The massive MIMO (multiple-input multiple-output) technology plays a key role in the next-generation (5G) wireless communication systems, which are equipped with a large number of antennas at the base station (BS) of a network to improve cell capacity for network communication systems. However, activating a large number of BS antennas needs a large number of radio-frequency (RF) chains that introduce the high cost of the hardware and high power consumption. Our objective is to achieve the optimal combination subset of BS antennas and users to approach the maximum cell capacity, simultaneously. However, the optimal solution to this problem can be achieved by using an exhaustive search (ES) algorithm by considering all possible combinations of BS antennas and users, which leads to the exponential growth of the combinatorial complexity with the increasing of the number of BS antennas and active users. Thus, the ES algorithm cannot be used in massive MIMO systems because of its high computational complexity. Hence, considering the trade-off between network performance and computational complexity, we proposed a low-complexity joint antenna selection and user scheduling (JASUS) method based on Adaptive Markov Chain Monte Carlo (AMCMC) algorithm for multi-cell multi-user massive MIMO downlink systems. AMCMC algorithm is helpful for selecting combination subset of antennas and users to approach the maximum cell capacity with consideration of the multi-cell interference. Performance analysis and simulation results show that AMCMC algorithm performs extremely closely to ES-based JASUS algorithm. Compared with other algorithms in our experiments, the higher cell capacity and near-optimal system performance can be obtained by using the AMCMC algorithm. At the same time, the computational complexity is reduced significantly by combining with AMCMC.

Self-healing is one of the most important parts in self-organizing mobile communication network. It focuses on detecting the decline of service quality and finding out the cause of network anomalies and repairing it with high automation. Diagnosis is a particularly important task which identifies the fault cause of problematic cells or regions. To perform the diagnosis, this paper presents two modified ensemble classifiers by using Support Vector Machine (SVM) with different kernels, i.e., SVM with the radial basis function (RBF) kernel (RBFSVM in short) and SVM with the linear kernel (LSVM in short), as component classifier in Adaptive Boosting (AdaBoost), and we call the two ensemble classifiers as Adaptive Boosting based on RBFSVM (AdaBoostRBFSVM in short) and Adaptive Boosting based on linear kernel (AdaBoostLSVM in short). Different with previous AdaBoostSVM classifiers using weak component classifiers, in this paper, the performance of the classifiers is adaptively improved by using moderately accurate SVM classifiers (the training error is less than 50%). To solve the accuracy/diversity dilemma in AdaBoost and get good classification performance, the training error threshold is regulated to adjust the diversity of classifier, and the parameters of SVM (regularization parameter C and Gaussian width σ) are changed to control the accuracy of classifier. The accuracy and diversity will be well balanced through reasonable parameter adjustment strategy. Results show that the proposed approaches outperform individual SVM approaches and show good generalization performance. The AdaBoostLSVM classifier has higher accuracy and stability than LSVM classifier. Compared with RBFSVM, the undetected rate and diagnosis error rate of AdaBoostRBFSVM decrease slightly, but the false positive rate does reduce a lot. It means that the AdaBoostRBFSVM classifier is indeed available and can greatly reduce the number of normal class samples that have been wrongly classified. Therefore, the two ensemble classifiers based on the SVM component classifier can improve the generalization performance by reasonably adjusting the parameters. To set the parameter values of component classifiers in a more reasonable and effective way, genetic algorithm is introduced to find the set of parameter values for the best classification accuracy of AdaBoostSVM, and the new ensemble classifier is called AdaboostSVM based on genetic algorithm (GA-AdaboostSVM in short) (including AdaboostLSVM based on genetic algorithm and AdaboostRBFSVM based on genetic algorithm). Results show that GA-AdaboostSVM classifiers have a lower overall error than AdaboostSVM classifiers. Genetic algorithm could help to achieve a more optimal performance of the ensemble classifiers.

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