Neural Network Aided Decoding for Physical-Layer Network Coding Random Access

TL;DR

This paper proposes a neural network-based method to assist in decoding linear combinations in physical-layer network coding for random access, aiming to improve decoding success by predicting error probabilities.

Contribution

It introduces a machine learning approach to identify the most decodable linear combinations, addressing the lack of analytical tools for this task.

Findings

Neural networks can accurately estimate error probabilities for linear combinations.

The approach improves decoding success rates in simulated scenarios.

The method offers a low-complexity solution for decoding in coded random access systems.

Abstract

Hinging on ideas from physical-layer network coding, some promising proposals of coded random access systems seek to improve system performance (while preserving low complexity) by means of packet repetitions and decoding of linear combinations of colliding packets, whenever the decoding of individual packets fails. The resulting linear combinations are then temporarily stored in the hope of gathering enough linearly independent combinations so as to eventually recover all individual packets through the resolution of a linear system at the end of the contention frame. However, it is unclear which among the numerous linear combinations---whose number grows exponentially with the degree of collision---will have low probability of decoding error. Since no analytical framework exists to determine which combinations are easiest to decode, this makes the case for a machine learning algorithm to assist the receiver in deciding which linear combinations to target. For this purpose, we train neural networks that approximate the error probability for every possible linear combination based on the estimated channel gains and demonstrate the effectiveness of our approach by numerical simulations.