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Place, Date
Advances in Coding Algorithms
for 5G
Jossy Sayir, University of Cambridge
Athens, 17 March 2014
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5G REQUIREMENTS
! 1000 times higher mobile data volume perarea
!
10 times to 100 times higher number of connected devices
!
10 times to 100 times higher typical userdata rate
!
10 times longer battery life for low powerMachine-to-Machine-Communications
!
5 times reduced End-to-End latency
(source METIS / Neelie Kroes press release)
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ENERGYBOTTLENECK
•
Current computational cost of transmission:
approx. 6 nJ/bit
•
Current battery capacity 5.45 Wh
•
Target 100 Gbit / s
•
Resulting battery life: 32.7 seconds!!
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CODING FOR #G
•
Quantum leap from 2G to 3G with the
adoption of modern iteratively decodablecodes (Turbo and/or LDPC)
•
3G to 4G “more of the same”, adoption ofHARQ, but techniques essentially similar
•
In order to satisfy 5G requirements, we need aparadigm change in coding
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CODING WISH LIST
•
3/4G coding is essentially capacity-achieving
for point to point BIAWGN
•
Gains in transmission power efficiency to beexpected from
• Better spectral efficiency
• Multi-terminal coding / decoding
•
Gains in computational power efficiency
equally important
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OUR MISSION
!" $""% &'"()*+,,- ".(/"$) (0%/$1 2")30%& )3+)
(+$ 4" +'',/"% )0 25,67)"*2/$+, &("$+*/0&
8*",+-/$19 (00'"*+60$9 :;:<= >/)3 "?)*"2",- ,0>
(02',"?/)- "$(0%"*& +$% %"(0%"*&
•
Same applies to backhaul
•
Currently there is no single technique that ticks allthese boxes
•
There are a few promising new techniques in need of
further research
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CODINGTECHNIQUES
Spatially Coupled (Convolutional) LDPC Codes
•
LDPC code units that can be regular and geometrically
designed
•
Code units sparsely interconnected
•
Result is capacity-achieving for a wide range of channels
and rates
•
Windowed decoders being studied for reduced decoding
complexity
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CODINGTECHNIQUES
Non-binary LDPC codes
•
Better performance at low block lengths
•
No loss of optimality when code alphabet = modulationalphabet
•
Better spectral efficiency
•
Increased complexity with respect to binary LDPC codes
•
EMS, trellis-EMS and other new techniques being
developed to bring complexity down
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CODINGTECHNIQUES
Analog-Digital Belief Propagation
•
Decoding directly using parametric densities
•
No complexity increase for larger modulation alphabets!
• Essentially optimal spectral efficiency
•
Ring LDPC codes (addition mod M)
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CODINGTECHNIQUES
Sparse Regression Codes
•
Gaussian codebooks are optimal for the Gaussian channel
but were always thought impractical
•
Gaussian codebooks constructed from a library of
elementary Gaussian vectors, achieving capacity!
•
Decoding using linear regression has moderate complexity
but performance needs improvement
•
New low complexity decoding algorithms could propel
this technique forward
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CODINGTECHNIQUES
Polar Codes
•
First constructive coding technique to provably achieve
channel capacity
•
Codes constructed recursively using Kronecker product
•
Equivalent channels using successive decoding polarise
to capacities 0 and 1
•
Performance for finite length below LDPC codes, but
polar coding may have advantages for multi-terminal
scenarios
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SOME N# CODINGEXPERTS
Erdal Arikan
Bilkent Uni.
Inventor of
Polar Codes
Guido Montorsi
Politec. Torino
Analog Digital
Belief Propagation
Michael LentmaierLund University
Co-inventor of
Spatially Coupled
LDPC Codes
Ramji
Venkataramanan
Univ. of Cambridge
Sparse Regression
Codes
and many others!...