https://eej.aut.ac.ir/ AUT Journal of Electrical Engineering en daily 1 Fri, 01 May 2026 00:00:00 +0330 Fri, 01 May 2026 00:00:00 +0330 https://eej.aut.ac.ir/article_6025.html The continuous evolution of wireless communication technologies is playing a pivotal role in enabling intelligent, connected, and data-driven systems across diverse application domains. This special issue brings together a collection of high-quality research contributions that address recent advances in wireless communications, spanning theoretical analysis, emerging technologies, system design, and real-world applications. A major focus of this issue is on the performance limits and optimization of next-generation wireless networks. Several contributions investigate multi-user communication scenarios, including the achievable rate region of wireless multiple access channels assisted by unmanned aerial vehicle (UAV) relays with multiple independent transmitters. Complementing this, the study of upper bounds on the average achievable rate of non-orthogonal multiple access (NOMA) systems provides valuable insights into the spectral efficiency limits of future communication paradigms. Further advancements are presented in massive multiple-input multiple-output (MIMO) and millimeter-wave (mmWave) systems, where novel channel estimation techniques based on coherence-optimized measurement matrices are proposed, alongside performance analysis of local processors-assisted cell-free massive MIMO (CF-mMIMO) systems. These works collectively contribute to improving capacity, reliability, and scalability in modern wireless networks. In addition, this special issue highlights emerging technologies that are shaping intelligent wireless environments. In particular, the integration of reconfigurable intelligent surfaces (RIS) with minimum redundancy linear arrays (MRLAs) for high-precision direction-of-arrival (DOA) estimation demonstrates significant potential in enhancing spatial resolution and signal detection for closely spaced targets. Complementary to these advances, the design and implementation of a wideband antenna capable of simultaneous transmission and reception with improved isolation addresses key challenges in full-duplex communication systems, further advancing hardware capabilities for next-generation networks. Beyond theoretical and physical layer innovations, the issue also emphasizes the growing role of wireless communication in enabling smart and sustainable applications. Contributions on blockchain-enabled Internet of Things (IoT) frameworks for smart farming and IoT-based automated watering systems for smart gardens illustrate how integrated wireless and computing technologies can enhance efficiency, resource management, and automation in agricultural environments. Security and resilience of wireless systems are also key themes addressed in this issue. A centralized machine learning-based intrusion detection system for mitigating distributed denial-of-service (DDoS) attacks in wireless sensor networks (WSNs) is presented, highlighting the importance of intelligent security mechanisms in protecting modern communication infrastructures. Finally, recognizing the broader societal impact of wireless technologies, this issue includes a comprehensive review of radio frequency electromagnetic wave effects on human health and reproduction. This work provides critical insights into safety considerations, emphasizing the need for responsible deployment of wireless systems. In summary, the papers featured in this special issue collectively provide a comprehensive perspective on current research directions in wireless communications, encompassing theoretical foundations, enabling technologies, practical applications, and societal implications. We hope this collection will serve as a valuable resource for researchers, engineers, and practitioners, and will inspire further innovations in this rapidly evolving field. https://eej.aut.ac.ir/article_5832.html In recent years, with the development of emerging technologies in developing countries, smart farming and pre- cision agriculture are more and more popular, especially using blockchain Internet of Things (IoT) system. In this study, we pro- pose a four-layer architecture for designing a blockchain-based IoT system in the domain of smart farming. The architecture comprises device nodes, fog nodes, a cloud server, and end users. Hyperledger Fabric is employed as the underlying blockchain framework, ensuring permissioned access and secure data man- agement. Additionally, we incorporate Role-Based Access Con- trol (RBAC) to further enhance security. Through performance analysis, including measures such as throughput and latency, we evaluate the system’s efficiency and responsiveness. The results demonstrate the benefits of using Hyperledger Fabric and a permissioned blockchain approach in smart farming, providing valuable insights for stakeholders and farmers seeking secure and reliable IoT solutions for agricultural practices. https://eej.aut.ac.ir/article_5888.html Watering and maintaining garden plants are essential daily activities for garden owners. However, for busy individuals, this task is often neglected, causing the plants to dry out and eventually die. This study proposes an IoT-based automatic watering system designed to activate irrigation based on soil moisture readings. The system employs soil moisture sensors and the Blynk IoT platform for real-time monitoring and control. The experience results show that the automatic watering mechanism performs more effectively than manual watering, maintaining optimal soil conditions and ensuring plant health. Additionally, user can monitor system status remotely through a smartphone. The proposed system transforms an ordinary garden into a smart garden that support autonomous, efficient, and sustainable plant care. https://eej.aut.ac.ir/article_5882.html A novel method for enhancing the accuracy of direction of arrival estimation for two closely spaced targets by optimizing the geometric array configuration of a Binary-Phase Reconfigurable Intelligent Surface   based on Minimum Redundancy Linear Arrays is proposed. In Binary-Phase Reconfigurable Intelligent Surfaces, the phases of the reflected signals at the Reconfigurable Intelligent Surfaces elements remain unchanged or undergo a 180-degree phase shift, making it significantly more cost-effective in terms of hardware compared to traditional direction of arrival estimation systems. This cost reduction, however, leads to an increase in the correlation of dictionary atoms. To compensate for this drawback, we regularize the optimization problem using atomic norm. Subsequently, the problem is transformed into its dual form to facilitate solving with existing solvers. Simulation results demonstrate that the proposed method can estimate the direction of arrival with higher accuracy for closely spaced targets in the angular domain, compared to existing Reconfigurable Intelligent Surfaces-based array methods, while maintaining the same hardware complexity. https://eej.aut.ac.ir/article_5989.html This paper considers and characterizes a multiple access relay channel (MARC) with $k$ transmitters, one UAV relay, and a terrestrial receiver, where we derive a new achievable rate region for the continuous alphabet wireless with an unmanned aerial vehicle (UAV) relay. The derived rate region is then evaluated numerically and compared with those of multiple access channels (MAC) and MARC with terrestrial relays. The results show that the presence of a UAV relay increases the achievable rate region of MAC systems by exploiting higher line-of-sight (LoS) channel quality and time-varying spatial link gains due to the UAV’s position. This improvement in the achievable region is not merely due to the UAV’s presence but arises from the different channel geometry and dynamic link conditions introduced by the UAV relay compared with terrestrial relays. https://eej.aut.ac.ir/article_5914.html The Non Orthogonal Multiple Access (NOMA) is a popular candidate for the next generation of wireless networks. Two advantages of NOMA are that it can achieve a higher rate and support more users compared to orthogonal multiple access (OMA). By increasing the number of users occupied a subchannel in NOMA, the achievable rate and the number of supported users of the system have been increased. Thus, we can assume there are infinite users in the system wanting to share one subchannel to derive the upper bound of the achievable rate of NOMA. In this article, the optimal power allocation function of infinite users in NOMAis derived, which maximizes the average achievable rate of the system under the maximum power constraint. The performances of the proposed power allocation strategy are compared with the simple case with only two users in NOMA. The simulation results show the gap between the average achievable rate and the outage probability of infinite users and two users in the NOMA system. https://eej.aut.ac.ir/article_5642.html In the realm of millimeter-wave (mmWave) communications, despite their promise of high data rates and expansive bandwidths, channel estimation encounters formidable challenges due to conspicuous path loss and the limited multipath components. This paper presents an innovative method that leverages the inherent sparsity of mmWave channels by operating within the two-dimensional transformed domain, this approach treats the channel as a sparse image representation. We advance the accuracy of sparse equivalent vectorized channel recovery by optimizing the measurement matrix. The proposed optimization method significantly reduces the requisite measurements and accelerates the estimation process and minimizes the mean squared error between the true and estimated channel matrices. Through comprehensive simulations, we evaluate our method against two scenarios: one where the compression rate is zero, and the sparse channel matrix recovery relies on the number of observations equating the number of channel matrix elements, and another where the compression rate is non-zero, but the measurement matrix remains unoptimized and randomly selected. Results demonstrate that our method outperforms the latter scenario and achieves accuracy comparable to the former, with significantly reduced computational overhead and accelerated computation speed. https://eej.aut.ac.ir/article_5794.html This paper proposes a local processors-assisted structure (LPs-AS) for cell-free massive multiple-input multiple-output (CF-mMIMO) systems, consisting of a central processor (CP), several access points (APs), and some local processors (LPs). Each LP is connected to the CP and a subset of APs and is used as a precoding unit in downlink (DL). This proposed LPs-AS enables us to implement DL precoders with a semi-distributed approach. In our proposed semi-distributed implementation (SDI), we design precoders at the LPs. This approach differs from centralized implementation (CI) where the precoders are implemented at the CP and distributed implementation (DI) where the precoders are designed at the APs. We evaluate LPs-AS in terms of spectral efficiency (SE) and analytically derive its achievable SE. Furthermore, we propose a power control algorithm to maximize its sum SE, compute the computational complexity (CC) of its minimum mean square error (MMSE) precoders, and compare this CC with its counterpart in CI and DI. Numerical results demonstrate that employing an optimal number of LPs (between 2 and 4) in our proposed LPs-AS, not only enables us to design DL precoders with significantly low CC but also results in an efficient SDI that effectively addresses the problem of low SE in DI. https://eej.aut.ac.ir/article_5734.html Human life has been increasingly affected by the rapid advancement of electronic technology and the widespread use of devices emitting electromagnetic radiation (EMR), such as Wi-Fi and mobile phones. While much remains unclear, studies suggest that electromagnetic fields (EMFs) can influence human health, particularly reproduction and the nervous system. EMF exposure, including from non-ionizing radiation produced by Wi-Fi and mobile phones, has been linked to potential effects on the male and female reproductive systems, embryonic development, and neuronal health. Key mechanisms include oxidative stress, thermal effects, changes in neurotransmitter metabolism, receptor function, nerve cell apoptosis, and ion channel dynamics. However, the long-term health risks, especially in children and adolescents due to prolonged exposure, remain a topic of debate. Despite current studies not confirming that RF-EMW from Wi-Fi exceeds safety guidelines, further research is essential to fully understand the implications of RF-EMW exposure on human health, particularly regarding reproduction and neurological effects. This review highlights the need for updated safety standards, more refined regulatory frameworks, and long-term investigations to clarify the potential biological and neurobiological consequences of EMF exposure. https://eej.aut.ac.ir/article_5895.html This paper proposes a dual-layer, microstrip patch antenna for simultaneous sending and receiving in the telecommunications industry. In this design, the harmonic suppression method is used. The proposed antenna has two ports with improved isolation and combines several telecommunication elements, including filters, duplexers, and radiators, into a single device, which reduces size, weight, and cost. The proposed antenna is designed, fabricated, and measured at C-band to verify and design methodology. The measurement results agree with the simulation results, which represent a complete two-way antenna in the frequency bands of (4.56-5) GHz (9.2%) and (6.25-7.39) GHz (16.8%) with an isolation of over 33 dB. The proposed antenna gain for the first and second bands is 5.6 dBi and 6.3 dBi, and the 3-dB beamwidths in two frequencies (f = 4.8 GHz and f = 6.8 GHz) are 83° and 85°, and the cross-polarization levels are −22 and −23 dB in the E- and H-planes, respectively. The antenna exhibits pure linear polarization with minimal cross-polarization levels observed in both E- and H-planes. https://eej.aut.ac.ir/article_4724.html This article discusses a multi-stage transimpedance amplifier (known as TIA), which is based on three stages of a modified inverter structure. The traditional inverter structures’ performances are improved adding two cascoded transistors. This new structure benefits from elimination of the Miller-capacitances in comparison with the traditional inverters, which can provide higher speed and wider frequency bandwidth. Manipulating the trade-offs among bandwidth, gain and power consumption beside using Gm/ID technique, this paper introduces a low-power transimpedance amplifier for high-bit rates in optical communication receiver systems. Moreover, active types of inductors are also used to lesson the occupied area and increase the frequency bandwidth. Transferring poles of the improved circuit to higher frequencies means less required DC current for a fixed bandwidth range, which results in low-power characteristic. The proposed circuit is simulated using 90nm CMOS technology parameters. Results for frequency response show the transimpedance gain of 42.3 dBΩ and the bandwidth of 3.47 GHz. Moreover, the circuit consumes 2.7mW using 1V supply, which is relatively close to the targeted 2.5mW power, using gm/ID. Other required simulations such as eye diagram, Monte Carlo, temperature and VDD variations, post layout simulation beside its mathematical equations are also added to manuscript to justify ts performance. https://eej.aut.ac.ir/article_5995.html Unexpected failures in essential industrial systems can cause operational disruptions and financial losses. To mitigate unplanned downtime and maintain safe, efficient functioning of critical assets, predictive maintenance strategies are essential. However, with the rapid increase in sensor-equipped machinery, the overwhelming volume of generated data has outpaced the capabilities of traditional machine learning models to provide accurate, real-time diagnostics. This research introduces a model-free deep reinforcement learning (DRL) approach tailored for predictive maintenance within sensor-integrated equipment networks. Each machine is equipped with a sensor module that captures real-time data and detects anomalies. Unlike conventional opaque regression-based methods, the proposed framework autonomously determines optimal maintenance policies and delivers actionable insights for each individual device. Experimental evaluations indicate the potential of this adaptive learning method to extend across diverse maintenance scenarios. https://eej.aut.ac.ir/article_5997.html In general, symmetrical and asymmetrical capacitor-clamped boost converters and direct current capacitor voltage, unbalancing specially with lower output current Total Harmonic Distortion are frequent problems for inverters. In order to improve voltage quality, a boost converter with a flying capacitor and grid tie inverter is suggested in this study. It manages the direct current link voltage asymmetrically. Additionally, the boost converter with flying capacitor grid tie inverter system's output voltage dynamic responses are enhanced and simulated using MATLAB Simulink which in turn benchmarked using a scaled-down hardware module. Proportional Integral and Model Predictive Controller control strategies are suggested and implemented in the built hardware. The suggested system's voltage, current, and dynamic performance are examined. The results show that a 360 Watt output power can be delivered by the suggested combination of the described converter system. Additionally, grid-connected power converters and flying capacitor boost converters have lower current harmonics and better voltage regulation direct/alternating current converters, demonstrating the developed system's great suitability for power usage in home photovoltaic systems. https://eej.aut.ac.ir/article_6008.html This paper addresses voltage stability challenges in distribution networks through a coordinated approach using Distributed Generation (DG) and Static Synchronous Compensator (STATCOM) placement. A novel hybrid Grey Wolf Optimization-Particle Swarm Optimization (GWO-PSO) algorithm is proposed to optimize the placement and sizing of these components with the objective of enhancing static voltage stability. The Fast Voltage Stability Index (FVSI) is employed as the primary metric for assessing voltage stability, where lower values (approaching zero) indicate improved stability. The proposed hybrid algorithm leverages GWO's exploration capabilities and PSO's exploitation strengths to overcome the limitations of individual algorithms. The methodology is validated on a 35-bus distribution system with a total load demand of 1.89 MW and 1.3455 MVAr. Results show that the hybrid GWO-PSO achieves an average FVSI reduction of 16.98%, significantly outperforming both standalone GWO (12.45%) and PSO (14.32%) implementations. The voltage profile across all buses is substantially improved, with the hybrid approach maintaining voltages closer to nominal values of 1.0 p.u. compared to the base case where many buses operate under low voltage conditions. The hybrid algorithm demonstrates faster convergence, reaching optimal solutions within 100 iterations compared to individual GWO and PSO implementations. The coordinated placement strategy determined optimal DG and STATCOM sizes and locations, effectively addressing voltage stability concerns in distribution systems experiencing rapid load growth with insufficient reactive power support. https://eej.aut.ac.ir/article_6009.html WECS are dynamic and intricate, considered by uncertainties and external disturbances. This research introduces an innovative pitch control strategy designed to improve energy stabilization and extraction predominantly for WT affected by unmodeled system dynamics to ensure stable operation under high wind speeds. For optimizing power capture, the ANN controller adapts dynamically to changing wind conditions by regulating the turbine blade’s pitch angle. For grid integration, a PWM rectifier transforms the variable-frequency AC power from the turbine into DC power. The simulations are conducted in MATLAB/Simulink tool to evaluate the control framework. It reveals that the superior performance of the ANN controller in reducing mechanical loads on turbines and platforms and maximizing power generation. Thus, it achieving reduced power and speed fluctuations, minimal overshoot and enhances the dynamic behaviour of WT. https://eej.aut.ac.ir/article_6010.html Accurate estimation of battery parameters, particularly State of Charge (SOC) and State of Health (SOH), is critical for the operational reliability and safety of electric vehicles (EVs). These parameters influence driving range, charging strategy, and long-term battery lifespan. Traditional methods such as Coulomb counting, equivalent circuit models, and Kalman filters have been standard for battery state estimation but struggle with noisy data, variable loads, and nonlinear battery ageing. Recently, deep learning has shown promise in addressing these challenges by offering more robust and adaptive performance. A recent review proposes a 4C framework—Correctness, Compute, Calibration, and Compliance—to evaluate deep learning models for next-generation Battery Management Systems (BMS). This scheme prioritises practical deployment aspects alongside accuracy. The review covers over 60 studies from 2019 to 2024, assessing model architectures, input features, training methods, and deployment readiness. It highlights advances such as physics-informed and uncertainty-aware models and offers a comparative evaluation of accuracy and computational efficiency on public datasets. Deep learning methods consistently outperform traditional approaches, achieving SOC errors below 2% and SOH deviations within ±3%. Transformer-based and hybrid models improve accuracy by 10–20% compared to simpler recurrent models. Lightweight architectures like GRUs offer fast inference (less than 20 milliseconds), suitable for in-vehicle real-time applications. Despite promising results, challenges remain around data generalizability, explainability, and real-time deployment. The 4C framework offers a roadmap for bridging laboratory advances with reliable, production-ready BMS technologies. https://eej.aut.ac.ir/article_6018.html Currently, the combination of Renewable Energy Sources (RES), particularly Photovoltaic (PV) systems, into power networks has grown in importance for sustainable energy generation. Therefore, this research develops the control approach for a high gain Triple Winding Max Gain Boost (TWMGB) converter incorporated with a Maximum Power Point Tracking (MPPT) controller for PV systems.  The developed converter exploits a triple winding inductor structure to attain an improved voltage gain, making it appropriate for low voltage PV system needs effective step-up capability. An innovative MPPT control approach based on Walrus Optimization Algorithm (WOA) tuned Radial Basis Function Fuzzy Inference System (RBFFIS) is utilized to extract the upmost power from the PV array in dynamic ecological conditons.  It assures fast convergence to the globalMPP and enahnces tracking accuracy even in partial shading scenarios. Moreover, the coordinated interaction among the TWMGB converter and adaptive control approach assures better performance interms of diminshed voltage stress and ripple. The performance of a system is applied via MATLAB/Simulink tool, demonstrating its adaptability and robustness with converter efficacy of 97.61%. The developed system offers a consistent and scalable solution for advanced PV based power systems, contributing to sustainable energy conversion and utilization.  https://eej.aut.ac.ir/article_6019.html At present power system faces certain Power Quality (PQ) issues, due to large amount of power usage, fluctuations and other uncertainties in non-linear loads. Thus, Distribution Static Compensator (DSTATCOM) is deployed for mitigating PQ problems. A three-phase AC source system, supplying a non-linear load using parallel-Voltage Source Inverter (VSI) based DSTATCOM at Point of Common Coupling (PCC) is deployed to DC-Link which functions based on current sharing principle. A novel Re-lift Single Ended Primary Inductor Converter (SEPIC) converter is integrated with Photovoltaic (PV) to boost PV power generation, assuring consistent and sustainable power supply to DC-Link capacitor of DSTATCOM is correctly charged. To further enhance the system performance, D-Q theory/Neural Network-based Synchronous Reference Frame (SRF) theory is utilized for generating reference current for DSTATCOM. These control topologies enable accurate compensation of reactive power and harmonic currents in real-time, assuring improved grid voltage stability and rectifying distortions. Proposed system is executed using MATLAB simulation and acquired outcomes validate improved system functioning with better PQ mitigation at PCC under varying load conditions. Thus, demonstrating the impacts of integrating renewable energy with advanced control approaches. https://eej.aut.ac.ir/article_6022.html In most areas and power systems, Photovoltaic (PV) energy is rapidly becoming a significant component of the energy balance because of its rapid annual growth rate. Therefore, this research presents the PV-fed improved SEPIC-Zeta converter with a cascaded fuzzy algorithm based Maximum Power Point Tracking (MPPT) for efficient DC power transfer. At the beginning, the improved SEPIC-Zeta (ISZ) converter is exploited to enhance the PV system’s voltage. Then, the Cascaded Fuzzy MPPT algorithm is introduced for tracking the upmost power from PV system. Also, the high frequency inverter transmutes the DC to AC power and isolation is provided by the isolation transformer for ensuring safety and mitigating harmonic distortion on the source and load side. Additionally, the interleaved synchronous rectifier is exploited for converting the AC into a DC supply. The implemented research is validated in MATLAB tool, which demonstrates that the proposed work has a converter efficacy of 95.12 %, which handle fluctuations and disturbances more effectively, enhancing the reliability of overall system.        https://eej.aut.ac.ir/article_6029.html This work presents an intelligent Photovoltaic (PV) grid integration system employing a Northern Goshawk Optimization Algorithm (NGOA)-based Radial Basis Function Neural Network (RBFNN) for Maximum Power Point Tracking (MPPT) and Soft Clamp X-Gain Boost (SC-XGB) converter for enhanced voltage regulation. The proposed system aims to optimize energy harvesting from PV source and ensure stable power delivery to a three-phase grid. The RBFNN is trained offline using comprehensive PV datasets and directly predicts the optimal duty cycle from measurable PV inputs during real-time operation, eliminating the need for auxiliary MPP pre-estimation algorithms, while NGOA enhances RBFNN’s learning capability by fine-tuning its weights and biases for rapid and accurate MPPT performance even under varying irradiance and temperature conditions. PV output is connected to SC-XGB, which efficiently raises the Direct Current (DC) voltage and is thus controlled by Pulse Width Modulation (PWM) signals which are generated according to MPPT output. Regulated DC output is then transformed into a three-phase Alternating Current (AC) by a Voltage Source Inverter (VSI), the output of which is taken through an LC filter to reduce harmonics before the power is fed into grid. Simulation is done in MATLAB showing the capacity of NGOA-RBFNN to track Maximum Power Point (MPP) at very high speed and accuracy. The system achieves superior voltage regulation of 95.24% efficiency, reduced Total Harmonic Distortion (THD) and enhanced dynamic performance over traditional MPPT control methods. https://eej.aut.ac.ir/article_6030.html This work presents a compact, hexagon-shaped wideband antenna designed for wearable applications in the Industrial, Scientific, and Medical band. The antenna is fabricated on a flexible felt substrate with a dielectric constant of 1.22 and a loss tangent of 0.016, achieving a compact footprint of 35 × 30 × 2 mm³. A Coplanar Waveguide feed integrated with a Stepped Impedance Resonator is employed to enhance impedance matching and bandwidth performance. The felt substrate exhibits excellent mechanical durability and electromagnetic stability under repeated bending and typical wear conditions, ensuring reliable long-term operation. The proposed antenna achieves a peak gain of 7.31 dB at 5.82 GHz and maintains a Specific Absorption Rate of 1.08 W/kg for 1 g of tissue, which is well within regulatory safety limits. Beyond communication, the antenna demonstrates moisture-sensing capability, exhibiting a consistent downward shift in resonant frequency with increasing substrate humidity. While stable under bending, excessive moisture leads to detuning and impedance mismatch. The strong correlation between simulated and measured results validates the proposed design as a robust and multifunctional solution for wearable biomedical and ultra-wideband sensing applications. https://eej.aut.ac.ir/article_6033.html The optimal design of a Switched Reluctance Motor (SRM) requires an accurate model. Analytical models presented for SRM do not meet required accuracy. Approximate models also have varying degrees of accuracy in predicting SRM characteristics. Hence, in this paper, two Neural Network (NN) models, Radial Basis Function (RBF) and Multilayer Perceptron (MLP), have been proposed to predict torque ripple and average torque, respectively. To train and test the models, 100 samples were extracted, 90% for training and 10% for testing. Furthermore, the Finite Element Method (FEM) has been used to solve the samples. The influencing parameters of the proposed NN models (number of hidden layers, number of neurons in hidden layers, bias, etc.) also have been determined to achieve the desired accuracy and minimal complexity. To evaluate the performance of the models, two criteria, Root Mean Square Error (RMSE) and Mean Relative Error (MRE), have been used. Both criteria indicate that the MLP model is successful in predicting torque ripple, while the RBF model excels in predicting average torque.