Abstract
The neural successive cancellation (NSC) decoder with tanh-based modified log-likelihood ratio (LLR) is proposed for reducing the decoding latency of polar codes over free space optical (FSO) turbulence channel. The conventional successive cancellation (SC) decoder is partitioned into multiple sub-blocks, which are replaced by multiple sub neural network (NN) decoders with tanh-based modified LLR. The recursive characteristic of the polar sequences reliability ranking given in 5G standard enables the sub-NN decoder to be uniquely determined by code length and the number of information bits. Confirmed by the simulation, the bit error rate (BER) performance of NSC decoder with tanh-based modified LLR is close to the conventional SC decoder over turbulence channel for the practical-length polar codes. Regarding turbulence-stability, the NSC decoder trained in moderate and strong turbulence conditions have stable performance in a wide range of turbulence conditions. Moreover, in comparison of decoding latency, the NSC decoder with tanh-based modified LLR takes less than 25% time steps of SC decoder in the same code length.
Highlights
Deep learning has drawn growing attention and achieved astounding results in many fields, such as computer vision, natural language processing and so on [1], [2]
The neural successive cancellation (NSC) decoder with tanh-based modified likelihood ratios (LLRs) takes less than 25% time steps in the same code length compared to the successive cancellation (SC) decoder
The NSC decoder with tanh-based modified LLR is investigated over free space optical (FSO) turbulence channel
Summary
Deep learning has drawn growing attention and achieved astounding results in many fields, such as computer vision, natural language processing and so on [1], [2]. In practical-length polar codes, the concept of NN decoder is mainly restricted by the curse of dimensionality, i.e., an exponential increasing of training complexity with the number of information bits To solve this problem, the polar decoder can be partitioned into multiple sub-blocks and each sub-block can be replaced by NN-based components [9], [10]. The neural networks are adopted to establish the polar decoder over FSO turbulence channel and the tanh-based modified LLR is proposed as the input of NN decoder to combat the turbulence-induced fading, which is the dominant factor degrading the system performance [11]. The NSC decoder with tanh-based modified LLR is proposed to reduce the decoding latency of polar codes over turbulence channel.
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