Compressive Sensing-Based Active User Detection and Channel Estimation for HF Skywave Massive MIMO-OFDM System

In this paper, we consider massive access in a high frequency (HF) skywave massive multi-input multi-output (MIMO) system with orthogonal frequency division multiplexing (OFDM) modulation and present an active user detection (AUD) and channel estimation (CE) scheme based on compressive sensing (CS). Specifically, we employ non-orthogonal pseudorandom pilot design for uplink broadband and formulate the AUD and CE task as a multiple-measurement-vector (MMV) compressive sensing problem. By exploiting the space-frequency structured sparsity of the channel matrix, we present a simultaneous orthogonal matching pursuit (SOMP)-based algorithm to recover both the active user set and the channel coefficients. To operate without prior knowledge of the channel sparsity level at the base station (BS), we use the norm of the residual to determine whether all active users have been detected, and estimate the channel matrix in the angular domain. The simulation results demonstrate that the proposed scheme reliably detects the active user set using fewer time slots than the total number of potential users and, with sufficient observations, achieves a performance close to that of an oracle least-square (LS) estimator.