Connected autonomous vehicles(CAVs)are a promising paradigm for implementing intelligent transportation systems.However,in CAVs scenarios,the sensing blind areas cause serious safety hazards.Existing vehicle-to-vehicl...Connected autonomous vehicles(CAVs)are a promising paradigm for implementing intelligent transportation systems.However,in CAVs scenarios,the sensing blind areas cause serious safety hazards.Existing vehicle-to-vehicle(V2V)technology is difficult to break through the sensing blind area and ensure reliable sensing information.To overcome these problems,considering infrastructures as a means to extend the sensing range is feasible based on the integrated sensing and communication(ISAC)technology.The mmWave base station(mmBS)transmits multiple beams consisting of communication beams and sensing beams.The sensing beams are responsible for sensing objects within the CAVs blind area,while the communication beams are responsible for transmitting the sensed information to the CAVs.To reduce the impact of inter-beam interference,a joint multiple beamwidth and power allocation(JMBPA)algorithm is proposed.By maximizing the communication transmission rate under the sensing constraints.The proposed non-convex optimization problem is transformed into a standard difference of two convex functions(D.C.)problem.Finally,the superiority of the lutions.The average transmission rate of communication beams remains over 3.4 Gbps,showcasing a significant improvement compared to other algorithms.Moreover,the satisfaction of sensing services remains steady.展开更多
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
High spectrum efficiency(SE)requirement and massive connections are the main challenges for the fifth generation(5G)and beyond 5G(B5G)wireless networks,especially for the case when Internet of Things(IoT)devices are l...High spectrum efficiency(SE)requirement and massive connections are the main challenges for the fifth generation(5G)and beyond 5G(B5G)wireless networks,especially for the case when Internet of Things(IoT)devices are located in a disaster area.Non-orthogonal multiple access(NOMA)-based unmanned aerial vehicle(UAV)-aided network is emerging as a promising technique to overcome the above challenges.In this paper,an emergency communications framework of NOMA-based UAV-aided networks is established,where the disasters scenarios can be divided into three broad categories that have named emergency areas,wide areas and dense areas.First,a UAV-enabled uplink NOMA system is established to gather information from IoT devices in emergency areas.Then,a joint UAV deployment and resource allocation scheme for a multi-UAV enabled NOMA system is developed to extend the UAV coverage for IoT devices in wide areas.Furthermore,a UAV equipped with an antenna array has been considered to provide wireless service for multiple devices that are densely distributed in disaster areas.Simulation results are provided to validate the effectiveness of the above three schemes.Finally,potential research directions and challenges are also highlighted and discussed.展开更多
BACKGROUND:Rapid on-site triage is critical after mass-casualty incidents(MCIs)and other mass injury events.Unmanned aerial vehicles(UAVs)have been used in MCIs to search and rescue wounded individuals,but they mainly...BACKGROUND:Rapid on-site triage is critical after mass-casualty incidents(MCIs)and other mass injury events.Unmanned aerial vehicles(UAVs)have been used in MCIs to search and rescue wounded individuals,but they mainly depend on the UAV operator’s experience.We used UAVs and artificial intelligence(AI)to provide a new technique for the triage of MCIs and more efficient solutions for emergency rescue.METHODS:This was a preliminary experimental study.We developed an intelligent triage system based on two AI algorithms,namely OpenPose and YOLO.Volunteers were recruited to simulate the MCI scene and triage,combined with UAV and Fifth Generation(5G)Mobile Communication Technology real-time transmission technique,to achieve triage in the simulated MCI scene.RESULTS:Seven postures were designed and recognized to achieve brief but meaningful triage in MCIs.Eight volunteers participated in the MCI simulation scenario.The results of simulation scenarios showed that the proposed method was feasible in tasks of triage for MCIs.CONCLUSION:The proposed technique may provide an alternative technique for the triage of MCIs and is an innovative method in emergency rescue.展开更多
With the emergence of the 6G technology,integrated sensing and communication(ISAC)has become a hot-spot vertical application.The low-altitude scenario is considered to be a significant use case of the ISAC.However,the...With the emergence of the 6G technology,integrated sensing and communication(ISAC)has become a hot-spot vertical application.The low-altitude scenario is considered to be a significant use case of the ISAC.However,the existing channel model is hard to meet the demands of the sensing function.The radar-cross-section(RCS)is a critical feature for the sensing part,while accurate RCS data for the typical frequency band of ISAC are still lacking.Therefore,this paper conducts measurements and analysis of the RCS data of the unmanned aerial vehicles(UAVs)under multiple poses and angles in real flying conditions.The echo from a UAV is acquired in an anechoic chamber,and the RCS values are calculated.The results of different flying attitudes are analyzed,providing RCS features for the ISAC applications.展开更多
基金China Tele-com Research Institute Project(Grants No.HQBYG2200147GGN00)National Key R&D Program of China(2020YFB1807600)National Natural Science Foundation of China(NSFC)(Grant No.62022020).
文摘Connected autonomous vehicles(CAVs)are a promising paradigm for implementing intelligent transportation systems.However,in CAVs scenarios,the sensing blind areas cause serious safety hazards.Existing vehicle-to-vehicle(V2V)technology is difficult to break through the sensing blind area and ensure reliable sensing information.To overcome these problems,considering infrastructures as a means to extend the sensing range is feasible based on the integrated sensing and communication(ISAC)technology.The mmWave base station(mmBS)transmits multiple beams consisting of communication beams and sensing beams.The sensing beams are responsible for sensing objects within the CAVs blind area,while the communication beams are responsible for transmitting the sensed information to the CAVs.To reduce the impact of inter-beam interference,a joint multiple beamwidth and power allocation(JMBPA)algorithm is proposed.By maximizing the communication transmission rate under the sensing constraints.The proposed non-convex optimization problem is transformed into a standard difference of two convex functions(D.C.)problem.Finally,the superiority of the lutions.The average transmission rate of communication beams remains over 3.4 Gbps,showcasing a significant improvement compared to other algorithms.Moreover,the satisfaction of sensing services remains steady.
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.
文摘High spectrum efficiency(SE)requirement and massive connections are the main challenges for the fifth generation(5G)and beyond 5G(B5G)wireless networks,especially for the case when Internet of Things(IoT)devices are located in a disaster area.Non-orthogonal multiple access(NOMA)-based unmanned aerial vehicle(UAV)-aided network is emerging as a promising technique to overcome the above challenges.In this paper,an emergency communications framework of NOMA-based UAV-aided networks is established,where the disasters scenarios can be divided into three broad categories that have named emergency areas,wide areas and dense areas.First,a UAV-enabled uplink NOMA system is established to gather information from IoT devices in emergency areas.Then,a joint UAV deployment and resource allocation scheme for a multi-UAV enabled NOMA system is developed to extend the UAV coverage for IoT devices in wide areas.Furthermore,a UAV equipped with an antenna array has been considered to provide wireless service for multiple devices that are densely distributed in disaster areas.Simulation results are provided to validate the effectiveness of the above three schemes.Finally,potential research directions and challenges are also highlighted and discussed.
基金Sanming Project of Medicine in Shenzhen(No.SZSM201911007)Shenzhen Stability Support Plan(20200824145152001)。
文摘BACKGROUND:Rapid on-site triage is critical after mass-casualty incidents(MCIs)and other mass injury events.Unmanned aerial vehicles(UAVs)have been used in MCIs to search and rescue wounded individuals,but they mainly depend on the UAV operator’s experience.We used UAVs and artificial intelligence(AI)to provide a new technique for the triage of MCIs and more efficient solutions for emergency rescue.METHODS:This was a preliminary experimental study.We developed an intelligent triage system based on two AI algorithms,namely OpenPose and YOLO.Volunteers were recruited to simulate the MCI scene and triage,combined with UAV and Fifth Generation(5G)Mobile Communication Technology real-time transmission technique,to achieve triage in the simulated MCI scene.RESULTS:Seven postures were designed and recognized to achieve brief but meaningful triage in MCIs.Eight volunteers participated in the MCI simulation scenario.The results of simulation scenarios showed that the proposed method was feasible in tasks of triage for MCIs.CONCLUSION:The proposed technique may provide an alternative technique for the triage of MCIs and is an innovative method in emergency rescue.
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No.HC-CN-20220622006。
文摘With the emergence of the 6G technology,integrated sensing and communication(ISAC)has become a hot-spot vertical application.The low-altitude scenario is considered to be a significant use case of the ISAC.However,the existing channel model is hard to meet the demands of the sensing function.The radar-cross-section(RCS)is a critical feature for the sensing part,while accurate RCS data for the typical frequency band of ISAC are still lacking.Therefore,this paper conducts measurements and analysis of the RCS data of the unmanned aerial vehicles(UAVs)under multiple poses and angles in real flying conditions.The echo from a UAV is acquired in an anechoic chamber,and the RCS values are calculated.The results of different flying attitudes are analyzed,providing RCS features for the ISAC applications.
