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By Fakhruddin Mahmood
Anlei Rao
OutlineIntroductionChannel Polarization
Channel CombiningChannel Splitting
Polar CodesPolar codingSuccessive Decoding
Conclusion
IntroductionShannon’s proof of noisy channel coding theorem is
the random coding method that he used to show the existence of capacity achieving code sequences.
Construction of capacity-achieving code sequences has been an elusive goal
Polar codes [Arikan] were the first provably capacity achieving codes for any symmetric B-DMC
Low encoding and decoding complexity O(NlogN)Main idea of polar codes is based on the
phenomenon of channel polarization
IntroductionBy recursively combining and splitting individual
channels, some channels become error free while others turn into complete noise
Those fraction of channels that become noiseless are given by I(W) which is the symmetric capacity
I(W) is equal to Shannon capacity C under the condition that the B-DMC is symmetric
Shannon capacity C is the highest rate at which reliable communication is possible across W using the inputs letters of the channel with equal probability.
IntroductionPolar coding is the construction of codes that
achieve I(W) by taking advantage of the polarizing effect.
Basic idea is to create a coding system where each coordinate channel can be accessed individually and send data only through those whose capacity is close to I(W)
Channel PolarizationAn operation converting N ind. copies of B-
DMC W to a polarized channel set of { }
Channel PolarizationAn operation converting N ind. copies of B-
DMC into a polarized channel set of { }The polarized channel becomes either noisy
or noiseless as block length N goes to infinity.
Channel PolarizationAn operation converting N ind. copies of B-
DMC into a polarized channel set of { }The polarized channel becomes either noisy
or noiseless as block length N goes to infinity.By sending the information bits through
these noiseless channels, we can achieve the symmetric capacity of B-DMC.
Channel PolarizationAn operation converting N ind. copies of B-
DMC into a polarized channel set of { }The polarized channel becomes either noisy
or noiseless as block length N goes to infinity.By sending the information bits through
these noiseless channels, we can achieve the symmetric capacity of B-DMC.
Channel Polarization consists of two parts: channel combining and channel splitting
Channel PolarizationChannel Combining:
with the transition prob:
Channel PolarizationChannel Combining:
with the transition prob: : generating matrix calculated in a
recursive way:
Channel PolarizationChannel Combining:
with the transition prob: : generating matrix calculated in a
recursive way:
: {1, 2, 3……N} {1, 3……N-1, 2, 4……N}
Channel PolarizationStructure :
Example with N=8
Channel Combining:
Example with N=8
With simulation we can calculate the generating matrix for N=8:
Channel PolarizationChannel Splitting:
with the transition prob:
Example with N=8
After channel combining:
Example with N=8
Example with N=8
Example with N=8
Example with N=8
Example with N=8
Example with N=8
Polar CodesPolar CodingBased on the process of channel combining
Polar CodesPolar CodingBased on the process of channel combiningUsing the generating matrix for coding:
Polar CodesPolar CodingBased on the process of channel combiningUsing the generating matrix for coding:
Choose the information set S={i: }
Polar CodesPolar CodingBased on the process of channel combiningUsing the generating matrix for coding:
Choose the information set S={i: }
Choose the frozen bits at will
Polar CodesSuccessive DecodingBased on the process of channel splitting
Polar CodesSuccessive DecodingBased on the process of channel splittingUse ML rule to make decisions
Polar CodesSuccessive DecodingBased on the process of channel splittingUse ML rule to make decisionsProbability of block error bounded as
Polar CodesSuccessive DecodingBased on the process of channel splittingUse ML rule to make decisionsProbability of block error bounded asCoding and decoding complexity: O(NlogN)
Example of N=8
Example of N=8
Example of N=8
ConclusionBy combining and splitting the N-ind. copies of
B-DMCs, we can get error free or pure-noise polarized channels.
Transmitting information bits only through noiseless channels while fixing symbols transmitted through the pure-noise ones, the Shannon capacity of the symmetric B-DMC can be achieved.
Polar codes, based on the phenomenon of channel polarization, are capacity-achieving for any symmetric B-DMC with low encoding and decoding complexity O(NlogN) and block error