Massive Unsourced Random Access Under Carrier Frequency Offset

Abstract: Unsourced random access (URA) is a new perspective of massive access which aims at supporting numerous machine-type users. With the appearance of carrier frequency offset (CFO), joint activity detection and channel estimation, which is vital for multiple-input and multiple-output URA, is a challenging task. To handle the phase corruption of channel measurements under CFO, a novel compressed sensing algorithm is proposed, leveraging the parametric bilinear generalized approximate message passing framework with a Markov chain support model that captures the block sparsity structure of the considered angular domain channel. An uncoupled transmission scheme is proposed to reduce system complexity, where slot-emitted messages are reorganized relying on clustering unique user channels. Simulation results reveal that the proposed transmission design for URA under CFO outperforms other potential methods.

Keywords: activity detection; channel estimation; frequency offset; massive machine-type communication; massive MIMO