Energy-Efficient Power Control: A Look at 5G Wireless Technologies

Abstract

Abstract-This work develops power control algorithms for energy efficiency (EE) maximization (measured in bit/Joule) in wireless networks. Unlike previous related works, minimum-rate constraints are imposed and the signal-to-interference-plus-noise ratio takes a more general expression, which allows one to encompass some of the most promising 5G candidate technologies. Both network-centric and user-centric EE maximizations are considered. In the network-centric scenario, the maximization of the global EE and the minimum EE of the network are performed. Unlike previous contributions, we develop centralized algorithms that are guaranteed to converge, with affordable computational complexity, to a Karush-Kuhn-Tucker point of the considered non-convex optimization problems. Moreover, closed-form feasibility conditions are derived. In the user-centric scenario, game theory is used to study the equilibria of the network and to derive convergent power control algorithms, which can be implemented in a fully decentralized fashion. Both scenarios above are studied under the assumption that single or multiple resource blocks are employed for data transmission. Numerical results assess the performance of the proposed solutions, analyzing the impact of minimum-rate constraints, and comparing the network-centric and user-centric approaches.

Achieving Energy Efficiency Fairness in Multicell MISO Downlink

“…Another important problem is to maintain the fairness of the achievable EE among involving parties. To do so, a widely used method is to maximize the minimum EE of the system, which was studied in, e.g., [2], [3], [4].…”

“…The numerical results show that the proposed algorithm greatly improves the convergence rate and processing time compared to the existing schemes. It is worth mentioning that sequential convex programming (similar to inner approximation method) was used to study the problem of energy-efficient power control in [4].…”

Resource Allocation for Energy Efficiency in 5G Wireless Networks

“…However, besides the benefits of massive MIMO, the beamforming design, which involves a large-scale size of beamformer, leads to the dramatically high computational complexity. On the other hand, EE performance has been designed for 100-fold increase as a requirement in 5G communication systems [8,25,60]. However, in massive MIMO HetNets, the use of low power largescale antennas at MBS and the higher channel gain by SBSs coverage offers better QoS; however, both MBS and SBSs consume a large amount of power including transmission and non-transmission powers, which are proportional to the number of their antennas and the number of SBSs.…”

“…However, in massive MIMO HetNets, the use of low power largescale antennas at MBS and the higher channel gain by SBSs coverage offers better QoS; however, both MBS and SBSs consume a large amount of power including transmission and non-transmission powers, which are proportional to the number of their antennas and the number of SBSs. Unfortunately, the objective EE functions in massive MIMO HetNets [40] may not belong to the classical of fractional programming, i.e., the ratios of concave and convex functions [60]. Therefore, the Dinkelbach's procedure does not provide an easy way to find its solution for a difficult nonconvex EE optimization problem.…”

“…First of all, the network EE performance is defined as the ratio of the total information throughput and the total energy consumption [6,60]. Secondly, to satisfy users' QoS requirement and minimize the transmit power, ZF and RZF beamforming design are exploited via new path-following procedures for EE maximization problem under practical massive MIMO scenarios.…”