Energy efficiency maximization oriented resource allocation in 5G ultra-dense network: Centralized and distributed algorithms

Abstract

Spurred by both economic and environmental concerns, energy efficiency (EE) has now become one of the key pillars for the fifth generation (5G) mobile communication networks. To maximize the downlink EE of the 5G ultra dense network (UDN), we formulate a constrained EE maximization problem and translate it into a convex representation based on the fractional programming theory. To solve this problem, we first adopt a centralized algorithm to reach the optimum based on Dinkelbach’s procedure. To improve the efficiency and reduce the computational complexity, we further propose a distributed iteration resource allocation algorithm based on alternating direction method of multipliers (ADMM). For the proposed distributed algorithm, the local and dual variables are updated by each base station (BS) in parallel and independently, and the global variables are updated through the coordination and information exchange among BSs. Moreover, as the noise may lead to imperfect information exchange among BSs, the global variables update may be subject to failure. To cope with this problem, we propose a robust distributed algorithm, for which the global variable only updates as the information exchange is successful. We prove that this modified robust distributed algorithm converges to the optimal solution of the primal problem almost surely. Simulation results validate our proposed centralized and distributed algorithms. Especially, the proposed robust distributed algorithm can effectively eliminate the impact of noise and converge to the optimal value at the cost of a little increase of computational complexity.