IET Communications最新文献

This paper introduces a novel, dual-band, compact 1:4 feed network employing a parallel power divider architecture designed to operate at 0.915 and 2.44 GHz, covering both industrial, scientific, and medical and ultra high frequency bands. The design leverages the non-linear phase characteristics of composite right/left-handed transmission lines to achieve dual-band functionality with high precision. Simulation results confirm the efficacy of the proposed network, which delivers quadrature-phase outputs with a 90° phase shift and uniform power distribution across all output ports, facilitating wideband circular polarisation in array antenna applications. Compared to traditional series power dividers, the parallel power divider offers significant advantages in terms of fabrication simplicity, reduced size, and lower manufacturing costs. The design avoids the use of non-radiating composite right/left-handed transmission lines and addresses impedance-matching challenges through the implementation of only three resistors, effectively isolating the output ports. The proposed architecture is highly scalable and can be easily adapted to various output port configurations, frequencies, and power division ratios, offering broad flexibility for a wide range of microwave applications.

Wireless sensor networks (WSNs) is a wireless system including the set of distributed sensor nodes used for physical or environmental observation. A network energy expenditure is considered as a significant concern because of battery restricted sensors of the WSN. Clustering and multi hop routing are considered as effective approaches to enhance the network lifecycle and communication. Achieving the anticipated objective of reducing the energy expenditure, thereby increasing the network lifecycle, is considered as an optimisation issue. In recent times, a nature inspired meta-heuristic approaches are extensively utilised for solving the different optimisation issues. In this context, this research aims to accomplish the objective by proposing the multiobjective-perturbed learning and mutation strategy based artificial rabbits optimisation namely M-PMARO for an optimum cluster head (CH) selection and route discovery. The proposed M-PMARO incorporates an experience based perturbed learning (EPL) and mutation strategy to identify the capable regions over the search space for enhancing the exploration and avoiding the local optima issue. To formulate the multiobjective, the residual energy, average intracluster distance, average base station (BS) distance, CH balancing factor (CHBF) and node centrality are incorporated for optimum CH discovery while the residual energy and average BS distance are considered for multi hop routing. The M-PMARO is analysed based on alive nodes, dead nodes, energy expenditure, throughput and data received in BS and network lifecycle. The viability of M-PMARO is validated by comparing it with existing approaches such as fitness based glowworm swarm with fruitfly algorithm (FGF), energy balanced particle swarm optimisation (EBPSO), improved bat optimisation algorithm (IBOA), graph neural network (GNN) and fuzzy logic and particle swarm optimisation (PSO) based clustering routing protocol namely PFCRE. The alive node count of M-PMARO is 100 for 1200 rounds, which is higher than the EBPSO.

This study proposed a complex fraud detection framework called CRAFIC (complex relationship analysis for fraud identification and cost management), which combines deep learning and graph neural networks to address complex fraud behaviours such as multi account collaborative fraud. The study used the DataCo Global supply chain dataset and the IEEE-CIS fraud detection dataset to extract order features using convolutional neural networks and long short term memory networks, and analysed the relationships between orders using graph attention networks to reveal complex fraud patterns. The results show that the CRAFIC framework performs well in both single order and collaborative fraud detection tasks. In single order fraud detection, the accuracy of the CRAFIC framework increased from the initial 45.21% to 93.75%, and the loss value decreased from 1.19 to 0.14, significantly better than other models. In collaborative fraud detection, the accuracy of the CRAFIC framework reached 90.3%, once again surpassing other models. These results validate the advantages of the CRAFIC framework in multimodal data fusion and complex relationship modelling. The CRAFIC framework reveals complex fraud patterns, optimizes internal controls and audit processes, enhances data security measures, prevents system vulnerabilities from being exploited, and enhances market reputation and customer trust.

The development of reconfigurable intelligent surface (RIS) makes direction-of-arrival (DOA) estimation possible for single-antenna receivers. However, non-ideal situations such as spectral aliasing occur when facing communication signals using orthogonal frequency division multiplexing modulation. This paper proposes a RIS-based single-channel DOA estimation method for communication signals. Specifically, by extending the time intervals to dynamically reduce the RIS state change rate, a real-time DOA estimation is achieved while mitigating the impact of non-ideal spectral shifts on communication. Then, based on the compressed sensing and mutual incoherence property, the method exploits the sparse property of the signal in space to reduce the estimation time while improving the estimation accuracy. Simulation results show an $��86�\%�$ reduction in computation time for the proposed method compared to the traditional CVX tool. Additionally, the estimation accuracy is as low as $��0.02�deg�$. To verify the practicality and robustness, we develop a prototype system and conduct extensive experiments. The results are consistent with our theoretical analysis, and the method proposed in this paper can realize single-path DOA estimation with an accuracy of less than $��0.1�deg�$ within $��0.4276��s�$. More excitingly, the presented experimental platform achieves DOA estimation for two coherent paths.

Satellite communications have emerged as a promising extension of terrestrial networks in future 6G network research due to their extensive coverage in remote areas and their ability to support the increasing traffic rate and heterogeneous networks. Like other wireless communication technologies, satellite signals are transmitted in a shared medium, making them vulnerable to attacks such as eavesdropping, jamming, and spoofing. A good candidate to overcome these issues is physical layer security (PLS), which utilizes physical layer characteristics to provide security, mainly due to its suitability for resource-limited devices such as satellites and IoT devices. This paper provides a comprehensive and up-to-date review of PLS solutions to secure satellite communication. Main satellite applications are classified into five domains: satellite-terrestrial, satellite-based IoT, satellite navigation systems, FSO-based, and inter-satellite. In each domain, how PLS can improve the overall security of the system, preserve desirable security properties, and resist widespread attacks are discussed and investigated. Finally, some gaps in the related literature are highlight and open research problems, including uplink secrecy techniques, smart threat models, authentication and integrity techniques, PLS for inter-satellite links, and machine learning-based PLS, are discussed.

Inter-cell interference and smart jammer attacks significantly impair the performance of non-orthogonal multiple access (NOMA) networks. This issue is particularly critical when considering strategic interactions with malicious actors. To address this challenge, the power allocation problem is framed in a two-cell NOMA network as a sequential game. In this game, each base station acts as a leader, choosing a power allocation strategy, while the smart jammer acts as a follower, reacting optimally to the base stations' choices. To address this multi-agent scenario, four multi-agent reinforcement learning algorithms are proposed: Q-learning based unselfish (QLU), deep QLU, hot booting deep QLU, and decreased state deep QLU. A game-theoretic analysis that demonstrates the algorithms' convergence to the optimal network-wide strategy with high probability is provided. Simulation results further confirm the superiority of our proposed algorithms compared to the Q-learning-based selfish NOMA power allocation method.

Accurate signal-to-noise ratio (SNR) estimation is critical in wireless communication systems as it directly impacts system performance and the assessment of signal quality. Recent advances in deep learning-based SNR estimation have significantly improved estimation accuracy in low SNR conditions. This paper presents a novel deep learning approach that uses a power spectrum generated through overlapping segmentation as input to a neural network for SNR estimation. The performance of SNR estimation has been enhanced by integrating an augmented squeeze-and-excitation (SE) attention mechanism with a residual block fusion module, employing multiple residual structures, and deepening the network architecture. To validate the efficacy of this method, extensive simulation experiments were conducted under various scenarios, including additive white Gaussian noise (AWGN), Rayleigh, and Rician channel conditions. The results demonstrate that this method outperforms state-of-the-art techniques in high SNR environments and across diverse channel conditions. Furthermore, there is only minimal performance degradation under low signal-to-noise ratio conditions.

As the guaranteed basis for providing communication services, the power communication network plays a vital role in the smart grid. However, during natural disasters, wired communication networks have inherent limitations and come with substantial construction and maintenance costs, which makes it difficult to function effectively. Therefore, it is imperative to apply wireless communication to smart grids and power communication networks in emergency scenarios. To solve the problems of spectrum resource scarcity and insufficient spectrum utilization in wireless communication, the integration of cognitive radio networks (CRNs) into smart grids and power communication networks is considered, which can effectively solve the problems and promote their development. Based on the deep reinforcement learning (DRL) and federated learning (FL) algorithms, this paper proposes a novel dynamic spectrum sharing framework which is applied to smart grids and power communication networks in emergency scenarios. In the proposed framework, the maximum entropy based multi-agent actor-critic (ME-MAAC) algorithm is used as the local learning model, which can not only improve system performance but also help power users to choose an optimum dynamic spectrum sharing strategy. It can be seen from the simulation results that the proposed scheme has better performance in reward value, access rate, and convergence speed.

Nowadays, wireless mesh networks (WMNs) are rapidly spreading around the world due to their competitive advantages. Beyond this, their adaptability is evident in supporting a wide range of applications: from powering broadband home networks and educational programs to driving healthcare advancements, simplifying building automation systems, assisting in expedited rescue operations, and even serving military needs. The routing within WMNs plays a pivotal role in enhancing reliability and optimizing performance. Notably, WMNs can consist of several cascading channels, however, the literature lacks routing algorithms based on the concept of information entropy for these channels. This work introduces an information-entropy-based algorithm specifically designed for wireless cascaded channels within WMNs. The proposed algorithm aims to enhance bandwidth in WMNs by constructing routes based on the maximum value of entropy of the cascading binary channel. This algorithm builds a route with the maximum value of the path entropy and bandwidth in WMNs. Furthermore, this study validates the applicability and competitiveness of the proposed method through a comparative analysis against established algorithms like Dijkstra, ACO, OLSR, AODV, and a previous method by the authors.

To better provide fast computing services, vehicular edge computing can improve the quality of service and quality of experience for intelligent transportation in 6G by reducing task transmission delay. However, vehicular edge networks face network capability limitations and privacy issues in practice. High-speed vehicles and the time-varying environment make them unpredictable. In the meantime, smart vehicles with distinct computation capabilities need to process various tasks with different resource requirements, which will inevitably cause untimely task offloading and massive energy consumption. This paper proposes to use the space-air-ground integrated network with blockchain to enhance the network capability and the privacy protection of vehicular edge networks. The digital twin is taken to better capture the dynamic characteristics of vehicles and the entire environment. The urgency level is introduced to meet the delay requirements of different tasks, while considering the impact of digital twin deviation on task offloading. Moreover, the selection algorithm and the task distribution algorithm based on the improved genetic algorithmare are proposed to obtain the optimal offloading strategy. Simulation results demonstrate that, compared with the existing algorithms, the proposed scheme can maximize the system utility while diminishing the total time for task processing.