Title
Joint resource allocation and power control scheme for device-to-device communication underlaying cellular networks
Abstract
Device-to-device (D2D) communication is a promising technique of future cellular networks which can improve the system throughput and extend the battery lifetime of user equipments. However, enabling D2D communications in a cellular network causes interference because of spectrum sharing. In this paper, to mitigate the interference and make full use of the advantage of D2D communications, we propose a joint spectrum resource allocation and power control (RAPC) scheme to maximize the total throughput while guaranteeing the minimum required rate for both D2D users and cellular users. We formulate the optimization problem of resource allocation and power control and we obtain the near-optimal solution by quantum particle swarm optimization (QPSO) method, in which the quantum particles represent the solutions of resource allocation and power control. A penalty function is formulated to delete the infeasible solutions. Simulation results show that the proposed scheme has a better performance on system throughput, power efficiency and minimum rate satisfactory than other schemes.
Year
DOI
Venue
2014
10.1109/WPMC.2014.7014882
WPMC
Keywords
Field
DocType
d2d communications,power control,cellular radio,quantum particle swarm optimization,particle swarm optimisation,resource allocation,joint spectrum resource allocation and power control scheme,optimization problem,device-to-device communication,telecommunication control,battery lifetime,cellular networks,quantum particle swarm optimization method,resource management,optimization,particle swarm optimization,throughput,wireless communication,interference
Particle swarm optimization,Resource management,Wireless,Computer science,Power control,Computer network,Resource allocation,Cellular network,Throughput,Optimization problem,Distributed computing
Conference
ISSN
Citations 
PageRank 
1347-6890
2
0.36
References 
Authors
9
4
Name
Order
Citations
PageRank
Tao Liang1266.42
Tiankui Zhang248762.41
Jinlong Cao3345.36
Chunyan Feng430538.57