IET Communications最新文献

This paper investigates a multi-UAV cargo delivery scenario, where each UAV picks up goods from one location and delivers them to another destination while maintaining connectivity with the ground cellular network. Optimizing task assignment and UAV trajectory design to minimize completion time under the constraints is a significant challenge. To address this, the approach is structured into two principal phases. First, Dijkstra's algorithm is utilized to derive the shortest paths between points while ensuring communication connectivity meets specific quality constraints. Second, these paths are integrated with a novel hybrid optimization algorithm fusing a genetic algorithm and an ant colony algorithm to solve the coupled task assignment and route planning problem subject to communication and payload limitations. The hybrid approach efficiently balances exploration and exploitation, leading to superior task allocation and route planning. Numerical results show that our proposed method is effective in balancing task allocation and reducing overall completion time by comparing it with other integrated optimization techniques.

Nowadays, the vehicular ad hoc network (VANET) technology is used to improve the quality of transportation systems and road safety between vehicles (Vs). The main routing challenges in VANETs include their dynamic and unstable structure, the energy limitations of the Vs, and the use of intermediate vs. Clustering is used to balance the overhead, increase lifetime, and enhance data collection in VANETs. Finding the optimal cluster head (CH) is an NP-hard problem, and heuristic and metaheuristic methods are often employed to solve it. In this paper, we propose a method for routing and optimal CH selection among all. In each cycle and across all diverse VANETs, various Vs features are first collected, and then, using a heuristic method, the fuzzy inference system (FIS), the optimization fitness function (OFF) value of all Vs is calculated to determine the optimal CHs. Additionally, the metaheuristic moth flame optimization (MFO) algorithm is used to tune and set the coefficients and rules of the FIS. Finally, to train and test VANET behaviour patterns across various topologies, decision trees (DTs) based on the random forest (RF) ensemble machine learning (ML) method are utilized. Simulation results show that the proposed method outperforms clustering-based routing protocols such as low energy adaptive clustering hierarchy (LEACH), ad hoc on-demand distance vector (AODV), dedicated short-range communications (DSRC), cluster-based routing protocol (CBRP), and greedy perimeter stateless routing (GPSR) in VANETs in terms of the number of alive and dead Vs, average network lifetime, routing overhead, end-to-end delay, throughput, packet delivery rate, and execution time.

With the increasing demand for high-quality, computing-intensive mobile services, the next-generation networks must provide users with instantly available and sufficient computational resources. As a promising solution to this challenge, Unmanned Aerial Vehicle-assisted Mobile Edge Computing (UAV-assisted MEC) has gained significant attention in recent years. However, due to the limited energy available to the user equipment and UAVs, minimising the energy consumption remains a significant challenge. This article tackles the problem of energy-efficient joint resource management and UAV trajectory optimisation in such networks by incorporating the Non-Orthogonal Multiple Access (NOMA). To solve this non-convex optimisation problem, it is divided into two sub-problems, and the optimal solution to the main problem is then obtained by iteratively solving these two sub-problems. According to the simulation results, incorporating Non-Orthogonal Multiple Access (NOMA) method achieves a significant reduction of 44.44% in the overall utility function of the optimisation problem.

Spectrum maps are visualization tools that reflect the underlying spectral environment, enabling advanced functions such as spectrum decision-making and emitter identification. To enhance mapping accuracy and optimize resource utilization, this study addresses the sensor node selection problem in ground-based sensing scenarios. We propose an entropy-greedy node selection (EGNS) framework that employs a two-stage scheduling strategy: the first stage performs coarse sensing via spatial sector partitioning to obtain an initial estimate of emitter locations, and the second stage executes an enhanced greedy selection algorithm to iteratively minimize the signal reconstruction error. Simulation results on real-world spectrum datasets show that the proposed method achieves superior reconstruction accuracy and lower sensing costs compared to conventional sampling approaches, making it well-suited for dynamic electromagnetic monitoring applications under constrained budgets.

Accurate channel acquisition remains a fundamental challenge in terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) communication systems, primarily due to near-field propagation effects and the large-scale nature of antenna arrays. To address the limitations of existing compressed sensing and deep learning-based approaches, a novel framework referred to as transformer-U-Net acquisition network (TU-AcqNet) is proposed. This hybrid model integrates multi-head self-attention-based transformer encoders with a U-Net-inspired decoder to jointly capture global pilot signal dependencies and reconstruct high-resolution estimates of channel parameters The novelty lies in its dual-stage architecture that combines temporal sequence modelling with spatial feature reconstruction, enabling highly accurate channel parameter estimation even under sparse pilot constraints. TU-AcqNet emphasizes dominant pilot features through attention mechanisms and estimates key channel parameters—including angles of arrival, path distances, and complex path gains—with high resolution. The proposed scheme achieves a normalized mean square error (NMSE) improvement of up to 6 dB and increases spectral efficiency by more than 4 bits/s/Hz in high signal-to-noise ratio scenarios. Overall, the framework yields up to 80% reduction in NMSE relative to state-of-the-art baselines, highlighting its potential for practical deployment in next-generation THz UM-MIMO systems.

Automatic modulation recognition holds significant application value in dynamic spectrum access, electromagnetic spectrum monitoring, and communication security. However, existing deep learning methods commonly suffer from parameter redundancy and high computational complexity, severely limiting deployment efficiency on resource-constrained devices. To address this, we propose a dual-path spatio-temporal joint network (DSJ-Net) featuring a dual-path feature fusion architecture for joint spatial-temporal learning: 1) A multi-branch spatial feature extraction module employs multi-scale feature fusion via 1D convolutional layers and grouped convolution to process high-order amplitude/phase sequences, enhancing spatial feature discriminability while reducing parameters; 2) A hierarchical temporal dynamics module captures time-varying characteristics of enhanced in-phase/quadrature sequences signals through gated recurrent units. Evaluated on the public RadioML2016.10B dataset, DSJ-Net contains only 12,002 parameters, achieves 92.4% average recognition accuracy at SNRs ≥0 dB, reduces parameters by 83% compared to baseline models, and improves classification performance by 1.3 percentage points.

With the evolution of wireless communication technologies towards the sixth generation (6G) mobile communication system, the space-air-ground-sea integrated network architecture has emerged as a critical development direction for achieving global seamless coverage. Focusing on the unmanned aerial vehicle (UAV)-to-ship maritime communication scenario within this network framework, a three-dimensional (3D) geometry-based stochastic model is proposed. The model adopts a combined structure of elliptical and cylindrical components to comprehensively characterize multipath propagation mechanisms, including line-of-sight, sea surface reflection, as well as single-bounced and double-bounced components. By introducing the wave equation of sea surface to establish the 3D motion trajectory model of the ship and integrating it with the 3D rotational motion model of the UAV, the time-varying propagation distance-induced channel non-stationarity is accurately captured. Based on this model, key statistical characteristics such as the space-time-frequency correlation function (STF-CF) and Doppler power spectral density are derived. Furthermore, the impacts of sea surface wind speed, UAV rotation, ship oscillation, and ship size on channel statistical properties and space-time non-stationarity are thoroughly analysed. These numerical results provide theoretical foundations for the design and performance optimization of UAV-assisted communication systems in complex maritime environments.

Dense radio frequency identification (RFID) networks suffer from severe reader collisions, redundant reads, and inefficient resource utilization, particularly in large-scale deployments. Existing approaches, including centralized and hybrid scheduling schemes, fail to scale effectively due to their reliance on global coordination and static allocation mechanisms. This paper presents NEMO (neighbourhood-aware efficient management and optimization), a fully decentralized neighbourhood-aware RFID network framework that dynamically optimizes scheduling, power control, and frequency allocation without requiring global coordination. NEMO leverages a novel adaptive scheduling mechanism to mitigate collisions while ensuring fair and efficient tag interrogation. Extensive universal software radio peripheral-based hardware experiments and large-scale simulations with up to 5000 readers and 1,000,000 tags demonstrate that NEMO outperforms state-of-the-art protocols by achieving 25% higher throughput, 30% fewer collisions, 40% reduction in redundant reads, and improved energy efficiency by 18%. Additionally, NEMO exhibits scalability and robustness under extreme network congestion by maintaining high performance even as the numbers of readers and tags increase. The proposed framework is highly applicable to real-world RFID deployments in warehouses, logistics, smart retail, Internet of Things, and industrial automation, where dense RFID environments demand efficient, adaptive, and decentralized resource management.

To tackle the challenge of recognizing highly similar interleaved signals, this paper proposes a novel recognition method leveraging a spatial dimensional upgrading transformation squeeze and excitation (SD-SE) block. Unlike traditional attention mechanisms reliant on internal back-propagation, SD-SE block offers enhanced controllability and effectiveness. It introduces SD-SE, a pre-weighted training network, using a lightweight, manually controllable approach. The block addresses issues of initial weight randomness and incomplete signal characteristic utilization in attention mechanisms. Furthermore, SD-SE mitigates CNN's tendency to lose key information during feature extraction. Experiments show SD-SE achieves superior accuracy in recognizing highly similar interleaved signals, resolving challenges in complex signal recognition. Notably, this lightweight module is compatible with various attention networks, broadening its applicability.

As a new type of supply chain (SC) based on “Internet plus Innovation”, crowdsourcing supply chain (CSC) emphasizes mass participation and personalized demands more than traditional SC. Most of the current CSC systems are based on a centralized structure. With the development of crowdsourcing business, problems such as single point of failure, malicious data leakage or fairness are prone to occur. Deploying the CSC system onto the decentralized blockchain can solve the above problems to a certain extent. However, deploying CSC applications on the blockchain is facing issues like service matching efficiency and new security concerns. In this paper, a novel CSC platform is proposed based on ontology and blockchain. The matching of tasks and candidate workers is automatically achieved by designing some ontologies and semantic web rule language (SWRL) rules. The quality of the submitted solutions can be effectively evaluated by the proposed improved confidence-weighted voting algorithm and semi-monopoly dividend algorithm. To better ensure data confidentiality and identity anonymity, a task-matching privacy protection algorithm combining ontology with proxy re-encryption bilinear pairing technology is proposed. Finally, a software prototype is implemented on the Ethereum public test network by using the CSC dataset. The experimental results show that the time cost of the proposed scheme is within an acceptable range, while the gas consumption is saved by approximately 15%–25%.