IET Communications · 2015 · 91 citations · 34 references
Energy ConsumptionWireless CommunicationsCellular NetworksEngineeringDevice-to-device CommunicationEnergy EfficiencyEnergy ManagementSpectrum ManagementInterference ManagementComputer EngineeringSystems EngineeringDevice-to-deviceResource AllocationNash EquilibriumWireless SystemsEnergy-efficient CommunicationResource OptimizationEnergy-efficient Networking
Device‑to‑device communication offers benefits but introduces co‑channel interference and battery constraints that previous work has largely overlooked. The study aims to maximize each user equipment’s energy efficiency while meeting quality‑of‑service and power limits. The authors formulate a non‑cooperative game and propose a distributed, interference‑aware algorithm based on nonlinear fractional programming, also deriving closed‑form EE‑SE trade‑off expressions. They prove the algorithm’s solution is the Nash equilibrium of the game, achieving optimal energy efficiency.
Despite the numerous benefits brought by device‐to‐device (D2D) communications, the introduction of D2D into cellular networks poses many new challenges in the resource allocation design because of the co‐channel interference caused by spectrum reuse and limited battery life of user equipment's (UEs). Most of the previous studies mainly focus on how to maximise the spectral efficiency and ignore the energy consumption of UEs. In this study, the authors study how to maximise each UE's Energy Efficiency (EE) in an interference‐limited environment subject to its specific quality of service and maximum transmission power constraints. The authors model the resource allocation problem as a non‐cooperative game, in which each player is self‐interested and wants to maximise its own EE. A distributed interference‐aware energy‐efficient resource allocation algorithm is proposed by exploiting the properties of the nonlinear fractional programming. The authors prove that the optimal solution obtained by the proposed algorithm is the Nash equilibrium of the non‐cooperative game. The authors also analyse the tradeoff between EE and SE and derive closed‐form expressions for EE and SE gaps.
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On Nonlinear Fractional Programming
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