Receiver Technologies And Coding Optimization For MIMO Systems

Multiple-input multiple-output (MIMO) technology has been identified as the most dominant solution in future broadband wireless communication systems, because it has the potential of achieving remarkably high channel capacity and spectrum efficiency in rich scattering environments without increasing the bandwidth or transmitted power. However, the performance of the MIMO system strongly depends on the signal design at the transmitter and the detection algorithm at the receiver, and hence one of the key problems is how to design appropriate signaling and practical receiver schemes to realize the promised capacity gain. Motivated by the observation, this thesis intensively investigates receiver technologies and coding optimization for MIMO systems on the basis of the up-to-date international research works, and presents some new results. The main contributions can be summarized as follows:1. To overcome the drawback of the high complexity of an enhanced minimum mean square error (EMMSE) based detection algorithm, two low-complextiy detection algorithms are proposed. The first one computes the equalizer matrices by adopting recursive method based on matrix inversion formula, and can decrease the complexity to some extent. The second one applies pre-processing combined with minimum mean square error (MMSE) filtering, takes the symbols decision errors into account in detecting symbols and calculating the bit log-likelihood ratios (LLRs), and removes the expected values of the decision errors when performing interference cancellation. The complexity of the second algorithm is significantly less than that of EMMSE algorithm. Moreover, simulation results show that, its performance is almost the same as that of EMMSE algorithm when low-order modulations are employed, and is much better than that of EMMSE algorithm when high-order modulations are employed.2. A low-complexity iterative receiver for MIMO bit-interleaved coded modulation (BICM) systems is proposed. In the proposed scheme, we apply linear MMSE filtering in the first iteration and utilize a new low-complexity MMSE combining with soft interference cancellation (SIC) in the following iterations to suppress residual interference and noise. By adopting the strategy that the different components of the residuals and the noise are approximated by uncorrelated random variables after the SIC, the complexity of the algorithm can be greatly reduced and the algorithm can be easily performed as the matrix inversion for MMSE is not necessary. Computer simulation results confirm that the proposed scheme with much lower complexity can achieve almost the same performance as the conventional MMSE combining with SIC.3. To overcome the disadvantage of the degraded system performance of the iterative tree search (ITS) detection resulting from the fact that it needs LLR clipping when calculating some bit LLRs, a new M-algorithm based soft-MIMO detection scheme, called path-choosing-entending (PCE) algorithm, is proposed. The scheme ensures that the LLR value of each bit can be calculated without resorting to clipping and provides highly reliable LLR, by appropriately choosing some discarded paths, retaining them and extending them to full-length paths. The rule to choose suitable discarded paths is presented and the low-complexity approach to extend them to full length is given. Computer simulation results show that the proposed algorithm can provide much better performance than ITS and achieve good performance-complexity trade-off.4. A new partial a posteriori probabilities based soft detection, called PAPP, is proposed. The scheme recursively calculates the a posterior probabilities of partial symbol sequences at each stage of the tree, based on which the LLRs of those bits from the first stage to the current one are approximately computed, and then, by using M-algorithm, retains partial symbol sequences and extends them to the next stage. Considering that the LLRs of some bits may be evaluated several times, a reduced-complexity implementation method is also given. In addition, a simple approach for calculating the a posterior probabilities of symbol sequences is suggested. Simulation results show that, with a given complexity, the proposed algorithm can obtain better performance than ITS.5. Joint design and optimization of both low density parity check (LDPC) codes and M-algorithm based soft detectors including ITS, PCE and PAPP in MIMO systems is investigated via the tool of extrinsic information transfer (EXIT) charts. First, we present EXIT analysis for ITS, PCE and PAPP. We indicate that the extrinsic information transfer curves of ITS and PCE obtained by Monte Carlo simulations based on output LLRs are not true EXIT curves, and the explanation for such phenomena is given. While, for PAPP, the true EXIT curves can be computed, enabling the code design. Then, we propose a new design rule and method for LDPC code degree profile optimization in MIMO systems. The algorithm can make the EXIT curves of the inner decoder and outer decoder match each other properly, and can easily attain the desired code with the target rate. Also, it can transform the optimization problem into a linear one, which is computationally simple. Numerical and simulation results show that the optimized MIMO systems significantly outperform the non-optimized ones, confirming the effectiveness of the proposed optimization approach and the importance of the joint optimization.