Joint channel selection and power control for video streaming over D2D communications based cognitive radio networks

作     者：Gao Ya Zhang Hailin Lu Xiaofeng 

作者机构：State Key Laboratory of Integrated Services NetworksXidian University College of Physics and Electronic InformationLuoyang Normal University 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2018年第25卷第1期

页      面：1-14页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 08[工学] 0839[工学-网络空间安全] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China ( 61371127,61671347) the 111 Project of China ( B08038 ) the Fundamental Research Funds for the Central Universities ( 7214603701 ) the Key Technology R&D Program of Henan Province ( 142102210572) 

主　　题：channel selection power control cognitive radio networks D2D communications video streaming convex optimization 

摘      要：A joint channel selection and power control scheme is developed for video streaming in device-to-device （D2D） communications based cognitive radio networks. In particular, physical queue and virtual queue models by applying ＇M/G/1 queue＇ and ＇M/G/1 queue with vacations＇ theories are built up, respectively, to evaluate the delays experienced by various video traffics. Such delays play a vital role in calculating the packet loss rate for video streaming, which reflects the video distortion. Based on the distortion model, a video distortion minimization problem is formulated, subject to the rate constraint, maximum power constraint, primary users＇ tolerant interference constraint, and secondary users＇ minimum data rate requirement constraint. The optimization problem turns out to be a mixed integer nonlinear programming （MINLP） , which is generally nondeterministic in polynomial time. A Lagrangian dual method is thus employed to reformulate the video distortion minimization problem, based on which the sub-gradient algorithm is used to determine a relaxed solution. Thereafter, applying the iterative user removal yields the optimal joint channel selection and power control solution to the original MINLP problem. Extensive simulations validate our proposed scheme and demonstrate that it significantly increases the peak signal- to-noise ratio （PSNR） compared with the existing schemes.