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QoS‐driven resource allocation in green OFDMA wireless networks
Author(s) -
Sinaie Mahnaz,
Azmi Paeiz
Publication year - 2015
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.2949
Subject(s) - subcarrier , computer science , quality of service , transmitter power output , orthogonal frequency division multiple access , mathematical optimization , maximization , resource allocation , efficient energy use , parametric programming , minification , throughput , energy consumption , fractional programming , orthogonal frequency division multiplexing , wireless , parametric statistics , computer network , telecommunications , transmitter , mathematics , nonlinear programming , electrical engineering , channel (broadcasting) , statistics , physics , nonlinear system , quantum mechanics , programming language , engineering
Summary In this paper, we present a QoS‐driven energy efficient power and subcarrier allocation in green orthogonal frequency division multiple access networks. Our proposed scheme aims at maximizing the effective energy efficiency (EEE), defined as overall effective capacity per total power consumption, subject to given user delay‐QoS requirements and an average sum transmit power constraints. We analytically obtain the uniquely global optimal power and subcarrier using fractional programming, which transforms the fractional program into an equivalent parametric convex program that has a tractable solution. Analytical results show that the optimal solution has the same structure as effective capacity maximization and overall power minimization solutions. Simulation results show that there exists a trade‐off between EEE and delay. When the delay is more stringent, the EEE decreases. Moreover, when the number of subcarriers increase, EEE increases, where a fixed optimal EEE can be achieved regardless of stringency of delay‐QoS requirements. Furthermore, our proposed resource allocation provides significant EEE gains over both the independent resource allocation and overall power minimization schemes. Copyright © 2015 John Wiley & Sons, Ltd.