High-frequency communications strongly depend on the line-of-sight (LoS) path, and obstacle blockage can severely degrade the received signal power and achievable rate. Near-field Airy beams with curved trajectories can circumvent obstacles, offering a promising way to alleviate blockage. However, since an Airy beam carries most useful energy along a single curved trajectory, existing Airy beamforming methods are highly sensitive to estimation errors of transmitter-obstacle-receiver geometry. That is to say, even a small error in the estimated geometry may cause the mismatched Airy trajectory, leading to severe performance loss. To address this problem, we propose a multi-Airy beamforming scheme for blockage-robust near-field communications. Specifically, we first reveal and analyze the sensitivity mechanism of single-Airy beamforming. This mechanism motivates us to extend the single-Airy generation method to a coordinated multi-Airy generation method by deriving the phase offsets required to coherently combine multiple Airy beams at the target user. Based on this coordinated generation method, we partition the transmit array into multiple sub-arrays and configure a tailored Airy beam for each sub-array, so that the resulting Airy beams formed by multiple curved trajectories can be coherently combined at the target user. Simulation results verify the sensitivity of single-Airy beamforming and the robustness of multi-Airy beamforming under estimation errors of transmitter-obstacle-receiver geometry. Moreover, the proposed scheme achieves higher achievable rates than single-Airy beamforming in blocked scenarios without geometry estimation errors.

Integrated Sensing and Communication (ISAC) is a pivotal technology enabling next-generation 6G networks by seamlessly combining communication and sensing functionalities for emerging applications. However, practical ISAC deployments are often hindered by performance degradation in complex environments. Ground-based ISAC systems are susceptible to line-of-sight (LoS) blockages stemming from urban infrastructure and irregular terrain. While uncrewed aerial vehicle (UAV)-based ISAC has gained prominence, current research predominantly focuses on single-UAV architectures, which exhibit limitations in sensing coverage, accuracy, and vulnerability to ground clutter interference. This paper presents a collaborative multi-UAV ISAC framework designed to address these shortcomings through coordinated transmit-receive beamforming and trajectory optimization. A system-level model characterizing cooperative sensing performance of multiple UAVs in clutter-rich environments is developed. An optimization problem is formulated to maximize the signal-to-clutter-plus-noise ratio (SCNR) subject to communication quality-of-service (QoS) constraints. The resulting non-convex problem is effectively solved utilizing an alternating optimization algorithm, decomposing it into tractable beamforming design and trajectory optimization sub-problems, both of which are converted into convex forms. Numerical simulations conducted under clutter scenarios demonstrate significant improvements in sensing robustness and communication reliability achieved by the proposed framework compared to single-UAV and clutter-agnostic approaches.

Integrated sensing and communication (ISAC) is expected to become a crucial component of the sixth-generation (6G) networks owing to its outstanding spectrum management capability. However, improving the cooperative sensing capabilities of multiple ISAC user equipments (ISAC-UEs) in complex interference environment presents a significant research challenge. This paper focuses on the multi-user cooperative sensing in uplink orthogonal frequency division multiplexing (OFDM) ISAC system. By utilizing the stochastic geometry, we model the distribution of communication UEs (COM-UEs) as a one-dimensional Matern hard-core point process (1-D MHCP), and derive a closed-form expression for interference power. To further enhance cooperative sensing accuracy while maintaining quality of service (QoS) in communication, we perform waveform optimization by jointly optimizing the weighted range-velocity Cramer-Rao lower bound (CRLB) subject to communication data rate (CDR) and subcarrier power ratio (SPR) constraints. This approach involves selecting the optimal subcarriers for sensing and allocating the corresponding power on each subcarrier for communication and sensing subsystems. By employing the convex relaxation and the cyclic minimization algorithm (CMA), we decompose the complex optimization problem into three sub-problems, simplifying the original NP-hard problem into a solvable one via a cyclic optimization framework. The simulation results validate the effectiveness of our optimization strategy, and evaluate the influence of CDR and SPR constraints using the CRLB and root mean square error (RMSE).

The rapid development of low altitude Unmanned Aerial Vehicles (UAVs) as a new mode of transportation has injected a new driving force into the market development, but at the same time, unreported "black flight" UAVs have also created new risks in civil aviation safety, citizen privacy protection and other social security areas. In this regard, the Integrated Sensing And Communication (ISAC) capability of Base Station (BS) can provide an effective means of communication and supervision of low-altitude UAVs. For example, by demarcating the electronic fence area, the ISAC BS can realize automatic detection of illegal invasion of UAVs, effectively guaranteeing low-altitude safety in the context of low-altitude economy. By leveraging the high mobility of UAVs and their strong air-ground Line-of-Sight (LoS) channels, UAV-enabled ISAC is anticipated to provide superior sensing and communication coverage, and enhanced sensing and communication performance compared to terrestrial ISAC. However, existing work mainly focus on single BS sensing with the assistance of communication, which may not fully activate ISAC's potential and achieve high-precision long-range sensing. Given the above considerations, this paper provides a cellular-connected UAV system, where the BS and connected UAV are employed to perform cooperative detection tasks for precise detection. To unleash the potential of ISAC in cellular-connected UAV systems, on the one hand, we propose an Extended Kalman Filtering (EKF) based data fusion algorithm to provide precise environment information and achieve beyond LoS sensing. On the other hand, according to the fusion results, we optimize the communication rate performance by jointly designing the transmit beamforming and trajectory subject to the power and practical fight constraints to combat the effect of mobility, while ensuring the sensing requirements, which can achieve a positive feedback loop. Extensive simulation results demonstrate that the proposed data fusion algorithm improves the estimation accuracy by 67% and the joint design of beamforming and trajectory algorithm improves the communication data rate by more than 31%.

Objective:The field of cellular mobile communication is advancing toward post-5G(5.5G,Beyond 5G,5G Advanced)and 6th Generation(6G)standards.This evolution involves a shift from traditional sub-6 GHz operating frequency bands to higher frequency ranges,including millimeter wave(mmWave),terahertz(THz),and even visible light frequencies,which intersect with radar operating bands.Technologies such as Orthogonal Frequency Division Multiplexing(OFDM)and Multiple Input Multiple Output(MIMO)have gained widespread application in both wireless communication and radar domains.Given the shared characteristics and commonalities in signal processing and operating frequency bands between these two fields,"Integrated Sensing And Communication(ISAC)"has emerged as a significant research focus in wireless technologies like 5G Advanced(5G-A),6G,Wireless Fidelity(WiFi),and radar.This development points toward a network perception paradigm that combines communication,sensing,and computing.The"ISAC"concept aims to unify wireless communication systems(including cellular and WiFi)with wireless sensing technologies(such as radar)and even network Artificial Intelligence(AI)computing capabilities into a cohesive framework.By integrating these elements,the physical layer can share frequencies and Radio Frequency(RF)hardware resources,leading to several advantages:spectrum conservation,cost reduction,minimized hardware size and weight,and enhanced communication perception.In this article,the focus of communication perception integration is primarily on radar communication.ISAC necessitates that both communication and sensing utilize the same radio frequency band and hardware resources.The diverse characteristics of multiple frequency bands,along with the varying hardware requirements for communication and sensing,present increased challenges for ISAC hardware design.Effective hardware design for ISAC systems demands a well-considered architecture and device design for RF transceivers.Key considerations include the receiver's continuous signal sensing,link budget,and noise figure,all of which are sensitive to factors such as system size,weight,power consumption,and cost.A comprehensive review of relevant literature reveals that while studies on overall architecture,waveform design,signal processing,and THz technology exist within the ISAC domain,they often center on theoretical models and software simulation.Hardware design and technical verification methodologies are sporadically addressed across different studies.Although some literature details specific hardware designs and validation approaches,these are limited in number compared to the rich body of theoretical and algorithmic research,indicating a need for more comprehensive and systematic reviews focused specifically on ISAC hardware design. Methods:This paper summarizes the hardware designs,verification technologies,and systemic hardware verification platforms pertinent to beyond 5G,6G,and WiFi ISAC systems.Additionally,recent researches on related hardware designs and verification both domestically and internationally are reviewed.The analysis addresses the challenges in hardware design,including the conflicting requirements between communication and sensing systems,In Band Full Duplex(IBFD)Self-Interference Cancellation(SIC),Power Amplifier(PA)efficiency,and the need for more accurate circuit performance modeling. Results and Discussions:Initially,the design of ISAC transceiver architectures from existing research is summarized and compared.Subsequently,an overview and analysis of current ISAC IBFD self-interference suppression strategies,low Peak to Average Power Ratio(PAPR)waveforms,high-performance PA designs,precise device modeling techniques,and systemic hardware verification platforms are presented.Finally,the paper provides a summary of the findings.Future challenges in ISAC hardware design are discussed,including the effects of hardware defects on sensing accuracy,ultra-large scale MIMO systems,high-frequency IBFD,and ISAC hardware designs for Unmanned Aerial Vehicle(UAV)applications.The performance metrics of ISAC IBFD architectures are compared,while the various ISAC transceiver architectures are outlined.Representative hardware verification platforms for ISAC systems are presented.The different ISAC transceiver architectures summarized in this paper are illustrated. Conclusions:In recent years,preliminary research has been conducted on integrated air interface architecture,transceiver hardware design,systematic hardware verification,and demonstration of sensing technologies such as 5G-A,6G,and WiFi,both domestically and internationally.However,certain limitations persist.Beyond 5G networks,post-5G and 6G ISAC hardware verification platforms primarily operate at the link level rather than at the network system level.This focus on ISAC without the integration of computing functions leads to increased volume and power consumption costs and a reliance on commercial instruments and SDR platforms.Furthermore,the IBFD self-interference suppression technology has yet to fully satisfy the demands of future ultra-large-scale MIMO systems,necessitating further integration with large-scale artificial intelligence model technologies.In light of impending technological challenges and issues of openness,it is crucial for academia and industry to collaborate in addressing these challenges and researching viable solutions.To expedite testing optimization and industrial implementation,practical hardware design transition solutions are required that balance advancements in high-frequency support,receiver architecture,and networking architecture,facilitating the efficient realization of the"ideal"of ISAC.

Millimeter-wave Integrated Sensing and Communications (ISAC) with multi-beam design holds significant promise for vehicular networks, offering multi-target omnidirectional sensing and high-capacity communication services concurrently. Nonetheless, the considerable challenge of potential mutual interference arises due to the high mobility and density of transmitters in such networks. To address this challenge effectively, we propose leveraging inter-vehicle communication to schedule communication and sensing signals for vehicles, thereby enhancing networked sensing capabilities. We first introduce an analytical framework to characterize the mutual interference among multiple vehicles. Subsequently, we evaluate the effectiveness of our proposed interference mitigation method in terms of interference probability, duration, and the achievable detectable density. Additionally, recognizing the different performance requirements of communication and sensing functions, we investigate a joint resource allocation problem catering to both aspects. Simulation results demonstrate a notable enhancement in the proposed ISAC-based interference mitigation, with a 58% reduction in interference probability compared to benchmarking schemes.

Communicating on millimeter wave (mmWave) bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission. However, mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by obstacles. To overcome these challenges, highly directional beams are exploited to achieve robust links by hybrid beamforming. Accurately aligning the transmitter and receiver beams, i.e. beam training, is vitally important to high data rate transmission. However, it may cause huge overhead which has negative effects on initial access, handover, and tracking. Besides, the mobility patterns of users are complicated and dynamic, which may cause tracking error and large tracking latency. An efficient beam tracking method has a positive effect on sustaining robust links. This article provides an overview of the beam training and tracking technologies on mmWave bands and reveals the insights for future research in the 6th Generation (6G) mobile network. Especially, some open research problems are proposed to realize fast, accurate, and robust beam training and tracking. We hope that this survey provides guidelines for the researchers in the area of mmWave communications.

Indoor positioning is a thriving research area, which is slowly gaining market momentum. Its applications are mostly customized, ad hoc installations; ubiquitous applications analogous to Global Navigation Satellite System for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the indoor positioning and indoor navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site tracks. In contrast with on-site tracks where competitors bring their systems on-site-which were impossible to organize during 2021 and 2022-in off-site tracks competitors download prerecorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem.

The joint utilization of the Fifth Generation Communications Technology (5G) and the Global Navigation Satellite System (GNSS) serves as a promising solution to address the challenges associated with insufficient visible satellites and lower observation quality in urban environments. 5G allows for the angle and distance measurements, augmenting the performance of Real-Time Kinematic (RTK) positioning. To quantify the improvement of 5G observations on RTK positioning, this paper proposes a float solution gain factor and the Ambiguity Dilution of Precision (ADOP) gain factor. Based on these gain factors, the theoretical analysis and simulation are performed. This study designs an extended Kalman filter for 5G-assisted BeiDou Navigation Satellite System (BDS) RTK positioning, employing both the Full Ambiguity Resolution (FAR) and Partial Ambiguity Resolution (PAR) modes. Our experiment verified the effectiveness of 5G-assisted BDS RTK positioning in mitigating outlier occurrences and improving the ambiguity fixing rate as well as the positioning accuracy. In the FAR and PAR modes, the Three-Dimensional (3D) spatial accuracy increased by 48% and 18.8%, respectively, and the results are consistent with theoretical analysis based on gain factors. The fixing rate of RTK increased from 11.11% to 13.93%, while it increased from 32.58% to 44.43% for the PAR mode. The assistance of 5G observations reduced the median error for the FAR mode from over 1.3m to 0.9 m, and the third quartile from 2.1m to 1.05 m. For the PAR mode, the median error decreased from 0.5m to 0.12 m, and the third and fourth quartiles decreased from 0.65m to 0.38 m.

3GPP has completed the standardization of 5G NR positioning in Release 16 (Rel-16) and Rel-17, and the Rel-18 study on expanded and improved positioning was finished in November, 2022. The standard solutions enable several positioning techniques in efforts to improve accuracy and power efficiency, and to decrease the latency. RAT-independent positioning techniques have also been standardized to support user equipment positioning via satellites and sensors. This article provides the latest progress on 3GPP 5G positioning including requirements, use cases, and the newly supported positioning techniques in Release-17 and Release-18. Finally, future challenges are discussed.

Many existing works have applied distribution test to solve the modulation classification problem and achieved good results. There are various test statistics for distribution test, such as KS test statistic, CvM test statistic, and AD test statistic. Most of the work only considers using one type of test statistic for modulation classification. In addition, most existing literature assumes that the additive noise is Gaussian noise while actual system noise exhibits obvious spike pulse characteristics. This paper proposes a method based on joint distribution test and Bayesian fusion to proceed with subcarrier modulation classification (SMC) of MIMO-OFDM system with non-Gaussian noise. We use these three test statistics together by constructing a joint distribution function for the three test statistics. Using Bayesian theory and maximum likelihood estimation, we find the mode that makes the joint probability maximum as the classification result by solving the posterior probability under different modes. Simulation results show that the proposed method has good results and robustness for SMC of MIMO-OFDM system.

Ternary Content Addressable Memory (TCAM) is an essential hardware component in SDN-enabled switches, which supports fast lookup speed and flexible matching patterns. However, TCAM's limited storage capacity has long been a scalability challenge to enforce fine-grained forwarding policies in SDN. Based on the observation of traffic locality, the rule-caching mechanism employs a combination of TCAM and Random Access Memory (RAM) to maintain the forwarding rules of large and small flows, respectively. However, previous works cannot identify large flows timely and accurately, and suffer from high computational complexity when addressing rule dependencies in TCAM. Worse still, TCAM only caches the forwarding rules of large flows but ignores the latency requirements of small flows. Small flows encounter cache-miss in TCAM and then will be diverted to RAM, where they have to experience slow lookup processes. To jointly optimize the performance of both high-throughput large flows and latency-sensitive small flows, we propose a hybrid rule-caching framework, H-Cache, to scale traffic-aware forwarding policies in SDN. H-Cache identifies large flows through a collaboration of learning-based and threshold-based methods to achieve early detection and high accuracy, and proposes a time-efficient greedy heuristic to address rule dependencies. For small flows, H-Cache establishes default paths in TCAM to speed up their lookup processes, and also reduces their TCAM occupancy through label switching and region partitioning. Experiments with both real-world and synthetic datasets demonstrate that H-Cache increases TCAM utilization by an average of 11% and reduces the average completion time of small flows by almost 70%.

As a key sensor for Advanced Driving Assistance System (ADAS), millimeter automotive radar has been a promising candidate for fulfilling tasks including adaptive cruise control and collision avoidance. However, the widely deployment of millimeter automotive radars may cause serious mutual interference among vehicles, thus degrading radar ranging performance severely. In this article, we analyze the mutual interference among multiple Frequency Modulated Continuous Wave (FMCW) radars. On one hand, we model the interference precisely by employing Matern Hard-Core Process (MHCP) model to characterize the distribution of vehicle nodes in practical bidirectional two-lane and multi-lane scenarios. Besides, the interference is analyzed in terms of the channel fading, the directional antenna pattern and the fluctuation of the target Radar Cross-Section (RCS) in two-lane and multi-lane scenarios. Besides, we analyze the reflected interference in detail. On the other hand, we evaluate the interference mitigation performance of the Random Frequency Division Multiplexing (RFDM) and Frequency Hopping (FH) approaches in terms of the probability of false detection and miss detection, effective detectable density and maximum number of interference-free radar. Finally, a novel AFH-PM mitigation approach is proposed to further improve the interference mitigation performance, which combines the adaptive FH technology with the binary phase modulation. Simulation results verify the proposed framework for interference analysis by employing Monte Carlo method, and the performance improvement of RFDM, FH and AFH-PM is 6.7 dB, 7.6 dB and 8.2 dB, respectively.

With requests of high accuracy location for indoor industry applications, 5G is studying the technical feasibility of carrier phase positioning to reach centimeter accuracy. In this paper, we propose positioning method, i.e., differential phase difference of arrival (DPDOA), based on the phase measurement. To be specific, a positioning reference unit is introduced to help the calibration of imperfect factors such as initial phase offset, synchronization error and coordinate error between gNBs. Practical field test in an indoor factory is conducted to verify the feasibility of carrier phase positioning. Results show that DPDOA can achieve centimeter level accuracy with over 80% of all the measurements.

In this paper, we propose an effective target localization strategy for Internet of Things (IoT) scenarios, where positioning is performed by resource-constrained devices. Target-anchor links may be impaired by Non-Line-Of -Sight (NLOS) communication conditions. In order to derive a feasible IoT-oriented positioning strategy, we rely on the acquisition, at the target, of a sequence of consecutive measurements of the Received Signal Strength Indicator (RSSI) of the wireless signals transmitted by the anchors. We then consider a pragmatic approach according to which the NLOS channels are pre-mitigated and "transformed"into equivalent Line-Of-Sight (LOS) channels to estimate more accurately each target-anchor distance. The estimated distances feed "agnostic"localization algorithms, operating as if all links were LOS. We experimentally assess the performance of our approach in indoor (IEEE 802.11-based) and outdoor (Long Term Evolution, LTE-based) scenarios, considering both geometric and Particle Swarm Optimization (PSO)-based localization algorithms. Even if NLOS mitigation per single communication link is very effective, our results show that, in a given environment, it is possible to derive an "average"NLOS mitigation strategy regardless of the specific position of the target in the given environment. This is crucial to limit the computational complexity at IoT nodes performing localization, yet guaranteeing a relatively high (for IoT scenarios) localization accuracy, especially in an IEEE 802.11-based indoor case (with six anchors). The obtained performance compares favorably (in relative terms) with that obtained with more sophisticated wireless technologies (e.g., Ultra-WideBand, UWB).

Ground-based positioning systems can be an alternative or a booster for the Global Navigation Satellite System. Groundbased positioning systems with synchronized clocks can obtain centimeter-level positioning accuracy using carrier phase measurements. But, clock synchronization can be time-consuming or sometimes even impossible. Asynchronous positioning systems take less time for deployment but suffer accuracy loss because of asynchronous clocks. A new framework is proposed to improve the positioning accuracy for asynchronous ground-based positioning systems. This framework introduces a static reference station with no requirement for knowing its position. The reference station estimates differenced clock drifts between the asynchronous pseudolite base stations and transmits the results to the user receiver. The receiver utilizes carrier phase measurements and the estimations from the reference station to acquire positioning results via an Extended Kalman Filter (EKF). Simulation shows that root-mean-square error(RMSE) for 2-D positioning can reach the centimeter level. A field experiment using an asynchronous pseudolite system is also conducted. The horizontal RMSE for the experiment is 7.97 cm. The field experiment shows the viability of the framework.

Distribution test, a method to evaluate the goodness-of-fit, has been applied to automatic modulation classification (AMC) in recent years. It compares the empirical cumulative distribution function (ECDF) of the sample signal with the theoretical cumulative distribution function (TCDF) under each candidate modulation format. Most existing works in MIMO systems assume the additive noise is Gaussian. However, additive noise often exhibits non-Gaussian characteristics in practical systems. The TCDF of each modulation developed for Gaussian noise can not perform well in practical system. To solve this issue, this paper proposes a practical MIMO system model, which assumes that the noise is Cauchy-Gaussian bi-parameter mixture and analyzes the TCDF corresponding to different modulation modes under this noise model. The Kolmogorov-Smirnov (KS) distribution test is used to AMC in flat-fading channel for both Quadrature Amplitude Modulation and Phase Shift Keying. Extensive simulation results demonstrate that, compared with two-sample KS test based classifier, one-sample KS test based classifier can achieve good recognition results under the non-Gaussian noise model.

The next-generation communications impose requirements on integrated sensing and communication. However, the non-line-of-sight propagation in indoor complex environments poses great challenges to common localization techniques. In this letter, we propose a signal denoising network based on the transformer and temporal attention to improve the angle-of-arrival estimation accuracy. In the proposed network, the channel impulse response is denoised and reconstructed to mitigate errors. Then, two database are constructed based on self-built ultra-wideband transceivers in indoor environments for validation. Results show that the proposed network outperforms other machine learning methods in terms of angle-of-arrival estimation accuracy.

3GPP is paving the way to enhance the 5G NR positioning for general use cases as well as vertical industry scenarios, which attracts wide research from industry and academy. Among the varied requirements the most important one is to reduce the power consumption of the positioning device while keeping high accuracy in order to dominate the increasing market of positioning. In this paper, we propose a LPHAP solution under the 3GPP NR standard framework. For reduced power consumption, a new category or capability for positioning device is defined where RRC_INACTIVE state positioning with user-centric positioning area. For accuracy enhancement, we propose to improve TOA measurement by stitching the SRSs in multiple bands and analyze the Cramer-Rao Bound of TOA estimation. Uplink time difference of arrival (UL-TDOA) with expected angle-of-arrival is investigated to enhance TDOA measurements. Performance evaluations show that the device can reach the accuracy of 0.3m@90% and the device battery life can be extended 20 times compared to Rel-17 UE.