Joint Activity Detection and Data Decoding in Massive Random Access via a Turbo Receiver

TL;DR

This paper introduces a turbo receiver that iteratively combines activity detection and data decoding for massive random access, significantly improving performance through bilinear inference and belief propagation techniques.

Contribution

It presents a novel turbo receiver framework that jointly performs activity detection and data decoding using bilinear generalized approximate message passing, enhancing accuracy in grant-free massive access.

Findings

Significant performance improvements over conventional methods

Effective joint activity detection and data decoding

Enhanced channel decoding accuracy through extrinsic information

Abstract

In this paper, we propose a turbo receiver for joint activity detection and data decoding in grant-free massive random access, which iterates between a detector and a belief propagation (BP)-based channel decoder. Specifically, responsible for user activity detection, channel estimation, and soft data symbol detection, the detector is developed based on a bilinear inference problem that exploits the common sparsity pattern in the received pilot and data signals. The bilinear generalized approximate message passing (BiG-AMP) algorithm is adopted to solve the problem using probabilities of the transmitted data symbols estimated by the channel decoder as prior knowledge. In addition, extrinsic information is derived from the detector to improve the channel decoding accuracy of the decoder. Simulation results show significant improvements achieved by the proposed turbo receiver compared with conventional designs.