Energy-Efficient Resource Allocation in Single-RF Load-Modulated Massive MIMO HetNets

• Published in: IEEE open journal of the Communications Society 2020, Vol.1, p.1738-1764
• Main Authors: Ataeeshojai, Mahtab, Elliott, Robert C., Krzymien, Witold A., Tellambura, Chintha, Melzer, Jordan
• Format: Article
• Language: English
• Subjects: 5G mobile communication; Algorithms; Antennas; Beamforming; Cellular communication; Cellular structure; Circuit reliability; Clean energy; Clustering; Communications systems; coordinated transmission; Design optimization; Energy consumption; Energy efficiency; heterogeneous cellular networks (HetNets); Interference; interference management; Massive MIMO; Multiple-input-multiple-output (MIMO) system; Network reliability; Optimization; Quality of service; Radio frequency; radio resource allocation; Resource allocation; Resource management; small cells; User requirements; Wireless networks
• ISSN: 2644-125X; 2644-125X
• DOI: 10.1109/OJCOMS.2020.3032351

Due to the dramatic increase in wireless data traffic and the associated increase in energy consumption, designing energy-efficient wireless networks with improved spectral efficiency is a pressing concern. The focus of this article is the design of a green, highly energy-efficient cellular heterogeneous network (HetNet) by taking advantage of multiple-input-multiple-output (MIMO) structure and deployment of small cells. We consider the downlink of a two-tier HetNet, in which multiple-antenna small cells are coordinated to serve users. Even though the deployment of MIMO together with small cells improves the communication system's performance in terms of data rate and reliability, circuit energy consumption in such a network is a critical issue. To address this, an energy-efficient antenna selection and radio resource block assignment algorithm is proposed for the small cells, and a single radio-frequency (RF) chain structure is considered for the massive MIMO macro base station. Then, while coordinating transmissions between cells subject to user-centric clustering, an energy-efficient beamforming design and power allocation optimization problem with respect to the quality of service requirement of users, transmit power budget of base stations, and fronthaul capacity is formulated; the problem is solved using the Dinkelbach method. Simulation results demonstrate the performance potential of our proposed algorithm in terms of energy efficiency and spectral efficiency.