Joint Active User Detection, Timing Offset and Channel Estimation for FBMC-Based Uplink Massive Access Systems

The robustness against timing offsets of filter bank multi-carrier (FBMC) is appealing for grant-free massive access scenarios that mainly adopt asynchronous transmissions. In this work, we propose a compressed sensing based algorithm for joint active user detection as well as timing offset and channel estimation in uplink communication under the combination of FBMC and grant-free massive access systems, which is critical for subsequent decoding or other processes at receiver. The channel estimation part is based on generalized approximate massage passing (GAMP). The active user detection and timing offset estimation are based on loopy belief propagation (LBP) rules, where the expressions of message passing and belief distributions are derived. Besides, since the receiver may have no prior knowledge about some parameters such as noise variance and activity probability, we introduce the expectation maximization (EM) approach into the proposed algorithm. Moreover, we develop a preamble design method to improve the detection and estimation performance. Simulation results show that the proposed EM-LBP-GAMP algorithm can achieve satisfying performance in terms of missed activity detection probability, timing offset estimation error and normalized mean square error of channel estimation.