In this paper, the application of non-binary low-density parity-check (NBLDPC) codes to MIMO systems which employ hundreds of antennas at both the transmitter and the receiver has been proposed. Together with the well-known low-complexity MMSE detection, the moderate length NBLDPC codes can operate closer to the MIMO capacity, e.g., capacity-gap about 3.5 dB (the best known gap is more than 7 dB). To further reduce the complexity of MMSE detection, a novel soft output detection that can provide an excellent coded performance in low SNR region with 99% complexity reduction is also proposed. The asymptotic performance is analysed by using the Monte Carlo density evolution. It is found that the NBLDPC codes can operate within 1.6 dB from the MIMO capacity. Furthermore, the merit of using the NBLDPC codes in large MIMO systems with the presence of imperfect channel estimation and spatial fading correlation which are both the realistic scenarios for large MIMO systems is also pointed out.

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system's performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

Recently, it has been proved that both MMSE and MF detectors are near optimal detection for large scale MIMO systems, e.g., MIMO systems with hundreds of antennas. In order to attain near capacity region with reasonable complexity, lowcomplexity detector with soft-output generation is necessary for coded large MIMO systems. We show in this paper that the nonbinary LDPC codes and well-known soft-output MMSE detector can be utilized to significantly reduce the gap to capacity. We also propose a novel soft-output MF-based detector for the non-binary LDPC coded large MIMO systems. With this proposed detector, capacity approaching performance, i.e., the gap to capacity of 1.6 dB, can be achieved with ultra low-complexity detection, e.g., just 0.28% of MMSE detection. Moreover, use of the proposed scheme in large MIMO systems is found to be robust to the presence of imperfect channel estimation and spatial fading correlation which are both the realistic scenarios for large MIMO systems.

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