Cooperative Downlink Max-Min Energy-Efficient Precoding for Multicell MIMO Networks

Abstract

Energy efficiency (EE) optimization of wireless systems has attracted increasing attention recently due to its beneficial impacts on the environment and operational savings. Max-min user energy-efficient precoding for multicell multiple-input–multiple-output (MIMO) cooperative networks is investigated. The optimization problem is a nonconvex fractional programming problem, and optimal solutions are hard to find. Two different local optimization methods, namely, block coordinate descent and sequential convex approximation, are proposed to transform the original problem into a series of convex subproblems. In the former method, the relationship between the user rate and the minimum mean square error (MMSE) is utilized, and optimization of the transmitter matrix is formulated as a semidefinite program; the latter method is based on the convex approximation of the nonconvex rate function. The fractional subproblem is transformed into a parameterized subtractive form by exploiting the generalized fractional programming theorem in both methods. Two iterative-fairness-based energy-efficient algorithms are proposed with proved local convergence. Numerical results illustrate that the proposed max-min EE algorithms can improve EE with a performance loss in terms of the sum rate at a high signal-to-noise ratio (SNR) and achieve EE fairness among base stations with different transmit power levels.