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In recent years, the proliferation of IoT technologies and the widespread adoption of wireless sensors across various critical infrastructures such as power plants, service monitoring systems, space and earth exploration missions, and water treatment facilities have resulted in the generation of vast quantities of multivariate time series data. Within this context, unsupervised anomaly detection has emerged as a pivotal yet challenging problem in time series research, necessitating machine learning models capable of identifying rare anomalies amidst massive datasets. Traditionally, unsupervised methods have approached this issue by learning representations of primary patterns within sequences and detecting deviations through reconstruction errors. However, the effectiveness of this approach is often limited due to the intricate dynamics and diverse patterns inherent in these dynamic systems. Moreover, many existing unsupervised anomaly detection techniques fail to fully exploit inter-feature relationships within multivariate time series data, thereby overlooking a crucial criterion for accurate detection. To address these shortcomings, this paper introduces a novel unsupervised method for multivariate time series anomaly detection based on normalized flows and autoencoders. Central to our approach is the incorporation of a channel shuffling mechanism during training, enhancing the model’s capacity to discern inter-channel patterns and anomalies. Concurrently, the application of normalized flows within the autoencoder framework serves to constrain the latent space, effectively isolating anomalies and improving detection accuracy. Experimental validation conducted on two large-scale public datasets demonstrates the efficacy of the proposed method compared to established benchmarks, highlighting its superior performance.

In the Vehicle-to-Grid (V2G) scenario, a multitude of coordinated electric vehicles (EVs) equipped with high-capacity batteries actively participate in power grid dispatching as energy carriers, aiming to achieve a tripartite objective encompassing peak load reduction and valley filling, enhanced utilization of renewable energy sources, and added benefits for electric vehicle owners. To address the existing limitations in the charging–discharging decision-making process for electric vehicles based on V2G, such as the lack of consideration for charging pile constraints, EV profitability, EV transportation timeliness, and high costs associated with central servers, we proposed a reinforcement learning-based Multi-vehicle Joint Routing and Charging–Discharging Decision algorithm (MJRCDD). Firstly, the Markov decision process (MDP) was established to describe the problem, and the route selection and charging–discharging behavior of the vehicle were innovatively integrated in the vehicle action space. Secondly, the multi-vehicle joint route planning and charging–discharging decision problem was solved by multi-agent reinforcement learning. Finally, the effectiveness of MJRCDD was verified by simulation and comparison experiments based on PeMS.

In this paper, we devise a deep SARSA reinforcement learning (DSRL) user scheduling algorithm for a base station (BS) that uses a high-altitude platform station (HAPS) as a backup to serve multiple users in a wireless cellular network. Considering a realistic scenario, we assume that only the outdated channel state information (CSI) of the terrestrial base station (TBS) is available in our defined user scheduling problem. We model this user scheduling problem using a Markov decision process (MDP) framework, aiming to maximize the sum-rate while minimizing the number of active antennas at the HAPS. Our performance analysis shows that the sum-rate obtained with our proposed DSRL algorithm is close to the optimal sum-rate achieved with an exhaustive search method. We also develop a heuristic optimization method to solve the user scheduling problem at the BS. We show that for a scenario where perfect CSI is not available, our proposed DSRL algorithm outperforms the heuristic optimization method.

Underwater optical wireless communication (UOWC) systems face significant challenges due to oceanic turbulence and pointing errors (PE), which can degrade system performance. Moreover, interference is another challenge in UOWC, considering the consistent increase in underwater optical devices. In this study, the performance of rate splitting multiple access (RSMA)-based UOWC systems is investigated over an exponential-generalized gamma (EGG)-distributed oceanic turbulence channel with generalized PE. The RSMA scheme is employed to facilitate communication between a source and multiple users in the UOWC system while accounting for the combined effects of oceanic turbulence and generalized PE. The statistical characterization of the UOWC system is analyzed, including the probability density function (PDF) of the signal-to-noise ratio (SNR), outage probability, throughput, and sum ergodic capacity. Additionally, closed-form expressions for the outage probability and throughput are derived, and asymptotic expressions for the outage probability in the high SNR regime are provided. Moreover, the diversity order of the system is evaluated, and the impact of different parameters on the system performance is discussed. Our results demonstrate the effectiveness of the RSMA-based UOWC system in mitigating the adverse effects of oceanic turbulence and PE while achieving improved performance in terms of capacity and outage probability. The findings of this study provide valuable insights for the design and optimization of UOWC systems in challenging underwater environments.

Our most recent findings on multi-hop LoRa networks imply that earlier developed multi-hop routing methods, such as our previously suggested Extended-DRESG scheme, had overlooked some essential information. Although the researchers employed uniform node distributions for their multi-hop models, a full capacity LoRa network may be treated as a Gaussian distribution. Extended-DRESG performs better with a Gaussian distribution than with a uniform distribution of nodes. In contrast to optimal ring-hop combinations created earlier, this observation led to the suggested algorithm for optimal ring positioning and ring-hop combinations in Extended-DRESG. The Extended-DRESG cluster developing technique, which may produce much superior performance in terms of energy efficiency when uniform node distribution is encountered, is further suggested in light of the observations made in this research. Future multi-hop models can benefit from the current observations in two ways: (i) a better understanding of the packet forwarding structure, and (ii) a good cluster formation technique when clusters are established based on their distances from the gateway.

Terahertz (THz) communication is envisioned as one of the candidate technologies for future wireless communications to enable achievable data rates of up to several terabits per second (Tbps). However, the high pathloss and molecular absorption in THz band communications often limit the transmission range. To overcome these limitations, this paper proposes intelligent reconfigurable surface (IRS)-aided THz networks with imperfect channel state information (CSI). Specifically, we present an angle-based trigonometric channel model to facilitate the performance evaluation of IRS-aided THz networks. In addition, to maximize the sum rate, we formulate the transmitter (Tx)-IRS-receiver (Rx) matching problem, which is a mixed-integer nonlinear programming (MINLP) problem. To address this non-deterministic polynomial-time hard (NP-hard) problem, we propose a Gale-Shapley algorithm-based solutions to obtain stable matching between transmitters and IRSs, and receivers and IRSs, in the first and second sub-problems, respectively. The impact of the transmission power, the number of IRS elements, and the network area on the sum rate are investigated. Furthermore, the proposed algorithm is compared to an exhaustive search, nearest association, greedy search, and random allocation to validate the proposed solution. The complexity and convergence analysis demonstrate that the computational complexity of our algorithm is lower than that of the ES method.

Although many proposals have been developed for the sixth-generation (6G) technology, realizing 6G is fraught with numerous fundamental interdisciplinary, multidisciplinary, and transdisciplinary challenges. To mitigate some of these challenges, goal-oriented semantic communication (SemCom) has emerged as a promising 6G technology enabler. This enabler employs only semantically-relevant information for successful task execution while minimizing power usage, bandwidth consumption, and transmission delay. On the other hand, 6G is essential for realizing major goal-oriented SemCom use cases such as autonomous transportation. These paradigms of 6G for goal-oriented SemCom and goal-oriented SemCom for 6G call for a tighter integration of 6G and goal-oriented SemCom. To facilitate this purpose, this survey paper exposes the fundamental challenges of 6G; details the notion of goal-oriented SemCom and its state-of-the-art research landscape; presents state-of-the-art trends, use cases, and frameworks of goal-oriented SemCom; exposes the fundamental and major challenges of goal-oriented SemCom; and offers promising future research directions for goal-oriented SemCom. Consequently, this survey article stimulates numerous lines of research on goal-oriented SemCom theories, algorithms, and realization.

Cognitive radio networks (CRNs) mitigate spectrum scarcity by leveraging the holes in the licensed spectrum to enable Internet of Things (IoT) devices to opportunistically access the spectrum. However, IoT devices need to sense the spectrum before they can access it, which is an energy-intensive process and hinders the practical implementation of opportunistic spectrum access for energy-constrained IoT devices. In this context, reinforcement learning-based algorithms that encourage cooperation among IoT devices to eliminate the need for constant sensing are promising candidates for practical CRN implementation. As exciting as the application of reinforcement learning to CRNs is, benchmarking the performance of different algorithms is a huge challenge due to a lack of standardized comparison metrics, especially for hybrid action spaces that comprise both discrete and continuous actions. We propose a hybrid discrete-continuous space deep reinforcement learning algorithm that maximizes the energy efficiency of CRNs by optimizing sensing, cooperation, and transmission by IoT devices. We also analyze the algorithm’s performance by setting the theoretical upper bound for throughput and find that it reaches 99.4% of the theoretical upper bound, while its discrete action-space version reaches 96% and other baseline algorithms range between 70% and 86%.

In the rapidly evolving sixth-generation (6G) networks, achieving ultra-high reliability for short packets poses a crucial challenge for network designers as classical Shanon capacity bounds become obsolete. This paper addresses this imperative task and presents a performance analysis of ultra-reliable low-latency communication (URLLC) systems in clustered networks with short packet communication (SPC) in the finite blocklength regime. In clustered networks, where multiple interferers are present, multiple ground users are grouped into a cluster and communicate with a central node called a cluster head (CH). The system adopts a CH as a wireless relay to accomplish URLLC between a base station and ground users. In this context, we first derive a closed-form expression for the overall block error rate of the system. The analysis is based on a theoretical model that considers the packet size, blocklength, and maximum achievable rate. Moreover, the paper proposes a transfer learning approach using fine-tuned target models with domain adaptation for real-time prediction of the system’s performance. The prediction is necessary to accurately assess the performance of URLLC in statistically independent and not necessarily identically distributed (Non-IID) interference networks. Deep transfer learning is utilized to develop a model that can generalize interference scenario and provide reliable predictions. The transfer learning approach involves using a source model and fine-tuning it with domain-specific data to improve the prediction accuracy. Overall, the paper provides insights into the performance of URLLC systems with SPC and proposes an approach to improve the real-time prediction’s accuracy. The proposed approach has the potential to facilitate the deployment of URLLC systems in practical applications where real-time performance prediction is crucial.

In recent years, due to their ability to transmit and relay wireless signals in challenging terrains, Unmanned Aerial Vehicles (UAVs) and High Altitude Platform Stations (HAPS) have become indispensable in various operations in security, emergency, and military campaigns. However, these networks’ ad-hoc structure and open nature make them highly vulnerable to numerous threats and, in particular, to severe jamming attacks. Furthermore, the communication link between a HAPS and multiple UAVs is also under the threat of multiple and different jamming attacks. Addressing these challenges requires innovative and novel methods capable of interactive and proactive defence strategies. To this end, in this study, we propose a method that combines a pseudo-random (PR) algorithm for initial channel selection with a Transformer-based module to predict jammer behavior. This proactive approach significantly enhances the robustness of UAV communications. Our results demonstrate substantial improvements in transmission success rates and prediction accuracy, offering a robust solution for secure UAV and HAPS communications under adverse conditions.

This article addresses the joint power allocation and channel assignment (JPACA) problem in uplink non-orthogonal multiple access (NOMA) networks, an essential consideration for enhancing the performance of wireless communication systems. We introduce a novel methodology that integrates convex optimization (CO) and machine learning (ML) techniques to optimize resource allocation efficiently and effectively. Initially, we develop a CO-based algorithm that employs an alternating optimization strategy to iteratively solve for channel and power allocation, ensuring quality of service (QoS) while maximizing the system’s sum-rate. To overcome the inherent challenges of real-time application due to computational complexity, we further propose a ML-based approach that utilizes a stacking ensemble model combining convolutional neural network (CNN), feed-forward neural network (FNN), and random forest (RF). This model is trained on a dataset generated via the CO algorithm to predict optimal resource allocation in real-time scenarios. Simulation results demonstrate that our proposed methods not only reduce the computational load significantly but also maintain high system performance, closely approximating the results of more computationally intensive exhaustive search methods. The dual approach presented not only enhances computational efficiency but also aligns with the evolving demands of future wireless networks, marking a significant step towards intelligent and adaptive resource management in NOMA systems.

Advancements in sixth-generation (6G) networks, coupled with the evolution of multimodal sensing in vehicle-to-everything (V2X) networks, have opened avenues for transformative research into multimodal-based artificial intelligence (AI) applications for wireless communication and network management. However, this promising research direction is often constrained by the limited availability of suitable datasets. In response, this paper introduces a comprehensive configurable co-simulation framework that integrates the state-of-the-art CARLA and Sionna simulators to generate a multimodal multi-view V2X (MVX) dataset. We present novel AI-based models to predict future line-of-sight (LoS) blockages and optimal beam direction as well as an innovative antenna position optimization (APO) solution, all of which are underpinned by the multimodal dataset MVX. Our framework capitalizes on collaborative perception and significantly enhances V2X communication by integrating LiDAR and wireless data. Thorough evaluations demonstrate that our collaborative perception approach outperforms traditional methods of both beam and blockage prediction in terms of accuracy and efficiency. Additionally, we evaluate the importance of infrastructural elements in V2X systems and conduct a computational study to illustrate that our framework is suitable for various operational scenarios and can be used as a digital twin solution. This work not only contributes to the field of V2X wireless communications by providing a versatile framework for network management but also sets the stage for future research on multi-sensor fusion in AI applications for V2X wireless communication environments to enhance the efficiency and resilience of future 6G networks.

As wireless communication technology progresses towards the sixth generation (6G), high-frequency millimeter-wave (mmWave) communication has emerged as a promising candidate for enabling vehicular networks. It offers high data rates and low-latency communication. However, obstacles such as buildings, trees, and other vehicles can cause signal attenuation and blockage, leading to communication failures that can result in fatal accidents or traffic congestion. Predicting blockages is crucial for ensuring reliable and efficient communications. Furthermore, the advent of 6G technology is anticipated to integrate advanced sensing capabilities, utilizing a variety of sensor types. These sensors, ranging from traditional RF sensors to cameras and Lidar sensors, are expected to provide access to rich multimodal data, thereby enriching communication systems with a wealth of additional contextual information. Leveraging this multimodal data becomes essential for making precise network management decisions, including the crucial task of blockage detection. In this paper, we propose a Deep Learning (DL)-based approach that combines Convolutional Neural Networks (CNNs) and customized Vision Transformers (ViTs) to effectively extract essential information from multimodal data and predict blockages in vehicular networks. We train and evaluate our proposed method on the DL dataset framework for vision-aided wireless communications (ViWi) and demonstrate its potential for predicting blockages in vehicular networks through simulations. The results show that the proposed approach achieves over 95% accurate predictions, proving its potential for integration into 6G vehicular networks to enhance communication reliability and support advanced applications such as autonomous driving and smart city infrastructure. These findings underscore the practical significance and future impact of our work in advancing ultra-reliable and low-latency communication systems.

The rapid expansion of Internet of Things (IoT) networks has paved the way for their integration into mission-critical applications requiring secure and reliable monitoring, such as smart grid utilities. However, these advanced power grids face significant challenges in maintaining reliable wireless communication, particularly in hostile environments like high-voltage substations and power plants. These environments are characterized by intense bursts of interference, known as impulsive noise with memory. To address this problem, in this study, we introduce a two-process receiver design. The first process is a multi-step receiver parameter estimation process. The second process is a novel memory-aware log-likelihood ratio (LLR) calculation method designed to mitigate the effects of impulsive noise with memory using the parameters estimated from the first process. This method is computationally efficient, which makes it suitable for IoT devices with limited computational capabilities. Simulation results obtained show that the proposed method achieves a bit error rate (BER) similar to the corresponding BERs of the best-performing algorithms with perfect noise parameters. Furthermore, it outperforms the Viterbi algorithm amid imperfect noise parameters. Notably, it method achieves these benchmarks while substantially improving execution time.

In this study, aiming to address the challenges posed by interference from communication systems and jammers, we investigate the application of deep learning (DL) in electronic support measures (ESM) radar systems. Our primary objective is to detect, classify, and forecast interference that can disrupt detection of low probability of intercept (LPI) and low probability of detection (LPD) signals. The proposed algorithm uses a time-frequency distribution (TFD) and received interference strength (RIS) to detect and predict interference. To ensure high precision,we develop a new DL-based outlier detection (OD) technique that is based on the relationship between true positive rate (TPR) and latent space. More specifically, the OD technique applies a new dual-threshold mechanism to the TFD representation for interference detection. We also introduce a DL-enabled classifier designed using the OD architecture to identify the source of interference. Finally, we forecast the RIS by proposing a new DL autoregressive (AR) model through a sliding window designed using the classifier’s output. By integrating OD in classifier design and using its output for forecasting, our approach achieves superior accuracy as compared to independent models. Simulation results demonstrate that the proposed algorithm outperforms others, particularly in low signal-to-interference plus noise ratio (SINR) conditions. Specifically, in terms of interference detection, our algorithm achieves 0.9978 TPR, 0.9415 recall, and 0.0004 false positive ratio (FPR). With regard to classification, it records 0.9784 precision and 0.7847 recall. In forecasting, it achieves a 0.2100 mean average error (MAE), thus significantly enhancing ESM radar awareness. The TFD feature also proves to be more accurate than in-phase and quadrature features. These strengths, coupled with an optimal balance of cost and accuracy, make our framework robust and resistant to interference.

With the growing interest that is being shown in marine resources, the concept of the Internet of Things (IoT) has been extended to underwater scenarios, which has given rise to the Internet of Underwater Things (IoUT). The IoUT encompasses a network of interconnected intelligent underwater devices that can be used to monitor underwater environments and support various applications, such as underwater exploration, disaster prevention, and environmental monitoring. Advances in underwater wireless communication and sensor technologies have propelled the IoUT concept forward. However, the IoUT faces significant challenges. The harsh and vast underwater environment makes information sensing particularly difficult and leads to insufficient or inaccurate data being collected. Additionally, underwater conditions like pressure variation, hydrological characteristics, temperature changes, water currents, and topography hinder conventional communication models and make data transmission difficult and inefficient. Security in IoUT networks is a critical concern due to hardware limitations and seawater channel imperfections. Constrained sensor nodes and spatial-temporal uncertainty introduced by node mobility further complicate security provisioning. This survey paper addresses these challenges by offering a comprehensive overview of IoUT security. The investigation thoroughly examines both traditional and classic machine learning techniques and focuses on deploying advanced technologies such as federated learning and digital twin. The study effectively addresses integration challenges and open issues and provides a roadmap for future directions to play a pivotal role in formulating robust security mechanisms for IoUT networks.

Now a days, the revolution or advancements in Information Communication Technology (ICT) are changing the way of handling the situations in the present environment. In our society, several people are present who live with physical difficulties and can not complete their basic household tasks for their own. The disabled person is all alone at home and caring the house where intermediator may gain the benefit of the opportunity. Though the modern era is revolutionizing the whole communication and interaction process among environment and human. The disabled person who cannot walk, and is deaf completely unable to hear, run or handle the current situation, the automatic systems or alarm may be compromised by the intruder and can access the house with the mean of steal. The aim of this paper is to propose a secure and competent communication mechanism for AI and edge-computing based home automation in case of disabled persons handling the situation. The proposed mechanism integrates the distrust-based recommendation system along with Social Contact Similarity mechanism that improves the efficiency and quick action decision corresponding to the altered device that may cause severe harm at the edge-level or to the person. The proposed mechanism is simulated and substantiated against various existing scheme over several performance metrics such as delay, alteration rate, accuracy and response time.

Disability has traditionally posed a significant barrier for millions of individuals who are unable to benefit from current technologies such as digital interfaces, the internet, and personal computers. Nowadays, developments in information communication and technology (ICT) are transforming the way we handle and interact with our environment, making it easier and more convenient. However, individuals with disabilities face numerous restrictions and challenges, hindering their full involvement and access to the devices. This paper proposes an efficient and trusted communication mechanism that utilizes knowledge factors and friendship similarity schemes to improve the accessibility and accuracy of information transmission in a networked environment for disabled people. The proposed mechanism ensures the involvement of trusted devices in the network for accuracy and immediate decisions for disabled individual. The proposed approach is validated and compared to existing methods using various security metrics, including social trust, objective trust, experienced trust, and recommended trust. Through this research, we aim to address the barriers faced by individuals with disabilities and provide them with equal opportunities to benefit from modern technological advancements.

To ensure better facilitation of vehicular services and improve driving safety in the Internet of Vehicles (IoV), context prediction among vehicles plays a very crucial role. However, as more malicious IoV devices get involved in the network, the context prediction accuracy shared among various servers may degrade severely. Existing schemes have used cryptographic mechanisms to securely and accurately identify malicious devices. However, time and the subsequent delay in identifying and rating the legitimate communicating IoV devices emerge as a crucial issue. Hence, to solve this critical problem, we put forth an efficient and reliable trust framework where trust and context prediction is achieved by Tidal Trust Mechanism (TTM) and Contract Theory (CT). TTM can successfully rate the degree of trust between the devices with a high level of accuracy, whereas CT can verify the context prediction reliably. The proposed mechanism based on TTM and CT ensures that trusted IoV devices are identified with high accuracy and verified reliably. The proposed framework is simulated over real-world data set in MATLAB for various performance metrics, such as altered records, accuracy prediction, response time, and utilities of IoV devices. Simulation results show that the proposed framework provides a significant improvement of approximately 87% in comparison to existing (baseline) approaches while analyzing the accuracy, record alteration, and resource utility among the devices in the network.

Improving the healthcare system is imperative for increasing efficiency and reducing costs. The rapid adoption of Internet of Medical Things (IoMT) has facilitated a broad range of healthcare applications and services, from real-time and critical care monitoring to telemedicine. These services typically have highly distinct quality of service (QoS) requirement for communication networks and thus cannot be served with a uniform network. In such a context, network slicing technologies in 5G can be employed to create virtually independent and customized communication networks for these use cases to meet their QoS requirement. In this wok, we model network slicing for healthcare services as a virtual network embedding (VNE) problem and propose a two-sided matching theory-based virtual network embedding (MT-VNE) solution. In MT-VNE, four novel preference indexes are devised to construct differential preference lists. The deferred acceptance and modified shortest path-based algorithms are utilized to perform the virtual node and link mapping, respectively. Extensive simulations demonstrate that MT-VNE outperforms other baselines in terms of accepting more healthcare services and effectively utilizing physical network resources. Moreover, MT-VNE also significantly reduces service embedding time.

Emotional aware intelligent healthcare mechanism is seen as one of the emergent techniques that can be used to overcome the traditional communication issues like storage overhead and delay in transmission process in healthcare systems. It is a smart technique primarily used to ensure faster connectivity and transmission as well as healthcare data processing through various heterogeneous networks by understanding human emotions. Researchers have focused on various processing and transmitting techniques of emotional aware intelligent systems. However, very few of them have focused on the need for security while transmitting or reading the patients’ record. The aim of this paper is to propose a secure and transparent communication mechanism in intelligent emotional aware systems using Analytical Hierarchical Process (AHP), blockchain system and a mathematical model. AHP and the mathematical model are used to ensure accuracy during the data transmission process by analysing the legitimacy of each device. In addition, the identity based trust and blockchain mechanism is used to ensure the continuous analysis and transparency among network entities while transferring the information. The proposed framework is further analysed against existing models over various security matrices such as data alteration, report generation, accuracy etc. The out performance of proposed mechanism is approximate 89% as compare to existing mechanism.

This study analyzes the physical layer security (PLS) performance of a differential chaos shift keying (DCSK) modulation-based Power Line Communication (PLC) system by exploiting the novel Farlie-Gumbel-Morgenstern (FGM) Copula approach. A power line Wyner’s wiretap channel model is investigated, where the main channel and the wiretap channel are assumed to be correlated and Log-normally distributed. The Gamma approximation to the Log-normal distribution is employed to simplify the computation. Concurrently, the PLC channel noise is modeled as a Bernoulli-Gaussian random process. Utilizing a Copula based approach to model the dependence among the correlated PLC channels, the PLS performance of the PLC system is evaluated in terms of the secure outage probability (SOP) and the strictly positive secrecy capacity (SPSC). It is revealed through the asymptotic SOP analysis that the secrecy diversity order depends on the shaping parameter $(m_{\gamma _{M}})$ of the main channel. We also propose an algorithm to maximize the secrecy throughput under SOP constraints. Based on the insights from this analysis, it has been seen that the SOP performance degrades when the value of the dependence parameter $(\theta)$ increases. Also, the secrecy throughput performance improves with a lower optimal threshold value of the signal-to-noise ratio (SNR), $\gamma _{\textrm {th}}$ . Furthermore, some other insightful observations are presented by studying the impact of different parameters such as spreading factor $(\beta)$ , impulsive noise occurrence probability $(p)$ , transmitted power $(P_{T})$ , and impulsive noise index $(K)$ .

Semantic communication (SemCom) aims to convey the meaning behind a transmitted message by transmitting only semantically-relevant information. This semantic-centric design helps to minimize power usage, bandwidth consumption, and transmission delay. SemCom and goal-oriented SemCom (or effectiveness-level SemCom) are therefore promising enablers of 6G and developing rapidly. Despite the surge in their swift development, the design, analysis, optimization, and realization of robust and intelligent SemCom as well as goal-oriented SemCom are fraught with many fundamental challenges. One of the challenges is that the lack of unified/universal metrics of SemCom and goal-oriented SemCom can stifle research progress on their respective algorithmic, theoretical, and implementation frontiers. Consequently, this survey paper documents the existing metrics– scattered in many references– of wireless SemCom, optical SemCom, quantum SemCom, and goal-oriented wireless SemCom. By doing so, this paper aims to inspire the design, analysis, and optimization of a wide variety of SemCom and goal-oriented SemCom systems. This article also stimulates the development of unified/universal performance assessment metrics of SemCom and goal-oriented SemCom, as the existing metrics are purely statistical and hardly applicable to reasoning-type tasks that constitute the heart of 6G and beyond.

While sixth-generation (6G) wireless systems are expected to bring about an explosion of accessible user information and novel technologies, along with new threats to terrestrial and non-terrestrial networks, major concerns associated with the development of 6G networks are privacy and security. Reconfigurable intelligent surfaces (RISs), which have recently emerged as promising candidates to support 6G physical platforms, have proved to be capable of boosting security of next-generation wireless systems. However, due to their easy reconfigurability and low cost, RISs are vulnerable to several security threats, and this vulnerability has not yet been thoroughly addressed in previous research. To fill this gap in the literature, in this review, we aim to thoroughly analyze the security challenges affecting RIS-empowered 6G wireless networks. To this end, we review the attributes of RISs that distinguish them from other relevant technologies, such as multiple-input-multiple-output (MIMO), conventional relaying, backscatter communication (BackCom), as well as outline security and privacy attacks in RIS-assisted 6G applications. Our specific focus is on security and privacy threats associated with the use of RISs with different vital 6G technologies, including millimeter wave (mmWave), terahertz (THz), device-to-device (D2D) communication, Internet of Things (IoT) networks, multi-access edge computing (MEC), integrated sensing and communication (ISAC), simultaneous wireless information and power transfer (SWIPT), and non-terrestrial network. The review concludes with an outline of open research challenges and promising future directions to further increase secrecy of RIS-assisted 6G applications. The results of this review contribute to previous research on 6G network security, in general, and RIS-based 6G network security, in particular.

Industrial Control Systems (ICSs) have entered an era of modernization enabled by the recent progress in Information Technologies (IT), particularly the Industrial Internet of Things (IIoT). This enables better automation of industrial processes but now exposes the ICSs to cyber-attacks that exploit the IIoT vulnerabilities. Thus, to ensure ICSs security, numerous research works have focused on designing Intrusion Detection and Prevention Systems (IDPSs), and deep learning has recently received considerable attention, as it has the potential to improve detection accuracy. However, most of the proposed deep learning solutions focus only on the model’s accuracy without considering latency, which is an essential requirement in many ICSs. The novelty of this paper is the time complexity analysis of Deep Neural Networks (DNNs) and the design of a low latency and robust deep learning-based collaborative IDPS. The proposed architecture employs two classification models. In the first model, a lightweight DNN is used to perform a binary classification, i.e., normal or attack, which ensures rapid intrusion detection. A second model ensures the identification of the type of attacks by performing a multi-class classification of the detected anomaly, which is handled by a robust and complex DNN in order to achieve higher accuracy. This research also proposes intrusion response measures to deal with detected attacks, first after the anomaly detection, and then after the identification of the attack type. An experimental evaluation has been provided using various detection features, datasets, DNN algorithms, and the results demonstrate the effectiveness of the proposed solution.

Conventional coherent chaos-based communication systems require synchronization of chaotic signals, which is still practically unattainable in a noisy environment. Moreover, in non-coherent schemes, a part of the bit duration is spent sending non-information-bearing reference samples, which deteriorates the Bit Error Rate performance (BER) of these systems. To tackle these problems, this paper designs an $M$ -ary Deep Learning Chaos Shift Keying $(M$ -ary DLCSK) system. The proposed receiver uses a Convolutional Neural Network (CNN)-based classifier that recovers $M$ -ary modulated data. The trained NN model grasps different chaotic maps, estimates channels, and classifies the received signals effectively. Moreover, we consider a Transfer Learning (TL) framework that enhances the noise performance and classification results. Due to the generalization capabilities of TL, the trained NN can work in different Signal-to-Noise Ratio (SNR) conditions without the need for re-training. We compare the BER performance, complexity, and bandwidth efficiency of the $M$ -ary DLCSK receiver with existing receivers. The results demonstrate that the $M$ -ary DLCSK receiver is the first practical system that achieves the theoretical BER performance of the coherent CSK systems under Rayleigh fading channels. Moreover, the proposed system provides a considerable performance advantage compared to the existing DL-based receivers under Rayleigh fading channels. For example, the BER performance of 8-ary DLCSK shows a gain of 0.1 over the Long Short-Term Memory (LSTM)-aided DNN systems at the target $E_{b}/N_{0}=14dB$ . These features make $M$ -ary DLCSK an attractive candidate for several applications, such as Massive Multiple-Input Multiple Output (MIMO), Vehicle-to-everything (V2X), Quantum, and optical communication systems.

Underwater acoustic cluster networks (UACNs) are commonly used due to their adaptability in dynamic underwater environments. While the low-energy adaptive clustering hierarchy (LEACH) protocol was initially designed for radio frequency (RF) cluster networks, it has also been applied to UACNs. However, the LEACH protocol uses lengthy overheads per packet due to its use of global or wide-scale IDs, leading to increased communication latency and energy usage per packet. To address this issue, we propose the low-latency low-energy adaptive clustering hierarchy ( $\text{L}^{3}$ EACH) protocol. The $\text{L}^{3}$ EACH protocol is a comprehensive framework that integrates ID assignment, time slot reservation, packet routing, and self-network organization. The protocol uses shorter overheads by assigning local IDs instead of global or wide-scale IDs. It assigns unique IDs to nodes within a cluster and reassigns the same IDs to nodes in other clusters, i.e., spatial ID reuse. The protocol also allocates IDs and time slots on demand to maximize network resources. To further enhance the protocol, we introduce the $\text{L}^{3}$ EACH-Version 2 ( $\text{L}^{3}$ EACH-V2) protocol, which modulates the preamble bits to embed the IDs in the overhead rather than inserting extra bits. We also provide the computational complexity of the $\text{L}^{3}$ EACH-V2 protocol. Compared to the DIVE protocol, the $\text{L}^{3}$ EACH protocol reduces the ID length and average energy per packet by 50% and 13%, respectively. Furthermore, the $\text{L}^{3}$ EACH-V2 protocol reduces the average energy per packet by 27% and increases the network throughput by 16% compared to the $\text{L}^{3}$ EACH protocol, making it an efficient and scalable solution, especially, for dense UACNs.

Abstract Large volumes of sensitive data are being transferred among devices as the Internet of Things (IoT) grows in popularity. As a result, security measures must be implemented to ensure that unauthorized parties do not obtain access to the data. It is well acknowledged that IoT devices have restricted resources, such as limited battery life, memory, and hence reaction time. Classical encryption approaches and methods become inefficient for IoT devices due to memory limits. Large volumes of sensitive data are being transferred between devices as the Internet of Things (IoT) grows in popularity. This involves the implementation of security safeguards to ensure that unauthorized parties do not obtain access to the data. IoT devices are notorious for having limited resources, such as battery life, memory, and hence response time. Classical encryption approaches and methods become inefficient for IoT devices due to memory limits. As a result, a Lightweight cryptosystem that fits the needs of Lightweight devices and ubiquitous computing systems has emerged. The goal of this study is to present a Lightweight cryptosystem (LWC) that may be used as a plugin to secure data transfers in IoT devices and pervasive computing. To that goal, the researchers employ several simple measuring techniques. The suggested system was then implemented on a field-programmable gate array (FPGA) board using the Verilog programming language to demonstrate its appropriateness for actual security applications. FPGA is also utilized in hardware applications to assess the system’s resource usage and performance. Finally, a comparison of the proposed system with previous lightweight cryptography systems is performed to reinforce the major goal of this work, which is to present a new lightweight cryptosystem.

Intelligent transportation systems (ITSs) have become increasingly popular because they support effective coordination in connected vehicles. ITSs present an integrated approach for exchanging relevant information in order to improve the safety, efficiency, and reliability of road transportation systems. A variant of mobile ad-hoc networks (MANETs) called vehicular ad-hoc networks (VANETs) are an integral component of ITSs. VANETs consist of interconnected vehicles with sensing abilities that exchange information related to traffic, positioning, weather, and emergency services. In general, vehicle-to-everything (V2X) refers to communications between any entity and a vehicle, where the entity may be a vehicle, a cloud-based network, a pedestrian, or equipment installed along a road. One of the crucial challenges in V2X is the reliable and timely circulation of information among vehicular nodes to allow drivers to make decisions that increase road safety. In this context, efficient V2X routing protocols play a key role in supporting reliability and safety, and enhancing the overall quality of service (QoS) in VANETs. However, VANETs have distinct characteristics, such as high vehicular node mobility, unsteady connectivity, rapid changes in network topology, and unbounded network size, that can significantly affect routing in the network. Various routing protocols for V2X communication exist in the open technical literature. In this survey, we categorize the routing mechanisms as non-learning- and learning-based approaches, and discuss existing V2X routing protocols and their contributions to and impacts on VANET performance. Here, the learning-based approach implies the use of machine learning algorithms. This survey also summarizes open challenges in designing effective V2X routing protocols and future research directions to consider when developing smart routing mechanisms for next-generation intelligent VANET technologies.

The 6LoWPAN (IPv6 over low-power wireless personal area networks) standard enables resource-constrained devices to connect to the IPv6 network, blending an IPv6 header compression protocol. For this network technology, a new routing protocol called Routing Protocol for Low Power Lossy network (RPL) has been designed. The latter is a lightweight protocol that determines the route across the nodes based on rank values. This protocol is known to be non-resilient against Rank attacks, which aim at creating non-optimized routes for packet forwarding, hence overwhelming the constrained 6LoWPAN. With 5G, Software-Defined Networks (SDNs) have been developed to facilitate simple programmable control plane, Quality of Service (QoS) provisioning, and route configuration services for 6LoWPAN. However, there is still a lack of optimization mechanisms to protect 6LoWPAN against Rank attacks in SDN-based deployment. To this end, in this paper, a Reinforcement-Learning (RL) agent is leveraged to assist and complement an SDN controller in achieving cost-efficient route optimization, and QoS provisioning packet forwarding to prevent rank attacks. Experimental results confirm that our approach effectively prevents Rank attacks while providing an adequate delay and radio duty cycle. Meanwhile, it maximizes the packet delivery ratio, facilitating practical implementations in software-defined Low Power Internet of Things (IoT) networks.

The performance of the envisioned 6G network is fundamentally constrained by the uncontrollable and random wireless communication channel. Intelligent reflecting surfaces (IRSs) have emerged as one of the potential solutions to overcome this challenge by smartly controlling the incident signal to enhance the energy efficiency and spectrum efficiency of the 6G network. In addition, the future 6G network will incorporate several enabling technologies, including artificial intelligence and machine learning (AI/ML), integrated terrestrial and non-terrestrial (TNT) networks, multi-access edge computing (MEC), non-orthogonal multiple access (NOMA), and terahertz/millimeter wave (THz/mmWave) communication techniques. Therefore, this paper provides a contemporary and comprehensive overview of the envisioned IRS-empowered 6G networks from the perspective of its architecture, deployment strategy, integration of IRS technology with other 6G-enabling technologies, and physical layer security (PLS). Finally, we highlight design challenges and future research directions aimed at improving the 6G network performance.

Abstract Physical layer security (PLS) has been proposed to afford an extra layer of security on top of the conventional cryptographic techniques. Unlike the conventional complexity-based cryptographic techniques at the upper layers, physical layer security exploits the characteristics of wireless channels, e.g., fading, noise, interference, etc., to enhance wireless security. It is proved that secure transmission can benefit from fading channels. Accordingly, numerous researchers have explored what fading can offer for physical layer security, especially the investigation of physical layer security over wiretap fading channels. Therefore, this paper aims at reviewing the existing and ongoing research works on this topic. More specifically, we present a classification of research works in terms of the four categories of fading models: (i) small-scale, (ii) large-scale, (iii) composite, and (iv) cascaded. To elaborate these fading models with a generic and flexible tool, three promising candidates, including the mixture gamma (MG), mixture of Gaussian (MoG), and Fox’s H-function distributions, are comprehensively examined and compared. Their advantages and limitations are further demonstrated via security performance metrics, which are designed as vivid indicators to measure how perfect secrecy is ensured. Two clusters of secrecy metrics, namely (i) secrecy outage probability (SOP), and the lower bound of SOP; and (ii) the probability of nonzero secrecy capacity (PNZ), the intercept probability, average secrecy capacity (ASC), and ergodic secrecy capacity, are displayed and, respectively, deployed in passive and active eavesdropping scenarios. Apart from those, revisiting the secrecy enhancement techniques based on Wyner’s wiretap model, the on-off transmission scheme, jamming approach, antenna selection, and security region are discussed.

The controller area network (CAN), which is still today the most used in-vehicle network, does not provide any security or authentication mechanism by design. Since current vehicles, which have numerous connectivity technologies, such as Bluetooth, Wi-Fi, and cellular radio, can be easily accessed from the exterior world, they can be easy targets of cyber-attacks. It is therefore urgently necessary to enhance vehicle security by detecting and stopping cyber-attacks. In this paper, we propose a novel unsupervised intrusion prevention system (IPS) for automotive CANs that detects and hinders attacks without modifying the architecture of the electronic control units (ECUs) or requiring information that is restricted to car manufacturers. We compare two machine learning algorithms’ ability to detect fuzzing and spoofing attacks, and evaluate which of them is most accurate with the fewest number of data bytes. The fewer data bytes required, the sooner detection can start and the sooner attacking frames can be detected. Experiment results show that our proposed detection mechanism achieves accuracy higher than 99%, F1-scores higher than 97%, and detection times shorter than $80 ~\mu s$ for the types of attacks considered. Moreover, when compared to four state-of-the-art intrusion detection systems, it is the only solution that is capable of discarding attacking frames before damage occurs while being deployed on inexpensive Raspberry Pi. Such an inexpensive deployment is particularly desirable, as cost is one of the automotive industry’s primary concerns.

Online social networking is expanding gradually in our professional as well as personal life in a variety of natures, beliefs, attitudes, and personalities. During communicating through the networks, the trust plays a very significant role while undertaking the communication process. This article proposes a secure trusted hypothetical mathematical model for ensuring secure communication among devices by computing the individual trust of each node. In addition, a decision making model is integrated with the hypothetical model for further speeding up the real time communication decision within the network. The proposed phenomenon is validated against variety of security threats by considering both ideal and adversarial models. Furthermore, the proposed framework is compared to a baseline approach against various security threats such as system accuracy, DDoS attack, data falsification threat,and number of processed requests. The proposed scheme is verified by simulating over synthesized data-set.

In this paper, an artificial noise (AN) injection technique is incorporated in a free-space optical (FSO) communication system with the aim of enhancing the secrecy performance of the system. An intensity modulated direct detection (IM/DD) FSO link which is subjected to Malaga ($\mathcal {M}$) distributed turbulence with pointing errors is considered in this paper. The performance of FSO systems is evaluated by deriving novel closed-form expressions for the secrecy outage probability (SOP), strictly positive secrecy capacity (SPSC), and throughput of the system. By formulating a constrained optimization problem, we discuss an optimal power allocation strategy for throughput maximization in the considered system. It is shown through the results that the proposed technique is very effective in improving the secrecy performance of FSO systems.

COVID-19 is an extremely dangerous disease because of its highly infectious nature. In order to provide a quick and immediate identification of infection, a proper and immediate clinical support is needed. Researchers have proposed various Machine Learning and smart IoT based schemes for categorizing the COVID-19 patients. Artificial Neural Networks (ANN) that are inspired by the biological concept of neurons are generally used in various applications including healthcare systems. The ANN scheme provides a viable solution in the decision making process for managing the healthcare information. This manuscript endeavours to illustrate the applicability and suitability of ANN by categorizing the status of COVID-19 patients' health into infected (IN), uninfected (UI), exposed (EP) and susceptible (ST). In order to do so, Bayesian and back propagation algorithms have been used to generate the results. Further, viterbi algorithm is used to improve the accuracy of the proposed system. The proposed mechanism is validated over various accuracy and classification parameters against conventional Random Tree (RT), Fuzzy C Means (FCM) and REPTree (RPT) methods.

The advances in the Internet of Things (IoT) provide several chances to develop a variety of innovations supporting smart home users in several industries including healthcare, energy management, etc. Ubiquitous support by intelligent appliances at modern homes, which constantly work to gather information can help us to solve everyday issues. In this article, we present a comparative study of recent advances in smart home development. The study aims to present the main trends in this field. During the analysis of the research reports and patents, we identify the propositions that constitute the main research streams. Through extensive analysis, we provide an outlook on the wide spectrum of the proposed solutions. We also analyze the main market to present which publishers are leading with the innovative science in this field. We also show the leaders of science and technology in the World. Finally, we define the ratio of the developments and outline the next stage of the development in the smart home industry.

The ever increasing thirst for the higher capacity demands radical changes in the design of cellular networks, such as a leap from single-tier homogeneous networks to multi-tier heterogeneous networks and the use of millimeter wave frequency band. A typical point-to-point full-duplex transmission link can double the link rate by simultaneously using the same spectrum for bidirectional traffic. However, the characterization of full-duplex two-tier heterogeneous networks is not as straightforward as that of point-to-point full-duplex systems, specially when the different tiers of the heterogeneous network use different frequency bands (millimeter wave and microwave) for their transmissions. This paper characterizes a full-duplex two-tier heterogeneous network with decoupled access, where both tiers operate on different frequency bands (millimeter wave and microwave). To evaluate the achievable spectral efficiency and association behavior of users and base stations, a two-tier heterogeneous network model is proposed in which all users and base stations are modeled using Poisson point processes. First a signal-to-interference-plus-noise-ratio optimal user association scheme is characterized. Based on the user association scheme, the spectral efficiencies of the uplink and downlink transmission links are derived. In addition, a thorough analysis of the signal-to-interference-plus-noise-ratio coverage is also provided. Moreover to render the analytical model more comprehensive and robust, i.e., different from the convention of noise-limited millimeter wave network, the interference in millimeter wave networks is also accounted for in the analytical model. Lastly, the pragmatic value of full-duplex heterogeneous networks and decoupled access is discussed in detail through numerous numerical and simulation results.

Conventional models in the intelligent transportation system (ITS) are confronted by large computational overheads and how they react during real-time scenarios. To appropriately manage the communication process in real-time, a trust-based mechanism can provide an efficient approach to acclimatize its deeds based on indecision sensory information. However, the computational models are not fully demoralized by the businesses owing to the lack of automated integration. In this study, we perform agent-based modeling (ABM) and population-based modeling (PBM) in the ITS mechanism during data transmission and record exchange for real-time communication. In addition, a trust evaluation process is performed to legitimize each device with the integration of ABM and PBM models. The simulation results show that the proposed mechanism is 89% more efficient than baseline methods in various networking scenarios, such as message alteration, distributed denial of service attacks, and information falsification threats.

In this paper, we derive a closed-form expression for the evaluation of the average vectorsymbol error rate (VER) of group detection followed by maximum-likelihood (GD-ML) receivers in large multi-user multiple-input multiple-output (MU-MIMO) systems with transmit side correlated Rayleigh channels. We assume M antennas at the base station (BS), N closely-located, single-antenna user equipment (UEs) with load factor λ = N/M , and N ≫ 1; consequently, we evaluate the performance of GD-ML receivers as the load factor grows to unity. The derived expression requires a negligible correlation at the receive side of the communication channel. Hence, from a practical point-of-view, when considering scenarios with a large number of UEs, the derived analytical expression is generally more applicable for systems with a distributed massive number of BS antennas. Numerical results are provided to validate our derived expression. We observe that the GD-ML with Nu group size achieves a diversity order proportional to M - N + Nu. Moreover, we show that for small group sizes, the analytical and simulation results remain close, and at moderate to high signal-to-noise ratio (SNR), the derived expression very closely matches the simulations, whereas this match becomes perfect as the users' side correlation increases. We also demonstrate that GD-ML outperforms the zero-forcing (ZF) and minimum mean-squared error (MMSE) receivers, in terms of VER; where for high λ, GD-ML exploits the maximum spatial multiplexing gain. Moreover, in terms of floating-point operations (FLOPs), we show that GD-ML receivers have almost the same complexity as ZF and MMSE where the ML detection stage adds a negligible complexity compared to the channel matrix inversion operation.

This paper investigates non-orthogonal multiple access (NOMA), cooperative relaying, and energy harvesting to support device-to-device (D2D) transmission. In particular, we deploy multiple relay nodes and a cell-center D2D device which can operate in full-duplex (FD) or half-duplex (HD) mode to communicate with a cell-edge D2D device. In this context, there are two possible signal transmission paths from the base station (BS) to the far D2D user either through multiple decode-and-forward (DF) relay nodes or through a near D2D user. Consequently, we propose three schemes to support D2D-NOMA systems, namely non-energy harvesting relaying (Non-EHR), energy harvesting relaying (EHR) and quantize-map-forward relaying (QMFR) schemes. For each of the proposed schemes, closed-form expressions of the outage probabilities of both D2D users are derived. Extensive Monte-Carlo simulation results are provided to validate the derived analytical expressions. The study results show that the proposed schemes can improve the outage performance compared to conventional orthogonal multiple access (OMA) schemes. Moreover, it is shown that the Non-EHR scheme achieves the best outage performance among the three considered schemes.

In this study, we address the problem of chaotic synchronization over a noisy channel by introducing a novel Deep Chaos Synchronization (DCS) system using a Convolutional Neural Network (CNN). Conventional Deep Learning (DL) based communication strategies are extremely powerful but training on large data sets is usually a difficult and time-consuming procedure. To tackle this challenge, DCS does not require prior information or large data sets. In addition, we provide a novel Recurrent Neural Network (RNN)-based chaotic synchronization system for comparative analysis. The results show that the proposed DCS architecture is competitive with RNN-based synchronization in terms of robustness against noise, convergence, and training. Hence, with these features, the DCS scheme will open the door for a new class of modulator schemes and meet the robustness against noise, convergence, and training requirements of the Ultra Reliable Low Latency Communications (URLLC) and Industrial Internet of Things (IIoT).

Dynamic and non-linear systems have been used to generate random bits in high-security applications for decades. In this perspective, due to their stochastic characteristic, chaotic systems have been emerging as the natural choice for the generation of random bits. This paper presents the design and the implementation of a chaos-based true random number generator and a chaos-key based data encryption scheme for secure communications. The mathematical expression of the dynamic system is presented and analyzed to evaluate the possibility of chaos occurrence. Then, the chaotic system is realized at the circuit level using 130 nm CMOS technology to generate random bit sequences, which are utilized in data encryption. Chaotic signal outputs of the chaos-based random number generator circuit are sampled at a maximum frequency of 50 MHz, enabling a high throughput of random bits. The core of the chaotic circuit consumes $630~\mu \text{W}$ in static mode and a maximum of $660~\mu \text{W}$ in running mode. The chaos-based one-time pad encryption scheme using the chaos-key generator shows the advantages of using this random number generator in secure communications. In this context, the data secrecy is compared to the advanced encryption standard AES128. Moreover, the design is simulated in different working conditions such as voltage supply and temperature variations, where it is shown that the random bit output benefits from a high entropy per bit and passes the standard statistical test suite (NIST) for cryptographic applications.

Decision-making is of critical significance in Internet-of-Vehicles (IoV), where vehicles need to quickly make decisions in real-time when sharing or transferring the information. In addition, it is necessary to identify the significant factors of an entity while measuring its legitimacy or to record the real-time data generated by it. Traditional automated schemes in IoV are confronted by the issues related to real-time processing and the manner they respond, such as traffic congestion information, fastest route selection, and road accidental information. The exchange of accurate information among vehicles is critical, but the decision-making for IoV has still not been fully investigated in the literature. Further, the involvement of malicious devices in the network may disgrace the network performance by consuming network resources. In this paper, we propose a hybrid decision-making scheme in vehicular informatics for data transferring and processing through VIKOR and analytic hierarchy process (AHP) methods. The proposed model is scrutinized and verified rigorously through several sensing and decision-making metrics against a conventional solution. The simulation results depict that the proposed model leads to 93 percent competence in terms of decision-making, identification of legitimate sensors, and data alteration process when sharing the information through various sensors in IoV.

In this paper, we present a new multi-user chaos-based communication system using Faster-than-Nyquist sampling to achieve higher data rates and lower energy consumption. The newly designed system, designated Multi-user Faster Than Nyquist Differential Chaos Shift Keying (MU-FTN-DCSK), uses the traditional structure of Differential Chaos Shift Keying (DCSK) communication systems in combination with a filtering system that goes below the Nyquist limit for data sampling. The system is designed to simultaneously enable transmissions from multiple users through multiple sampling rates resulting in semi-orthogonal transmissions. The design, performance analysis, and experimental results of the MU-FTN-DCSK system are presented to demonstrate the utility of the newly proposed system in enabling multi-user communications and enhancing the spectral efficiency of the basic DCSK design without the addition of new blocks. The MU-FTN-DCSK system presented in this paper demonstrates spectral gains for one user of up to 23% and a combined gain of 25% for four (U=4) users. In this paper, we present a proof of concept demonstrating a new degree of freedom in the design of Chaos-based communication systems and their improvement in providing wireless transmissions without complicated signal processing tools or advanced hardware designs.

This paper considers a dual-hop vehicle-to-vehicle (V2V) communication system equipped with full-duplex relays (FDRs) in the millimeter-wave networks. The performance of this network is studied in a dense multi-lane highway considering cooperative best vehicular relay selection strategy. In fact, two different FDR schemes are analyzed for the given network. In the first scheme that is called self-interference (SI) vehicle relaying selection, the FDR models the source-relay and the relay-relay links by one channel state information (CSI) coefficient (i.e., a single channel estimation of the two links), while in the second scheme that is called individual SI vehicle relaying selection, the CSI of the source-relay and the relay-relay links are estimated separately. Moreover, relay-destination link is modeled by one CSI coefficient in both aforementioned schemes. Dissimilar to traditional V2V schemes where either line-of-sight (LOS) or non-line-of-sight (NLOS) propagation is considered, and the effect of the blockage on V2V communications is not taken into account when analyzing the performance, in this paper, we propose a probabilistic model capturing the occurrences of LOS and NLOS propagations, which depend on the movement of the vehicles between the central and adjacent lanes. Thus, the movement of the vehicles between any two adjacent lanes is assumed to take place independently and is deemed to be according to the Poisson distribution. This vehicle movement model considers the blockage between the source and destination links. In this vein, a LOS link is considered available when the center lane is clear of vehicles between the source and destination nodes, and an NLOS link is assumed otherwise. Furthermore, a Nakagami-m fading channel model is considered due to its excellent representation of the V2V communication environment. The LOS/NLOS probabilities combined together with the probability of blockage are used to analyze the system performance. The cumulative density function expression of the signal-to-interference-plus-noise ratio at the relay is derived for both relaying schemes. Moreover, the lower bound expressions of the end-to-end outage probabilities are developed and used to express throughput. Finally, the theoretical results postulated and presented here are verified by the numerical simulation for a variety of parameters.

In this paper, we propose a solution for video transmission over a low-bandwidth network that enables a physician to take in charge of remote trauma patient. We propose and analyze a method based on an H.264 compression scheme that relies on transmitting high-quality video of a moving and dynamic region of interest while scarifying quality in the background. Our method is motivated by the problem of limited bandwidth usually encountered in air-to-ground communication channels. We propose to use a region of interest with smoothed edges to increase the video quality of the transition between the regions of various qualities. The moving region of interest, covering the torso and the head, is segmented and tracked by using the skeleton information provided by a Kinect camera. Our proposed compression scheme respects the real-time, low-complexity, and interoperability constraints. We have analyzed the results of our method obtained with various bit rate targets and have shown that a visual assessment of a patient is achievable over very low bandwidth.

This paper brings forward a Deep Learning (DL)-based Chaos Shift Keying (DLCSK) demodulation scheme to promote the capabilities of existing chaos-based wireless communication systems. In coherent Chaos Shift Keying (CSK) schemes, we need synchronization of chaotic sequences, which is still practically impossible in a disturbing environment. Moreover, the conventional Differential Chaos Shift Keying (DCSK) scheme has a drawback, that for each bit, half of the bit duration is spent sending non-information bearing reference samples. To deal with this drawback, a Long Short-Term Memory (LSTM)-based receiver is trained offline, using chaotic maps through a finite number of channel realizations, and then used for classifying online modulated signals. We presented that the proposed receiver can learn different chaotic maps and estimate channels implicitly, and then retrieves the transmitted messages without any need for chaos synchronization or reference signal transmissions. Simulation results for both the AWGN and Rayleigh fading channels show a remarkable BER performance improvement compared to the conventional DCSK scheme. The proposed DLCSK system will provide opportunities for a new class of receivers by leveraging the advantages of DL, such as effective serial and parallel connectivity. A Single Input Multiple Output (SIMO) architecture of the DLCSK receiver with excellent reliability is introduced to show its capabilities. The SIMO DLCSK benefits from a DL-based channel estimation approach, which makes this architecture simpler and more efficient for applications where channel estimation is problematic, such as massive MIMO, mmWave, and cloud-based communication systems.

Non-orthogonal multiple access (NOMA) has emerged as a promising technology that allows for multiplexing several users over limited time-frequency resources. Among existing NOMA methods, sparse code multiple access (SCMA) is especially attractive; not only for its coding gain using suitable codebook design methodologies, but also for the guarantee of optimal detection using message passing algorithm (MPA). Despite SCMA’s benefits, the bit error rate (BER) performance of SCMA systems is known to degrade due to nonlinear power amplifiers at the transmitter. To mitigate this degradation, two types of detectors have recently emerged, namely, the Bussgang-based approaches and the reproducing kernel Hilbert space (RKHS)-based approaches. This paper presents analytical results on the error-floor of the Bussgang-based MPA, and compares it with a universally optimal RKHS-based MPA using random Fourier features (RFF). Although the Bussgang-based MPA is computationally simpler, it attains a higher BER floor compared to its RKHS-based counterpart. This error floor and the BER’s performance gap are quantified analytically and validated via computer simulations.

Cooperative communications are expected to be a centerpiece for 5G cellular networks. Using cooperation, a wider and more uniform network coverage is attained, network capacity is enhanced, and power consumption is drastically reduced. While these latter heavily rely on collaboration among network elements, there is barely a packet-level perspective on what tangible gains cooperation is able to achieve. Motivated by this fact, different from studies on the capacity analysis of cooperative protocols, this paper tackles cooperation as a packet-level problem. The latter perspective empowers us to broaden our understanding of cooperation through characterizing high-level quantities, such as delay, throughput, fairness, and buffer length, as more tangible measures of the instant service quality that users/devices experience. In the pursuit of achieving this goal, the theory of BCMP queueing networks is leveraged. The proposed modeling approach can be used to analyze networks with an arbitrary number of relays, traffic classes, generic service time distributions, and several serving disciplines. To showcase the generality of this framework, we establish the queueing models for some of the most well-known cooperative protocols, such as amplify and forward amplify-forward, decode-forward, selection-relaying, incremental-relaying, and opportunistic-relaying, and evaluate their performances through the above-mentioned metrics. Moreover, a distributed cooperative protocol based on space-time block codes is proposed, its corresponding BCMP model is derived, and its performance is compared with other cooperative protocols.