Energy-Efficiency of Distributed Antenna Systems Relying on Resource Allocation

• Published in: KSII transactions on Internet and information systems 2019, 13(3), , pp.1325-1344
• Main Authors: Huang, Xiaoge, Zhang, Dongyu, Dai, Weipeng, Tang, She
• Format: Article
• Language: English
• Subjects: Algorithms; Antennas (Electronics); Design and construction; Energy efficiency; Methods; Resource allocation; 컴퓨터학
• ISSN: 1976-7277; 1976-7277
• DOI: 10.3837/tiis.2019.03.012

Recently, to satisfy mobile users' increasing data transmission requirement, energy efficiency (EE) resource allocation in distributed antenna systems (DASs) has become a hot topic. In this paper, we aim to maximize EE in DASs subject to constraints of the minimum data rate requirement and the maximum transmission power of distributed antenna units (DAUs) with different density distributions. Virtual cell is defined as DAUs selected by the same user equipment (UE) and the size of virtual cells is dependent on the number of subcarriers and the transmission power. Specifically, the selection rule of DAUs is depended on different scenarios. We develop two scenarios based on the density of DAUs, namely, the sparse scenario and the dense scenario. In the sparse scenario, each DAU can only be selected by one UE to avoid co-channel interference. In order to make the original non-convex optimization problem tractable, we transform it into an equivalent fractional programming and solve by the following two sub-problems: optimal subcarrier allocation to find suitable DAUs; optimal power allocation for each subcarrier. Moreover, in the dense scenario, we consider UEs could access the same channel and generate co-channel interference. The optimization problem could be transformed into a convex form based on interference upper bound and fractional programming. In addition, an energy-efficient DAU selection scheme based on the large scale fading is developed to maximize EE. Finally, simulation results demonstrate the effectiveness of the proposed algorithm for both sparse and dense scenarios. Keywords: Distributed antenna systems, resource allocation, energy efficiency, virtual cell