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.

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.

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.

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.

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.

The benefits of mobile edge computing (MEC) systems cannot be fully exploited when the communication link is blocked or the communication signal is weak. Intelligent reflective surface (IRS) technology is introduced to build an IRS-assisted MEC system and to solve this issue. In this paper, devices offload part of their computing tasks to MEC through the multi-antenna access point with the help of the IRS, thereby reducing the completion time of computing tasks. We consider the weighted sum-latency minimization for single-device and multi-device scenarios in the uplink, which are constrained by computing offload allocation, edge node computational capability, IRS practical phase shift, beamforming, and device transmitting power. Firstly, block coordinate descent technology is used to decouple latency minimization problem into two subproblems of computation and communication. Secondly, in single-device scenario, the original problem is simplified and solved by the continuous refinement scheme. In multi-device scenario, an algorithm that combines alternating optimization and the Jaya algorithm is proposed for the first time to solve the weighted sum-latency minimization problem. In addition, compared with the conventional MEC systems without IRS, the effectiveness and high-performance gain of the proposed algorithm are proved through simulations.

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.

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.

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.