Tasks-Oriented Joint Resource Allocation Scheme for the Internet of Vehicles with Sensing, Communication and Computing Integration

With the development of artificial intelligence(AI)and 5G technology,the integration of sensing,communication and computing in the Internet of Vehicles(Io V)is becoming a trend.However,the large amount of data transmission and the computing requirements of intelligent tasks lead to the complex resource management problems.In view of the above challenges,this paper proposes a tasks-oriented joint resource allocation scheme(TOJRAS)in the scenario of Io V.First,this paper proposes a system model with sensing,communication,and computing integration for multiple intelligent tasks with different requirements in the Io V.Secondly,joint resource allocation problems for real-time tasks and delay-tolerant tasks in the Io V are constructed respectively,including communication,computing and caching resources.Thirdly,a distributed deep Q-network(DDQN)based algorithm is proposed to solve the optimization problems,and the convergence and complexity of the algorithm are discussed.Finally,the experimental results based on real data sets verify the performance advantages of the proposed resource allocation scheme,compared to the existing ones.The exploration efficiency of our proposed DDQN-based algorithm is improved by at least about 5%,and our proposed resource allocation scheme improves the m AP performance by about 0.15 under resource constraints.

Integrated Sensing and Communication:Who Benefits More?

This paper compares the benefits of communication-assisted sensing and sensing-assisted communication in the context of integrated sensing and communication(ISAC).Communication-assisted sensing leverages the extensive cellular infrastructure to create a vast and cooperative sensor network,enhancing environmental perception accuracy and coverage.On the other hand,sensing-assisted communication utilizes advanced sensing technologies to improve predictive beamforming and channel estimation performance in high-frequency and highmobility scenarios,thereby increasing communication efficiency and reliability.To validate our analysis,we present an example of channel knowledge map(CKM)-assisted beam tracking.This example demonstrates the practical advantages of incorporating CKM in enhancing beam tracking accuracy.Our analysis confirms that communication-assisted sensing may offer greater development potential due to its wide coverage and cost-effectiveness in large-scale applications.

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.

Sensing and Communication Integrated Fast Neighbor Discovery for UAV Networks

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.

Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control

In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.Such challenges can be potentially overcome by integrating communication,computing,caching,and control(i4C)technologies.In this survey,we first give a snapshot of different aspects of the i4C,comprising background,motivation,leading technological enablers,potential applications,and use cases.Next,we describe different models of communication,computing,caching,and control(4C)to lay the foundation of the integration approach.We review current stateof-the-art research efforts related to the i4C,focusing on recent trends of both conventional and artificial intelligence(AI)-based integration approaches.We also highlight the need for intelligence in resources integration.Then,we discuss the integration of sensing and communication(ISAC)and classify the integration approaches into various classes.Finally,we propose open challenges and present future research directions for beyond 5G networks,such as 6G.

Trajectory Tracking for MmWave Communication Systems via Cooperative Passive Sensing

A cooperative passive sensing framework for millimeter wave(mmWave)communication systems is proposed and demonstrated in a scenario with one mobile signal blocker.Specifically,in the uplink communication with at least two transmitters,a cooperative detection method is proposed for the receiver to track the blocker’s trajectory,localize the transmitters and detect the potential link blockage jointly.To facilitate detection,the receiver collects the signal of each transmitter along a line-of-sight(LoS)path and a non-line-of-sight(NLoS)path separately via two narrow-beam phased arrays.The NLoS path involves scattering at the mobile blocker,allowing its identification through the Doppler frequency.By comparing the received signals of both paths,the Doppler frequency and angle-of-arrival(AoA)of the NLoS path can be estimated.To resolve the blocker’s trajectory and the transmitters’locations,the receiver should continuously track the mobile blocker to accumulate sufficient numbers of the Doppler frequency and AoA versus time observations.Finally,a gradient-descent-based algorithm is proposed for joint detection.With the reconstructed trajectory,the potential link blockage can be predicted.It is demonstrated that the system can achieve decimeterlevel localization and trajectory estimation,and predict the blockage time with an error of less than 0.1 s.

Cognitive J^(2)SAC:Joint Jamming,Sensing,and Communication Under Antagonistic Environment

Integrated sensing and communication(ISAC)is regarded as a recent advanced technology,which is expected to realize the dual functions of sensing and communication simultaneously in one system.Nevertheless,it still faces the challenges of the information security and transmission robustness caused by the openness of wireless channel,especially under antagonistic environment.Hence,this article develops a generalized framework,named cognitive joint jamming,sensing and communication(cognitive J2SAC),to empower the current sensing/communication/jamming system with a“brain”for realizing precise sensing,reliable communication and effective jamming under antagonistic environment.Three kinds of gains can be captured by cognitive J2SAC,including integrated gain,cooperative gain and cognitive gain.Moreover,we highlight the enabling mechanism among jamming,sensing,and communication,as well as illustrating several typical use cases of cognitive J2SAC.Furthermore,several key enabled technologies are analyzed and a typical sensing enhance integrated communication and jamming case study is discussed to verify the effectiveness of the proposed method.Last but not the least,the future directions are listed before concluding this article.Integrated sensing and communication(ISAC)is regarded as a recent advanced technology,which is expected to realize the dual functions of sensing and communication simultaneously in one system.Nevertheless,it still faces the challenges of the information security and transmission robustness caused by the openness of wireless channel,especially under antagonistic environment.Hence,this article develops a generalized framework,named cognitive joint jamming,sensing and communication(cognitive J2SAC),to empower the current sensing/communication/jamming system with a“brain”for realizing precise sensing,reliable communication and effective jamming under antagonistic environment.Three kinds of gains can be captured by cognitive J2SAC,including integrated gain,cooperative gain and cognitive gain.Moreover,we highlight the enabling mechanism among jamming,sensing,and communication,as well as illustrating several typical use cases of cognitive J2SAC.Furthermore,several key enabled technologies are analyzed and a typical sensing enhance integrated communication and jamming case study is discussed to verify the effectiveness of the proposed method.Last but not the least,the future directions are listed before concluding this article.

Satellite Communications with 5G, B5G, and 6G: Challenges and Prospects

Satellite communications, pivotal for global connectivity, are increasingly converging with cutting-edge mobile networks, notably 5G, B5G, and 6G. This amalgamation heralds the promise of universal, high-velocity communication, yet it is not without its challenges. Paramount concerns encompass spectrum allocation, the harmonization of network architectures, and inherent latency issues in satellite transmissions. Potential mitigations, such as dynamic spectrum sharing and the deployment of edge computing, are explored as viable solutions. Looking ahead, the advent of quantum communications within satellite frameworks and the integration of AI spotlight promising research trajectories. These advancements aim to foster a seamless and synergistic coexistence between satellite communications and next-gen mobile networks.

Channel estimation in integrated radar and communication systems with power amplifier distortion

To reduce the negative impact of the power amplifier(PA)nonlinear distortion caused by the orthogonal frequency division multiplexing(OFDM)waveform with high peak-to-average power ratio(PAPR)in integrated radar and communication(RadCom)systems is studied,the channel estimation in passive sensing scenarios.Adaptive channel estimation methods are proposed based on different pilot patterns,considering nonlinear distortion and channel sparsity.The proposed methods achieve sparse channel results by manipulating the least squares(LS)frequency-domain channel estimation results to preserve the most significant taps.The decision-aided method is used to optimize the sparse channel results to reduce the effect of nonlinear distortion.Numerical results show that the channel estimation performance of the proposed methods is better than that of the conventional methods under different pilot patterns.In addition,the bit error rate performance in communication and passive radar detection performance show that the proposed methods have good comprehensive performance.

Architecture for Cellular Enabled Integrated Communication and Sensing Services

There is growing interest in the integrated sensing and communication(ISAC)to extend the 5G+/6G network capabilities by introducing sensing capability.While the solutions for mono-static or bi-static ISAC have shown feasibility and benefits based on existing 5G physical layer design,whether and how to coordinate multiple ISAC devices to better exert networking performance are rarely discussed.3 rd Partnership Project(3GPP)has initiated the ISAC use cases study,and the follow-up studies for network architecture could be anticipated.In this article,we focus on gNB-based sensing mode and propose ISAC functional framework with given of highlevel service procedures to enable cellular based ISAC services.In the proposed ISAC framework,three types of network functions for sensing service as Sensing Function(SF),lightweight-Edge Sensing Function(ESF)and full-version-ESF are designed with interaction with network nodes to fulfill the latency requirements of ISAC use cases.Finally,with simulation evaluations and hardware testbed results,we further verify the performance benefit and feasibility to enable ISAC in 5G for the gNB-based sensing mode with new design on SF and related signaling protocols.

Asymptotic Performance Limits of Vehicular Location and Velocity Detection Towards 6G mmWave Integrated Communication and Sensing

In this paper,joint location and velocity estimation(JLVE)of vehicular terminals for 6G integrated communication and sensing(ICAS)is studied.We aim to provide a unified performance analysis framework for ICAS-based JLVE,which is challenging due to random fading,multipath interference,and complexly coupled system models,and thus the impact of channel fading and multipath interference on JLVE performance is not fully understood.To address this challenge,we exploit structured information models of the JLVE problem to render tractable performance quantification.Firstly,an individual closedform Cramer-Rao lower bound for vehicular localization,velocity detection and channel estimation,respectively,is established for gaining insights into performance limits of ICAS-based JLVE.Secondly,the impact of system resource factors and fading environments,e.g.,system bandwidth,the number of subcarriers,carrier frequency,antenna array size,transmission distance,spatial channel correlation,channel covariance,the number of interference paths and noise power,on the JLVE performance is theoretically analyzed.The associated closed-form JLVE performance analysis can not only provide theoretical foundations for ICAS receiver design but also provide a perfor mance benchmark for various JLVE methods。

Integrated Sensing and Communication Enabled Multiple Beamwidth and Power Allocation for Connected Automated Vehicles

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.

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.

Demonstration of radar-aided flexible communication in a photonics-based W-band distributed integrated sensing and communication system for 6G

This paper experimentally demonstrates a distributed photonics-based W-band integrated sensing and communication(ISAC) system, in which radar sensing can aid the communication links in alignment and data rate estimation. As a proof-of-concept, the ISAC system locates the users, guides the alignment, and sets a communication link with the estimated highest data rate. A peak net data rate of 68.6 Gbit/s and a target sensing with a less-than-1-cm error and a sub-2-cm resolution have been tested over a 10-km fiber and a 1.15-m free space transmission in the photonics-based W-band ISAC system. The achievable net data rates of the users at different locations estimated by sensing are experimentally verified.

A polarisation coding scheme based on an integrated sensing and communication system

Integrated sensing and communication(ISAC)technology enhances the spectrum utilization of the system by interchanging the spectrum between communication and sensing,which has gained popularity in scenarios such as vehicle-to-everything(V2X).With the aim of providing more dependable services for vehicles in high-speed mobile scenarios,we propose a scheme based on sense-assisted polarisation coding.Specifically,the base station acquires the vehicle's positional information and channel strength parameters through the forward time slot echo information.This information informs the creation of the coding architecture for the following time slot.This approach not only optimizes resource consumption but also enhances system dependability.Our simulation results confirm that the introduced scheme displays a notable improvement in the bit error rate(BER)when compared to traditional communication frameworks,maintaining this advantage across both unimpeded and compromised channel conditions.

Hybrid Beamforming Design and Signal Processing with Fully-Connected Architecture for mmWave Integrated Sensing and Communications

Millimeter-wave (mmWave) is capable of achieving gigabit/second communication capacity and centimeter-level sensing accuracy and has become one of the main frequency bands for integrated sensing and communications (ISAC) research. Hybrid beamforming techniques have attracted much attention for solving the high path loss of mmWave and further reducing the hardware cost of the system. However, the related studies based on multicarrier and fully-connected hybrid architectures are still limited. For this reason,this paper investigates the orthogonal frequency division multiplexing (OFDM) based mmWave fully-connected hybrid architecture ISAC system to form a stable communication beam and dynamically varying sensing beam. In order to realize the aforementioned multifunctional beams, the hybrid beamformer design problem based on weighted error minimization of multicarrier is proposed and solved efficiently using the penalty dual decomposition (PDD) algorithm. Meanwhile, based on the echo model, the multicarrier multiple signal classification (MUSIC) algorithm for target angle of arrival estimation and the two-dimensional discrete Fourier transform(2D-DFT)algorithm for distance and velocity estimation are proposed, respectively. Numerical simulation results show that by adjusting the weighting factor,a flexible trade-off can be formed between the communication spectrum efficiency and the sensing accuracy error.

Sensing-Assisted Accurate and Fast Beam Management for Cellular-Connected mmWave UAV Network

Beam management,including initial access(IA)and beam tracking,is essential to the millimeter-wave Unmanned Aerial Vehicle(UAV)network.However,the conventional communicationonly and feedback-based schemes suffer a high delay and low accuracy of beam alignment,since they only enable the receiver to passively“hear”the information of the transmitter from the radio domain.This paper presents a novel sensing-assisted beam management approach,the first solution that fully utilizes the information from the visual domain to improve communication performance.We employ both integrated sensing and communication and computer vision techniques and design an extended Kalman filtering method for beam tracking and prediction.Besides,we also propose a novel dual identity association solution to distinguish multiple UAVs in dynamic environments.Real-world experiments and numerical results show that the proposed solution outperforms the conventional methods in IA delay,association accuracy,tracking error,and communication performance.

Integrated sensing and communication based outdoor multi-target detection,tracking,and localization in practical 5G Networks

The 6th generation(6G)wireless networks will likely to support a variety of capabilities beyond communication,such as sensing and localization,through the use of communication networks empowered by advanced technologies.Integrated sensing and communication(ISAC)has been recognized as a critical technology as well as a usage scenario for 6G,as widely agreed by leading global standardization bodies.ISAC utilizes communication infrastructure and devices to provide the capability of sensing the environment with high resolution,as well as tracking and localizing moving objects nearby.Meeting both the requirements for communication and sensing simultaneously,ISAC-based approaches celebrate the advantages of higher spectral and energy efficiency compared to two separate systems to serve two purposes,and potentially lower costs and easy deployment.A key step towards the standardization and commercialization of ISAC is to carry out comprehensive field trials in practical networks,such as the 5th generation(5G)networks,to demonstrate its true capacities in practical scenarios.In this paper,an ISAC-based outdoor multi-target detection,tracking and localization approach is proposed and validated in 5G networks.The proposed system comprises of 5G base stations(BSs)which serve nearby mobile users normally,while accomplishing the task of detecting,tracking,and localizing drones,vehicles,and pedestrians simultaneously.Comprehensive trial results demonstrate the relatively high accuracy of the proposed method in practical outdoor environment when tracking and localizing single targets and multiple targets.