  We consider the problem of efficient ultra-massive multiple-input
multiple-output (UM-MIMO) data detection in terahertz (THz)-band non-orthogonal
multiple access (NOMA) systems. We argue that the most common THz NOMA
configuration is power-domain superposition coding over quasi-optical
doubly-massive MIMO channels. We propose spatial tuning techniques that modify
antenna subarray arrangements to enhance channel conditions. Towards recovering
the superposed data at the receiver side, we propose a family of data detectors
based on low-complexity channel matrix puncturing, in which higher-order
detectors are dynamically formed from lower-order component detectors. We first
detail the proposed solutions for the case of superposition coding of multiple
streams in point-to-point THz MIMO links. We then extend the study to
multi-user NOMA, in which randomly distributed users get grouped into narrow
cell sectors and are allocated different power levels depending on their
proximity to the base station. We show that successive interference
cancellation is carried with minimal performance and complexity costs under
spatial tuning. We derive approximate bit error rate (BER) equations, and we
propose an architectural design to illustrate complexity reductions. Under
typical THz conditions, channel puncturing introduces more than an order of
magnitude reduction in BER at high signal-to-noise ratios while reducing
complexity by approximately 90%.
