Deep Reinforcement Learning Based Antenna Selection for Cell Outage Compensation

Mobile networks require high availability to provide reliable connectivity for various mobile services. Therefore, when a service outage occurs due to mobile base station (BS) failures, mobile network operators need to immediately resolve the effects of the outage. Cell Outage Compensation (COC) is the critical technology that resolves the outage. The COC method is composed of two steps: antenna selection from antennas of neighboring BSs and optimization of the tilts of selected antennas to provide coverage in the outage area. Although most existing works on COC methods focus on the tilt optimization algorithm, the antenna selection algorithm has not been fully discussed. Since the COC method obtains a solution by optimizing the tilts of selected antennas, a poor antenna selection causes performance degradation of the COC solution. This paper proposes an antenna selection algorithm considering the positional relationship between the outage area and neighboring antennas. Moreover, we use deep reinforcement learning (DRL) in our algorithm to find the optimal antenna selection policy. The simulation results show that the COC method with our algorithm finds a practical solution within one minute and outperforms existing selection algorithms in terms of coverage in the outage area and overlap of coverage areas.