D2D communication is widely considered as one of the key technologies in 5G communication systems. The introduction of D2D communication can improve system throughput and spectrum utilization in cellular systems. Considering the scenario that D2D users are larger than cellular users(concerts or gyms), aiming at the problem of co-channel interference caused by D2D users multiplexing uplink resources of cellular users, an improved resource allocation algorithm based on fuzzy clustering is proposed to effectively suppress interference. First, the D2D users are divided into several D2D user groups so that multiple D2D users can reuse the same CU user resources. Subsequently, on the basis of guaranteeing the QoS of cellular users and D2D users, an improved KM algorithm is used to match the optimal multiplexing resources for each D2D user group. The simulation results show that the proposed algorithm can effectively reduce the interference between users, and the system throughput has been significantly improved.
Since the study of fuel consumptions is of great importance and the related data is accessible, many researches about factors affecting fuel consumptions have appeared.To sum up, the driving style of drivers, automobile emissions and the type of the routes are the mainly three factors.Classification is relatively single.In order to improve the classification accuracy’ this text studies that there exist some special roads,when drivers drive through them,their fuel consumptions will be similar because of road qualities.To achieve this goal, the first step is to calculate fuel consumptions per 100km of all city roads.Recognizing and examining special roads based on the ST-Matching algorithm.Third is to analyze road qualities of special roads like the length of roads, the speed of driving, and compare them with other common roads.Then we choose 4 cases to analyze. We find that when driving through special roads, fuel consumptions would be similar because of road qualities.Besides, the average length of special roads is longer than common roads’ and the mean velocity, the speed of getting and off special roads are faster than common roads’.The findings in this paper can filter out special road segments as noisy data in the study of relationship between driving styles and fuel consumptions,and it also has very high practical significance on recommending fuel-efficient paths.
In the view of solving the combinatorial optimization problems, there are some faults for Ant Colony Optimization(ACO), such as the long compution and easy to fall into local optimum. To solve these problems, the improved ACO based Differential Evolution(DETCACS) is presented. Different from other DEACO, the transforming between natural number coding and real number is applied in the path planning in the new algorithm ,so that the multiple populations differential evolution and guiding cross can be used to ensuring the diversity. Moreover ,The cross removing strategy are applied to accelerate the convergence process. At last, combined with classic Traveling Salesman Problem(TSP) instances in MATLAB, the DETCACS algorithm shows good performance.
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