Penalty-Based Algorithm for Joint Activity and Data Detection in Grant-Free Massive Access

Abstract

Grant-free random access is a promising mechanism to support modern massive machine-type communications in which devices are sporadically active with small payloads. Under this random access, a unique challenge is the detection of device activity without the cooperation from devices. Furthermore, for only a few bits of data, it is more efficient to embed the data to the signature sequences so that the activity and data detection can be jointly carried out. However, compared with the vanilla device activity detection problem, joint activity and data detection has an extra discontinuous sparsity constraint, which makes the detection problem more challenging. In contrast to the prevalent way of first neglecting the discontinuous sparsity constraint and re-enforcing it at the end, this paper proposes a novel penalty-based algorithm to gradually enforce the discontinuous sparsity constraint during the optimization procedure. Simulation results demonstrate that the proposed method achieves around 10 times better detection performance than state-of-the-art approaches.