Two-tier channel estimation aided near-capacity MIMO transceivers relying on norm-based joint transmit and receive antenna selection

We propose a norm-based joint transmit and receive antenna selection (NBJTRAS) aided near-capacity multiple-input multiple-output (MIMO) system relying on the assistance of a novel two-tier channel estimation scheme. Specifically, a rough estimate of the full MIMO channel is first generated using a low-complexity, low-training-overhead minimum mean square error based channel estimator, which relies on reusing a modest number of radio frequency (RF) chains. NBJTRAS is then carried out based on this initial full MIMO channel estimate. The NBJTRAS aided MIMO system is capable of significantly outperforming conventional MIMO systems equipped with the same modest number of RF chains, while dispensing with the idealised simplifying assumption of having perfectly known channel state information (CSI). Moreover, the initial subset channel estimate associated with the selected subset MIMO channel matrix is then used for activating a powerful semi-blind joint channel estimation and turbo detector-decoder, in which the channel estimate is refined by a novel block-of-bits selection based soft-decision aided channel estimator (BBSB-SDACE) embedded in the iterative detection and decoding process. The joint channel estimation and turbo detection-decoding scheme operating with the aid of the proposed BBSB-SDACE channel estimator is capable of approaching the performance of the near-capacity maximumlikelihood (ML) turbo transceiver associated with perfect CSI. This is achieved without increasing the complexity of the ML turbo detection and decoding process

Reduced-complexity near-capacity joint channel estimation and three-stage turbo detection for coherent space-time shift keying

We propose a low-complexity joint channel estimation (CE) and three-stage iterative demapping-decoding scheme for near-capacity coherent space-time shift keying (CSTSK) based multiple-input multiple-output (MIMO) systems. In the proposed scheme, only a minimum number of space-time shift keying training blocks are employed for generating an initial least square channel estimate, which is then used for initial data detection. As usual, the detected soft information is first exchanged a number of times within the inner turbo loop between the unity-rate-code (URC) decoder and the CSTSK soft-demapper, and the information gleaned from the inner URC decoder is then iteratively exchanged with the outer decoder in the outer turbo loop. Our CE scheme is embedded into the outer turbo loop, which exploits the a posteriori information produced by the CSTSK soft-demapper to select a sufficient number of high-quality decisions only for CE. Since the CE is embedded into the iterative three-stage demapping-decoding process, no additional iterative loop is required for exchanging information between the decision-directed channel estimator and the three-stage turbo detector. Hence, the computational complexity of the proposed joint CE and three-stage turbo detection remains similar to that of the three-stage turbo detection-decoding scheme with the given channel estimate. Moreover, our proposed low-complexity semiblind scheme is capable of approaching the optimal maximum likelihood turbo detection performance attained with the aid of perfect channel state information, with the same low number of turbo iterations as the latter, as confirmed by our extensive simulation result