The space-air-ground integrated networks (SAGIN) has emerged as a critical paradigm to address the growing demands for global connectivity and enhanced communication services. This paper gives a thorough review of the...The space-air-ground integrated networks (SAGIN) has emerged as a critical paradigm to address the growing demands for global connectivity and enhanced communication services. This paper gives a thorough review of the strategies and methodologies for resource allocation within SAGIN, focusing on the challenges and solutions within its complex structure. With the advent of technologies such as 6G, the dynamics of resource optimization have become increasingly complex, necessitating innovative approaches for efficient management. We examine the application of mathematical optimization, game theory, artificial intelligence (AI), and dynamic optimization techniques in SAGIN,offering insights into their effectiveness in ensuring optimal resource distribution, minimizing delays, and maximizing network throughput and stability. The survey highlights the significant advances in AI-based methods,particularly deep learning and reinforcement learning, in tackling the inherent challenges of SAGIN resource allocation. Through a critical review of existing literature, this paper categorizes various resource allocation strategies, identifies current research gaps, and discusses potential future directions. Our findings highlight the crucial role of integrated and intelligent resource allocation mechanisms in realizing the full potential of SAGIN for next-generation communication networks.展开更多
The combination of integrated sensing and communication(ISAC)with mobile edge computing(MEC)enhances the overall safety and efficiency for vehicle to everything(V2X)system.However,existing works have not considered th...The combination of integrated sensing and communication(ISAC)with mobile edge computing(MEC)enhances the overall safety and efficiency for vehicle to everything(V2X)system.However,existing works have not considered the potential impacts on base station(BS)sensing performance when users offload their computational tasks via uplink.This could leave insufficient resources allocated to the sensing tasks,resulting in low sensing performance.To address this issue,we propose a cooperative power,bandwidth and computation resource allocation(RA)scheme in this paper,maximizing the overall utility of Cramer-Rao bound(CRB)for sensing accuracy,computation latency for processing sensing information,and communication and computation latency for computational tasks.To solve the RA problem,a twin delayed deep deterministic policy gradient(TD3)algorithm is adopted to explore and obtain the effective solution of the RA problem.Furthermore,we investigate the performance tradeoff between sensing accuracy and summation of communication latency and computation latency for computational tasks,as well as the relationship between computation latency for processing sensing information and that of computational tasks by numerical simulations.Simulation demonstrates that compared to other benchmark methods,TD3 achieves an average utility improvement of 97.11%and 27.90%in terms of the maximum summation of communication latency and computation latency for computational tasks and improves 3.60 and 1.04 times regarding the maximum computation latency for processing sensing information.展开更多
算力网络(Computing Power Network,CPN)主要通过网络控制面板将资源信息分发,有机实现多维度、多资源的信息整合过程。在算力网络中,大量算力交易、网络订购业务也能相互关联起来并得以实现,形成相对统一的技术体系架构,满足多重类型...算力网络(Computing Power Network,CPN)主要通过网络控制面板将资源信息分发,有机实现多维度、多资源的信息整合过程。在算力网络中,大量算力交易、网络订购业务也能相互关联起来并得以实现,形成相对统一的技术体系架构,满足多重类型资源的优化分配。介绍了算力网络及其资源内涵,并就算力网络实现一体化服务的相关技术内容进行深层次分析,讨论算力网络交易系统的设计与实践具体技术过程。展开更多
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
The ubiquitous and deterministic communication systems are becoming indispensable for future vertical applications such as industrial automation systems and smart grids.5G-TSN(Time-Sensitive Networking)integrated netw...The ubiquitous and deterministic communication systems are becoming indispensable for future vertical applications such as industrial automation systems and smart grids.5G-TSN(Time-Sensitive Networking)integrated networks with the 5G system(5GS)as a TSN bridge are promising to provide the required communication service.To guarantee the endto-end(E2E)QoS(Quality of Service)performance of traffic is a great challenge in 5G-TSN integrated networks.A dynamic QoS mapping method is proposed in this paper.It is based on the improved K-means clustering algorithm and the rough set theory(IKCRQM).The IKC-RQM designs a dynamic and loadaware QoS mapping algorithm to improve its flexibility.An adaptive semi-persistent scheduling(ASPS)mechanism is proposed to solve the challenging deterministic scheduling in 5GS.It includes two parts:one part is the persistent resource allocation for timesensitive flows,and the other part is the dynamic resource allocation based on the max-min fair share algorithm.Simulation results show that the proposed IKC-RQM algorithm achieves flexible and appropriate QoS mapping,and the ASPS performs corresponding resource allocations to guarantee the deterministic transmissions of time-sensitive flows in 5G-TSN integrated networks.展开更多
This study designs and proposes a method for evaluating the configuration of energy storage for integrated re-newable generation plants in the power spot market,which adopts a two-level optimization model of“system s...This study designs and proposes a method for evaluating the configuration of energy storage for integrated re-newable generation plants in the power spot market,which adopts a two-level optimization model of“system simulation+plant optimization”.The first step is“system simulation”which is using the power market simu-lation model to obtain the initial nodal marginal price and curtailment of the integrated renewable generation plant.The second step is“plant optimization”which is using the operation optimization model of the integrated renewable generation plant to optimize the charge-discharge operation of energy storage.In the third step,“sys-tem simulation”is conducted again,and the combined power of renewable and energy storage inside the plant is brought into the system model and simulated again for 8,760 h of power market year-round to quantify and compare the power generation and revenue of the integrated renewable generation plant after applying energy storage.In the case analysis of the provincial power spot market,an empirical analysis of a 1 GW wind-solar-storage integrated generation plant was conducted.The results show that the economic benefit of energy storage is approximately proportional to its capacity and that there is a slowdown in the growth of economic benefits when the capacity is too large.In the case that the investment benefit of energy storage only considers the in-come of electric energy-related incomes and does not consider the income of capacity mechanism and auxiliary services,the income of energy storage cannot fulfill the economic requirements of energy storage investment.展开更多
基金supported by the Key Area Research and Development Program of Guangdong Province under Grant 2020B0101110003in part by Dongguan Science and Technology Special Commissioner Foundation under Grant 20231800500222.
文摘The space-air-ground integrated networks (SAGIN) has emerged as a critical paradigm to address the growing demands for global connectivity and enhanced communication services. This paper gives a thorough review of the strategies and methodologies for resource allocation within SAGIN, focusing on the challenges and solutions within its complex structure. With the advent of technologies such as 6G, the dynamics of resource optimization have become increasingly complex, necessitating innovative approaches for efficient management. We examine the application of mathematical optimization, game theory, artificial intelligence (AI), and dynamic optimization techniques in SAGIN,offering insights into their effectiveness in ensuring optimal resource distribution, minimizing delays, and maximizing network throughput and stability. The survey highlights the significant advances in AI-based methods,particularly deep learning and reinforcement learning, in tackling the inherent challenges of SAGIN resource allocation. Through a critical review of existing literature, this paper categorizes various resource allocation strategies, identifies current research gaps, and discusses potential future directions. Our findings highlight the crucial role of integrated and intelligent resource allocation mechanisms in realizing the full potential of SAGIN for next-generation communication networks.
基金supported by the National Natural Science Foundation of China(62231020)Innovation Capability Support Program of Shaanxi(2024RS-CXTD-01).
文摘The combination of integrated sensing and communication(ISAC)with mobile edge computing(MEC)enhances the overall safety and efficiency for vehicle to everything(V2X)system.However,existing works have not considered the potential impacts on base station(BS)sensing performance when users offload their computational tasks via uplink.This could leave insufficient resources allocated to the sensing tasks,resulting in low sensing performance.To address this issue,we propose a cooperative power,bandwidth and computation resource allocation(RA)scheme in this paper,maximizing the overall utility of Cramer-Rao bound(CRB)for sensing accuracy,computation latency for processing sensing information,and communication and computation latency for computational tasks.To solve the RA problem,a twin delayed deep deterministic policy gradient(TD3)algorithm is adopted to explore and obtain the effective solution of the RA problem.Furthermore,we investigate the performance tradeoff between sensing accuracy and summation of communication latency and computation latency for computational tasks,as well as the relationship between computation latency for processing sensing information and that of computational tasks by numerical simulations.Simulation demonstrates that compared to other benchmark methods,TD3 achieves an average utility improvement of 97.11%and 27.90%in terms of the maximum summation of communication latency and computation latency for computational tasks and improves 3.60 and 1.04 times regarding the maximum computation latency for processing sensing information.
文摘算力网络(Computing Power Network,CPN)主要通过网络控制面板将资源信息分发,有机实现多维度、多资源的信息整合过程。在算力网络中,大量算力交易、网络订购业务也能相互关联起来并得以实现,形成相对统一的技术体系架构,满足多重类型资源的优化分配。介绍了算力网络及其资源内涵,并就算力网络实现一体化服务的相关技术内容进行深层次分析,讨论算力网络交易系统的设计与实践具体技术过程。
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 by National Key Research and Development Project under Grant No.2020YFB1710900Sichuan International Cooperation Project of Science and Technology Innovation under Grant No.2022YFH0022。
文摘The ubiquitous and deterministic communication systems are becoming indispensable for future vertical applications such as industrial automation systems and smart grids.5G-TSN(Time-Sensitive Networking)integrated networks with the 5G system(5GS)as a TSN bridge are promising to provide the required communication service.To guarantee the endto-end(E2E)QoS(Quality of Service)performance of traffic is a great challenge in 5G-TSN integrated networks.A dynamic QoS mapping method is proposed in this paper.It is based on the improved K-means clustering algorithm and the rough set theory(IKCRQM).The IKC-RQM designs a dynamic and loadaware QoS mapping algorithm to improve its flexibility.An adaptive semi-persistent scheduling(ASPS)mechanism is proposed to solve the challenging deterministic scheduling in 5GS.It includes two parts:one part is the persistent resource allocation for timesensitive flows,and the other part is the dynamic resource allocation based on the max-min fair share algorithm.Simulation results show that the proposed IKC-RQM algorithm achieves flexible and appropriate QoS mapping,and the ASPS performs corresponding resource allocations to guarantee the deterministic transmissions of time-sensitive flows in 5G-TSN integrated networks.
基金funded by the China Energy Investment Cor-poration under the program“Simulation of energy storage application scenarios in China and research on development strategy of China En-ergy Investment Corporation”(Grant No.:GJNY-21-143).
文摘This study designs and proposes a method for evaluating the configuration of energy storage for integrated re-newable generation plants in the power spot market,which adopts a two-level optimization model of“system simulation+plant optimization”.The first step is“system simulation”which is using the power market simu-lation model to obtain the initial nodal marginal price and curtailment of the integrated renewable generation plant.The second step is“plant optimization”which is using the operation optimization model of the integrated renewable generation plant to optimize the charge-discharge operation of energy storage.In the third step,“sys-tem simulation”is conducted again,and the combined power of renewable and energy storage inside the plant is brought into the system model and simulated again for 8,760 h of power market year-round to quantify and compare the power generation and revenue of the integrated renewable generation plant after applying energy storage.In the case analysis of the provincial power spot market,an empirical analysis of a 1 GW wind-solar-storage integrated generation plant was conducted.The results show that the economic benefit of energy storage is approximately proportional to its capacity and that there is a slowdown in the growth of economic benefits when the capacity is too large.In the case that the investment benefit of energy storage only considers the in-come of electric energy-related incomes and does not consider the income of capacity mechanism and auxiliary services,the income of energy storage cannot fulfill the economic requirements of energy storage investment.
