Study On Key Techniques In MIMO Systems

Multi-input and multi-output (MIMO) technology is an important means to improve the performance of high-speed wireless communications, such as multi-media communications and wireless internet access. It is well known that over multipath fading channels, a MIMO system has much higher spectral efficiency than a conventional single-input and single-output (SISO) system. MIMO technology offers a variety of research fields, including channel capacity, space-time coding, parameter estimation, signal detection, and so on. This dissertation deals with the problem of parameter estimation and signal detection, its main contributions are as follows.1. The problem of joint frequency offset and channel estimation is studied for correlated MIMO channels. The channels are assumed to be frequency-selective and block fading with both spatial and multi-path correlations. A maximum-likelihood (ML) frequency offset (FO) estimator is proposed by using the Bayesian approach. The mean and variance of the FO estimation are derived. To evaluate the performance of the FO estimator, its Cramer-Rao low bound (CRLB) is also developed. It is shown that the proposed FO estimator is asymptotically optimal. Based on the FO estimate, we derive the linear minimum mean square error (LMMSE) channel estimator and analytically investigate the effect of frequency offset estimation error on the mean square error (MSE) performance of the channel estimator.2. The problem of joint frequency offset and channel estimation is studied for MIMO systems in the presence of timing error. Two equivalent signal models with frequency offset and timing error are given, and then a joint estimation method is derived. The proposed estimation method consists of two steps. Firstly, a ML frequency offset estimator is proposed based on the second signal model. Secondly, based on the FO estimate, we formulate the timing error and channel estimation as a problem of composite hypothesis testing according to the first signal model, and then solve the problem by the composite hypothesis testing approaches. Simulation results are performed to show the effectiveness of the proposed method.3. The problem of frequency offsets and channel estimation is studied for a MIMO system in flat-fading channels, where the frequency offsets are possibly different for each transmit antenna is considered. Two estimation methods are given. The first one is based on the multiple signal classification (MUSIC) and the ML algorithms. This estimation method has three steps. A subset of frequency offsets is first estimated with the MUSIC algorithm. Then all frequency offsets in the subset are identified with the ML algorithm. Finally channel gains are estimated with the ML estimator. The second one is based the particle swarm optimization theory. This estimation method has two steps. Frequency offsets are first estimated by the particle swarm optimization theory. Then channel gains are estimated by the ML estimator. Theoretical analyses and simulation results show that the first estimation method is suboptimal and that the second one is asymptotically optimal.4. The problem of signal detection is studied for vertical Bell-labs layered space-time (V-BLAST) systems. Firstly, a discrete particle swarm optimization (DPSO) detection algorithm is proposed by applying discrete particle swarm optimization to the signal detection of the V-BLAST system. Secondly, aiming at solving the premature convergence problem in DPSO detection algorithm, another detection algorithm, named by hybrid discrete particle swarm optimization (HDPSO) detection algorithm, is proposed. The HDPSO detection algorithm can be obtained by redesigning the evolution equation of the DPSO detection algorithm and introducing the mutation operator of the genetic algorithm, which improves the performance of the DPSO detection algorithm. Analyses and simulation results show that the proposed detection algorithms have lower computational complexity than the optimal detection algorithm and better detection performance than the suboptimal detection algorithms, to find a new method to solve the detection problem in V-BLAST systems.5. In order to further improve the performance of the HDPSO detection algorithm, two detection approaches that employ the HDPSO and parallel interference cancellation (PIC) algorithms in V-BLAST system are proposed. One of approaches is that the HDPSO algorithm is used as the first stage of the PIC to provide a good initial point for successive stages of the PIC, the other is that PIC is embedded into the HDPSO algorithm to further improve the fitness of the population at each generation. Such a hybridization of the HDPSO with the PIC can speed up its convergence. In addition, a better initial data estimate supplied by the HDPSO algorithm improves the performance of the PIC, and the embedded PIC improves the performance of the HDPSO.