Constant Weight Codes with Gabor Dictionaries and Bayesian Decoding for Massive Random Access

TL;DR

This paper introduces a novel massive random access scheme using constant-weight codes and Gabor dictionary design, combined with an extended AMP decoder, achieving efficient, low-latency communication without channel state information.

Contribution

It proposes a new coding and decoding framework for massive random access that leverages Gabor dictionaries and constant-weight codes with an extended AMP algorithm.

Findings

Outperforms existing schemes in energy efficiency and user capacity.

Operates non-coherently in fading scenarios, avoiding channel state information.

Uses smaller codewords, reducing latency and complexity.

Abstract

This paper considers a general framework for massive random access based on sparse superposition coding. We provide guidelines for the code design and propose the use of constant-weight codes in combination with a dictionary design based on Gabor frames. The decoder applies an extension of approximate message passing (AMP) by iteratively exchanging soft information between an AMP module that accounts for the dictionary structure, and a second inference module that utilizes the structure of the involved constant-weight code. We apply the encoding structure to (i) the unsourced random access setting, where all users employ a common dictionary, and (ii) to the "sourced" random access setting with user-specific dictionaries. When applied to a fading scenario, the communication scheme essentially operates non-coherently, as channel state information is required neither at the transmitter nor at the receiver. We observe that in regimes of practical interest, the proposed scheme compares favorably with state-of-the art schemes, in terms of the (per-user) energy-per-bit requirement, as well as the number of active users that can be simultaneously accommodated in the system. Importantly, this is achieved with a considerably smaller size of the transmitted codewords, potentially yielding lower latency and bandwidth occupancy, as well as lower implementation complexity.