Cooperative perception enabled by Vehicle-to-Everything communication has shown great promise in enhancing situational awareness for autonomous vehicles and other mobile robotic platforms. Despite recent advances in perception backbones and multi-agent fusion, real-world deployments remain challenged by hard detection cases, exemplified by partial detections and noise accumulation which limit downstream detection accuracy. This work presents Diffusion on Reinforced Cooperative Perception (DRCP), a real-time deployable framework designed to address aforementioned issues in dynamic driving environments. DRCP integrates two key components: (1) Precise-Pyramid-Cross-Modality-Cross-Agent, a cross-modal cooperative perception module that leverages camera-intrinsic-aware angular partitioning for attention-based fusion and adaptive convolution to better exploit external features; and (2) Mask-Diffusion-Mask-Aggregation, a novel lightweight diffusion-based refinement module that encourages robustness against feature perturbations and aligns bird's-eye-view features closer to the task-optimal manifold. The proposed system achieves real-time performance on mobile platforms while significantly improving robustness under challenging conditions. Code will be released in late 2025.

Internet of Vehicles (IoV) is a typical extension of Internet of Things (IoT). Specifically, Federated Learning (FL) is capable of alleviating the knowledge sharing and privacy protection problems of IoV, and further enhancing the driving experience and service quality. However, dueto the scanty data results of new environment and the high-cost of expert data-labeling, posing an imminent challenge of how to reinforcement the vehicles’ data. In this letter, a data reinforcement mechanism is proposed to utilize the vehicles’ unlabeled dataset sufficiently and enhance the vehicle collaboration ultimately. Particularly, the dataset distribution characteristics of vehicles’ datasets are calculated to measure the dataset similarity. Furthermore, the labeling models are distributed to vehicles to empower the unlabeled data with the assistance of dataset distribution characteristics. Experimental results show that the proposed data reinforcement mechanism is capable of labeling the unlabeled data accurately and improving the performance of FL.

This paper presents a novel beamforming design that seamlessly integrates sensing, communication, and over-the-air computation (AirComp), enabling a critical multi-purpose functionality for next-generation wireless networks. Firstly, we formulate an optimization problem with the objective of minimizing the mean squared error of AirComp, subject to constraints that ensure the performance of both sensing and communication. The optimization problem is then parameterized and solved using unsupervised learning, employing real-valued and complex-valued deep neural networks (DNNs), respectively. For the complex-valued DNNs, we introduce its mechanism and then apply it for an intelligent beamforming design. Numerical results validate the convergence, ergodic rate, and ergodic mean square error of the proposed algorithms for the integrated sensing, communication, and computation. Also, our findings show that complex-valued DNNs outperform real-valued DNNs.

Cooperative perception has emerged as a promising solution to overcome the limitations of individual sensing in autonomous driving. However, its performance in real-world scenarios is often inconsistent due to dynamic environments and fluctuating wireless communication quality. In particular, the quality of service (QoS) in both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) links is frequently affected by physical constraints, while limited communication and computation resources further exacerbate system latency and degrade perception accuracy. To address these challenges, we propose a novel communication-sensing-computation integrated cooperative perception framework for connected vehicular networks. This framework considers V2V and V2I cooperation and introduces a unified optimization strategy that simultaneously handles resource allocation, fusion method selection, and cooperative agent selection. We formulate this as a mixed-integer nonlinear programming (MINLP) problem and decompose it into two subproblems, which are efficiently solved using a block coordinate descent method. To enable adaptive and scalable decision-making, we design a deep reinforcement learning (DRL)-based joint resource allocation scheme for V2I, and extend it with a federated learning (FL) variant for decentralized V2V scenarios. Extensive multi-factor simulations demonstrate that our method significantly improves perception accuracy, reduces latency, and enhances robustness under diverse network conditions. These results validate the effectiveness and practicality of our proposed framework in real-world cooperative driving environments.

This paper proposes a movable array-enabled localization (MAL) framework for high-resolution and cost-efficient direction-of-arrival (DoA) estimation. A base station equipped with a movable uniform linear array (ULA) transmits sensing signals and receives echoes along a linear slide. By modeling the round-trip Doppler shifts caused by motion, we construct a spatio-temporal signal model and reinterpret the temporal phase variations as spatial shifts. This enables the synthesis of a virtual array with an aperture up to twice the physical displacement. A sparse recovery algorithm based on simultaneous orthogonal matching pursuit (SOMP) is employed for efficient DoA estimation. Cramér-Rao bound (CRB) analysis shows that the CRB scaling improves from first-order to third-order with respect to observation time, demonstrating the efficiency of motion-induced aperture synthesis. Simulations validate the analysis and confirm that MAL achieves accurate localization with minimal physical antennas, including the single-antenna case.

Emerging as a promising technology to bridge the trust gap among multiple participants, blockchain has been envisioned to enable dynamic spectrum sharing in a decentralized manner. However, satellites with limited resources may struggle to support the frequent interactions required by blockchain networks. Additionally, due to the large coverage area of satellites, the differentiated spectrum sharing needs in various regions can make traditional blockchain approaches inadequate. In this paper, a two-tier multi-region blockchain-based dynamic spectrum sharing approach (TMB-DSS) is proposed. This approach enables regions to manage spectrum autonomously while jointly maintaining a unified blockchain ledger. Moreover, a theoretical framework using stochastic geometry is derived to evaluate the stability performance of TMB-DSS. Finally, numerical results are presented to validate the proposed approach.

Satellite networks play a vital role in providing global connectivity to remote areas, including mountains, forests, and regions affected by natural disasters. The primary challenge lies in the limited communication timeframe between satellites and earth stations (ESs) due to the swift motion of satellites, making timely satellite data downloads through ESs challenging. To address this, we propose a method named priority-aware and throughput-optimized intersatellite cooperative data transmission (PACT). PACT optimizes network throughput while maximizing download priorities for ESs by leveraging intersatellite links (ISLs), considering diverse download priorities for different data types. To comprehensively capture constellation characteristics, PACT models low Earth orbit (LEO) constellations using a spatiotemporal graph. Within the graph, data priorities are assigned as edge weights, and the allocation of initial download windows to ESs is achieved by maximum weighted matching. Subsequently, PACT organizes a contact plan through cooperative scheduling leveraging ISLs. A bipartite graph is constructed based on data awaiting download and link allocation to redistribute remaining download windows optimally through maximum matching. This iterative process enhances network throughput and maintains data priority. Performance assessments in the ndnSIM framework, covering diverse load scenarios, demonstrate the efficiency and benefits of PACT, particularly in prioritizing data downloads.

Vehicular digital twin networks (VDTN) is an emerging paradigm integrating physical vehicular networks with their virtual digital twins (DT) mirror, enabling real-time mapping, simulation, and optimization of complex systems. However, constrained resources, high data synchronization costs, and dynamic network conditions in vehicular networks may degrade the performance of DT. We consider the interaction between task performance guarantees and node resource constraints to adaptively determine collaborative task computation transfer optimization in VDTN. In this paper, we design a semantic-aware multi-task vehicular digital twin network model, where vehicles extract semantic representations to achieve lightweight data transmission and efficient DT synchronization. We formulate a problem of maximizing the average utility of DT tasks by jointly considering the synchronization performance of DT tasks and the resource consumption among heterogeneous nodes. To solve the formulated problem, we develop a dynamic collaborative task computation transfer algorithm involving the high mobility of vehicles and heterogeneous resources of nodes. The algorithm is optimized in two phases to maximize average utility. A coarse-grained policy space is first obtained through an adaptive multi-agent deep reinforcement learning approach, aiming to alleviate the policy space explosion caused by dynamic task requirements and heterogeneous node collaboration. Subsequently, a fine-grained policy is derived via a resource-aware refinement mechanism. Numerical results validate the effectiveness and robustness of our proposed algorithm.

As in-vehicle services grow, the increasing size of cached content prolongs wait times for users. For electric vehicles, balancing efficient communication with reduced energy consumption remains a challenge. In this paper, a vehicle clustering cooperative caching model for urban Internet of Vehicles (IoV) systems is proposed. This model decreases system energy consumption and task delay by leveraging buses as regular mobile Roadside Units (RSUs) and pre-caching nodes. It includes a kinetic energy recovery scheme for vehicles and an Energy Harvesting (EH) mechanism for RSUs, both intended to further reduce energy consumption. Terahertz (THz) technology is harnessed for Vehicle-to-Vehicle (V2V) communication to accelerate caching tasks and reduce tasks delay. To address these challenges, we propose the Deep Deterministic Policy Gradient (DDPG)-based Power Splitting (DPS) algorithm to address the needs of information transmission and energy recharging of buses while in motion. The proposed ($1+1$)-Evolutionary Strategy (ES)-based joint Task Decomposition and Bandwidth Allocation (TDBA) algorithm, which transmits the decomposed task file segments in parallel on different paths, reduces the additional delay and energy consumption caused by frequent task switching. Furthermore, the proposed Time-Location Preference User Rated Recommendation (TLPURR) algorithm recommends appropriate content based on the vehicle user’s time and location preferences, reducing the delay and energy consumption of obtaining content from remote cloud resources. Simulation results demonstrate that our proposed algorithms have a significant improvement in the delay and energy consumption metrics compared to other algorithms.

When communication signals are weak, the advantages of on-board edge intelligence cannot be fully utilized. To tackle this challenge, the introduction of a key technology in the sixth-generation mobile network (6G)—reconfigurable intelligent surface that can simultaneously transmit and reflect signals (star-RIS)—is proposed. In star-RIS-enhanced intelligent transportation system (ITS), intelligent vehicles use star-RIS to upload local training models and perform global model training on the roadside unit (RSU) side. In this paper, the goal is to minimize system delay and loss function of the learning model, and comprehensively considering constraints such as system bandwidth, star-RIS phase shift, vehicle transmission power and beamforming, and vehicle selection. Firstly, for the optimization of phase shift, transmission power and beamforming caused by the introduction of star-RIS, the block coordinate descent method and Lagrange dual algorithm are used to simplify the solution. Secondly, for system delay and global model training, federated learning (FL) algorithm based on double deep Q-network (DDQN) is utilized. This approach leverages policy optimization and data privacy protection to provide an intelligent resource optimization scheme for ITS. In addition, the effectiveness of the proposed algorithm is validated through extensive simulations and numerical analyses. The results show that the algorithm enhances the adaptability and service quality of the system significantly.

Benefiting from a large amount of unallocated bandwidth, millimeter-wave (mmWave) communications have been regarded as one of the most promising technologies. To overcome high pathloss of mmWave signals, the beamforming technique plays a fundamental role. In recent years, with the success of deep learning (DL), DL-based beam prediction methods have been widely studied to reduce the training overhead of traditional beam scanning methods. In this paper, a novel DL-based low-overhead beam prediction scheme is proposed, which is motivated by two important observations: (1) The optimal beam prediction is difficult for non-line of sight (NLOS) scenario, which limits the overall prediction accuracy. (2) On the contrary, the optimal beam can be precisely predicted with low computational costs under line of sight (LOS) scenario. Therefore, we propose a flexible network architecture, namely multi-stage network (MSN), to conduct the optimal beam prediction. Firstly, MSN contains multiple branches with gradually increasing computational complexity, and each branch carries with a classifier, which enables the MSN to have the capability of adaptively and dynamically allocating computational resources. Meanwhile, to combine the advantages of convolutional neural network (CNN) and transformer for feature extraction in MSN, we design joint CNN and transformer (JCT) module and its simplified module, namely Ghost-JCT. Secondly, we propose two pre-training strategies to effectively improve the performance of classifiers without additional computational costs. Finally, we propose confidence-based and Markov-based classifier selection strategies, which could select the appropriate classifier to strike a balance between accuracy and computational complexity. Simulation results demonstrate that MSN enjoys significant superiority in terms of computational complexity and prediction accuracy compared to its traditional counterparts.

This paper investigates a novel client selection for the volatile Federated Learning (FL) systems, where volatility means that the state of the client set, client datasets, and client training status will change over time. We study how to select clients dynamically to mitigate the volatility. Particularly, the volatile client selection problem is formulated as a classification problem, and we propose two new metric features. The Multi-Feature Volatile Client Selection (MFVCS) algorithm, which considers client training capacity, client-weighted data quality, and client historical selection entropy, is proposed to solve the volatile client selection problem. Moreover, we have developed an adaptive dynamic weighting algorithm that allows for dynamic updating of the weight for each feature. We propose a volatility ratio to measure client volatility. The experimental results indicate that the proposed algorithm demonstrates strong robustness and better performance under different volatility ratios of the client set. In particular, the proposed MFVCS algorithm improves the model accuracy at most by $\mathbf{9.2\%}, \mathbf{9.6\%}$ and $\mathbf{12.5\%}$ under 0.01 volatility ratio, 0.05 volatility ratio and 0.1 volatility ratio, respectively.

With the increasing number of Internet of Things devices and sensors in vehicular network, a huge amount of data is generated. Vehicle Edge Computing (VEC) utilises the computation resources at the edge of the network and can efficiently process this big data through computational offloading techniques. However, due to the neglect of communication network relationships among vehicles, current Vehicle-to-Vehicle (V2V) computation offloading schemes encounter challenges such as high communication latency, substantial communication overhead, and the wastage of computation resources. To address these challenges, we design a computation offloading mechanism based on Federated Graph Neural Network (GNN) for vehicular networks, that is, vehicular FedGNN (V-FedGNN). Firstly, our modeling approach considers features including vehicle speed, location, available resources, and wireless network, which are embedded in the graph structure. Secondly, we design weighted vehicular communication network topology and propose weighted total delay optimization problem. Finally, this paper proposes a prediction model based on Federated Learning (FL) and GNN to minimize the weighted computation offloading delays among vehicle nodes. Experimental results demonstrate that our proposed scheme achieves high offloading prediction accuracy, with an average value of 98.1% and achieves low offloading latency, correspondingly.

User selection has became crucial for improving energy efficiency in communication of federated learning (FL) over wireless networks. However, centralized user selection causes additional system complexity. This study proposes a network intrinsic approach of distributed user selection that leverages the radio resource competition mechanism in random access. Taking the carrier sensing multiple access (CSMA) mechanism as an example of random access, we manipulate the contention window (CW) size to prioritize certain users for obtaining radio resources in each round of training. Training data bias is used as a target scenario for FL with user selection. Prioritization is based on the distance between the newly trained local model and the global model of the previous round. To avoid “excessive contribution” by certain users, a counting mechanism is used to ensure fairness. Simulations with various datasets demonstrate that the proposed method can rapidly achieve convergence similar to that of the centralized user selection approach.

Federated Learning (FL) is envisioned as the cornerstone of the next-generation mobile system, whereby integrating FL into the network edge elements (i.e., user terminals and edge/cloud servers), it is expected to unleash the potential of network intelligence by learning from the massive amount of users' data while concurrently preserving privacy. In this paper, we develop an analytical framework that quantifies the interplay of user mobility, a fundamental property of mobile networks, and data heterogeneity, the salient feature of FL, on the model training efficiency. Specifically, we derive the convergence rate of a hierarchical FL system operated in a mobile network, showing how user mobility exacerbates the divergence caused by data heterogeneity. The theoretical findings are corroborated by experimental simulations.

With cutting-edge sensors and learning algorithms developed for vehicular perception, breakthrough advancements have been made in the perception-based end-to-end autonomous driving in recent years. However, the reliability of autonomous driving systems could be compromised by the vulnerability of perception module to occlusion. To address this issue, the integration of vehicle-to-vehicle communication enabled perception data sharing in the dynamic driving task has been proposed and has yielded notable results, as demonstrated by COOPERNAUT, a cooperative system based on distributed lidar perception. In this paper, we introduce ICOP, an end-to-end driving system based on multi-agent camera cooperative perception, to select sensor sharing nodes and to fuse intermediate image data features for learning a driving policy. In the ICOP system, each agent encodes image information into Bird’s Eye View (BEV) representations individually, and these representations are then transmitted as payloads of V2X (vehicle-to-everything) messages via wireless connection, thus enables capturing global spatial interactions among agents to form comprehensive BEV perception information used for final control decision-making. Supported by our designed mechanism of vehicle-to-vehicle communication and transformer block to achieve acceptable image sensory data size for transmission, the experiments suggest that the proposed cooperative perception driving system achieves better results than lidar-based systems in challenging driving situations compared to prior works.

Unlocking the Potential of Smart Cities: Our paper details a groundbreaking field test validating the direct camera-to-RSU connection in a C-V2X pedestrian warning scenario. By leveraging local AI computing within the Sony IMX500 smart camera, our approach eliminates the need for traditional computing servers, leading to significant improvements in pedestrian warning speed. Test results demonstrate a reduction in response time by up to approximately 1 second, showcasing the efficiency gains and transformative benefits of building smarter cities.

In an integrated access and backhaul (lAB) net-works with multihop characteristics, congestion may occur in the middle node during uplink transmission. Severe congestion will cause data packet loss and long user waiting delays, which causes the network performance dropping. A suitable congestion control scheme effectively alleviate node congestion when congestion occurs and prevent the user's service quality from being greatly affected. The existing researches on congestion issues mainly focus on short-term congestion, and the congestion relieves by limiting the upload rates of the child nodes of the congested nodes and its UEs, which further effects system throughput. In this paper, congestion control schemes for the long-term congestion problem of lAB network are proposed, which includes long-term congestion trigger conditions and control schemes. The approach effectively solves the long-term congestion problem of the lAB by improving the node's backhaul capability and balancing the node's ingress and egress rate. We also demonstrate the effectiveness of the proposed method and by means of simulation results. The effectiveness of the proposed congestion control method are demonstrated by the evaluation results of uplink packet delivery rate (PDR) and user datagram protocol (UDP) delay.

Platoon-based vehicular network in NR-V2X has been considered as a promising technology to assist reducing traffic congestion, saving vehicle fuel, and enhancing driving experience. Resource allocation is the basis for ensuring stable and safety vehicular networks. In this paper, we propose a Distributed Resource Allocation algorithm using Federated Multi agent Deep Reinforcement Learning (DRAFRL), which mathematically utilize the federated averaging (FedAvg) mechanism to reduce the variance between agents and achieve better transmission performance. The proposed algorithm consists of four steps: Firstly, each agent updates local model by deep deterministic policy gradient (DDPG) algorithm. Secondly, the agents upload local model parameters to the base station (BS) for federated aggregation. Thirdly, the BS performs weight aggregation using the FedAvg method and updates the global model. Finally, the BS distributes the optimized global model parameters to each agent. The simulation results show that the proposed algorithm outperforms other baseline algorithms while reducing the variance between agents by 93.5% and 99.1% compared with two baselines.

In the context of the burgeoning Industrial Internet of Things (IIoT), the proliferation of interconnected devices has created a reservoir of data resources distributed across diverse domains. However, due to the conflict between proprietary data and the use of data, it is a challenge to fully obtain data value in an efficient and legal way. To release the data value in an efficient and legal way, blockchain is considered a promising technology for data security and privacy, which has been widely introduced to cross-domain data governance. In this paper, we propose a blockchain-assisted cross-domain data sharing (BCDS) in IIoT. Specifically, by deploying the permissioned blockchain, we design a zero-knowledge proof scheme to verify data ownership under the criterion of confidence and anonymity. Besides, to prevent the thrid-party from decrypting data, we design a key agreement protocol to ensure that only recipient is authorized to decrypt data based on private key. Furthermore, we theoretically analyze the security performance of schemes. Extensive experiments in simulation computer systems and testbed deployment are conducted to demonstrate the effectiveness and efficiency of the proposed scheme.

As an important research field in time series processing, traffic prediction has a profound impact on people’s daily lives and social development. Conventional traffic prediction relies on complete observation data. However, data missing is common in cities due to equipment failure, network interruption, etc., which poses a huge obstacle to traffic prediction. In this paper, we design a novel Spatiotemporal Ego-graph Domain Adaptation framework (SEDA) to predict traffic state in data missing scenarios. Based on the multi-dimensional topological information of local network (ego-graph), isomorphic ego-graphs are aligned across the missing data in target domain and the external data in source domain to obtain alternative data. Furthermore, a Dual-branch Cross reCoupling method (DCC) is proposed to reconstruct missing features according to the alternative data. Experimental results on real public datasets with 10%-40% missing show that SEDA averagely outperforms both the state-of-the-art knowledge transfer-based prediction baselines and the incomplete data prediction baselines by more than 0.45% and 0.86%. Ablation experiments and visualization analysis further demonstrate the effectiveness of SEDA components.

Cooperative spectrum sensing (CSS) is a key enabling technology of cognitive radio networks with multiple secondary users (SUs). In conventional CSS systems, when the primary signals are weak, the SUs require long sensing time to achieve a high detection probability for protecting the transmission of the primary user (PU), leading to little remaining time for secondary transmissions. To address this issue, we propose a reconfigurable intelligent surface (RIS) assisted CSS system, where multiple RISs are employed to improve the CSS performance within limited sensing time. Considering that the dependency of the CSS performance on the received primary signal strengths at the SUs differs across various CSS schemes, the RIS configurations could also be optimized differently regarding these CSS schemes. Motivated by this, we investigate the phase shift matrix (PSM) optimization problems to maximize the cooperative detection probability given a maximum tolerable false alarm probability, and we consider two typical kinds of CSS schemes, namely, data fusion and decision fusion. As it is intractable to directly solve these problems due to the complex expressions of the cooperative detection probability with respect to the PSMs, we show that the solutions can be obtained by transforming these problems into channel gain-related optimization problems. Furthermore, we show that the proposed PSM optimization methods can be extended to the more practical scenarios where instantaneous channel state information (CSI) is unavailable. In such cases, we leverage statistical CSI to improve the CSS performance in the sense of expectation. Subsequently, we conduct a numerical analysis on the number of reflecting elements required to achieve a target detection probability in the statistical CSI case. Finally, simulation results demonstrate that the proposed PSM optimization methods can significantly improve the CSS performance within limited sensing time.

The future integrated sensing and communication (ISAC) networks is expected to equip with sufficient computation resources. However, current research focuses on single-domain resource allocation in ISAC and computing force networks, leaving the joint optimization of sensing, communication, and computation resource allocation unexplored. In this paper, we propose a novel approach to this problem by deep incorporating computation resources, combined with a federated learning framework, while considering sensing precision and power consumption. Firstly, a multi-objective optimization is designed, involving Cramer-Rao Bound, sum rate of ISAC networks, and power consumption of computing force networks. Subsequently, the multi-objective optimization is transformed into a multi-task learning model. We aim to obtain joint optimization of sensing, communication, and computation resource allocation via deep learning techniques. Towards the multi-task learning model, the multiple-gradient descent algorithm is utilized to obtain the multi-objective optimization. Furthermore, a practical low-complexity the multiple-gradient descent algorithm is developed to reduce the computational cost. Finally, the effectiveness of the proposed deep learning algorithms is verified by simulations results.

In non-cooperative communications, malicious electromagnetic interference attacks communication systems and causes higher probability of communication disruption. In order to address the challenges posed by electromagnetic interference, the wireless interference recognition technique has emerged, which identifies the interference signals without priori information. In recent years, the success of deep learning (DL) has sparked interest in introducing DL in the field of wireless interference recognition. However, most DL-based interference identification methods improve accuracy by dramatically increasing network sizes while ignoring the important effect of network inputs. For this reason, we extensively investigate the impact of different signal transformation forms of interference (called signal modalities) on performance. The artificial features of the interference signal are also utilized as one of the refined modalities, which breaks the inherent concept that artificial features are only used in the methods of feature extraction. Convolution and transformer are combined in the extraction of different modal features. In order to reduce the complexity of transformer, a dual transformer module (DTM) is proposed. Furthermore, to overcome the imbalance of modal optimization during the training process, an adaptive gradient modulation (AGM) strategy is proposed, which leads to better convergence for the multimodal training. Finally, modal information selection mechanism (MISM) selects the most appropriate modalities for each input sample, which saves computational costs. Extensive experiments demonstrate that combining multiple interference modalities is more effective than trying different networks.

Unmanned aerial vehicle (UAV) and sidelink technology are becoming more and more important in emergency communication. To optimize overall energy efficiency, joint subchannel, transmit power, and multi-UAV trajectory optimization algorithms are examined in a multi-cell heterogeneous network of UAV and sidelink with quality of service (QoS) sensitivity restrictions. To allocate subchannel appropriately in each period, a grouping and matching approach is first developed that can handle the subchannel assignment of multi-cell. Then, successive convex approximation method is used to approximate the non-deterministic polynomial hard problem of power allocation. Taylor expansion approximation method is finally introduced to deal with multi-UAV trajectory optimization. In addition, the complexity analysis is provided and numerical results confirm the optimization methods’ reasonableness.

One of the main challenges of popularizing the integration of sensing and communication (ISAC) network is mutual interference between the two functions. A viable solution is the time division scheme where communication and sensing are separated in time domain. This paper considers a multi-cluster ISAC network model, where the time-domain radio resources are allocated to sensing and communication. At the same time, the time resources can be reused in space-domain. Particularly, the terminals in different work phases can access radio resources of different or the same clusters simultaneously, depending on interference. In this way, the interference is isolated while the resources utilization is improved. Two different resource allocation approaches are proposed according to how interference is considered. The aim is to maximize the sensing detection probability under the constraint of network throughput. The performance improvement in terms of target detection probability brought by the proposed schemes is shown by numerical results compared with benchmark methods.

Federated Learning (FL) is an emerging distributed learning architecture that allows multiple agents to share knowledge in machine learning. However, in the scenario with mobile agents, the mobility of agents significantly affects the learning performance. Besides, frequent exchange of local models could result in high communication overhead. In this paper, we propose a Mobility-aware Federated Reinforcement Learning (MFRL) framework. In MFRL, we model the influences of agent mobility on communication quality and data correlation, and devise a mobility-aware node selection algorithm, so as to accelerate the training procedure and improve the learning performance, taking learning quality, wireless channel quality, and data correlation of agents into consideration. A knowledge distillation (KD) based model compression method is integrated into the MFRL to reduce the communication overhead as well as accelerate the inference process. Finally, taking deep reinforcement learning (DRL) based collision avoidance of intelligent vehicles as a study case, the effectiveness of MFRL is verified. Numerical results demonstrate that the proposed MFRL can accelerate the training process and improve the learning performance.

The straggler effect is the main bottleneck for Federated Learning (FL), where the performance of training is degraded by the slowest member. Another significant problem is unreliable communication, which somehow has been neglected in previous studies. That is, the transmission of local models is not successful every time. In this paper, we find that the problems of straggler effect and unreliable communication are implicitly caused by time divergence of User Equipments (UEs) in each training round. Based on this, we propose our solutions for these two problems and show that our solutions can be merged into a general one: the problem of the straggler effect and unreliable communication can be solved with a simple UE selection method. This method consists of two steps: First, we cluster UEs into several groups based on UEs' physical parameters or performance metrics; Second, in each training round, only UEs from the same group are chosen for FL operation. Full explanations are given why the time divergence is statistically reduced, and therefore it can mitigate the aforementioned two problems. Our solutions are further illustrated with some examples and validated by simulations.

Leveraging the unique characteristics of blockchain, secure and efficient dynamic spectrum sharing (DSS) can be achieved, which has been regarded as a promising solution to meet the spectrum requirement in future wireless communication systems. However, proper incentive mechanism with guaranteed fairness is essential for blockchain-enabled DSS. In this paper, we investigate fairness-oriented spectrum auction, where multiple access points can share resources on the blockchain platform with smart contract. Specifically, we propose a fairness factor to adjust users’ satisfaction considering both the historical spectrum allocation results and current spectrum auction results. Then, an improved virtual auction mechanism is proposed to balance the long-term satisfaction of participants. Simulation results show that the multi-round fairness-based auction algorithm (FBAA) can enhance the fairness of spectrum allocation and increase the number of radio users served.

In this paper, we propose a transmit-receiver alternating minimization (TR-AltMin) algorithm for integrated sensing and communication (ISAC) systems in vehicular ad-hoc networks (VANETs) with hybrid analog-digital (HAD) beamforming structure. Given the ideal beamformer for radar and the channel information for communication, consider the minimum beamformer error for radar and the minimum mean square error (MMSE) or weighted MMSE (WMMSE) metric for communication, a weighted summation optimization problem is obtained, and the TR-AltMin algorithm is used to get the optimal results of the HAD beamformer and combiner. Numerical simulation results show that the proposed method has better performance in terms of the communication-radar (C-R) trade-off and C-R functionalities.

Integrated sensing and communication (ISAC) aims to use the basic functions of the wireless communication system to achieve sensing (by sharing the same frequency, signalling, hardware, etc.). At the same time, the results of wireless sensing are used in turn to optimize wireless communication. In this way, it is possible to realize the dual promotion of wireless communication system and wireless sensing, improve and fully differentiate the spectrum utilization, and reach a high level of integration and simplification of the equipment, and achieve accurate sensing. This paper reports an ongoing study on ISAC conducted by the Service & System Aspects Work Group 1 (SA WG1) of the third generation partnership project (3GPP), which is defining to study use cases and potential requirements for the enhancement of the 5G systems (5GS) to provide ISAC services addressing various target verticals/applications. We summarize the objectives of the ISAC Study Item (SI), and discuss some of the most interesting proposed use cases discussed thus far. We also introduce the potential new requirement for 5GS as well as a summary of potential research objective pertaining to ISAC in standardization evolution.

In this paper, an adaptive millimeter-wave (mmWave) beam tracking scheme is proposed to flexibly adjust the angular range of beam tracking based on the user-specific speeds for reducing the tracking overhead, where deep learning is exploited to accurately extract the user movement features. Specifically, long short-term memory network is utilized to predict the possible optimal beams according to the received signals of previous beam tracking. Based on the predicted probabilities, the sum-probability criterion is proposed to track the subset of maximum-probability beams whose sum-probability is larger than the predefined threshold, where the beam with the highest received power is selected as the optimal one. Considering the limited number of received beam tracking signals, a two-stage training strategy is further proposed to stabilize the model optimization. Simulation results demonstrate that our proposed scheme could effectively reduce the overhead of beam tracking in guarantee of high beamforming gains, compared with the conventional schemes.

This paper considers a cluster-based relay vehicle selection scheme that adjusts the K-means-type algorithm to find real center mean vehicles under a restricted seeding range. With the help of the K-means-based VANET seeding principle, a local minimum cluster center is proposed, together with a rigorous proof by means of Lagrange multipliers. Unlike other existing works, relay vehicles in this correspondence are categorized into centroids and connecting vehicles to minimize the number of relay vehicles used and maximize the broadcasting power efficiency. To evaluate the system performance, we use different metrics to accommodate the realistic V2V scenario with NLOS signal dissemination, and empirical results show that both the algorithms themself and the system performance by implementing proposed algorithms are in the advantageous position over exsting appoaches.

The huge training overhead for obtaining channel state information (CSI) at the BS has been recognized as a major challenge in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) cellular networks. To solve this problem, we propose an angular domain pilot design and channel estimation scheme to reduce the required overhead by exploiting the angle domain channel sparsity. Specifically, we firstly propose the downlink dominant angular set estimation by utilizing the directional reciprocity of FDD channels, where an index calibration algorithm is introduced to handle the effect of different wavelengths in FDD systems. Then, two kinds of angular domain pilots design schemes, named complete orthogonal pilot design and partial orthogonal pilot design, together with their corresponding feedback frameworks are proposed for channel estimation. Simulation results demonstrate that our proposed angular domain pilot design and channel estimation scheme could provide good mean square error (MSE) performance with much reduced pilot overhead, and consequently achieve much larger downlink throughput in comparison to the conventional scheme adopted in LTE.

Sparse code multiple access (SCMA) is being considered as a promising multiple access solution for 5G systems. A distinguishing feature of SCMA is that it combines the procedures of bit to constellation symbol mapping and subsequent spreading using multidimensional codebooks differentiated by users. Such codebooks dominate the system implementation as a main source of not only performance gain but also design complexity. This letter presents a joint constellation with mapping matrix design for SCMA codebooks, which formulates the constellations optimization as a nonconvex quadratically constrained quadratic programming problem based on a set of well-constructed mapping matrices. We elaborately solve the problem to achieve outperformance over existing SCMA design in terms of bit error rate (BER). For improving practicality, an approximate approach is further proposed to reduce the complexity significantly with a limited BER loss.

Full-dimensional multiple-input multiple-output (FD-MIMO) systems, whereby each base station is equipped with a uniformly spaced rectangular antenna array (URA), provides a practical means of realizing massive MIMO systems. However, the spectral efficiency of URA is considerably lower than that of its uniformly spaced linear array counterpart having the same number of antenna elements. In this paper, we first introduce a discrete angular resolution metric for quantifying the low resolution of URA in the antenna-elevation domain. This motivates us to propose a novel antenna device design, referred to as the structured non-uniformly spaced rectangular array (NURA), in which the antenna elements are non-uniformly distributed in the elevation-angle domain. Specifically, we conceive a structured NURA device for which the nonuniform distribution of the elevation-domain antenna elements is controlled by a single parameter. The design of the optimally structured NURA for the given nonlinear antenna-element-positioning function then becomes a single-parameter optimization, namely, that of maximizing the spectral efficiency of the FD-MIMO system, which can be solved efficiently. Our simulation results demonstrate that our structured NURA design significantly outperforms the standard URA in terms of achievable spectral efficiency. Our proposed structured NURA design therefore offers an effective practical framework for enhancing the achievable performance of FD-MIMO systems.