International Journal of Communication Systems最新文献_第10页

AI-Driven Performance Forecasting of Metasurface-Enhanced Two-Port DRA for mm-Wave Applications 用于毫米波应用的超表面增强双端口DRA的ai驱动性能预测

IF 1.8 4区计算机科学

International Journal of Communication Systems Pub Date : 2026-06-01 DOI: 10.1002/dac.70528

Shweta Shree, Amit Prakash

{"title":"AI-Driven Performance Forecasting of Metasurface-Enhanced Two-Port DRA for mm-Wave Applications","authors":"Shweta Shree, Amit Prakash","doi":"10.1002/dac.70528","DOIUrl":"https://doi.org/10.1002/dac.70528","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper describes the design, fabrication, and experimental validation of a metasurface-suspended two-port dielectric resonator antenna (DRA) with integrated machine learning (ML)–based performance prediction for millimeter-wave applications. The dielectric resonator's circular polarization is excited by an asymmetrical plus-shaped aperture, and strong interport isolation is ensured by an orthogonal port arrangement. The antenna directivity and gain are increased by adding a double-negative (DNG) metasurface superstrate. The suggested design concurrently achieves better gain (≈13 dBi), larger impedance bandwidth (1.65 GHz), and improved port isolation (> 30 dB) in comparison to previously published mm-wave dielectric MIMO antennas, indicating a significant overall performance increase. The |S<sub>11</sub>| response of the antenna is successfully predicted using two ML approaches: Artificial Neural Network (ANN) and Random Forest (RF). The design is validated by measuring a manufactured prototype. The suggested antenna is a strong contender for n257/n258 mm-wave 5G communication systems because it works in the 32.18–33.83 GHz range with steady circular polarization and excellent MIMO diversity features.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 10","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148173907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Optimized Transformer–BiLSTM Framework for RF-Based Drone Detection in Noisy Wireless Communication Systems 噪声无线通信系统中基于射频的无人机检测优化变压器- bilstm框架

IF 1.8 4区计算机科学

International Journal of Communication Systems Pub Date : 2026-05-29 DOI: 10.1002/dac.70530

S. R. Sam Kingsley, V. R. S. Mani

{"title":"An Optimized Transformer–BiLSTM Framework for RF-Based Drone Detection in Noisy Wireless Communication Systems","authors":"S. R. Sam Kingsley, V. R. S. Mani","doi":"10.1002/dac.70530","DOIUrl":"https://doi.org/10.1002/dac.70530","url":null,"abstract":"<div>\u0000 \u0000 <p>The fast emergence of unmanned aerial vehicles (UAVs) has increased significant security and privacy concerns. Radio frequency (RF)-based drone detection has emerged as an emerging solution because of its potential in operating independently of lighting conditions and line-of-sight constraints. However, RF signals captured in real-time environments are generally affected by interference, noise, and complex temporal dynamics. This makes accurate drone detection a challenging task. To tackle these problems, this work presents an optimization-based hybrid deep learning (DL) model, Transformer-Bidirectional Long Short-Term Memory (BiLSTM) model for robust RF-based drone detection and classification. In the suggested work, RF signals are first transformed into time-frequency representations by short-time Fourier transform (STFT). A Transformer encoder with multi-head self-attention is presented for capturing long-range temporal dependencies and the BiLSTM captures sequential characteristics of RF signals. To further improve detection performance, the Enhanced Nutcracker Optimization Algorithm (ENOA) is integrated for optimizing major hyperparameters of the model. Experimental analysis on noisy drone RF signal datasets shows that the suggested model outperformed existing DL on the basis of different measures. The outcome confirms the effectiveness of the suggested model for reliable RF-based drone detection in noisy electromagnetic environments.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 10","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent Energy-Aware Routing in Wireless Sensor Networks: A Survey of Deep Reinforcement Learning Approaches 无线传感器网络中的智能能量感知路由：深度强化学习方法综述

IF 1.8 4区计算机科学

International Journal of Communication Systems Pub Date : 2026-05-28 DOI: 10.1002/dac.70523

Aditi Sharma, Rita Mahajan, Deepak Bagai

{"title":"Intelligent Energy-Aware Routing in Wireless Sensor Networks: A Survey of Deep Reinforcement Learning Approaches","authors":"Aditi Sharma, Rita Mahajan, Deepak Bagai","doi":"10.1002/dac.70523","DOIUrl":"https://doi.org/10.1002/dac.70523","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless sensor networks (WSNs) are increasingly required to operate efficiently under stringent energy and scalability constraints. Conventional routing and clustering protocols often face limitations in large-scale and dynamically changing network environments, particularly when traffic patterns and topology vary over time. Deep reinforcement learning (DRL) has emerged as a promising paradigm for intelligent routing optimization. It integrates deep neural network-based feature representation with adaptive decision-making mechanisms. This survey provides a comprehensive review of DRL-based energy-efficient routing approaches in WSNs, covering prominent learning architectures such as deep Q-networks (DQNs), double DQN (DDQN), deep deterministic policy gradient (DDPG), proximal policy optimization (PPO), and actor–critic frameworks. Existing methods are systematically categorized based on their learning paradigms, optimization objectives, and network design assumptions. A comparative analysis with classical routing protocols is presented in terms of energy consumption, network lifetime, convergence behavior, and scalability. In addition, a statistical assessment of recent literature highlights key research trends in DRL-based energy-efficient routing that provides insight into the growing adoption of specific learning models and application domains. In closing, this survey identifies open challenges and outlines future directions for developing sustainable, adaptive, and energy-aware routing frameworks for next-generation IoT sensor networks.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 10","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 1.8 4区计算机科学

International Journal of Communication Systems Pub Date : 2026-05-25

引用次数: 0

IF 1.8 4区计算机科学

International Journal of Communication Systems Pub Date : 2026-05-25

引用次数: 0

IF 1.8 4区计算机科学

International Journal of Communication Systems Pub Date : 2026-05-24

引用次数: 0

IF 1.8 4区计算机科学

International Journal of Communication Systems Pub Date : 2026-05-22