Can the smart radio environment paradigm measurably enhance the performance of contemporary urban macrocells? In this study, we explore the impact of reconfigurable intelligent surfaces (RISs) on a real-world sub-6 GHz MIMO channel. A rooftop-mounted macrocell antenna has been adapted to enable frequency domain channel measurements to be ascertained. A nature-inspired beam search algorithm has been employed to maximize channel gain at user positions, revealing a potential 50% increase in channel capacity in certain circumstances. Analysis reveals, however, that the spatial characteristics of the channel can be adversely affected through the introduction of a RIS in these settings. The RIS prototype schematics, Gerber files, and source code have been made available to aid in future experimental efforts of the wireless research community.

With the continuous evolution of wireless communication and the explosive growth in data traffic, decentralized spectrum sensing has become essential for the optimal utilization of wireless resources. In this direction, we propose an over-the-air aggregation-based Federated Learning (FL) for a technology recognition model that can identify signals from multiple Radio Access Technologies (RATs), including Wi-Fi, Long Term Evolution (LTE), 5G New Radio (NR), Cellular Vehicle-to-Everything PC5 (C-V2X PC5), and Intelligent Transport Systems G5 (ITS-G5). In the proposed FL-based technology recognition framework, we consider edge network elements as clients to train local models and a central server to create the global model. In each client, a Convolutional Neural Network (CNN)-based model is trained from Inphase and Quadrature (IQ) samples collected from a certain combination of RATs. The possible combination of RATs considered in the clients is selected based on the capabilities of the real-world network elements that can be used as a client. The FL framework involves a process where multiple clients periodically send updates derived from local data to a central server, which then integrates these contributions to enhance a shared global model. This method ensures that the system stays current with the evolving real-world environment while also minimizing bandwidth required for training data transfer and allowing for the maintenance of personalized local models on each clientâ\euro™s end.

The escalating growth of vehicles has amplified the complexity of Vehicle Ad-hoc Networks (VANETs), necessitating innovative strategies to combat communication delays and disruptions. In this study, we covered a literature review from 2009 to 2023 where we present a comprehensive exploration of solutions by integrating Software-Defined Networking (SDN), Programming Protocol-independent Packet Processors (P4), and Service Function Chaining (SFC). Our research addresses the core challenges of VANETs, emphasizing the specific problem of communication delays. We conducted an in-depth analysis of SDNâ\euro™s application in VANETs, showcasing its potential to enhance network efficiency and adaptability. Additionally, we delve into the practical implementation of P4 architecture in traffic management and multihoming environments, elucidating its pivotal role in improving VANET performance. Simultaneously, we explore the intricacies of SFC and its support for services and policies in VANETs. Our study reveals novel approaches to mitigate delays in VANETs, presenting valuable insights for both researchers and practitioners. By integrating SDN, P4 architecture, and SFC in a multihoming environment, our research not only advances the understanding of VANET challenges but also provides practical and implementable solutions for achieving efficient and low-latency vehicular communication networks. This work do not only contributes to the academic discourse but also holds significant promise for real-world applications, marking a pivotal step towards the future of seamless vehicular communications.

The paper aims to try to push the current conecpt about reduced switch count Multi-level inverter (RSC-MLI) for battery sotage systems. They can be used to reduce the count of Mosefts used in Home storage systems(HSS); However, it would require precise Voltage measuring instruments and a complexer code to control the switching of Mosfets. To smooth the on /off step function, one can also use an inductor. The research sparked interest during Covid phase when the prices of certain Mosfets increased 20 folds.Â

This experimental research aims to investigate the potential benefits of integrating augmented reality (AR) technology into the classroom setting. The study hypothesizes that the use of AR technology will enhance student engagement and lead to improved learning outcomes. A sample of participants from a local high school will be involved in this research. The research employs a pre-test/post-test design to assess the impact of AR technology on student engagement and learning outcomes. Data will be collected and analysed to determine the effectiveness of AR technology in enhancing classroom education.

This abstract delves into the intricate relationship between the abstract concept of intelligence and its embodiment, drawing attention to the lack of coherence in understanding intelligence and its implications for artificial intelligence (AI). Intelligence, inherently grounded in human experience, attempts to transcend its embodiment, posing challenges for the development of artificial general intelligence (AGI). The concept of embodiment extends beyond physical instantiation, encompassing the interdependence of an autopoietic system on its environment. The pursuit of autonomy and general capability in AGI necessitates the recreation of the organism’s natural condition of embodiment. However, the feasibility , controllability, and overall advantages of such artificial embodiment remain uncertain. This abstract explores the complex interplay between intelligence, embodiment, and the quest for AGI, raising critical questions about the path forward in the development of intelligent systems.Â

Exams are a widely used evaluation tool in educational institutions to assess students’ strengths and weaknesses. However, students tend to cheat during physical exams by exchanging papers, using hidden notes, or fulfilling their parents’ expectations, among other things. Due to physical limitations, traditional invigilation methods cannot effectively monitor exams while maintaining their integrity. To address this issue, a study proposes an automated method based on computer vision that uses closed- circuit television (CCTV) cameras to detect suspicious behavior during physical exams. The proposed method employs You Only Look Once (YOLOv3) with residual networks as the backbone architecture to inspect cheating. The results demonstrate that the proposed method is reliable and efficient, achieving 88.03% accuracy in detecting cheating in the classroom environment. Overall, this work shows promising results for invigilating students during exams.

Data from interconnected vehicles may contain sensitive information such as location, driving behavior, personal identifiers, etc. Without adequate safeguards, sharing this data jeopardizes data privacy and system security. The current centralized data-sharing paradigm in these systems raises particular concerns about data privacy. Recognizing these challenges, the shift towards decentralized interactions in technology, as echoed by the principles of Industry 5.0, becomes paramount. This work is closely aligned with these principles, emphasizing decentralized, human-centric, and secure technological interactions in an interconnected vehicular ecosystem. To embody this, we propose a practical approach that merges two emerging technologies: Federated Learning (FL) and Blockchain.Â

Drones are used for public safety missions because of their communication capabilities, unmanned mission, flexible deployment, and low cost. Recently, drone-assisted emergency communication systems in disasters have been developed where instead of a single large drone, flying ad hoc networks (FANETs) are proposed through clustering. Although cluster size has an impact on the proposed system’s performance, no method is provided to effectively regulate cluster size. In this paper, optimum cluster size is obtained through two distinct meta-heuristic optimization algorithms - the Cuckoo Search Algorithm (CUCO) and the Particle Swarm Algorithm (PSO). Flowcharts and algorithms of CUCO and PSO are provided. A presentation of an analytical investigation based on the Markov chain model is provided. To further validate the analytical study, simulation results are presented. Simulation shows the improvement in terms of throughput and packet dropping rate (PDR).

This research proposes a new malware detection technique using a multi-dimensional evaluation function, which comprehensively assesses multiple indicators such as power consumption, network traffic, and CPU usage. This method enables the rapid scoring of malware risks and prioritizes the detection of malware that presents a risk score above a certain threshold. Moreover, this technique surpasses the detection speed of existing security software and provides flexibility by utilizing machine learning to adapt to new patterns and behaviors of malware. These characteristics are expected to significantly improve the efficiency and accuracy of malware detection.

This paper presents a Millimeter wave Microstrip Patch antenna(MPA) with a metaplate which consists of Split Ring resonators(SRR) design. The gain and bandwidth of MPA are improved by using 4×3 array unit cells printed on both the sides of the metaplate. Simulation results show that the Gain of the antenna was increased by 4.82 dBi and 4.53 dBi, bandwidth was increased by 2.25% and 6.21% in CST and HFSS softwares respectively using the Metaplate along with the MPA. The center frequency of the proposed antenna is 28.5 GHz. Thus the proposed antenna has a very small size of 18×22 mm2 and is suitable for Millimeter wave applications Â

This paper proposes a scalable framework for the throughput analysis of the grant-free power-domain non-orthogonal multiple access (GF-NOMA) and presents the achievable performance in the optimized offered load at each power level (called per-level offered load). Our analytical model reflects packet errors caused by \textit{power collisions}, characterized by GF-NOMA, based on the power level design guaranteeing the required signal-to-interference-and-noise ratio (SINR). This idea enables analyzing the throughput of a large-scale GF-NOMA system more accurately than the existing models and thus optimizing the per-level offered load rather than a uniform one in the throughput maximization or energy minimization problem with a throughput condition. Our analytical results highlighted some key insights into designing future access control methods in GF-NOMA. First, our analytical model achieved an approximation error percentage of only 0.4% for the exact throughput obtained by the exhaustive search at five power levels; then, the existing one provided that of 25%. Next, by using our framework, the optimal per-level offered load restrictively improved the throughput above the optimally uniform per-level offered load. Finally, our framework discovered a 27% more energy-efficient per-level offered load than the existing framework at five power levels while providing higher throughput than the uniform per-level offered load optimized by our framework.

Insider threats, is one of the most challenging threats in cyberspace, usually responsible for causing significant loss to organizations.The topic of insider threats has long been studied and many detection techniques were proposed to deal with insider threats. This paper focuses on using different anomaly detection algorithms- Locality Outlier Factor Algorithm and Isolation forest Algorithm and does a comparative analysis between their performance. A hybrid model incorporating advantages of both LOF Algorithm and IF Algorithm is proposed in this paper which gives better performance than the individual models for detecting insider threats. The hybrid model was able to achieve whooping 99.99\% accuracy while detecting insider threats.

 Amid the global rollout of fifth-generation (5G) services, researchers in academia, industry, and national labo?ratories have been developing proposals for the sixth generation (6G). Despite the many 6G proposals, the materialization of 6G as presently envisaged is fraught with many fundamental interdisciplinary, multidisciplinary, and transdisciplinary (IMT) challenges. To alleviate some of these challenges, semantic com?munication (SemCom) has emerged as promising 6G technology enabler. SemCom is designed to transmit only semantically?relevant information and hence help to minimize power usage, bandwidth consumption, and transmission delay. Thus, SemCom embodies a paradigm shift that can change the status quo that wireless connectivity is an opaque data pipe carrying messages whose context-dependent meaning have been ignored. On the other hand, 6G is critical for the materialization of major SemCom use cases. These paradigms of 6G for SemCom and SemCom for 6G call for a tighter integration and marriage of 6G and SemCom. To facilitate this integration and marriage, this comprehensive survey paper first provides the fundamentals of semantics and semantic information, semantic representation, semantic information, and semantic entropy. It then builds on this understanding and details the state-of-the-art research landscape of SemCom; exposes the fundamental and major challenges of SemCom; and offers promising future research directions for SemCom theories, algorithms, and realization. Accordingly, this survey article stimulates major streams of research on SemCom theories, algorithms, and implementation.Â

A document by Volkan Ozduran . Click on the document to view its contents.

The application of Reed-Solomon (RS) codes in distributed storage systems is widespread. A lot of literature have proposed repair schemes for RS codes with one or multiple failed symbols, which can reduce communication cost during repair (i.e. repair bandwidth).  However, for repair schemes of RS codes with multiple failed symbols,  the existing distributed repair schemes can only repair $e=2,3$ erasures. In this paper, by intelligently dividing the set of evaluation points corresponding to failed symbols, a distributed repair scheme for RS codes with $e\geq 4$ erasures are proposed.

This paper explores the experimental localization of single and multiple ground RF transmitters using both traditional localization and machine learning algorithms. For the localization of a single transmitter, the setup is evaluated in two unlicensed frequency bands with and without interference. A threshold approach is proposed to improve accuracy in the presence of interference. To localize multiple transmitters, the RSSI data are divided into clusters by a k-means clustering algorithm and fed into a localization algorithm. These experimental results are preceded by an analysis phase where the UAV flight path and data collection are simulated using the QuaDRiGa channel model.

Explicit results are obtained using simple and exact methods for the joint queue-length distribution of the M/M/c queue with an arbitrary number of non-preemptive priority levels. This work is the first to provide explicit results for the joint probability generating function and joint probability mass function for a general number of priority levels. A fixed-point iteration is developed for the stationary balance equations, which enables direct computation of the joint queue-length distribution. A multi-variate probability generating function is also derived, from which the joint probability mass function can be computed by means of a multi-dimensional fast Fourier transform method.

A document by Naga Subrahmanya Vamsi Mohan Yarra . Click on the document to view its contents.

We propose a solar sensor-based smart farm system to provide high monitoring quality while preserving sensor energy in the presence of adversarial attacks. Since solar sensors are attached to cows to monitor their health under varying weather conditions, ensuring that the system provides energy-adaptive, high-quality monitoring services is critical. Further, the smart farm system should be robust against diverse adversarial attacks that will disrupt its monitoring quality. We use deep reinforcement learning (DRL) to identify the optimal policy for maximizing monitoring quality and prolonging the systemâ\euro™s lifetime while maintaining sufficient energy. We introduce transfer learning (TR) into the DRL process to achieve fast learning by DRL without experiencing a cold start problem. In addition, we develop an uncertainty-aware anomaly data detection method to filter out deceptive data caused by adversarial attacks. Via extensive comparative performance analysis conducted in our experiments based on real datasets, we demonstrate the superior performance of TL-based DRL strategies over other competitive counterparts regarding system lifetime, monitoring quality, learning convergence time, and energy consumption.

The dataset descriptor paper presents an essential and structured documentation of the Internet Protocol Stack Dataset, offering a comprehensive overview of its contents and potential applications in the field of networking and telecommunications. This descriptor paper details the dataset’s organization into tunable parameters, non-tunable parameters, and performance metrics for VoIP and HTTP sessions, providing insights into its significance for network performance evaluation. Researchers, network engineers, and data scientists can utilize this descriptor as a guide to effectively access and utilize the dataset for various purposes, including network optimization, quality of service assessment, and network modeling, ultimately contributing to advancements in network performance and infrastructure development.Â

The first thing that comes to everyoneâ\euro™s mind, when we talk about wireless communications is â\euroœThe Radioâ\euro?. Starting from the 1800â\euro™s till date the radio has undergone several iterations of development. This paper is focused to bring out the three major development stages that are Hardware Defined Radios, Software Defined Radios & Cognitive Radios. With the introduction of cognizance and decision-making algorithms in Cognitive Radios, the scope for incorporation of Artificial Intelligence and Machine Learning has become significant. Intelligent Radios can be considered as the fourth generation of Radio in times to come, which will incorporate Artificial Intelligence and Machine Learning at the core processing unit to improve the overall capability of radio in a congested, constrained and contested electromagnetic spectrum. The Intelligent Radios would be capable of generating hypothesis based on environmental conditions on its own and take intelligent decisions as per the situation reducing the human effort. As the world is moving constantly towards information age, it is clear that future age warfare will be network and electromagnetic spectrum centric. Hence, it is very important for the military to achieve freedom operation in electromagnetic spectrum space in operational scenario. Intelligent Radios will be the weapon of future age warfare by aiding in efficient spectrum management, powerful electromagnetic warfare in active mode and easy and quick decision making leading to timely escalation and achievement of tactical, strategic, and political objectives.Â

This letter proposes a low-complexity, two-step angle, range and delay signature estimation algorithm for a sparse multipath near-field spatial-wideband system. With upcoming sub-THz systems expected to have a large number of antennas, the transmitted wideband signal is not only sensitive to the physical propagation delay across the array aperture but the resulting wavefront is no longer locally planar. We use a combination of discrete linear chirp transform and discrete Fourier transform based techniques to estimate the spatial wideband signature of such a system. Further, we propose an iterative neighbourhood search to acquire super-resolution estimates of the scatterer locations, delays and angle of arrival of the multipaths and show that they are asymptotically optimal approaching their respective Cramér-Rao bounds. This letter proposes a low-complexity one-snapshot two-step signature estimation algorithm for a sparse multipath near-field spatial-wideband system. With upcoming sub-THz systems expected to have a large number of antennas, the transmitted wideband signal is not only sensitive to the physical propagation delay across the array aperture but the resulting wavefront is no longer locally planar. We use a combination of discrete Fourier and discrete linear chirp transform-based techniques to estimate the spatial wideband signature of such a system. Further, we propose an iterative neighbourhood search to acquire super-resolution estimates of the scatterer locations, delays and angle of arrival of the multipaths and show that they are asymptotically optimal approaching their respective Cramer- Rao bounds.Â

Co-Channel Interference is a classical problem in cellular systems that has been studied extensively and several methods have been proposed to overcome it. These include interference rejection techniques as well as joint detection techniques. We have previously proposed a joint detection technique for MSK-type signals that works quite well in certain conditions. In this paper, we formally present what we call Orthogonal MSK and postulate that if two MSK signals have a 90-degree phase offset between them then both can be detected successfully increasing the spectral efficiency two-fold. This technique works well even if the two signals are near equal power and have the same carrier frequency.