Open AccessMultiple resource allocation in device‐to‐device communication underlaying cellular networks from an end‐to‐end energy‐efficient perspective
Author(s) -
Xu Quansheng,
Ji Hong,
Li Xi
Publication year - 2015
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2014.0994
Subject(s) - mathematical optimization , computer science , resource allocation , fractional programming , integer programming , efficient energy use , energy (signal processing) , convex optimization , integer (computer science) , power (physics) , optimization problem , channel (broadcasting) , regular polygon , mathematics , nonlinear programming , computer network , statistics , physics , geometry , nonlinear system , quantum mechanics , electrical engineering , programming language , engineering
In this study, a novel energy‐efficient resource allocation (RA) scheme is proposed for device‐to‐device communication underlaying cellular networks from an end‐to‐end energy‐efficient perspective. The time slot, sub‐channel (frequency) and power resources are allocated together to optimise the energy‐efficiency (EE) performance. Furthermore, to match the practical communication situations and achieve the best EE performance, the time–frequency resource units (RUs) are used in a complete‐shared pattern. Then, the multiuser interference is very severe and complex. With all these considerations, the energy‐efficient RA problem is formulated as a mixed integer and non‐convex optimisation problem, which is an non‐deterministic polynominal (NP)‐hard problem and extremely difficult to solve. To obtain a desirable solution with a reasonable computation cost, the authors tackle this problem with two steps. Step 1, the RU allocation policy is obtained via a greedy search method, and the original optimisation problem is reduced to a non‐convex fractional programming problem. Step 2, exploiting the properties of fractional programming and after some manipulations, they transform the reduced problem to a concave optimisation problem, and obtain the sub‐optimal power allocation strategy through the Lagrange dual approach. Finally, simulation results are presented to validate the effectiveness of the proposed RA scheme.