Smart Antennas offer significant benefits for wireless networks including increased network capacity and improved transmission quality. They work by directing antenna beams towards desired users to increase their signal strength while placing nulls towards interfering signals. This allows serving more users per base station. Smart Antennas adapt their antenna weights based on algorithms that estimate signal directions and qualities. Their introduction impacts radio network planning by requiring consideration of directional channel characteristics and spatially dependent interference.
RF testing has remained hype for most of us. But seriously it is not so. It can be very interesting and one can develop a lot of interest in this if given an opportunity.
In this paper, authors have started with the some basic concepts of radio engineering which we studied in engineering and built upon these concepts to use in practical applications.
We have also described the basic principles of Signal Analyzer and Signal Generator which are the most common test tools used for any radio testing.
SIMULATIONS OF ADAPTIVE ALGORITHMS FOR SPATIAL BEAMFORMINGijiert bestjournal
The main aim of this paper is to simulate different types of Adaptive Algorithms for Spatial Beam forming,which is achieved by combinin g elements of a phased array in such a way that signals at particular angles experi ence constructive interference while others experience destructive interference. Here,s imulations are done on different types of Adaptive Algorithms in MATLAB and Simulink to de termine the desired signal from clutter/noise by updating its weight value for bett er execution speed and computational complexity and the characteristics of individual al gorithms are compared and their area of applications. Adaptive filter is a filter that s elf-adjusts its transfer function according to an optimization algorithm driven by an error sig nal. The adaptive beamforming algorithms are used to update the weight vectors pe riodically to track the signal source in time varying environment by adaptively modifying the system�s antenna pattern so that nulls are generated in the directions of the i nterference sources.
This summary provides the key details from the document in 3 sentences:
The document discusses troubleshooting methods for improving microwave links used by TATA DOCOMO in India. It proposes a system to control the power of indoor units using water sensors and control diesel generators using auxiliary ports. The document also describes the various acknowledgment alarms generated in NEC microwave systems and their associated troubleshooting methods to reduce call drops.
This document discusses multihop/direct forwarding (MDF) for wireless sensor networks deployed in 3D environments. It analyzes the behavior of MDF and compares it to other forwarding schemes. The key points are:
1) MDF is analyzed for its ability to balance energy consumption across sensor nodes in a 3D network model, with the goal of prolonging network lifetime.
2) The network is divided into logical nodes based on distance from the base station. Equations are derived for dividing packet flows between nodes to optimize battery lifespan.
3) Simulation results show MDF balances energy use better than other schemes like closest forwarding, leading to longer network lifetime when applied in 3D wireless sensor networks.
Performance analysis of radar based on ds bpsk modulation techniqueIAEME Publication
This document summarizes the performance of a radar system based on direct sequence spread spectrum (DSSS) binary phase-shift keying (BPSK) modulation. It describes the DSSS-BPSK modulation technique, the radar model implemented in MATLAB/Simulink, and simulation results. Key findings include that bit error rate decreases as the signal-to-noise ratio increases, detection range varies with chip rate, and the system can accurately detect targets from 20cm to 8m distance.
COMPARISON OF BER AND NUMBER OF ERRORS WITH DIFFERENT MODULATION TECHNIQUES I...Sukhvinder Singh Malik
This paper provides analysis of BER and Number of Errors for MIMO-OFDM wireless communication system by using different modulation techniques. Wireless designers constantly seek to improve the spectrum efficiency/capacity, coverage of wireless networks, and link reliability. So the performances of the wireless communication systems can be enhanced by using multiple transmit and receive antennas, which is generally referred to as the MIMO technique. Here analysis will be carried out for an OFDM wireless communication system using different modulation techniques and considering the effect and the wireless channel like AWGN, fading. Performance results will be evaluated numerically and graphically using the plots of BER versus SNR and plots of number of errors versus SNR.
The document describes a simulation project for a communication link using AM and PSK modulation. Students are asked to design and simulate a communication link using AM modulation to transmit an audio signal, investigating the effects of different message signal frequencies and modulation indices. They also simulate communication links using BPSK and QPSK modulation schemes, comparing the performance of each in terms of bandwidth efficiency and required signal power. The project uses Matlab and Simulink to generate signals, design modulators and demodulators, and simulate the overall communication links.
This document presents information on smart antennas. It discusses that smart antennas dynamically adjust their radiation pattern to improve performance by maximizing gain towards desired signals and minimizing it towards interferers. It describes the main types as switched beam antennas which form fixed beams and adaptive arrays which can create infinite patterns adjusted in real time. Adaptive arrays can customize patterns for each user and suppress interference more effectively. Smart antennas provide advantages like extended range, higher capacity and reduced interference. Their applications include cellular networks, satellites and electronic warfare.
RF testing has remained hype for most of us. But seriously it is not so. It can be very interesting and one can develop a lot of interest in this if given an opportunity.
In this paper, authors have started with the some basic concepts of radio engineering which we studied in engineering and built upon these concepts to use in practical applications.
We have also described the basic principles of Signal Analyzer and Signal Generator which are the most common test tools used for any radio testing.
SIMULATIONS OF ADAPTIVE ALGORITHMS FOR SPATIAL BEAMFORMINGijiert bestjournal
The main aim of this paper is to simulate different types of Adaptive Algorithms for Spatial Beam forming,which is achieved by combinin g elements of a phased array in such a way that signals at particular angles experi ence constructive interference while others experience destructive interference. Here,s imulations are done on different types of Adaptive Algorithms in MATLAB and Simulink to de termine the desired signal from clutter/noise by updating its weight value for bett er execution speed and computational complexity and the characteristics of individual al gorithms are compared and their area of applications. Adaptive filter is a filter that s elf-adjusts its transfer function according to an optimization algorithm driven by an error sig nal. The adaptive beamforming algorithms are used to update the weight vectors pe riodically to track the signal source in time varying environment by adaptively modifying the system�s antenna pattern so that nulls are generated in the directions of the i nterference sources.
This summary provides the key details from the document in 3 sentences:
The document discusses troubleshooting methods for improving microwave links used by TATA DOCOMO in India. It proposes a system to control the power of indoor units using water sensors and control diesel generators using auxiliary ports. The document also describes the various acknowledgment alarms generated in NEC microwave systems and their associated troubleshooting methods to reduce call drops.
This document discusses multihop/direct forwarding (MDF) for wireless sensor networks deployed in 3D environments. It analyzes the behavior of MDF and compares it to other forwarding schemes. The key points are:
1) MDF is analyzed for its ability to balance energy consumption across sensor nodes in a 3D network model, with the goal of prolonging network lifetime.
2) The network is divided into logical nodes based on distance from the base station. Equations are derived for dividing packet flows between nodes to optimize battery lifespan.
3) Simulation results show MDF balances energy use better than other schemes like closest forwarding, leading to longer network lifetime when applied in 3D wireless sensor networks.
Performance analysis of radar based on ds bpsk modulation techniqueIAEME Publication
This document summarizes the performance of a radar system based on direct sequence spread spectrum (DSSS) binary phase-shift keying (BPSK) modulation. It describes the DSSS-BPSK modulation technique, the radar model implemented in MATLAB/Simulink, and simulation results. Key findings include that bit error rate decreases as the signal-to-noise ratio increases, detection range varies with chip rate, and the system can accurately detect targets from 20cm to 8m distance.
COMPARISON OF BER AND NUMBER OF ERRORS WITH DIFFERENT MODULATION TECHNIQUES I...Sukhvinder Singh Malik
This paper provides analysis of BER and Number of Errors for MIMO-OFDM wireless communication system by using different modulation techniques. Wireless designers constantly seek to improve the spectrum efficiency/capacity, coverage of wireless networks, and link reliability. So the performances of the wireless communication systems can be enhanced by using multiple transmit and receive antennas, which is generally referred to as the MIMO technique. Here analysis will be carried out for an OFDM wireless communication system using different modulation techniques and considering the effect and the wireless channel like AWGN, fading. Performance results will be evaluated numerically and graphically using the plots of BER versus SNR and plots of number of errors versus SNR.
The document describes a simulation project for a communication link using AM and PSK modulation. Students are asked to design and simulate a communication link using AM modulation to transmit an audio signal, investigating the effects of different message signal frequencies and modulation indices. They also simulate communication links using BPSK and QPSK modulation schemes, comparing the performance of each in terms of bandwidth efficiency and required signal power. The project uses Matlab and Simulink to generate signals, design modulators and demodulators, and simulate the overall communication links.
This document presents information on smart antennas. It discusses that smart antennas dynamically adjust their radiation pattern to improve performance by maximizing gain towards desired signals and minimizing it towards interferers. It describes the main types as switched beam antennas which form fixed beams and adaptive arrays which can create infinite patterns adjusted in real time. Adaptive arrays can customize patterns for each user and suppress interference more effectively. Smart antennas provide advantages like extended range, higher capacity and reduced interference. Their applications include cellular networks, satellites and electronic warfare.
Adaptive array antennas and switched beam array antennas have become core components in future mobile networks. Adaptive arrays can steer beams in any direction of interest while nulling interfering signals, allowing them to track and locate signals. Switched beam arrays have several fixed beam patterns and must decide which beam to access at any given time, with the overall goal of increasing gain based on the user's location.
Smart antenna arrays use digital signal processing to transmit and receive signals in an adaptive and spatially sensitive manner. They have applications in cellular networks, radar, electronic warfare, and satellite systems. Smart antenna arrays provide benefits like higher capacity, coverage, bit rate, link quality, and spectral efficiency compared to conventional antennas. The key elements are the radiating elements, a combining/dividing network, and a control unit. Smart antenna arrays aim to maximize gain in the desired direction and minimize it for interferers.
Investigation and Analysis of SNR Estimation in OFDM systemIOSR Journals
Estimation of signal to noise ratio (SNR) of received signal and to transmit the signal effectively for
the modern communication system. The performance of existing non-data-aided (NDA) SNR estimation methods
are substantially degraded for high level modulation scheme such as M-ary amplitude and phase shift keying
(APSK) or quadrature amplitude modulation (QAM).In this paper SNR estimation proposed method which uses
zero point auto-correlation of received signal per block and auto/cross- correlation of decision feedback signal
in orthogonal frequency division multiplexing (OFDM) system. Proposed method can be studied into two types;
Type 1 can estimate SNR by zero point auto-correlation of decision feedback signal based on the second
moment property. Type 2 uses both zero point auto-correlation and cross-correlation based on the fourth
moment property. In block-by-block reception of OFDM system, these two SNR estimation methods can be
possible for the practical implementation due to correlation based the estimation method and they show more
stable estimation performance than the earlier SNR estimation methods.
The document discusses smart antennas in 3G networks. It provides an introduction to smart antennas, how they form adaptive beams to improve communication links. Smart antennas can increase network capacity and coverage by directing beams toward desired users and nulling interference. This is done through algorithms that calculate antenna weights to maximize signal strength and minimize interference using techniques like beamforming and null steering. Smart antennas allow strategies like interference reduction, rejection, and spatial division multiple access to improve system performance. When applied in 3G base stations, smart antennas can form multiple beams to cover cells, track users to adaptively change beam patterns, and null interference to further increase capacity and coverage for mobile users.
Capacity Enhancement of MIMO-OFDM System in Rayleigh Fading ChannelIOSR Journals
MIMO-OFDM system in Rayleigh Fading Channel is very popular technique for mobile
communication now a day’s for research. Here we want increase the capacity of MIMO-OFDM of system by
using adaptive modulation, Algebraic Space-Time Codes (ASTC) encoder for MIMO Systems are based on
quaternion algebras .we found that ergodic capacity has some limitation which reduce the system’s
performance to overcome this we use ASTC code . ASTC code are full rank, full rate and non vanishing constant
minimum determinant for increasing spectral efficiency and reducing Peak to Average Power Ratio (PAPR) .
This ppt is about Smart Antenna which includes history, Introduction, Working of smart antenna and where this smart antennas can be used.This ppt also tells about the types of smart antenna and the main principle of working of smart antenna. Smart antennas mainly categorized as Adaptive and switched beam array.Among these two adaptive antenna is used for the efficient utilisation of frequency spectrum.
Smart Antenna Report for third year Electronics and Communication Students .
Smart Antenna is a really nice topic to discover and present for third year students of electronics and communication engineering branch. So this Report covers it all .
This document discusses the design of smart antennas for RFID systems. It begins with an introduction to RFID technology, including the components of an RFID system (tags and readers). It then covers basic antenna concepts and different types of antennas. Smart antennas are introduced as antennas with multiple elements that can adaptively process signals. The benefits of using smart antennas for RFID readers are provided, such as improved capacity and interference rejection. Finally, the document outlines the design process for RFID antennas, including designing patch antennas for readers and PIFA antennas for tags using simulation software. It also discusses evaluating the RFID system performance using a evaluation kit.
Its exploring the technique for spatially successive interference cancellation and superposition of transmission for upcoming radio communication 5G technology.
This document outlines a course on fundamentals of wireless communication. The course aims to study the fundamentals and new research developments in the field in a unified way. The topics covered include basics of the wireless channel, diversity techniques, capacity of wireless channels, MIMO systems, and wireless networks. Spatial multiplexing, channel modeling, diversity-multiplexing tradeoff, and opportunistic communication in multiuser systems are some specific concepts discussed. Modern wireless systems like GSM, CDMA2000, and OFDM are used as examples to illustrate the concepts.
The document contains information about the course EC6501 Digital Communication including the units covered in the course and their brief descriptions. It discusses topics like sampling and quantization, waveform coding, baseband transmission, digital modulation schemes and error control coding. It provides the course outcomes listing the highest cognitive level for each outcome. The subsequent sections provide more details about digital modulation formats including ASK, PSK, FSK and their generation and detection. It also discusses coherent and non-coherent receivers along with their probability of error analysis.
Digital communication viva questions.( 50+)
MCQ of digital communication (50+)
communication systems MCQ. (50+)
communication systems viva questions (50+)
covered topic list:
sampling,quantization,digital,discrete,AM,FM,PM,ASK,FSK,PSK,DM,DPCM,QPSK,ADM,differences,modulation,block diagram,applications,PAM,PWM,PPM,line encoding,polar encoding,bipolar encoding,unipolar encoding,RZ,NRZ,AMI,HDB3,B8ZS
The document discusses smart antennas, which are antenna arrays that dynamically adjust their radiation pattern through techniques like beamforming. It describes the basic components of a smart antenna like antenna arrays and discusses techniques used in smart antennas like switched beam systems, adaptive arrays, direction of arrival estimation algorithms like MUSIC and ESPRIT. The summary provides an overview of smart antennas and their advantages in improving wireless communication systems by enhancing coverage, reducing interference and improving capacity.
This document summarizes research on performance analysis of adaptive multi-user OFDM systems. It describes using adaptive modulation to maximize throughput by selecting modulation schemes on a per-subcarrier basis to maintain bit error rate while maximizing spectral efficiency. Adaptive user allocation is also analyzed to improve signal power by optimizing user-subcarrier combinations based on frequency selective fading differences between users. Simulation results show adaptive modulation providing 12-16dB SNR improvement over fixed modulation. Adaptive user allocation provides an additional 3-5dB average signal power gain. The document concludes these adaptive techniques allow OFDM systems to approach channel capacity limits given constraints of the radio channel, transmitter power and quality of service requirements.
On the performance of non-orthogonal multiple access (NOMA) using FPGAIJECEIAES
In this paper, non-orthogonal multiple access (NOMA) is designed and implemented for the fifth generation (5G) of multi-user wireless communication. Field-programmable gate array (FPGA) is considered for the implementation of this technique for two users. NOMA is applied in downlink phase of the base-station (BS) by applying power allocation mechanism for far and near users, in which one signal contains the superposition of two scaled signals depending on the distance of each user from the BS. We assume an additive white Gaussian noise (AWGN) channel for each user in the presence of the interference due to the non-orthogonality between the two users’ signals. Therefore, successive-interference cancellation (SIC) is exploited to remove the undesired signal of the other user. The outage probability and the biterror rate performance are presented over different signal-to-interference-plus-noise ratio (SINR). Furthermore, Monte-Carlo simulations via Matlab are utilized to verify the results obtained by FPGA, which show exact-close match.
COMPARISON OF BIT ERROR RATE PERFORMANCE OF VARIOUS DIGITAL MODULATION SCHEME...ijasa
This document compares the bit error rate (BER) performance of different digital modulation schemes (BPSK, QPSK, 16-QAM) over AWGN and Rayleigh fading channels using Simulink simulations. It finds that BPSK outperforms QPSK and 16-QAM in both channels. The BER is evaluated for these modulation schemes using two equalization techniques: constant modulus algorithm (CMA) and maximum likelihood sequence estimation (MLSE). According to the results, BPSK has better BER performance than QPSK and 16-QAM when using either equalizer, especially at lower SNR values. CMA equalization works better than MLSE equalization for all modulation schemes based on the BER values obtained.
Ber performance of ofdm with discrete wavelet transform for time dispersive c...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Data encoding and modulation techniques are discussed. Modulation involves varying properties of a high-frequency carrier signal according to a message signal. This allows transmission of baseband signals over long distances. Common modulation types are amplitude modulation (AM), frequency modulation (FM), and phase modulation (PM). Encoding converts data into formats for transmission, storage, processing and more. Common encoding schemes for digital data transmission include non-return to zero (NRZ) encoding and Manchester encoding. Pulse modulation can transmit signals as pulses using techniques like pulse code modulation (PCM).
Plasma Antennas Ltd is a UK-based company founded in 2001 that develops multi-beam antenna technologies using plasma devices. They address markets for mobile broadband, secure networks, and homeland security. Their product portfolio includes conventional electronically scanned antenna arrays and ultra-wideband technologies. Their plasma device technology has benefits over conventional arrays such as lower cost, wider bandwidth, and more beams. They are working on applications for 5G, satellites, autonomous vehicles, and more.
The document summarizes research on comparing the performance of different adaptive beamforming algorithms for smart antenna systems. Simulation results showed that training sequence algorithms like recursive least squares (RLS) and least mean squares (LMS) formed the best main lobes towards the desired user but had limitations in interference rejection. The constant modulus algorithm (CMA) provided better interference rejection but a higher bit error rate for a single antenna element. RLS was found to have the fastest convergence rate, making it the best choice. Increasing the step size for LMS affected its performance. Overall, RLS was found to perform best across parameters like beampattern, amplitude response, error, and bit error rate.
A robust doa–based smart antenna processor for gsm base stationsmarwaeng
This document summarizes a robust smart antenna processor for GSM base stations that uses direction-of-arrival (DOA) estimation. It estimates DOAs in the uplink using multiple algorithms, including unitary ESPRIT and Capon's beamformer. It then tracks DOAs separately for uplink and downlink to form antenna patterns that suppress interference. By adapting weights within each GSM frame, it provides up to a 35dB improvement in signal-to-noise-and-interference ratio and outperforms conventional beamformers that place sharp nulls.
An overview of adaptive antenna technologies for wireless communication marwaeng
This document provides an overview of adaptive antenna technologies for wireless communications. It discusses how smart antenna systems can enhance performance by manipulating antenna patterns in the spatial domain to reduce interference and increase capacity. The key benefits are reduced fading, increased power efficiency, and higher capacity. The document reviews smart antenna architectures, direction of arrival estimation techniques, spatial filtering methods like beamforming, and applications such as spatial division multiple access. It also discusses challenges in implementation and opportunities for future research.
Adaptive array antennas and switched beam array antennas have become core components in future mobile networks. Adaptive arrays can steer beams in any direction of interest while nulling interfering signals, allowing them to track and locate signals. Switched beam arrays have several fixed beam patterns and must decide which beam to access at any given time, with the overall goal of increasing gain based on the user's location.
Smart antenna arrays use digital signal processing to transmit and receive signals in an adaptive and spatially sensitive manner. They have applications in cellular networks, radar, electronic warfare, and satellite systems. Smart antenna arrays provide benefits like higher capacity, coverage, bit rate, link quality, and spectral efficiency compared to conventional antennas. The key elements are the radiating elements, a combining/dividing network, and a control unit. Smart antenna arrays aim to maximize gain in the desired direction and minimize it for interferers.
Investigation and Analysis of SNR Estimation in OFDM systemIOSR Journals
Estimation of signal to noise ratio (SNR) of received signal and to transmit the signal effectively for
the modern communication system. The performance of existing non-data-aided (NDA) SNR estimation methods
are substantially degraded for high level modulation scheme such as M-ary amplitude and phase shift keying
(APSK) or quadrature amplitude modulation (QAM).In this paper SNR estimation proposed method which uses
zero point auto-correlation of received signal per block and auto/cross- correlation of decision feedback signal
in orthogonal frequency division multiplexing (OFDM) system. Proposed method can be studied into two types;
Type 1 can estimate SNR by zero point auto-correlation of decision feedback signal based on the second
moment property. Type 2 uses both zero point auto-correlation and cross-correlation based on the fourth
moment property. In block-by-block reception of OFDM system, these two SNR estimation methods can be
possible for the practical implementation due to correlation based the estimation method and they show more
stable estimation performance than the earlier SNR estimation methods.
The document discusses smart antennas in 3G networks. It provides an introduction to smart antennas, how they form adaptive beams to improve communication links. Smart antennas can increase network capacity and coverage by directing beams toward desired users and nulling interference. This is done through algorithms that calculate antenna weights to maximize signal strength and minimize interference using techniques like beamforming and null steering. Smart antennas allow strategies like interference reduction, rejection, and spatial division multiple access to improve system performance. When applied in 3G base stations, smart antennas can form multiple beams to cover cells, track users to adaptively change beam patterns, and null interference to further increase capacity and coverage for mobile users.
Capacity Enhancement of MIMO-OFDM System in Rayleigh Fading ChannelIOSR Journals
MIMO-OFDM system in Rayleigh Fading Channel is very popular technique for mobile
communication now a day’s for research. Here we want increase the capacity of MIMO-OFDM of system by
using adaptive modulation, Algebraic Space-Time Codes (ASTC) encoder for MIMO Systems are based on
quaternion algebras .we found that ergodic capacity has some limitation which reduce the system’s
performance to overcome this we use ASTC code . ASTC code are full rank, full rate and non vanishing constant
minimum determinant for increasing spectral efficiency and reducing Peak to Average Power Ratio (PAPR) .
This ppt is about Smart Antenna which includes history, Introduction, Working of smart antenna and where this smart antennas can be used.This ppt also tells about the types of smart antenna and the main principle of working of smart antenna. Smart antennas mainly categorized as Adaptive and switched beam array.Among these two adaptive antenna is used for the efficient utilisation of frequency spectrum.
Smart Antenna Report for third year Electronics and Communication Students .
Smart Antenna is a really nice topic to discover and present for third year students of electronics and communication engineering branch. So this Report covers it all .
This document discusses the design of smart antennas for RFID systems. It begins with an introduction to RFID technology, including the components of an RFID system (tags and readers). It then covers basic antenna concepts and different types of antennas. Smart antennas are introduced as antennas with multiple elements that can adaptively process signals. The benefits of using smart antennas for RFID readers are provided, such as improved capacity and interference rejection. Finally, the document outlines the design process for RFID antennas, including designing patch antennas for readers and PIFA antennas for tags using simulation software. It also discusses evaluating the RFID system performance using a evaluation kit.
Its exploring the technique for spatially successive interference cancellation and superposition of transmission for upcoming radio communication 5G technology.
This document outlines a course on fundamentals of wireless communication. The course aims to study the fundamentals and new research developments in the field in a unified way. The topics covered include basics of the wireless channel, diversity techniques, capacity of wireless channels, MIMO systems, and wireless networks. Spatial multiplexing, channel modeling, diversity-multiplexing tradeoff, and opportunistic communication in multiuser systems are some specific concepts discussed. Modern wireless systems like GSM, CDMA2000, and OFDM are used as examples to illustrate the concepts.
The document contains information about the course EC6501 Digital Communication including the units covered in the course and their brief descriptions. It discusses topics like sampling and quantization, waveform coding, baseband transmission, digital modulation schemes and error control coding. It provides the course outcomes listing the highest cognitive level for each outcome. The subsequent sections provide more details about digital modulation formats including ASK, PSK, FSK and their generation and detection. It also discusses coherent and non-coherent receivers along with their probability of error analysis.
Digital communication viva questions.( 50+)
MCQ of digital communication (50+)
communication systems MCQ. (50+)
communication systems viva questions (50+)
covered topic list:
sampling,quantization,digital,discrete,AM,FM,PM,ASK,FSK,PSK,DM,DPCM,QPSK,ADM,differences,modulation,block diagram,applications,PAM,PWM,PPM,line encoding,polar encoding,bipolar encoding,unipolar encoding,RZ,NRZ,AMI,HDB3,B8ZS
The document discusses smart antennas, which are antenna arrays that dynamically adjust their radiation pattern through techniques like beamforming. It describes the basic components of a smart antenna like antenna arrays and discusses techniques used in smart antennas like switched beam systems, adaptive arrays, direction of arrival estimation algorithms like MUSIC and ESPRIT. The summary provides an overview of smart antennas and their advantages in improving wireless communication systems by enhancing coverage, reducing interference and improving capacity.
This document summarizes research on performance analysis of adaptive multi-user OFDM systems. It describes using adaptive modulation to maximize throughput by selecting modulation schemes on a per-subcarrier basis to maintain bit error rate while maximizing spectral efficiency. Adaptive user allocation is also analyzed to improve signal power by optimizing user-subcarrier combinations based on frequency selective fading differences between users. Simulation results show adaptive modulation providing 12-16dB SNR improvement over fixed modulation. Adaptive user allocation provides an additional 3-5dB average signal power gain. The document concludes these adaptive techniques allow OFDM systems to approach channel capacity limits given constraints of the radio channel, transmitter power and quality of service requirements.
On the performance of non-orthogonal multiple access (NOMA) using FPGAIJECEIAES
In this paper, non-orthogonal multiple access (NOMA) is designed and implemented for the fifth generation (5G) of multi-user wireless communication. Field-programmable gate array (FPGA) is considered for the implementation of this technique for two users. NOMA is applied in downlink phase of the base-station (BS) by applying power allocation mechanism for far and near users, in which one signal contains the superposition of two scaled signals depending on the distance of each user from the BS. We assume an additive white Gaussian noise (AWGN) channel for each user in the presence of the interference due to the non-orthogonality between the two users’ signals. Therefore, successive-interference cancellation (SIC) is exploited to remove the undesired signal of the other user. The outage probability and the biterror rate performance are presented over different signal-to-interference-plus-noise ratio (SINR). Furthermore, Monte-Carlo simulations via Matlab are utilized to verify the results obtained by FPGA, which show exact-close match.
COMPARISON OF BIT ERROR RATE PERFORMANCE OF VARIOUS DIGITAL MODULATION SCHEME...ijasa
This document compares the bit error rate (BER) performance of different digital modulation schemes (BPSK, QPSK, 16-QAM) over AWGN and Rayleigh fading channels using Simulink simulations. It finds that BPSK outperforms QPSK and 16-QAM in both channels. The BER is evaluated for these modulation schemes using two equalization techniques: constant modulus algorithm (CMA) and maximum likelihood sequence estimation (MLSE). According to the results, BPSK has better BER performance than QPSK and 16-QAM when using either equalizer, especially at lower SNR values. CMA equalization works better than MLSE equalization for all modulation schemes based on the BER values obtained.
Ber performance of ofdm with discrete wavelet transform for time dispersive c...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Data encoding and modulation techniques are discussed. Modulation involves varying properties of a high-frequency carrier signal according to a message signal. This allows transmission of baseband signals over long distances. Common modulation types are amplitude modulation (AM), frequency modulation (FM), and phase modulation (PM). Encoding converts data into formats for transmission, storage, processing and more. Common encoding schemes for digital data transmission include non-return to zero (NRZ) encoding and Manchester encoding. Pulse modulation can transmit signals as pulses using techniques like pulse code modulation (PCM).
Plasma Antennas Ltd is a UK-based company founded in 2001 that develops multi-beam antenna technologies using plasma devices. They address markets for mobile broadband, secure networks, and homeland security. Their product portfolio includes conventional electronically scanned antenna arrays and ultra-wideband technologies. Their plasma device technology has benefits over conventional arrays such as lower cost, wider bandwidth, and more beams. They are working on applications for 5G, satellites, autonomous vehicles, and more.
The document summarizes research on comparing the performance of different adaptive beamforming algorithms for smart antenna systems. Simulation results showed that training sequence algorithms like recursive least squares (RLS) and least mean squares (LMS) formed the best main lobes towards the desired user but had limitations in interference rejection. The constant modulus algorithm (CMA) provided better interference rejection but a higher bit error rate for a single antenna element. RLS was found to have the fastest convergence rate, making it the best choice. Increasing the step size for LMS affected its performance. Overall, RLS was found to perform best across parameters like beampattern, amplitude response, error, and bit error rate.
A robust doa–based smart antenna processor for gsm base stationsmarwaeng
This document summarizes a robust smart antenna processor for GSM base stations that uses direction-of-arrival (DOA) estimation. It estimates DOAs in the uplink using multiple algorithms, including unitary ESPRIT and Capon's beamformer. It then tracks DOAs separately for uplink and downlink to form antenna patterns that suppress interference. By adapting weights within each GSM frame, it provides up to a 35dB improvement in signal-to-noise-and-interference ratio and outperforms conventional beamformers that place sharp nulls.
An overview of adaptive antenna technologies for wireless communication marwaeng
This document provides an overview of adaptive antenna technologies for wireless communications. It discusses how smart antenna systems can enhance performance by manipulating antenna patterns in the spatial domain to reduce interference and increase capacity. The key benefits are reduced fading, increased power efficiency, and higher capacity. The document reviews smart antenna architectures, direction of arrival estimation techniques, spatial filtering methods like beamforming, and applications such as spatial division multiple access. It also discusses challenges in implementation and opportunities for future research.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document provides an overview of smart antennas. It begins with a brief history, noting that smart antennas were initially developed for military communications and intelligence gathering. The document defines smart antennas as antenna arrays that can adaptively change their pattern in response to signal environments to improve communication channel performance. It describes the key components and functioning of smart antennas, including digital beamforming and direction of arrival estimation techniques. The main types of smart antennas are explained as adaptive array antennas and switched beam array antennas. Advantages like reduced interference and increased capacity are outlined. Applications in mobile communications, satellites, and wireless networks are also mentioned.
Channel Overlapping Between IMT-Advanced Users and Fixed Satellite ServiceEECJOURNAL
This document summarizes a research paper that proposes a new algorithm to mitigate interference between IMT-Advanced base stations and fixed satellite services. The algorithm aims to form nulls in the radiation pattern of base stations towards satellites by extracting null directions using MUSIC algorithm and controlling handover. It studies two mobile users moving around a satellite and simulates calculating the shortest separation distance after identifying critical points. Results show the algorithm can enable good coexistence and spectrum sharing between the different wireless services in the C-band frequency range.
The document is a seminar report on smart antenna systems submitted by Ashok Behuria in partial fulfillment of the requirements for a Bachelor of Engineering degree. It discusses different types of smart antenna systems including switched beam and adaptive array systems. The report provides an overview of smart antennas, explaining that they combine antenna arrays with signal processing to optimize radiation and reception patterns automatically based on the signal environment.
This document presents information on smart antennas. It discusses different types of smart antennas including switched beam antennas and adaptive array antennas. Switched beam antennas form multiple fixed beams while adaptive array antennas can dynamically adjust patterns in response to the signal environment. Space division multiple access is described as an advanced technique that employs smart antennas. Key advantages of smart antennas are also summarized such as improved coverage, interference reduction, and increased system capacity. Applications and limitations of smart antenna systems are provided.
This document discusses the use of smart antennas in 4G mobile communications. It begins by defining smart antennas as antenna arrays connected to a digital signal processor that can enhance wireless links through diversity gain, array gain, and interference suppression. This allows for higher data rates or more simultaneous users. The document then discusses key principles such as using antenna arrays to distinguish propagation paths and encode independent data streams. It also covers applications like space division multiple access and beamforming basics. Specific benefits of smart antennas for mobile communications discussed include increased antenna gain, decreased inter-symbol interference, and spatial filtering/nulling of interference.
11.smart antennas in 0004www.iiste.org call for paper_gAlexander Decker
This document summarizes a paper on smart antennas in 4G systems. It discusses how smart antennas work using an array of antenna elements and a digital signal processor to form beams. This allows for diversity gain, array gain, and interference suppression, improving capacity. Smart antennas can distinguish between propagation paths to transmit independent data streams or redundantly encode data. They can also suppress interference for conventional transmitters. Applications discussed include space division multiple access, beamforming basics, switched beam antennas, and use in mobile communications for increased gain and reduced interference.
A survey of Adaptive Beamforming Strategy in Smart Antenna for Mobile Communi...IRJET Journal
This document summarizes research on adaptive beamforming strategies for smart antennas in mobile communication. It first defines smart antennas and discusses their advantages over traditional fixed beam antennas. It then reviews literature on different approaches to smart antenna design, including switched beam and adaptive beamforming. The main challenges are reducing interference and multipath effects to improve capacity and performance. The proposed work is to use an adaptive smart antenna that can steer its main beam toward the desired user while generating nulls toward interferers based on direction of arrival estimation. This approach aims to enhance capacity in mobile systems.
Smart antennas are antenna arrays that dynamically adapt their beam patterns to improve communication performance. They use signal processing algorithms to electronically steer antenna beams towards desired signals and away from interference. This allows them to improve signal strength, reduce interference, and increase channel capacity. Smart antennas work by estimating the direction of incoming signals using direction of arrival estimation methods. They can form switched beams that steer in different directions, or use adaptive arrays with complex algorithms to nullify interference. Smart antenna technologies like SDMA can further improve capacity by separating multiple users within the same frequency band.
ADAPTIVE AND DYNAMIC WIRELESS ROUTERS WITH SMART ANTENNcscpconf
In the recent evolution of wireless technologies, the power management has been a worrying
factor. In order to overcome the power shortage, steps are taken to find new kind of energy
harvesting methods, power attenuation reduction methods and power saving techniques. Wireless
routers even though consume not much of power, battery powered devices require a lot. Omni
directional antenna embedded with multiple antennae focusing the beam of radio wave signals in
the direction of nodes with least transmission angle can be a solution for this problem which is called as “Smart Antenna”. To reduce power maceration we are going for adaptive and dynamic transmission wherein the transmission angle of antennae is varied in accordance with the movement of nodes. Apart from saving the power considerably, it also improves the signal strength
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1) Smart antennas use antenna arrays that can change their radiation patterns in response to the signal environment to improve wireless system performance.
2) There are two main types of smart antennas: phased beam antennas which form a finite number of fixed patterns, and adaptive array antennas which can form an infinite number of patterns.
3) Adaptive array antennas can direct their main beam toward the desired signal while suppressing interference by adapting their pattern, allowing them to customize coverage for each user.
Beamforming with per antenna power constraint and transmit antenna selection ...sipij
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Field trial with a gsmdcs1800 smart antenna base station marwaeng
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1) Measurements in a static line-of-sight scenario demonstrated the potential to suppress interference by 25dB.
2) Field measurements in a microcell setup confirmed the system's ability to track mobiles even in multipath environments.
3) In non-line-of-sight situations an average signal-to-noise gain of 7.4dB was achieved, increasing to 8.3dB in line-of-sight environments.
4) Angular diversity provided an additional 5.8dB diversity gain at a 1% bit-error ratio
This document discusses smart antenna technology. It defines smart antennas as antenna systems that combine multiple antenna elements with signal processing to optimize radiation and reception patterns in response to the signal environment. The document describes two main types of smart antennas: switched beam antennas which form fixed beams and adaptively switch between them, and adaptive array antennas which can form an infinite number of patterns in real-time to maximize desired signals and minimize interference. It compares the advantages and drawbacks of each type and discusses applications of smart antenna technology in fields like wireless networks and satellite systems.
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Smart antennas for umts
1. Smart Antennas - A Technical Introduction
SYMENA Software & Consulting GmbH
Wiedner Hauptstraße 24/15, A-1040 Vienna, Austria
Phone: [+43-1] 585 51 01-0, Fax: [+43-1] 585 51 01-99
info@symena.com, www.symena.com
Abstract— Smart Antennas are recognized characteristics. This weight adaptation is the
as a key technology for capacity increase in ”smart” part of the Smart Antennas, which
3G radio networks. Smart Antennas offer a should hence (more precisely) be called
mixed service capacity gain of more than ”adaptive antennas”.
100% and hence reduce to less than half
the number of base stations required. They
are one of the most promising technologies
for the enabling of high capacity wireless
networks. Since Smart Antennas are more
expensive than conventional base stations,
they should be used where they are truly
needed.
In this paper we provide a brief overview
of Smart Antennas, their benefits and how
they actually work.
I. SMART ANTENNA BASICS
Conventional base station antennas in
existing operational systems are either
omnidirectional or sectorized. There is a waste
of resources since the vast majority of
transmitted signal power radiates in directions
other than toward the desired user. In addition, Fig. 1. Smart antenna patterns in a multi-
signal power radiated throughout the cell area service UMTS system with high data rate
will be experienced as interference by any other interferers and desired low data rate users.
user than the desired one. Concurrently the
base station receives ”interference” emanating Smart Antennas can be used to achieve
from the individual users within the system. different benefits. The most important is higher
Smart Antennas offer a relief by transmitting / network capacity, i.e. the ability to serve more
receiving the power only to / from the desired users per base station, thus increasing
directions. revenues of network operators, and giving
A Smart Antenna consists of M antenna customers less probability of blocked or
elements, whose signals are processed dropped calls. Also, the transmission quality
adaptively in order to exploit the spatial can be improved by increasing desired signal
dimension of the mobile radio channel. In the power and reducing interference. A schematic
simplest case, the signals received at the model of how Smart Antennas work is shown in
different antenna elements are multiplied with Figure 1. The example cell serves several low
complex weights, and then summed up; the data rate users and a few high data rate users.
weights are chosen adaptively. Not the antenna The latter are indicated by mobile terminals
itself, but rather the complete antenna system with large screen and keyboard. Let us consider
including the signal processing is adaptive or the uplink first: Without Smart Antennas the
smart. All M elements of the antenna array high data rate users heavily interfere with the
have to be combined (weighted) in order to more distant desired user. The former have to
adapt to the current channel and user send with higher TX power in order to fulfill the
1
2. requirements at the receiver. Using Smart we will provide an overview of Smart Antenna
Antennas means the antenna beams are classifications such as switched beam
directed towards and focused on the desired antennas, spatial processing, space-time-
user and hence this user can be ”heard” much processing, and space-time detection. Then we
better. The interference from the high data rate will present an overview of the adaptation
interferers is reduced by setting broad nulls algorithms and, finally, we will show the effects
of the introduction of Smart Antennas on radio
network planning.
II. SMART ANTENNA RECEIVER CLASSIFICATIONS
Smart Antennas can basically be divided
into: switched beam, spatial processing, space-
time-processing, and space-time detection. The
simplest implementation is the so-called
switched beam system, in which a single
transceiver is connected to the RF-
beamforming unit. If the number of antenna
elements is M, one out of the predefined set of
beams (N ≤ M) is selected, based on maximum
received signal power or minimum bit error
ratio (BER) [1] [2]. The best signal is selected
for further processing by a standard receiver.
This technique benefits from its simplicity.
Fig. 2. Antenna pattern of a eight-element
However, maxima and nulls of the antenna
uniform linear array. The signal arrives at 10°.
pattern can not be put into arbitrary directions,
Two interfering signals are shown, one at -35°
but can only be chosen from one of N possible
and a stronger one at 32°. The smart antenna
positions.
algorithms compute the antenna weights for all
A more sophisticated approach is the spatial
eight antenna elements so that the Signal-to-
Noise-and-Interference ratio (SNIR) becomes
filter or spatial processing. The received signals
are converted down to base band and sampled.
an optimum.
This procedure requires M receiver chains. The
signals of each receiver chain are multiplied
in the antenna pattern towards their main with complex weights w, and then summed up.
direction of arrival. This interference reduction The resulting output signal can then be
corresponds to an increase in the uplink processed like any signal from a normal
coverage in a UMTS network. This is also antenna. In wideband systems like UMTS, the
shown in Figure 2. signal is fed into a conventional equalizer1,
Further benefits include a possible which combines the signal components with
reduction of the delay spread, allowing higher different delays, leading to the term time or
data rates, and a reduction of the transmission temporal processing. The combination of these
power in both uplink and downlink. The latter two involves simultaneous filtering in space and
is responsible for the downlink capacity time and is called space-time processing.
limitation in UMTS networks. The less base Space-only processing works best if each
station transmission power is required for a antenna element shows the same time
single link, the more users can be served. dispersion, i.e. the same shape of the impulse
Hence, Smart Antennas can increase both the response. If this is not true, each antenna
uplink and the downlink capacity of UMTS element should have a separate equalizer. If we
radio networks. use a linear equalizer of length L , the total
structure has then M spatial and L temporal
Having reviewed how a Smart Antenna can complex weights, leading to a complexity of
improve the performance of a mobile system, M * L . Instead of calculating the spatial and
we shall now look at how to achieve the
1
individual improvements. In the following text In narrowband systems, a decision device can
follow immediately
2
3. temporal weight vectors in a sequential combining methods for the diversity signals,
manner, we can calculate them jointly, leading the SNIR can finally be optimized [4] [3].
to a weight matrix of size M * L . The receiver is In beam forming, one exploits the close
then also known as joint space-time receiver or proximity of antenna elements in order that an
joint space-time equalizer. The output signal is appreciable correlation between the antenna
then fed into a decision device for recovering elements is present. The close proximity of
the received bitstream. antenna elements allows forming a unique
Finally, we could also do the space-time antenna pattern that enhances the desired
equalization and the detection jointly, leading signal and suppresses the interference.
to a so called joint space-time detection.
Space-time detection offers best performance, III. WEIGHT ADAPTATION ALGORITHMS
but also the highest degree of complexity.
In the beamforming case the major question
Figure 3 shows block diagrams of both a
is: How to calculate the complex weights w for
decoupled space-time and a joint space-time
the individual antenna elements for each user?
receiver2.
Before answering this question one should
Smart Antennas can also be classified in a
reflect upon the different processes in the
different way: whether they use diversity or
baseband signal processing unit, before the
beamforming. Diversity relies essentially upon
antenna weights can be adapted. Basically the
the statistical independence of the signals at
signal processing unit is responsible for the
different antenna elements. In the simplest
user identification, user separation and beam-
case, one exploits the high improbability that
forming. First, the base station has to estimate
the signals of all the elements are
the directions of arrival of all multipath
simultaneously in a fading dip.
components. Next, it has to determine whether
the echo from a certain direction comes from a
desired user or from an interferer. Finally, it
can compute the antenna weights in order to
increase the SNIR as much as possible.
Adaptation algorithms are designed to
process the above mentioned demands. They
can basically be classified as temporal
reference (TR), spatial reference (SR) and blind
(BA) algorithms.
A. Temporal Reference Algorithms (TR)
TR algorithms are based on the prior
knowledge of the time structure of parts of the
received signals. The training sequences of
both 2G (a midamble in GSM) and 3G (pilot
bits in UMTS) systems fulfill this requirement.
The receiver adjusts the complex weights in
Fig. 3. Space-Time receiver structures. (a) such a way that the difference between the
separate space and time domain weight combined signal at the output and the known
adaptation, (b) joint space time filtering. training sequence is minimized. Those weights
are then used for the reception of the actual
data. The temporal reference approach can be
In order to achieve statistical independence
used in conjunction with both diversity and
various diversity techniques can be applied [3].
beamforming methods, although it is more
By using more advanced, but well known
common with the former.
2
In literature, the separated space-time receiver B. Spatial Reference Algorithms (SR)
structure is also named ”decoupled space-time
rake”, ”beamformer rake”, ”2D-rake” and ”vector SR algorithms estimate the direction of
Rake - single beamformer”. arrival (DOA) of both the desired and interfering
3
4. signals. They are based on the prior knowledge structure of the transmitted signal, e.g. finite
of the physical antenna geometry. In most alphabet, or cyclostationarity. If training
mobile communication systems, the time a sequences are used in combination with blind
wavefront takes to pass through the antenna algorithms, they are called semi-blind
array is much smaller than the bit (or chip) algorithms which show better performance than
interval Tb (Tc). Therefore, the narrowband temporal reference algorithms or blind
assumption for antenna arrays is valid (see algorithms alone [5]. Currently, all blind or
Figure 4). This makes it possible to model the semi-blind algorithms require too much
time delays of the wave between the antenna computation time to be employed in real time,
elements as phase shifts. Hence, a received but semi-blind algorithms are close to real-time
signal impinging at the antenna array at angle θ implementation.
can be expressed as
IV. EFFECTS ON RADIO NETWORK PLANNING
T
− j 2π λ sin (θ )
d
− j 2π sin (θ )( M −1)
d
The effects of Smart Antennas on the radio
c(θ ) = 1, e ,K, e λ
(1) network planning process are various. The most
important technical innovation regarding smart
antenna radio network planning is the
where c(θ) is the array steering vector, d, λ¸ consideration of the spatial behavior of the
and M denote the inter-element spacing, the mobile radio propagation channel. Within the
wavelength and the number of antenna European research initiative COST 259 [6]
elements. The notation (.)T indicates the several channel models have been developed.
transpose. For the estimation of the individual They are aimed at UMTS and HIPERLAN3,
DOAs no additional information is needed. with particular emphasis on Smart Antennas
After user identification (e.g. by utilizing the and directional channels. They have been
training sequence) the signals can be separated introduced in the 3rd generation
and detected. standardization process by 3GPP [7].
The spatial behavior of the received
C. Blind Algorithms (BA) interference is another significant issue
regarding the complex smart antenna radio
Instead of using a training sequence or the
network planning. If the interference is
properties of the receiver array, “blind”
spatially white, i.e. the interferers are equally
algorithms can be applied for weight adaptation
distributed in the coverage area, the gain due
as well. Blind Algorithms basically try to extract
to Smart Antennas only has to be taken into
the unknown channel impulse response and the
account in the link budget. This can be easily
unknown transmitted data from the received
implemented by utilizing look-up-tables, where
signal at the antenna elements. Even though
the smart antenna gains are listed in order of
they do not know the actual bits, Blind
the experienced signal to noise and
Algorithms use additional knowledge about the
interference ratio (SNIR).
The simplifying assumption of spatial
. . . .
whiteness holds in second generation CDMA
. systems at least approximately, where mainly
speech users with almost identical data rates
are served. It can be shown that this is no
longer true in multi-service high data rate
UMTS networks [8]. The consequence is that
smart antenna adaptation algorithms have to be
Fig. 4. Principle of SR algorithms. The phase considered even in the planning process! While
shift between two antenna elements is defined simple beamsteering algorithms only consider
by the antenna geometry and the angle of the desired signal, more sophisticated
incidence. k=2π/ λ, where λ is the wavelength, algorithms take the interferers into account.
d is the interelement spacing and M is the Finally, Smart Antennas also affect the radio
number of antenna elements. resource management (RRM). The RRM
3
HIgh PERformance Local Area Network
4
5. algorithms are important for the planning [9] A. Paulraj and C. B. Papadias, “Space-time
process when the main concerns are about the processing for wireless communications”, IEEE
number of served packet switched users and Signal Processing Mag., vol. 14, pp. 49–83,
November 1997.
the quality of service (QoS) in the network.
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V. SOLUTIONS OFFERED BY SYMENA
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software solutions for Smart Antenna radio
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software solutions help operators to invest their Proc. IEEE Vehicular Technology Conference,
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it is not. 1997.
Detailed information about the products can [14] A. F. Naguib, A. Paulraj, and T. Kailath,
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