IRO Journal on Sustainable Wireless Systems
Classification of RF Signal Using Deep Learning
Automatic Modulation Classification (AMC) is a fundamental component of intelligent wireless communication systems that guarantees robust recognition of modulation types in complex Radio Frequency (RF) channels. This research proposes an end-to-end RF communication model based on a Convolutional Long Short-Term Memory Deep Neural Network (CLDNN) for AMC and communication performance estimation. The RadioML 2016.10a dataset including eleven modulation types was normalized and trained via a novel hybrid CLDNN network which consists of feature extraction module using convolution and temporal sequences learning model. The proposed framework implements not only the classical classification procedure but also synthesis of I/Q signals, their transmission through the TCP channel, and receiver performance evaluation via Bit Error Rate (BER). The obtained experimental results showed 62.48% validation accuracy at 10 dB SNR, while signal reconstruction guaranteed the real-world application of the proposed approach.
Automatic Modulation Classification (AMC)
Convolutional Long Short-Term Memory Deep Neural Network (CLDNN)
Intelligent Wireless Communication Systems
Hybrid Feature Selection with Adaptive Threshold Optimization for Efficient Machine Learning–based Intrusion Detection
With the increasing use of computer networks and internet services, network security becomes crucially important. The existing intrusion detection methods are based on signature analysis or rules, but these methods are inefficient against zero-day attacks and evolving cyber threats. In order to overcome the disadvantages of existing solutions, this study proposes a novel IDS framework with hybrid feature selection method and adaptive threshold optimization. The IDS under consideration uses the UNSW-NB15 dataset. First, the data pre-processing algorithms such as encoding, scaling and class balancing are used. Hybrid feature selection is then implemented by means of a combination of chi-squared filtering and Particle Swarm Optimization (PSO). Several supervised learning algorithms, including K-Nearest Neighbors, Decision Tree, Logistic Regression and Random Forest are used for training and testing. Moreover, Adaptive Threshold Testing Algorithm (ATTA) is applied for dynamic optimization of decision thresholds. The results show that this solution considerably increases IDS efficiency and decreases false positive rates.
Metamaterial-Enhanced Compact MIMO Antenna for 6G Vehicular and IoT Communications
6G wireless communication enables ultrareliable, low latency, and high data rate services for various applications like vehicular communication, Internet of Things (IoT), and Extended Reality (ER). On the other hand, the antenna design for millimeter wave (mmWave) and terahertz (THz) frequencies encounters certain challenges such as limited bandwidth, reduced gain, and mutual coupling effect in MIMO systems. Therefore, this research proposes a new MIMO antenna using a metamaterial, which is suitable for 6G technology. In this case, the concept of metamaterial is used in a superstrate with a microstrip MIMO antenna for enhanced antenna performance. The 2×2 MIMO antenna is designed and studied using the software CST Microwave Studio, where important parameters like return loss, Voltage Standing Wave Ratio (VSWR), gain, efficiency, and Envelope Correlation Coefficient (ECC) were investigated. The simulation results show a significant performance improvement when compared with the standard microstrip antenna in terms of bandwidth and the reduction of mutual coupling effect.
Internet of Things (IoT) and Mutual Coupling Reduction
PG-GAT based Channel Estimation for Enhanced OFDM Performance
Accurate channel estimation is essential in enhancing the performance and spectral efficiency of an Orthogonal Frequency Division Multiplexing (OFDM) system especially in high mobility environment. This paper presents a Physics-Guided Graph Attention Network (PG-GAT mini v2) to be used in pilot-based channel estimation through combining the concepts of wireless channel physics and graph attention learning. Multi-head graph attention, residual learning, and Layer Normalization are used in order to optimize initial channel estimates by means of LS interpolation as well as to ensure stability of training procedure. Performance of the method is estimated in terms of NMSE, BER and standard deviation in the conditions of the high mobility Rayleigh fading channel with the carrier frequency of 2 GHz, SNRs from 0 to 30 dB and receiver velocities from 0 to 300 km/h. According to experimental results, PG-GAT mini v2 decreases the total NMSE from 0.7846 to 0.3079, which corresponds to 60.76% performance gain compared to LS interpolation.
OFDM (Orthogonal Frequency Division Multiplexing)
AI-Powered Digital Twin Framework for Real-Time Smart City Infrastructure Optimization
A smart city requires efficient management and coordination of connected urban infrastructures, which could effectively cope with the rising requirements on energy, water, transport, and buildings. The traditional methods of infrastructure management do not allow making proper predictions due to independent functioning in different spheres. This paper proposes UrbanMirror – a novel smart city framework that integrates Digital Twin technique, Physics-Informed Neural Networks (PINNs), and Swarm Optimization Reinforcement Learning (SwarmOpt-RL) engine in order to provide real-time infrastructure monitoring, prediction, and optimizationMulti-agent SwarmOpt-RL approach is applied for coordination of decision-making process within the domains of energy, water, mobility, and buildings, whereas the role of the coordinator (Meta-Agent) in cross-domain optimization and resource allocation is fulfilled by the Meta-Agent. The proposed framework was tested using a large-scale smart city simulation setting including up to 50,000 infrastructure nodes interconnected. Experimental results show significant improvement of operational efficiency, resource consumption, sustainability, and infrastructure resilience, having achieved the overall predictive accuracy of 95.5%. Scalability analysis proves the applicability of the developed.
Corner Perturbation-Enabled Dual-Resonant Circularly Polarized Antenna for Resilient UAV Networks
In this paper, the development process of a miniature dual-band CP (circularly polarized) microstrip patch antenna is presented for wireless onboard communication in unmanned aerial vehicles (UAVs). It is designed to work in 2.4GHz and 5.2GHz ISM (industrial, scientific, and medical) Wi-Fi bands for achieving high spectral efficiency and communication reliability for critical UAV applications like autonomous monitoring, swarm operations, and emergency communications. The CP radiation technique is used in order to eliminate polarization loss as it is sensitive to the different orientations of aerial platforms. The proposed antenna is composed of an integrated U-slot and two stubs and has asymmetrical corners for obtaining orthogonal modal excitation required for CP operation. A DGS (defected ground structure) cell is also embedded to extend the bandwidth range. The proposed antenna is made up of a FR-4 dielectric substrate, with a compact footprint of 48 × 39 × 1.6 mm³. According to electromagnetic modeling studies, the highest realized gain for the proposed antenna occurs at 3.29 dBi at 2.4 GHz and 2.54 dBi at 5.2 GHz, with impedance bandwidths of 163 MHz and 600 MHz, respectively.
A Comprehensive Review of Semiconductor and Optical Wireless Communication Technologies for 5G and 6G Systems
The rapid evolution of wireless communication systems calls for ever-increasing data rate, reduced latency, enhanced spectral efficiency and dependable connectivity of many mobile devices. The transition from fifth generation (5G) communication systems to sixth generation (6G) necessitates advancements in semiconductor technology and novel approaches in communication. This paper reviews some of the semiconductor materials and optical wireless communication systems suitable for future communications systems. Some of the semiconductor materials that can be used in future communication systems include silicon (Si), silicon-germanium (SiGe), gallium nitride (GaN), gallium arsenide (GaAs), and indium phosphide (InP). These materials have been selected for this study considering their suitability in applications of high frequency and power. Also, emerging materials and future devices are considered in the discussion. Optical wireless communication technologies reviewed in this paper include visible light communication (VLC), light fidelity (LiFi), and free-space optical (FSO) communication technologies. Future communication technologies face several challenges including device scaling, heat dissipation, and complex systems integration. This paper also addresses possible future research areas for future communication systems including hybrid radio frequency-optical networks and intelligent communication systems.
Copyright (c) 2026 IRO Journal on Sustainable Wireless Systems
A Compact Multiband Microstrip Antenna with Metamaterial Loading for Simultaneous Band Control and Gain Enhancement in 5G IoT Systems
A small multiband microstrip antenna loaded with metamaterial elements is proposed in this paper for band control and gain improvement for 5G IoT applications. The antenna is based on a planar microstrip structure with split ring resonators. The metamaterial loading alters the effective permittivity and permeability and creates additional resonances. This allows for the controlled design of multiple bands in the sub-6 GHz frequency range. The antenna is designed using an FR4 substrate with a compact structure. The proposed design shows that the reflection coefficient values are found to be below −10 dB at 2.45 GHz, 3.50 GHz, 5.20 GHz, and 6.70 GHz at the operating bands, and the VSWR is less than 2 at these frequency bands. The gain is improved because of the use of metamaterials. The radiation pattern is consistent at various frequencies. The design is free of complex structures and additional components. It is shown that the antenna is suitable for multiband 5G and IoT applications.
Geo-Fencing Based Cattle Monitoring System Using LoRa
The current research work proposes a geo-fencing-based cattle monitoring system that uses LoRa (Long-Range) technology for livestock tracking and boundary monitoring in smart agriculture applications. In the proposed monitoring system, RSSI is used to calculate the distance from the base station to the cattle collar unit. Based on the distance from the base station, the movement of the cattle is categorized as falling within the safe, warning, and critical zone regions. In the proposed geo-fencing-based monitoring system, ESP32 microcontroller, LoRa communication module, GPS, GSM, and buzzer are used to provide effective long-range communication and real-time alerting capabilities. In order to conserve energy, the GPS sensor remains off except when there is a need to determine whether the cattle have moved out of the specified geo-fencing region. Experimental testing revealed that the proposed monitoring system can communicate effectively up to 1 km range while ensuring low packet losses and power efficiency with about 93% geo-fencing accuracy. About 35% of battery-saving efficiency was achieved due to conditional GPS operation.
Advancing Healthcare with IoT: Digital Twins and AI-Driven Cardiac Diagnostics
Healthcare monitoring techniques have gone through substantial evolution over the years, and IoT coupled with Digital Twin technology represents one of the most promising ways towards providing quality healthcare services in a more patient-centric manner. In this regard, the current study proposes to design a real-time cardiac monitoring solution based on ESP32 microcontroller, AD8232 electrocardiogram sensor, MAX30100 pulse oximetry and heart rate sensor, as well as DS18B20 digital temperature sensor. Data about the vital signs will be uploaded to the cloud and used to create a Digital Twin that will be constantly updated with the new information. Healthcare specialists can interact with the digital twin to analyze historical information, run different simulations of possible health problems and come up with customized treatment strategies without having direct contact with a patient. Programming will be done in the Arduino environment, data will be stored and analyzed using Firebase Firestore, and the web application interface will use React platform. Preliminary results indicate that the suggested approach can dramatically decrease time needed to deliver a response after the abnormal cardiac condition occurs, as well as decrease in-person visit frequency required by patients from rural or underdeveloped areas.
Pole-Mounted Distribution Transformers in Rural Areas During Disaster Times Using Wireless Communication
The pole-mounted transformer is very important when distributing electricity to rural areas since these transformers are exposed to unfavorable environmental conditions. In case of disasters such as cyclones, floods, and earthquakes, the transformer can easily be affected by problems such as oil leakage, overload of electricity, fire danger, and pole failure. Conventional monitoring of these transformers is conducted manually and the process is inefficient in terms of cost and time in remote and disaster-ridden environments. This research describes the development of an Internet of Things (IoT)-based system for monitoring the health status of pole-mounted transformers using wireless technology. This system comprises various types of sensors such as MQ sensors for oil leakages, voltage and current sensors for the flow of electricity, flame sensors to detect fire, and MEMS sensors to measure pole tilt and vibration. The data obtained from the sensors is transmitted by ESP32 microcontroller through the HC-12 wireless transmitter module to the remote monitoring center.
Design and Optimization of RFID Antennas for Enhanced Read Range and Efficiency
RFID (Radio Frequency Identification) is used in various applications such as logistics and tracking in IoT-based devices with limited factors such as low read range and poor efficiency caused by improper design of the antenna. This paper discusses the design and optimization of a microstrip patch antenna at 915 MHz to overcome such limitations. The performance parameters such as return loss (S11), voltage standing wave ratio (VSWR), gain and radiation pattern are studied using simulation techniques. The optimized antenna design shows improved performance of return loss is improved to -20 dB from -8 dB. Similarly, the VSWR is improved to 1.2 from 2.5. The gain is also improved to 5-7 dBi from 2 dBi. This shows that the design has improved performance of impedance matching and gain. This design is better than the traditional RFID antenna in impedance matching and gain. This design is structurally optimized. The read range is improved to 8-12 m from 3 m. This demonstrates that the design is both dependable and efficient performance. This design may be utilized in many applications including logistics and tracking in IoT-based devices.
AI-Driven Adaptive Differential Privacy Framework for Secure Location-Based Services
Location-Based Services (LBS) is an integral component of the modern digital environment, enabling the delivery of applications like smart city management and personalized mobile services. However, the service is challenged by privacy concerns due to the perpetual requirement for accurate spatial-temporal data. Although conventional privacy protection mechanisms like Differential Privacy (DP) offer strong theoretical guarantees of privacy, the approach is often based on a static privacy budget (ε) that does not offer satisfactory results when considering the data utility-privacy trade-off across various applications. In the present study, an adaptive Differential Privacy approach is proposed, enabled by the power of Artificial Intelligence (AI) technology. The approach allows the system to adapt the static privacy budget by adjusting the value of ε based on the contextual sensitivity of the query. Using the Random Forest algorithm, the system is able to evaluate the risks of the query by considering various parameters like the nature of the location, time factor, and user behavior. The experimental results based on a synthetic dataset for 5,000 location queries show that the proposed method significantly enhances the privacy utility trade-off. In specific terms, the proposed method reduces the success rate of inference attack by 49.2%, while utility is preserved with an increase in MAE by 63.7% for a controlled scenario. This demonstrates the applicability of the proposed method for adaptive and scalable privacy protection using ML and DP for LBS environments.
Microstrip Antenna with Low SAR for Wearable IoT Applications
This article discusses the methods used to design and analyze a small, flexible microstrip antenna for IoT devices. The antenna operates at 2.4 GHz, which is in the ISM band and is usually used for short-range communication. The radiating patch has a circular slot in it to acheive wide bandwidth and even current flow. To maintain stable antenna characteristics and prevent unwanted radiation from reaching the human body, a full ground plane is used. The bandwidth of the antenna is about 200 MHz, and the resonance frequency is 2.4 GHz with a return loss of about -28 dB. The radiation pattern of the antenna is quasi-omnidirectional with a maximum gain in the range of 3.2 to 3.5 dBi. The efficiency in open space is in the range of 80-85%, while when the antenna is in contact with the human body, it is more than 75%. The results of the bending analysis show that the performance of the antenna is almost unaffected by bending. Additionally, the SAR value is within the allowed range.
Green Wireless Communications: A Review of Sustainable Architectures and Protocols
The rapid growth of wireless communication systems and devices has increased global energy consumption in wireless communication networks. Therefore, wireless communication technologies are advancing towards the development of fifth-generation wireless communication systems and beyond, i.e., towards the development of sixth-generation wireless communication systems. It is important that the energy efficiency of wireless communication systems considered a significant parameter along with data rate, delay and reliability. Green wireless communication systems aim to reduce the power consumption of wireless communication systems minimize carbon emissions and make wireless communication systems environmentally sustainable. This paper presents a comprehensive review of sustainable wireless communication systems and energy-efficient protocols for green wireless communication systems. This study discusses various architectural strategies such as energy-efficient base stations, heterogeneous and ultra-dense networks, cloud radio access networks, adaptive networks, renewable energy and their role enables wireless networks to modify their behavior according to the nature of the data and availability of resources. Further, the paper discusses the role of sustainable wireless protocols in all layers of wireless communication, where techniques like power control, duty cycle, energy-efficient routing and transport protocols are playing a significant role in removing the unnecessary communication and utilizing resources efficiently. It also discusses enabling technologies that can be utilized in energy-efficient network operation including energy harvesting, cognitive radio, artificial intelligence, edge computing and network virtualization. Moreover, some of the major challenges in energy-efficient wireless communication including decisions, complexity, security, cost, and regulatory issues, are addressed. This study provides an sustainable wireless communication can be achieved based on recent developments and existing research gaps in wireless communication particularly in future 5G and 6G networks.
Design and Optimization of Metamaterial Antenna for Enhancing Satellite Communication
This paper presents the design and optimization of a multi-band antenna intended for use in satellite communications. The antenna consists of a pentagonal patch multi-band antenna that uses a metamaterial of split ring resonators to enhance its electromagnetic performance. Some disadvantages associated with conventional antennas include being large, having a limited bandwidth and producing high levels of signal reflection. The performance of the antenna is effective in the C, X, and Ku satellite communication bands. The proposed SRR-based pentagonal patch antenna has a low level of reflection with S11<-10dB; a gain of 4.96dB; and a VSWR<2 indicating that very little of the transmitted signal will be reflected back from the antenna. The radiation pattern of the proposed antenna is also very concentrated in 2D space, resulting in higher signal strength and lower interference. Ansys HFSS simulation of the proposed antenna design indicated potential applications for satellite internet, global positioning, remote sensing, and Internet of Things (IoT) systems. Because of its low weight, low cost, and scalable design characteristics, the proposed antenna is expected to be fully compatible with next generation satellite systems. Future improvements to the proposed antenna may include adaptive beamforming and tunable metamaterials to further enhance its satellite communication capabilities.
Analysis of SDN-Orchestrated Solutions for Heterogeneous IoT in 6G Networks
The sixth generation (6G) networks facilitate the provision of large-scale, heterogeneous Internet of Things (IoT) applications with a variety of requirements such as latency, reliability, scalability and data rates that are higher than the limits of traditional network architectures. The drawbacks of current network architectures for implementing heterogeneous IoT applications increased in dynamicity and complexity connected to these kinds of networks. SDN Solutions provide an effective approach to implement the heterogeneous IoT for 6G Networks. The purpose of this review is to provide a comprehensive overview of SDN-based architecture, orchestration mechanism and control strategies includes to connect and improve the performance of related technologies such as NFV, edge and fog computing, network slicing and AI. This paper will explain the SDN-based orchestration improves interoperability, resource allocation, quality of service and security for heterogeneous IoT use cases. This study concludes with future research directions for smart, autonomous and resilient SDN-enabled IoT systems in next-generation 6G Networks.
A Temporal-Aware Dynamic Graph Neural Network for Learning Evolving Graph Representations
Many computer science studies, including communication networks, software dependency graphs, transaction systems and knowledge graphs produce dynamic graphs in which nodes and edges are continuously updated. Standard Graph Neural Networks (GNNs) are mainly designed for static network architectures and do not accurately capture temporal dynamics or structural changes. When designing real-time dynamic systems, this issue includes data loss and low performance. To solve this issue, this study introduces a Temporal-Aware Dynamic Graph Neural Network (TDGNN) that generates both structural and temporal representations from building graphs. The method combines temporal encoding with graph convolution to maintain previous connections to change the new nodes and edges. A memory-based temporal aggregation method is provided to maintain long-term data knowledge without retraining the model from the initial stage. The experimental evaluation of standard dynamic graph datasets proposed TDGNN performs traditional static and snapshot-driven GNN models in terms of accuracy and stability. The results show that implementing temporal awareness significantly improves representation learning for dynamic graph data, make the proposed method suitable for next-generation graph-based software systems.
Evaluating CRC-8 Detection Performance in Cloud Traffic
The cloud infrastructure settings used to maintain security across distributed servers, lively and reliable data transport is necessary. Error detection techniques like Cyclic Redundancy Check (CRC) provides accuracy with low cost is important for increasing traffic volumes. This paper examines the binary format and functional role of the CRC-8 polynomial to keep data validity during server-to-server communication. The CRC identify the most common error patterns by this analysis. If it fails in some situations then sudden errors will match the generating polynomial. The performance of CRC has the detection accuracy, correction capability, bandwidth cost and relevance across various networking environments proposed in this paper compared to Forward Error Correction and Two-Dimensional Parity Checking. CRC receives 50,000 messages daily. A 1 KB payload with 16-bit CRC capability is included in every transfer. According to the test, CRC produces a 0.195% delay per message and needs 0.095MB of bandwidth. Furthermore, CRC prevents 2% of retransmissions by saving ten times more bandwidth. These result show that the polynomial provides high error detection with less resource usage is one of the most effective techniques for high-volume cloud traffic. However, FEC or hybrid algorithms are the most suitable for settings that require strong correction capabilities such as satellite connections, radio channels, or latency-sensitive systems.
Enhancing Wi-Fi Link-Layer Reliability for Real-Time Collaboration: A Simulation-Based Performance Evaluation
A reliable Wi-Fi network is required to enable real-time collaboration for situations like campus hackathons that participants utilize wireless connectivity for tasks like screen sharing and code synchronization. The consumers experience of missed content or duplicate updates suggests issues with the link layer rather than the physical layer, despite a variety of strong network signals. Basic processes that directly affect data consistency and integrity across different devices and operating systems are handled by the link layer including framing, error detection, flow control, acknowledgments and retransmissions. This study explains the influence of link-layer behavior on data dependability in dense heterogeneous Wi-Fi networks. It demonstrates that differences in driver implementation, retransmission policy and acknowledgment processing result in irregular content delivery. A WLAN simulation was built using Cisco Packet Tracer to simulate real-time data transport under various loads and interference conditions. The purpose of simulating the link layer services is to transmit content without duplication or corruption, the simulation examines packet retransmission, acknowledgment latency and frame loss. The results demonstrate that adjustments to retransmission limits, block ACK activation, and adaptive flow control design improve dependability. Wi-Fi 6/6E access points, QoS prioritization (IEEE 802.11e/WMM), and comparable NIC firmware versions on devices improve stability and lower latency. The study results, indicate that link-layer efficiency in wireless dependability is affected as much by signal strength as by other factors.
Smart Grid Topology and Error Detection in Smart Meter Networks using CRC
In smart grid infrastructure, where Smart Meters continuously send usage data to central servers for invoicing, monitoring, and optimization, data transmission reliability is crucial. However, transmission mistakes brought on by noise, interference, and signal attenuation can impair system performance. In this study, the Cyclic Redundancy Check (CRC) is used and tested as an error detection method in Smart Meter communication networks. In comparison to conventional parity approaches, CRC offers greater safety by using polynomial division to identify single-bit and burst mistakes with high accuracy. To investigate the operational efficacy, bandwidth effect, and error detection properties of CRC, a smart grid network was simulated using Cisco Packet Tracer. These findings confirm that CRC is a cost-effective, scalable, and safe method for ensuring dependable communication in wide-area smart grid configurations.
Design and Testing of an X-Band Microstrip Patch Antenna for Microwave Breast Imaging
Breast cancer continues to be the leading cause of death in women, indicating the need for effective, non-destructive, and radiation-free imaging technology. Microwave imaging is an adequate instrument with the potential to differentiate between cancerous and normal tissue based on contrast in dielectric properties. The design, simulation, fabrication, and measurement of a 7.97 GHz X-band microstrip patch antenna for biomedical high-resolution applications of microwave breast imaging are presented in this paper. The antenna is fabricated on jean material with a thickness of 1 mm as a wearable flexible substrate for biomedical applications. The return loss value and VSWR, calculated with Ansys HFSS Student Edition, are -26.3 dB and nearly 1, indicating good impedance matching as well as a lack of signal reflection. Operation at high frequencies offers better spatial resolution for more precise tumor detection. The new antenna will be simulated and tested at an experimental level to verify its effectiveness in practice. Future work will include optimization of depth penetration, bandwidth, and incorporation into a multi-element imaging system for diagnostic applications.
Metaheuristic based Data Management in IoT-Wireless Sensor Networks
The integration of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) has produced smart connected systems capable of collecting a large number of data and then making decisions based on that data. These systems encounter unique challenges such as limited power, excessive redundant data, and inefficient routing protocols. This review work provides solutions to these problems by employing metaheuristic-based algorithms that could better manage data in IoT-WSNs, focusing mainly on data fusion and reduction methods, finding the best route selection, and energy conservation. This study will also compare the most common metaheuristic-based algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Firefly Algorithm (FA) and it will discuss the different constraints which they may encounter.
A Comprehensive Introduction to Artificial Intelligence Techniques for Advanced Wireless Networks: 5G and Beyond
The fast development of wireless communication technologies, especially the move to the next generation of 6G wireless networks, has encouraged the demand for smart, flexible, and competent system design. Artificial Intelligence (AI) has become a game-changing enabler, providing robust solutions to handle the high level of complexity and dynamics in advanced wireless networks. This review paper introduces an in-depth overview of how AI methods can be used in the planning, optimization, and operation of 5G and beyond wireless networks. It offers a systematic inference about the incorporation of various AI approaches into major frameworks such as resource distribution, mobility management, network slicing, interference avoidance, and predictive maintenance. Moreover, the article discusses the difficulties associated with the application of AI-driven architectures in practice, pointing out concerns regarding data privacy, the interpretability of the models, computational load, and scalability. The review also presents emerging research directions and open challenges, aiming to provide researchers and practitioners with a foundational understanding of the integration of AI and next-generation wireless communication systems.
Development of Reflectarray Antenna with Twin Cut Resonator for High Gain X Band Applications
This paper presents a high-gain reflect array antenna design that utilizes a double-cut ring structure to enhance performance in terms of gain and bandwidth. Reflect array antennas are recognized for their lightweight, low-profile characteristics and capability to achieve high directivity. However, their performance is often constrained by the phase shift range and accuracy of the reflecting elements. To overcome these limitations, the proposed design incorporates an innovative double-cut ring element structure, which improves phase control across a broad frequency range, resulting in enhanced beam-steering capabilities and increased gain. The double-cut ring structure is optimized to achieve a substantial phase shift range, contributing to the antenna's high-gain performance by facilitating precise phase correction throughout the array. The proposed structure illustrates the effectiveness of the double-cut ring configuration as a viable solution for improving the gain and operational bandwidth of reflect array antennas. The maximum gain achieved is 44.5 dBi at 10 GHz, and the Voltage Standing Wave Ratio (VSWR) plot indicates a value of 1.04 at the resonant frequency of 10 GHz.
Feed Analysis of a Hexagonal Microstrip Patch Antenna
The feeding methods of coaxial, microstrip line, and proximity-coupled feeds significantly influence the performance of microstrip patch antennas. While proximity-coupled microstrip patch antennas have been explored for various applications, there remains a lack of understanding regarding the optimal positioning and length of the proximity-coupled feed. This study investigates the impact of feed line length on the performance of a proximity-coupled microstrip patch antenna. The findings reveal that the resonance frequency is directly affected by the length of the feed line; specifically, as the feed line length increases, the antenna resonates at a higher frequency.
Self-Powered RF Sensor Antenna for Implantable and Ingestible Medical Devices
Radio frequency (RF) devices such as antennas play a crucial role in implantable and ingestible medical devices (IMDs) by enabling wireless communication, energy harvesting, and localization links between transmitter (Tx) and receiver (Rx). Implantable and ingestible medical devices (IMDs) have transformed modern healthcare by enabling real-time monitoring and improving treatment outcomes. Radio frequency (RF) sensor antennas are key components in these devices and are widely used in biomedical applications for early diagnosis, treatment, and continuous health monitoring. RF-enabled IMDs monitor vital parameters such as temperature, blood pressure, glucose levels, oxygen saturation, pH, and blood composition. These antennas also support devices like pacemakers and defibrillators in managing cardiac conditions. In addition to human healthcare, they are applied in animal health monitoring and veterinary diagnostics. Implanted antennas detect physiological changes and wirelessly transmit data to external processing units, allowing real-time access for medical experts. This integration of RF sensing in IMDs enhances the ability to detect diseases early and deliver timely interventions, making them essential tools in both clinical and remote healthcare settings. This article presents two RF sensor antennas—FR-4 integrated (Ant-1) and miniaturized helical (Ant-2)—designed for implantable and ingestible medical devices, particularly in veterinary applications, operating in the 400–500 MHz range. Ant-1 is fabricated on an FR-4 substrate (51×28×1.6 mm³, ℇr = 4.4, tanδ = 0.002), resonating at 434 MHz with a bandwidth of 32.13 MHz and a total gain of approximately –36.92 dB without the phantom model and with the phantom in an acceptable range. Ant-2 is a miniaturized normal-mode helical antenna made of 0.9 mm diameter copper wire, also resonating at 434 MHz and both antennas integrate a surface acoustic wave (SAW) sensor to enhance wireless sensing. SAW sensors utilize acoustic waves on piezoelectric surfaces to detect parameters like temperature, pressure, strain, and displacement, enabling real-time, wireless physiological monitoring in biomedical applications.
Implantable and Ingestible Medical Devices (IMDs)
Impact of Design Parameters on FSO System under Rain Conditions
In wireless communication, FSO promises to provide high-speed communication under extremely vulnerable atmospheric conditions. This study evaluates the effect of system design parameters for moderate rain conditions by simulating the proposed model using Optisystem 22.0. In this work, the divergence angle and the aperture diameter were varied, and their effects on the system were observed in terms of the signal quality, Bits in error, and the receiver's optical power. The findings show that increasing the divergence angle degrades system performance, whereas an increase in the receiver’s aperture diameter improves the signal quality at the receiver side. The observed eye diagram reveals that the received signal quality is improved as it has less Inter-Symbol Interference.
BiLSTM based Precise Estimation of Rayleigh Fading Channel
Accurate wireless channel estimation is essential for optimizing communication systems, particularly in 5G and future wireless networks. This study explores a deep learning-based approach to estimate the received power in a wireless channel, using distance, fading effects, and noise characteristics as input features. The deep neural network is designed with multiple hidden layers, incorporating batch normalization, dropout regularization, and L2 weight decay to enhance generalization and stability. The model is trained on synthetically generated channel data, simulating path loss, Rayleigh fading, and additive noise to represent realistic propagation conditions. The proposed deep learning model is trained using Mean Squared Error (MSE) loss and optimized with the Adam optimizer, along with a learning rate scheduler to improve convergence. Evaluation shows that the model achieves a low Mean Absolute Error (MAE), indicating strong predictive accuracy. The simulated and predicted power curves demonstrate minimal deviation, confirming the model's capability to generalize well across different channel conditions. Results suggest that this deep learning-based channel estimation approach effectively captures complex propagation characteristics, making it suitable for real-time applications in wireless communication systems. The model can aid in beamforming, resource allocation, and interference management, contributing to enhanced network efficiency.
Design and Optimization of a Smart Wearable Antenna for Biomedical Telemetry Applications
The development of a compact, efficient, and multifunctional wearable antenna is specifically designed to support biotelemetry applications such as continuous physiological monitoring, remote diagnostics, and data transmission for medical purposes. The proposed antenna incorporates novel materials and design strategies, including high flexibility, a lightweight structure, and biocompatibility, ensuring comfort and wearability for long-term use. Optimization techniques, such as parameter variation, simulation-driven design, and fabrication considerations, are employed to achieve desirable radiation characteristics, minimal interference with body tissues, and optimal integration with wearable sensors. The antenna's gain, bandwidth, and operating frequency are all designed to minimize power consumption and satisfy the demanding needs of biotelemetry systems. The antenna is crafted from Polyimide Lossy material and has overall dimensions of 40 mm × 30 mm × 0.1 mm. The completed antenna has a 10 dB bandwidth that is centered at 2.4 GHz and functions at that frequency. According to simulation data, it has minimal effects on efficiency, bandwidth, gain, and reflection coefficient