Decentralized Energy-Efficient Coordinated Beamforming for Multicell Systems

Energy-efficient transmission is crucial for future wireless communication systems and has attracted much attention. In this paper, we study coordinated beamforming optimization for multicell multiple-input-single-output (MISO) downlink systems using energy efficiency as a criterion, which is still an open problem to the best of our knowledge. The optimization problem of interest is nonconvex and in a fractional form. To solve it, we first reveal that finding the solution of the problem is equivalent to searching for a particular point on the Pareto boundary of the newly defined energy-efficiency rate tuples. Then, we propose to use a set of interference temperature (IT)-constrained beamforming optimization problems to characterize the energy-efficiency rate Pareto boundary. Based on that, two efficient iterative algorithms are developed to reach the Pareto optimality and thus to solve the primary problem. We show that the proposed algorithms can be carried out in a decentralized manner and are guaranteed to converge. Then, these algorithms are extended to consider imperfect channel state information (CSI) using the worst-case design. Numerical results are finally provided to verify the effectiveness of the proposed schemes and they exhibit the great potential of the coordinated beamforming optimization in improving the energy efficiency of the cellular network. In particular, the results also illustrate that our proposed algorithms achieve most of the achievable performance gain with a small number of iterations and thereby with limited backhaul overhead in a time-division duplexing (TDD) system.