Energy-Efficient Small Cell With Spectrum-Power Trading

• Published in: IEEE journal on selected areas in communications Vol. 34; no. 12; pp. 3394 - 3408
• Main Authors: Qingqing Wu, Ye Li, Geoffrey, Wen Chen, Ng, Derrick Wing Kwan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bandwidth; Bandwidths; Electronic mail; Energy efficiency; Green communications; Green offloading; Linear programming; non-convex optimization; Optimization; Power consumption; Power demand; Quality of service; Resource allocation; Resource management; Signal to noise ratio; small cell; Spectrum allocation; spectrum-power trading; User requirements
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2016.2612061

In this paper, we investigate spectrum-power trading between a small cell (SC) and a macro cell (MC), where the SC consumes power to serve the MC users (MUs) in exchange for some bandwidth from the MC. Our goal is to maximize the system energy efficiency (EE) of the SC while guaranteeing the quality of service of each MU as well as SC users (SUs). Specifically, given the minimum data rate requirement and the bandwidth provided by the MC, the SC jointly optimizes MU selection, bandwidth allocation, and power allocation while guaranteeing its own minimum required system data rate. The problem is challenging due to the binary MU selection variables and the fractional-form objective function. We first show that the bandwidth of an MU is shared with at most one SU in the SC. Then, for a given MU selection, the optimal bandwidth and power allocation are obtained by exploiting the fractional programming. To perform MU selection, we first introduce the concept of the trading EE to characterize the data rate obtained as well as the power consumed for serving an MU. We then reveal a sufficient and necessary condition for serving an MU without considering the total power constraint and the minimum data rate constraint: the trading EE of the MU should be higher than the system EE of the SC. Based on this insight, we propose a low complexity MU selection method and also investigate the optimality condition. Simulation results verify our theoretical findings and demonstrate that the proposed resource allocation achieves near-optimal performance.