{"title":"基于协同波束形成的无人机辅助MWSNs上行数据传输","authors":"Aimin Wang, Yuxin Wang, Geng Sun, Jiahui Li, Shuang Liang, Yanheng Liu","doi":"10.1109/GLOBECOM46510.2021.9685853","DOIUrl":null,"url":null,"abstract":"Unmanned aerial vehicles (UAVs) have attracted growing attention in enhancing the performance of mobile wireless sensor networks (MWSNs) since they can act as the aerial base stations (ABSs) and have the autonomous nature to collect data. In this paper, we consider to construct a virtual antenna array (VAA) consists of mobile sensor nodes (MSNs) and adopt the collaborative beamforming (CB) to achieve the long-distance and efficient uplink data transmissions with the ABSs. First, we formulate a high data transmission rate multi-objective optimization problem (HDTRMOP) of the CB-based UAV-assisted MWSN to simultaneously improve the total transmission rates, suppress the total maximum sidelobe levels (SLLs) and reduce the total motion energy consumptions of MSNs by jointly optimizing the positions and excitation current weights of MSN-enabled VAA, and the order of communicating with different ABSs. Then, we propose an improved non-dominated sorting genetic algorithm-III (INSGA-III) with chaos initialization, average grade mechanism and hybrid-solution generate strategy to solve the problem. Simulation results verify that the proposed algorithm can effectively solve the formulated HDTRMOP and it has better performance than some other benchmark methods.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Uplink Data Transmission Based on Collaborative Beamforming in UAV-assisted MWSNs\",\"authors\":\"Aimin Wang, Yuxin Wang, Geng Sun, Jiahui Li, Shuang Liang, Yanheng Liu\",\"doi\":\"10.1109/GLOBECOM46510.2021.9685853\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Unmanned aerial vehicles (UAVs) have attracted growing attention in enhancing the performance of mobile wireless sensor networks (MWSNs) since they can act as the aerial base stations (ABSs) and have the autonomous nature to collect data. In this paper, we consider to construct a virtual antenna array (VAA) consists of mobile sensor nodes (MSNs) and adopt the collaborative beamforming (CB) to achieve the long-distance and efficient uplink data transmissions with the ABSs. First, we formulate a high data transmission rate multi-objective optimization problem (HDTRMOP) of the CB-based UAV-assisted MWSN to simultaneously improve the total transmission rates, suppress the total maximum sidelobe levels (SLLs) and reduce the total motion energy consumptions of MSNs by jointly optimizing the positions and excitation current weights of MSN-enabled VAA, and the order of communicating with different ABSs. Then, we propose an improved non-dominated sorting genetic algorithm-III (INSGA-III) with chaos initialization, average grade mechanism and hybrid-solution generate strategy to solve the problem. Simulation results verify that the proposed algorithm can effectively solve the formulated HDTRMOP and it has better performance than some other benchmark methods.\",\"PeriodicalId\":200641,\"journal\":{\"name\":\"2021 IEEE Global Communications Conference (GLOBECOM)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE Global Communications Conference (GLOBECOM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GLOBECOM46510.2021.9685853\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Global Communications Conference (GLOBECOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GLOBECOM46510.2021.9685853","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
摘要
由于无人机可以充当空中基站(abs)并具有自主收集数据的特性,因此在提高移动无线传感器网络(MWSNs)的性能方面受到越来越多的关注。本文考虑构建由移动传感器节点(msn)组成的虚拟天线阵列(VAA),并采用协同波束形成(CB)技术与移动传感器节点实现远距离、高效的上行数据传输。首先,我们提出了基于cb的无人机辅助MWSN的高数据传输速率多目标优化问题(HDTRMOP),通过联合优化使能MWSN的VAA的位置和激励电流权重,以及与不同abs的通信顺序,同时提高总传输速率,抑制总最大旁瓣电平(SLLs),降低MWSN的总运动能耗。然后,我们提出了一种改进的非支配排序遗传算法- iii (INSGA-III),该算法具有混沌初始化、平均等级机制和混合解生成策略。仿真结果验证了所提算法能有效求解拟定的HDTRMOP,且性能优于其他基准算法。
Uplink Data Transmission Based on Collaborative Beamforming in UAV-assisted MWSNs
Unmanned aerial vehicles (UAVs) have attracted growing attention in enhancing the performance of mobile wireless sensor networks (MWSNs) since they can act as the aerial base stations (ABSs) and have the autonomous nature to collect data. In this paper, we consider to construct a virtual antenna array (VAA) consists of mobile sensor nodes (MSNs) and adopt the collaborative beamforming (CB) to achieve the long-distance and efficient uplink data transmissions with the ABSs. First, we formulate a high data transmission rate multi-objective optimization problem (HDTRMOP) of the CB-based UAV-assisted MWSN to simultaneously improve the total transmission rates, suppress the total maximum sidelobe levels (SLLs) and reduce the total motion energy consumptions of MSNs by jointly optimizing the positions and excitation current weights of MSN-enabled VAA, and the order of communicating with different ABSs. Then, we propose an improved non-dominated sorting genetic algorithm-III (INSGA-III) with chaos initialization, average grade mechanism and hybrid-solution generate strategy to solve the problem. Simulation results verify that the proposed algorithm can effectively solve the formulated HDTRMOP and it has better performance than some other benchmark methods.