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Outline
• M2M
• Differences between M2M and IoT
• SDN and NFV/VNF
• Massive MIMO
• Beamforming
Book website: http://www.internet-of-things-book.com
Machine-to-Machine (M2M)
• Machine-to-Machine (M2M) refers to networking of machines (or devices)
for the purpose of remote monitoring and control and data exchange.
Machine-to-Machine (M2M)
• An M2M area network comprises machines (or M2M nodes) which have
embedded hardware modules for sensing, actuation and communication.
• Various communication protocols can be used for M2M local area networks,
such as ZigBee, Bluetooth, ModBus, M-Bus, Wireless M-Bus, Power Line
Communication (PLC), 6LoWPAN, IEEE 802.15.4, etc.
• The communication network provides connectivity to remote M2M area
networks.
• The communication network can use either wired or wireless networks (IP-
based).
• While the M2M area networks use either proprietary or non-IP based
communication protocols, the communication network uses IP-based
networks.
M2M Gateway
• Since non–IP-based protocols are used within M2M area networks, the M2M
nodes within one network cannot communicate with nodes in an external
network.
• To enable communication between remote M2M area networks, M2M
gateways are used.
Difference between IoT and M2M
• Communication Protocols
• M2M and IoT can differ in how the communication between the machines or
devices happens.
• M2M uses either proprietary or non–IP-based communication protocols for
communication within the M2M area networks.
• Machines in M2M vs Things in IoT
• The "Things" in IoT refers to physical objects that have unique identifiers and
can sense and communicate with their external environment (and user
applications) or their internal physical states.
• M2M systems, in contrast to IoT, typically have homogeneous machine types
within an M2M area network.
Difference between IoT and M2M
• Hardware vs Software Emphasis
• While the emphasis of M2M is more on hardware with embedded modules, the
emphasis of IoT is more on software.
• Data Collection & Analysis
• M2M data is collected in point solutions and often in on-premises storage
infrastructure.
• In contrast to M2M, the data in IoT is collected in the cloud (can be public, private or
hybrid cloud).
• Applications
• M2M data is collected in point solutions and can be accessed by on-premises
applications such as diagnosis applications, service management applications and on-
premises enterprise applications.
• IoT data is collected in the cloud and can be accessed by cloud applications such as
analytics applications, enterprise applications, remote diagnosis and management
applications, etc.
Communication in IoT vs M2M
SDN
• Software-Defined Networking (SDN)
is a networking architecture that
separates the control plane from the
data plane and centralizes the
network controller.
• Software-based SDN controllers
maintain a unified view of the
network and make configuration,
management and provisioning
simpler.
• The underlying infrastructure in SDN
uses simple packet forwarding
hardware as opposed to specialized
hardware in conventional networks.
Key Elements of SDN
• Centralized Network Controller
• With decoupled control and data planes and a centralized network controller, the
network administrators can rapidly configure the network.
• Programmable Open APIs
• SDN architecture supports programmable open APIs for interface between the SDN
application and control layers (Northbound interface).
• Standard Communication Interface (OpenFlow)
• SDN architecture uses a standard communication interface between the control and
infrastructure layers (Southbound interface).
• OpenFlow, which is defined by the Open Networking Foundation (ONF), is the broadly
accepted SDN protocol for the Southbound interface.
NFV
• Network Function Virtualization
(NFV) is a technology that leverages
virtualization to consolidate the
heterogeneous network devices
onto industry-standard high-volume
servers, switches and storage.
• NFV is complementary to SDN as
NFV can provide the infrastructure
on which SDN can run.
Key Elements of NFV
• Virtualized Network Function (VNF)
• VNF is a software implementation of a network function which is capable of running
over the NFV Infrastructure (NFVI).
• NFV Infrastructure (NFVI)
• NFVI includes computer, network and storage resources that are virtualized.
• NFV Management and Orchestration
• NFV Management and Orchestration focuses on all virtualization-specific management
tasks and covers the orchestration and life-cycle management of physical and/or
software resources that support infrastructure virtualization and the life-cycle
management of VNFs.
NFV – Use Case
• NFV can be used to virtualize the Home Gateway. The NFV Infrastructure in the cloud hosts a virtualized
Home Gateway.
• The virtualized gateway provides private IP addresses to the devices in the home. The virtualized gateway
also connects to network services such as VoIP and IPTV.
What is MIMO ?
• MIMO (Multiple Input Multiple Output) antenna technology is a way of increasing the capacity of a radio
link using multiple transmit antennas and multiple receive antennas.
• Due to multipath propagation and decorrelated paths between the transmitter and receiver, multiple data
streams can be sent over the same radio channel, thus increasing the peak data rate per user along with the
capacity of the cellular network.
• MIMO has been part of LTE since the 1st release. LTE started with a 2×2 MIMO which means 2 transmit
antennas at the base station (BS) side and 2 receive antennas at the UE side.
• LTE allow applications of up to 8 spatial layers in DL direction and up to 4 spatial layers in UL direction.
Commercial LTE networks tend to use 2 or 4 spatial layers.
SU-MIMO and MU-MIMO -
• If different data streams are sent to the same receiver, it is referred to as Single User MIMO (SU-MIMO),
• if the data streams are transmitted to different users, it is referred to as Multi-User MIMO (MU-MIMO)
• With 5G NR, there is possibility of having up to 256 transmit antenna at the BS side and that is where the term
‘massive MIMO’ comes into picture.
• Massive MIMO antennas uses a large number of antenna elements but operate at frequencies below 6 GHz.
Essentially, they exploit many elements to realize a combination of BF and spatial multiplexing.
MIMO Vs MU-MIMO -
Why massiveMIMO -
• Spectral Efficiency: Massive MIMO provides higher spectral efficiency by allowing its
antenna array to focus narrow beams towards a user. Spectral efficiency more than ten
times better than the current MIMO system used for 4G/LTE can be achieved.
• Energy Efficiency: As antenna array is focused in a small specific section, it requires less
radiated power and reduces the energy requirement in massive MIMO systems.
• High Data Rate: The array gain and spatial multiplexing provided by massive MIMO
increases the data rate and capacity of wireless systems.
• User Tracking: Since massive MIMO uses narrow signal beams towards the user; user
tracking becomes more reliable and accurate.
• Low Power Consumption: Massive MIMO is built with ultra lower power linear amplifiers,
which eliminates the use of bulky electronic equipment in the system. This power
consumption can be considerably reduced.
• Less Fading: A Large number of the antenna at the receiver makes massive MIMO
resilient against fading
Why massiveMIMO -
• Low Latency: Massive MIMO reduces the latency on the air interface.
• Robustness: Massive MIMO systems are robust against unintended interference and
internal Jamming. Also, these systems are robust to one or a few antenna failures due to
large antennas.
• Reliability: A large number of antennas in massive MIMO provides more diversity gain,
which increases the link reliability.
• Enhanced Security: Massive MIMO provides more physical security due to the orthogonal
mobile station channels and narrow beams.
• Low Complex Linear Processing: More number of base station antenna makes the simple
signal detectors and precoders optimal for the system.
Challenges massiveMIMO -
Challenges massiveMIMO -
• Pilot Contamination -
• In massive MIMO systems, the base station needs the
channel response of the user terminal to get the estimate
of the channel.
• The uplink channel is estimated by the base station when
the user terminal sends orthogonal pilot signals to the
base station.
• Furthermore, with the help of channel reciprocity
property of massive MIMO, the base station estimates the
downlink channel towards the user terminal.
• If the pilot signals in the home cell and neighboring cells
are orthogonal, the base station obtains the accurate
estimation of the channel. However, the number of
orthogonal pilot signals in given bandwidth and period is
limited, which forces the reuse of the orthogonal pilots in
neighbouring cells
Challenges massiveMIMO -
• Channel Estimation -
• For signal detection and decoding, massive MIMO relies on Channel State Information
(CSI).
• CSI is the information of the state of the communication link from the transmitter to the
receiver and represents the combined effect of fading, scattering, and so forth.
• If the CSI is perfect, the performance of massive MIMO grows linearly with the number of
transmitting or receive antennas, whichever is less.
Challenges massiveMIMO -
• Channel Estimation -
• For a system using Frequency Division Duplexing
(FDD), CSI needs to be estimated both during
downlink and uplink.
• During uplink, channel estimation is done by the
base station with the help of orthogonal pilot signals
sent by the user terminal.
• During the downlink, the base station sends pilot
signals towards the user, and the user acknowledges
with the estimated channel information for the
downlink transmission.
• For a massive MIMO system with many antennas,
the downlink channel estimation strategy in FDD
becomes very complex and infeasible to implement
Challenges massiveMIMO -
• Channel Estimation -
• TDD provides the solution for the problem during
downlink transmission in FDD systems.
• In TDD, by exploiting the channel reciprocity
property, the base station can estimate the
downlink channel with the help of channel
information during uplink.
• During uplink, the user will send the orthogonal
pilot signals towards the base station, and based
on these pilot signals, the base station will
estimate the CSI to the user terminal
Challenges massiveMIMO -
• Precoding -
• Precoding is a concept of beamforming which supports the multi-stream transmission in
multi-antenna systems.
• Precoding plays an imperative role in massive MIMO systems as it can mitigate the effect
created by path loss and interference, and maximizes the throughput.
• In massive MIMO systems, the base station estimates the CSI with the help of uplink pilot
signals or feedback sent by the user terminal.
• The received CSI at the base station is not uncontrollable and not perfect due to several
environmental factors on the wireless channel
Challenges massiveMIMO -
• Precoding -
Challenges massiveMIMO -
• User Scheduling -
• Massive MIMO equipped with a large number of antennas at the base station can
communicate with multiple users simultaneously.
• Simultaneous communication with multiple users creates multi-user interference and
degrades the throughput performance.
• Precoding methods are applied during the downlink to reduce the effect of multi-user
interference, as shown in Fig.
• Since the number of antennas is limited in massive MIMO base station, if the number of
users becomes more than the number of antennas, proper user scheduling scheme is
applied before precoding to achieve higher throughput and sum rate performance.
Challenges massiveMIMO -
• User Scheduling -
Challenges massiveMIMO -
• Signal Detection -
• In massive MIMO systems, due to a large number of antennas, the uplink signal detection
becomes computationally complex and reduces the achievable throughput.
• Also, all the signals transmitted by users superimpose at the base station to create
interference, which also contributes to the reduction of throughput and spectral
efficiency.
• Fig. shows a massive MIMO system with N user terminal and M antenna at the
base station.
• All the signals transmitted by N user terminal travel through a different wireless
path and superimpose at the base station, which makes signal detection at the
base station complex and inefficient.
Challenges massiveMIMO -
• Signal Detection -
MIMO Implementation -
1. Diversity: Multiple transmit and receive antennas are used to increase coverage (increased SINR).
Transmit diversity means to have multiple antennas at the sending side and receive diversity means to
have multiple antennas at the receiver side to increase the captured radio energy.
2. Spatial Multiplexing: When multiple antennas are used by both sender and receiver, multiple streams can
be sent with different information for increased user data bit rate. Transmission of data uses several layers
with small phase shift between the layers, enabling a receiver to decode the layers separately.
3. Beamforming (BF): Multiple transmit antennas will direct the radio energy in a narrow sector to increase
the SINR and thereby increasing the coverage (or increase the bitrate to the UE at a certain distance from
the BS).
Massive MIMO -
• Uplink Transmission –
• The uplink channel is used to transmit data and the
pilot signal from the user terminal to the base
station, as shown in Fig.
• Let us consider a massive MIMO uplink system
equipped with M antennas at the base station and
simultaneously communicating with N (M>>N)
single-antenna users.
• the signal received at the base station during uplink
is given as,
• The interference added is independent of the user
signal x, but it can be dependent on the channel H.
Massive MIMO -
• Downlink Transmission –
• The downlink channel is used to transmit data or
estimate the channel between user and base station.
• The base station uses training pilots to estimate the
channel. A downlink transmission with several UE and a
base station is shown in Fig.
• Let us consider a downlink massive MIMO system,
where base station equipped with M antennas, and it is
serving N users having a single antenna simultaneously.
• The base station sends independent information to
multiple users simultaneously
• The signal received,
Beamforming -
• Beamforming is a well-known and established antenna technology.
• Beamforming is the ability of the base station to adapt the radiation pattern of the antenna and
helps the base station to find a suitable route to deliver data to the user, and it also reduces
interference with nearby users along the route
• It has more importance in 5G cellular communications as it allows deployment of 5G in higher
frequency ranges such as cm-wave and mm-wave frequency spectrum where it is necessary to
achieve enough cell coverage i.e. to compensate for high path loss at these frequencies.
• The ability to steer beams dynamically is equally important since blockage scenarios are likely to
occur due to moving objects such as cars or even a human body which can block the line of sight
path.
Beamforming -
1. In a fixed wireless access scenario, the customer premises equipment (CPE) in a
household connects to an outdoor 5G BS. Here, no mobility is involved and a beam
sweeping procedure would identify the best beam to be used.
2. In contrast, Beamforming needs to be dynamic (steerable or switchable) when a moving
car on a road is connected.
Beamforming -
• Beamforming is an essential capability in 5G NR which impacts the physical layer and
higher layer resources.
• It is based on 2 fundamental physical resources: SS/PBCH blocks and the capability to
configure channel state information reference signals (CSI-RS).
Beamforming -
• The principle of Beamforming is to use the large number of antennas
in, for example, an array.
• Each antenna can be controlled with a phase shifter and an
attenuator.
• The antennas are usually half a wavelength of the signals they are
optimized.
• The phase of each antenna is then adjusted in order to control the
direction of the beam.
• Preferably, the beam should be sent in the same direction as the UE
transmitted in the UL. i.e. the antennas and the logic controlling them
must be able to measure the so called ‘angle of arrival’.
Beamforming -
• If a signal comes from a direction in front of the antenna, all
elements will receive a phase front of the signal at the same time.
• For Example: if the angle is 45 degrees, the antennas will receive
the phase front of the signal with the time spread.
• By measuring the time delay between the arriving phase front to
the antennas, it is possible to calculate the angle of arrival.
• To send the signal in the same direction, the phase front of the
transmitted signal should be sent with the same time spread.
• Phase shifting can be done in the digital domain or analog domain.
Beamforming -
• Beamforming in 5G NR should be able to direct beams not only in horizontal direction but vertical
direction as well, which is sometimes referred to as 3D MIMO .
• Antennas need to be put in a square, termed as Uniform Square Array (USA). Below is an example of
128 cross polarized antennas
Beamforming -
• Beamforming in 5G NR should be able to direct beams not only in
horizontal direction but vertical direction as well, which is sometimes
referred to as 3D MIMO as well.
• Antennas need to be put in a square, termed as Uniform Square
Array (USA). Below is an example of 128 cross polarized antennas
• The antennas are put in a USA with cross-polarized antennas with 32, 64
or 256 antennas.
• Behind the Digital-to-analog-converter (DAC) is the baseband part which
creates and analyzes the signals in the digital form, which comprises of
number of Digital signal processors (DSP) with high capacity.
Beamforming -
• Beamforming in 5G NR should be able to direct beams not only in
horizontal direction but vertical direction as well, which is sometimes
referred to as 3D MIMO as well.
• Antennas need to be put in a square, termed as Uniform Square
Array (USA). Below is an example of 128 cross polarized antennas
• The antennas are put in a USA with cross-polarized antennas with 32, 64
or 256 antennas.
• Behind the Digital-to-analog-converter (DAC) is the baseband part which
creates and analyzes the signals in the digital form, which comprises of
number of Digital signal processors (DSP) with high capacity.
Operations in Beam Management -
1. Beam Measurement: UE provides measurement reports to the Base Station on a per beam basis.
2. Beam Detection: UE identifies the best beam based on power measurements related to
configured thresholds.
3. Beam Recovery: UE is configured with basic information to recover a beam in case the
connection is lost.
4. Beam Sweeping: Using multiple beams at the Base Station to cover a geographic area and sweep
through them at prespecified intervals.
5. Beam Switching: UE switches between different beams to support mobility scenarios.
Assignment 1 -
1. Differentiate historical trend and evolution of different mobile generations up to 5G.
2. Explain different objectives for next generation wireless 5G network.
3. Compare 4G and 5G in terms of various capabilities.
4. Enlist different use cases of 5G and explain any one in details.
5. Discuss 5G Spectrum in details.
6. Draw and Explain core architecture of 5G in details.
7. Explain concept of IoT in details

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M2M.pptx

  • 1. Outline • M2M • Differences between M2M and IoT • SDN and NFV/VNF • Massive MIMO • Beamforming Book website: http://www.internet-of-things-book.com
  • 2. Machine-to-Machine (M2M) • Machine-to-Machine (M2M) refers to networking of machines (or devices) for the purpose of remote monitoring and control and data exchange.
  • 3. Machine-to-Machine (M2M) • An M2M area network comprises machines (or M2M nodes) which have embedded hardware modules for sensing, actuation and communication. • Various communication protocols can be used for M2M local area networks, such as ZigBee, Bluetooth, ModBus, M-Bus, Wireless M-Bus, Power Line Communication (PLC), 6LoWPAN, IEEE 802.15.4, etc. • The communication network provides connectivity to remote M2M area networks. • The communication network can use either wired or wireless networks (IP- based). • While the M2M area networks use either proprietary or non-IP based communication protocols, the communication network uses IP-based networks.
  • 4. M2M Gateway • Since non–IP-based protocols are used within M2M area networks, the M2M nodes within one network cannot communicate with nodes in an external network. • To enable communication between remote M2M area networks, M2M gateways are used.
  • 5. Difference between IoT and M2M • Communication Protocols • M2M and IoT can differ in how the communication between the machines or devices happens. • M2M uses either proprietary or non–IP-based communication protocols for communication within the M2M area networks. • Machines in M2M vs Things in IoT • The "Things" in IoT refers to physical objects that have unique identifiers and can sense and communicate with their external environment (and user applications) or their internal physical states. • M2M systems, in contrast to IoT, typically have homogeneous machine types within an M2M area network.
  • 6. Difference between IoT and M2M • Hardware vs Software Emphasis • While the emphasis of M2M is more on hardware with embedded modules, the emphasis of IoT is more on software. • Data Collection & Analysis • M2M data is collected in point solutions and often in on-premises storage infrastructure. • In contrast to M2M, the data in IoT is collected in the cloud (can be public, private or hybrid cloud). • Applications • M2M data is collected in point solutions and can be accessed by on-premises applications such as diagnosis applications, service management applications and on- premises enterprise applications. • IoT data is collected in the cloud and can be accessed by cloud applications such as analytics applications, enterprise applications, remote diagnosis and management applications, etc.
  • 8. SDN • Software-Defined Networking (SDN) is a networking architecture that separates the control plane from the data plane and centralizes the network controller. • Software-based SDN controllers maintain a unified view of the network and make configuration, management and provisioning simpler. • The underlying infrastructure in SDN uses simple packet forwarding hardware as opposed to specialized hardware in conventional networks.
  • 9. Key Elements of SDN • Centralized Network Controller • With decoupled control and data planes and a centralized network controller, the network administrators can rapidly configure the network. • Programmable Open APIs • SDN architecture supports programmable open APIs for interface between the SDN application and control layers (Northbound interface). • Standard Communication Interface (OpenFlow) • SDN architecture uses a standard communication interface between the control and infrastructure layers (Southbound interface). • OpenFlow, which is defined by the Open Networking Foundation (ONF), is the broadly accepted SDN protocol for the Southbound interface.
  • 10. NFV • Network Function Virtualization (NFV) is a technology that leverages virtualization to consolidate the heterogeneous network devices onto industry-standard high-volume servers, switches and storage. • NFV is complementary to SDN as NFV can provide the infrastructure on which SDN can run.
  • 11. Key Elements of NFV • Virtualized Network Function (VNF) • VNF is a software implementation of a network function which is capable of running over the NFV Infrastructure (NFVI). • NFV Infrastructure (NFVI) • NFVI includes computer, network and storage resources that are virtualized. • NFV Management and Orchestration • NFV Management and Orchestration focuses on all virtualization-specific management tasks and covers the orchestration and life-cycle management of physical and/or software resources that support infrastructure virtualization and the life-cycle management of VNFs.
  • 12. NFV – Use Case • NFV can be used to virtualize the Home Gateway. The NFV Infrastructure in the cloud hosts a virtualized Home Gateway. • The virtualized gateway provides private IP addresses to the devices in the home. The virtualized gateway also connects to network services such as VoIP and IPTV.
  • 13. What is MIMO ? • MIMO (Multiple Input Multiple Output) antenna technology is a way of increasing the capacity of a radio link using multiple transmit antennas and multiple receive antennas. • Due to multipath propagation and decorrelated paths between the transmitter and receiver, multiple data streams can be sent over the same radio channel, thus increasing the peak data rate per user along with the capacity of the cellular network. • MIMO has been part of LTE since the 1st release. LTE started with a 2×2 MIMO which means 2 transmit antennas at the base station (BS) side and 2 receive antennas at the UE side. • LTE allow applications of up to 8 spatial layers in DL direction and up to 4 spatial layers in UL direction. Commercial LTE networks tend to use 2 or 4 spatial layers.
  • 14. SU-MIMO and MU-MIMO - • If different data streams are sent to the same receiver, it is referred to as Single User MIMO (SU-MIMO), • if the data streams are transmitted to different users, it is referred to as Multi-User MIMO (MU-MIMO) • With 5G NR, there is possibility of having up to 256 transmit antenna at the BS side and that is where the term ‘massive MIMO’ comes into picture. • Massive MIMO antennas uses a large number of antenna elements but operate at frequencies below 6 GHz. Essentially, they exploit many elements to realize a combination of BF and spatial multiplexing.
  • 16. Why massiveMIMO - • Spectral Efficiency: Massive MIMO provides higher spectral efficiency by allowing its antenna array to focus narrow beams towards a user. Spectral efficiency more than ten times better than the current MIMO system used for 4G/LTE can be achieved. • Energy Efficiency: As antenna array is focused in a small specific section, it requires less radiated power and reduces the energy requirement in massive MIMO systems. • High Data Rate: The array gain and spatial multiplexing provided by massive MIMO increases the data rate and capacity of wireless systems. • User Tracking: Since massive MIMO uses narrow signal beams towards the user; user tracking becomes more reliable and accurate. • Low Power Consumption: Massive MIMO is built with ultra lower power linear amplifiers, which eliminates the use of bulky electronic equipment in the system. This power consumption can be considerably reduced. • Less Fading: A Large number of the antenna at the receiver makes massive MIMO resilient against fading
  • 17. Why massiveMIMO - • Low Latency: Massive MIMO reduces the latency on the air interface. • Robustness: Massive MIMO systems are robust against unintended interference and internal Jamming. Also, these systems are robust to one or a few antenna failures due to large antennas. • Reliability: A large number of antennas in massive MIMO provides more diversity gain, which increases the link reliability. • Enhanced Security: Massive MIMO provides more physical security due to the orthogonal mobile station channels and narrow beams. • Low Complex Linear Processing: More number of base station antenna makes the simple signal detectors and precoders optimal for the system.
  • 19. Challenges massiveMIMO - • Pilot Contamination - • In massive MIMO systems, the base station needs the channel response of the user terminal to get the estimate of the channel. • The uplink channel is estimated by the base station when the user terminal sends orthogonal pilot signals to the base station. • Furthermore, with the help of channel reciprocity property of massive MIMO, the base station estimates the downlink channel towards the user terminal. • If the pilot signals in the home cell and neighboring cells are orthogonal, the base station obtains the accurate estimation of the channel. However, the number of orthogonal pilot signals in given bandwidth and period is limited, which forces the reuse of the orthogonal pilots in neighbouring cells
  • 20. Challenges massiveMIMO - • Channel Estimation - • For signal detection and decoding, massive MIMO relies on Channel State Information (CSI). • CSI is the information of the state of the communication link from the transmitter to the receiver and represents the combined effect of fading, scattering, and so forth. • If the CSI is perfect, the performance of massive MIMO grows linearly with the number of transmitting or receive antennas, whichever is less.
  • 21. Challenges massiveMIMO - • Channel Estimation - • For a system using Frequency Division Duplexing (FDD), CSI needs to be estimated both during downlink and uplink. • During uplink, channel estimation is done by the base station with the help of orthogonal pilot signals sent by the user terminal. • During the downlink, the base station sends pilot signals towards the user, and the user acknowledges with the estimated channel information for the downlink transmission. • For a massive MIMO system with many antennas, the downlink channel estimation strategy in FDD becomes very complex and infeasible to implement
  • 22. Challenges massiveMIMO - • Channel Estimation - • TDD provides the solution for the problem during downlink transmission in FDD systems. • In TDD, by exploiting the channel reciprocity property, the base station can estimate the downlink channel with the help of channel information during uplink. • During uplink, the user will send the orthogonal pilot signals towards the base station, and based on these pilot signals, the base station will estimate the CSI to the user terminal
  • 23. Challenges massiveMIMO - • Precoding - • Precoding is a concept of beamforming which supports the multi-stream transmission in multi-antenna systems. • Precoding plays an imperative role in massive MIMO systems as it can mitigate the effect created by path loss and interference, and maximizes the throughput. • In massive MIMO systems, the base station estimates the CSI with the help of uplink pilot signals or feedback sent by the user terminal. • The received CSI at the base station is not uncontrollable and not perfect due to several environmental factors on the wireless channel
  • 25. Challenges massiveMIMO - • User Scheduling - • Massive MIMO equipped with a large number of antennas at the base station can communicate with multiple users simultaneously. • Simultaneous communication with multiple users creates multi-user interference and degrades the throughput performance. • Precoding methods are applied during the downlink to reduce the effect of multi-user interference, as shown in Fig. • Since the number of antennas is limited in massive MIMO base station, if the number of users becomes more than the number of antennas, proper user scheduling scheme is applied before precoding to achieve higher throughput and sum rate performance.
  • 26. Challenges massiveMIMO - • User Scheduling -
  • 27. Challenges massiveMIMO - • Signal Detection - • In massive MIMO systems, due to a large number of antennas, the uplink signal detection becomes computationally complex and reduces the achievable throughput. • Also, all the signals transmitted by users superimpose at the base station to create interference, which also contributes to the reduction of throughput and spectral efficiency. • Fig. shows a massive MIMO system with N user terminal and M antenna at the base station. • All the signals transmitted by N user terminal travel through a different wireless path and superimpose at the base station, which makes signal detection at the base station complex and inefficient.
  • 28. Challenges massiveMIMO - • Signal Detection -
  • 29. MIMO Implementation - 1. Diversity: Multiple transmit and receive antennas are used to increase coverage (increased SINR). Transmit diversity means to have multiple antennas at the sending side and receive diversity means to have multiple antennas at the receiver side to increase the captured radio energy. 2. Spatial Multiplexing: When multiple antennas are used by both sender and receiver, multiple streams can be sent with different information for increased user data bit rate. Transmission of data uses several layers with small phase shift between the layers, enabling a receiver to decode the layers separately. 3. Beamforming (BF): Multiple transmit antennas will direct the radio energy in a narrow sector to increase the SINR and thereby increasing the coverage (or increase the bitrate to the UE at a certain distance from the BS).
  • 30. Massive MIMO - • Uplink Transmission – • The uplink channel is used to transmit data and the pilot signal from the user terminal to the base station, as shown in Fig. • Let us consider a massive MIMO uplink system equipped with M antennas at the base station and simultaneously communicating with N (M>>N) single-antenna users. • the signal received at the base station during uplink is given as, • The interference added is independent of the user signal x, but it can be dependent on the channel H.
  • 31. Massive MIMO - • Downlink Transmission – • The downlink channel is used to transmit data or estimate the channel between user and base station. • The base station uses training pilots to estimate the channel. A downlink transmission with several UE and a base station is shown in Fig. • Let us consider a downlink massive MIMO system, where base station equipped with M antennas, and it is serving N users having a single antenna simultaneously. • The base station sends independent information to multiple users simultaneously • The signal received,
  • 32. Beamforming - • Beamforming is a well-known and established antenna technology. • Beamforming is the ability of the base station to adapt the radiation pattern of the antenna and helps the base station to find a suitable route to deliver data to the user, and it also reduces interference with nearby users along the route • It has more importance in 5G cellular communications as it allows deployment of 5G in higher frequency ranges such as cm-wave and mm-wave frequency spectrum where it is necessary to achieve enough cell coverage i.e. to compensate for high path loss at these frequencies. • The ability to steer beams dynamically is equally important since blockage scenarios are likely to occur due to moving objects such as cars or even a human body which can block the line of sight path.
  • 33. Beamforming - 1. In a fixed wireless access scenario, the customer premises equipment (CPE) in a household connects to an outdoor 5G BS. Here, no mobility is involved and a beam sweeping procedure would identify the best beam to be used. 2. In contrast, Beamforming needs to be dynamic (steerable or switchable) when a moving car on a road is connected.
  • 34. Beamforming - • Beamforming is an essential capability in 5G NR which impacts the physical layer and higher layer resources. • It is based on 2 fundamental physical resources: SS/PBCH blocks and the capability to configure channel state information reference signals (CSI-RS).
  • 35. Beamforming - • The principle of Beamforming is to use the large number of antennas in, for example, an array. • Each antenna can be controlled with a phase shifter and an attenuator. • The antennas are usually half a wavelength of the signals they are optimized. • The phase of each antenna is then adjusted in order to control the direction of the beam. • Preferably, the beam should be sent in the same direction as the UE transmitted in the UL. i.e. the antennas and the logic controlling them must be able to measure the so called ‘angle of arrival’.
  • 36. Beamforming - • If a signal comes from a direction in front of the antenna, all elements will receive a phase front of the signal at the same time. • For Example: if the angle is 45 degrees, the antennas will receive the phase front of the signal with the time spread. • By measuring the time delay between the arriving phase front to the antennas, it is possible to calculate the angle of arrival. • To send the signal in the same direction, the phase front of the transmitted signal should be sent with the same time spread. • Phase shifting can be done in the digital domain or analog domain.
  • 37. Beamforming - • Beamforming in 5G NR should be able to direct beams not only in horizontal direction but vertical direction as well, which is sometimes referred to as 3D MIMO . • Antennas need to be put in a square, termed as Uniform Square Array (USA). Below is an example of 128 cross polarized antennas
  • 38. Beamforming - • Beamforming in 5G NR should be able to direct beams not only in horizontal direction but vertical direction as well, which is sometimes referred to as 3D MIMO as well. • Antennas need to be put in a square, termed as Uniform Square Array (USA). Below is an example of 128 cross polarized antennas • The antennas are put in a USA with cross-polarized antennas with 32, 64 or 256 antennas. • Behind the Digital-to-analog-converter (DAC) is the baseband part which creates and analyzes the signals in the digital form, which comprises of number of Digital signal processors (DSP) with high capacity.
  • 39. Beamforming - • Beamforming in 5G NR should be able to direct beams not only in horizontal direction but vertical direction as well, which is sometimes referred to as 3D MIMO as well. • Antennas need to be put in a square, termed as Uniform Square Array (USA). Below is an example of 128 cross polarized antennas • The antennas are put in a USA with cross-polarized antennas with 32, 64 or 256 antennas. • Behind the Digital-to-analog-converter (DAC) is the baseband part which creates and analyzes the signals in the digital form, which comprises of number of Digital signal processors (DSP) with high capacity.
  • 40. Operations in Beam Management - 1. Beam Measurement: UE provides measurement reports to the Base Station on a per beam basis. 2. Beam Detection: UE identifies the best beam based on power measurements related to configured thresholds. 3. Beam Recovery: UE is configured with basic information to recover a beam in case the connection is lost. 4. Beam Sweeping: Using multiple beams at the Base Station to cover a geographic area and sweep through them at prespecified intervals. 5. Beam Switching: UE switches between different beams to support mobility scenarios.
  • 41. Assignment 1 - 1. Differentiate historical trend and evolution of different mobile generations up to 5G. 2. Explain different objectives for next generation wireless 5G network. 3. Compare 4G and 5G in terms of various capabilities. 4. Enlist different use cases of 5G and explain any one in details. 5. Discuss 5G Spectrum in details. 6. Draw and Explain core architecture of 5G in details. 7. Explain concept of IoT in details