Joint Parameter Selection for Massive MIMO: An Energy-Efficient Perspective

Abstract

In this paper, the energy-efficient massive MIMO design problem is investigated in both single-cell and multi-cell scenarios with the aim of selecting appropriate parameters to maximize the overall system energy utilization. To this end, a more realistic parameter selection model is established by considering the channel estimation error and pilot contamination (PC). In the proposed model, we not only optimize the number of antennas, the number of users, and the transmission power to balance the improved radiated energy efficiency and the increased circuit power consumption, but also optimize the length of pilot sequences to make a desired trade-off between improving channel estimation accuracy and reserving more resources for data transmission. We first focus on the single-cell scenario, and strictly prove the quasi-concavity of the objective function in each dimension after suitable variable transformation, based on which an alternative optimization plus bisection searching (AO-BS) algorithm is proposed to solve the problem quickly. Then, the research is extended into the multi-cell scenario by increasingly considering PC and multi-cell interference. Since PC is related to the relative pilot length, i.e., the ratio between the length of pilot sequences and the number of users, the formulated problem is difficult to deal with directly. For effective solving, we divide the original problem into multiple subproblems according to the setting of pilot reusing factor, and employ the proposed AO-BS algorithm to solve each subproblem. Through numerical simulations, it is observed that the proposed AO-BS algorithm converges rapidly and is almost able to achieve the globally optimal solution. Meanwhile, from the perspective of energy efficiency maximization, massive MIMO is more preferable in the case of large cell coverage, and selecting a large relative pilot length is beneficial, always resulting in higher energy utilization in both single-cell and multi-cell scenarios.