Joint Active and Passive Beamforming Design for Hybrid RIS-Aided Integrated Sensing and Communication

Integrated sensing and communication(ISAC) is considered an effective technique to solve spectrum congestion in the future. In this paper, we consider a hybrid reconfigurable intelligent surface(RIS)-assisted downlink ISAC system that simultaneously serves multiple single-antenna communication users and senses multiple targets. Hybrid RIS differs from fully passive RIS in that it is composed of both active and passive elements, with the active elements having the effect of amplifying the signal in addition to phase-shifting. We maximize the achievable sum rate of communication users by collaboratively improving the beamforming matrix at the dual function base station(DFBS) and the phase-shifting matrix of the hybrid RIS, subject to the transmit power constraint at the DFBS, the signal-to-interference-plus-noise-ratio(SINR) constraint of the radar echo signal and the RIS constraint are satisfied at the same time. The builtin RIS-assisted ISAC design problem model is significantly non-convex due to the fractional objective function of this optimization problem and the coupling of the optimization variables in the objective function and constraints. As a result, we provide an effective alternating optimization approach based on fractional programming(FP) with block coordinate descent(BCD)to solve the optimization variables. Results from simulations show that the hybrid RIS-assisted ISAC system outperforms the other benchmark solutions.

Federated Learning for 6G:A Survey From Perspective of Integrated Sensing,Communication and Computation

With the rapid advancements in edge computing and artificial intelligence,federated learning(FL)has gained momentum as a promising approach to collaborative data utilization across organizations and devices,while ensuring data privacy and information security.In order to further harness the energy efficiency of wireless networks,an integrated sensing,communication and computation(ISCC)framework has been proposed,which is anticipated to be a key enabler in the era of 6G networks.Although the advantages of pushing intelligence to edge devices are multi-fold,some challenges arise when incorporating FL into wireless networks under the umbrella of ISCC.This paper provides a comprehensive survey of FL,with special emphasis on the design and optimization of ISCC.We commence by introducing the background and fundamentals of FL and the ISCC framework.Subsequently,the aforementioned challenges are highlighted and the state of the art in potential solutions is reviewed.Finally,design guidelines are provided for the incorporation of FL and ISCC.Overall,this paper aims to contribute to the understanding of FL in the context of wireless networks,with a focus on the ISCC framework,and provide insights into addressing the challenges and optimizing the design for the integration of FL into future 6G networks.

Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications,sensing and computing

Task offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing resources.However,the overwhelming upload traffic may lead to unacceptable uploading time.To tackle this issue,for tasks taking environmental data as input,the data perceived by roadside units(RSU)equipped with several sensors can be directly exploited for computation,resulting in a novel task offloading paradigm with integrated communications,sensing and computing(I-CSC).With this paradigm,vehicles can select to upload their sensed data to RSUs or transmit computing instructions to RSUs during the offloading.By optimizing the computation mode and network resources,in this paper,we investigate an I-CSC-based task offloading problem to reduce the cost caused by resource consumption while guaranteeing the latency of each task.Although this nonconvex problem can be handled by the alternating minimization(AM)algorithm that alternatively minimizes the divided four sub-problems,it leads to high computational complexity and local optimal solution.To tackle this challenge,we propose a creative structural knowledge-driven meta-learning(SKDML)method,involving both the model-based AM algorithm and neural networks.Specifically,borrowing the iterative structure of the AM algorithm,also referred to as structural knowledge,the proposed SKDML adopts long short-term memory(LSTM)networkbased meta-learning to learn an adaptive optimizer for updating variables in each sub-problem,instead of the handcrafted counterpart in the AM algorithm.Furthermore,to pull out the solution from the local optimum,our proposed SKDML updates parameters in LSTM with the global loss function.Simulation results demonstrate that our method outperforms both the AM algorithm and the meta-learning without structural knowledge in terms of both the online processing time and the network performance.

Cooperative sensing,communication and computation resource allocation in mobile edge computing-enabled vehicular networks

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.

6G通感算智融合架构和场景赋能探索

面向未来移动通信领域的无人机识别、脑机协同、XR、工业协作机器人等新兴移动业务场景,移动网络将成为集感知、计算和智能多要素能力于一体的创新平台,满足创新在移动网络基础设备内部实施。阐述了移动通信网络由5G向6G演进过程中,因为新兴的业务需求驱动,形成了通感融合、通智融合和通算融合领域技术分支,提出了一种6G通感算智一体化的架构,具备快速动态加载通感算智各种原子能力,实现多要素能力智能调度。并在此基础上,探讨了该架构在无人机识别和XR沉浸式通信两个典型应用场景的应用,以达到服务边界拓展和整体系统效率优化。

面向边缘智能网络的通-感-算融合:架构、挑战和展望

通-感-算融合作为边缘智能网络的赋能手段,可以有效实现海量数据的实时采集、传输和处理,从而为各行各业提供高效、实时的智能服务。首先,基于边缘智能网络的特点,明确了通-感-算功能的定义,提出了通-感-算融合赋能的网络架构和系统架构,给出了通-感-算融合的典型应用场景。其次,重点分析了在边缘智能网络中实现通-感-算融合的关键挑战,包括海量数据处理、复杂干扰协调和联合资源管理,并提供了相应的解决方案。最后,展望了未来可行的研究方向。

6G智简无线网络

5G的发展激发了更多的业务创新,同时也带来了更多维的业务需求。为了更高效支持不同场景的差异化、碎片化、定制化需求,6G无线网络需结合5G的经验,在服务模式、技术能力、发展范式等方面进行突破创新,通过构建异构硬件云平台底座、通感算智融合内核、按需动态服务编排中枢,提升网络的灵活性、开放性、共享性,实现网络对千行百业的精准赋能。

STAR-RIS辅助通感算一体化系统波束成形设计

为提升无线网络的全域覆盖能力,STAR-RIS作为一种全新的RIS架构被提出。针对未来6G网络通信、感知和计算的需求,研究STAR-RIS辅助的通感算一体化系统。旨在联合优化主动、被动波束成形和计算资源分配,在满足通信和感知性能的同时,提升系统能效。所考虑的优化问题是非凸且强耦合的,难以直接求解。为此,提出一种基于交替优化、闭式分式规划、半正定松弛和连续凸近似的算法求解该问题。仿真结果表明,该算法能够极大提升系统性能。

立体交通系统通感算一体化关键技术

以无人机、智能网联汽车、道路基础设施等空地平台为载体的立体交通系统是智慧城市综合交通在6G网络中发展的前沿方向。随着海量无线通信终端、智能传感器等物理设备与交通运输环境、载运工具和道路基础设施深度融合,立体交通系统对通信感知的需求日益增长;然而,当前系统的发展受网络体系结构、通信资源、计算资源和感知能力的制约,其信息交互与综合处理能力亟待协同提高。另一方面,通信感知与边缘计算一体化(ISACEC)技术发展及其在空地协同网络中应用,为立体交通系统演进提供了新的推动力。面向立体交通系统应用场景,总结了ISAC-EC技术发展与应用现状,然后构建了空对地、地对空通信感知联合优化模型,给出了通信波束和雷达波束联合设计的关键技术思路;最后,针对典型应用场景开展仿真实验,结果展现了通感算一体化关键技术在立体交通系统中的应用潜力和优势,可为系统发展建设提供指导。

自动驾驶车联网中通感算融合研究综述与展望

为了应对自动驾驶车联网极低的通信时延、极高的可靠性、更高的传输速率等极致性能需求,亟需破解现有车联网中通信、感知、计算相互割裂与独立分治的问题,实现“云-边-端”一体化协同感知、协同传输和协同决策.为此,急需对自动驾驶车联网的通感算融合开展研究,实现三者的高效融合.首先论述了目前在通信、感知、计算融合领域的研究进展,然后给出了通感算融合网络的定义,论述了通感助算、通算助感以及感算助通的研究进展.针对自动驾驶车联网的应用场景,创造性地提出了“五层四面”通感算融合的网络架构,横向五层自下而上分别是:多元接入层、统一网络层、多域资源层、协同服务层、管理与应用层;纵向四面分别是:通信面、感知面、算力面、智能融合面,通过五层四面的深度融合,进一步提升了自动驾驶车联网中通感算融合网络的性能.其次,提出了评价通感算融合网络的性能指标体系,最后针对目前研究存在的问题以及未来发展方向给出了四点可行性建议.

面向6G通感算深度融合的云-网-边智能协同

通信-感知-计算(通感算)深度融合对6G网络端到端传输时延、可靠性等通信指标提出了极高的要求,因此提出一种云-网-边智能协同方案。首先,将云-网-边智能协同系统抽象为“三域四层三面”分布式体系结构,包括:计算域、感知域、通信域;基础设施层、虚拟化层、功能层、应用层;智能面、控制面、管理编排面。然后,通过云-网-边协同训练与推理,以支撑通感算智能协同。最后,搭建面向6G通感算深度融合的云-网-边智能协同实验平台。实验结果表明:云-网-边智能协同可提高系统可靠性,降低传输负载和业务时延。

6G通感智算一体化无线网络技术研究

6G将实现真实物理世界与虚拟数字世界的深度融合。为满足业务应用“智慧化、沉浸化、全域化”的发展趋势,未来网络发展需将网络世界、数字世界与物理世界无缝融合,构建通感智算一体化网络架构体系。通过研究6G通感智算一体化的技术需求、架构及方案等,提出了更具体的通感智算一体化无线网络演进方向和技术,分析了在通感智算一体化无线网络基础上的智能节能、智能编排、物理层智能等应用案例。

智能超表面辅助6G通感算深度融合关键技术

为支撑新兴业务实施和产业升级,未来无线通信网络将提供通信、感知、计算(通感算)的融合服务。智能超表面作为6G重要的备选技术,能有效提升系统的通信、感知、计算性能,具有构建通感算一体化系统的潜力,近年来得到广泛关注。为此,首先介绍了智能超表面辅助的通感算融合系统的研究现状,进一步介绍智能超表面辅助通感算融合的关键技术,最后分析了智能超表面辅助通感算融合网络的技术挑战。对智能超表面在通感算领域的应用做了系统地阐述,为未来通感算融合网络提供技术支撑。

通感算智一体化技术发展模式

面向5G-Advanced演进系统和未来6G新系统,与通感算智相关的新功能、新业务被不断地开发拓展并融入系统中。阐述了在5G-Advanced和6G不同阶段,通感算智一体化技术场景用例和未来技术模式功能的需求预期,从中提炼出其技术五大发展模式特征,并以此指导未来5G-Advanced系统开发和6G新系统的标准化开展,使通感算智一体化技术能得到有效的落地应用和未来商业价值最大化彰显。

感知-通信-计算融合的智能车联网挑战与趋势

针对单车感知无法满足未来自动驾驶安全需求的现状,面向多车传感器信息融合与时效性共享问题,提出了基于感知-通信-计算融合的智能车联网方法与解决思路。该方法有助于提高自动驾驶车辆的协同环境感知能力,并通过移动边缘计算(MEC)技术降低车间感知信息传输负载,提高多车协同的信息融合与处理效率,最终实现基于多车智能协同的安全自动驾驶。

面向智能持续感知的“传感-计算”共融架构和芯片设计

智能物联网技术是人工智能技术和物联网技术的快速融合发展下的必然产物,设计和实现具有持续智能感知能力的集成物联网节点是各种终端设备实现智能化的必由之路,也是解决当前物联网系统功耗、实时性和安全隐私性等难题的关键技术。对当今新兴的面向智能持续感知的“传感-计算”共融体系架构和集成电路设计新范式进行梳理,首先以智能视觉感知集成系统为突破点,目标是在新型处理架构下将人工智能处理任务与视觉传感器紧密融合,实现能够持续工作的超低功耗智能视觉采集和分析芯片,并进一步拓展到听觉感知和触觉感知等多种感知模态。介绍了多维度可扩展的“传感-计算”共融智能持续感知架构探索方法,高能效“传感+存算一体”混合信号集成电路芯片设计方法,以及跨越设计层次的软硬件联合设计优化思路,解决混合信号共融架构和电路的多种非理想因素,提升低功耗智能芯片的性能和可靠性。

Federated Learning for 6G:Applications,Challenges,and Opportunities

Standard machine-learning approaches involve the centralization of training data in a data center,where centralized machine-learning algorithms can be applied for data analysis and inference.However,due to privacy restrictions and limited communication resources in wireless networks,it is often undesirable or impractical for the devices to transmit data to parameter sever.One approach to mitigate these problems is federated learning(FL),which enables the devices to train a common machine learning model without data sharing and transmission.This paper provides a comprehensive overview of FL applications for envisioned sixth generation(6G)wireless networks.In particular,the essential requirements for applying FL to wireless communications are first described.Then potential FL applications in wireless communications are detailed.The main problems and challenges associated with such applications are discussed.Finally,a comprehensive FL implementation for wireless communications is described.

面向6G的智能机器通信与网络

随着智慧城市、智能制造等产业的快速发展,机器类通信得到学术界和产业界的广泛关注。机器类通信网络的功能集成化、智能化程度越来越高,对网络的时延、可靠性、连接设备数、峰值速率、安全性和可扩展性等指标提出了更严苛的要求。总结分析了4G、5G至未来6G时代的机器类通信网络需求的变化以及相应网络架构与关键技术的演化,在此基础上提出了面向6G机器类通信网络的感传算一体化智能机器通信网络架构。

面向6G的超可靠低延迟通信关键技术:研究进展与展望

URLLC作为5G和6G蜂窝网络的主要通信服务之一,对于支持各种新兴的关键任务应用至关重要。首先,介绍了6G的背景和URLLC的重要性,随后回顾了URLLC的基础知识、6G URLLC的应用需求及其面临的主要挑战。接着,重点讨论了6G URLLC的几项关键技术及其研究机遇,包括实现全覆盖的空天地一体化网络、高频段通信、智能计算、可重构智能表面,以及人工智能驱动的网络优化。最后,展望了未来通信感知计算一体化、元宇宙与数字孪生支持的无线网络演化及分布式框架的演进。这些技术的发展将显著提升6GURLLC的性能和可靠性,为未来新兴应用提供坚实的基础。