TY - GEN
T1 - THz-band, Tbps MIMO Communications: A Joint Data Detection and Decoding Framework
AU - Jemaa, Hakim
AU - Sarieddeen, Hadi
AU - Alouini, Mohamed-Slim
AU - Al-Naffouri, Tareq Y.
N1 - KAUST Repository Item: Exported on 2023-03-10
PY - 2023/3/7
Y1 - 2023/3/7
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/690214
UR - https://ieeexplore.ieee.org/document/10051850/
U2 - 10.1109/ieeeconf56349.2022.10051850
DO - 10.1109/ieeeconf56349.2022.10051850
M3 - Conference contribution
BT - 2022 56th Asilomar Conference on Signals, Systems, and Computers
PB - IEEE
ER -