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2168 communication, networking and broadcast technologies Preprints

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communication, networking and broadcast technologies Augmented Reality multihoming channel inversion interference rejection m-rat malware (computer software) received signal strength indicator (rssi) latency over-the-air aggregation radio signal transmission vehicular networks queue-length distribution reconfigurable intelligent surfaces intelligent reflecting surfaces spatial wideband signature estimation balancing charge energy-aware queueing theory experiment semcom research landscape Spectrum Sensing cst 6g + show more keywords
k-means clustering algorithm software defined radio (sdr) refractive index structure MIMO cheating detection delay technology recognition sic fedrated learning smart invigilation ship-to-shore oqpsk p4 semantic information semcom dft millimeter waves cooperative relaying dual-polarized home energy storage systems spectral efficiency varactor reed-solomon codes, outdoor measurements object detection student aig security & privacy orthogonality rsu cyberattacks turbulence semcom challenges and future directions power-domain noma fso field trials unmanned aerial vehicles (uav's) localization spatial structure 1-bit power collision multi-rat network stack cyber sec fields, waves and electromagnetics battery applications sub-thz malware analysis ai joint detection Embodiment low power computing and processing field measurements distributed repair schemes. anomaly detection gnu radio engineering profession mmtc Transfer learning ris superstrate insider threats wireless sensor network (wsn) co-channel interferences 5g and beyond split ring resonator (srr) grant-free drone robotics and control systems metamaterial sfc 5G macrocell components, circuits, devices and systems performance analysis signal processing and analysis Federated Learning multiple erasures, repair bandwidth, SDN per-level offered load radio network planning markovian queue artificial intelligence safety applications smart farming (sf) intelligent driver model classroom radio access networks irs deep learning intelligent transportation system deep reinforment learning blockchain interference hfss protocol stack minimum shift keying (msk) vanet general topics for engineers non-orthogonal multiple access near-field ssps massive MIMO yolov3 emergency communication power, energy and industry applications distributed storage systems, meta-heuristic optimization algorithms dlct multi-level inverter microstrip patch antenna agi network protocols & wireless communication non-preemptive priority
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Please note: These are preprints and have not been peer reviewed. Data may be preliminary.
RIS-Enhanced MIMO Channels in Urban Environments: Experimental Insights
James Rains
anvar tukmanov

James Rains

and 3 more

November 22, 2023
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.
Over-the-air Aggregation-based Federated Learning for Technology Recognition in Multi...
Merkebu Girmay
Mohamed Seif

Merkebu Girmay

and 6 more

November 22, 2023
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 potential of SDNs and multihoming in VANETs: A Comprehensive Survey
Richmond Sarpong
Bruno Sousa

Richmond Sarpong

and 2 more

November 22, 2023
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.
Rotational Balancing for Parallel Connection of Batteries and Reducing the Number of...
Muhammad Hamid
Jian Xie

Muhammad Hamid

and 1 more

November 22, 2023
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
Augmented Reality (AR) Technology on Student Engagement: An Experimental Research Stu...
KHRITISH SWARGIARY

KHRITISH SWARGIARY

November 22, 2023
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.
Challenges of Replicating Embodiment in Artificial General Intelligence
Budee U Zaman

Budee U Zaman

November 21, 2023
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
Automatic Cheating Detection In Exam Hall
Roopikha Saravanan

Roopikha Saravanan

November 20, 2023
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.
Blockchain-Enabled Federated Learning Approach for Vehicular Networks
Shirin Sultana
Fida Hasan

Shirin Sultana

and 1 more

November 20, 2023
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
Improvement of Emergency Communication Systems Using Drones in 5G and Beyond for Safe...
A F M Shahen Shah
Muhammet Ali Karabulut

A F M Shahen Shah

and 2 more

November 20, 2023
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).
Application and optimization of multidimensional evaluation functions for faster malw...
Taku Tanaka
HIROTAKA CHIKARAISHI

Taku Tanaka

and 4 more

November 16, 2023
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.
Millimeter wave MPA using Metamaterial-substrate Antenna array for Gain Enhancement
Rishitha Komatineni
Chinta Samson Hruday

Rishitha Komatineni

and 3 more

November 13, 2023
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
Power-Level-Design-aware Scalable Framework for Throughput Analysis of GF-NOMA in mMT...
Takeshi Hirai
Rei Oda

Takeshi Hirai

and 2 more

November 13, 2023
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.
Hybrid Model using LOF and iForest Algorithms for Detection of Insider Threats
Anvita Mahajan

Anvita Mahajan

November 13, 2023
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.
Semantic Communication: A Survey on Research Landscape, Challenges, and Future Direct...
Tilahun Getu
Georges Kaddoum

Tilahun Getu

and 2 more

November 13, 2023
 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
Relay-Aided Uplink NOMA Under Non-Orthogonal CCI and Imperfect SIC in 6G Networks
Volkan Ozduran
Nikolaos Nomikos

Volkan Ozduran

and 4 more

November 13, 2023
A document by Volkan Ozduran . Click on the document to view its contents.
Distributed Repairing Multiple Erasures in Reed-Solomon Codes
Xing Lin

Xing Lin

November 13, 2023
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.
Differentiation and Localization of Ground RF Transmitters Through RSSI Measures from...
Vineeth Teeda
Stefano Moro

Vineeth Teeda

and 4 more

November 09, 2023
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.
Joint Queue-Length Distribution for the Non-Preemptive Multi-Server Multi-Level Marko...
Josef Zuk
David Kirszenblat

Josef Zuk

and 1 more

November 09, 2023
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.
Performance evaluation of the ship-to-shore FSO system under various weather situatio...
Naga Subrahmanya Vamsi Mohan Yarra
Sivanantha Raja A

Naga Subrahmanya Vamsi Mohan Yarra

and 2 more

November 09, 2023
A document by Naga Subrahmanya Vamsi Mohan Yarra . Click on the document to view its contents.
Energy-Adaptive, Robust Monitoring for Solar Sensor-based Smart Farms Under Adversari...
Dian Chen
Qisheng Zhang

Dian Chen

and 4 more

November 09, 2023
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.
Data Acquisition and Utilization of Cognitive Protocol Stack Parameters for Efficient...
Ankit Srivastava
Manoj B. S.

Ankit Srivastava

and 1 more

November 09, 2023
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
Intelligent Radios & Itsâ\euro™ Military Applications
Anupam Sharma
R.M Bodade

Anupam Sharma

and 1 more

November 09, 2023
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
Low Complexity Signature Estimation of Near-Field Spatial-Wideband Systems
Shrayan Das
Debarati Sen

Shrayan Das

and 2 more

December 07, 2023
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
Orthogonal Minimum Shift Keying: A New Perspective on Interference Rejection
Yasir Ahmed
Jeffrey Reed

Yasir Ahmed

and 1 more

November 09, 2023
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.
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