Efficient data detection and decoding are addressed under terahertz (THz)-band channel conditions and terabit-persecond (Tbps) baseband processing constraints. We investigate the performance and complexity tradeoffs of candidate data detectors in correlated ultra-massive multiple-input multiple-output (UM-MIMO) THz channels. Under high correlation, channel-matrix puncturing in subspace detectors can significantly reduce computational complexity and introduce much-needed parallelizability. Simulation results demonstrate that subspace detectors outperform conventional detectors in typical line-of-sight-dominated THz channel conditions. We advocate for a joint data detection and decoding framework that does not parallelize channel-code decoders to satisfy stringent Tbps baseband constraints but parallelizes data sources through channel puncturing and adopts short codes instead.
|Title of host publication
|2022 56th Asilomar Conference on Signals, Systems, and Computers
|Published - Mar 7 2023