高帅
北京交通大学电子信息工程学院 北京 100044
基金项目:国家自然科学基金(61571038)，国家科技重大专项(2016ZX03001011-004)，中央高校基本科研业务费专项资金(2016JBZ003)
详细信息
作者简介:徐少毅：女，1975年生，副教授，研究方向为无线资源管理
高帅：男，1994年生，硕士生，研究方向为无线资源管理
通讯作者:徐少毅 shyxu@bjtu.edu.cn
中图分类号:TN929.5计量
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被引次数:0
出版历程
收稿日期:2018-12-19
修回日期:2019-07-24
网络出版日期:2019-08-22
刊出日期:2019-12-01
Energy Efficiency and System Capacity Based Multi-Objective Radio Resource Management in M2M Communications
Shaoyi XU,,Shuai GAO
School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
Funds:The National Natural Science Foundation of China(61571038), The Important National Science & Technology Specific Projects of China(2016ZX03001011-004), The Fundamental Research Funds for the Central Universities (2016JBZ003)
摘要
摘要:机器对机器(M2M)通信和设备到设备(D2D)通信都是5G中的关键技术。而M2M通信特别需要考虑提高设备的能量效率(EE)以延长设备的生存周期。该文将M2M技术与D2D技术相结合，考虑M2M设备使用D2D技术进行通信，同时M2M设备复用蜂窝网络中的人对人(H2H)通信的频谱资源。为了同时保证两种系统的服务质量(QoS)需求，建立了最大化M2M的能量效率，最大化H2H系统容量和，以及最小化M2M系统对H2H系统干扰的多目标优化问题(MOOP)。为了解决该问题，采用惩罚函数的方法将二进制变量松弛约束，进而采用凹凸过程(CCCP)方法将非凸的单目标优化问题转化为凸优化问题，并最终通过加权切比雪夫算法得到原多目标优化问题的Pareto最优解。通过与传统的加权和算法进行比较，仿真结果证明了该算法的有效性。
关键词:机器对机器/
人对人/
多目标优化问题/
凹凸过程/
加权切比雪夫
Abstract:Machine-to-Machine (M2M) and Device-to-Device (D2D) communications are both key technologies in the Fifth Generation (5G) mobile communication systems. In M2M communications, the Energy Efficiency (EE) especially needs to be improved to extend the life cycle of the M2M equipment. In this paper, the M2M and D2D technologies are combined and the D2D technology is used to realize M2M transmission. At the same time, M2M users are allowed to reuse spectrum resources with Human-to-Human (H2H) devices in the cellular networks. To guarantee the Quality of Service (QoS) of these two systems simultaneously, a Multi-Objective Optimization Problem (MOOP) is then formulated to maximize the sum throughput of H2H systems, and the sum EE of M2M systems and to minimize the interference from M2M communications to H2H networks. To solve this MOOP, the penalty function method is firstly adopted to relax the original binary variables, and then the ConCave-Convex Procedure (CCCP) method is used to convert the non-convex single-objective problems into convex problems. Finally, the weighted Tchebyshev algorithm is utilized to obtain the Pareto solution of the original MOOP. By comparing with the traditional weighted sum method, the effectiveness of the proposed method is proved by simulation results.
Key words:Machine-to-Machine (M2M)/
Human-to-Human (H2H)/
Multi-Objective Optimization Problem (MOOP)/
ConCave-Convex Procedure (CCCP)/
Weighted Tchebyshev
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