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This paper is to design and optimize a non-orthogonal and noncoherent massivemultiple-input multiple-output (MIMO) framework towards enabling scalableultra-reliable low-latency communications (sURLLC) in wireless systems beyond5G. In this framework, the huge diversity gain associated with the large-scaleantenna array in massive MIMO systems is leveraged to ensure ultrahighreliability. To reduce the overhead and latency induced by the channelestimation process, we advocate the noncoherent communication technique whichdoes not need the knowledge of instantaneous channel state information (CSI)but only depends on the large-scale fading coefficients for informationdecoding. To boost the scalability of the system considered, we enable thenon-orthogonal channel access of multiple users by devising a new differentialmodulation scheme to assure that each transmitted signal matrix can be uniquelydetermined in the noise-free case and be reliably estimated in noisy cases whenthe antenna array size is scaled up. The key idea is to make the transmittedsignals from multiple users be superimposed properly over the air such thatwhen the sum-signal is correctly detected, the signals sent by all users can beuniquely determined. To further improve the average error performance when thearray antenna number is large, we propose a max-min Kullback-Leibler (KL)divergence-based design by jointly optimizing the transmitted powers of allusers and the sub-constellation assignment among them. Simulation results showthat the proposed design significantly outperforms the existing max-minEuclidean distance-based counterpart in terms of error performance. Moreover,our proposed approach also has a better error performance than the conventionalcoherent zero-forcing (ZF) receiver with orthogonal channel training,particularly for cell-edge users.

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