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Performance analysis of neural network-based polar decoding algorithms with different code rates

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Abstract

Neural network approach with deep learning is of interest of late in channel decoding. Polar code is a desirable candidate in 5G and replaces Turbo code with better error correction capacities. In view of this, there is a need to explore polar decoding with neural network techniques. In this paper, the performance of polar decoding using conventional successive cancellation (SC) algorithm and belief propagation (BP) algorithm are evaluated in additive white Gaussian noise (AWGN) in python platform. Also, an effort is made to test the performance of decoding by implementing belief propagation algorithm using neural network, called as belief propagation neural network (BPNN). BPNN is chosen, as it requires a minimum number of iterations to complete the decoding operation. Performance is compared with that of conventional decoding. Performance analysis is carried out in terms of bit error rate (BER) for different input code lengths and code rates. Block length error rate (BLER) analysis of algorithms is also investigated. It is observed that BPNN performs better with improvement in the performance with modified neural network architecture and training sets.

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Abbreviations

NN:

Neural network

SC:

Successive cancellation

BP:

Belief propagation

BPNN:

Belief propagation neural network

AWGN:

Additive white Gaussian noise

BPSK:

Binary phase shift keying

GA:

Greedy algorithm

LR:

Likelihood ratio

LLR:

Log likelihood ratio

BER:

Bit error rate

BLER:

Block length error rate

FPGA:

Field programmable gate array

SNR:

Signal-to-noise ratio

k:

Bits of information

I:

Mutual information

N:

Code word length

\(z\left(w\right)\) :

Bhattacharyya parameter

\({x}^{N}\) :

Input data

\({y}^{N}\) :

Output data

\({w}^{N}\) :

Channel

K i :

Estimated value of reliability

u :

Input to the encoder

n :

Nodes in the encoder structure

f :

F-function

g :

G-function

hi :

Hard decision function

frz :

Frozen bits

\({\widehat{b}}_{i}\) :

Output of the decoder

\({b}_{i}\) :

Input of the decoder

\(\alpha ,\beta ,\delta ,\chi \) :

Coefficients of BP algorithm

q:

Value of the bit inside decoder

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Global Academy of Technology, Bangalore, Karnataka, India

    Shridhar B. Devamane

  2. Department of Electronics and Communication Engineering, KLE Institute of Technology, Hubballi, Karnataka, India

    Rajeshwari L. Itagi

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  1. Shridhar B. Devamane
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Correspondence to Shridhar B. Devamane.

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Devamane, S.B., Itagi, R.L. Performance analysis of neural network-based polar decoding algorithms with different code rates. Iran J Comput Sci 5, 83–97 (2022). https://doi.org/10.1007/s42044-021-00094-2

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