This document provides an introduction to MIMO (Multiple Input Multiple Output) technology. MIMO uses multiple antennas at the transmitter and receiver to improve wireless communication performance. It can be used to improve robustness through spatial diversity or increase data rates through spatial multiplexing. MIMO has been adopted in wireless standards like 3GPP UMTS, WiMAX, WLAN, and LTE to enhance data throughput and coverage. Rohde & Schwarz offers signal generators, analyzers, and systems to test devices implementing MIMO technology specified in these standards.
This document provides an introduction to MIMO (Multiple Input Multiple Output) technology. MIMO uses multiple antennas at the transmitter and receiver to improve wireless communication performance. It allows for spatial diversity, spatial multiplexing, and beamforming. Many modern wireless standards have adopted MIMO, including 3GPP UMTS, WiMAX, WLAN, and LTE to provide higher data rates and better coverage. Rohde & Schwarz offers signal generators, analyzers, testers and systems to test devices implementing these MIMO standards.
1) MIMO (Multiple Input Multiple Output) uses multiple antennas at the transmitter and receiver to improve data rates and link reliability in wireless communication systems.
2) This document discusses various MIMO techniques including spatial diversity, spatial multiplexing, and beamforming and how they are implemented in standards like 3GPP UMTS, WiMAX, WLAN, and LTE.
3) It also provides an overview of Rohde and Schwarz's test solutions for verifying MIMO capabilities in devices that support these various wireless communication standards.
This document discusses OFDM-MIMO wireless broadcasting systems. It first introduces the need for high data rate and bandwidth efficient wireless communication systems using multiple transmitters and receivers. It then describes different wireless systems including SISO, SIMO, MISO and MIMO. OFDM and its advantages for multicarrier transmission are also discussed. Finally, the document reviews literature on adaptive filtering, channel estimation, equalization and modeling for OFDM-MIMO systems and sets the objective to develop a channel model for such wireless broadcasting systems.
Questions about Understanding benefits of mimo technology (article)Yaseen
MIMO (multiple-input multiple-output) technology uses multiple antennas at both the transmitter and receiver to enhance wireless throughput and performance. It achieves higher data rates than traditional single antenna SISO systems by utilizing the spatial dimension of the wireless channel. MIMO works by using knowledge of the communications channel gained from multiple signal paths to transmit independent data streams from each transmitter antenna. This allows the receiver to recover independent streams and achieve throughput close to double that of SISO for a 2x2 MIMO system. Key challenges for MIMO implementation include antenna design to address multiple antennas, multi-channel synchronization across transceivers, and more sophisticated digital signal processing algorithms.
Overview about MIMO
Contents:
Diversity Definition
Why Diversity
Types of Diversity
Types of combining
MIMO Definition
Why MIMO ?
MIMO Advantages and disadvantages
Applications of MIMO
This document discusses beamforming and the eight transmission modes in LTE Release 9. It begins with introductions to MIMO technology and beamforming basics. It then explains the eight transmission modes, including single transmit antenna mode, transmit diversity, open and closed loop spatial multiplexing, multi-user MIMO, and two beamforming modes that use UE-specific reference signals. Key aspects of LTE such as physical channels and the reference signal structure are also summarized.
This document provides an introduction to MIMO (Multiple Input Multiple Output) technology. MIMO uses multiple antennas at the transmitter and receiver to improve wireless communication performance. It allows for spatial diversity, spatial multiplexing, and beamforming. Many modern wireless standards have adopted MIMO, including 3GPP UMTS, WiMAX, WLAN, and LTE to provide higher data rates and better coverage. Rohde & Schwarz offers signal generators, analyzers, testers and systems to test devices implementing these MIMO standards.
1) MIMO (Multiple Input Multiple Output) uses multiple antennas at the transmitter and receiver to improve data rates and link reliability in wireless communication systems.
2) This document discusses various MIMO techniques including spatial diversity, spatial multiplexing, and beamforming and how they are implemented in standards like 3GPP UMTS, WiMAX, WLAN, and LTE.
3) It also provides an overview of Rohde and Schwarz's test solutions for verifying MIMO capabilities in devices that support these various wireless communication standards.
This document discusses OFDM-MIMO wireless broadcasting systems. It first introduces the need for high data rate and bandwidth efficient wireless communication systems using multiple transmitters and receivers. It then describes different wireless systems including SISO, SIMO, MISO and MIMO. OFDM and its advantages for multicarrier transmission are also discussed. Finally, the document reviews literature on adaptive filtering, channel estimation, equalization and modeling for OFDM-MIMO systems and sets the objective to develop a channel model for such wireless broadcasting systems.
Questions about Understanding benefits of mimo technology (article)Yaseen
MIMO (multiple-input multiple-output) technology uses multiple antennas at both the transmitter and receiver to enhance wireless throughput and performance. It achieves higher data rates than traditional single antenna SISO systems by utilizing the spatial dimension of the wireless channel. MIMO works by using knowledge of the communications channel gained from multiple signal paths to transmit independent data streams from each transmitter antenna. This allows the receiver to recover independent streams and achieve throughput close to double that of SISO for a 2x2 MIMO system. Key challenges for MIMO implementation include antenna design to address multiple antennas, multi-channel synchronization across transceivers, and more sophisticated digital signal processing algorithms.
Overview about MIMO
Contents:
Diversity Definition
Why Diversity
Types of Diversity
Types of combining
MIMO Definition
Why MIMO ?
MIMO Advantages and disadvantages
Applications of MIMO
This document discusses beamforming and the eight transmission modes in LTE Release 9. It begins with introductions to MIMO technology and beamforming basics. It then explains the eight transmission modes, including single transmit antenna mode, transmit diversity, open and closed loop spatial multiplexing, multi-user MIMO, and two beamforming modes that use UE-specific reference signals. Key aspects of LTE such as physical channels and the reference signal structure are also summarized.
To MIMO or Not To MIMO in Mobile Satellite Broadcasting SystemsJithin Prasad
This document discusses the potential advantages of using a dual polarization per beam (DPPB) architecture instead of the conventional single polarization per beam (SPPB) for mobile satellite broadcasting systems. It analyzes aspects of using linear or circular dual polarization for the satellite signal. The document also examines the capacity and performance of SISO, 2xSISO, and MIMO configurations over a dual polarization channel based on the DVB-SH standard. The advantages of DPPB include increasing data rates and decreasing error rates while disadvantages include the high costs of satellite launches and limited bandwidth. Applications include mobile TV broadcasting and digital video services.
Human: Thank you for the summary. You captured the key points effectively in 3 concise sentences as
The document compares MIMO features in WiMAX and LTE mobile communication standards. Both standards use MIMO techniques like spatial multiplexing and beamforming to achieve high data rates and spectral efficiency. However, LTE typically has higher uplink spectral efficiency using SC-FDMA, while WiMAX utilizes more advanced receivers and feedback techniques for improved performance. Overall, both WiMAX and LTE are well-suited to meet 4G requirements through the use of similar MIMO channel access methods.
BER performance simulation in a multi user MIMO PresentationVikas Pandey
This document summarizes a simulation of bit error rate (BER) performance for a multi-user MIMO system using transmit antenna selection/maximal ratio combining (TAS/MRC) over a Nakagami-m fading channel. Modulation schemes evaluated include BPSK, QPSK, and QAM. Simulations were conducted for systems with 200 and 500 users. Results show BER decreasing as the number of users increases or modulation order increases from BPSK to QAM. Findings can inform the design of future MIMO devices.
This document provides an overview of MIMO (Multiple Input Multiple Output) radar. MIMO radar uses multiple transmit and receive antennas. This allows it to achieve higher angular resolution than traditional phased array radars with fewer antennas. MIMO radar works by having receive antennas separately process signals from different transmit antennas, using techniques like time division multiplexing and binary phase modulation. The virtual array concept enables MIMO radar to greatly increase its degrees of freedom beyond the physical number of antennas. Potential applications of MIMO radar include air surveillance, clutter mitigation, and moving target detection.
1) MIMO systems use multiple antennas at both the transmitter and receiver to improve wireless communication capabilities. This allows for increased data rates and signal strength.
2) Traditional wireless systems use a single antenna at both ends (SISO) while MIMO can have multiple at both, known as MISO, SIMO, or fully multiple-input multiple-output (MIMO).
3) MIMO provides higher capacity through spatial multiplexing and increases spectrum efficiency. The Shannon capacity can increase linearly with the number of antennas or data streams.
The document discusses MIMO (Multiple Input Multiple Output) systems. It motivates MIMO by explaining how system designers aim to achieve high data rates and quality while minimizing complexity, transmission power, and bandwidth. It describes MIMO antenna configurations including SISO and MIMO. MIMO systems use multiple transmit and receive antennas to achieve high capacity. The document outlines diversity as a design criterion for MIMO systems to achieve reliable reception. It also discusses Alamouti's space-time coding scheme and how MIMO can be combined with OFDM to further improve performance. In conclusions, MIMO brings us closer to gigabit speeds while also providing reliable communications.
This document discusses various MIMO techniques including single-user and multi-user MIMO. It begins with an overview of STBC, which is used in 802.11ac and provides transmit diversity with low cost. Spatial multiplexing allows multiple parallel channels to improve throughput. For single-user MIMO, transmit beamforming enhances signal reception through precoding techniques like SVD that establish parallel channels. Multi-user MIMO further increases capacity but introduces interference that must be managed through precoding and receiver techniques like zero-forcing. Channel feedback is also required to implement beamforming and precoding.
This document outlines and describes space-time coding techniques for MIMO wireless systems. It introduces MIMO system models and derives MIMO capacity. It then discusses space-time coding performance analysis, including diversity-multiplexing tradeoffs and error analysis. Finally, it describes specific space-time coding schemes, including Alamouti codes, space-time block codes, and space-time trellis codes.
IRJET- Performance Analysis of MIMO-OFDM System using Different Antenna Confi...IRJET Journal
This document analyzes the performance of a MIMO-OFDM wireless communication system using different antenna configurations through simulation in MATLAB. It finds that the system performs better when diversity is increased at both the transmitter and receiver sides. Specifically, it shows that bit error rate decreases and signal-to-noise ratio increases as the number of receiving antennas is increased when using BPSK modulation over an AWGN channel. The performance of MIMO-OFDM is evaluated for different detection methods and antenna configurations, and zero-forcing with successive interference cancellation is found to improve bit error rate compared to other techniques.
Multi user-MIMO Broadcast Channel techniquesIRJET Journal
This document provides an overview of techniques for multi-user MIMO broadcast channels, including block diagonalization, dirty paper coding, and zero forcing channel inversion. Block diagonalization uses precoding to suppress interference between users by making each user's channel orthogonal. Dirty paper coding codes signals to cancel out interference that is known at the transmitter. Zero forcing channel inversion inverts the channel and applies precoding to eliminate multi-user interference. These techniques aim to reconstruct signals and remove interference to maintain reliable data transmission in multi-user MIMO systems.
MIMO system (potential candidate for 4G system)Virak Sou
This document provides an overview of MIMO (multiple-input multiple-output) systems in wireless communications. It discusses how MIMO can provide various performance improvements, including array gain through signal combining, diversity gain to combat fading, and multiplexing gain to increase spectral efficiency. It also covers MIMO channel capacity calculations for different channel models, as well as techniques for maximizing diversity or throughput such as space-time coding and spatial multiplexing. The key advantages of MIMO for future wireless systems are higher data rates, quality of service, coverage, and spectral efficiency.
The document summarizes the performance analysis of orthogonal space-time block codes exploiting channel state information in MIMO systems. It begins with an introduction to MIMO wireless communication systems and space-time coding techniques. It then discusses space-time block codes, orthogonal space-time block codes, and generalized OSTBCs. The document outlines the objectives of analyzing and comparing the performance of OSTBCs with and without channel state information feedback in MIMO systems. It also describes the data processing and encoding/decoding methods used in OSTBC systems.
The document discusses MIMO (multiple-input multiple-output) technology in 4G wireless networks. It describes how MIMO uses multiple antennas at both the transmitter and receiver to provide benefits like increased throughput, robustness to fading, and the ability to support new broadband applications. It discusses various MIMO techniques including antenna diversity, beamforming, and space division multiplexing and how they improve the signal-to-noise ratio and mitigate multipath interference. MIMO has been adopted in technologies like WiFi, WiMAX, and LTE to provide these benefits and enhancements to wireless communications.
The document describes the simulation of an OFDM system using MATLAB. It discusses key aspects of OFDM including how it divides the frequency selective fading channel into narrow flat subchannels. It also discusses the transmitter and receiver blocks including modulation, channel effects, demodulation and error calculation. The MATLAB code simulates transmission of 256 bits using QAM modulation over an AWGN channel. Results show the transmitted and received OFDM signal spectra and constellation diagrams, validating the simulation.
This document discusses a project analyzing the performance of MIMO-OFDM systems in Rayleigh fading channels. MIMO-OFDM is a popular technique for mobile communications that uses multiple antennas at the transmitter and receiver to improve data rates and capacity. The project compares the ergodic and outage capacities of MIMO-OFDM systems with varying numbers of transmit and receive antennas and analyzes performance metrics like SNR and BER. It aims to evaluate MIMO-OFDM prototype performance and investigate methods for cost reduction.
This document provides an introduction to Multiple Input Multiple Output (MIMO) technology. MIMO involves using multiple antennas at both the base station (eNB) and user equipment (UE). It explains that MIMO can transmit more data than Single Input Single Output (SISO) using the same transmission power by exploiting spatial multiplexing through independent data streams sent from different antennas. An analogy is provided comparing SISO to a single car transporting people, while MIMO could transport the same number of people using multiple cars and less fuel, similar to how MIMO can increase data rates with the same transmission power. Finally, it discusses machine learning algorithms and Python libraries that can help develop low complexity MIMO detection algorithms for applications like 5G
This document discusses OFDM and OFDMA technologies. It begins with an outline of topics including the need for multi-carrier transmission, how OFDM addresses this need using FFT and IFFT, guard time insertion using cyclic prefixes, drawbacks of OFDM including high PAPR, channel estimation techniques, and an OFDM block diagram. It then discusses OFDMA which allows simultaneous transmissions to multiple users using OFDM signaling. Diversity techniques including time, frequency, and spatial diversity are also summarized.
Min input and min output of the system can be conduct by the process of interest in the process of view of synchronisation and software Engineer at the system can be a good idea for this colour combination of view of the best part of the system can be conduct by the process of view till now I'm interested for the system based engineering and technology related issues in college and software engineer h today’s increasing demand for security, especially in public places such as
airports, train stations, supermarkets, schools, and crowded street, surveillance cameras are
used for monitoring daily activities and detecting abnormal events. This task focuses on the
localization of anomalies using both temporal and partial information in videos. Anomalies
can be defined as events deviating from normal behavior [1], e.g., fighting, sneaking,
or unattended bags at an airport. The purpose of using surveillance cameras is the early
detection of anomalous human behaviors. This is a critical task in many cases where human
intervention is necessary, e.g., for crime prevention or countering terrorism. However,
this process requires labor-intensive and continuous human attention, which is a tedious
process, since abnormal events only happen 0.01% of the time and 99.9% of the surveillance
time is wasted [2]. Moreover, a surveillance system produces a lot of redundant video data,
which require unnecessary storage space. For reducing human errors and storage costs, it
is necessary to build an efficient surveillance system for detecting any strange behaviors
that may lead to dangerous situations. This requires deep and comprehensive study of
human activity recognition, to understand the features representative of each action.
Anomaly detection in video has a wide range of applications, such as for traffic
accident detection, criminal activity detection, and illegal activity detection. In addition,
detecting anomalous items or abandoned objects, such as guns or knifes, is necessary in
sensitive area
A New Transmission Scheme for MIMO – OFDMijsrd.com
This contribution introduces a new transmission scheme for multiple-input multiple-output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems. The new scheme is efficient and suitable especially for symmetric channels such as the link between two base stations or between two antennas on radio beam transmission. This survey Paper presents the performance analysis of V-BLAST based multiple inputs multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system with respect to bit error rate per signal to noise ratio (BER/SNR) for various detection techniques. A 2X2 MIMO-OFDM system is used for the performance evaluation. The simulation results shows that the performance of V-BLAST based detection techniques is much better than the conventional methods. Alamouti Space Time Block Code (STBC) scheme is used with orthogonal designs over multiple antennas which showed simulated results are identical to expected theoretical results. With this technique both Bit Error Rate (BER) and maximum diversity gain are achieved by increasing number of antennas on either side. This scheme is efficient in all the applications where system capacity is limited by multipath fading.
To MIMO or Not To MIMO in Mobile Satellite Broadcasting SystemsJithin Prasad
This document discusses the potential advantages of using a dual polarization per beam (DPPB) architecture instead of the conventional single polarization per beam (SPPB) for mobile satellite broadcasting systems. It analyzes aspects of using linear or circular dual polarization for the satellite signal. The document also examines the capacity and performance of SISO, 2xSISO, and MIMO configurations over a dual polarization channel based on the DVB-SH standard. The advantages of DPPB include increasing data rates and decreasing error rates while disadvantages include the high costs of satellite launches and limited bandwidth. Applications include mobile TV broadcasting and digital video services.
Human: Thank you for the summary. You captured the key points effectively in 3 concise sentences as
The document compares MIMO features in WiMAX and LTE mobile communication standards. Both standards use MIMO techniques like spatial multiplexing and beamforming to achieve high data rates and spectral efficiency. However, LTE typically has higher uplink spectral efficiency using SC-FDMA, while WiMAX utilizes more advanced receivers and feedback techniques for improved performance. Overall, both WiMAX and LTE are well-suited to meet 4G requirements through the use of similar MIMO channel access methods.
BER performance simulation in a multi user MIMO PresentationVikas Pandey
This document summarizes a simulation of bit error rate (BER) performance for a multi-user MIMO system using transmit antenna selection/maximal ratio combining (TAS/MRC) over a Nakagami-m fading channel. Modulation schemes evaluated include BPSK, QPSK, and QAM. Simulations were conducted for systems with 200 and 500 users. Results show BER decreasing as the number of users increases or modulation order increases from BPSK to QAM. Findings can inform the design of future MIMO devices.
This document provides an overview of MIMO (Multiple Input Multiple Output) radar. MIMO radar uses multiple transmit and receive antennas. This allows it to achieve higher angular resolution than traditional phased array radars with fewer antennas. MIMO radar works by having receive antennas separately process signals from different transmit antennas, using techniques like time division multiplexing and binary phase modulation. The virtual array concept enables MIMO radar to greatly increase its degrees of freedom beyond the physical number of antennas. Potential applications of MIMO radar include air surveillance, clutter mitigation, and moving target detection.
1) MIMO systems use multiple antennas at both the transmitter and receiver to improve wireless communication capabilities. This allows for increased data rates and signal strength.
2) Traditional wireless systems use a single antenna at both ends (SISO) while MIMO can have multiple at both, known as MISO, SIMO, or fully multiple-input multiple-output (MIMO).
3) MIMO provides higher capacity through spatial multiplexing and increases spectrum efficiency. The Shannon capacity can increase linearly with the number of antennas or data streams.
The document discusses MIMO (Multiple Input Multiple Output) systems. It motivates MIMO by explaining how system designers aim to achieve high data rates and quality while minimizing complexity, transmission power, and bandwidth. It describes MIMO antenna configurations including SISO and MIMO. MIMO systems use multiple transmit and receive antennas to achieve high capacity. The document outlines diversity as a design criterion for MIMO systems to achieve reliable reception. It also discusses Alamouti's space-time coding scheme and how MIMO can be combined with OFDM to further improve performance. In conclusions, MIMO brings us closer to gigabit speeds while also providing reliable communications.
This document discusses various MIMO techniques including single-user and multi-user MIMO. It begins with an overview of STBC, which is used in 802.11ac and provides transmit diversity with low cost. Spatial multiplexing allows multiple parallel channels to improve throughput. For single-user MIMO, transmit beamforming enhances signal reception through precoding techniques like SVD that establish parallel channels. Multi-user MIMO further increases capacity but introduces interference that must be managed through precoding and receiver techniques like zero-forcing. Channel feedback is also required to implement beamforming and precoding.
This document outlines and describes space-time coding techniques for MIMO wireless systems. It introduces MIMO system models and derives MIMO capacity. It then discusses space-time coding performance analysis, including diversity-multiplexing tradeoffs and error analysis. Finally, it describes specific space-time coding schemes, including Alamouti codes, space-time block codes, and space-time trellis codes.
IRJET- Performance Analysis of MIMO-OFDM System using Different Antenna Confi...IRJET Journal
This document analyzes the performance of a MIMO-OFDM wireless communication system using different antenna configurations through simulation in MATLAB. It finds that the system performs better when diversity is increased at both the transmitter and receiver sides. Specifically, it shows that bit error rate decreases and signal-to-noise ratio increases as the number of receiving antennas is increased when using BPSK modulation over an AWGN channel. The performance of MIMO-OFDM is evaluated for different detection methods and antenna configurations, and zero-forcing with successive interference cancellation is found to improve bit error rate compared to other techniques.
Multi user-MIMO Broadcast Channel techniquesIRJET Journal
This document provides an overview of techniques for multi-user MIMO broadcast channels, including block diagonalization, dirty paper coding, and zero forcing channel inversion. Block diagonalization uses precoding to suppress interference between users by making each user's channel orthogonal. Dirty paper coding codes signals to cancel out interference that is known at the transmitter. Zero forcing channel inversion inverts the channel and applies precoding to eliminate multi-user interference. These techniques aim to reconstruct signals and remove interference to maintain reliable data transmission in multi-user MIMO systems.
MIMO system (potential candidate for 4G system)Virak Sou
This document provides an overview of MIMO (multiple-input multiple-output) systems in wireless communications. It discusses how MIMO can provide various performance improvements, including array gain through signal combining, diversity gain to combat fading, and multiplexing gain to increase spectral efficiency. It also covers MIMO channel capacity calculations for different channel models, as well as techniques for maximizing diversity or throughput such as space-time coding and spatial multiplexing. The key advantages of MIMO for future wireless systems are higher data rates, quality of service, coverage, and spectral efficiency.
The document summarizes the performance analysis of orthogonal space-time block codes exploiting channel state information in MIMO systems. It begins with an introduction to MIMO wireless communication systems and space-time coding techniques. It then discusses space-time block codes, orthogonal space-time block codes, and generalized OSTBCs. The document outlines the objectives of analyzing and comparing the performance of OSTBCs with and without channel state information feedback in MIMO systems. It also describes the data processing and encoding/decoding methods used in OSTBC systems.
The document discusses MIMO (multiple-input multiple-output) technology in 4G wireless networks. It describes how MIMO uses multiple antennas at both the transmitter and receiver to provide benefits like increased throughput, robustness to fading, and the ability to support new broadband applications. It discusses various MIMO techniques including antenna diversity, beamforming, and space division multiplexing and how they improve the signal-to-noise ratio and mitigate multipath interference. MIMO has been adopted in technologies like WiFi, WiMAX, and LTE to provide these benefits and enhancements to wireless communications.
The document describes the simulation of an OFDM system using MATLAB. It discusses key aspects of OFDM including how it divides the frequency selective fading channel into narrow flat subchannels. It also discusses the transmitter and receiver blocks including modulation, channel effects, demodulation and error calculation. The MATLAB code simulates transmission of 256 bits using QAM modulation over an AWGN channel. Results show the transmitted and received OFDM signal spectra and constellation diagrams, validating the simulation.
This document discusses a project analyzing the performance of MIMO-OFDM systems in Rayleigh fading channels. MIMO-OFDM is a popular technique for mobile communications that uses multiple antennas at the transmitter and receiver to improve data rates and capacity. The project compares the ergodic and outage capacities of MIMO-OFDM systems with varying numbers of transmit and receive antennas and analyzes performance metrics like SNR and BER. It aims to evaluate MIMO-OFDM prototype performance and investigate methods for cost reduction.
This document provides an introduction to Multiple Input Multiple Output (MIMO) technology. MIMO involves using multiple antennas at both the base station (eNB) and user equipment (UE). It explains that MIMO can transmit more data than Single Input Single Output (SISO) using the same transmission power by exploiting spatial multiplexing through independent data streams sent from different antennas. An analogy is provided comparing SISO to a single car transporting people, while MIMO could transport the same number of people using multiple cars and less fuel, similar to how MIMO can increase data rates with the same transmission power. Finally, it discusses machine learning algorithms and Python libraries that can help develop low complexity MIMO detection algorithms for applications like 5G
This document discusses OFDM and OFDMA technologies. It begins with an outline of topics including the need for multi-carrier transmission, how OFDM addresses this need using FFT and IFFT, guard time insertion using cyclic prefixes, drawbacks of OFDM including high PAPR, channel estimation techniques, and an OFDM block diagram. It then discusses OFDMA which allows simultaneous transmissions to multiple users using OFDM signaling. Diversity techniques including time, frequency, and spatial diversity are also summarized.
Min input and min output of the system can be conduct by the process of interest in the process of view of synchronisation and software Engineer at the system can be a good idea for this colour combination of view of the best part of the system can be conduct by the process of view till now I'm interested for the system based engineering and technology related issues in college and software engineer h today’s increasing demand for security, especially in public places such as
airports, train stations, supermarkets, schools, and crowded street, surveillance cameras are
used for monitoring daily activities and detecting abnormal events. This task focuses on the
localization of anomalies using both temporal and partial information in videos. Anomalies
can be defined as events deviating from normal behavior [1], e.g., fighting, sneaking,
or unattended bags at an airport. The purpose of using surveillance cameras is the early
detection of anomalous human behaviors. This is a critical task in many cases where human
intervention is necessary, e.g., for crime prevention or countering terrorism. However,
this process requires labor-intensive and continuous human attention, which is a tedious
process, since abnormal events only happen 0.01% of the time and 99.9% of the surveillance
time is wasted [2]. Moreover, a surveillance system produces a lot of redundant video data,
which require unnecessary storage space. For reducing human errors and storage costs, it
is necessary to build an efficient surveillance system for detecting any strange behaviors
that may lead to dangerous situations. This requires deep and comprehensive study of
human activity recognition, to understand the features representative of each action.
Anomaly detection in video has a wide range of applications, such as for traffic
accident detection, criminal activity detection, and illegal activity detection. In addition,
detecting anomalous items or abandoned objects, such as guns or knifes, is necessary in
sensitive area
A New Transmission Scheme for MIMO – OFDMijsrd.com
This contribution introduces a new transmission scheme for multiple-input multiple-output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems. The new scheme is efficient and suitable especially for symmetric channels such as the link between two base stations or between two antennas on radio beam transmission. This survey Paper presents the performance analysis of V-BLAST based multiple inputs multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system with respect to bit error rate per signal to noise ratio (BER/SNR) for various detection techniques. A 2X2 MIMO-OFDM system is used for the performance evaluation. The simulation results shows that the performance of V-BLAST based detection techniques is much better than the conventional methods. Alamouti Space Time Block Code (STBC) scheme is used with orthogonal designs over multiple antennas which showed simulated results are identical to expected theoretical results. With this technique both Bit Error Rate (BER) and maximum diversity gain are achieved by increasing number of antennas on either side. This scheme is efficient in all the applications where system capacity is limited by multipath fading.
The aim of this paper is to determine the viability of Indoor Optical Wireless Communication System. This paper introduces Visible Light Communication along with its merits, demerits and applications. Then the main characteristics of VLC system are described, around which the project is designed. Multiple Input-Multiple Output (MIMO) technique is used in the project in order to enhance the data rate of transmission. Instead of using a system of only one LED and one APD, which transmits only one bit at a time, a system of 4 LEDs and 4 APDs is introduced, which increases the data rates by 300% from the previous case. We observe the signal, noise, SNR, BER etc. across the room dimension. Finally, in the last chapter we summarize our results on the basis of MATLAB simulations and propose some modifications to this model that can be implemented in future.
This document summarizes recent advances in wireless communication through the implementation of OFDM-MIMO systems. It discusses how OFDM can transmit multiple signals simultaneously using orthogonal subcarriers to improve data rates. MIMO uses multiple antennas at the transmitter and receiver to provide diversity gain and increase capacity. The combination of OFDM and MIMO (OFDM-MIMO) results in increased data rates and efficiency by overcoming problems like frequency selective fading. It then describes how OFDM-MIMO systems can transmit a single signal using transmit diversity and relay selection with decode-and-forward or amplify-and-forward protocols to further improve performance. Simulations show the OFDM-MIMO system achieves a lower bit error rate than
MIMO-OFDM (Multi Input Multi Output- Orthogonal Frequency Division Multiplexing) system is very popular technique for mobile communication. We found that Ergodic channel capacity has some limitation in MIMO-OFDM system. So Ergodic channel capacity optimization is necessary to improve the performance of MIMO-OFDM System.
This document presents an overview of MIMO systems. It discusses the motivations for developing MIMO, including achieving high data rates and minimizing error rates. It describes MIMO antenna configurations using multiple transmit and receive antennas. It covers design criteria for MIMO like diversity to achieve independent fading paths. MIMO-OFDM is also discussed as a technique to combine MIMO with orthogonal frequency division multiplexing. The document concludes that MIMO systems are helping to achieve data rates closer to 1Gbps while also providing reliable communications.
International Journal of Engineering Research and DevelopmentIJERD Editor
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This document describes a simulator designed to analyze bit error rates using orthogonal frequency division multiplexing (OFDM) under different modulation schemes and channel conditions. The simulator was implemented in MATLAB and allows users to choose modulation types, channel types (AWGN, Rayleigh, Rician), and other parameters. It then generates plots of bit error rate versus signal-to-noise ratio for performance analysis. Screenshots of the user interface are provided along with sample output plots and discussion of the simulator design and capabilities.
This document discusses smart antennas and MIMO technology for wireless communications. It defines key concepts like MIMO, SISO, SIMO and MISO antenna configurations. It describes how MIMO uses multiple antennas to send and receive multiple spatial streams simultaneously, increasing spectral efficiency. The document also discusses different MIMO techniques like precoding, spatial multiplexing and diversity coding. It provides examples of how MIMO antenna configurations can improve signal strength and SNR. The document concludes that technologies like MIMO and smart antennas can improve wireless network capacity and coverage area by reducing interference.
Training document e ran2.2_lte tdd system multiple antenna techniques(mimo an...ProcExpl
The document is an internal training presentation on LTE system multiple antenna techniques. It provides an overview of MIMO and beamforming concepts and principles, including the advantages of multi-antenna techniques, classifications of MIMO techniques, principles of multi-antenna receive and transmit MIMO, open-loop and closed-loop spatial multiplexing, and adaptive mode configuration. The goal is for trainees to understand the concepts and basic principles of MIMO and beamforming in LTE systems.
Multiple Input Multiple Output (MIMO) technology uses multiple antennas at both the transmitter and receiver to improve channel robustness and throughput. By utilizing reflected signals, MIMO can provide gains in channel robustness and throughput. MIMO was initially developed in the 1990s after additional processing power made it possible to utilize both spatial diversity and spatial multiplexing. MIMO systems provide either spatial multiplexing gain to maximize transmission rate or diversity gain to minimize errors and prioritize reliability. MIMO is now used in many wireless communication standards and ongoing research aims to develop more advanced MIMO techniques.
Error Control and performance Analysis of MIMO-OFDM Over Fading ChannelsIOSR Journals
ABSTRACT: Multiple Input Multiple Output is a wireless technology that uses multiple transmitters and
receivers to transfer more data at the same time. Orthogonal Frequency Division Multiplexing, an FDM
modulation technique which splits the signal into multiple smaller sub-signals that are then transmitted
simultaneously at different frequencies to the receiver. OFDM technique spreads the data over number of
carriers which are at specific predefined frequencies. This reduces or eliminates the ISI. Forward error
correction or channel coding is a technique used for controlling errors in data transmission over unreliable or
noisy communication channels. The objective of our proposed paper is to implement the FEC into the MIMO
OFDM systems and its performance is analysed by using MATLAB over different fading channels. For
modulation it employs M-QAM which combines both ASK and PSK thereby enabling several bits to be
transmitted per symbol. The performance of MIMO-OFDM system is evaluated by BER Vs SNR when the bits
propagates through the different fading channels.
Keywords– OFDM, MIMO, QAM, FEC, BER.
Performance Analysis of 2x2 MIMO for OFDM-DSSS Based Wireless SystemAM Publications
In today’s 3G world moving to 4G requires high data rate support in applications like multimedia services,
internet access and video streaming services. Such applications are always in need of very high speed data rate
support which increases the requirement of efficient usage of spectrum and high capacity systems. Thus the major
challenges to be taken care of in designing the next generation wireless communications system should provide or
accommodate capacity, the spectral efficiency, improved link reliability and multimedia services. So we can establish a
distributed system in terms of multi-carrier, multi-antenna and coded pulse. It gives rise to hybrid technology based on
DSSS, OFDM, and MIMO system which can be the ultimate solution for wireless cellular communication systems. In
this paper we analysis the performance of MIMO-OFDM-DSSS system. This paper also includes comparison of
performances of MIMO-OFDM-DSSS system with ZF and MMSE equalizer on the basis of BER using different
modulation techniques in a scattering environment.
This document discusses an iterative MMSE-PIC detection algorithm for MIMO-OFDM systems. It begins with an introduction to MIMO and OFDM technologies and how their combination can provide high spectrum efficiency and diversity gain against fading channels. It then describes the iterative MMSE-PIC detection algorithm, which utilizes parallel interference cancellation and iteration to improve detection performance compared to other detectors like ZF and MMSE in noisy environments. The document provides details on the system model and MIMO techniques like spatial multiplexing and diversity schemes before introducing the proposed iterative MMSE-PIC detection algorithm for MIMO-OFDM systems.
Design and analysis of mimo system for uwb communicationijwmn
Multiple transmit and receive antennas are used MIMO system. The system creates parallel MIMO
subchannels to transmit independent streams of data under the appropriate channel conditions. Similarly,
Ultrawideband (UWB) communication has attracted great interest for various applications in recent days.
Spatially multiplexed (SM) multiple-input multiple-output (MIMO) systems gains the spectral efficiency as
well as high data rates without consuming additional power, bandwidth or time slots. In this paper, we
extend the concept of MIMO to UWB systems. The correlated channel for such purpose is considered and
the performance has been analyzed for spatial multiplexing SM-UWB-MIMO system which is required for
estimation. The system performance substantially degrades in the presence of high values of spatial
correlation. To avoid the degradation of such system, it has been designed for virtual UWB-MIMO Time
Reversal (TR) system, so that it is not affected by the transmit correlation. Another novel method to reduce
the effect of correlation has been chosen by taking the Eigen value of the channel matrix for the
computation of the system performance. The result shows its performance.
Multicarrier modulation can be implemented by using Orthogonal Frequency Division Multiplexing (OFDM) to achieve utmost bandwidth exploitation and soaring alleviation attributes profile besides multipath fading. To support delay sensitive and band bandwidth demanding multimedia applications and internet services, MIMO in addition with other techniques can be used to achieve high capacity and reliability. To obtain high spatial rate by transmitting data on several antennas by using MIMO with OFDM results in reducing error recovery features and the equalization complexities arise by sending data on varying frequency levels. Three parameters frequency OFDM, Spatial (MIMO) and time (STC) can be used to achieve diversity in MIMO-OFDM. This technique is dynamic and well-known for services of wireless broadband access. MIMO if used with OFDM is highly beneficial for each scheme and provides high throughput. There are several space time block codes to exploit MIMO OFDM; one of the techniques is called Alamouti Codes. The paper investigates adaptive Alamouti Codes and their application in IEEE 802.11n.
Ber analysis of 2x2 mimo spatial multiplexing under awgn and rician channels ...ijwmn
Multiple-input–multiple-output (MIMO) wireless systems use multiple antennas at transmitting and
receiving end to offer improved capacity and data rate over single antenna systems in multipath channels.
In this paper we have investigated the Spatial Multiplexing technique of MIMO systems. Here different
fading channels like AWGN and Rician are used for analysis purpose. Moreover we analyzed the technique
using high level modulations (i.e. M-PSK for different values of M). Detection algorithms used are Zero-
Forcing and Minimum mean square estimator. Performance is analyzed in terms of BER (bit error rate) vs.
SNR (signal to noise ratio).
Article on MIMO-OFDM printed in BSNL telecom JournalSushil Kumar
The document summarizes MIMO-OFDM technology for high-speed wireless communication. It describes that MIMO uses multiple antennas at the transmitter and receiver to minimize errors and optimize data speed. It can increase channel capacity while obeying Shannon's law. OFDM divides data into small sub-signals transmitted through different frequencies using IFFT and FFT. Combining MIMO with OFDM provides higher throughput and link reliability. Industry standards like 802.11n, 802.16a, LTE/LTE Advanced have adopted MIMO-OFDM to achieve data rates up to 1Gbps.
Performance Analysis of 802.lln MIMO OFDM TransceiverIJERA Editor
The increasing demand on real time application to achieve high throughput, reliable wireless system and network capacity for fourth generation wireless local area networks is to combine MIMO wireless technology with OFDM. Orthogonal Frequency Division Multiplexing (OFDM), which offers reliable high bit rate wireless system with reasonable low complexity. OFDM does provide large data rates with sufficient robustness to radio channel impairments. OFDM is a combination of modulation and multiplexing and are able to maximize spectral efficiency without causing adjacent channel interference. This paper first focuses on 802.11n standard, MIMO-OFDM system. This paper further reviews different work done on implementation of MIMO-OFDM transceiver for 802.11n standard.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
1. Introduction to MIMO
Application Note
Products:
| R&S SMU200A
| R&S AMU200A
| R&S SMATE200A
| R&S TS8980
| R&S TS8970
| R&S TS8975
| R&S CMW270
| R&S CMW500
| R&S FSQ
| R&S FSG
| R&S FSV
Modern radio communication systems have
to provide higher and higher data rates. As
conventional methods like using more
bandwidth or higher order modulation types
are limited, new methods of using the
transmission channel have to be used.
Multiple antenna systems (Multiple Input,
Multiple Output – MIMO) gives a significant
enhancement to data rate and channel
capacity.
This application note gives an introduction to
basic MIMO concepts and terminology and
explains how MIMO is implemented in
different radio communications standards.
ApplicationNote
Schindler,Schulz
07.2009-1MA142_0e
2. Table of Contents
1MA142_0e Rohde & Schwarz Introduction to MIMO 2
Table of Contents
1 Introduction......................................................................................... 3
2 MIMO.................................................................................................... 3
2.1 Conventional Radio System (SISO) ...........................................................................3
2.2 Multiple Antenna Systems ..........................................................................................4
2.2.1 Spatial Diversity ...........................................................................................................6
2.2.1.1 RX Diversity..................................................................................................................6
2.2.1.2 TX Diversity ..................................................................................................................7
2.2.2 Spatial Multiplexing .....................................................................................................8
2.2.3 Beamforming................................................................................................................9
3 MIMO in Radio Communications Systems ....................................... 9
3.1 3GPP UMTS ................................................................................................................10
3.1.1 HSPA+ (3GPP Release 7/8) .......................................................................................10
3.1.2 LTE (3GPP Release 8)................................................................................................11
3.2 WiMAX
TM
(802.16e-2005) ...........................................................................................12
3.3 WLAN (802.11n)..........................................................................................................13
3.4 Outlook .......................................................................................................................15
4 Rohde & Schwarz Solutions ............................................................ 15
4.1 Signal Generators......................................................................................................15
4.2 Signal Analyzers ........................................................................................................17
4.3 Mobile Radio Testers.................................................................................................17
4.4 Systems ......................................................................................................................18
5 Appendix ........................................................................................... 20
5.1 References..................................................................................................................20
5.2 Additional Information ..............................................................................................21
6 Ordering Information........................................................................ 22
3. Introduction
1MA142_0e Rohde & Schwarz Introduction to MIMO 3
1 Introduction
All radiocommunications systems, regardless of whether mobile radio networks like
3GPP UMTS or wireless radio networks like WLAN, must continually provide higher
data rates. In addition to conventional methods, such as introducing higher modulation
types or providing larger bandwidths, this is also being achieved by using multiple
antenna systems (Multiple Input, Multiple Output – MIMO).
This application note gives an introduction to basic MIMO concepts and terminology
and explains how MIMO is implemented in the different radiocommunications
standards. The solutions offered by Rohde & Schwarz are presented in the conclusion.
The MIMO terminology refers to the channel, thus the transmitter is the channel input
and the receiver the channel output.
2 MIMO
Several different diversity modes are used to make radiocommunications more robust,
even with varying channels. These include time diversity (different timeslots and
channel coding), frequency diversity (different channels, spread spectrum, and OFDM),
and also spatial diversity. Spatial diversity requires the use of multiple antennas at the
transmitter or the receiver end. Multiple antenna systems are typically known as
Multiple Input, Multiple Output systems (MIMO). Multiple antenna technology can also
be used to increase the data rate (spatial multiplexing) instead of improving
robustness.
In practice, both methods are used separately or in combination, depending on the
channel condition.
2.1 Conventional Radio System (SISO)
Conventional systems use one transmit and one receive antenna. In MIMO
terminology, this is called Single Input, Single Output (SISO) (Figure 1).
Figure 1: SISO antenna configuration
Shannon-Hartley theorem
According to Shannon, the capacity C of a radio channel is dependent on bandwidth B
and the signal-to-noise ratio S/N. The following applies to a SISO system:
4. MIMO
1MA142_0e Rohde & Schwarz Introduction to MIMO 4
Formula 1: Shannon-Hartley theorem for SISO
2.2 Multiple Antenna Systems
A MIMO system typically consists of m transmit and n receive antennas (Figure 2). By
using the same channel, every antenna receives not only the direct components
intended for it, but also the indirect components intended for the other antennas. A
time-independent, narrowband channel is assumed. The direct connection from
antenna 1 to 1 is specified with h11, etc., while the indirect connection from antenna 1
to 2 is identified as cross component h21, etc. From this is obtained transmission matrix
H with the dimensions n x m.
Formula2: Matrix H
Figure 2: General MIMO
The following transmission formula results from receive vector y, transmit vector x, and
noise n:
5. MIMO
1MA142_0e Rohde & Schwarz Introduction to MIMO 5
y = Hx + n .
Formula 3: MIMO transmission
Data to be transmitted is divided into independent data streams. The number of
streams M is always less than or equal to the number of antennas; in the case of
asymmetrical (m E n) antenna constellations, it is always smaller or equal the minimum
number of antennas. For example, a 4x4 system could be used to transmit four or
fewer streams, while a 3x2 system could transmit two or fewer streams.
Theoretically, the capacity C increases linearly with the number of streams M.
Formula 4: Shannon-Hartley theorem for MIMO
Single User MIMO (SU-MIMO)
When the data rate is to be increased for a single UE, this is called Single User MIMO
(SU-MIMO)
Figure 3: SU-MIMO
Multi User MIMO (MU-MIMO)
When the individual streams are assigned to various users, this is called Multi User
MIMO (MU-MIMO). This mode is particularly useful in the uplink because the
complexity on the UE side can be kept at a minimum by using only one transmit
antenna. This is also called 'collaborative MIMO'.
6. MIMO
1MA142_0e Rohde & Schwarz Introduction to MIMO 6
Figure 4: MU-MIMO
Cyclic delay diversity (CDD)
CDD introduces virtual echoes into OFDM-based systems. This increases the
frequency selectivity at the receiver. In the case of CDD, the signals are transmitted by
the individual antennas with a time delay. Because CDD introduces additional diversity
components, it is particularly useful as an addition to spatial multiplexing.
2.2.1 Spatial Diversity
The purpose of spatial diversity is to make the transmission more robust. There is no
increase in the data rate. This mode uses redundant data on different paths.
2.2.1.1 RX Diversity
RX diversity uses more antennas on the receiver side than on the transmitter side. The
simplest scenario consists of two RX and one TX antenna (SIMO, 1x2).
Figure 5: SIMO antenna configuration
Because special coding methods are not needed, this scenario is very easy to
implement. Only two RF paths are needed for the receiver.
7. MIMO
1MA142_0e Rohde & Schwarz Introduction to MIMO 7
Figure 6: RX diversity
Because of the different transmission paths, the receiver sees two differently faded
signals. By using the appropriate method in the receiver, the signal-to-noise ratio can
now be increased. Switched diversity always uses the stronger signal, while maximum
ratio combining uses the sum signal from the two signals (see Figure 6).
2.2.1.2 TX Diversity
When there are more TX than RX antennas, this is called TX diversity. The simplest
scenario uses two TX and one RX antenna (MISO, 2x1).
Figure 7: MISO antenna configuration
In this case, the same data is transmitted redundantly over two antennas. This method
has the advantage that the multiple antennas and redundancy coding is moved from
the mobile UE to the base station, where these technologies are simpler and cheaper
to implement.
To generate a redundant signal, space-time codes are used. Alamouti developed the
first codes for two antennas.
8. MIMO
1MA142_0e Rohde & Schwarz Introduction to MIMO 8
Space-time codes additionally improve the performance and make spatial diversity
usable. The signal copy is transmitted not only from a different antenna but also at a
different time. This delayed transmission is called delayed diversity. Space-time codes
combine spatial and temporal signal copies as illustrated in Figure 8. The signals s1
and s2 are multiplexed in two data chains. After that, a signal replication is added to
create the Alamouti space-time block code.
Figure 8: Alamouti coding
Additional pseudo-Alamouti codes were developed for multiple antennas [14][15].
The coding can also be handled in the frequency domain. This is called Space-
frequency coding.
2.2.2 Spatial Multiplexing
Spatial multiplexing is not intended to make the transmission more robust; rather it
increases the data rate. To do this, data is divided into separate streams; the streams
are transmitted independently via separate antennas.
Because MIMO transmits via the same channel, transmissions using cross
components not equal to 0 will mutually influence one another.
Figure 9: MIMO 2x2 antenna configuration
If transmission matrix H is known, the cross components can be calculated on the
receiver.
In the open-loop method, the transmission includes special sections that are also
known to the receiver. The receiver can perform a channel estimation.
In the closed-loop method, the receiver reports the channel status to the transmitter via
a special feedback channel. This makes it possible to respond to changing
circumstances.
9. MIMO in Radio Communications Systems
1MA142_0e Rohde & Schwarz Introduction to MIMO 9
2.2.3 Beamforming
Antenna technologies are the key in increasing network capacity. It started with
sectorized antennas. These antennas illuminate 60 or 120 degrees and operate as one
cell. In GSM, the capacity can be tripled, by 120 degree antennas. Adaptive antenna
arrays intensify spatial multiplexing using narrow beams. Smart antennas belong to
adaptive antenna arrays but differ in their smart direction of arrival (DoA) estimation.
Smart antennas can form a user-specific beam. Optional feedback can reduce
complexity of the array system.
Beamforming is the method used to create the radiation pattern of an antenna array. It
can be applied in all antenna array systems as well as MIMO systems.
Smart antennas are divided into two groups:
- Phased array systems (switched beamforming) with a finite number of
fixed predefined patterns
- Adaptive array systems (AAS) (adaptive beamforming) with an infinite
number of patterns adjusted to the scenario in realtime
Figure 10: Switched beamformer and adaptive beamformer
Switched beamformers electrically calculate the DoA and switch on the fixed beam.
The user only has the optimum signal strength along the center of the beam. The
adaptive beamformer deals with that problem and adjusts the beam in realtime to the
moving UE. The complexity and the cost of such a system is higher than the first type.
3 MIMO in Radio Communications Systems
Various mobile radio and network standards use MIMO. This section provides a brief
overview of the various implementations. In principle, all standards use TX diversity
and spatial multiplexing.
More detailed explanations for the individual standards are provided in the dedicated
sections.
10. MIMO in Radio Communications Systems
1MA142_0e Rohde & Schwarz Introduction to MIMO 10
3.1 3GPP UMTS
The 3GPP mobile radio standard (UMTS) has undergone numerous phases of
development. Starting with WCDMA, various data acceleration methods have been
introduced, including HSDPA and HSUPA. The newest releases cover HSPA+ and
Long Term Evolution (LTE).
3.1.1 HSPA+ (3GPP Release 7/8)
A transmit diversity mode had already been introduced in Release 99 (WCDMA).
Release 7 of the 3GPP specification (HSPA+) expanded this approach to MIMO and
again increased the data rate with respect to Release 6 (HSDPA). The introduction of
64QAM modulation and MIMO in the downlink makes a peak data rate of 28 Mbps
(Rel. 7) possible. In Rel. 7 MIMO and 64QAM can not be used simultaneously. Since
Rel. 8 the simultaneous use is possible which leads to peak data rates up to 42 Mbps.
Uplink MIMO is not provided.
MIMO was introduced in the form of a double transmit antenna array (D-TxAA) for the
high speed downlink shared channel (HS-DSCH).
Figure 11: HSPA+ MIMO
With D-TxAA, two independent data streams can be transmitted simultaneously over
the radio channel using the same WCDMA channelization codes. The two data
streams are indicated with blue and green color in Figure 11. After spreading and
scrambling, precoding based on weight factors is applied to optimize the signal for
transmission over the mobile radio channel. Four precoding weights w1 to w4 are
available. The first stream is multiplied with w1 and w2, the second stream is multiplied
with w3 and w4. The weights can take the following values:
11. MIMO in Radio Communications Systems
1MA142_0e Rohde & Schwarz Introduction to MIMO 11
Formula 5
Note that w1 is always fixed, and only w2 can be selected by the base station. Weights
w3 and w4 are automatically derived from w1 and w2, because they have to be
orthogonal. The base station selects the optimum weight factors based on proposals
reported by the UE in the uplink.
In addition to the use of MIMO in HS-DSCH, the weight information must be
transmitted to the UE via the HS-SCCH control channel. Although MIMO is not
provided in the uplink, MIMO-relevant information still does have to be transmitted in
the uplink. The UE sends a precoding control indication (PCI) and a channel quality
indication (CQI) in the HS-DPCCH, which allows the base station to adapt the
modulation, coding scheme, and precoding weight to the channel conditions.
For more information on HSPA+, refer to [7].
3.1.2 LTE (3GPP Release 8)
UMTS Long Term Evolution (LTE) was introduced in 3GPP Release 8. The objective is
a high data rate, low latency and packet optimized radio access technology. LTE is
also referred to as E-UTRA (Evolved UMTS Terrestrial Radio Access) or E-UTRAN
(Evolved UMTS Terrestrial Radio Access Network).
The basic concept for LTE in downlink is OFDMA (Uplink: SC-FDMA), while MIMO
technologies are an integral part of LTE. Modulation modes are QPSK, 16QAM, and
64QAM. Peak data rates of up to 300 Mbps (4x4 MIMO) and up to 150 Mbps (2x2
MIMO) in the downlink and up to 75 Mbps in the uplink are specified.
For an introduction to LTE, refer to [2] [3] [4]. For more information on MIMO in LTE,
refer to [6].
Downlink
The following transmission modes are possible in LTE:
N Single antenna transmission, no MIMO
N Transmit diversity
N Open-loop spatial multiplexing, no UE feedback required
N Closed-loop spatial multiplexing, UE feedback required
N Multi-user MIMO (more than one UE is assigned to the same resource block)
N Closed-loop precoding for rank=1 (i.e., no spatial multiplexing, but precoding is
used)
N Beamforming
12. MIMO in Radio Communications Systems
1MA142_0e Rohde & Schwarz Introduction to MIMO 12
Figure 12: LTE downlink
In LTE, one or two code words are mapped to one to four layers ("layer mapper"
block). To achieve multiplexing, a precoding is carried out ("precoding" block). In this
process, the layers are multiplied by a precoding matrix W from a defined code book
and distributed to the various antennas. This precoding is known to both the transmitter
and the receiver. In the specification, code books are defined for one, two, and four
antennas, as well as for spatial multiplexing (with and without CDD) and transmit
diversity. Table 1 shows the code book for spatial multiplexing with two antennas as an
example. Code books for four antennas are also defined.
Spatial multiplexing LTE
Code book
index
Number of layers
1 2
0
1
1
2
1
10
01
2
1
1
1
1
2
1
11
11
2
1
2
j
1
2
1
jj
11
2
1
3
j
1
2
1
-
Table 1: LTE precoding matrix for a maximum of two layers
Uplink
In order to keep the complexity low at the UE end, MU-MIMO is used in the uplink. To
do this, multiple UEs, each with only one Tx antenna, use the same channel.
3.2 WiMAXTM
(802.16e-2005)
WiMAX
TM
promises a peak data rate of 74 Mbps at a bandwidth of up to 20 MHz.
Modulation types are QPSK, 16QAM, and 64QAM.
13. MIMO in Radio Communications Systems
1MA142_0e Rohde & Schwarz Introduction to MIMO 13
Downlink
The WiMAX
TM
802.16e-2005 standard specifies MIMO in WirelessMAN-OFDMA mode.
This standard defines a large number of different matrices for coding and distributing to
antennas. In principle, two, three or four TX antennas are possible. For all modes, the
matrices A, B, and C are available. In the "STC encoder" block, the streams are
multiplied by the selected matrix and mapped to the antennas (Figure 13).
Figure 13: WiMAXTM
downlink
In actual systems typically only matrices A and B are implemented:
Formula 6: WiMAXTM
matrices A and B for two antennas
Matrix A corresponds to TX diversity, while matrix B corresponds to spatial multiplexing
(known in the literature as "True MIMO").
Corresponding matrices also exist for three and four antennas.
Uplink
In Uplink-MIMO only different pilot patterns are used. Coding and mapping is the same
like in non-MIMO case. In addition to single user MIMO (SU-MIMO) two different user
can use the same channel (collaborative MIMO, MU-MIMO).
For more information on WiMAX
TM
, refer to [10] [11].
3.3 WLAN (802.11n)
WLAN as defined by the 802.11n standard promises a peak data rate of up to 600
Mbps at a bandwidth of 40 MHz. Modulation types are BPSK,QPSK,16QAM, and
64QAM. It is backward compatible with the previous standards 802.11 a/b/g.
With up to four streams, it supports up to a maximum of four antennas.
For more information on WLAN, refer to [12] [13].
14. MIMO in Radio Communications Systems
1MA142_0e Rohde & Schwarz Introduction to MIMO 14
WLAN differentiates between spatial streams (SS) and space-time streams (STS). If
NSS < NSTS, then a space-time block encoder ("STBC") distributes the SS to the STS
and adds transmit diversity by means of coding (Downlink block diagram Figure 14).
Figure 14: WLAN downlink
Figure 15 shows the matrix for NSS = 1 and NSTS = 2 as an example.
Figure 15: Coding for SS -> STS (NSS = 1, NSTS = 2)
In the "spatial Mapping" block, the STS is mapped to the transmit chains (NTX). Three
different methods are provided:
N Direct mapping
– 1-to-1 mapping from STS to TC.
N Spatial expansion
– Additional multiplication with a matrix. Figure 16 gives an example of two STS
and three TX antennas.
Figure 16: Example matrix for two STS and three TX
N Beamforming
– Additional multiplication with a steering vector
15. Rohde & Schwarz Solutions
1MA142_0e Rohde & Schwarz Introduction to MIMO 15
3.4 Outlook
Future standards will continue to use MIMO technology. At present, the following
standards with MIMO are being worked on:
N LTE Advanced
The goal is to provide 1 Gbps at 100 MHz bandwidth in downlink direction.
N 1xEV-DO Rev. C
The goal is to provide 18 Mbps at 1.25 MHz bandwidth in forward link.
N WiMAX
TM
802.16m
The goal is to provide 300 Mbps at 20 MHz bandwidth in downlink direction.
4 Rohde & Schwarz Solutions
Rohde & Schwarz offers various instruments and systems for the individual radio
communications standards that use MIMO.
Signal generators are able to perform receiver test in MIMO conditions for up- and
downlink in non-signaling. Signal analyzers are used to test the transmitter side. Mobile
radio testers provide additionally signaling tests for RF testing or protocol testing. RF
test systems provide full RF conformance tests.
4.1 Signal Generators
SMU200A vector signal generator
The UE receiver tests can be performed using SMU signal generators from Rohde &
Schwarz. The SMU can generate the individual antennas for all MIMO standards
(802.11n, 802.16e-2005, and 3GPP Rel. 7 and Rel. 8).
In addition, the SMU performs the channel simulation (MIMO), whereby the individual
correlations can be modified. In addition, the SMU realtime fading is available with
predefined profiles for all standards. AWGN can be added for both channels.
16. Rohde & Schwarz Solutions
1MA142_0e Rohde & Schwarz Introduction to MIMO 16
Figure 17: SMU overview
In a single instrument, two antennas can be generated with fading and MIMO for the
individual standards. By connecting two instruments together, up to four antennas can
be simulated.
By adding the "Phase Coherence" option (SMU-B90), precise phase relationships can
be ensured for up to four antennas for beamforming.
AMU200A baseband signal generator and fading simulator
In addition to the baseband generation of various radiocommunications standards, the
AMU provides MIMO fading via digital inputs/outputs for the CMW as well as for the
SMATE. The combination of AMU and SMATE has the same functionality like a two
channel SMU but provides additionally full frequency range up to 6 GHz on both
channels.
17. Rohde & Schwarz Solutions
1MA142_0e Rohde & Schwarz Introduction to MIMO 17
4.2 Signal Analyzers
For the transmitter test, the FSQ, FSG, and FSV signal/spectrum analyzers are
available. They can carry out both the SISO measurements for the individual standards
as well as most of the MIMO measurements.
In addition to the SISO measurements, a single FSx can also complete certain MIMO
measurements, such as TX diversity and special spatial multiplexing modes. By
connecting multiple FSx instruments together (maximum four), up to four antennas can
be measured or demodulated simultaneously. A lot of the MIMO measurements
anyhow can be performed by a single instrument.
4.3 Mobile Radio Testers
18. Rohde & Schwarz Solutions
1MA142_0e Rohde & Schwarz Introduction to MIMO 18
CMW500 wideband radio communication tester
The CMW500 wideband radio communication tester is a scalable tester for all stages
of UE testing from R&D up to conformance. As an LTE protocol tester, it allows
verification of all protocol layers up to the user plane. Signaling test scenarios can be
flexibly created via powerful programming interfaces. CMW500 supports MIMO testing
as well.
CMW270 WiMAX™ communication tester
The CMW270 is the first all-in-one test solution for WiMAX
TM
mobile stations: from
realistic mobile station test in full signaling mode to high-speed, low-cost test in
non-signaling mode for RF alignment. In addition, the CMW270 supports WiMAX™
MIMO tests, including matrix A and matrix B verification with two antennas.
4.4 Systems
Complex RF test scenarios need to be covered to thoroughly verify MIMO handsets.
MIMO also plays an important role in RF conformance testing and UE certification.
Rohde & Schwarz provides custom-tailored RF test systems for WiMAX™ and LTE,
addressing test applications from R&D up to conformance.
R&S®TS8980 LTE RF test system
The TS8980 supports LTE mobile phone development with fully automatic RF
transmitter and receiver tests. It has a modular design and can be configured to
support precompliance through conformance tests and is MIMO-ready.
19. Rohde & Schwarz Solutions
1MA142_0e Rohde & Schwarz Introduction to MIMO 19
R&S®TS8970 mobile WiMAX
TM
RCT
The TS8970 RCT was developed in response to a call for proposals from the WiMAX
Forum®. The system includes BS and MS radio certification test cases for Mobile
WiMAX
TM
, including MIMO tests, adaptive modulation/coding, and beamforming
verification. With its scalability and outstanding measurement accuracy, the TS8970 is
optimally suited for applications in R&D, quality assurance, precompliance and, of
course, for the certification testing of WiMAX
TM
devices.
R&S®TS8975 WiMAX™ RF preconformance test system
The TS8975 handles most of the tests offered by the TS8970 and is the cost-effective
solution for quality assurance and precompliance testing.
20. Appendix
1MA142_0e Rohde & Schwarz Introduction to MIMO 20
5 Appendix
5.1 References
LTE
[1] 3GPP TS 36.211 V8.4.0; Physical Channels and Modulation (Release 8)
[2] Rohde & Schwarz: UMTS Long Term Evolution (LTE) Technology Introduction,
Application Note 1MA111, September 2008
[3] Rohde & Schwarz: LTE Measurement Guide, Application Note RSI004, August
2007
[4] Rohde & Schwarz: RF Chipset Verification for UMTS LTE with SMU200A and
FSQ, Application Note 1MA138, November 2008
[5] Rohde & Schwarz: E-UTRA Base Station Testing acc. to 3GPP TS 36.141,
Application Note 1MA134, December 2008
[6] Rohde & Schwarz: LTE Downlink MIMO Verification with R&S
®
SMU200A and
R&S
®
FSQ, Application Note 1MA143, June 2009
HSPA+
[7] Rohde & Schwarz: HSPA+ Technology Introduction, Application Note 1MA121,
March 2008
[8] 3GPP TS 25.212; Multiplexing and Channel Coding (FDD), Release 8
WiMAX
TM
[9] IEEE: Std 802.16e™-2005 and Std 802.16™-2004/Cor1-2005, February 2006
[10] Rohde & Schwarz: WiMAX - General information about the standard 802.16,
Application Note 1MA96, June 2006
[11] Rohde & Schwarz: WiMAX - Generating and analyzing 802.16-2004 and
802.16e-2005 signals, Application Note 1MA97, July 2006
WLAN
[12] IEEE: 802.11n™/D8.0: Wireless LAN Medium Access Control
(MAC) and Physical Layer (PHY) specifications, Amendment 5. Enhancements for
Higher Throughput, January 2009
[13] Rohde & Schwarz: WLAN Tests According to Standard 802.11a/b/g,
Application Note 1MA121, July 2004
21. Appendix
1MA142_0e Rohde & Schwarz Introduction to MIMO 21
MIMO
[14] H. Jafarkhani: “Space-Time Coding Theory and Practice”, Cambridge, 2000
[15] M. Jankiraman: “Space-Time Codes and MIMO Systems” Artech House, (2004)
5.2 Additional Information
Please send your comments and suggestions regarding this application note to
TM-Applications@rohde-schwarz.com
Please also visit the technology sites at www.rohde-schwarz.com/technologies/mimo .
22. Ordering Information
1MA142_0e Rohde & Schwarz Introduction to MIMO 22
6 Ordering Information
Please visit our website www.rohde-schwarz.com and contact your local Rohde &
Schwarz sales office for further assistance.
Ordering Information
Vector Signal Generator
SMU200A Vector Signal Generator 1141.2005.02
SMATE200A Vector Signal Generator 1400.7005.02
AMU100A Baseband Signal Generator 1402.4090.02
Signal Analyzers, Spectrum Analyzers
FSQ Up to 3, 8, 26, 31 or 40 GHz 1155.5001.xx
FSG Up to 8 or 13 GHz 1309.0002.xx
FSV Up to 3 or 7 GHz 1307.9002.0x
xx stands for the different frequency ranges (e.g. 1155.5001.26 up to 26 GHz)
Note: Available options are not listed in detail.
Please contact your local Rohde & Schwarz sales office for further assistance.
Radio Communication Tester
CMW270 WiMAXTM
Communication
Tester
1201.0002K75
CMW500 Wideband Radio
Communication Tester
1201.0002.50
Systems
TS8990 LTE RF Test System 1510.6002.02
TS8970 WiMAXTM
RCT 1162.0001.02
TS8975 WiMAXTM
RF Preconformance
Test System
1510.6954.02
23. About Rohde & Schwarz
Rohde & Schwarz is an independent group
of companies specializing in electronics. It is
a leading supplier of solutions in the fields of
test and measurement, broadcasting,
radiomonitoring and radiolocation, as well as
secure communications. Established 75
years ago, Rohde & Schwarz has a global
presence and a dedicated service network
in over 70 countries. Company headquarters
are in Munich, Germany.
Regional contact
Europe, Africa, Middle East
+49 1805 12 42 42* or +49 89 4129 137 74
customersupport@rohde-schwarz.com
North America
1-888-TEST-RSA (1-888-837-8772)
customer.support@rsa.rohde-schwarz.com
Latin America
+1-410-910-7988
customersupport.la@rohde-schwarz.com
Asia/Pacific
+65 65 13 04 88
customersupport.asia@rohde-schwarz.com
This application note and the supplied
programs may only be used subject to the
conditions of use set forth in the download
area of the Rohde & Schwarz website.
Rohde & Schwarz GmbH & Co. KG
Mühldorfstraße 15 | D - 81671 München
Phone + 49 89 4129 - 0 | Fax + 49 89 4129 – 13777
www.rohde-schwarz.com