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As the quest for higher data rates continues, future generations of wireless communications are expected to concur even higher frequency bands, particularly at terahertz (THz) frequencies. Even though the vast bandwidths at the THz band promise terabit-per-second (Tbps) data rates, current baseband technologies do not support such high rates. In particular, the complexities of Tbps channel code decoding and ultra-massive multiple-input multiple-output data detection are prohibitive. This work addresses the efficient data detection and channel-code decoding problem under THz-band channel conditions and Tbps baseband processing limitations. We propose ultra-massive multiple-input multiple-output THz channel models, then investigate the corresponding performance of several candidate data detection and coding schemes. We further investigate the complexity of different detectors and decoders, motivating parallelizability at both levels. We recommend which detector to combine best with which channel code decoder under specific THz channel characteristics.

Soft iterative detection and decoding techniques have been shown to be able to achieve near-capacity performance in multiple-antenna systems. To obtain the optimal soft information by marginalization over the entire observation space is intractable; and the current literature is unable to guide us towards the best way to obtain the suboptimal soft information. In this thesis, several existing soft-input soft-output (SISO) detectors, including minimum mean-square error-successive interference cancellation (MMSE-SIC), list sphere decoding (LSD), and Fincke-Pohst maximum-a-posteriori (FPMAP), are examined. Prior research has demonstrated that LSD and FPMAP outperform soft-equalization methods (i.e., MMSE-SIC); however, it is unclear which of the two scheme is superior in terms of performance-complexity trade-off. A comparison is conducted to resolve the matter. In addition, an improved scheme is proposed to modify LSD and FPMAP, providing error performance improvement and a reduction in computational complexity simultaneously. Although list-type detectors such as LSD and FPMAP provide outstanding error performance, issues such as the optimal initial sphere radius, optimal radius update strategy, and their highly variable computational complexity are still unresolved. A new detection scheme is proposed to address the above issues with fixed detection complexity, making the scheme suitable for practical implementation. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2008-07-08 19:29:17.66