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

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.

Channel Rank Improvement in Urban Drone Corridors Using Passive Intelligent Reflectors

“…• Objective Function: The objective of the process is to find the IRS-ASA pairs that maximizes the average rates throughout the drone corridor, where ξ is the cardinality of ASA set, [U], and R u,µ is the channel rate from (5), achieved when all the IRSs in subset-µ serve the u th ASA. A higher s max yields increased computational complexity; however, this is not a concern as the optimization process runs offline and the IRSs are placed only once.…”

“…Optimal placement of BSs has been studied with several different performance criteria in the literature, such as cost, coverage, and capacity [4,5]. The optimal placement of reflectors is also studied with similar motivations.…”

Temporal deep learning assisted UAV communication channel model for application in EH-MIMO-NOMA set-up

“…In [37], authors have reported the application of a deep learning based approach to model path loss on fixed wireless access techniques in suburban scenarios. In [38] and [39], learning aided approaches for proper utilization of the UAV channel have been reported. Despite the reported use of several learning based approaches for UAV channel modeling, there are opportunities for obtaining performance improvements and optimal computation if the input feed mechanisms are expanded with tapped delay line (TDL) [31], [32] blocks and configuring gated functions in innovative combinations to facilitate detailed feature learning, missing content recovery, tracking time variations and preserving context of the processed content.…”

“…So the recovery of x 1 from y R1 is related to the consideration of x 2 as an interference signal. Using (37) and (38),…”

Channel Rank Improvement in Urban Drone Corridors Using Passive Intelligent Reflectors

“…Optimal placement of BSs has been studied with several different performance criteria in the literature, such as cost, coverage, and capacity [4], [5].…”