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    • 7. 发明申请
    • METHOD FOR MANAGING WIRELESS COMMUNICATION NETWORKS BY PREDICTION OF TRAFFIC PARAMETERS
    • US20180262921A1
    • 2018-09-13
    • US15759931
    • 2015-09-30
    • TELECOM ITALIA S.p.A.
    • Andrea BULDORINIAndrea SCHIAVONI
    • H04W24/02H04W28/02H04W16/18
    • H04W24/02H04W16/18H04W24/08H04W28/02
    • A method (100) for managing a wireless network, comprising: collecting (105) a sequence of traffic data samples ordered in time, and arranging said collected data samples in at least one level-0 residual matrix having at least one dimension, said dimension of said level-0 residual matrix corresponding to a respective time scale comprising an ordered sequence of time units, said ordered sequences of time units defining a first time window; performing at least once a cycle, each n-th iteration of the cycle, starting from n=0, comprising a sequence of phases A), B), C), D), E): A) for at least one dimension of a level-(n) residual matrix, sub-dividing (110) the corresponding time scale in such a way to group the time units thereof in a respective level-(n+1) partition of time units so as to subdivide the traffic data samples in corresponding level-(n+1) traffic data sample sets; B) for each level-(n+1) traffic data sample set, calculating (115) a corresponding functional which fits said level-(n+1) traffic data sample set; C) for each level-(n+1) traffic data sample set, calculating (115) a corresponding approximation of the level-(n+1) traffic data sample set by applying the corresponding functional to the corresponding level-(n+1) partition of time units; D) joining together (115) the approximations of the level-(n+1) traffic data sample sets to calculate a level-(n+1) approximated matrix, said level-(n+1) approximated matrix being an approximated version of the level-(n) residual matrix; E) calculating (120) the difference between the level-(n) residual matrix and the calculated level-(n+1) approximated matrix so as to obtain a level-(n+1) residual matrix; forecasting (130) traffic data trend in a second time window different from the first time window by generating predicted data samples by applying the calculated functional to a partition of time units comprising an ordered sequence of time units corresponding to at least one among said second time window and said first time window; using (140) said forecasted traffic data trend to manage the wireless network.