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Register a new userOutdoor mmWave Base Station Placement: A Multi-Armed Bandit Learning Approach
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Added by
Fatih Erden
Publication date
2020
fields
Electronic Engineering
and research's language is
English
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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.
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The use of millimeter-wave (mmWave) bands in 5G networks introduce a new set of challenges to network planning. Vulnerability to blockages and high path loss at mmWave frequencies require careful planning of the network to achieve the desired service quality. In this paper, we propose a novel 3D geometry-based framework for deploying mmWave base stations (gNBs) in urban environments by considering first-order reflection effects. We also provide a solution for the optimum deployment of passive metallic reflectors (PMRs) to extend radio coverage to non-line-of-sight (NLoS) areas. In particular, we perform visibility analysis to find the direct and indirect visibility regions, and using these, we derive a geometry-and-blockage-aided path loss model. We then formulate the network planning problem as two independent optimization problems, placement of gNB(s) and PMRs, to maximize the coverage area with a certain quality-of-service constraint and minimum cost. We test the efficacy of our proposed approach using a generic map and compare our simulation results with the ray-tracing solution. Our simulation results show that considering the first-order reflections in planning the mmWave network helps reduce the number of PMRs required to cover the NLoS area and the gNB placement aided with PMRs requires fewer gNBs to cover the same area, which in turn reduces the deployment cost.
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