Upload
vuonghuong
View
225
Download
2
Embed Size (px)
Citation preview
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
Outline
I What is new in 5G
I Multihop Communications for 5G
I Channel coding for 5G
Outline
I What is new in 5G
I Multihop Communications for 5G
I Channel coding for 5G
Outline
I What is new in 5G
I Multihop Communications for 5G
I Channel coding for 5G
5G - What is New?
I Applications
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
5G - What is New?
I Applications
I Requirements
I Architecture - Common network platform
5G and Spectrum
Design
I Low frequencies: widecoverage
I mmW band: short range,low complexity
Ultra-dense Networks in mmW Bands
Dense deployments
I Due to limited range
I For higher throughput
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
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
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?
Information Theory: Relay Channel is 44 Years Old
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
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
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
Multihop Communications for 5G
Multihop Backhaul for Ultra-dense Networks
Multihop MTC?
70000 tracking devices 9 Gbyte/user/hour
480 Gbps/km2
Multihop Backhaul
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
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
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
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
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
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
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
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]
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]
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]
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
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]
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]
Relay Selection
Group relaying
source
destination
Relay Selection
Group relaying
source
destination
Relay Selection
Group relaying
source
destination
Relay Selection
Group relaying
source
destination
Relay Selection
Layered network
destinationsource
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?
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?
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?
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?
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)
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
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
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!
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!
Channel Coding for 5G
Informationsource
Sourceencoder
Channelencoder
Modulator
Channel
User Sourcedecoder
Channeldecoder
Demodulator
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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)
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
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)
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]
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
Summary
Constructed family of rate-compatible polar codes
I Achieves capacity
I Can be used for HARQ-IR
Thank You!