In unmanned aerial vehicle(UAV)networks,the high mobility of nodes leads to frequent changes in network topology,which brings challenges to the neighbor discovery(ND)for UAV networks.Integrated sensing and communicati...In unmanned aerial vehicle(UAV)networks,the high mobility of nodes leads to frequent changes in network topology,which brings challenges to the neighbor discovery(ND)for UAV networks.Integrated sensing and communication(ISAC),as an emerging technology in 6G mobile networks,has shown great potential in improving communication performance with the assistance of sensing information.ISAC obtains the prior information about node distribution,reducing the ND time.However,the prior information obtained through ISAC may be imperfect.Hence,an ND algorithm based on reinforcement learning is proposed.The learning automaton(LA)is applied to interact with the environment and continuously adjust the probability of selecting beams to accelerate the convergence speed of ND algorithms.Besides,an efficient ND algorithm in the neighbor maintenance phase is designed,which applies the Kalman filter to predict node movement.Simulation results show that the LA-based ND algorithm reduces the ND time by up to 32%compared with the Scan-Based Algorithm(SBA),which proves the efficiency of the proposed ND algorithms.展开更多
目的:系统评价穴位贴敷治疗颈椎病的有效性与安全性。方法:检索中国知网(CNKI)、维普(VIP)、万方(Wanfang Data)、PubMed、Web of science等数据库有关穴位贴敷治疗颈椎病的随机对照试验(RCT),检索时间从建库至2022年9月,结局指标选用...目的:系统评价穴位贴敷治疗颈椎病的有效性与安全性。方法:检索中国知网(CNKI)、维普(VIP)、万方(Wanfang Data)、PubMed、Web of science等数据库有关穴位贴敷治疗颈椎病的随机对照试验(RCT),检索时间从建库至2022年9月,结局指标选用总有效率、痊愈率,以及治疗后疼痛视觉模拟评分(VAS)、Northwick Park颈痛量表(NPQ)评分。采用RevMan 5.4软件对数据进行Meta分析,再使用TSA V0.9软件对研究结果进行试验序贯分析。结果:共纳入7篇RCT,1121例患者,Meta分析结果显示,试验组总有效率[OR=3.04,95%CI(2.13,4.34),P<0.00001]、痊愈率[OR=3.10,95%CI(1.73,5.53),P=0.0001]均高于对照组,VAS评分低于对照组[MD=-0.61,95%CI(-0.94,-0.29),P=0.0002];试验组NPQ评分与对照组比较,差异无统计学意义(P>0.05);试验组的不良反应发生率低于对照组(P<0.05)。试验序贯分析结果显示,总有效率的累计Z曲线穿过传统界值和试验序贯分析界值,但样本量未达到期望信息值量。结论:与传统中西药治疗比较,联合或单用穴位贴敷治疗颈椎病的疗效确切,安全性更高,值得进一步推广应用。但鉴于纳入文献数量较少,质量偏低,仍需开展高质量的RCT,以进一步验证其临床疗效。展开更多
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant No.2024ZCJH01in part by the National Natural Science Foundation of China(NSFC)under Grant No.62271081in part by the National Key Research and Development Program of China under Grant No.2020YFA0711302.
文摘In unmanned aerial vehicle(UAV)networks,the high mobility of nodes leads to frequent changes in network topology,which brings challenges to the neighbor discovery(ND)for UAV networks.Integrated sensing and communication(ISAC),as an emerging technology in 6G mobile networks,has shown great potential in improving communication performance with the assistance of sensing information.ISAC obtains the prior information about node distribution,reducing the ND time.However,the prior information obtained through ISAC may be imperfect.Hence,an ND algorithm based on reinforcement learning is proposed.The learning automaton(LA)is applied to interact with the environment and continuously adjust the probability of selecting beams to accelerate the convergence speed of ND algorithms.Besides,an efficient ND algorithm in the neighbor maintenance phase is designed,which applies the Kalman filter to predict node movement.Simulation results show that the LA-based ND algorithm reduces the ND time by up to 32%compared with the Scan-Based Algorithm(SBA),which proves the efficiency of the proposed ND algorithms.
基金自然资源保护协会资助项目“Value evaluation and policy study of energy storage on power generation side for carbon emission reduction under the background of carbon”(合同编号:M10)。
