Low-Complexity Iterative Detection Algorithms for Multi-Antenna Systems

Multiple input multiple output (MIMO) techniques have been widely employed by different wireless systems with many advantages. By using multiple antennas, the system is able to transmit multiple data streams simultaneously and within the same frequency band. The methods known as spatial multiplexing (SM) and spatial diversity (SD) improves the high spectral efficiency and link reliability of wireless communication systems without requiring additional transmitting power. By introducing channel coding in the transmission procedure, the information redundancy is introduced to further improve the reliability of SM links and the quality of service for the next generation communication systems. However, the throughput performance of these systems is limited by interference. A number of different interference suppression techniques have been reported in the literature. Theses techniques can be generally categorised into two aspects: the preprocessing techniques at the transmitter side and the decoding techniques at the receiver side. Generally speaking, in the ideal case, the preprocessing techniques orthogonalize the interfering channels, and therefore, the receiver experiences interference free transmission. However, a feedback channel is required to provide the channel information. On the other hand, in this thesis we are interested in the decoding part which uses various techniques to improve the signal-to-interference-and-noise ratio (SINR) of the desired symbols. To achieve this goal, a number of low-complexity iterative detection algorithms have been investigated. In the context of the thesis, the mainly focus is on the interference cancellation techniques. Firstly, we investigate the traditional successive interference cancellation (SIC) algorithm. SIC has the ability to separate the spatially multiplexed signals on a MIMO channel. However, the low detection diversity order as well as the error propagation effect restrict the bit error performance of such detectors. We propose a multiple feedback SIC (MF-SIC) method to enhance the performance of conventional SIC detection by introducing feedback candidates and reliability checking. This algorithm is able to provide significant performance gains with little additional complexity without the protection from channel codes. The MF-SIC algorithm is then incorporated into an iterative detection and decoding (IDD) scheme to process soft information. Secondly, in the case that the MIMO channel is time-varying, the conventional detection algorithms generally bring about expensive complexity in the time domain. In order to address this problem, a decision feedback algorithm is introduced and adaptive algorithms are derived to update the forward and backward filters to perform the detection in each time instant. A constellation based estimation refinement scheme is also introduced in the system and the performance is significantly improved. The proposed decision feedback algorithm is incorporated into an IDD scheme that performs iterative (turbo) interference cancellation. At last, the inter-cell interference is considered in a multi-cell, high frequency reuse scenario. The distributed iterative detection (DID) algorithms are investigated. A large amount of information need to be transmitted via a wired backhaul network where optimal distributed detection exchange all the soft estimates among adjacent base stations (BSs). To address this problem we consider a reduced message passing (RMP) technique in which each BS generates a detection list with the probabilities for the desired symbol that are sorted according to the calculated probability density. RMP introduces low backhaul overhead compared with the hard bit exchange and outperforms the previously reported hard/soft information exchange algorithms