Soft-Decision-Driven Channel Estimation for Pipelined Turbo Receivers

We consider channel estimation specific to turbo equalization for multiple-input multiple-output (MIMO) wireless communication. We develop a soft-decision-driven sequential algorithm geared to the pipelined turbo equalizer architecture operating on orthogonal frequency division multiplexing (OFDM) symbols. One interesting feature of the pipelined turbo equalizer is that multiple soft-decisions become available at various processing stages. A tricky issue is that these multiple decisions from different pipeline stages have varying levels of reliability. This paper establishes an effective strategy for the channel estimator to track the target channel, while dealing with observation sets with different qualities. The resulting algorithm is basically a linear sequential estimation algorithm and, as such, is Kalman-based in nature. The main difference here, however, is that the proposed algorithm employs puncturing on observation samples to effectively deal with the inherent correlation among the multiple demapper/decoder module outputs that cannot easily be removed by the traditional innovations approach. The proposed algorithm continuously monitors the quality of the feedback decisions and incorporates it in the channel estimation process. The proposed channel estimation scheme shows clear performance advantages relative to existing channel estimation techniques.Comment: 11 pages; IEEE Transactions on Communications 201

Soft-Decision-Driven Sparse Channel Estimation and Turbo Equalization for MIMO Underwater Acoustic Communications

Multi-input multi-output (MIMO) detection based on turbo principle has been shown to provide a great enhancement in the throughput and reliability of underwater acoustic (UWA) communication systems. Benefits of the iterative detection in MIMO systems, however, can be obtained only when a high quality channel estimation is ensured. In this paper, we develop a new soft-decision-driven sparse channel estimation and turbo equalization scheme in the triply selective MIMO UWA. First, the Homotopy recursive least square dichotomous coordinate descent (Homotopy RLS-DCD) adaptive algorithm, recently proposed for sparse single-input single-output system identification, is extended to adaptively estimate rapid time-varying MIMO sparse channels. Next, the more reliable a posteriori soft-decision symbols, instead of the hard decision symbols or the a priori soft-decision symbols, at the equalizer output, are not only feedback to the Homotopy RLS-DCD-based channel estimator but also to the minimum mean-square-error (MMSE) equalizer. As the turbo iterations progress, the accuracy of channel estimation and the quality of the MMSE equalizer are improved gradually, leading to the enhancement in the turbo equalization performance. This also allows the reduction in pilot overhead. The proposed receiver has been tested by using the data collected from the SHLake2013 experiment. The performance of the receiver is evaluated for various modulation schemes, channel estimators, and MIMO sizes. Experimental results demonstrate that the proposed a posteriori soft-decision-driven sparse channel estimation based on the Homotopy RLS-DCD algorithm and turbo equalization offer considerable improvement in system performance over other turbo equalization schemes

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