A Proximal Jacobian ADMM Approach for Fast Massive MIMO Signal Detection in Low-Latency Communications

One of the 5G promises is to provide Ultra Reliable Low Latency Communications (URLLC) which targets an end to end communication latency that is <; 1ms. The very low latency requirement of URLLC entails a lot of work in all networking layers. In this paper, we focus on the physical layer, and in particular, we propose a novel formulation of the massive MIMO uplink detection problem. We introduce an objective function that is a sum of strictly convex and separable functions based on decomposing the received vector into multiple vectors. Each vector represents the contribution of one of the transmitted symbols in the received vector. Proximal Jacobian Alternating Direction Method of Multipliers (PJADMM) is used to solve the new formulated problem in an iterative manner where at every iteration all variables are updated in parallel and in a closed form expression. The proposed algorithm provides a lower complexity and much faster processing time compared to the conventional MMSE detection technique and other iterative-based techniques, especially when the number of single antenna users is close to the number of base station (BS) antennas. This improvement is obtained without any matrix inversion. Simulation results demonstrate the efficacy of the proposed algorithm in reducing detection processing time in the multi-user uplink massive MIMO setting.

Elgabli Anis, Elghariani Ali, Aggarwal Vaneet, Bennis Mehdi, Bell Mark R.

A4 Article in conference proceedings

2019 IEEE International Conference on Communications, ICC 2019; Shanghai; China; 20-24 May 2019 : Proceedings

A. Elgabli, A. Elghariani, V. Aggarwal, M. Bennis and M. R. Bell, "A Proximal Jacobian ADMM Approach for Fast Massive MIMO Signal Detection in Low-Latency Communications," ICC 2019 - 2019 IEEE International Conference on Communications (ICC), Shanghai, China, 2019, pp. 1-6. doi: 10.1109/ICC.2019.8761844

https://doi.org/10.1109/ICC.2019.8761844 http://urn.fi/urn:nbn:fi-fe202003249096