DECCO: Deep-Learning Enabled Coverage and Capacity Optimization for Massive MIMO Systems

Abstract

System capacity and service coverage are the most critical performance metrics in cellular wireless communication networks. Usually, system capacity enhancements are at the expense of service coverage degradations, and vice versa. This capacity-coverage tradeoff and the associated joint optimization problem becomes very challenging in massive multiple-input multiple-output (MIMO) wireless systems, due to a large amount of antenna tilt values to be configured and very sophisticated inter-cell interference conditions, under massive antenna scenarios. This paper proposes a novel approach, namely group alignment of user signal strength (GAUSS), to efficiently support the user scheduling for the massive MIMO system, and thus serve as an effective parameter for the coverage and capacity optimization (CCO) problem. Together with a unified threshold of Quality of Service, i.e. the minimum signal-to-interference-plus-noise ratio ( $\textit {SINR}_{min}$ ) for user satisfaction, GAUSS can effectively control the variance of signal strengths of multiple users in the neighborhood. Moreover, an intelligent and efficient deep-learning enabled coverage and capacity optimization (DECCO) algorithm is proposed and evaluated, which adopts a pre-trained deep policy gradient-based neural network to dynamically derive GAUSS and $\textit {SINR}_{min}$ during CCO. Furthermore, an inter-cell interference coordination (ICIC) is proposed to enhance the CCO performance. Analytical and simulation results show that the proposed DECCO algorithm can effectively achieve a much better performance balance between system capacity and service coverage than traditional fixed optimization (FO) and proportional fair optimization (PFO) algorithms. Specifically, DECCO significantly increases the overall spectrum efficiency by 24% and 40%, respectively, than FO and PFO in a typical massive MIMO system.