Sequence Design and User Activity Detection for Uplink Grant-Free NOMA in mMTC Networks

Abstract

Massive machine type communications (mMTC) is one of the three major scenarios in 5G wireless networks. mMTC is an uplink-dominant scenario that supports massive connectivity of small-size packets and sporadic traffic. To meet the requirement of mMTC, non-orthogonal multiple access (NOMA) with grant-free transmission is considered a promising technology because of its higher spectrum efficiency and lower signaling overhead. Request-and-grant-based scheduling process is eliminated in grant-free transmission and thus the base stations need to identify active users first before data detection. Previously, several user activity detection (UAD) algorithms were proposed and they can obtain good performance in flat-fading channels. However, the performance of these works degrades significantly in doubly selective fading channels. In this work, we propose an effective solution for uplink grant-free NOMA with a new user sequence scheme and a novel UAD algorithm suitable for practical channel scenarios. Simulation results validated that the proposed sequences can mitigate the inter-user interference at the base stations, and the proposed UAD algorithm can achieve better detection accuracy. Moreover, detailed complexity analysis showed that the proposed UAD algorithm has relatively low complexity and good scalability.