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。

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.

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.

M3SC:A Generic Dataset for Mixed Multi-Modal(MMM)Sensing and Communication Integration

The sixth generation(6G)of mobile communication system is witnessing a new paradigm shift,i.e.,integrated sensing-communication system.A comprehensive dataset is a prerequisite for 6G integrated sensing-communication research.This paper develops a novel simulation dataset,named M3SC,for mixed multi-modal(MMM)sensing-communication integration,and the generation framework of the M3SC dataset is further given.To obtain multimodal sensory data in physical space and communication data in electromagnetic space,we utilize Air-Sim and WaveFarer to collect multi-modal sensory data and exploit Wireless InSite to collect communication data.Furthermore,the in-depth integration and precise alignment of AirSim,WaveFarer,andWireless InSite are achieved.The M3SC dataset covers various weather conditions,multiplex frequency bands,and different times of the day.Currently,the M3SC dataset contains 1500 snapshots,including 80 RGB images,160 depth maps,80 LiDAR point clouds,256 sets of mmWave waveforms with 8 radar point clouds,and 72 channel impulse response(CIR)matrices per snapshot,thus totaling 120,000 RGB images,240,000 depth maps,120,000 LiDAR point clouds,384,000 sets of mmWave waveforms with 12,000 radar point clouds,and 108,000 CIR matrices.The data processing result presents the multi-modal sensory information and communication channel statistical properties.Finally,the MMM sensing-communication application,which can be supported by the M3SC dataset,is discussed.

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.

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.

Thermal integrity profiling of cast-in-situ piles in sand using fiber-optic distributed temperature sensing

Defects in cast-in-situ piles have an adverse impact on load transfer at the pile‒soil interface and pile bearing capacity. In recent years, thermal integrity profiling (TIP) has been developed to measure temperature profiles of cast-in-situ piles, enabling the detection of structural defects or anomalies at the early stage of construction. However, using this integrity testing method to evaluate potential defects in cast-in-situ piles requires a comprehensive understanding of the mechanism of hydration heat transfer from piles to surrounding soils. In this study, small-scale model tests were conducted in laboratory to investigate the performance of TIP in detecting pile integrity. Fiber-optic distributed temperature sensing (DTS) technology was used to monitor detailed temperature variations along model piles in sand. Additionally, sensors were installed in sand to measure water content and matric suction. An interpretation method against available DTS-based thermal profiles was proposed to reveal the potential defective regions. It shows that the temperature difference between normal and defective piles is more obvious in wet sand. In addition, there is a critical zone of water migration in sand due to the water absorption behavior of cement and temperature transfer-induced water migration in the early-age concrete setting. These findings could provide important insight into the improvement of the TIP testing method for field applications.

Integrated Evaluation of Ecological Security at Different Scales Using Remote Sensing: A Case Study of Zhongxian County, the Three Gorges Area, China

Based on related literature and this research, an ecological security evaluation from the pixel scale to the small watershed or county scale was presented using remote sensing data and related models. With the driver-pressure, state and exposure to pollution-response （DPSER） model as a basis, a conceptual framework of regional ecological evaluation and an index system were established. The extraction and standardization of evaluation indices were carried out with GIS techniques, an information extraction model and a data standardization model. The conversion of regional ecological security results from the pixel scale to a small watershed or county scale was obtained with an evaluation model and a scaling model. Two conceptual scale conversion models of regional ecological security from the pixel scale to the county scale were proposed： 1） scale conversion of ecological security regime results from plxel to small watershed; and 2） scale conversion from pixel to county. These research results could provide useful ideas for regional ecological security evaluation as well as ecological and environmental management.

INTEGRATED VEGETATION CLASSIFICATION AND MAPPINGUSING REMOTE SENSING AND GIS TECHNIQUES

NOAA-AVHRR data have been more and more used by scientists because of its short temporal resolution,large scope, inexpensive cost and broad wave bands. On macro and middle scale of vegetation remote sensing, NOAAAVHRR possesses an advantage when compared with other satellites. However, because NOAA-AVHRR also problem of low resolution, data distortion and geometrical distortion, in the area of application of NOAA-AVHRR in largescale vegetation - mapping, the accuracy of vegetation classification should be improved. This paper discuss the feasibilityof integrating the geographic data in GIS(Geographical Information System) and remotely sensed data in GIS. Under theenvironment of GIS, temperature, precipitation and elevation, which serve as main factors affecting vegetation growth,were processed by a mathematical model and qualified into geographic image under a certain grid system. The geographicimage were overlaid to the NOAA-AVHRR data which had been compressed and processed. In order to evaluate the usefulness of geographic data for vegetation classification, the area under study was digitally classified by two groups of interpreter: the proposed methodology using maximum likelihood classification assisted by the geographic database and a conventional maximum likelihood classification only. Both result were compared using Kappa statistics. The indices to both theproposed and the conventional digital classification methodology were 0. 668(yew good) and 0. 563(good), respetively.The geographic database rendered an improvement over the conventional digital classification. Furthermore, in this study,some problems related to multi-sources data integration are also discussed.

Internet of Lamps for Future Ubiquitous Communications:Integrated Sensing,Hybrid Interconnection,and Intelligent Illumination

The ubiquitous deployment and restricted consumption are the requirements restricting the development of Internet of Things.Thus,a promising technology named Internet of Lamps(Io L)is discussed in this paper to address these challenges.Compared with other communication networks,the remarkable advantage of Io L is that it can make full use of the existing lighting networks with sufficient power supply.The lamps can be connected to the Internet through wired power line communication and/or wireless communication,while the integration of integrated sensing,hybrid interconnection,and intelligent illumination is realized.In this paper,the Io L is discussed from three aspects including sensing layer,network layer,and application layer,realizing the comprehensive upgrade based on the conventional communication and illumination systems.Meanwhile,several novel technologies of Io L are discussed based on the requirements of sensing,communication,and control,which have put forward practical solutions to the issues faced by Io L.Moreover,the challenges and opportunities for Io L are highlighted from various parts of the system structure,so as to provide future insights and potential trends for researchers in this field.

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.

A novel voltage output integrated circuit temperature sensor

The novel integrated circuit (IC) temperature sensor presented in this paper works similarly as a two terminal Zener, has breakdown voltage directly proportional to Kelvin temperature at 10 mV/℃, with typical error of less than ±1.0℃ over a temperature range from -50℃ to +125℃. In addition to all the features that conventional IC temperature sensors have, the new device also has very low static power dissipation ( 0.5 mW ) , low output impedance ( less than 1Ω), excellent stability, high reproducibility, and high precision. The sensor's circuit design and layout are discussed in detail. Applications of the sensor include almost any type of temperature sensing over the range of -50℃-+125℃. The low impedance and linear output of the device make interfacing the readout or control circuitry especially easy. Due to the excellent performance and low cost of this sensor, more applications of the sensor over wide temperature range are expected.

Fuzzy Integral Based Information Fusion for Water Quality Monitoring Using Remote Sensing Data

To improve the monitoring precision of lake chlorophyll a (Chl-a), this paper presents a fusion method based on Choquet Fuzzy Integral (CFI) to estimate the Chl-a concentration. A group of BPNN models are designed. The output of multiple BPNN model is fused by the CFI. Meanwhile, to resolve the over-fitting problem caused by a small number of training sets, we design an algorithm that fully considers neighbor sampling information. A classification experiment of the Chl-a concentration of the Taihu Lake is conducted. The result shows that, the proposed approach is superior to the classification using a single neural network classifier, and the CFI fusion method has higher identification accuracy.

Microarrow sensor array with enhanced skin adhesion for transdermal continuous monitoring of glucose and reactive oxygen species

Conventional blood sampling for glucose detection is prone to cause pain and fails to continuously record glucose fluctuations in vivo.Continuous glucose monitoring based on implantable electrodes could induce pain and potential tissue inflammation,and the presence of reactive oxygen species(ROS)due to inflammationmay affect glucose detection.Microneedle technology is less invasive,yet microneedle adhesion with skin tissue is limited.In this work,we developed a microarrow sensor array(MASA),which provided enhanced skin surface adhesion and enabled simultaneous detection of glucose and H_(2)O_(2)(representative of ROS)in interstitial fluid in vivo.The microarrows fabricated via laser micromachining were modified with functional coating and integrated into a patch of a three-dimensional(3D)microneedle array.Due to the arrow tip mechanically interlocking with the tissue,the microarrow array could better adhere to the skin surface after penetration into skin.The MASA was demonstrated to provide continuous in vivo monitoring of glucose and H_(2)O_(2) concentrations,with the detection of H_(2)O_(2) providing a valuable reference for assessing the inflammation state.Finally,the MASA was integrated into a monitoring system using custom circuitry.This work provides a promising tool for the stable and reliable monitoring of blood glucose in diabetic patients.

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.

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.

Research on Cultural Consumption and Urban Integration of Floating Population

This paper examined the relationship between cultural consumption and the floating population’s integration into host cities based on data from China’s Seventh National Population Census and the 2018 China Migrants Dynamic Survey(CMDS).The findings indicate that improving the floating population’s consumption level and quality,especially the quality of development-oriented cultural consumption,can significantly improve the level of their integration.Moreover,development-oriented cultural consumption has a positive effect on the floating population’s integration and social participation,while entertainment-oriented cultural consumption facilitates their integration mainly by improving their sense of well-being.These findings could guide policymakers in developing targeted cultural consumption policies,implementing specific regional industry adjustments,and expanding domestic consumption demand.

Groundwater Resource Mapping through the Integration of Geology, Remote Sensing, Geographical Information Systems and Borehole Data in Arid-Subarid Lands at Turkana South Sub-County, Kenya

The integrated approach of various techniques which historically have been used independently is key to successful exploration, development, exploitation and management of the groundwater resources. The integration of Remote Sensing (RS), Geographical Information Systems (GIS) and Borehole data has been used in the study area to assess their applicability in groundwater investigation. The area of study lies in the arid and semi-arid lands (ASALs) where principally remote sensing data has been used in extraction of various thematic maps (lithology, lineament, drainage density, and Digital Elevation Model Maps) for groundwater assessment. The GIS platform was used in integrating the RS data and data of productive boreholes. The lineaments generated through remote sensing agree well with structural geology of the area, where high density lineament points overlays the points of intense faulting. Lineaments found in the area correlate well with fault zones, fractures, and lithological contrasts as supported by geological map and structural map. Weathering, faulting and fracturing of the rocks mean a possible increase or a reduction in specific capacities as observed in productive boreholes in sedimentary rocks or igneous/basaltic rocks of the area. Similarly, it is noted that the degree of faulting affects the degree of radius of influence of a borehole in a particular area. These analyses show that groundwater potential within the Sub-County varies spatially with high dependency on geological structures in the basement region and more on geology within the volcanic and younger sediments.

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.