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.

With the blossom of Internet of Things (IoT) technology, the big data volumes raised by the large number of IoT devices have posed great burden on the communication and computing network. Considering the advantages of reflecting intelligent surface (RIS), mobile edge computing (MEC), and unmanned aerial vehicle (UAV), this article proposes a flying-RIS-assisted MEC system to assist offloading services to alleviate the ground computation burden in IoT. The UAV equipped with RIS is dispatched to fly over a specific area to assist in offloading the ground’s computing mission to MEC server situated nearby access point (AP) in IoT. The cost of the IoT device is introduced as the weighted sum of the device’s energy consumption and the time consumed to accomplish all computation tasks. To prolong the lifetime and guarantee the communication quality of the IoT devices, this article minimizes the sum cost of all IoT devices by collaboratively planning UAV’s trajectory, scheduling the IoT devices’ association with flying-RIS, and optimizing the phase shift value of each reflecting components. To address the posed nonconvex optimization challenge, a deep deterministic policy gradient (DDPG)-based algorithm is brought forward. Besides, the state and action normalization mechanism is used to ease up on the training difficulty. Finally, the numerical simulation results prove the superiority of the proposed algorithm compared with other benchmark schemes.

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.

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.

The digital twin (DT) and terahertz (THz) wireless communication technologies have promoted the innovative development and application of 6G networks. Combining DT can obtain efficient, collaborative, and intelligent management for THz wireless networks. However, the conflicts between large amounts of twin data and limited network resources make it difficult to improve the performance of DT networks. In this paper, a DT architecture for THz wireless networks is proposed, which maps a physical network in the THz band into a virtual DT network and represents the DT network as a graph structure. Furthermore, the THz channel model is provided, and the resource management problem with weighted mean rate as the optimization objective is proposed, which is transformed into a graph optimization problem. Based on this, a distributed message propagation algorithm is proposed, which uses the graph neural network to provide a solution. Simulation results show that the proposed scheme improves the weighted mean rate of the DT network for the THz band and outperforms the benchmark methods. It is also proved that the proposed distributed message propagation algorithm is scalable and can maintain good performance under different conditions.