Energy efficiency-driven mobile base station deployment strategy for shopping malls using modified improved differential evolution algorithm

Sun, Xingping; Zhang, Tian; Xu, Jing; Zhang, Haigang; Kang, Hongwei; Shen, Yong; Chen, Qingyi

doi:10.1007/s10489-022-03358-x

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…From the perspective of UAVs, their flight distance is restricted by battery endurance, and severe weather conditions, such as strong winds, rain, and snow, pose potential threats to their operations [11]. On the contrary, terrestrial vehicle-mounted mBSs are playing an increasingly important role in various existing networks [12][13][14][15][16][17][18]. For instance, Ref.…”

Section: Introductionmentioning

confidence: 99%

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A Joint Optimization Algorithm for Trajectory Planning and Resource Allocation of Vehicle Mobile Base Stations for On-Demand Coverage Networks

Zhao,

Zhu

2024

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In today’s urban hotspot regions, service traffic exhibits dynamic variations in both time and location. Traditional fixed macro base stations (FMBSs) are unable to meet these dynamic demands due to their fixed coverage and capacity. Therefore, this paper introduces a novel algorithm for the joint optimization of the placement of terrestrial vehicle-mounted mobile micro base stations (mBSs), the correlation of service clusters (SCs) with mBSs, and resource assignments. The objective is to maximize the matching degree between network capacity and service demands while adhering to constraints related to the power, coverage, and bandwidth of mBSs, as well as the data rate required for the services. Additionally, we investigate the mobility of the mBSs towards the SCs in the spatiotemporal changing service demand network and obtain optimal trajectories for the mBSs. We begin by formulating the problem of maximizing the matching degree by analyzing the capacity provided by the base stations and the network service demand. Subsequently, we derive solutions to the optimization problem using our algorithm. The simulation results demonstrate that the proposed algorithm can effectively meet the capacity demand of dynamically changing hotspot regions and achieve on-demand, resilient coverage of hotspot regions in the network.

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Section: Introductionmentioning

confidence: 99%

“…In response to surges of traffic within shopping centers, Ref. [15] introduces a green shopping center traffic model based on mBSs with dynamic sleep strategies. Moreover, Ref.…”

Section: Introductionmentioning

confidence: 99%