Jiankang Zhang

On the Design of Minimum BER Linear Space-Time Block Codes for MIMO Systems Equipped With MMSE Receivers

In this paper, we consider the design of a full-rate linear space-time block code for coherent multiple-input multiple-output (MIMO) communication systems under a quasi-static Rayleigh flat-fading environment. Our design targets specifically at the use of a linear minimum mean-square error (MMSE) receiver that minimizes the asymptotic average bit error rate (BER) when the transmitted signal is selected from a 4-QAM constellation. This optimization problem is solved in two main stages: 1) a lower bound on the BER is first minimized, and 2)how this minimized lower bound can be achieved is then shown. By exploiting a rigorous convex optimization technique without any assumption on the code, we prove that individual unitary and trace-orthogonal structures are the necessary and sufficient conditions to assure the minimum asymptotic average BER with an MMSE detector. An algorithm is provided for an efficient generation of our codes, and simulation results confirm that our optimally designed codes are indeed superior in performance compared to some other commonly used codes

A Novel Signaling Scheme for Blind Unique Identification of Alamouti Space-Time Block-Coded Channel

In this paper, we present a simple signaling scheme to blindly and uniquely identify the Alamouti space-time block-coded channel, first under a noise-free environment, and then, under a complex Gaussian noise environment in which pth- and qth-order statistics (p and q are coprime) of the received signals are available. In both cases, closed-form solutions to determine the channel coefficients are obtained by exploiting specific properties of the Alamouti space-time block code (STBC) and the linear Diophantine equation theory. Under Gaussian noise, when the length of the received data is finite, we propose to use the semidefinite relaxation (SDR) algorithm to approximate maximum-likelihood (ML) detection so that the joint estimation of the channel and symbols can be efficiently implemented. Simulation results show that while other existing blind methods fail, our signaling scheme works well

Turbo Multi-User Detection for OFDM/SDMA Systems Relying on Differential Evolution Aided Iterative Channel Estimation

A differential evolution (DE) algorithm aided iterative channel estimation and turbo multi-user detection (MUD) scheme is proposed for multi-user multi-input multiple-output aided orthogonal frequency-division multiplexing / space-division multiple-access (OFDM/SDMA) systems. The proposed scheme iteratively exchanges the estimated channel information and the detected data between the channel estimator and MUD employing a turbo technique, which gradually improves the accuracy of the channel estimation and the MUD, especially for the first iteration. Quadrature amplitude modulation (QAM) is employed in most wireless standards by virtue of providing a high throughput. However, the optimal maximum likelihood (ML)-MUD becomes extremely complex for employment in QAM-aided multi-user systems. Hence, two different DE aided MUD schemes, the DE aided minimum symbol error rate (MSER)-MUD as well as the discrete DE aided ML-MUD, were developed, and their achievable performance versus complexity was characterized. The simulation results demonstrate that the proposed DE aided channel estimator is capable of approaching the Cramer-Rao lower bound with just two or three iterations. The ultimate bit error rate lower-bound of the single-user additive white Gaussian noise scenario has been approached in the range of Eb / N0 ≥ 10 dB and Eb / N0 ≥ 6 dB for the DE aided MSER-MUD and the discrete DE aided ML-MUD, respectively.

Evolutionary-Algorithm-Assisted Joint Channel Estimation and Turbo Multiuser Detection/Decoding for OFDM/SDMA

The development of evolutionary algorithms (EAs), such as genetic algorithms (GAs), repeated weighted boosting search (RWBS), particle swarm optimization (PSO), and differential evolution algorithms (DEAs), have stimulated wide interests in the communication research community. However, the quantitative performance-versus-complexity comparison of GA, RWBS, PSO, and DEA techniques applied to the joint channel estimation (CE) and turbo multiuser detection (MUD)/decoding in the context of orthogonal frequency-division multiplexing/space-division multiple-access systems is a challenging problem, which has to consider both the CE problem formulated over a continuous search space and the MUD optimization problem defined over a discrete search space. We investigate the capability of the GA, RWBS, PSO, and DEA to achieve optimal solutions at an affordable complexity in this challenging application. Our study demonstrates that the EA-assisted joint CE and turbo MUD/decoder is capable of approaching both the Cramér-Rao lower bound of the optimal CE and the bit error ratio (BER) performance of the idealized optimal maximum-likelihood (ML) turbo MUD/decoder associated with perfect channel state information, respectively, despite imposing only a fraction of the idealized turbo ML-MUD/decoders complexity.

Joint Decision-Directed Channel and Noise-Variance Estimation for MIMO OFDM/SDMA Systems Based on Expectation-Conditional Maximization

A joint channel impulse response (CIR) and noise-variance estimation scheme is proposed for multiuser multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing/space-division multiple access (OFDM/SDMA) systems, which is based on the expectation-conditional maximization (ECM) algorithm. Multiple users communicating over fading channels exhibiting a range of different characteristics are considered in this paper. Channel estimation becomes quite challenging in this scenario since an increased number of independent transmitter-receiver links having different statistical characteristics have to be simultaneously estimated for each subcarrier. To cope with this scenario, we design an ECM-based joint CIR and noise-variance estimator for multiuser MIMO OFDM/SDMA systems, which is capable of simultaneously estimating diverse CIRs and noise variance. Furthermore, we propose a forward error code (FEC)-aided decision-directed channel estimation scheme based on the ECM algorithm, which further improves the ECM algorithm by exploiting the error correction capability of an FEC decoder for iteratively exchanging information between the decoder and the ECM algorithm.

Decision-directed channel estimation based on iterative linear minimum mean square error for orthogonal frequency division multiplexing systems

A decision-directed (DD) channel estimation based on iterative linear minimum mean square error (LMMSE) is proposed for orthogonal frequency division multiplexing systems. Existing DD channel estimation is well known to have the problem of error propagation because of symbol-by-symbol detection. The proposed algorithm can estimate the correction term of current channel state information (CSI) according to the error vector of previous CSI by applying the orthogonality principle, and corrects the current CSI with this correction term. Analysis and simulation results have shown that this method has no error propagation problem. The performance of the proposed algorithm is much better than the conventional DD channel estimation, and close to the optimal LMMSE estimator, but with much less computational complexity compared with the optimal LMMSE estimator.

Joint Channel, Carrier-Frequency-Offset and Noise-Variance Estimation for OFDM Systems Based on Expectation Maximization

In this paper, a joint channel, carrier-frequency-offset (CFO) and noise-variance estimation scheme is proposed for OFDM systems which is based on Expectation and Maximization (EM) algorithm. The channel parameters are estimated using training sequences incorporated at the beginning of each transmission frame. Based on the assumption that the amplitude and CFO of different paths are independent, the received multipath components may be decomposed into

Optimal Pilot Design for Pilot Contamination Elimination/Reduction in Large-Scale Multiple-Antenna Aided OFDM Systems

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Differential Evolution Algorithm Aided Minimum Symbol Error Rate Multi-User Detection for Multi-User OFDM/SDMA Systems

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Joint Channel Impulse Response and Noise-Variance Estimation for OFDM/SDMA Systems Based on Expectation Maximization

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