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Advanced Signal Processing
- Advanced Space-Time Processing: Channel Equalisation and Channel Estimation
- Advanced Signal Detection: Minimum Bit Error Ratio, Sphere Detection and Markov Chain Monte Carlo
- Synchronization and Initial Acquisition
- Evolutionary Algorithms: Genetic Algorithm, Ant Colony Optimisation, Particle Swarm Optimisation and Harmony Search
Advanced Space-Time Processing: Beamforming, Channel Equalisation and Channel Estimation |
The ever-increasing demand for an improved throughput in wireless communication has motivated the development of adaptive antenna- It is well known that digital communication signal detection, in general, can be viewed as a classification problem, where the receiver detector simply classifies the received multidimensional channel-impaired signal into the most likely transmitted symbol constellation point or class. For the multiple-antenna-aided beamforming receiver, if one is willing to extend the beamforming process to nonlinear, substantial performance enhancement can be achieved over the linear beamforming at the cost of an increased complexity. The idea of nonlinear beamforming has recently been developed for wireless systems with the real-valued binary phase shift-keying modulation, where a symmetric radial basis function (SRBF) network is proposed to adaptively implement the optimal nonlinear beamforming solution. This study extends nonlinear beamforming to wireless systems that employ the complex-valued quadrature phase-shift keying (QPSK) modulation scheme. For QPSK systems, the optimal Bayesian detection solution can be expressed as a complex-valued radial basis function (RBF) network. We further exploit the inherent symmetry of the optimal nonlinear beamforming solution and propose a novel complex-valued SRBF network for adaptively implementing |
Advanced Signal Detection: Minimum Bit Error Ratio, Sphere Detection and Markov Chain Monte Carlo |
The most potent space-time detection method found in the literature is constituted by the Sphere Decoder (SD), which is capable of achieving the optimum performance of the Maximum Likelihood (ML) detector at a relatively low computational complexity. Against this background, we propose a novel SDM detection method, which we refer to as the Soft-output OPtimized HIErarchy (SOPHIE) Spatial Division Multiplexing (SDM) detector. The proposed method may be regarded as an advanced extension of the SD methods. More specifically, our method can be employed in the above-mentioned rank-deficient and hence overloaded scenario, where the number of transmit antenna elements exceeds that of the receive antenna elements. Furthermore our scheme is suitable for high-throughput modulation schemes such as 16- and 64-QAM. We introduce a list of optimization rules, which facilitate the achievement of the near optimum BER performance of a Log-MAP detector at a relatively low computational complexity. The trade-off between the achievable BER performance and the associated computational complexity is controlled using two parameters. The proposed detection method exhibits two major advantages over all previously proposed techniques: 1) The bit-related soft information, which facilitates the achievement of near-optimum Log-MAP performance, is attained at the expense of a modest complexity increase over that of hard-decision ML detection. 2) Our method exhibits a particularly low polynomial complexity in both the low- and high-SNR regions. In the critical range of SNR values, which corresponds to the “waterfall” region of the BER versus SNR curve, the detection complexity versus the number of transmit antennas remains exponential. Nevertheless, we demonstrate that the complexity can be dramatically reduced at the cost of a minor BER degradation. [top] |
Synchronization and Initial Acquisition |
This sequential estimation method exploits the principle of iterative soft-in–soft-out (SISO) decoding for enhancing the acquisition performance, and that of differential preprocessing for the sake of achieving an enhanced acquisition performance, when communicating over |
Evolutionary Algorithms: Genetic Algorithm, Ant Colony Optimisation, Particle Swarm Optimisation and Harmony Search |
Multiple-Input-Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems have recently attracted substantial research interest. However, compared to Single-Input-Single-Output (SISO) systems, channel estimation in the MIMO scenario becomes more challenging, owing to the increased number of independent transmitter receiver links to be estimated. In the context of the Bell LAyered Space-Time architecture (BLAST) or Space Division Multiple Access (SDMA) multi-user MIMO OFDM systems, none of the known channel estimation techniques allows the number of users to be higher than the number of receiver antennas, which is often referred to as a “rank-deficient” scenario, owing to the constraint imposed by the rank of the MIMO channel matrix. Against this background, we propose a new Genetic Algorithm (GA) assisted iterative Joint Channel Estimation and Multi-User Detection (GA-JCEMUD) approach for multi-user MIMO SDMA-OFDM systems, which provides an effective solution to the multi-user MIMO channel estimation problem in the above-mentioned rank-deficient scenario. Furthermore, the GAs invoked in the data detection literature can only provide a hard-decision output for the Forward Error Correction (FEC) or channel decoder, which inevitably limits the system’s achievable performance. By contrast, our proposed GA is capable of providing “soft” outputs and hence it becomes capable of achieving an improved performance with the aid of FEC decoders. A range of simulation results are provided to demonstrate the superiority of the proposed scheme. |