Toward Massive Active Connectivity: Performance Analysis and Near-Optimal Detectors for Grant-Free Random Access Systems

In this paper, theoretical performance of optimal maximum likelihood (ML) detector and near-optimal simulated detectors are designed for massive multiple-input multiple-output (MIMO) aided grant-free (MM-GF) systems. Specifically, the approximated average bit error probability (ABEP) bounds are firstly derived by exploiting the relationship between the Hamming distance (HD) and the pairwise error probability (PEP), which are confirmed by the simulation results. Moreover, an extended alphabet based expectation propagation (EA-EP) and an adaptive subspace matching pursuit (ASMP) algorithm are devised for signal detection of MM-GF without the prior information (PI) of active users. Simulation results show that the proposed detectors are able to outperform the classic oracle least squares (OLS) benchmark and are capable of approaching the theoretical ABEP bounds of ML.

Automated summary

This paper investigates the theoretical performance of optimal maximum likelihood (ML) detector and near-optimal simulated detectors in massive MIMO aided grant-free (MM-GF) systems. The approximated average bit error probability (ABEP) bounds are derived by exploiting the relationship between Hamming distance (HD) and pairwise error probability (PEP), which are confirmed by simulation results. Additionally, an extended alphabet based expectation propagation (EA-EP) and an adaptive subspace matching pursuit (ASMP) algorithm are proposed for signal detection in MM-GF without prior information (PI) of active users. Simulation results show that the proposed detectors outperform the classic oracle least squares (OLS) benchmark and approach the theoretical ABEP bounds of ML.