Design of Non-orthogonal and Noncoherent Massive MIMO for Scalable URLLC Beyond 5G

This paper is to design and optimize a non-orthogonal and noncoherent massive multiple-input multiple-output (MIMO) framework towards enabling scalable ultra-reliable low-latency communications (sURLLC) in wireless systems beyond 5G. In this framework, the huge diversity gain associated with the large-scale antenna array in massive MIMO systems is leveraged to ensure ultrahigh reliability. To reduce the overhead and latency induced by the channel estimation process, we advocate the noncoherent communication technique which does not need the knowledge of instantaneous channel state information (CSI) but only depends on the large-scale fading coefficients for information decoding. To boost the scalability of the system considered, we enable the non-orthogonal channel access of multiple users by devising a new differential modulation scheme to assure that each transmitted signal matrix can be uniquely determined in the noise-free case and be reliably estimated in noisy cases when the antenna array size is scaled up. The key idea is to make the transmitted signals from multiple users be superimposed properly over the air such that when the sum-signal is correctly detected, the signals sent by all users can be uniquely determined. To further improve the average error performance when the array antenna number is large, we propose a max-min Kullback-Leibler (KL) divergence-based design by jointly optimizing the transmitted powers of all users and the sub-constellation assignment among them. Simulation results show that the proposed design significantly outperforms the existing max-min Euclidean distance-based counterpart in terms of error performance. Moreover, our proposed approach also has a better error performance than the conventional coherent zero-forcing (ZF) receiver with orthogonal channel training, particularly for cell-edge users.