Low-Complexity Channel Estimation and Multi-User Detection for Uplink Grant-Free NOMA Systems

Abstract

Grant-free non-orthogonal multiple access (NOMA) scheme is a promising candidate to accommodate massive connectivity with reduced signalling overhead for Internet of Things (IoT) services in massive machine-type communication (mMTC) networks. In this letter, we propose a low-complexity compressed sensing (CS) based sparsity adaptive block gradient pursuit (SA-BGP) algorithm in uplink grant-free NOMA systems. Our proposed SA-BGP algorithm is capable of jointly carrying out channel estimation (CE), user activity detection (UAD) and data detection (DD) without knowing the user sparsity level. By exploiting the inherent sparsity of transmission signal and gradient descend, our proposed method can enjoy a decent detection performance with substantial reduction of computational complexity. Simulation results demonstrate that the proposed method achieves a balanced trade-off between computational complexity and detection performance, rendering it a viable solution for future IoT applications.