IET Communications最新文献

This study presents the design and optimization of a microstrip monopole antenna for 5G sub-6 GHz applications, employing a deep learning-based surrogate model combined with honeybee mating optimization (HBMO). The studied antenna structure employs air via arrays, intended to enhance antenna performance, including improved impedance matching and increased bandwidth. It is important to note that, unlike conventional antennas, the proposed design does not include a fully enclosed metallic cavity similar to a substrate integrated waveguide (SIW) antenna designs. A sensitivity analysis was conducted to assess the impact of these parameters, emphasizing the need for optimal tuning. To generate training and test datasets efficiently, Latin hypercube sampling (LHS) was used. A convolutional neural network (CNN) surrogate model was trained, outperforming other machine learning (ML) algorithms in predictive accuracy and generalization. The proposed CNN-HBMO framework reduced computational costs by minimizing the need for expensive electromagnetic (EM) simulations, enabling rapid design space exploration. The optimized antenna was fabricated and validated through experimental measurements, achieving 2–3 dBi gain and 𝑆₁₁ < −10 dB across the 2.7–5.2 GHz band. Compared to existing designs, the proposed antenna offers a compact size (34 × 34 mm) with competitive performance, making it suitable for multi-band 5G applications.

Reconfigurable intelligent surface (RIS)-assisted received spatial modulation (RIS-RSM) has emerged as a promising technique to enhance spectral and energy efficiency in next-generation wireless systems. However, performing accurate signal detection without channel state information (CSI) remains a critical challenge, particularly in blind detection scenarios. In this paper, we propose a novel clustering-based blind detector named energy-tiered structure initialization (ETSI). The proposed method exploits the amplitude heterogeneity of modulation symbols—originating from the unequal energy levels of QAM or hybrid constellations—by partitioning the signal space into multiple energy tiers. Each receive antenna is associated with a structure prototype, representing a normalized statistical channel pattern, which is initialized using the distribution of the underlying fading model (e.g., Rayleigh) rather than instantaneous CSI. During clustering, these prototypes are iteratively refined through tier-wise averaging of normalized signal samples, thereby enforcing structural consistency and mitigating amplitude-induced bias. After convergence, the constellation-aligned cluster centres are reconstructed by combining the updated prototypes with their corresponding modulation amplitudes, inherently enabling the joint detection of both the modulation symbol and the RIS-assisted spatial index. Simulation results show that ETSI achieves around 0.5–1 dB SNR gain over amplitude–phase aware clustering under 8PSK, and about 1–1.5 dB improvement under 16QAM, while outperforming the CSI-based greedy detector (GD) across both modulations. Moreover, ETSI achieves BER performance close to that of the perfect-CSI maximum likelihood detector, confirming its accuracy, scalability and practical feasibility for blind RIS-RSM detection.

Reconfigurable intelligent surface (RIS) holds potential for enhancing spectral efficiency (SE) and energy efficiency (EE). Nevertheless, the variations in these two metrics are not synchronous. Additionally, acquiring precise channel state information (CSI) presents substantial difficulties in actual implementations. In this work, an RIS-aided multi-user network with imperfect CSI is investigated. Correspondingly, we focus on balancing SE and EE through the collaborative design of the base station (BS)'s precoding matrix and the RIS's phase shifts, which consider the transmit power budget and the channel uncertainties constraints. The non-convexity of the joint optimization problem motivates the use of two-stage AO, which decouples the problem into tractable subproblems for BS beamforming and RIS phase-shift design, ensuring convergence to a locally optimal solution. In the outer-stage problem, the quadratic transformation (QT) method is applied to convert the fractional objective into a linear form. By introducing auxiliary variables, a closed-form solution is obtained. In the inner-stage problem, by employing successive convex approximation (SCA), introducing auxiliary variables, and utilizing S-procedure, the SE with uncertain channels is transformed into a concave form and further converted into equivalent linear matrix inequalities (LMIs). Simulation results illustrate that the proposed design effectively attains a favorable tradeoff in both SE and EE, and demonstrate certain superiority compared to the baseline.

As a cornerstone of the internet of things, massive grant-free random multiple access (MGFRMA) has attracted wide attention in recent years. However, due to lack of coordination, it is difficult to remove access collisions among randomly activated users, which makes the existing solutions unsatisfactory. To solve the issue, this paper introduces cognitive radio (CR) and preamble delay (PD) to reduce access collisions, together with non-orthogonal multiple access (NOMA) to improve spectrum efficiency, and then develops a CR-NOMA-PD-based MGFRMA scheme. First, a three-step MGFRMA protocol is advanced. It helps users to obtain the channel occupancy state before uplink transmission with spectrum sensing so as to consciously avoid access conflicts. Then, an optimisation strategy for jointly selecting access channel and power level is designed according to the sensing results. The competitive transmission scheme with PD is formulated, based on which uplink signals are well modelled. Finally, a multi-user detection algorithm involving channel filtering, power level detection, preamble detection, and data recovery is proposed. The performance of the CR-NOMA-PD-based MGFRMA scheme is also analysed and simulated. Simulation results indicate that the proposed scheme improves the access ratio of users and system overload capacity significantly compared to the existing schemes. It also has better robustness and scalability.

Channel state information (CSI) is pivotal for assuring high performances of wireless communication systems. In particular, multiple-input multiple-output transmission is only beneficial when CSI is known. A large number of subcarriers are desired in Orthogonal Frequency Division Multiplex (OFDM) systems to boost overall throughput, which makes channel estimation a more challenging task, especially to extract channel features in a more dynamic environment without causing a significant overhead transmission. Conventional least squares-based methods are affected by the noise and interference that inherently exist in the acquired data for processing. We proposed the deep neural network (DNN)-based method to estimate CSI, and one distinguishing characteristic is to adopt a Discrete Fourier Transform (DFT) operation-based method to mitigate the impact of noise before carrying out the DNN procedure; hence, the accuracy of the learning outcome significantly improved. The effectiveness of the proposed scheme is verified with simulations under a variety of propagation scenarios. The proposed method has demonstrated a high performance for channel estimation. It has shown a particular advantage in more dynamic and noisy environments for wireless communications.

The sixth-generation (6G) mobile network is expected to trigger an unprecedented surge in data traffic. unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising paradigm for emergency communications, yet dynamic user mobility and heterogeneous task demands pose severe challenges. Existing reinforcement learning (RL) methods (such as proximal policy optimization (PPO)), while effective in continuous control, often suffer from unstable convergence and neglect task-priority differentiation, leading to excessive delays for critical tasks. To address these issues, we propose a novel KM-PPO framework that integrates interference-aware k-means clustering with PPO. The clustering stage aggregates users by spatial distribution and task urgency, yielding an interference-suppressed initial UAV deployment. The trajectory optimization stage employs PPO with clipped probability ratios, which serve to constrain policy updates and maintain stability, and a priority-sensitive reward function, enabling UAVs to adaptively adjust motion trajectories under dynamic conditions. Compared with the baseline algorithms of k-means integrated with twin delayed deep deterministic policy gradient (KM-TD3) and k-means integrated with soft actor-critic (KM-SAC), our method reduces average system latency by 15.9% and 24.6%, respectively, while achieving an 86.7% success rate for high-priority tasks. These results demonstrate that KM-PPO not only ensures stable convergence in the environments but also guarantees quality of service (QoS) for mission-critical tasks, highlighting viability for UAV MEC deployments.

Although specific emitter identification (SEI) networks based on deep learning (DL) have made a great improvement on signal classification tasks, they still show vulnerability when faced with adversarial examples due to the close proximity of different classes samples in the feature space learned by DL model. To counter this, a novel loss function named inter-class distance loss (ICD loss) is proposed and a joint defense framework based on ICD loss and denoising autoencoder (DAE) is implemented to defense against the adversarial examples. Specifically, ICD loss tries to force the model to learn a feature space where the feature clusters for each class is maximally separated from the clusters of other classes and DAE is used to filter out the perturbation in adversarial examples. Experimental results show that the joint defense frame is effective enough to defend against adversarial samples when uses 8 classes ADS-B radiation source signals as the dataset, with a 13% and 70% higher accuracy than the defense based on prototype conformity loss (PC loss) and the model without defense in the classification tasks when attacks occur, respectively.

In this paper, we investigate the anti-jamming communication challenge for unmanned aerial vehicles (UAVs) in urban environments with strong jammers. Jamming power often far exceeds the UAVs' inherent anti-jamming capability threshold, causing anti-jamming measures to fail and even interrupt normal communication. To address this challenge, we propose an innovative strategy that leverages the natural shielding effect of urban buildings to enhance the anti-jamming performance of UAV communication links. The core of this strategy lies in leveraging multiple UAVs working collaboratively to form an end-to-end anti-jamming communication network in the urban environment. Specifically, we first introduce a UAV formation control mechanism for end-to-end collaboration—‘resonant motion’ transmission. Second, we propose an anti-jamming algorithm for urban environments with strong jammers, combining ‘resonant motion’ transmission with the artificial potential field (APF) algorithm and rapidly exploring random tree star (RRT*) to develop a novel anti-jamming path planning algorithm. Finally, we leverage prior knowledge of jammers, UAV formation and urban environment to enable UAV formation to evade obstacles and strong jammers in urban environment, find optimal communication positions and thereby build more robust communication links. The anti-jamming strategy proposed in this paper provides a practical new approach to addressing the technical challenge of difficult UAV communication in urban environment with strong jammers. Simulation experiments demonstrate that UAVs can effectively address the challenge of UAV formation in urban environment through collaborative operations and intelligent algorithms, achieving reliable end-to-end transmission for UAV formation, outperforming traditional algorithms in both anti-jamming performance and energy consumption.

Owing to the growing Internet-of-things (IoT) infrastructure and the vast amounts of data involved, the demands of the IoT ecosystem are growing. Sixth generation (6G) networks are essential for meeting these demands. Due to its ability to provide high data rates with extended network coverage, visible light communication (VLC) in 6G optical networks is attracting more attention. The primary issue, though, is that these VLC networks are susceptible to signal obstructions that lower line-of-sight (LoS) link quality. Intelligent reflecting surfaces (IRSs), which provide improved non-LoS channel gains, are used in the optical domain to get around this. This paper presents a novel framework for the IRS-aided VLC system and optimizes it for maximum data rate. The mathematical formulations for the presented optimization methodology is also provided. Further, an association algorithm is proposed that associates each IRS element with each transmitter-receiver pair. The time-space complexity analysis is also carried out. It is observed that the proposed association scheme increases the achievable data rate in the IRS-assisted VLC network by 7%. For various transmit powers $�P_T$, the effect of increasing the number of user nodes and blockages in the system model on the achievable data rate is assessed. Additionally, the suggested association scheme and random association scheme are used to analyse the outage performance of the IRS-assisted VLC system. At $�P_T$ of 10 W, the suggested association outperforms random association by 46.6% in outage performance. Finally, an IRS-assisted VLC system use case scenario for indoor communication in a corporate office is also covered.

Multiple reconfigurable intelligent surface (RIS)-assisted rate-splitting multiple access (RSMA) systems have been extensively studied. In this paper, we propose a method to minimise the system's power consumption. First, we formulate an optimisation problem to minimise the system's power, using the base station's precoding vectors, common rate allocation, and phase shifts of RISs as optimisation parameters, with the user's rate requirement and phase shift of RIS as constraints. Next, the optimisation problem is decomposed into two subproblems: optimising the base station's precoding vectors and common rate allocation, and optimising the phase shifts of RISs. These parameters are then alternately optimised using an iterative approach. In each iteration, the base station's precoding vectors and common rate allocation are optimised using successive convex approximation (SCA), while the phase shifts of RISs are optimised using semidefinite relaxation (SDR). Simulation results demonstrate that the system's minimum power decreases as the number of RISs increases, and the proposed scheme achieves lower power consumption compared to existing methods in the same scenario.