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![Page 1: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/1.jpg)
Information Theoretic Concepts of 5G
Ivana MaricEricsson Research
Joint work withSong-Nam Hong, Dennis Hui and Giuseppe Caire (TU Berlin)
IEEE 5G Silicon Valley SummitNovember 16, 2015
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Outline
I What is new in 5G
I Multihop Communications for 5G
I Channel coding for 5G
![Page 3: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/3.jpg)
Outline
I What is new in 5G
I Multihop Communications for 5G
I Channel coding for 5G
![Page 4: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/4.jpg)
Outline
I What is new in 5G
I Multihop Communications for 5G
I Channel coding for 5G
![Page 5: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/5.jpg)
5G - What is New?
I Applications
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5G - What is New?
I Applications
I RequirementsI 1000x mobile data, 100x user data rates, 100x connected
devices, 10x battery life, 5x lower latencyI Sustainable, secure
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5G - What is New?
I Applications
I Requirements
I Architecture - Common network platform
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5G and Spectrum
Design
I Low frequencies: widecoverage
I mmW band: short range,low complexity
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Ultra-dense Networks in mmW Bands
Dense deployments
I Due to limited range
I For higher throughput
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Ultra-dense Networks in mmW Bands
Backhaul for thousands ofaccess points?
I Backhaul today:P2P, line-of-sight
I Tomorrow:Wireless multihop backhaul
I Access points relay eachother’s data
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Ultra-dense Networks in mmW Bands
Backhaul for thousands ofaccess points?
I Backhaul today:P2P, line-of-sight
I Tomorrow:Wireless multihop backhaul
I Access points relay eachother’s data
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Remove Houses: Mesh Network
source 2
source 3
source 1
User 1
User 3
User 2
User 4
Efficient multihop scheme? What should relays do?
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Information Theory: Relay Channel is 44 Years Old
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Multihop Schemes in Practice
I Large body of IT results
I Efficient multihop schemes developed; capacity bounds, scalinglaws and capacity in some cases determined
I Not much practical impact
I Too complex?
I There was no need?
I 5G will deploy multihop communications
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Multihop Schemes in Practice
I Large body of IT results
I Efficient multihop schemes developed; capacity bounds, scalinglaws and capacity in some cases determined
I Not much practical impact
I Too complex?
I There was no need?
I 5G will deploy multihop communications
![Page 16: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/16.jpg)
Multihop Schemes in Practice
I Large body of IT results
I Efficient multihop schemes developed; capacity bounds, scalinglaws and capacity in some cases determined
I Not much practical impact
I Too complex?
I There was no need?
I 5G will deploy multihop communications
![Page 17: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/17.jpg)
Multihop Communications for 5G
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Multihop Backhaul for Ultra-dense Networks
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Multihop MTC?
70000 tracking devices 9 Gbyte/user/hour
480 Gbps/km2
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Multihop Backhaul
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Current Proposal for 5G
Interference-avoidance routing
I Each relay performsstore-and-forward
I Establish routes iteratively
I Works well in lowinterference
Does not work in high interference
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Current Proposal for 5G
Interference-avoidance routing
I Each relay performsstore-and-forward
I Establish routes iteratively
I Works well in lowinterference
Does not work in high interference
![Page 23: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/23.jpg)
Current Proposal for 5G
Interference-avoidance routing
I Each relay performsstore-and-forward
I Establish routes iteratively
I Works well in lowinterference
Does not work in high interference
![Page 24: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/24.jpg)
Current Proposal for 5G
Interference-avoidance routing
I Each relay performsstore-and-forward
I Establish routes iteratively
I Works well in lowinterference
Does not work in high interference
![Page 25: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/25.jpg)
Current Proposal for 5G
Interference-avoidance routing
I Each relay performsstore-and-forward
I Establish routes iteratively
I Works well in lowinterference
Does not work in high interference
![Page 26: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/26.jpg)
Current Proposal for 5G
Interference-avoidance routing
I Each relay performsstore-and-forward
I Establish routes iteratively
I Works well in lowinterference
Does not work in high interference
![Page 27: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/27.jpg)
Current Proposal for 5G
Interference-avoidance routing
I Each relay performsstore-and-forward
I Establish routes iteratively
I Works well in lowinterference
Does not work in high interference
![Page 28: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/28.jpg)
Decode vs. Quantize
Routing
I Each relay has to decodemessages
I Worst relay is a bottleneck
Quantize
I Any relay can quantize sourcesignal
I Noisy network coding (NNC)[Avestimehr et.al, 2009], [Lim
et.al, 2011],[Hou & Kramer, 2013]
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Decode vs. Quantize
Routing
I Each relay has to decodemessages
I Worst relay is a bottleneck
Quantize
I Any relay can quantize sourcesignal
I Noisy network coding (NNC)[Avestimehr et.al, 2009], [Lim
et.al, 2011],[Hou & Kramer, 2013]
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Decode vs. Quantize
Routing
I Each relay has to decodemessages
I Worst relay is a bottleneck
Quantize
I Any relay can quantize sourcesignal
I Noisy network coding (NNC)[Avestimehr et.al, 2009], [Lim
et.al, 2011],[Hou & Kramer, 2013]
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Noisy Network Coding
I No interference at relays: every signal is useful
I A relay sends a mix of data flows
I Can outperform other schemes
I Achieves constant gap to the multicast capacity
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Implementation: NNC Challenges
I Full-duplex assumption
I Channel state information
I Relay selection
I Decoder complexity
I Rate calculation
We developed a scheme that has a lower complexity and improvedperformance [Hong, Maric, Hui & Caire, ISIT 2015, ITW 2015]
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Implementation: NNC Challenges
I Full-duplex assumption
I Channel state information
I Relay selection
I Decoder complexity
I Rate calculation
We developed a scheme that has a lower complexity and improvedperformance [Hong, Maric, Hui & Caire, ISIT 2015, ITW 2015]
![Page 34: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/34.jpg)
Relay Selection
Group relaying
source
destination
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Relay Selection
Group relaying
source
destination
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Relay Selection
Group relaying
source
destination
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Relay Selection
Group relaying
source
destination
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Relay Selection
Layered network
destinationsource
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To Improve Performance: Adaptive Scheme
A relay chooses a forwarding scheme based on SNR
I Relays with good channels decode-and-forward
I The rest of relays quantize
How much to quantize?
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To Improve Performance: Adaptive Scheme
A relay chooses a forwarding scheme based on SNR
I Relays with good channels decode-and-forward
I The rest of relays quantize
How much to quantize?
![Page 41: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/41.jpg)
To Improve Performance: Adaptive Scheme
A relay chooses a forwarding scheme based on SNR
I Relays with good channels decode-and-forward
I The rest of relays quantize
destinationsource
quantize
quantize
quantize
quantize
decode
decode
How much to quantize?
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To Improve Performance: Adaptive Scheme
A relay chooses a forwarding scheme based on SNR
I Relays with good channels decode-and-forward
I The rest of relays quantize
destinationsource
quantize
quantize
quantize
quantize
decode
decode
How much to quantize?
![Page 43: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/43.jpg)
To Improve Performance: Optimized Quantization
A relay chooses number of quantization levels based on SNR
I Optimal quantization decreases the gap to capacity fromlinear to logarithmic
I NNC with noise-level quantization [Avestimehr et. al., 2009]
R(K) = log(1 + SNR) − K
I Optimal quantization [Hong & Caire, 2013]
R(K) ≥ log(1 + SNR) − log(K + 1)
![Page 44: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/44.jpg)
To Reduce Complexity: Successive Decoding
Destination successively decodes messages from different layers
I Does not decrease performance in the considered network[Hong & Caire, 2013]
destinationsource
quantize
quantize
quantize
quantize
decode
decode
![Page 45: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/45.jpg)
Summary
destination
message 1
quantize
quantize
quantize
quantize
decode
decode
source
message 2
I Relay selection via interference-harnessing
I Adaptive scheme: each relay chooses to decode or quantize
I Quantization level is optimized
I Destination performs successive decoding
I Successive relaying [Razaei et.al., 2008]
I Rate splitting reduces interference at DF relays
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Performance Gains
I Derived closed form solution for the rate, for any relayconfiguration [Hong, Maric , Hui & Caire, ISIT 2015, ITW 2015]
I Better performance with a simpler scheme!
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Performance Gains
I Derived closed form solution for the rate, for any relayconfiguration [Hong, Maric , Hui & Caire, ISIT 2015, ITW 2015]
I Better performance with a simpler scheme!
![Page 48: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/48.jpg)
Channel Coding for 5G
Informationsource
Sourceencoder
Channelencoder
Modulator
Channel
User Sourcedecoder
Channeldecoder
Demodulator
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Choosing Channel Codes for 5G
I Main considerationsI Performance, complexity, rate-compatibility
I LTE deploys turbo codes [Berrou et. al., 1993]I Perform within a dB fraction from channel capacity
I Why Beyond Turbo Codes?
I LDPC codes
I New classes of codes that are capacity-achieving with lowcomplexity encoder and decoder
Polar & spatially-coupled LDPC codes
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Choosing Channel Codes for 5G
I Main considerationsI Performance, complexity, rate-compatibility
I LTE deploys turbo codes [Berrou et. al., 1993]I Perform within a dB fraction from channel capacity
I Why Beyond Turbo Codes?
I LDPC codes
I New classes of codes that are capacity-achieving with lowcomplexity encoder and decoder
Polar & spatially-coupled LDPC codes
![Page 51: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/51.jpg)
Choosing Channel Codes for 5G
I Main considerationsI Performance, complexity, rate-compatibility
I LTE deploys turbo codes [Berrou et. al., 1993]I Perform within a dB fraction from channel capacity
I Why Beyond Turbo Codes?
I LDPC codes
I New classes of codes that are capacity-achieving with lowcomplexity encoder and decoder
Polar & spatially-coupled LDPC codes
![Page 52: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/52.jpg)
Choosing Channel Codes for 5G
I Main considerationsI Performance, complexity, rate-compatibility
I LTE deploys turbo codes [Berrou et. al., 1993]I Perform within a dB fraction from channel capacity
I Why Beyond Turbo Codes?
I LDPC codes
I New classes of codes that are capacity-achieving with lowcomplexity encoder and decoder
Polar & spatially-coupled LDPC codes
![Page 53: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/53.jpg)
Polar Codes [Arikan, 2009]
I First provably capacity-achieving codes with lowencoding/decoding complexity
I Outperform turbo codes for large block length n
I Best performance for short block length n
I Complexity O(nlogn)
I Better energy-efficiency for large n than other codes
I Code construction is deterministic
I No error floor
![Page 54: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/54.jpg)
Polar Codes [Arikan, 2009]
I First provably capacity-achieving codes with lowencoding/decoding complexity
I Outperform turbo codes for large block length n
I Best performance for short block length n
I Complexity O(nlogn)
I Better energy-efficiency for large n than other codes
I Code construction is deterministic
I No error floor
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Channel Polarization
I n instances of a channel are transformed into a set of channelsthat are either noiseless or pure-noise channels
I Polar code: send information bits over good channels
I Fraction of good channels approaches the capacity of theoriginal channel
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Channel Polarization
I n instances of a channel are transformed into a set of channelsthat are either noiseless or pure-noise channels
I Polar code: send information bits over good channels
I Fraction of good channels approaches the capacity of theoriginal channel
![Page 57: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/57.jpg)
Channel Polarization
I n instances of a channel are transformed into a set of channelsthat are either noiseless or pure-noise channels
I Polar code: send information bits over good channels
I Fraction of good channels approaches the capacity of theoriginal channel
![Page 58: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/58.jpg)
Channel Polarization
I n instances of a channel are transformed into a set of channelsthat are either noiseless or pure-noise channels
I Polar code: send information bits over good channels
I Fraction of good channels approaches the capacity of theoriginal channel
![Page 59: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/59.jpg)
Channel Polarization
I n instances of a channel are transformed into a set of channelsthat are either noiseless or pure-noise channels
I Polar code: send information bits over good channels
I Fraction of good channels approaches the capacity of theoriginal channel
![Page 60: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/60.jpg)
Channel Polarization
I n instances of a channel are transformed into a set of channelsthat are either noiseless or pure-noise channels
I Polar code: send information bits over good channels
I Fraction of good channels approaches the capacity of theoriginal channel
![Page 61: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/61.jpg)
Channel Polarization
I n instances of a channel are transformed into a set of channelsthat are either noiseless or pure-noise channels
I Polar code: send information bits over good channels
I Fraction of good channels approaches the capacity of theoriginal channel
![Page 62: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/62.jpg)
Wireless Channel is Time-Varying
I Hybrid ARQ with Incremental Redundancy (HARQ-IR)I Send additional coded bits until decoding is successful
NACK
NACK
ACK
TX RX
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Wireless Channel is Time-Varying
I Hybrid ARQ with Incremental Redundancy (HARQ-IR)I Send additional coded bits until decoding is successful
NACK
NACK
ACK
TX RX
![Page 64: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/64.jpg)
HARQ-IR
I Encode for degraded channels W1 � W2 � . . . � WK withcapacities C1 ≥ C2 ≥ . . . ≥ CK
n1
k information bits
1st transmission
Rate R1 = k/n1
n2
2nd transmission
R2 = k/(n1 + n2)
![Page 65: Information Theoretic Concepts of 5G (slides)](https://reader030.fdocuments.us/reader030/viewer/2022021503/5875faae1a28abd8348b605f/html5/thumbnails/65.jpg)
Problem: HARQ-IR with Polar Codes?
I HARQ-IR requires the same information set for all codesI Polar code designed for fixed length n
I Information sets are different for different lengths
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Our Solution: Parallel-Concatenated Polar Codes
I Encoder R1 > R2 (ex. K = 2)
information bits
DividerPolar encoderC(n1,R1)
Polar encoderC(n2,R2)
D
I Decoder
ReceiverPolar decoderC(n2,R2)
Polar decoderC(n1,R1)
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Our Solution: Parallel-Concatenated Polar Codes
I Encoder R1 > R2 (ex. K = 2)
information bits
DividerPolar encoderC(n1,R1)
Polar encoderC(n2,R2)
D
I Decoder
ReceiverPolar decoderC(n2,R2)
Polar decoderC(n1,R1)
frozenbits
R2
I To choose D, used nested property of polar codes[Korada, 2009]
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Capacity Result
Theorem [Hong, Hui & Maric, 2015]
For any sequence of degraded channels W1 � W2 � . . . � WK
there exists a sequence of rate-compatible punctured polar codesthat is capacity-achieving
I Details: arxiv.org/pdf/1510.01776v1.pdf
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Summary
Constructed family of rate-compatible polar codes
I Achieves capacity
I Can be used for HARQ-IR
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Thank You!