International Journal of Communication Systems最新文献

This paper presents a linearly polarized, ground-defected 2 × 2 rectangular patch antenna array with microstrip inset feeding. A simple structure is realized by placing the rectangular patches and the feeding network on the same layer. The proposed microstrip inset-fed configuration facilitates proper impedance matching and easy array formation. This work provides an in-depth examination of the antenna array's characteristics and performance. The antenna array is fabricated on an RT Duroid substrate and integrates a dumbbell-shaped defected ground structure (DGS) in a triangular form with four radiating elements. The primary objective of incorporating the DGS is to improve the return loss. The proposed compact antenna array has overall dimensions of 106.3 × 112 × 1.575 mm³ and is intended for wireless communication applications. The designed array operates over the desired frequency band of 3.52–3.61 GHz, achieving a reflection coefficient better than −10 dB and an impedance bandwidth of 25.09%. The antenna exhibits stable radiation characteristics with a peak gain of approximately 13.08 dBi across the operating band. The array is analyzed using HFSS, and fabrication and measurements are carried out to validate the simulated results. The measured outcomes show good agreement with the simulations, demonstrating the suitability of the proposed antenna for 5G midband mobile broadband wireless communication applications.

Deep learning-based automatic modulation recognition (AMR) techniques are particularly well-suited to facilitate the development of non-cooperative communication systems, providing a robust foundation for the automatic processing of complex communication signals. However, existing models for AMR often fail to capture fine-grained temporal features and exhibit limited robustness against noisy or adversarial perturbations. To address these challenges, we introduce Tr-AMR, a robust transformer-based framework designed for high-accuracy AMR. The core of Tr-AMR is an enhanced architecture that integrates gated attention units and a feed-forward network (FFN) equipped with gated linear units activated by Gaussian error linear units, replacing the transformer's original self-attention mechanism and FFN components. These strategies not only significantly enhance the model's ability to capture intricate temporal patterns embedded in signals but also improve its capacity to extract global information through patch segmentation, position embeddings, and class embeddings, thereby enabling accurate recognition of in-phase and quadrature signal modulation types. The results of validation experiments on multiple datasets demonstrate that Tr-AMR outperforms all baseline models across all metrics, highlighting its superior performance.

This article is focused on miniaturized, reconfigurable UWB monopole antenna loaded with metasurface for directional and multiband. This work is carried out in two stages. In the first stage, the radiating patch is modified to achieve both narrowband and wideband frequency ranges. In the second stage, a metasurface reflector is incorporated to achieve directional radiation and enhanced gain in all configurations. The miniaturized antenna is 20 mm $��\times �$ 20 mm $��\times �$ 1.6 mm in size. This proposed antenna achieved the nine frequency bands of range 1.4–1.9, 2.5–3.2, 2.5–4.5, 2.4–5.4, 2.6–10.36, 4.2–10.0, 5.4–6.1, 5.8–10.06, and 7.3–9.5 GHz. For the realization of reconfigurable nine bands, two PIN diodes were introduced in the radiator structures. After integrating the metasurface reflector, the antenna operates over a wide bandwidth from 1.4 to 13 GHz. The unit cell of metasurface reflector is $��9�\times �9�$ array, which is in the square shape with slot in the middle; this metasurface reflector is situated in 5 mm above the patch antenna. The placement of the metasurface above the antenna improves the gain significantly, by approximately 3–4 dB in all states, and makes the radiation pattern directional. The efficiency of the antenna is 98.8% overall and in minimum efficiency around 84%. This compact antenna is simulated on HFSS software with evidence that all the antenna parameters $��{�S�}_{�11�}�$, radiation pattern, SCD, gain, and radiation efficiency are up to the mark. This antenna is well suited for V2X and automotive radar platforms, because of directional property and high gain and multiband feature support various application like wireless communication systems, satellite links, and intelligent transport systems.

Wireless sensor networks (WSNs) are essential to cyber–physical systems with their multiple hop, self-organized arrangement of mobile sensor components. WSNs sense, aggregate, process, and transfer data across a geographical network area, ultimately forwarding it to the network's head. Nevertheless, existing intrusion detection systems (IDS) for WSNs face various limitations such as less detection rates, high computational costs, and significant false alarm rates due to resource constraints, redundancy, and data correlation. To address these challenges, a self-attention–based cycle-consistent generative adversarial network (SACCGAN), enhanced using reptile search algorithm for intrusion detection in WSN environments (SACCGAN-RSA-IDS-WSN), is proposed. The data are collected through WSN-DS dataset and supplied to the preprocessing stage. Preprocessing involves adjusted quick shift phase preserving dynamic range compression (AQS-PDC) to address missing values and remove redundant data. The feature selection is done by Pelican optimization algorithm (POA) to identify ideal features. Utilizing the selected features, SACCGAN categorizes WSN data into normal and anomalous instances, including blackhole, grayhole, flooding, and scheduling attacks. However, SACCGAN lacks adaptation of optimization modes to ensure accurate WSN intrusion detection. Therefore, the reptile search algorithm (RSA) is introduced to enhance SACCGAN parameters effectively. The proposed SACCGAN-RSA-IDS-WSN is executed in Python using WSN-DS database. The SACCGAN-RSA-IDS-WSN approach is examined under some metrics like recall, precision, F-measure, specificity, accuracy, RoC, and computation time. The SACCGAN-RSA-IDS-WSN method attains 28.59%, 31.43%, and 30.18% higher accuracy; 33.45%, 37.67%, and 27.78% higher ROC; 31.73%, 33.49%, and 29.15% lower computational time when compared with existing techniques.

As the demand for high-performance computing and low-latency applications escalates in wireless metropolitan area networks (WMANs), efficient task offloading and resource management have become increasingly critical. This article presents a framework for decision-maker satisfaction in WMAN cloudlet computing optimization, addressing the challenge of balancing multiple objectives such as delay, energy consumption, and deployment cost in dynamic WMAN environments. Our method employs a variable-length multi-objective whale optimization integrated with differential evolution (VL-WIDE) to tackle the optimization problem, dynamically adjusting to the WMAN environment while optimizing delay, energy consumption (both user device and cloudlet), and cloudlet deployment cost. The analytical hierarchy process (AHP) is incorporated to balance the relative importance of these multiple criteria based on decision-maker preferences. Comprehensive time-series evaluations demonstrate that our framework significantly outperforms conventional systems and other benchmark algorithms, with VL-WIDE and AHP achieving the lowest root mean square error (RMSE) of 16.7, a 5.1% improvement over random selection. The proposed approach reduced execution and wireless delays by 40% (0.00015 s vs. 0.00025 s) compared to random selection, decreased mobile device energy consumption by 36.9% (118.69 Jol vs. 188.17 Jol), and demonstrated a 33.3% reduction in average renting rates compared to other algorithms. These results indicate that our proposed framework provides high satisfaction to decision-makers while significantly enhancing user experience in metropolitan areas, offering a robust solution to cloudlet-based computing optimization in WMANs and enabling higher efficiency and performance across multiple critical metrics. However, while the framework shows promising results, it is important to note that real-world implementation may face challenges not fully addressed in this study, such as network dynamics, scalability issues, and potential security concerns.

Wireless sensor networks (WSNs) require routing mechanisms that are both energy-efficient and resilient to security threats. While numerous studies have explored trust-based routing, optimization algorithms, and intrusion detection individually, their integration has often remained loosely coupled. This paper presents an adaptive secure routing framework that tightly integrates Learning Dynamic Deterministic Finite Automata (LDDFA), trust-based routing, and a hybrid Moth Flame–Firefly optimization (MFO–FA) algorithm under a unified decision layer. Unlike traditional hybrid models, the proposed framework enables bidirectional interaction: Trust metrics and KNN-based intrusion detection feedback dynamically influence route learning and optimization parameters. This co-adaptive design enhances both the security responsiveness and the energy efficiency of WSNs. Simulation results on various network scales demonstrate that the proposed model achieves superior performance in energy consumption, network lifetime, packet delivery ratio, and end-to-end delay, outperforming recent metaheuristic and trust-based routing protocols. The study thus contributes a novel cross-coupled optimization–security framework for WSNs.

The integration of software-defined networking (SDN) with the underwater acoustic sensor network (UASN) has introduced a more centralized and flexible approach to managing underwater sensor nodes. Existing methods of path planning and object detection models have challenges like limited network lifetime and resource constraints that affect efficient path planning and inaccurate object detection. UWSN performance is improved by SDN by providing flexible and centralized management, which can lead to simplified configuration, high energy efficiency, and better adaptability. This advantage makes the proposed model integrate SDN with UWSN; therefore, better energy efficiency can be achieved during path planning and object detection. In UWSN, the traffic flow and network configuration are supported by the SDN control panel. Initially, the nodes are localized in the network scenario using a simplified Kalman filter (SiKaF). Then, the efficient path planning among the AUV and base station is performed using the proposed improved reinforcement learning (ImRL) model. In this, the loss function optimization is employed using the novel adaptive mother optimization (AdMo) algorithm. Finally, using the gathered data, object detection is employed at the base station using the advanced support vector machine (AdSVM) model. AdSVM is designed with skip-GRU-based temporal feature extraction and cascaded auto-encoder–based spatial feature extraction with SVM-based classification. The extracted spatial and temporal features are concatenated together and fed into the SVM for performing the object detection task. The proposed method acquired an accuracy, IoU, dice coefficient, and MSE of 99.28, 99.14, 0.92, and 0.04, respectively, for the object detection task. During path planning, the energy consumption of the proposed ImRL is 20.573, throughput is 3.022, latency is 2.1 ms, and network lifetime is 21.318.

This paper presents the design and implementation of a high-gain 1 × 2 active microstrip patch antenna array for sub-6-GHz 5G wireless communication applications. Three structures set apart the modified conventional patch design: an octagonal conductive patch embedded in a square frame and a circular ring. This patch shows improvements in critical parameters such as radiation efficiency, gain, impedance matching, directivity, bandwidth, and half-power beamwidth. With a bandwidth of 200 MHz, an HPBW of 44°, and a radiation efficiency of 82.3%, the measured results show that the suggested passive antenna array may achieve maximum gain and directivity of 6.1 and 7.27 dBi, respectively. The antenna array combines a new coupling-loss compensation method, an RF amplifier, and a redesigned circular patch construction. With passive components, such as an RF splitter and an RF combiner, the two antenna elements are connected to a common amplifier via 50-Ω arc transmission coupling lines. The methodology used in its development includes compact antenna geometry, prototype construction of a low-loss substrate, and experimental verification. Improvements in impedance matching, bandwidth, gain, and radiation efficiency can be achieved by combining amplifier coupling with structural optimization. With a maximum gain of 18.15 dBi, directivity of 19.2 dBi, bandwidth of 320 MHz, HPBW of 73°, and radiation efficiency of 94.7%, the experimental findings show a 99.9% increase in matching efficiency. The excellent radiation characteristics of the active antenna array establish an efficient pathway for advancing fifth-generation wireless communication systems, such as those used in tablets.

Wireless sensor networks (WSNs) are vulnerable to serious security risks because of their open communication settings, decentralized architecture, and resource constraints. To address these challenges, a novel Deep Learning Fuzzy-based Trust-aware Routing Protocol (DLF-TRP) is proposed. This model integrates long short-term memory (LSTM) networks and fuzzy C-means clustering to enhance node- and path-level security. The LSTM module monitors node behavior and assigns anomaly scores, which are processed using fuzzy C-means clustering to classify nodes into trust-based groups. This fuzzy-based trust evaluation allows for dynamic and context-aware trust score assignment, accommodating uncertainty and partial information in WSN environments. DLF-TRP performs intelligent path selection by favoring routes with high-trust nodes while avoiding suspicious or potentially malicious ones. The proposed FLLC (fuzzy C-means + LSTM + enhanced LEACH-C) routing model enhances secure and energy-efficient data transmission in WSNs. Simulation results show a 37.8% increase in network lifetime, 7.65% improvement in PDR, and 14.8% reduction in delay compared to existing DL-GMA and EEDLCR protocols. The model ensures adaptive trust evaluation and efficient cluster-head selection for robust performance.