This work presents an automated, intelligent methodology for optimizing power amplifier (PA) design by predicting the most suitable circuit topology-specifically, the input and output matching networks-for a given high electron mobility transistor (HEMT). A classification-based bidirectional long short-term memory (BiLSTM) deep neural network (DNN) is trained to determine the optimal PA topology, while multi-objective Pareto front-based optimization techniques refine the network’s hyperparameters, including the number of hidden layers and neurons. The proposed approach is adaptable to various HEMT models and is validated through the design and optimization of high-performance PAs using lumped elements and transmission lines, operating within the $1-2 \mathrm{GHz}$ frequency range. The method is demonstrated using the Cree CGH40010 GaN HEMT on a Rogers RO4350B substrate, achieving a power output of approximately 40 dBm, a power-added efficiency (PAE) of at least 50%, and a power gain exceeding 10dB.

Hyperdimensional Computing (HDC) has emerged as a hardware-efficient paradigm for embedded malware detection, offering strong parallelism and low complexity. However, the accuracy and robustness of HDC classifiers remain highly dependent on the diversity and quality of training data, leaving them vulnerable to novel threats. To address this challenge, we introduce a generative adversarial network (GAN)-assisted augmentation framework for the Microprocessor without Interlocked Pipelined Stages-32 (MIPS32) malware generation. The GAN is trained on real-world MIPS32 malware binaries to produce previously unseen instruction sequences. The synthetic code stacks are filtered using a custom MIPS32 assembler for syntactic validation and a Bidirectional Long Short-Term Memory (BiLSTM)-based semantic critic to ensure logical coherence. Only validated samples are retained to expand the training set for the HDC classifier, thereby strengthening generalization and resilience against novel malware variants. Our preliminary results show an average generator loss ($\mathbf{G}$) of 2.68 over 200 epochs and a discriminator loss (D) converging to 0.63, indicating that the GAN is learning to generate realistic and diverse outputs. This hybrid GAN-BiLSTM-HDC framework shows strong potential for enhancing classification accuracy, resilience, and efficiency in resource-constrained, real-time malware detection systems.

Abstract Within the evolving landscape of fifth‐generation (5G) wireless networks, the introduction of network‐slicing protocols has become pivotal, enabling the accommodation of diverse application needs while fortifying defences against potential security breaches. This study endeavours to construct a comprehensive network‐slicing model integrated with an attack detection system within the 5G framework. Leveraging software‐defined networking (SDN) along with deep learning techniques, this approach seeks to fortify security measures while optimizing network performance. This undertaking introduces network slicing predicated on SDN with the OpenFlow protocol and Ryu control technology, complemented by a neural network model for attack detection using deep learning methodologies. Additionally, the proposed convolutional neural networks‐long short‐term memory approach demonstrates superiority over conventional ML algorithms, signifying its potential for real‐time attack detection. Evaluation of the proposed system using a 5G dataset showcases an impressive accuracy of 99%, surpassing previous studies, and affirming the efficacy of the approach. Moreover, network slicing significantly enhances quality of service by segmenting services based on bandwidth. Future research will concentrate on real‐world implementation, encompassing diverse dataset evaluations, and assessing the model's adaptability across varied scenarios.

Wireless technologies are growing unprecedentedly with the advent and increasing popularity of wireless services worldwide. With the advancement in technology, profound techniques can potentially improve the performance of wireless networks. Besides, the advancement of artificial intelligence (AI) enables systems to make intelligent decisions, automation, data analysis, insights, predictive capabilities, learning, and adaptation. A sophisticated AI will be required for next-generation wireless networks to automate information delivery between smart applications simultaneously. AI technologies, such as machines and deep learning techniques, have attained tremendous success in many applications in recent years. Hances, researchers in academia and industry have turned their attention to the advanced development of AI-enabled wireless networks. This paper comprehensively surveys AI technologies for different wireless networks with various applications. Moreover, we present various AI-enabled applications that exploit the power of AI to enable the desired evolution of wireless networks. Besides, the challenges of unsolved research in this area, which represent the future research trends of AI-enabled wireless networks, are discussed in detail. We provide several suggestions and solutions that help wireless networks be more intelligent and sophisticated to handle complicated problems. In summary, this paper can help researchers deeply understand the up-to-the-minute wireless network designs based on AI technologies and identify interesting unsolved issues to be pursued in their research in a fast way.

Accurately classifying regions based on Wi-Fi signals can be a difficult task, especially when considering different frequency values. In this study, we aimed to improve the accuracy of indoor localization by developing a novel approach that does not rely on pre-trained models. To achieve this, fingerprints from the IEEE 802.11az standard were randomly selected, and the data samples were trained using parameterized station characteristics and neural network hyperparameters. The impact of each parameter on the localization accuracy was measured, and performance monitoring metrics such as F1-Measure and confusion matrix-based metrics were evaluated. Furthermore, the Thompson sampling (TS) algorithm was employed to determine the optimal parameters, which helped to achieve the best possible accuracy. The proposed approach demonstrated improved accuracy in region localization compared to conventional heuristic approaches which typically yield an accuracy range of 65% to 77%. The proposed approach achieved up to 80% accuracy in region localization and could be a promising solution for indoor localization in various settings.

The wireless communication systems are expecting to include the high-end service quality for the users and customers. In the last decade, the industry and academia are studying effectively on the sixth generation (6G) systems, since the high performance network connectivity will influence on the number of use cases and also varied applications. For this case, new challenging requirements with key specifications need to be considered deeply for fulfilling the technical drawbacks. In the very recently published papers, it is recognized that researchers focus on solving various problems appeared in the communication systems through advanced optimization methods based on the artificial intelligence (AI). This study devotes to summarize newly and recently published studies where they use optimization and AI-based approaches for tacking their problems related to the communication systems. This review will help readers to discover the suitable AI-based approach for their own challenges.

Biomedical implanted devices are typically used for interacting with organs and/or for investigating various physiological signals. Hence, enhanced performance devices for clinical uses have got the attention of researchers. In this study, a multi-band implanted microstrip antenna suitable for transmitting/receiving biomedical signals in the Industrial, Scientific and Medical (ISM) frequency bands is presented. The antenna is built on a bio-compatible substrate, as titanium dioxide (TiO2) with relative permittivity of 95. The ground plane is thought to be a bio-metallic implant located within a bone. The proposed antenna is compact in size, 14 × 18 × 1.6 mm3, and works in both 2.45 GHz and 5.8 GHz centered frequency bands. It is designed and optimized considering the actual biological tissues as bone, muscle, fat, and skin surroundings. The simulation results referring to a planar stratification prove that the multiband single microstrip antenna is working properly within the human body and it can be used for medical communication services.