Codebook Based Antenna Configuration: A New Network Planning Paradigm for mmWave Mobile Communication Systems

To configure the azimuths and downtilts of massive antennas optimally is of great importance for the mmWave mobile communication systems to support diverse services, however, it is always computationally forbidden due to the huge number of possible combinations in real-world mobile networks. In this paper, we develop an effective and efficient method to deal with this intractable optimization task. First, we explore the most promising azimuth and downtilt configurations of each antenna with a branch-and-bound procedure followed by a semi-definite relaxation algorithm, which finally generates a codebook with finite elements to reduce the unnecessary searches for similar or unpromising configurations during global search; second, a Monte Carlo tree search method is introduced to iteratively optimize the azimuths and downtilts of all antennas, aiming at alleviating the mutual interference of adjacent antennas to maximize the signal-to-interference ratio of the target service area, during which the generated antenna configuration codebook guides the iterative search process efficiently. Experiment results in real urban scenarios show that our proposed method can produce the best signal-to-interference ratio coverage as compared with state-of-the-art ones. Moreover, the proposed algorithm can scale up straightforwardly, making it a competitive choice for large-scale network optimization.