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Place, Date

Advances in Coding Algorithms

for 5G

Jossy Sayir, University of Cambridge

Athens, 17 March 2014



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)



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!!



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



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



OUR MISSION

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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



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



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



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)



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



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



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!...

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