When the gain of predictive resource allocation for content delivery is large?

作     者：Chenzuo ZHANG Jia GUO Chenyang YANG Chenzuo ZHANG;Jia GUO;Chenyang YANG

作者机构：School of Electronic and Information EngineeringBeihang University 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2023年第66卷第12期

页      面：179-193页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 

基　　金：supported by Key Project of National Natural Science Foundation of China (Grant No. 61731002) 

主　　题：predictive resource allocation performance analysis average data rates residual bandwidth user mobility 

摘      要：By predicting future information such as data rate with sensory wireless data, radio resources can be pre-allocated for content delivery. Such an integrated sensing and communications technique can help improve network performance and user experience. To justify the cost paid for predicting future information,it is important to understand in which scenarios predictive resource allocation yields a large gain over the non-predictive counterpart. In this paper, we strive to identify the key factors that affect the gain of predictive resource allocation by deriving the closed-form expression of the gain. We are concerned with minimizing the transmission time required for content delivery such as file downloading to users with an expected deadline,where the resources of base stations are shared with real-time services. Then, the performance gain is measured by the difference of the average time required by predictive and non-predictive resource *** by the solution of the optimization problem, we resort to the theory of order statistics for deriving the performance gain. We find that the gain depends on the statistics(i.e., mean value and standard deviation)of the user’s average data rates in the prediction window. Then, we separately analyze how the statistics of the bandwidth available for content delivery and user mobility affect the gain. We use simulation with a real dataset of traffic load to validate the analysis and quantify the impact of the key factors. Our results show that predictive resource allocation can reduce the transmission time even for non-moving users. The performance gain is high when the network is busy or the cell radius is large.