Energy-Efficient Interference-Aware Cognitive Machine-to-Machine Communications Underlaying Cellular Networks

被引:1
|
作者
Alhussien, Nedaa [1 ]
Gulliver, T. Aaron [1 ]
机构
[1] Univ Victoria, Dept Elect & Comp Engn, Victoria, BC V8W 2Y2, Canada
来源
IEEE ACCESS | 2022年 / 10卷
关键词
Resource management; Machine-to-machine communications; Quality of service; Power demand; Interference; Long Term Evolution; Energy consumption; Cellular user equipment (CUE); energy efficiency (EE); machine-to-machine (M2M) communications; power allocation; underlay cognitive radio (CR); RESOURCE-ALLOCATION; M2M COMMUNICATIONS; POWER ALLOCATION; MANAGEMENT; ACCESS; RADIOS;
D O I
10.1109/ACCESS.2022.3162252
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Machine-to-machine (M2M) communications can effectively utilize cognitive radio (CR) to coexist with cellular users in what is known as cognitive M2M (CM2M) communications. In this system, underlay CR is used to manage spectrum sharing among machine type communication devices (MTCDs) and cellular user equipment (CUE) where the CUE are considered to be primary users (PUs) and the MTCDs are secondary users (SUs). Moreover, due to the limited battery capacity of MTCDs and diverse quality-of-service (QoS) requirements of both MTCDs and CUE, energy efficiency (EE) is critical to prolonging network lifetime. This paper investigates the power allocation problem for energy-efficient CM2M communications underlaying cellular networks. Underlay CR is employed to manage the coexistence of MTCDs and CUE and exploit spatial spectrum opportunities to improve spectrum utilization. Two power allocation problems are proposed where the first targets MTCD power consumption minimization while the second considers MTCD EE maximization subject to MTCD transmit power constraints, MTCD minimum data rate requirements, and CUE interference limits. The proposed power consumption minimization problem is transformed into a geometric programming (GP) problem and solved iteratively. The proposed EE maximization problem is a nonconvex fractional programming problem. Hence, a parametric transformation is used to convert it into an equivalent convex form and this is solved using an iterative approach. Simulation results are presented which show that the proposed algorithms provide MTCD power allocation with lower power consumption and higher EE than the equal power allocation (EPA) scheme while satisfying the constraints.
引用
收藏
页码:33932 / 33942
页数:11
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