In this paper, we propose a soft-decoding near-ML MIMO demodulation scheme that achieves near optimal performance with fixed and low complexity. Exploiting the regular structure of bit-to-symbol mapping, the proposed scheme performs hard demodulation to find the first candidate symbol for each stream followed by selection of nearby candidate points such that at least one candidate exists for the computation of likelihood information of bit 0 and 1 without intermediate calculation of the Euclidean distance. This demodulation scheme enables an improvement in performance by guaranteeing the existence of candidates and a significant reduction in the number of distance calculations which is a major complexity burden. The performance is evaluated by computer simulation, and computational complexity is also assessed in terms of the number of complex multiplication.

Improving the overall performance of reliable speech communication in ultrashort wave radios over very noisy channels is of great importance and practical use. An iterative joint source-channel (de-)coding and (de-)modulation (JSCCM) algorithm is proposed for ITU-T Rec.G.729EV by both exploiting the residual redundancy and passing soft information throughout the receiver while introducing a systematic global iteration process. Being fully compatible with existing transmitter structure, the proposed algorithm does not introduce additional bandwidth expansion and transmission delay. Simulations show substantial error correcting performance and synthesized speech quality improvement over conventional separate designed systems in delay and bandwidth constraint channels by using the JSCCM algorithm.

Adaptive hybrid genetic algorithm concatenated with improved parallel interference cancellation, i.e. adaptive hybrid genetic algorithm parallel interference cancellation (AHGAPIC) was proposed. A study is conducted on the application of AHGAPIC to soft decoding high rate multi-user detection with diversity reception for dual-rate wideband DS-CDMA spread spectrum communications, aiming to mitigate the effect of multiple access interference. The relevant research has revealed that the local search capability of hybrid genetic algorithm (HGA) is still not good enough. Therefore, first, two evolutionary operations, i.e. inversion and insertion are merged into HGA to constitute a novel algorithm. With its moderate local search capability, this new algorithm can search for the global optimum region according to the information entropy, and then it is made adaptively vary its probabilities of crossover and mutation depending on the fitness values of the solutions to form the adaptive hybrid genetic algorithm (AHGA). Second, AHGA is utilized to effectively identify the better and better binary string to maximize the log-likelihood function of dual-rate multi-user detection. As AHGA converges to the optimum region, the control factor of the improved parallel interference cancellation (IPIC) detector is set to be the ratio of the average fitness value to the maximum fitness value of the population of AHGA. Finally, equipped with both the control factor and the binary string with the maximum fitness value as the initial data, the IPIC detector can rapidly find out the approximately optimum soft decoding vector. Then, it can obtain the approximately global optimum estimate point on the basis of the soft decoding rule, corresponding to the transmitted data bits. A lower bound of computational complexity has been achieved through simulations and qualitative analyses. The property of the proposed algorithm to converge rapidly leads to lower computational complexity. Emulation results have shown that the AHGAPIC soft decoding high rate multi-user detector is superior to other suboptimum detectors considered in this paper in terms of two points. They are the mitigation of multiple access interference and the resistance to near-far effects. Its performance is close to the sequential group optimum multi-user detector but with a shorter time delay.