Capacity augmentation of 3G cellular networks: a deep learning approach

摘要: Optimal enhancement of 3G cellular network capacity utilizes two components learning and optimization. First, a pair approaches are used to model measured in terms total number users carried predict breakpoints towers as function traffic loading. Then, an optimization problem is formulated maximize subject constraints user quality predicted breakpoints. Among alternatives, variant simulated annealing referred Block Coordinated Descent Simulated Annealing (BCDSA) presented solve the problem. Performance measurements show that BCDSA algorithm offers dramatically improved algorithmic success rate best characteristics utility, runtime, confidence range measures compared other solution alternatives. Accordingly, integrated iterative method, program, system described aiming at maximizing networks by redistributing from congested non-congested towers.