Dynamic Compressive Sensing-Based Multi-User Detection for Uplink Grant-Free NOMA

Non-orthogonal multiple access (NOMA) can support more users than OMA techniques using the same wireless resources, which is expected to support massive connectivity for Internet of Things in 5G. Furthermore, in order to reduce the transmission latency and signaling overhead, grant-free transmission is highly expected in the uplink NOMA systems, where user activity has to be detected. In this letter, by exploiting the temporal correlation of active user sets, we propose a dynamic compressive sensing (DCS)-based multi-user detection (MUD) to realize both user activity and data detection in several continuous time slots. In particular, as the temporal correlation of the active user sets between adjacent time slots exists, we can use the estimated active user set in the current time slot as the prior information to estimate the active user set in the next time slot. Simulation results show that the proposed DCS-based MUD can achieve much better performance than that of the conventional CS-based MUD in NOMA systems.

[1]  Zhiguo Ding,et al.  Nonorthogonal Multiple Access for 5G , 2018, 5G Networks: Fundamental Requirements, Enabling Technologies, and Operations Management.

[2]  Linglong Dai,et al.  Joint User Activity and Data Detection Based on Structured Compressive Sensing for NOMA , 2016, IEEE Communications Letters.

[3]  H. Vincent Poor,et al.  MIMO-NOMA Design for Small Packet Transmission in the Internet of Things , 2016, IEEE Access.

[4]  Olgica Milenkovic,et al.  Subspace Pursuit for Compressive Sensing Signal Reconstruction , 2008, IEEE Transactions on Information Theory.

[5]  Carsten Bockelmann,et al.  Compressive Sensing Multi-User Detection for Multicarrier Systems in Sporadic Machine Type Communication , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[6]  Wan Choi,et al.  Sparsity Controlled Random Multiple Access With Compressed Sensing , 2015, IEEE Transactions on Wireless Communications.

[7]  Chung Gu Kang,et al.  Iterative Order Recursive Least Square Estimation for Exploiting Frame-Wise Sparsity in Compressive Sensing-Based MTC , 2016, IEEE Communications Letters.

[8]  Namrata Vaswani,et al.  Recursive Recovery of Sparse Signal Sequences From Compressive Measurements: A Review , 2016, IEEE Transactions on Signal Processing.

[9]  Byonghyo Shim,et al.  Multiuser Detection via Compressive Sensing , 2012, IEEE Communications Letters.