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This paper proposes a new scheduling algorithm in downlink Long Term Evolution networks whichis adoptable with fast variations in channel conditions.It allocates resources in a fair manner among users so that it increases cell edge users' performance. Besides, it makes a good trade-off between throughput and fairness. The proposed method is simulated and compared with three scheduling algorithms available for Long Term Evolution. The results showgood degree of fairness at the cost of small decrease in amount of system throughput.

Feature selection refers to data reduction process by selecting the minimal subsets of features which are effective to preserve the meaning of the features and rarely dependent on other features. Fuzzy-rough set-based feature selection is a beneficial technique which not only satisfies these conditions but also can deal with imprecision and uncertainty. Many methods have been proposed for feature selection problem; however, most of them are able to find only one minimal data reduction while a dataset can have several minimal reducts. In this paper, we propose a Fuzzy-rough set-based feature selection, using particle swarm optimization (PSO) technique, able to find various minimal data reductions. The main contribution of this paper includes using a ring topology for a binary version of the PSO, utilizing the fuzzy-rough dependency degree as fitness. In addition, we present a new velocity updating rule. In order to obtain the efficiency of the proposed method, we compare it with some other meta-heuristic methods using 10 well-known UCI data sets. The results show that the performance of the fuzzy rough-based feature selection can be improved using this method for finding various data reductions.

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