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We consider the problem of efficient ultra-massive multiple-inputmultiple-output (UM-MIMO) data detection in terahertz (THz)-band non-orthogonalmultiple access (NOMA) systems. We argue that the most common THz NOMAconfiguration is power-domain superposition coding over quasi-opticaldoubly-massive MIMO channels. We propose spatial tuning techniques that modifyantenna subarray arrangements to enhance channel conditions. Towards recoveringthe superposed data at the receiver side, we propose a family of data detectorsbased on low-complexity channel matrix puncturing, in which higher-orderdetectors are dynamically formed from lower-order component detectors. We firstdetail the proposed solutions for the case of superposition coding of multiplestreams in point-to-point THz MIMO links. We then extend the study tomulti-user NOMA, in which randomly distributed users get grouped into narrowcell sectors and are allocated different power levels depending on theirproximity to the base station. We show that successive interferencecancellation is carried with minimal performance and complexity costs underspatial tuning. We derive approximate bit error rate (BER) equations, and wepropose an architectural design to illustrate complexity reductions. Undertypical THz conditions, channel puncturing introduces more than an order ofmagnitude reduction in BER at high signal-to-noise ratios while reducingcomplexity by approximately 90%.

Terahertz-Band MIMO-NOMA: Adaptive Superposition Coding and Subspace Detection