IET Communications最新文献

In unmanned aerial vehicle ad-hoc network (UANET), the node speed of unmanned aerial vehicles (UAVs) may reach up to 400 km/h. The fast or slow movement of UAV nodes leads to different speeds of topology change of the nodes. Traditional optimized link state routing (OLSR) protocol cannot adaptively adjust the routing update period when the network topology changes, which may lead to the nodes calculating incorrect routing tables. This increases the average end-to-end delay and packet loss rate for packet transmission. To enhance the adaptability of OLSR routing protocol to network topology changes, this paper proposes a multi-agent independent deep deterministic policy gradient-OLSR (MA-IDDPG-OLSR) routing protocol based on distributed multi-agent reinforcement learning. The protocol deploys DDPG algorithm on each UAV node, and each UAV node adaptively adjusts the Hello and TC message sending intervals, according to the one-hop neighbouring nodes as well as its own state. Simulation results show that the proposed protocol is able to improve the throughput and reduce the packet loss rate as compared to traditional AODV, GRP, OLSR, and distributed multiple-agent independent proximal policy optimization-OLSR (MA-IPPO-OLSR), distributed multiple-agent independent twin delayed deep deterministic policy gradient-OLSR (MA-ITD3-OLSR) routing protocols. Since MA-IDDPG-OLSR relies only on local information, there is a minor performance degradation in MA-IDDPG-OLSR compared to centralized single-agent DQN-OLSR routing protocol. But it is more suitable to a completely distributed UAV network without a centralized node.

Communication security has become particularly crucial with the rapid development of the Internet of Things (IoT). Frequency hopping spread spectrum (FHSS) technology, a prevalent method in wireless communication, has a wide range of applications in the Internet of Things. Enhancing the security of frequency hopping sequences is an essential means to improve the security of frequency hopping communication in the Internet of Things, as the performance of frequency hopping sequences plays a crucial role in frequency hopping systems. This paper proposes constructing secure adaptive frequency hopping sequence sets based on the advanced encryption standard (AES) algorithm. As a block cipher algorithm with superior security, the AES algorithm can provide a fundamental guarantee for the security of the proposed frequency hopping sequences. The mapping methods from ciphertext sequences to frequency hopping sequences proposed in this paper can achieve the construction of frequency hopping sequences of any frequency set size to meet the requirements of adaptive frequency hopping. In addition, we also model and analyse the problem of overlapping spectrum band of the IoT groups in the industrial, scientific, and medical (ISM) band, aiming to achieve better packet transmission performance by adjusting the frequency set size.

Reconfigurable intelligent surface (RIS) can provide unprecedented spectral efficiency gains and excellent ability to manipulate electromagnetic waves. This article considered a RIS-assisted multiuser multiple-input multiple-output (MIMO) downlink system, where the beamforming at the base station and RIS are jointly designed to maximize the sum-rate. For the large dimension scenario and high-rank beamforming matrix, the accurate deterministic approximations from random matrix theory are then utilized to simplify the RIS-assisted MIMO systems. The asymptotical signal-to-interference-plus-noise ratio values obtained through random matrix theory is infinitely close to the theoretical limits calculated by accurately iteration. And the performance of the proposed algorithm computed via the sharing second-order channel statistics matches that of the RIS algorithm with sharing full channel state information asymptotically. The deterministic approximations are instrumental to get improvement into the structure of the optimal beamforming and to reduce the implementation complexity in large-scale MIMO system. Numerical simulations results are provided to evaluate and verify the accuracy of the asymptotic results obtained from the proposed algorithm in the finite system regime. With the complex operation process of large dimension matrix reducing to the deterministic approximations, a lower computational complexity can be obtained compared with other methods.

The emergence of novel AI technologies and increasingly portable wearable devices have introduced a wider range of more liberated avenues for communication and interaction between human and virtual environments. In this context, the expression of distinct emotions and movements by users may convey a variety of meanings. Consequently, an emerging challenge is how to automatically enhance the visual representation of such interactions. Here, a novel Generative Adversarial Network (GAN) based model, AACOGAN, is introduced to tackle this challenge effectively. AACOGAN model establishes a relationship between player interactions, object locations, and camera movements, subsequently generating camera shots that augment player immersion. Experimental results demonstrate that AACOGAN enhances the correlation between player interactions and camera trajectories by an average of 73%, and improves multi-focus scene quality up to 32.9%. Consequently, AACOGAN is established as an efficient and economical solution for generating camera shots appropriate for a wide range of interactive motions. Exemplary video footage can be found at https://youtu.be/Syrwbnpzgx8.

In next-generation wireless communications, reconfigurable intelligent surface (RIS) has emerged as a cost-effective technique for enhancing physical layer security (PLS) in millimeter-wave (mmWave) communications, especially under challenging scenarios with adversarial entities and obstructions. However, the primary studies for RIS incorporation in mmWave communication systems utilized static deployments, lacking adaptability and efficiency in complex environments. To address this problem, a dynamic RIS deployment design framework is introduced for PLS enhancement in mmWave systems against jamming and eavesdropping attacks. For the design, it is aimed to maximize the secrecy rate by jointly optimizing the RIS selection with the beamforming design. The resulting optimization problem is challenging to solve due to the coupling of the RIS control factor, joint beamforming design, and non-convex constraints. To tackle these issues, an efficient multi-RIS-aided PLS enhancement algorithm is proposed. It transforms the objective into a series of subproblems and employs the fractional programming technique and prox-linear block coordinate descent updating method to solve them alternatively and obtain the optimal solution. The simulations demonstrate the advantage of the dynamic deployment, which exhibits enhanced security performance with reduced complexity compared with benchmarks. Further examinations also provide insight into optimal RIS activation configurations, achieving optimal balance for securing mmWave communications against emerging threats while maintaining system efficiency.

Worker selection is critical to the success of federated learning, but issues such as inadequate incentives and poor-quality data can negatively impact the process. The existing studies have used the multi-weight subjective logic model, but it is vulnerable to malicious evaluation and unfair to newly added nodes. In this paper, the authors propose an improved reputation evaluation algorithm that allows evaluations from different sources to influence each other and reduce the impact of malicious comments. The authors’ approach effectively distinguishes between malicious and honest users and improves worker selection and collaboration in federated learning.

Nowadays, the digital development of marine ranching requires a communication system with wide coverage, high transmission rate and stable communication links. It is known that fixed-wing unmanned aerial vehicles (UAVs) have great advantages in long-range applications. They have the potential to serve as low-altitude communication platforms for maritime communication. In this study, with a developmental perspective, considering the intense growth of marine terminals in the future, a new clustering algorithm applied to cluster nonorthogonal multiple access (C-NOMA) is proposed and its advantages are investigated. In addition, considering the limited energy of marine terminals, combining the wireless power communication (WPC) technology for the UAV to charge terminals, the charging and communication time are optimized with the Lagrange multiplier method and the bisection search method. After completing the above optimization content of charging and communication, combined with the optimization results, it is found the trajectory that maximizes the energy efficiency of the UAV with the convex optimization technique. Experimental results show that the proposed clustering algorithm has good throughput performance, better fairness and lower algorithm complexity, and the proposed trajectory optimization scheme has better energy efficiency.