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http://dx.doi.org/10.3837/tiis.2015.02.001

Optimal User Density and Power Allocation for Device-to-Device Communication Underlaying Cellular Networks  

Yang, Yang (Wireless Signal Processing and Network Lab, Beijing University of Posts & Telecommunications)
Liu, Ziyang (Beijing National Railway Research & Design Institute of Signal & Communication Co. Ltd.)
Min, Boao (Wireless Signal Processing and Network Lab, Beijing University of Posts & Telecommunications)
Peng, Tao (Wireless Signal Processing and Network Lab, Beijing University of Posts & Telecommunications)
Wang, Wenbo (Wireless Signal Processing and Network Lab, Beijing University of Posts & Telecommunications)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.9, no.2, 2015 , pp. 483-503 More about this Journal
Abstract
This paper analyzes the optimal user density and power allocation for Device-to-Device (D2D) communication underlaying cellular networks on multiple bands with the target of maximizing the D2D transmission capacity. The entire network is modeled by Poisson point process (PPP) which based on stochastic geometry. Then in order to ensure the outage probabilities of both cellular and D2D communication, a sum capacity optimization problem for D2D system on multiple bands is proposed. Using convex optimization, the optimal D2D density is obtained in closed-form when the D2D transmission power is determined. Next the optimal D2D transmission power is obtained in closed-form when the D2D density is fixed. Based on the former two conclusions, an iterative algorithm for the optimal D2D density and power allocation on multiple bands is proposed. Finally, the simulation results not only demonstrate the D2D performance, density and power on each band are constrained by cellular communication as well as the interference of the entire system, but also verifies the superiority of the proposed algorithm over sorting-based and removal algorithms.
Keywords
Device-to-Device communication; user density; power allocation; stochastic geometry; convex optimization;
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