Outdoor mmWave Base Station Placement: A Multi-Armed Bandit Learning Approach

TL;DR

This paper introduces a multi-armed bandit learning method for outdoor millimeter-wave base station placement, accounting for 3D environment geometry and dynamic channel conditions to optimize coverage and QoS.

Contribution

It presents a novel MAB-based approach that incorporates viewshed analysis and probabilistic blockage models for mmWave BS placement, capturing time-varying channel behavior.

Findings

Effective identification of optimal BS locations using MAB.

Improved coverage probability compared to traditional methods.

Adaptability to dynamic outdoor environments.

Abstract

Base station (BS) placement in mobile networks is critical to the efficient use of resources in any communication system and one of the main factors that determines the quality of communication. Although there is ample literature on the optimum placement of BSs for sub-6 GHz bands, channel propagation characteristics, such as penetration loss, are notably different in millimeter-wave (mmWave) bands than in sub-6 GHz bands. Therefore, designated solutions are needed for mmWave systems to have reliable quality of service (QoS) assessment. This article proposes a multi-armed bandit (MAB) learning approach for the mmWave BS placement problem. The proposed solution performs viewshed analysis to identify the areas that are visible to a given BS location by considering the 3D geometry of the outdoor environments. Coverage probability, which is used as the QoS metric, is calculated using the appropriate path loss model depending on the viewshed analysis and a probabilistic blockage model and then fed to the MAB learning mechanism. The optimum BS location is then determined based on the expected reward that the candidate locations attain at the end of the training process. Unlike the optimization-based techniques, this method can capture the time-varying behavior of the channel and find the optimal BS locations that maximize long-term performance.