A novel scheme of semi-blind joint maximum likelihood (ML) channel estimation and data detection is proposed for multipleinput multiple-output (MIMO) systems by decomposing the joint ML optimisation over channel and data into an iterative two-level optimisation loop. Particle swarm optimisation (PSO) is invoked at the upper level to identify the unknown MIMO channel while an enhanced ML sphere detector is used at the lower level to detect the transmitted data. The scheme is semi-blind as a minimum pilot overhead is employed to aid the initialisation of the PSO based channel estimator.
Abstract. Multiple-input multiple-output (MIMO) technologies are capable of substantially improving the achievable system's capacity, coverage and/or quality of service. The system's ability to approach the MIMO capacity depends heavily on the designs of MIMO receiver and/or transmitter, which are generally expensive optimisation tasks. Hence, researchers and engineers have endeavoured to develop efficient optimisation techniques that can solve practical MIMO designs with affordable costs. In this contribution, we demonstrate that particle swarm optimisation (PSO) offers an efficient means for aiding MIMO transceiver designs. Specifically, we consider PSO-aided semi-blind joint maximum likelihood channel estimation and data detection for MIMO receiver, and we investigate PSO-based minimum bit-error-rate multiuser transmission for MIMO systems. In both these two MIMO applications, the PSO-aided approach attains an optimal design solution with a significantly lower complexity than the existing state-of-the-art scheme. IntroductionMultiple-input multiple-output (MIMO) technologies are widely adopted in practice to improve the system's achievable capacity, coverage and/or quality of service [14,15,30,32,33,41,42,43,45]. The designs of MIMO receiver and/or transmitter critically influence the system's ability to approach the MIMO capacity. MIMO transceiver designs, which are typically expensive optimisation tasks, have motivated researchers and engineers to develop efficient optimisation techniques that can attain optimal MIMO designs with affordable costs. Hence, the particle swarm optimisation (PSO) as an advanced optimisation tool can offer an efficient means for aiding MIMO transceiver designs. PSO [25] is a population based stochastic optimisation technique inspired by social behaviour of bird flocking or fish schooling. The algorithm commences with random initialisation of a swarm of individuals, referred to as particles, within the problem's search space. It then endeavours to find
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