International Journal of Wireless and Microwave Technologies @ijwmt
Journal articles - International Journal of Wireless and Microwave Technologies
All articles: 648
Scientific article
One of the key aspects of 5G networks is the implementation of massive MIMO (Multiple Input Multiple Output) technology combined with adaptive beamforming. This study explores the use of a linear array antenna to manage and reduce unwanted signals such as jamming, interference, and noise, while also boosting the signal strength towards the intended user or device. The main challenge lay in optimizing the weights of the antenna elements, which was tackled by employing adaptive algorithms like LCMV (Linearly Constrained Minimum Variance) and RLS (Recursive Least Squares). To simplify the optimization process, two soft computing techniques—Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)—were utilized. The performance of the beamforming weights and radiation patterns was assessed in terms of minimizing unwanted signals and maximizing the desired signal. To check how well the proposed methods work, some commonly used algorithms like MVDR (Minimum Variance Distortionless Response) and LCMV are also applied. The outcomes were compared to those from other algorithms. A Differential Beamforming method is applied to examine how effectively the system can focus the signal in the target direction while minimizing unwanted interference from other directions. Additionally, the fminsearch algorithm, which is a basic local search method, is used to compare how well it can adjust the beamforming weights compared to the more advanced global optimization techniques. The results indicate that PSO and GA produce highly similar performance levels.
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Adaptive Cell Pride Traffic Load Balancing for Reliable 5G Mmwave Handovers
Scientific article
Handover (HO) management in millimeter-wave (mmWave) fifth-generation (5G) networks faces critical challenges including limited propagation distance, blockage, and frequent disconnections, particularly in dense urban environments. Most existing solutions target high mobility scenarios, while dense urban traffic with low-speed heterogeneous environments and frequent stop-and-go scenarios remains under-explored. This study proposes a novel concept of cell pride where the neighbouring cells cooperate and select the best performing cell for each user equipment (UE) instead of competing with each other. Based on this idea, the Adaptive Cell Pride Traffic Load Balancing (ACPT-LB) framework is developed to enhance the reliability of handover and connection stability in 5G mmWave networks by combining cooperative cell selection, adaptive load balancing, and a neighbour discovery mechanism. The simulated results showed Handover Success Rate (HSR) of 100%, Ping-Pong Avoidance Rate (PPAR) of 100%, and Connection Stability (CS) of more than 88% for all simulations with UE densities ranging from 1000 to 5000, highlighting the effectiveness of the framework in low mobility and high-density urban environments. These results confirm that ACPT-LB offers a scalable and robust solution for mobility and traffic management in 5G and Beyond 5G networks.
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Adaptive Cross-layer Resource Allocation by HNN in OFDM-MISO System
Scientific article
This paper presents an adaptive cross-layer resource allocation problem with the fairness in multi-user OFDM-MISO communication systems, and provides two solutions with Hopfield Neural Network (HNN) and Genetic Algorithm (GA) for the problem. We utilized HNN’s characteristics such as parallel processing, fast convergence speed and easy convergence to the optimum, to solve this problem under the conditions of proportional fairness for satisfying system performances and users’ requirements. The method is simplified in the computation by dividing the bit-loading matrix into three matrixes. The simulation results show that HNN and GA can effectively solve optimization problems of resource allocation in such system, and results of selected HNN and GA methods are more effective than that of the traditional method.
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Scientific article
This paper addresses the problem of efficient data transmission under dynamically changing network and computational conditions by proposing an adaptive data compression method based on context-aware selection of compression algorithms and their parameters. Unlike conventional static approaches, the proposed method performs real-time analysis of data characteristics, network bandwidth, latency, and available computational resources, enabling dynamic selection of the optimal compression strategy through multi-criteria optimization. The scientific novelty of the work lies in the integration of data-driven and environment-aware adaptation within a unified decision-making framework that simultaneously minimizes end-to-end transmission delay while balancing compression ratio and processing overhead. Experimental evaluation was conducted using both synthetic datasets and the standard Silesia Corpus benchmark. The synthetic datasets included repetitive low-entropy text (repeated.txt), structured JSON data (structured.json), moderate-complexity text (example.txt), and high-entropy binary streams (random.bin), representing realistic web content and raw data transmission scenarios. The Silesia Corpus, containing approximately 200 MB of heterogeneous real-world files, including text, binaries, and images, was used for validation and benchmarking. The proposed method was evaluated using compression algorithms such as LZ4, Zstandard, Brotli, and ZSTD under different network conditions and system loads. Experimental results show that the adaptive approach reduces total transmission time by an average of 23%, improves compression efficiency by 16%, and decreases computational resource consumption by 13% compared to conventional static compression methods. The software implementation is based on a modular service-oriented architecture that supports real-time monitoring, dynamic algorithm switching, and scalable deployment in distributed, cloud, streaming, and Internet of Things environments.
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Adaptive HEC-VPS: The Real-time Reliable Wireless Multimedia Multicast Scheme
Scientific article
To satisfy the reliability of real-time wireless multimedia multicast services, the existing erasure error correction schemes usually assume that the packet size in transmissions is fixed. However, recent studies have shown that Variable Packet Size (VPS) can deeply influence the performance of unicast wireless services. Accordingly, using a delay-limited general architecture of EEC for real-time wireless multicast, this paper proposes an Adaptive Hybrid Error Correction (AHEC) scheme with VPS. Comparing with the AHEC schemes with fixed packet size, the analysis results show that the AHEC with VPS scheme can improve the throughput by about 10% in some cases.
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Adaptive Multi User Detection for FD-MC-CDMA in Presence of CFO
Scientific article
The main targets of multi-carrier direct sequence code division multiple access (MC-DS-CDMA) mobile communication systems are to overcome the multi-path fading influences as well as the near-far effect and to increase its capacity. Different types of optimal and suboptimal multi-user detection schemes have been proposed and analyzed in literature. Unfortunately, most of them share the drawback of requiring an efficient practical solution. Genetic algorithm provides a more robust and efficient approach for solving complex real world problem such as multi user detection, but genetic algorithms are not computationally efficient. Computational complexity and performance of the genetic algorithms depends on number of generations and/or the population size, schemes involving genetic algorithms would compromise in computational complexity or performance. In this paper we propose adaptive population sizing genetic algorithm based multi user detection algorithm and compare its performance with existing multi user detection algorithms in various channels. Simulation results confirmed that the proposed adaptive genetic algorithm assisted multi user detection algorithm performs better compared to the existing multi user detection algorithms.
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Adaptive Multi-Stage Fuzzy Logic Model of Student Knowledge Assessment
Scientific article
Accurate and objective assessment of students’ knowledge remains a challenging problem due to the inherent uncertainty and subjectivity of traditional evaluation systems. Conventional grading approaches often fail to account for task complexity, discrimination power, and variability in student responses, which leads to inconsistent and biased results. This study proposes a multi-stage fuzzy logic–based decision-making model for knowledge assessment. The model integrates several key evaluation indicators, including task difficulty, discrimination index, response value, and response weight, within a unified fuzzy inference framework. A structured multi-factor evaluation mechanism is developed, where fuzzy membership functions and rule-based inference are used to transform qualitative judgments into quantitative assessment measures. Furthermore, a defuzzification process based on the Center of Gravity (COG) method is applied to obtain final scores, and a correction mechanism is introduced to refine evaluation outcomes. A comparative analysis was conducted using assessment data from 100 students across 5 tasks evaluated on a [0–10] scale. The results suggest that the proposed approach provides a more differentiated and consistent interpretation of student performance than the traditional assessment method. The proposed model provides a reliable and interpretable framework for evaluating students’ knowledge and supports the development of adaptive and intelligent educational assessment systems.
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Scientific article
Malicious user detection in spectrum sensing is a critical challenge in Cognitive Radio Networks (CRNs). Traditional rule-based mechanisms lack adaptability to dynamic behaviors, while existing AI techniques often overlook scalability and real-time constraints. This paper proposes a novel hybrid framework that integrates adaptive trust-based mechanisms with AI-powered anomaly detection and blockchain technology to achieve superior detection accuracy (>90%), energy efficiency (30% reduction), and scalability (supporting 500+ nodes with blockchain throughput >750 transactions/second). The framework dynamically updates trust scores using machine learning models and leverages blockchain for secure and transparent spectrum management. Comparative simulations demonstrate superior performance compared to existing methods. The proposed methodology addresses the limitations of static trust mechanisms and offers a robust solution for real-time malicious user detection in CRNs.
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Adversarial Deep Learning in Anomaly based Intrusion Detection Systems for IoT Environments
Scientific article
Using deep learning networks, anomaly detection systems have seen better performance and precision. However, adversarial examples render deep learning-based anomaly detection systems insecure since attackers can fool them, increasing the attack success rate. Therefore, improving anomaly systems' robustness against adversarial attacks is imperative. This paper tests adversarial examples against three anomaly detection models based on Convolutional Neural Network (CNN), Long Short-term Memory (LSTM), and Deep Belief Network (DBN). It assesses the susceptibility of current datasets (in particular, UNSW-NB15 and Bot-IoT datasets) that represent the contemporary network environment. The result demonstrates the viability of the attacks for both datasets where adversarial samples diminished the overall performance of detection. The result of DL Algorithms gave different results against the adversarial samples in both our datasets. The DBN gave the best performance on the UNSW dataset.
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Adversarial Machine Learning Attacks and Defenses in Network Intrusion Detection Systems
Scientific article
Machine learning is now being used for applications ranging from healthcare to network security. However, machine learning models can be easily fooled into making mistakes using adversarial machine learning attacks. In this article, we focus on the evasion attacks against Network Intrusion Detection System (NIDS) and specifically on designing novel adversarial attacks and defenses using adversarial training. We propose white box attacks against intrusion detection systems. Under these attacks, the detection accuracy of model suffered significantly. Also, we propose a defense mechanism against adversarial attacks using adversarial sample augmented training. The biggest advantage of proposed defense is that it doesn’t require any modification to deep neural network architecture or any additional hyperparameter tuning. The gain in accuracy using very small adversarial samples for training deep neural network was however found to be significant.
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Scientific article
Wireless Sensor Networks have emerged as a key technology enabling real time data collection and monitoring across various domains, including environmental monitoring, industrial control, healthcare, and security applications. However, despite their growing relevance, energy efficiency remains a fundamental design challenge due to the limited power supply of sensor nodes, which directly impacts overall network lifetime and reliability. This paper proposes an Adaptive Energy-Aware Clustering Protocol (EACP) designed to improve energy efficiency and extend the operational lifetime of homogeneous WSNs. The proposed protocol integrates three main mechanisms: Residual Energy-based Cluster Head Selection, to ensure balanced energy distribution; Mobility-Aware Cluster Head Reassignment, to maintain stable communication under node mobility; and Base Station Proximity Based Direct Transmission, which allows nodes near the BS to bypass CHs, thereby minimizing redundant energy use. These mechanisms allow the network to dynamically adapt to changing energy conditions and communication distances. The protocol was evaluated through extensive MATLAB simulations and compared with benchmark protocols including LEACH, HAC, and HSA. Simulation results demonstrate that the proposed EACP significantly improves network performance. Specifically, it achieves 50% to 94% improvement in network lifetime, reduces energy consumption by approximately 20% to 25%, and increases throughput by more than 2.5 times compared to the benchmark protocols. These results demonstrate that EACP offers a scalable, energy-efficient communication strategy well suited for large scale WSNs deployments.
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An Adaptive Hybrid Epidemic-PRoPHET Routing Framework for Opportunistic Internet of Things Networks
Scientific article
Opportunistic Internet of Things (O-IoT) networks operate in highly dynamic, infrastructureless environments where connectivity is intermittent and unpredictable, making efficient and reliable data delivery a persistent challenge. Traditional routing protocols such as Epidemic Routing and PRoPHET have been widely studied, yet both present significant drawbacks: Epidemic Routing ensures high delivery probability by replicating messages extensively, but this causes excessive buffer usage, bandwidth consumption, and energy drain, while PRoPHET employs probabilistic forwarding based on encounter histories, which is more resource-efficient but struggles in highly mobile or sparse networks where prediction accuracy decreases. To overcome these issues, this paper proposes a Hybrid Adaptive Routing framework that integrates the predictive capability of PRoPHET with a controlled epidemic fallback mechanism. The framework applies a predictability threshold of 0.6 to decide when to rely on probabilistic forwarding and when to activate epidemic replication, while carefully constraining the latter with EPIDEMIC_LIMIT = 5, HOP_LIMIT = 8, and TTL = 300 minutes to prevent resource exhaustion. . The framework was subsequently simulated and analyzed in an Opportunistic Network Environment (ONE) at different network densities and compared with the traditional routing protocols Epidemic and PRoPHET. The system's performance was evaluated using parameters such as delivery probability, overhead ratio, average latency, hop count, and buffer utilization. Experimental results confirm the approach’s effectiveness at high node density (246) nodes, where the hybrid protocol achieves a 19.03% improvement in delivery probability over Epidemic routing and 30.45% improvement over PRoPHET, alongside a 43.5% and 31.1% overhead reduction compared to Epidemic and PRoPHET respectively, and 35.5% and 37.1% latency reduction compared to Epidemic and PRoPHET respectively, making it a robust and resource-efficient solution for real-world O-IoT applications.
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Scientific article
Wireless Sensor Networks play a vital role in the Internet of Things, smart cities, and industrial automation, yet there are open ended challenges in terms of efficient energy management and reliable data transmission. This paper presents a novel, two-phase routing framework comprising Dynamic Channel Selection and Energy-Efficient Routing Optimization to address these issues. In the first phase, Deep Q-Learning is utilized to identify stable communication channels, thereby enabling congestion-free data transfer across the network. The second phase implements Coral Reef Optimization to derive energy-efficient routing paths, significantly minimizing power consumption. Additionally, Adaptive Modulation and coding dynamically adjusts transmission parameters in real time to improve data throughput and reduce network delays. Existing solutions have been limited by network instability, poor scalability, and inefficient spectrum usage; In contrast, the integrated approach leverages Deep Q-Learning for intelligent channel allocation and Coral Reef Optimization for optimized route selection, while Adaptive Modulation and Coding fine-tunes the communication process to achieve optimal performance. Compared to existing models which shows high packet drop ratio and scalability constraints, our model achieves a 68% reduction in energy consumption, increases network lifetime by 82%, lowers error rate by 77%, enhances routing stability by 85%, and boosts overall throughput by 79%. These results highlight the proposed model’s potential as a highly adaptive, low-latency, and scalable solution for next-generation wireless sensor network applications.
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An Analysis of VoIP Application in Fixed and Mobile WiMAX Networks
Scientific article
WiMAX is a wireless broadband technology, which promises maximum coverage area and high data rates. WiMAX provides last mile connectivity. This network defines two working models such as fixed WiMAX and mobile WiMAX. The main aim of this paper is to compare and also analyze the performance of the VoIP application over fixed and mobile WiMAX, with respect to various codecs such as G.711, G.723.1ar5.3, G.726ar24, G.728ar16 and G.729. We have considered few QoS parameters such as average end-to-end delay, throughput, average jitter, average one-way delay and average MOS. From the obtained result, codec G.711 and G.726ar24 performs better when compared to other codecs in both fixed and mobile WiMAX network.
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An Analytical Study of Cloud Security Enhancements
Scientific article
Enhancements and extensions in pervasive computing have enabled penetration of cloud computing enabled services into almost all walks of human life. The expansion of computational capabilities into everyday objects and processes optimizes end users requirement to directly interact with computing systems. However, the amalgamation of technologies like Cloud Computing, Internet of Things (IoT), Deep Learning etc are further giving way to creation of smart ecosystem for smart human living. This transformation in the whole pattern of living as well as working in enterprises is generating high expectations as well as performance load on existing cloud implementation as well as cloud services. In this complete scenario, there are simultaneous efforts on optimizing as well as securing cloud services as well as the data available on the cloud. This manuscript is an attempt at introducing how cloud computing has become pivotal in the current enterprise setting due to its pay-as -you -use character. However, the allurement of using services without having to procure and retain involved hardware and software also has certain risks involved. The main risk involved in choosing cloud is compromising security concerns. Many potential customers avoid migrating towards cloud due to security concerns. Security concerns for the cloud implementations in the recent times have grown exponentially for all the varied stakeholders involved. The aim of this manuscript is to analyze the current security challenges in the existing cloud implementations. We provide a detailed analysis of existing cloud security taxonomies enabling the reader to make an informed decision on what combination of services and technologies could be used or hired to secure their data available on the cloud.
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An Approach Towards Dynamic Opportunistic Routing in Wireless Mesh Networks
Scientific article
Opportunistic routing (OR) for multi-hop wireless networks was first proposed by Biswas and Morris in 2004, but again as a modified version in 2005 as Extremely Opportunistic Routing (ExOR). A few other variants of the same were also proposed in the meanwhile time. In this paper we propose a Dynamic Opportunistic Routing (DOR) protocol which depends on network density and also provides spatial diversity. Our routing protocol is distributed in nature and provides partial 802.11 MAC layer abstraction. To verify the results of our protocol we took a network with light-density of nodes and bigger in size (as OR performs better in higher node density). A wireless mesh network in “QualNet network simulator” was created, where the average end-to-end delay and throughput at every node are compared with that of other standard routing protocol OLSR-INRIA.
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An Approach to Micro-blog Sentiment Intensity Computing Based on Public Opinion Corpus
Scientific article
Based on the analysis of the status of network public opinion, the features of short content and nearly real-time broadcasting velocity in this paper, it constructs a public opinion corpus on the content of micro-blog information, and proposes an approach to marking corpus on the basis of sentiment tendency from the semantic point of view; Furthermore, considering the characteristics of micro-blog, it calculates the sentiment intensity from three levels on words, sentences and documents respectively, which improves the efficiency of the public opinion characteristics analysis and supervision. So as to provide a better technical support for content auditing and public opinion monitoring for micro-blog platform.
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An Automated Optimization Workflow for HFSS Using GA and PSO for Circular Patch Antenna Design
Scientific article
This paper presents the automated design and optimization of a compact circular microstrip patch antenna for C-band applications using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Microstrip patch antennas inherently suffer from narrow impedance bandwidth, making systematic optimization essential for wideband wireless applications. The antenna is implemented on an FR4 substrate (24 × 24 mm2, εr = 4.4, h = 1.6 mm) and optimized through ANSYS HFSS using the PyAEDT Python interface. Three key design parameters were tuned to enhance impedance bandwidth and minimize return loss GA achieved the best performance among the considered optimization methods, with an optimized bandwidth of 3.74 GHz and a minimum S11 of –37 dB, while the optimized PSO method reduced computation time by approximately 49% compared to manual tuning and 31% compared to GA. The final optimized design exhibits consistent gain performance (2.3–2.8 dB) and stable radiation patterns across the operational band, confirming reliable C-band operation. The results demonstrate that metaheuristic optimization integrated with HFSS automation provides a powerful and efficient antenna design framework, which can be extended toward hybrid algorithms and intelligent machine-learning-assisted antenna prediction models.
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An Efficient and Cloud Based Architecture for Smart Grid Security
Scientific article
Due to explosive growth of users, increasing energy demand and also the need to improve efficiency and maintain the stability of the electricity grid, smart grid is the only option available to electrical industry engineers. In fact, the smart grid is a physical-cyber system that provides coherent and integrated communication, processing and control functions. The smart grid provides control and management of millions of devices in the electricity industry in a reliable, scalable, cost-effective, real time and two-sided manner. Given the increasing growth of cyber threats in the last decade, the need to protect the electricity industry and its critical systems seems essential. The slightest disruption to the power industry's systems results in disruption to other industries, reduced productivity, and discontent. Hence we proposed an efficient cloud based architecture to improve smart grid performance. Proposed architecture provides data security and privacy against different types of cyber threats such as replay attack, modification attack and so on.
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An Efficient and Comapct Design of Coupled-Line Unequal Wilkinson Power Divider
Scientific article
In this paper, 3:1 and 10:1 unequal Wilkinson power dividers are designed and investigated. Unequal power dividers are an integral part of the feeding network for antenna array where their performance affects a group of elements rather than an individual element. Thus, there is a need for an accurate design for such dividers. A coupled-line section with two grounding via has been used to attain the high characteristic impedance line. This is done in order to outplay the microstrip fabrication constraints of printing very thin/fine conductor lines. Further to reduce the size of the structure, meandering of the transmission line has been done which procreates three designs, namely: 00 Serpentine Flexure, 1800 Serpentine Flexure and Compact Meandered Flexure. Verification of the design methodology has been done by creating a 10:1 Unequal WPD. The structures are implemented on a high-resistive silicon substrate (HRS) for a centre frequency of 1.575 GHz. Further, their EM analysis is done in terms of S-parameters such as return loss and insertion loss using commercially available FEM solver. Satisfactory RF performance, with return loss better than -10 dB and required power split for all the structures, has been achieved.
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