Tactical flying ad hoc networks (T-FANETs) are mission-critical networks that connect mobile airborne platforms, such as tactical unmanned aerial vehicles (TUAVs), to enhance operations at the tactical edge. These networks are vulnerable to adversarial attacks, node failures, intermittent connectivity, and frequent topology variations, which make packet routing challenging. Due to the complex nature of T-FANETs, classical routing methods, which rely on sequential processing, require longer processing times to discover optimal routes, which can hinder mission effectiveness in fast-paced military operations. In this paper, we propose hybrid quantum federated learning (HQFL) to address the high computational complexity and long processing times of classical approaches. The proposed HQFL-based routing solution uses the computational power of quantum computing (QC) and the scalability of classical neural networks to develop routing agents with hybrid quantum circuits (HQCs) in order to accelerate the learning of optimal routes. Unmanned surface vehicles (USVs) are then used as parameter servers to collaboratively train lightweight HQC models for routing in T-FANETs operating in maritime environments. In addition, we design an adversarial model that combines mobile jamming and physical attacks on TUAVs and evaluate the performance of the proposed routing scheme under these attacks. Our simulation results show that the proposed HQFL-based approach outperforms classical FL algorithms, reducing communication overhead by 95%, lowering end-to-end delay by 9.43%, and improving the packet delivery ratio and routing energy efficiency by 39.6% and 52.6%, respectively.

Next-generation wireless networks are progressing beyond conventional connectivity to incorporate emerging sensing and computing capabilities. This convergence gives rise to integrated systems that enable not only uninterrupted communication, but also environmental awareness, intelligent decision-making, and novel applications that take advantage of these combined features. At the same time, this integration brings substantial security challenges. As computing, sensing, and communication become more tightly intertwined, the overall complexity of the system increases, creating new vulnerabilities and expanding the attack surface. The widespread deployment of data-heavy artificial intelligence applications further amplifies concerns regarding data security and privacy. This paper presents a comprehensive survey of security and privacy threats, along with potential countermeasures, in integrated wireless systems. We first review physical-layer security techniques for communication networks, and then investigate the security and privacy implications of semantic and pragmatic communications and their associated cross-layer design methodologies. For sensing functionalities, we pinpoint security and privacy risks at the levels of signal sources, propagation channels, and sensing targets, and summarize state-of-the-art defense strategies for each. The growing computational requirements of these applications drive the need for distributed computing over the network, which introduces additional risks such as data leakage, weak authentication, and multiple points of failure. We subsequently discuss secure coded computing approaches that can help overcome several of these challenges. Finally, we introduce unified security frameworks tailored to integrated communication–sensing–computing architectures, offering an end-to-end perspective on protecting future wireless systems.

Reliable low Earth orbit satellite networks (LEO-SNs) should be capable of optimally adapting to dynamic environments and remaining resilient against smart jamming attacks. In this context, dynamic inter-satellite link (ISL) routing is crucial for enabling efficient and adaptive data transmission across any satellite network during smart jamming attacks. However, due to the environmental variability, ISL routing becomes a complex time-sequential optimization problem. Accordingly, in this study, we propose a digital twin-assisted federated quantum deep reinforcement learning (DT-FQDRL) framework to solve dynamic ISL routing with faster convergence and minimal-error solutions. The DT-FQDRL framework optimizes ISL routing by minimizing jamming success rate and total delay while maximizing energy efficiency. Specifically, a digital twin (DT) replicates the LEO-SN environment to simulate satellite interactions and jamming behaviors at each time step. In this virtual setting, each satellite employs quantum deep reinforcement learning (QDRL) for local training and long-term prediction. To enhance data privacy and prevent node conflicts, a hierarchical federated learning scheme aggregates local QDRL models within the DT. The optimized weights are then transferred to real satellites. Our numerical results demonstrate that the DT-FQDRL framework reduces jamming success rate by 48.16%, decreases total delay by 22.26%, and improves energy efficiency by 6.17% over existing benchmarks.

Wireless communication systems operating in industrial environments are often subject to bursty impulsive noise and frequency-selective fading that significantly degrade their performance. Under these conditions, conventional orthogonal frequency division multiplexing (OFDM) receivers struggle to maintain reliable communication. In this paper, we address these challenges by introducing a reliable receiver design consisting of two key components. First, we develop an adaptive memory-aware noise covariance estimation method using neural networks to dynamically predict the noise level for each received symbol. Second, we propose a channel state information (CSI) enhancement method using two-dimensional average pooling and interpolation (2DAPI) to smooth out inaccuracies in CSI caused by bursty impulsive noise. We further derive a theoretical bit error rate (BER) performance approximation and incorporate a lower bound benchmark for rigorous evaluation. The results of our extensive simulations demonstrate the extreme power efficiency of the proposed receiver, which requires less than 10% of the transmit power as compared to conventional OFDM receivers that use linear minimum mean square error (LMMSE) equalizers and least squares (LS) CSI estimation to achieve the same BER. In addition, performance analyses across various modulation schemes and fading models confirm the reliability and adaptability of our design in diverse operational environments.

Ensuring secure and efficient multi-user (MU) transmission is critical for vehicular communication systems. Chaos-based modulation schemes have garnered considerable interest owing to their inherent advantages in physical layer security. However, most existing MU chaotic communication systems, particularly those based on non-coherent detection, suffer from low spectral efficiency due to reference signal overhead and limited user connectivity under orthogonal multiple access (OMA). Although non-orthogonal schemes such as sparse code multiple access (SCMA)-based differential chaos shift keying (DCSK) have been explored, they incur high computational complexity and exhibit inflexible scalability owing to fixed codebook designs. This paper proposes a deep learning-assisted power-domain non-orthogonal multiple access chaos shift keying (DL-NOMA-CSK) system for vehicular communications. A deep neural network (DNN)-based demodulator is designed to learn the intrinsic characteristics of chaotic signals during offline training, thereby eliminating the need for chaotic synchronization or reference signal transmission. The demodulator employs a dual-domain feature extraction architecture that jointly processes time-domain and frequency-domain information of the received chaotic signals, enhancing feature learning under dynamic channel conditions. The DNN is integrated into a successive interference cancellation (SIC) framework to mitigate error propagation. Theoretical analysis and extensive simulations demonstrate that the proposed system achieves superior performance in terms of spectral efficiency (SE), energy efficiency (EE), bit error rate (BER), security, and robustness compared to conventional MU-DCSK and existing deep learning-aided schemes. These advantages confirm the practical viability of the proposed system for secure vehicular communications.

This paper presents a distributed beamforming framework for a constellation of airborne platform stations (APSs) in a massive Multiple-Input and Multiple-Output (MIMO) non-terrestrial network (NTN) that targets the downlink sum-rate maximization under imperfect local channel state information (CSI). We propose a novel entropy-based multi-agent deep reinforcement learning (DRL) approach where each non-terrestrial base station (NTBS) independently computes its beamforming vector using a Fourier Neural Operator (FNO) to capture long-range dependencies in the frequency domain. To ensure scalability and robustness, the proposed framework integrates transfer learning based on a conjugate prior mechanism and a low-rank decomposition (LRD) technique, thus enabling efficient support for large-scale user deployments and aerial layers. Our simulation results demonstrate the superiority of the proposed method over baseline schemes including WMMSE, ZF, MRT, CNN-based DRL, and the deep deterministic policy gradient (DDPG) method in terms of average sum rate, robustness to CSI imperfection, user mobility, and scalability across varying network sizes and user densities. Furthermore, we show that the proposed method achieves significant computational efficiency compared to CNN-based and WMMSE methods, while reducing communication overhead in comparison with shared-critic DRL approaches.

In recent years, the demand for seamless connectivity and highly efficient, reliable network services for low earth orbit (LEO) satellites has escalated. To meet these expectations, a critical issue that must be addressed is combating malicious jamming attacks on satellite networks, which occur due to the open nature of satellite-ground connections. Moreover, in the era of artificial intelligence (AI), AI-based jamming poses a severe threat to the security of satellite networks and disrupts secure communications, particularly given the dynamic movements of LEO satellites and the time-sequential complexity of such attacks. Accordingly, this paper proposes a quantum adversarial deep reinforcement learning (QADRL) approach to mitigate AI-based jamming attacks while enhancing the quality-of-service (QoS) for LEO satellite networks. Specifically, the proposed QADRL approach is based on a zero-sum Markov game utilizing two opposing learning networks: one optimizing satellite routing links to avoid jamming and improve QoS, while the other, focuses on the jammer, optimizes the trajectory, jamming nodes, and power of unmanned aerial vehicles (UAVs) to maximize jamming success. The results demonstrate that the proposed QADRL outperforms classical adversarial DRL (CADRL) by reducing the jamming success rate by 33.33% and increasing the average QoS of the satellite network by 18.4975%.

We propose a multiple chirp rate index modulation (MCR-IM) system based on Zadoff-Chu (ZC) sequences that overcomes the problems of low transmission rate and large-scale access in long-range wide-area network (LoRaWAN) using classical LoRa modulation. We demonstrate the extremely low cross-correlation of MCR-IM signals across different spread factors, showing that the proposed MCR-IM system also inherits the characteristics of ZC sequences modulation. Moreover, we derive an approximate approximate closed-form expression for the bit-error-rate (BER) of the proposed MCR-IM system over Nakagami-mfading channels. Simulation results confirm the accuracy of the derived approximate closed-form expression and demonstrate that the MCR-IM system achieves higher levels of spectral efficiency (SE) compared to existing systems like frequency-shift chirp spread spectrum with index modulation (FSCSS-IM), group-based CSS (GCSS), layered CSS (LCSS), and layered group-based CSS (LGCSS). In this context, assigning multiple chirp rates to each user results in a reduction in the number of parallel channels. To mitigate this issue, we propose a peak detection based successive interference cancellation (PD-SIC) algorithm to accommodate more users. Results further show that the proposed system with the PD-SIC algorithm achieves a lower BER and higher throughput than the orthogonal scatter CSS (OrthoRa) system under collision scenarios, making it a compelling solution for future large-scale, high-throughput Internet of Things network.

The digitalization of traffic networks has spurred the development of intelligent transportation systems. By utilizing reinforcement learning for dynamic traffic optimization, it efficiently handles real-world traffic complexities. However, as the demand for real-time, high-efficiency tasks increases, relying solely on reinforcement learning struggles to meet both goals. Integrating reinforcement learning with mobile communication technology offers a promising solution for efficient, low-overhead traffic networks. As an important physical layer technology for Integrated Sensing and Communications Systems, Spectrally Efficient Frequency Division Multiplexing (SEFDM) addresses the communication overhead challenge in reinforcement learning-enabled optimization. However, the main challenge of SEFDM is eliminating the inter-carrier interference (ICI) caused by non-orthogonal modulation. Considering that existing post-interference cancellation methods fail due to the ill-conditioning of the generalized channel matrix, which cannot be directly inverted, we propose a nonlinear precoding algorithm at the transmitter, instead of post-cancellation, that effectively eliminates interference and improves transmission reliability. We firstly use a nonlinear feedback structure to avoid power boost and error propagation. Besides that, Geometric Mean Decomposition (GMD) based interference matrix decomposition algorithm is used in the proposed precoding scheme to avoid matrix singularity and obtain diversity gain. Finally, the numerical results show that the proposed precoding method can achieve higher order QAM SEFDM signaling with higher spectral efficiency and get comparative BER performance.

The rapid development of the internet of electric vehicles (IoEV) and the advancement of electric vehicle (EV) charging technology are transforming energy management for both residential and commercial users. Vehicle-to-building (V2B) energy trading is emerging as a groundbreaking approach that incorporates the exchange of energy between EVs and buildings. Despite the fact that V2B energy trading is able to reduce energy costs, main-grid complexity, and greenhouse gas emissions, it faces challenges when it comes to cooperative decision-making, resource efficient computation, and user transaction security. To address these challenges, this study aims to enhance energy exchange efficiency and dynamic energy interactions with enhanced security in urban environments. With these objectives, this paper proposes a novel energy trading method that integrates transfer learning (TL) and blockchain technology. TL makes it possible to adapt the knowledge gathered from vehicle-to-vehicle (V2V) systems to V2B settings, which reduces the computational resources required and boosts the overall efficiency. Blockchain technology, on the other hand, provides a secure framework for transaction verification while granting users enhanced control over their data privacy. We demonstrate the effectiveness of our proposed technique using detailed simulations conducted with real-world data. Our simulation results indicate that the proposed approach improves the convergence speed by around 50% compared to training from scratch, while buildings achieve up to 38% higher profits relative to scenarios without our proposed strategy. We also simulate the system model using the Ethereum blockchain platform to determine its real-world feasibility. These experiments demonstrate that the system has the potential to facilitate efficient energy trading to ensure user security and economically beneficial transactions.

This paper investigates uplink radio resource optimization of a user-centric (UC) cell-free (CF) massive multiple-input multiple-output (mMIMO) system aided by the rate splitting multiple access (RSMA) technique subject to pilot contamination. We formulate problem to maximize the minimum spectral efficiency (SE) problem by jointly addressing decoding order selection, power allocation, and access point (AP) - user equipment (UE) association assignment. The envisioned optimization exhibits two challenges. First, it requires global channel state information (CSI) for near-optimal performance, which incurs substantial overhead and data collection costs in large-scale CF networks. Second, the optimization is intractable due to its NP-hard and discrete non-linear programming nature. To address the CSI acquisition issue, we utilize a digital twin (DT) of the CF mMIMO system, leveraging its context-awareness to acquire global CSI with reduced overhead. To address computational intractiablity of the optimization problem, we decompose it into three sub-problems. The power allocation sub-problem is transformed into a second-order cone programming problem and solved by the bisection method. Additionally, we propose a computationally efficient heuristic approach for power allocation. Next, we propose an analytical method for the decoding order selection by ranking the channels in descending order of strength. Simulation results validate the ability of the proposed approach to attain the near-optimal performance. Subsequently, the AP-UE association assignment problem is solved by a heuristic approach to further improve the SE performance. Finally, we solve the original NP-hard problem in a unified manner via the block-coordinate descent algorithm. Simulation results underscore a substantial 61% improvement in the SE performance when integrating the RSMA technique into a UC CF mMIMO system.

Low rank tensor ring based data recovery algorithms have been widely used in data-driven consumer electronics to recover missing data entries in the collecting data pre-processing stage for providing stable and reliable service. However, traditional recovery methods often fail to utilize the abundant prior knowledge of data and the non-local self-similarity of the data, thus leading to the failure to effectively capture the spatial relationships within high-dimensional data to recover them accurately. To address these problems, we present a novel Non-local Self-similarity and Low-rank Prior Knowledge based tensor ring completion method. Firstly, we incorporate the BM3D denoising operator within a Plug-and-Play framework to exploit the self-similarity in the data. Then a logarithmic determinant function is integrated to distinguish singular values in the cyclic unfolding matrix of the tensor and adopts a tensor ring completion approach based on weighted nuclear norms. Finally, in order to evaluate the effectiveness of our proposed method, we conducted a series of experiments by using the missing image dataset and the missing traffic data dataset respectively, and the experimental results show that our method achieves the highest level in terms of data recovery accuracy.

This paper addresses radio resource management (RRM) in highly dynamic device-to-device (D2D) networks. Existing heuristic and deep learning (DL) approaches often overlook the network’s time-varying nature and struggle with variable link counts and channel conditions. We propose a Continuous-Time Dynamic Graph (CTDG) model that captures network events (activations, updates, and deactivations) in real time, rather than relying on discrete snapshots. Our Temporal Graph Neural Network (TGNN) processes these events, updating node-wise and graph-wise memories to track historical context. The resulting temporal embeddings drive power and channel allocation decisions that adapt to changing network topologies and mobility. We evaluate this TGNN-based solution in a realistic D2D setting using mobility traces from SUMO. Results show near-optimal throughput under stringent constraints and significant performance gains over conventional DL networks and memoryless GNN-based methods. This work underscores the importance of continuous-time graph modeling for scalable, efficient RRM in next-generation wireless systems.

This paper addresses channel estimation error (CEE) mitigation in user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) systems under pilot contamination (PC). To suppress the PC, we formulate an optimization problem to minimize the normalized CEE through the joint treatment of the pilot power allocation and the pilot assignment. However, the NP-hard nature of the problem makes finding a global optimal solution computationally infeasible. To tackle this challenge, we decompose the problem into two sub-problems: the fractional programming (FP) technique is employed for pilot power allocation, and a multi-agent reinforcement learning (RL) approach is applied for pilot assignment. The RL-based scheme, however, involves iterative action generation and reward computation, leading to significant overhead from repeated information exchanges. To mitigate this issue, we leverage a digital twin (DT) of the CF mMIMO system, utilizing its virtual environment and contextual awareness to optimize CEE reduction with minimal overhead. By employing closed-form expressions, the proposed schemes achieve computational efficiency without reliance on complex numerical solvers. Finally, we solve the original NP-hard problem via the block-coordinate descent and sequential optimization methods. Numerical evaluations demonstrate that the proposed FP-based pilot power allocation scheme improves the 95%-likely spectral efficiency (SE) by up to 15% and reduces the average pilot transmission power by up to 83% compared to existing methods. Furthermore, the pilot assignment scheme enhances the average SE performance by up to 7.4% over state-of-the-art pilot assignment approaches. These results validate the effectiveness of our proposed framework in reducing CEE, leading to enhanced improvement in system performance in the presence of PC.

Harnessing the potential of smart grid networks relies on the efficient and secure communication between distributed energy resources (DERs) and the energy management system (EMS), particularly under variable channel conditions and adversarial interference. This interference is further exacerbated with the advent of artificial intelligence (AI)-driven adversarial devices capable of adaptively disrupting communication in real time. To address these challenges, in this study, we formulate the distributed channel access problem, incorporating dynamic channels and intelligent adversarial interference as a partially observable Markov game (POMG). Specifically, we propose a distributed framework based on a centralized training and distributed execution (CTDE) multi-agent reinforcement learning (MARL), enabling DERs to autonomously adapt to dynamic channels and mitigate intelligent interference using only local observations, i.e., without direct information sharing among DERs. Our simulation results indicate that, by integrating an advanced policy evaluation technique and a tailored utility maximization strategy, DERs can collaboratively optimize their transmission decisions, improving the network's aggregate packet success rate (APSR) and resilience. Additionally, the proposed framework outperforms existing methods, ensuring robust and scalable communication in smart grids under diverse conditions.

The challenge of handling missing data in time series is critical for maintaining the accuracy and reliability of machine learning (ML) models in applications like fifth generation mobile communication (5G) network management. Traditional methods for validating imputation rely on ground truth data, which is inherently unavailable. This paper addresses this limitation by introducing two statistical metrics, the wasserstein distance (WD) and jensen-shannon divergence (JSD), to evaluate imputation quality without requiring ground truth. These metrics assess the alignment between the distributions of imputed and original data, providing a robust method for evaluating imputation performance based on internal structure and data consistency. We apply and test these metrics across several imputation techniques. Results demonstrate that WD and JSD are effective metrics for assessing the quality of missing data imputation, particularly in scenarios where ground truth data is unavailable.

The advent of sixth-generation (6 G) networks introduces significant advancements for Vehicle-to-Everything (V2X) communications that offer ultra-low latency, massive connectivity, and enhanced spectral efficiency. However, the dynamic and heterogeneous nature of V2X environments, coupled with the use of high-frequency bands like millimeter-wave (mmWave) and terahertz (THz), poses significant challenges for Radio Resource Management (RRM). Traditional RRM techniques frequently fail to adapt to rapidly changing network conditions, which results in suboptimal performance. Attending to these concerns, in this paper, we propose a novel AI-driven RRM framework for 6G V2X networks that integrates multimodal collaborative perception through LiDAR-based environmental information with traditional wireless metrics. The proposed framework aims to optimize data rate, energy efficiency, and fairness across the network. The results of our extensive simulations demonstrate that our approach significantly outperforms traditional RRM methods, confirming the effectiveness of incorporating multimodal collaborative perception data and advanced deep learning time series techniques.

Non-terrestrial base stations (NTBSs), including high-altitude platform stations (HAPSs) and hot-air balloons (HABs), are integral to next-generation wireless networks, offering coverage in remote areas and enhancing capacity in dense regions. In this paper, we propose a distributed beamforming framework for a massive MIMO network with a constellation of aerial platform stations (APSs). Our approach leverages an entropy-based multi-agent deep reinforcement learning (DRL) model, where each APS operates as an independent agent using imperfect channel state information (CSI) in both training and testing phases. Unlike conventional methods, our model does not require CSI sharing among APSs, significantly reducing overhead. Simulations results demonstrate that our method outperforms zero forcing (ZF) and maximum ratio transmission (MRT) techniques, particularly in high-interference scenarios, while remaining robust to CSI imperfections. Additionally, our framework exhibits scalability, maintaining stable performance over an increasing number of users and various cluster configurations. Therefore, the proposed method holds promise for dynamic and interference-rich NTBS networks, advancing scalable and robust wireless solutions.

The emergence of movable antenna (MA) technology has marked a significant advancement in the field of wireless communication research, paving the way for enhanced connectivity, improved signal quality, and adaptability across diverse environments. By allowing antennas to adjust positions dynamically within a finite area at transceivers, this technology enables more favourable channel conditions, optimizing performance across applications like mobile telecommunications and remote sensing. However, throughout history, the introduction of every new technology has presented opportunities for misuse by malicious individuals. Just as MAs can enhance connectivity, they may also be exploited for disruptive purposes such as jamming. In this paper, we examine the impact of an MA-enhanced jamming system equipped with$M$movable antennas in a downlink multi-user communication scenario, where a base station (BS) with$N$antennas transmits data to$K$single-antenna users. We formulate an optimization problem where the jammer determines both the antenna locations and beamforming vectors to minimize the total system sum rate. Given the non-convex nature of the problem, it is decomposed into two sub-problems, which are solved alternately until convergence. Simulation results show that an adversary equipped with MAs reduce the system sum rate by 30 % more effectively than fixed-position antennas (FPAs). Additionally, MAs increase the outage probability by 25 % over FPAs, leading to a 20 % increase in the number of users experiencing outages. The highlighted risks posed by unauthorized use of this technology, underscore the urgent need for effective regulations and countermeasures to ensure its secure application.

In this paper, we address the challenge of power control and spectrum allocation in Device-to-Device (D2D) networks with the aim to maximize the long-term average network rate. The inherent time-varying nature of wireless channels, caused by user mobility, complicates radio resource management (RRM) in such networks. address this concern, in this study, we propose a novel two-level solution that integrates Channel State Information (CSI) prediction with a Graph Neural Network (GNN)-based RRM model. On the first level, we develop an attention-based recurrent neural network (RNN) to predict future CSIs using historical data. On the second level, we transform the past, current, and predicted CSIs into multiple graph structures. We then design a GNN-based RRM model that captures the geometric properties of the CSIs across the following three dimensions: within resource blocks (RBs), between RBs, and over time (both forward and backward). Specifically, the proposed model employs message passing within RBs to capture local interference patterns, between RBs to optimize spectral resource allocation, and across temporal states to use temporal dependencies. The results of our simulation demonstrate that our method outperforms benchmark schemes across various wireless scenarios, achieving a higher network throughput while satisfying Quality of Service (QoS) constraints.

This paper investigates the use of beyond diagonal reconfigurable intelligent surface (BD-RIS) with$N$elements to advance integrated sensing and communication (ISAC). We address a key gap in the statistical characterizations of the radar signal-to-noise ratio (SNR) and the communication signal-to-interference-plus-noise ratio (SINR) by deriving tractable closedform cumulative distribution functions (CDFs) for these metrics. Our approach maximizes the radar SNR by jointly configuring radar beamforming and BD-RIS phase shifts. Subsequently, zeroforcing is adopted to mitigate user interference, enhancing the communication SINR. To meet ISAC outage requirements, we propose an analytically-driven successive non-inversion sampling (SNIS) algorithm for estimating network parameters satisfying network outage constraints. Numerical results illustrate the accuracy of the derived CDFs and demonstrate the effectiveness of the proposed SNIS algorithm.

Recent years have witnessed the emergence of the concept of network slicing (NS) that enables the creation of independent, virtualized logical networks on the same physical infrastructure. Each NS is tailored to meet the needs of a particular service or application. NS is widely considered a key enabling technology for end-to-end (E2E) automation in managing resources within radio access networks (RAN). To achieve E2E automation in RAN slicing, it is essential to automate resource allocation at both the intra- and inter-slice levels to meet the demands of future applications and services. In this context, the present study focuses on the automated management of multiple radio resources at the inter-slice level. More specifically, we propose a self-optimizing inter-slice scheme based on the deep reinforcement learning (DRL) advantage actor-critic (A2C) algorithm, named SOIS-A2C. Our goal is to maximize the spectral efficiency of the system while maintaining high service quality by considering the effects of hardware distortions and intra-slicing interference. The results highlight the effectiveness of the proposed SOIS-A2C scheme, which demonstrates superior performance in maximizing spectral efficiency as compared to benchmark schemes such as the SOIS-based deep Q-network (DQN) and hard slicing in highly fluctuating environments and under ideal and non-ideal hardware conditions.

Home Energy Management Systems (HEMS) are essential for optimizing household energy consumption and reducing costs, particularly in smart grids, where renewable energy sources and demand side management play a critical role. We propose an advanced HEMS framework that utilizes Proximal Policy Optimization (PPO), a reinforcement learning (RL) algorithm to address the challenges of managing energy consumption in realistic and dynamic environments. Our approach provides a comprehensive smart home environment by incorporating a wide range of household appliances, each with distinct patterns of energy consumption and operational constraints. This enhances the realism and practical relevance of the system, allowing cost savings while respecting user preferences and the limitations of the appliance. Simulations reveal that the proposed HEMS framework significantly improves energy savings, reduces costs, and enhances user satisfaction compared to other baseline methods, achieving 38% lower costs and 3% higher satisfaction of the energy level of electric vehicles (EV) than the Soft Actor-Critic based HEMS. These results highlight the effectiveness of RL and realistic environment modeling in the development of adaptive and efficient HEMS solutions, paving the way for more sustainable energy management practices.

In this paper, we investigate user equipment (UE)-radio access technology (RAT) association and bandwidth assignment to maximize sum-rates in a multi-RAT network. To this end, we formulate an optimization problem that jointly addresses UE association and bandwidth allocation, adhering to practical constraints. Because of the NP-hard nature of this problem, finding a globally optimal solution is computationally infeasible. To address this challenge, we propose a centralized and computationally efficient heuristic algorithm that aims to maximize sumrates while enhancing quality of service (QoS). Yet, the proposed approach requires global channel state information (CSI) for near-optimal performance, which incurs substantial overhead and data collection costs in large-scale multi-RAT networks. To alleviate this burden, we use a digital twin (DT) of the multi-RAT network, leveraging its context-awareness to acquire global CSI with reduced overhead. Our numerical results reveal that our approach improves sum-rates by up to 43 % over baseline method, with less than a 5 % deviation from the theoretical optimal solution, while achieving up to a 43 % improvement in QoS. Further analysis reveals that our method not only surpasses the optimal solution in terms of QoS enhancement, but also ensures significant computational efficiency.

A broader coverage of low earth orbit (LEO) satellite networks has been an object of research interest in recent years and will remain to be of keen interest to both start-ups and established companies in the future. However, achieving optimal coverage remains a challenge today, as the satellites dynamically move along their orbit, requiring frequent constellation size and beamsteering adjustments. Such frequent adjustments and the number of variables that need to be optimized result in a high computational complexity. Aiming to attend to these concerns, the present study proposes a quantum adaptive learning (QAL) as a potential solution for coverage optimization of stochastic geometry-based LEO satellite networks with low computational complexity by taking advantage of quantum computing and adaptive learning. The QAL scheme utilizes quantum computing and a feedback mechanism to improve the learning network design and parameters for higher accuracy with low computational complexity. As a study case, considering the binomial point process (BPP) distribution of contact distance between satellites and user terminals, we use the QAL scheme to optimize the satellites' constellation size and their corresponding beamsteering at each time step to achieve maximum coverage probability. To evaluate performance, the simulation results of the QAL scheme are compared with those of quantum machine learning (QML), which lacks feedback and adaptive mechanisms. The results show that the proposed QAL outperforms QML by achieving higher coverage probability, higher accuracy, and faster convergence.

In modern wireless communication systems, using high-order modulation is often key to achieving high data rates while maintaining a good spectral efficiency. By packing more bits into each transmitted symbol, more data can be sent over a given bandwidth, but that also makes the transmission more vulnerable to fading and noise. Moreover, traditional methods often isolate the design and optimization of transmitters and receivers, leading to poor error rate performances. To tackle these challenges, we propose a novel end-to-end (E2E) optimization strategy that includes a trainable constellation at the transmitter and a convolutional neural network (CNN)based demapper at the receiver. The transmitter and receiver are jointly trained to handle channel fading and detect symbols with minimal errors. Our findings demonstrate that the proposed transceiver architecture exhibits a significantly lower bit-error rate (BER) than traditional modulation methods and earlier dense layer-based models, especially for high-order modulation.

Radio access network (RAN) slicing in unmanned aerial vehicle (UAV)-assisted tactical networks facilitates dynamic sharing of radio resources among slices with varying quality of service (QoS) requirements. However, the unpredictable and resource-constrained nature of tactical RAN environments can jeopardize slice isolation, significantly impacting the QoS provided to users. This paper introduces a UAV positioning and bandwidth allocation (UAV-PBA) mechanism that optimally positions UAVs to enhance coverage and allocates bandwidth among users while considering slice isolation and user mobility. UAV-PBA ensures robust slice isolation by satisfying both resource-based and performance-based constraints. The mechanism utilizes deep reinforcement learning (DRL) algorithms for effective UAV positioning and bandwidth allocation. We design two implementations of the UAV-PBA mechanism, each utilizing a different DRL algorithm, and evaluate their performance against baseline methods using several metrics.

In this paper, we introduce a novel generalized deep hierarchical reinforcement learning (DHRL) approach to address the joint user association and channel assignment problem in high-altitude platform station (HAPS)-integrated wireless networks. The proposed method partitions the action space into sub-action spaces, treating each user as an agent. We leverage a deep neural network structure, incorporating convolutional neural network (CNN) layers as a feature extractor, and demonstrate the model’s generalization capability across varying numbers of users without fine-tuning and its ability to adapt to new scenarios through transfer learning. Our proposed DHRL model exhibits strong performance, surpassing the genetic algorithm and achieves nearly identical results as the exhaustive search action selection method in terms of sum-rate maximization.

As modern power systems evolve to handle increasingly frequent challenges, such as weather anomalies and recurrent failures, innovative radio resource allocation solutions are essential to fortify microgrids against these challenges. In this letter, we investigate transmission power allocation for distributed energy resources (DERs) in a multi-microgrid system. Leveraging narrow band internet of things’ (NB-IoT) coverage enhancement (CE) feature, the deliberate control of CE radii at the base stations influences the weighted sum rate, thus necessitating the exploration of optimal CE radii. The inherent nonconvexity of the optimization problem is addressed by employing difference-of-convex techniques along with a heuristic procedure to determine the CE radii. Furthermore, to ensure reliability and robustness in practical implementations, we establish the algorithm’s rate of convergence using strong convexity. Our numerical simulations demonstrate the algorithm’s performance against relevant benchmarks, including the upper bound benchmark derived from the Lagrange error bound.

Accurate channel estimation is crucial for the improvement of signal processing performance in wireless communications. However, traditional model-based methods frequently experience difficulties in dynamic environments. Similarly, alternative machine-learning approaches typically lack generalization across different datasets due to variations in channel characteristics. To address this issue, in this study, we propose a novel domain adaptation approach to bridge the gap between the quasi-static channel model (QSCM) and the map-based channel model (MBCM). Specifically, we first proposed a channel estimation pipeline that takes into account realistic channel simulation to train our foundation model. Then, we proposed domain adaptation methods to address the estimation problem. Using simulation-based training to reduce data requirements for effective application in practical wireless environments, we find that the proposed strategy enables robust model performance, even with limited true channel information.

In this paper, we propose a novel framework to integrate the intelligent reconfigurable surface (IRS) in terahertz (THz) networks, with co-existing ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) services. To meet URLLC latency constraints, the URLLC traffic is scheduled along with eMBB traffic, which poses a significant resource allocation challenge. To address this concern, in this paper, we propose a joint power and service allocation (JPSA) framework to maximize URLLC reliability while ensuring eMBB data rates. Furthermore, to address the challenging NP-hard mixed-integer nonlinear programming (MINLP) problem, we decompose the resource allocation problem into the URLLC power allocation and service allocation sub-problems. More specifically, we suggest a one-to-one matching game for service allocation. Our simulation results demonstrate that the proposed scheme outperforms baseline methods, particularly in terms of enhancing the reliability of URLLC user equipment (uUEs).

While the Internet of Vehicles (IoV) can revolutionize transportation systems through intelligent connectivity, a critical challenge in realizing this potential lies in ensuring efficient channel allocation in the IoV ecosystem, particularly considering dynamic channel conditions and adversarial jamming exacerbated by the emergence of artificial intelligence (AI)-based jamming. To address these challenges, in this study, we use distributed edge intelligence (DEI) to propose a distributed channel access mechanism for the vehicle-to-infrastructure (V2I) mode of IoV networks. Specifically, using an actor-critic-based multiagent reinforcement learning (MARL) framework with a common critic, we model the distributed channel access problem in V2I communications under varying channel conditions and an intelligent jamming device$(\tt {iJD})$interference as a decentralized partially observable stochastic game (Dec-POSG). Furthermore, by addressing challenges, such as partial observations, nonstationarity, and credit assignment, our proposed approach fosters collaboration among intelligent vehicles ($\tt {iV}$s) without direct communication. In addition, our unique counterfactual reasoning-aided action evaluation mechanism and a novel utility function design enable the$\tt {iV}$s to learn mixed collaborative-competitive channel access policies, thereby enhancing channel utilization, mitigating the impact of the$\tt {iJD}$, and improving the network’s sum cross-layer achievable rate (SCLAR).

With the rapid advancement of sixth-generation (6G) network communication technology, improvements in data transmission rates, latency, and reliability have driven substantial growth in Internet of Vehicles (IoV) applications. Among these, the integration of 6G-enabled extremely large-scale antenna arrays (ELAAs) has extended the range of near-field (NF) communication, enabling their application in IoV to facilitate efficient and accurate environmental sensing. Through NF communication, vehicles can achieve high-accuracy localization and perception by analyzing the signal phase, channel state information, and beamforming calculations. However, positioning and sensing tasks place substantial computational and energy demands on edge devices, often exceeding traditional capacity limits. To address this challenge, task offloading has emerged as a solution, with mobile edge computing (MEC) offering a lower-latency alternative to centralized cloud computing by processing tasks at the network edge. Despite these advantages, MEC’s limited resources present challenges as the number of connected vehicles increases. Existing approaches to resource allocation often overlook the varied accuracy requirements of IoV tasks, where high-accuracy tasks like indoor navigation require stringent performance standards, while lower-accuracy tasks may tolerate reduced precision to save resources. Motivated by this, we propose an accuracy-based classification scheme for IoV positioning and sensing tasks, which dynamically adjusts accuracy requirements to reduce delay and energy consumption. Our approach maps total energy, accuracy loss, and delay to an overall quality of service (QoS) metric, and employs an optimization algorithm that leverages gradient descent and greedy strategies to balance resource allocation and accuracy selection. Extensive simulations demonstrate the effectiveness of the proposed scheme in reducing delay and energy consumption while maintaining high accuracy, significantly outperforming benchmark strategies.

Federated learning (FL) is a novel paradigm for distribute edge intelligence for the Internet-of-Vehicles (IoV) application, which can enable superior performance in model training without the need to share local data. However, in the actual architecture of FL, the existence of nonindependent and identically distributed (non-IID) data at the edge device, along with the involvement of randomly participating distributed nodes, can result in model bias and a subsequent decrease in overall performance. To solve this problem, a new federated self-supervised learning method based on prototypes clustering contrastive learning (FedPCC) is proposed, which can effectively addresses the issue of asynchronous edge training and global model bias by introducing an unsupervised prototypes layer. The prototypes layer maps edge features to a global space and performs clustering, facilitating the new aggregation method of global prototypes on the server. Then, models from other components are aggregated based on data weight. Besides that, during the parameter deployment phase, we replace the prototype layer to acquire global knowledge, while employing momentum updates to preserve the local knowledge of the other components. Finally, to assess the efficacy of our proposed approach, we carried out comprehensive experiments across the various data sets. The findings show that our method gains state-of-the-art performance, which also validates its effectiveness.

The Internet of Vehicles (IoV) generates massive data traffic and demands reliable end-to-end connectivity to achieve multi-Gbps throughput between vehicles and roadside units. Millimeter-wave (mmWave) bands, with their abundant bandwidth, are promising for high-throughput IoV networks. However, in this context, significant propagation losses, intermittent line-of-sight availability, and dynamic topology changes due to vehicle mobility present critical challenges. This article introduces resource allocation for vehicular networks ($\textsf {RAVEN}$), a centralized resource management framework designed to address these challenges effectively.$\textsf {RAVEN}$leverages a digital twin network (DTN) to optimize the end-to-end system capacity of multihop mmWave IoV networks by effectively managing co-channel interference among vehicles.$\textsf {RAVEN}$comprises the following three steps: 1) a channel prediction step that utilizes DTN’s awareness of vehicular mobility and environmental contexts to predict site-specific channel gains for vehicular communication links; 2) a clustering step that partitions vehicles into nonoverlapping clusters, allowing vehicles within each cluster to share the same mmWave channel for data transmission, while simultaneously reducing co-channel interference; and 3) a multihop connectivity optimization step that provides a connected vehicular networking topology by jointly optimizing vehicle-to-vehicle and vehicle-to-infrastructure connectivity using a graph theory approach. A proof-of-concept of$\textsf {RAVEN}$is developed by implementing a DTN on the Microsoft Azure Digital Twins platform while integrating real-world vehicular mobility traces, edge-cloud collaboration, and parallel computing. Extensive simulations demonstrate that$\textsf {RAVEN}$outperforms several benchmark schemes, and offers scalability and near real-time decision-making capabilities for managing interference in large-scale IoV networks.

In recent years, the rapid advancement of wireless communication technologies, particularly in the development of sixth-generation networks, brought about challenges in spectrum efficiency, security, and reliability. Machine learning-based automatic modulation classification (AMC) plays a critical role in addressing these challenges by enabling efficient signal classification in dynamic environments. However, such systems remain vulnerable to adversarial attacks, which can induce machine learning-based systems into making mistakes and, by doing so, compromise applications that rely on them. Accordingly, in this study, we propose a robust AMC framework based on Bayesian neural networks (BNN) to mitigate the impact of adversarial attacks. Our approach uses a regularization term on the weight variance of the BNN to reduce the likelihood of extreme weight values, thereby enhancing model stability in adversarial settings. We also incorporate the Sinh-Arcsinh Gaussian distribution as a flexible prior to control skewness and tail behavior, thus improving the trade-off between robustness and accuracy. Experimental evaluations against common white-box adversarial attacks, such as fast gradient sign method (FGSM), projected gradient descent (PGD), and automatic PGD (Auto-PGD), demonstrate that our proposed model outperforms conventional AMC models, achieving greater resilience in low perturbation-to-noise ratio conditions. Taken together, these findings highlight the potential of Bayesian methods in developing more secure and reliable intelligent wireless communication systems.

Digital twin (DT) is a promising technology for responding to Industry 4.0 and realizing comprehensive automation and virtualization. In the Web3.0-powered 5G/6G era, the expansion of the industrial data and closer interaction among cross-industrial entities pose new security challenges for DT industrial systems. As a prevalent computing paradigm, Graph Anomaly Detection provides an effective solution to ensure the security of DT industrial systems. However, the existing unsupervised graph anomaly detection methods tend to treat multiple graph attributes in isolation during the reconstruction process, resulting in insufficient semantics and suboptimal reconstruction performance. To overcome these challenges, we propose a multiattribute contrastive learning framework, which realizes graph anomaly detection by capturing both graph attribute patterns and their hidden relationships. First, we use an improved multiattribute aligned reconstruction approach to represent the anomaly information effectively. Besides that, the positive instance aggregation-based contrastive constraints are proposed, which can reduce the loss generated by mappings between different data dimension in feature representation space. Finally, to verify our proposal, extensive experiments have been conducted on five benchmark datasets, and the results show that our method obtains the state-of-the-art performance.

Chaotic signals are spread-spectrum nonlinear signals with initial value sensitivity that can potentially provide safe and anti-jamming digital communication systems. However, to reduce receiver complexity and achieve smooth demodulation, traditional chaos-based wireless communication systems require the transmission of additional chaotic reference signals, which reduces the spectral efficiency and degrades the security characteristics of the chaotic signals. Recent work has explored deep learning (DL)-aided transceivers to address these issues. However, these techniques have not yet fully exploited the inherent characteristics of chaotic signals, e.g., spectral properties. To maximize the potential of chaotic signals in wireless communication, this paper proposes a power spectral density-based deep learning chaos shift keying (PSD-DLCSK) receiver. The proposed PSD-DLCSK scheme effectively utilizes the spectral properties of chaotic signals, where the PSD of received signals serves as input to a deep neural network (DNN) for symbol detection. The proposed DNN architecture combines long short-term memory networks with self-attention mechanisms to effectively capture the temporal dependencies and spectral properties of chaotic signals, enabling reliable intelligent demodulation without chaotic reference signals. Extensive simulations demonstrate that PSD-DLCSK achieves superior bit error rate (BER) performance compared to traditional receivers as well as other DL-aided schemes over additive white Gaussian noise and multipath Rayleigh fading channels.

Although LoRa has emerged as the leading technology among the rapidly developing low-power wide-area networks, the performance of the LoRa system severely deteriorates over fading channels. To address this problem, in this paper, we introduce multiple reconfigurable intelligent surfaces (multi-RISs) into the LoRa system to improve its performance. Our specific focus is on the impact of outdated channel state information (CSI), the imperfection of estimated CSI, and the design of RIS discrete phase shifts on the performance. To this end, we first use the moment-matching method to obtain the end-to-end (E2E) channel coefficient of the joint outdated channels and erroneous channels over Nakagami-m fading. Moreover, the closed-form bit error rates (BERs) of the proposed system with non-coherent and coherent detections are derived. The results reveal that, in the high signal-to-noise ratio (SNR) regime, coherent detection encounters the error floor and performs worse than non-coherent detection. Furthermore, we also analyze delay outage rate, throughput, and achievable diversity order of the proposed system. The results show that, despite the presence of outdated CSI and channel estimation errors, the proposed system is still superior to RIS-aided LoRa systems adopting blind transmission and RIS-free ones. Finally, we also thoroughly investigate the effects of various important factors such as the correlation factor, channel estimation errors, the number of RIS reflecting elements, and the number of quantization bits for RIS discrete phase shifts on the performance.

The inherent vulnerability of wireless communication necessitates strategies to enhance its security, particularly in the face of jamming attacks. This paper uses the collaborations of multiple sensing nodes (SNs) in the wireless network to present a cooperative anti-jamming approach (CAJ) designed to neutralize the impact of jamming attacks. We propose an eigenvector (EV) method to estimate the direction of the channel vector from pilot symbols. Through our analysis, we demonstrate that with an adequate number of pilot symbols, the performance of the proposed EV method is comparable to the scenario where the perfect channel state information (CSI) is utilized. Both analytical formulas and simulations illustrate the excellent performance of the proposed EV-CAJ under strong jamming signals. Considering severe jamming, the proposed EV-CAJ method exhibits only a 0.7 dB degradation compared to the case without jamming especially when the number of SNs is significantly larger than the number of jamming nodes (JNs). Moreover, the extension of the proposed method can handle multiple jammers at the expense of degrees of freedom (DoF). We also investigate the method’s ability to remain robust in fast-fading channels with different coherence times. Our proposed approach demonstrates good resilience, particularly when the ratio of the channel’s coherence time to the time frame is small. This is especially important in the case of mobile jammers with large Doppler shifts.

Wireless communication can be simply subjected to malicious attacks due to its open nature and shared medium. Detecting jamming attacks is the first and necessary step to adopt the anti-jamming strategies. This paper presents novel cooperative jamming detection methods that use the low-rank structure of the received signal matrix. We employed the likelihood ratio test to propose detectors for various scenarios. We regarded several scenarios with different numbers of friendly and jamming nodes and different levels of available statistical information on noise. We also provided an analytical examination of the false alarm performance of one of the proposed detectors, which can be used to adjust the detection threshold. We discussed the synthetic signal generation and the Monte Carlo (MC)-based threshold setting method, where knowledge of the distribution of the jamming-free signal, as well as several parameters such as noise variance and channel state information (CSI), is required to accurately generate synthetic signals for threshold estimation. Extensive simulations reveal that the proposed detectors outperform several existing methods, offering robust and accurate jamming detection in a collaborative network of sensing nodes.

This paper studies the statistical characterization of ground-to-air (G2A) and reconfigurable intelligent surface (RIS)-assisted air-to-ground (A2G) communications in RIS-assisted AAV networks under the impact of channel aging. A comprehensive channel model is presented, which incorporates the time-varying fading, three-dimensional (3D) mobility, Doppler shifts, and the effects of channel aging on array antenna structures. We provide analytical expressions for the G2A signal-to-noise ratio (SNR) probability density function (PDF) and the corresponding cumulative distribution function (CDF), demonstrating that the G2A SNR follows a mixture of noncentral$\chi ^{2}$distributions. The A2G communication is characterized under RIS arbitrary phase-shift configurations, showing that the A2G SNR can be represented as the product of two correlated noncentral$\chi ^{2}$random variables (RVs). Additionally, we present the PDF and the CDF of the product of two independently distributed noncentral$\chi ^{2}$RVs, which accurately characterize the A2G SNR’s distribution. Our paper confirms the effectiveness of RIS-assisted communication in mitigating channel aging effects within the coherence time. Finally, we propose an adaptive spectral efficiency method that ensures consistent system performance and satisfactory outage levels when the AAV and the ground user equipment are in motion.

The Internet of Vehicles (IoV) enables technology that allows IoV and vehicles to connect everything. IoV has become an essential component of modern life. This exponential growth of IoV technology has introduced significant security and privacy issues, which pose potential threats to different types of attacks and cause different threats to the normal operation of vehicles. To prevent intelligent vehicle accidents and identify malicious attacks within IoV networks, various researchers have focused on machine learning (ML)-based methods to detect attacks. Intrusion detection systems (IDS) are a prominent solution for cyber attacks in IoV using ensemble learning. To achieve higher accuracy and detection rate, designing an improved detection framework using ensemble learning is a challenging task. The design of an ensemble-based IDS depends on two main challenges: selecting base classifiers and their combination methods. Therefore, in this study, we propose a hybrid ML model to detect various attacks in IoV. We have used different ML algorithms to develop an enhanced algorithm that can efficiently detect attacks in IoV networks. To evaluate the performance of the proposed system, we have used two well-known datasets, (CIC-IDS2017) and (UNSW-NB15). The proposed algorithm shows outstanding performance from the performance results, with an average attack detection accuracy of 99.75% and 100% and an F1 score of 99.74% and 100%, respectively, for both datasets. Further performance scores, that is, recall, precision, and F1 score metrics, validate the exceptional effectiveness of the proposed framework.

In quasi-static wireless networks characterized by infrequent changes in the transmission schedules of user equipment (UE), malicious jammers can easily deteriorate network performance. Accordingly, a key challenge in these networks is managing channel access amidst jammers and under dynamic channel conditions. In this context, we propose a robust learning-based mechanism for channel access in multi-cell quasi-static networks under jamming. The network comprises multiple legitimate UEs, including predefined UEs (pUEs) with stochastic predefined schedules and an intelligent UE (iUE) with an undefined transmission schedule, all transmitting over a shared, time-varying uplink channel. Jammers transmit unwanted packets to disturb the pUEs’ and the iUE’s communication. The iUE’s learning process is based on the deep reinforcement learning (DRL) framework, utilizing a residual network (ResNet)-based deep Q-Network (DQN). To coexist in the network and maximize the network’s sum cross-layer achievable rate (SCLAR), the iUE must learn the unknown network dynamics while concurrently adapting to dynamic channel conditions. Our simulation results reveal that, with properly defined state space, action space, and rewards in DRL, the iUE can effectively coexist in the network, maximizing channel utilization and the network’s SCLAR by judiciously selecting transmission time slots and thus avoiding collisions and jamming.

Internet-of-Vehicles (IoV) is envisioned to connect vehicles with each other, the surrounding environment, and central control centers. Spectrum sharing among active vehicular links is imperative to enhance the utilization of the spectrum licensed to IoV networks. However, co-channel interference among neighboring vehicular communication links poses a fundamental challenge when enabling spectrum sharing in IoV networks. This paper introduces a resource optimization framework, entitled PRISM (ProactiveResource optimization forInterference andSpectrumManagement), to mitigate co-channel interference in IoV networks. PRISM proactively allocates resources among a set of Vehicle-to-Infrastructure (V2I) communication links by accurately predicting the links’ positions and multi-path channel gains, thereby preventing outdated resource scheduling in dynamic IoV networks. PRISM is a three-step approach. In the first step, a multi-layer long short-term memory neural network and transfer learning are employed to predict the vehicles’ positions. In the second step, a digital twin network incorporating high-fidelity 3D maps and a ray tracing tool entitled$\mathrm {Sionna}^{\textrm {TM}}$is used to predict the V2I links’ multi-path channel gains. In the third step, a resource allocation algorithm is executed to efficiently determine V2I clusters and their transmit power allocations to maximize the overall system capacity. Simulation results show that PRISM enhances IoV network’s capacity up to 33% compared to non-proactive schemes, as validated through a simulation framework using real-world vehicular mobility traces.

A high-altitude platform station (HAPS) located in the stratosphere can provide connectivity over a large area. However, a HAPS can create only a limited number of uncorrelated beams. Therefore, we cannot dedicate a beam to each user, and we need to perform user scheduling to be able to serve a large number of users with a HAPS. To this end, in this paper, we group users into clusters using the K-means algorithm and allocate the users in each cluster to orthogonal frequency resource blocks. The frequencies are reused across the clusters. Since HAPS has limited power resources, we also propose a novel codebook-based hybrid beamforming scheme. The results of our simulations conclusively show that our proposed beamforming scheme outperforms the traditional steering vector-based beamforming scheme in high-rise urban areas. Furthermore, we propose a deep Q-network (DQN)-based power allocation method that considerably outperforms the baseline equal power allocation scheme for large coverage areas. Finally, we compare the interference management efficiency of our proposed orthogonal frequency-division multiplexing (OFDM)-based hybrid beamforming-enabled hemispherical array antenna and baseline rectangular and cylindrical array antennas.

In recent years, non-terrestrial network (NTN) communication has gained a significant interest with regard to its capacity to provide coverage in remote areas where deploying a terrestrial network (TN) may not be an option. A growing ecosystem between NTN service providers and TN providers is also developing in high-density urban areas where high-altitude platform stations (HAPs) can provide additional coverage. Maximizing energy efficiency of user equipment (UE) and spectral efficiency of both TN and NTN while minimizing interference to TN’s primary users (PU) and mutual interference of user equipment (UEs), all the while respecting the quality of service (QoS) constraints of all UEs is an NP-hard non-convex mixed integer problem requiring novel, adaptive, and scalable solutions. This paper presents a cooperative algorithm focused on UEs’ capabilities that optimizes sensing and transmission based on their sensing and computing capabilities to maximize their energy efficiency while respecting QoS constraints. The proposed algorithm adapts to the existing UEs or new ones joining the network by facilitating knowledge transfer among cooperative . The proposed algorithm relies on coalition structures for resource sharing, scalability, and cooperative mitigation of sensing data falsification (SDF) attacks. We test the algorithm’s performance in dense urban environments as per 3GPP specifications for channel and interference models and demonstrate the algorithm’s adaptability to new UEs entering or leaving the network.

We introduce the fluctuating Line-of-Sight (fLoS) fading model, characterized by parameters K, k, λ, and Ω. The fLoS fading distribution is expressed in terms of the multivariate confluent hypergeometric functions Ψ2, $\Phi _3^{(n)}$, and ${\Phi _3} = \Phi _3^{(2)}$ and encompasses well-known distributions, such as the Nakagami-m, Hoyt, Rice, and Rician shadowed fading distributions as special cases. An efficient method to numerically compute the fLoS fading distribution is also addressed. Notably, for a positive integer k, the fLoS fading distribution simplifies to a finite mixture of κ-µ distributions. Additionally, we analyze the outage probability and Ergodic capacity, presenting a tailored Prony’s approximation method for the latter. Numerical results are presented to show the impact of the fading parameters and verify the accuracy of the proposed approximation. Moreover, we illustrate an application of the proposed fLoS fading distribution for characterizing wireless systems affected by channel aging.

In the field of Integrated Sensing and Commu-nication (ISAC) systems, several challenges emerge, such as obtaining the infinitesimal Cramer-Ran lower bound (CRLB) for sensing outcomes and addressing the vulnerabilities of ISAC to adversarial machine learning (AML) attacks. To address this, we propose a Smart ISAC (S-ISAC) system, which incorporates a unique generative adversarial network (GAN) combined with a differentiable Kolmogorov-Smirnov (KS) loss function, named KSGAN. This KSGAN is tailor-made to identify AML attacks on range-Doppler heatmap features. Only after ensuring that the range-Doppler heatmap is free from AML attacks using KSGAN, do we apply the Constant False Alarm Rate (CFAR) for accurate estimation of target vehicle parameters. We implement a rigorous ISAC system under AML attacks using Matlab Toolboxes and the adversarial robustness toolbox (ART). Our numerical findings indicate that the proposed KSGAN offers greater accuracy in detecting AML than a standalone GAN. Additionally, our results show that the MIMO S-ISAC Beamforming surpasses the performance of the standalone ISAC system.

This paper explores downlink Cooperative Rate-Splitting Multiple Access (C-RSMA) in a multi-cell wireless network with the assistance of Joint-Transmission Coordinated Multipoint (JT-CoMP). In this network, each cell consists of a base station (BS) equipped with multiple antennas, a cell-center user (CCU), and a cell-edge user (CEU) located at the edge of adjacent cells. Through JT-CoMP, all BSs collaborate to simultaneously transmit the data to all users including the CCUs and CEU. To enhance the signal quality for the CEU, CCUs relay the common stream to the CEU by operating in half-duplex (HD) relaying mode. We aim to jointly optimize the beamforming vectors at the BS, the allocation of common stream rates, the transmit power at relaying users, i.e., CCU s, and the time slot fraction aiming to maximize the minimum achievable data rate. The formulated problem is non-convex and challenging to solve directly. To address this, we employ change-of-variables, first-order Taylor approximations and a low-complexity algorithm based on Successive Convex Approximation (SCA). We demonstrate the efficacy of the proposed scheme, in terms of average achievable data rate, and we compare its performance to that of four baseline schemes, including HD cooperative non-orthogonal multiple access (C-NOMA), NOMA, and RSMA without user cooperation. The results show improvements of 12% and 41 % over RSMA and HD C-NOMA, respectively in high channel disparity between the BS and UEs.

This paper studies the statistical characterization of ground-to-air (G2A) and reconfigurable intelligent surface (RIS)-assisted air-to-ground (A2G) communications with unmanned aerial vehicles (UAVs) in terrestrial and non-terrestrial networks under the impact of channel aging. We first model the G2A and A2G signal-to-noise ratios (SNRs) as non-central complex Gaussian quadratic random variables (RVs) and derive their exact probability density functions, offering a unique characterization for the A2G SNR as the product of two scaled non-central chi-square RVs. Moreover, we also find that, for a large number of RIS elements, the RIS-assisted A2G channel can be characterized as a single Rician fading channel. Our results reveal the presence of channel hardening in A2G communication under low UAV speeds, where we derive the maximum target spectral efficiency (SE) for a system to maintain a consistent required outage level. Meanwhile, high UAV speeds, exceeding 50 m/s, lead to a significant performance degradation, which cannot be mitigated by increasing the number of RIS elements.

In this paper, we present a novel hemispherical antenna array (HAA) designed for High-Altitude Platform Stations (HAPS). Traditional rectangular antenna arrays for HAPS suffer from a significant limitation - their antenna elements are perpetually oriented downward, resulting in low gain for distant users. Meanwhile, cylindrical antenna arrays were introduced to mitigate this drawback, but they, in turn, exhibit a distinct problem: their antenna elements continually face the horizon, leading to suboptimal gain for users located beneath the HAPS. To address these challenges, we introduce the HAA. In the HAA configuration, antenna elements are strategically distributed across the surface of a hemisphere, ensuring that each user receives direct alignment with specific antenna elements, thereby maximizing the gain for all users. We derive the achievable data rates for users within this proposed scheme, employing an analog beamforming technique that leverages the steering vectors of the selected antenna elements for each user. We also formulate an op-timization problem focused on maximizing the minimum Signal-to-Interference-plus-Noise Ratio (SINR) for users. Additionally, we introduce an antenna selection algorithm based on the gains of the antenna elements. To further enhance system performance, we employ the Bisection method to determine the optimal power allocation for each user. Our simulation results substantiate the superior rate performance of the proposed HAA when compared to the conventional rectangular and cylindrical baseline arrays. The proposed approach demonstrates to reach sum data rates of up to 14 Gigabit/s. Furthermore, in contrast to the baseline schemes, the proposed scheme achieves more consistent spectral efficiencies across the entire coverage area.

The problem of relay selection is pivotal in the realm of cooperative communication. However, this issue has not been thoroughly examined, particularly when the background noise is assumed to possess an impulsive characteristic with consistent memory as observed in smart grid communications and some other wireless communication scenarios. In this paper, we investigate the impact of this specific type of noise on the performance of cooperative Wireless Sensor Networks (WSNs) with the Decode and Forward (DF) relaying scheme, considering Symbol-Error-Rate (SER) and battery power consumption fair-ness across all nodes as the performance metrics. We introduce two innovative relay selection methods that depend on noise state detection and the residual battery power of each relay. The first method encompasses the adaptation of the Max-Min criterion to this specific context, whereas the second employs Reinforcement Learning (RL) to surmount this challenge. Our empirical outcomes demonstrate that the impacts of bursty impulsive noise on the SER performance can be effectively mitigated and that a balance in battery power consumption among all nodes can be established using the proposed methods.

This paper investigates a deep reinforcement learning (DRL)-based approach for managing channel access in wireless networks. Specifically, we consider a scenario in which an intelligent user device (iUD) shares a time-varying uplink wireless channel with several fixed transmission schedule user devices (fUDs) and an unknown-schedule malicious jammer. The iUD aims to harmoniously coexist with the fUDs, avoid the jammer, and adaptively learn an optimal channel access strategy in the face of dynamic channel conditions, to maximize the network's sum cross-layer achievable rate (SCLAR). Through extensive simulations, we demonstrate that when we appropriately define the state space, action space, and rewards within the DRL frame-work, the iUD can effectively coexist with other UDs and optimize the network's SCLAR. We show that the proposed algorithm outperforms the tabular Q-learning and a fully connected deep neural network approach.

Sparse code multiple access (SCMA) and mobile edge computing (MEC) can greatly enhance the capabilities of IoT networks by providing massive connectivity and timely computation. The paper presents a model and analysis of the performance for a large-scale grant-free (GF) SCMA aided MEC network. Firstly, stochastic geometry is used to derive closed-form solutions for offloading probability and SCMA ergodic rate. Then, the impact of SCMA on task completion time and energy cost in MEC networks is studied using queueing theory. Simulation results verify the validity of the theoretical expression and demonstrate that SCMA has advantages over orthogonal multiple access (OMA) in improving the offloading probability and ergodic rate, and reducing task latency and energy cost.

Traditional human-designed medium access control (MAC) protocols cannot tackle the heterogeneous requirements of the future 6G wireless networks. Reinforcement learning (RL) algorithms have been proposed, in which base stations (BSs) and user equipment's (UEs) act as agents to automatically learn the MAC protocols to satisfy the stringent quality of service (QoS) requirements of 6G networks. However, existing RL techniques result in a generalization issue where agents fail to identify and explore useful information in a sparse wireless environment. To tackle this challenge, we propose a cooperative multi-agent exploration (CMAE) framework in which the network state space is projected into a low-dimensional space instead of learning a policy in a high-dimensional space. Consequently, the agents start exploring from low-dimensional state space to high-dimensional space to learn the abstracted information from the wireless environment. In the proposed framework, the nodes and BSs collaborate to explore the under-explored wireless network states to jointly learn the channel access and signalling policy. Simulation results show that the proposed CMAE framework outperforms traditional baseline schemes in terms of good put and collision rate and has better generalization capabilities.

Terahertz (THz) communication is a promising technology for future wireless communications, offering data rates of up to several terabits-per-second (Tbps). However, the range of THz band communications is often limited by high pathloss and molecular absorption. To overcome these challenges, this paper proposes intelligent reconfigurable surfaces (IRSs) to enhance THz communication systems. Specifically, we introduce an angle-based trigonometric channel model to evaluate the effectiveness of IRS-aided THz networks. Additionally, to maximize the sum rate, we formulate the source-IRS-destination matching problem, which is a mixed-integer nonlinear programming (MINLP) problem. To solve this non-deterministic polynomial-time hard (NP-hard) problem, the paper proposes a Gale-Shapley-based solution that obtains stable matches between sources and IRSs, as well as between destinations and IRSs in the first and second sub-problems, respectively.

The performance of integrated satellite-aerial-terrestrial relay networks (ISATRNs) faces two main challenges, severe signal strength degradation over long transmission distances and limited spectrum resources. To address these issues, we consider the introduction of high altitude platforms (HAPs) and unmanned aerial vehicles (UAVs) carrying reconfigurable intelligent surface (RIS) as relays during transmission from satellites to the ground. Additionally, we employ rate splitting multiple access (RSMA) at HAPs to improve signal transmission robustness. To optimize system energy efficiency, we formulate a multi-objective problem that considers the active transmit beamforming vector, RIS phase shift, power splitting ratio, and UAV trajectory. To tackle the non-convex problem involving both discrete and continuous variables, we introduce a novel approach called access-free federated deep reinforcement learning (AF-DRL). The optimal transmit beamforming and power splitting ratio are obtained by allowing the UAV to plan its path and locally train, reducing computational overhead caused by high-dimensional UAV movement. Simulation results demonstrate that the proposed RSMA-based enhancement scheme achieves higher energy efficiency compared to the comparison scheme.

Jamming attacks are becoming increasingly common and pose a significant threat to the security and reliability of wireless sensor networks (WSNs). These attacks can be difficult to detect, as they often operate in a stealthy manner, disrupting communication between sensors. Artificial intelligence (AI) techniques have the potential to be highly effective in detecting jamming attacks. However, the adoption of AI-based techniques for detecting jamming attacks has been limited due to the scarcity of up-to-date and accurate data of these attacks. Privacy-aware collaboration among agents is expected to be essential in building robust AI-based models for detecting jamming attacks in WSNs. In this context, we propose a novel framework that uses collaborative federated learning (FL) to enable privacy-aware distributed learning among multiple agents without sharing their sensitive data. This can be particularly important in jamming attack detection, where data may contain sensitive information that should not be shared. In addition, we design a novel secure aggregation scheme to protect the FL aggregation service from reverse-engineering attacks. The effectiveness of our proposed framework was tested using the public Wireless Sensor Networks Dataset (WSN-DS) that includes four types of well-known jamming attacks (i.e., constant jamming, reactive jamming, random jamming, and deceptive jamming). The results of a thorough experiment using the WSN-DS data set demonstrate the effectiveness and high accuracy/F1-score (99%) in detecting jamming attacks while also maintaining participant privacy.

Rapid advancements in deep learning (DL) and the availability of the large data sets have made the adoption of DL highly appealing across various fields. Wireless communication systems, including future 6G systems are anticipated to incorporate intelligent components like automatic modulation classification (AMC) for the cognitive radio and dynamic spectrum access. However, DL-based AMC models are susceptible to the adversarial attacks, which consist of crafted perturbations that aim to alternate the decision of a victim model. This study focuses on investigating and uncovering modern modulation classifiers’ vulnerability to the adversarial threats. Though attacks of this nature inherently jeopardize DL-based classifiers, contemporary attack methods typically exhibit diminished impact at the lower perturbation levels. Therefore, we introduce a novel attack approach that exploits the Riemannian manifold properties of the intricate neural networks, yielding adversarial samples with heightened efficacy at the lower perturbation powers. We thoroughly evaluate how effective various defense techniques are and demonstrate our proposed attack method’s ability to thwart them. The findings of this study shed light on the limitations and vulnerabilities of the DL-based AMC models in the face of the adversarial attacks. By addressing these challenges, we can enhance the robustness and security of these models, and pave the way for their reliable deployment in practical wireless communication systems, including the future 6G networks.

As the Augmented Intelligence of Things (AIoT) advances within vehicle-road coordination systems, challenges related to road traffic data transmission and processing are being increasingly addressed. However, this progress also brings significant risks of privacy data leakage. Federated learning (FL), a distributed machine learning paradigm, effectively safeguards client data privacy by allowing multiple participants to collaboratively train models while keeping their data localized. Despite its benefits, FL faces challenges, such as model parameter leakage and Byzantine attacks. To tackle these issues, this article introduces an anonymous and efficient FL framework for vehicle-road coordination systems (AEFL), designed to ensure a secure and reliable vehicle data transmission process. This architecture incorporates a novel group pairing onion routing protocol, which leverages pairing cryptography principles for hierarchical data encryption. During the routing process, relay group nodes decrypt the corresponding layer, ensuring both data confidentiality and node anonymity. Additionally, a sampling method is proposed to accurately identify Byzantine vehicle nodes, enhancing the precision of FL aggregation without compromising overall model performance. Experimental results show that AEFL outperforms the classic TOR anonymous routing protocol, achieving a 100% message delivery rate more quickly. Under the same conditions, the anonymity of the source node and the destination node improves by 3.9% and 1.9%, respectively. When half of the nodes are compromised, path anonymity can be increased by 24.8%. Furthermore, our framework excels in FL aggregation efficiency, with a Byzantine adversary detection accuracy of up to 99%.

In this article, we propose a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)- and energy buffer-aided multiple-input-single-output (MISO) simultaneous wireless information and power transfer (SWIPT) nonorthogonal multiple access (NOMA) system, which consists of a STAR-RIS, an access point (AP), and reflection users and transmission users with energy buffers. In the proposed system, the multiantenna AP can transmit information and energy to several single-antenna reflection and transmission users simultaneously by the NOMA fashion in the downlink, where the power transfer and information transmission states of the users are modeled using Markov chains. The reflection and transmission users harvest and store the energy in energy buffers as additional power supplies, which are partially utilized for uplink information transmission. The power outage probability, information outage probability, sum throughput, and joint outage probability closed-form expressions of the proposed system are derived over Nakagami-m fading channels, which are validated via simulations. Results demonstrate that the proposed system achieves better performance as compared to the proposed system with discrete phase shifts, the STAR-RIS-aided MISO SWIPT-NOMA buffer-less, conventional reconfigurable intelligent surface (RIS)- and energy buffer-aided MISO SWIPT-NOMA, and STAR-RIS- and energy buffer-aided MISO SWIPT-time-division multiple access (TDMA) systems. Furthermore, a particle swarm optimization-based power allocation (PSO-PA) algorithm is designed to maximize the uplink sum throughput with a constraint on the uplink joint outage probability and Jain’s fairness index (JFI). Simulation results illustrate that the proposed PSO-PA algorithm can achieve an improved sum throughput performance of the proposed system.

Integrating multiaccess edge computing (MEC) with the Internet of Things (IoT) is able to provide IoT sufficient computational resources in addition to its capabilities of sensing and communication. In this article, given the limited computational and energy resources, IoT devices (IDs) are allowed to offload computational tasks to MEC servers for execution. However, as the number of IDs increases dramatically, jointly optimizing the usage of sensing, communication, and computational resources becomes challenging due to the exponential growth in interactions among the IDs. In this article, we address the energy-efficient joint optimization problem for sensing and computation in the MEC-assisted IoT system, aiming to ensure the freshness of the status update and minimize the energy consumption of IDs. To reduce the computation complexity, we introduce the concept of the general mean-field N-player Markov game (GMFG), and reformulate it as a mean-field game (MFG) with teams, leveraging the network structure of states. Considering the advantages of reinforcement learning (RL) for solving dynamic problems, we propose an MFG-based actor-critic algorithm (MFGAC) to minimize the long-term average system cost. Through extensive simulations, we demonstrate that the proposed method is effective and can outperform other schemes under different scenarios.

Amid the global rollout of fifth-generation (5G) services, researchers in academia, industry, and national laboratories have been developing proposals for the sixth-generation (6G), whose materialization is fraught with many fundamental challenges. To alleviate these challenges, a deep learning (DL)-enabled semantic communication (SemCom) has emerged as a promising 6G technology enabler, which embodies a paradigm shift that can change the status quo viewpoint that wireless connectivity is an opaque data pipe carrying messages whose context-dependent meanings have been ignored. Since 6G is also critical for the materialization of major SemCom use cases, the paradigms of 6G for SemCom and SemCom for 6G call for a tighter integration of 6G and SemCom. For this purpose, this comprehensive article provides the fundamentals of semantic information, semantic representation, and semantic entropy; details the state-of-the-art SemCom research landscape; presents the major SemCom trends and use cases; discusses current SemCom theories; exposes the fundamental and major challenges of SemCom; and offers future research directions for SemCom. We hope this article stimulates many lines of research on SemCom theories, algorithms, and implementation.

In this paper, we present a novel coexistence uplink-downlink stochastic mobile edge computing (MEC) system that considers the dynamic characteristics of both small cell base stations (SBSs) and user equipments (UEs). To devise an efficient radio resource management strategy encompassing user association, channel allocation, and power allocation, we formulate an optimization problem that considers time, energy, and achievable rate in the utility function. The formulated problem is a Mixed Integer Nonlinear Program (MINLP) and has been proven to be NP-hard. To address this complexity, we propose a unified nature-inspired optimization framework, which can be deployed for subproblems in various settings and can be integrated with the Whale Optimization Algorithm (WOA), Improved Whale Optimization Algorithm (IWOA), and Particle Swarm Optimization (PSO). Through our rigorous mathematical and numerical analysis, the proposed algorithms show that they can converge to a near-optimal solution while keeping negligible optimality gaps. Our numerical results show the advantages and drawbacks of the proposed algorithms, highlighting their potential for effective resource management in MEC systems. The results also show the performance evaluation of stochastic characteristics on the performance of MEC.

It has been proved that a long-range (LoRa) system can achieve long-distance and low-power transmission. However, the performance of LoRa systems can be severely degraded by fading. In addition, LoRa technology typically adopts an ALOHA-based access mechanism, which inevitably produces interfering signals for the target user. To overcome the effects of fading and interference, we introduce a reconfigurable intelligent surface (RIS) to LoRa systems. In this context, both non-coherent and coherent detections are considered and their bit error rate (BER) performance analyses are conducted. Moreover, we derive the closed-form BER expressions for the proposed system over Nakagami-m fading channels. Simulation results are used to verify the accuracy of our proposed analytical results. It is shown that in the presence of the interference, the proposed system outperforms RIS-free LoRa systems, and RIS-aided LoRa systems adopting blind transmission. In addition, we also compare the proposed system to single-user RIS-aided LoRa systems adopting blind transmission, and the results show that the proposed system maintains its superior performance even in the presence of the interference. Furthermore, the impacts of the spreading factor (SF), the number of reflecting elements, and the Nakagami-m fading parameters are investigated. It is shown that increasing the number of reflecting elements can remarkably enhance the BER performance, which is an affective measure for the proposed system to balance the trade-off between data rate and coverage range. We further observe that the BER performance of the proposed system is more sensitive to the fading parameter m at high signal-to-noise ratios.

It is crucial that communication between an unmanned aerial vehicle (UAV) and the ground station (GS) be secure, and both devices should mutually authenticate each other to ensure that an adversary cannot obtain communicated information. Moreover, dynamically adapting the session time of an authenticated session can decrease the idle time of a session and consequently reduce the window of opportunity for an adversary to interfere with the communication link. In light of these considerations, we design aphysically unclonable function (PUF)andfuzzy extractor-based UAV-GS authentication mechanism calledUAV Authentication with Adaptive Session (UAAS). In UAAS, both UAV-GS and UAV-UAV authentication are two-way. We use aThompson Sampling (TS)-based approach to intelligently adapt the duration of a session. Both formal and informal security proofs are presented along with a computation and communication cost analysis to analyze the performance of UAAS. It is noted that UAAS is secure against various well-known attacks and has a lower communication cost than several baseline mechanisms. Due to the noise reduction that occurs in PUFs, the computational cost of UAAS is higher than that of baselines that ignore noise. Also, our simulation shows that UAAS significantly outperforms baselines when it comes to network performance.

Unmanned aerial vehicles (UAVs)-aided mobile-edge computing (MEC) systems face several challenges that hinder their practical implementation. First, the broadcast nature of wireless communications can cause security issues. Second, UAVs have constrained onboard power. Finally, the UAV should be able to serve a maximum number of ground users (GUs). It is also crucial to maintain fairness such that all GUs get equal opportunities to securely offload tasks to UAVs. We seek to address the aforementioned challenges by designing an intelligent mechanism,FairLearn, which maximizes the fairness in secure MEC services by controlling the UAV 3D trajectory, transmission power, and scheduling time for task offloading by mobile GUs. To this end, we formulate a maximization problem and solve it using adeep neural network (DNN)-based model, where the UAVs collaboratively learn the model by utilizing afederated learning (FL)approach. Each UAV uses areinforcement learning (RL)-based approach to individually generate the training dataset, making the training data span different network scenarios. Our model is based on UAV pairs, where one UAV executes the GUs' offloaded tasks, while the other is a jammer that suppresses eavesdroppers. The simulation evaluation of FairLearn shows that it significantly improves the performance of UAV-enabled MEC systems.

Unmanned aerial vehicles (UAVs) are operated remotely without the presence of a unified system of identity authentication, and wireless communications in untrusted environments can cause the loss of valuable data carried by UAVs. Traditional UAV authentication mechanisms are centralized approaches, which suffer from a single point of failure problem and may incur high complexity computations. Therefore, it is crucial to establish a distributed authentication mechanism between the ground station controller (GSC) and a UAV. Moreover, in case of UAV swarms, the high mobility of the UAVs affects the stability of UAV communications, which leads to the degradation of the UAV authentication performance. Addressing these challenges, we design a blockchain-based distributed authentication mechanism, known asSwarmAuth, for UAV swarms, where the GSC and UAVs follow a mutual authentication approach using physical unclonable functions (PUFs), and the K-means clustering-based intelligent approach is used to dynamically create location-based clusters. The blockchain helps store UAVs’ authentication information in an immutable storage and the associated smart contracts provide a convenient access control model. The security analysis of SwarmAuth is carried out through both formal and informal proofs considering general attacks. Experimental evaluation shows that SwarmAuth can assure trustworthy communications and improve the network performance.

Information and Communication Technology (ICT) provides customers with utilities and smart grid solutions, enabling enhanced monitoring and control of energy management systems. This technology is poised to elevate the reliability, sustainability, and efficiency of future electric grids through the implementation of advanced metering infrastructure (AMI). However, current Supervisory Control and Data Acquisition (SCADA) systems lack trusted machine authentication in smart grid communications, leaving the electric grid vulnerable to cyberattacks via sophisticated network technologies such as wireless access points, sensors, routers, and gateways. Therefore, ensuring proper management of data integrity from field sensors is crucial to enhance the reliability of SCADA systems. In this context, the utilization of quantum key distribution (QKD) key pairs is proposed to uphold integrity in smart grid communications. This paper presents a fibre optic blockchain network designed to manage and utilize cryptographic keys, facilitating the authentication of peer-to-peer (P2P) communications in SCADA systems. This demonstration underscores the feasibility of employing QKD and blockchain to further strengthen the integrity and authentication of smart grid communications. Additionally, this paper delves into discussing the performance metrics and overhead expenses of the proposed scheme in comparison with existing state-of-the-art proposals. Simulation results highlight the significant impact of blockchain size on the system setup’s throughput and latency.

Tactical wireless sensor networks (T-WSNs) are used in critical data-gathering military operations, such as battlefield surveillance, combat monitoring, and intrusion detection. These networks have unique challenges, such as jamming attacks, which are not normally encountered in traditional WSNs. Jamming attacks on the networks’ links disrupt data communication and make packet routing in T-WSNs a difficult task. Consequently, T-WSN routing aims to find the most reliable routes, while meeting the stringent delay and energy requirements. To this end, we propose a distributed multi-agent deep reinforcement learning (MADRL)-based routing solution for multi-sink tactical mobile sensor networks to overcome link layer jamming attacks. Our proposed routing scheme captures the hop count to the nearest sink, the one-hop delay, the next hop’s packet loss rate (PLR), and the energy cost of packet forwarding in the action reward estimation. Furthermore, the proposed scheme outperforms benchmark algorithms in terms of the packet delivery ratio (PDR), packet delivery time, and energy efficiency.

Efficient renewable resource integration is a key pillar of the evolving smart grid vision to shift from fossil fuels. Microgrids (MGs), empowered by distributed energy resources (DERs), are a promising avenue toward this goal. In MG communications, robust radio resource management is essential, requiring mature wireless technologies that accommodate growing network size and diverse services. This work demonstrates the capabilities of coverage enhancement, which is a feature of the narrow-band Internet of things to support the performance of future massive MG networks. We formulate a problem to maximize the weighted sum-rate of DERs with different quality of service classes. Our objective is to optimally allocate power levels to the users and perform frequency scheduling. A three-step approach is proposed to solve the highly nonconvex problem, where difference-of-convex tools are invoked to address the power control subproblem. Next, a low-complexity distributed heuristic is used for sub-carrier scheduling, leveraging data-driven methods and mixed-integer nonlinear programming for network compression and coordination. The base stations’ coverage enhancement radii are optimized using a distributed multi-armed-bandit-based algorithm. Furthermore, numerical simulations reveal that our integrated solution not only outperforms benchmarks based on a genetic algorithm and round-robin scheduling but also performs comparably to solutions using pure mixed-integer nonlinear programming for optimal scheduling.

In this paper, we present a novel hemispherical antenna array (HAA) designed for high-altitude platform stations (HAPS). A significant limitation of traditional rectangular antenna arrays for HAPS is that their antenna elements are oriented downward, resulting in low gains for distant users. Cylindrical antenna arrays were introduced to mitigate this drawback; however, their antenna elements face the horizon leading to suboptimal gains for users located beneath the HAPS. To address these challenges, in this study, we introduce our HAA. An HAA’s antenna elements are strategically distributed across the surface of a hemisphere to ensure that each user is directly aligned with specific antenna elements. To maximize users’ minimum signal-to-interference-plus-noise ratio (SINR), we formulate an optimization problem. After performing analog beamforming, we introduce an antenna selection algorithm and show that this method achieves optimality when a substantial number of antenna elements are selected for each user. Additionally, we employ the bisection method to determine the optimal power allocation for each user. Our simulation results convincingly demonstrate that the proposed HAA outperforms the conventional arrays, and provides uniform rates across the entire coverage area. With a 20 MHz communication bandwidth, and a 50 dBm total power, the proposed approach reaches sum rates of 14 Gbps.

Although deep learning (DL)-enabled semantic communication (SemCom) has emerged as a 6G enabler by minimizing irrelevant information transmission – minimizing power usage, bandwidth consumption, and transmission delay, its benefits can be limited by radio frequency interference (RFI) that causes substantial semantic noise. Such semantic noise’s impact can be alleviated using an interference-resistant and robust (IR2) SemCom design, though no such design exists yet. To stimulate fundamental research on IR2SemCom, the performance limits of a popular text SemCom system namedDeepSCare studied in the presence of (multi-interferer) RFI. By introducing a principled probabilistic framework for SemCom, we show that DeepSC produces semantically irrelevant sentences as the power of (multi-interferer) RFI gets very large. We also derive DeepSC’s practical limits and a lower bound on its outage probability under multi-interferer RFI, and propose a (generic) lifelong DL-based IR2SemCom system. We corroborate the derived limits with simulations and computer experiments, which also affirm the vulnerability of DeepSC to a wireless attack using RFI.

This research paper introduces an unsupervised scalable probabilistic approach for radio resource management in device-to-device (D2D) communication networks, essential for enhancing wireless data service capacity. We propose a joint optimization framework for spectrum allocation and power control, aiming to optimize the network’s mean rate while meeting minimum data rate requirements. Although deep learning (DL) models have been explored for this purpose, their scalability is constrained by the fixed sizes of their input/output features, and their effectiveness is often limited by an insufficient understanding of the network’s geometric structure. Consequently, Graph Neural Networks (GNNs) were introduced to integrate the wireless network’s topology into the learning process. However, GNNs typically lose spatial correlation data when converting the tensorized channel state information (CSI) into a graph structure. To overcome this limitation, our solution combines GNNs, convolutional neural networks (CNNs), and variational autoencoders to extract meaningful embeddings from the CSI, preserving spatial and geometric features. We also introduce an innovative graph attention mechanism that enhances the model’s focus on crucial node and edge features. Our holistic approach exploits the wireless network’s topological and spatial relationships, offering a scalable, unsupervised, and generalizable solution without the need for retraining or architectural adjustments across various wireless setups. Our findings confirm our method’s superior performance and adaptability to different wireless environments.

The recent growth of the Internet of Things (IoT) has revolutionized vehicular networks into the Internet of Ve-hicles (IoV). Within the IoV infrastructure, modern vehicles are vulnerable to different and new types of cyber-attacks. Consequently, Intrusion Detection Systems (IDS) are extremely helpful in coping with such attacks, and there is a pressing need for the development of an advanced IDS that can efficiently detect attacks with a high detection rate and accuracy, and a low false alarm rate. Toward this end, we present a deep learning-based IDS for the identification and classification of potential cyber-attacks in the 10 V network. Specifically, a generative hybrid deep learning model that combines Long Short-Term Memory Variational AutoEncoder with Bidirectional Gated Recurrent Units (LSTMVAE-BiGRU) is proposed. Due to the integration of the LSTM network and the VAE in this architecture, the LSTM performs the encoding and decoding in the LSTMVAE paradigm. The LSTMVAE is forced to learn the most salient features of the training time series data by restricting the latent space to have a dimension that is less than the input. On the other hand, to learn latent representations, BiGRU collects local dependencies in a two-way time flow, or forward and backward. Lastly, the multi-class attack detection method is carried out using the softmax layer. Experimental findings demonstrate that the proposed IDS outperforms both baseline techniques that are widely used and cutting-edge approaches.

In this paper, we propose federated learning (FL)-based jamming detection algorithms for a stochastic, distributed, tactical terrestrial and non-terrestrial (SDT-TNT) network. Specifically, we consider an SDT-TNT network with multiple clusters, in which multiple unknown jammers might be present. Moreover, we employ the spectral correlation function (SCF) on local servers to estimate the cyclostationary properties of the received waveforms. We then use the SCF to train local convolutional autoencoders (CAEs). In the inference phase, we jointly use the latent representation of the trained CAE and the kernel density estimation (KDE) to detect the existence of jammers. Our proposed methods show very promising results in jamming detection and outperform non-FL approaches. We further demonstrate that using the SCF feature provides higher accuracy than using In-phase/Quadrature-phase (I/Q) features.

The advancements in the field of the Internet of Things (IoT) have fueled technological advancements in remotely handling agricultural operations, as well as the successful implementation of Precision Agriculture (PA) around the world. In this paper, we present Cluster-based Routing using an Optimized framework for Precision agricultural monitoring (CROP) that uses the recently developed Sooty Tern Optimization Algorithm (STOA). CROP specifically aims to detect unauthenticated entry into the agricultural field and various other factors related to PA. We perform a simulation analysis of CROP, and the performance of CROP is overwhelming, as it not only enhances stability period and network longevity by 58.9% and 65.5% respectively, but also proves to be scalable as compared to the Fuzzy-C-Means (FCM) algorithm and other routing protocols pertaining to PA.

Unmanned aerial vehicles (UAVs) combining with mobile edge computing (MEC) networks have promoted the application of Internet of Things (IoT) devices, providing enhanced coverage with flexible computing services. But the energy consumption of data processing is still a shortage in the UAV-assisted MEC architecture. Motivated by that, we propose a dynamic UAV-assisted MEC network and formulate a problem and jointly optimize association strategies, UAV trajectory, data offloading, and resource distribution for minimizing total energy consumption. To deal with this tricky problem, we devise a dichotomy-based joint iterative optimization algorithm. Specifically, we divide the problem into three sub-problems, solving by the integer programming, successive convex optimization, and dichotomy method. Finally, the simulation consequences prove that the devised network and algorithm significantly reduce total energy consuming.

Satellite-terrestrial integrated networks (STINs) are troubled with the serious jamming threats in the counterwork environment. Non-orthogonal multiple access (NOMA) approach can not only improve the resource utilization by resource sharing, but also has the potential advantages to be used for anti-jamming. In this paper, under the threat of smart jammer with adaptive jamming policies, we investigate the NOMA-based anti-jamming problem in multi-cell STINs by jointly considering the NOMA-based user grouping in each cell and the beam allocation among multiple cells. Specifically, for each cell, the users can enhance anti-jamming performance and improve the sum rate by NOMA-based users grouping, which is formulated as the anti-jamming Stackelberg game and grouping game to obtain the equilibrium solutions. Then, an adaptive beam allocation algorithm with a low complexity is proposed to avoid allocation conflicts and achieve fairness among multiple cells. Finally, simulation results prove the performance of the proposed scheme.

The resource-constrained IPV6-based low power and lossy network (6LowPAN) is connected through the routing protocol for low power and lossy networks (RPL). This protocol is subject to a routing protocol attack called a rank attack (RA). This paper presents a performance evaluation where leveraging model-free reinforcement-learning (RL) algorithms helps the software-defined network (SDN) controller achieve a cost-efficient solution to prevent the harmful effects of RA. Experimental results demonstrate that the state action reward state action (SARSA) algorithm is more effective than the Q-learning (QL) algorithm, facilitating the implementation of intrusion prevention systems (IPSs) in software-defined 6LowPANs.

Federated Learning (FL) is a distributed machine learning technique where multiple devices can collaboratively train a model without sharing their data. As a result, FL ensures distinct privacy benefits compared to centralized training approaches. However, despite its benefits, FL remains susceptible to reverse-engineering attacks that can uncover sensitive information about the training data from the local updates sent by each participant. To address this issue, we propose a framework for securely and efficiently aggregating the results of FL on multiple devices. We compare and evaluate the performance of two techniques, Homomorphic Encryption (HE) and Secure Multiparty Computation (SMPC), to determine the best method that allows devices to share their learning results without revealing their raw local data. Ultimately, we propose using SMPC protocol as the most effective solution to secure FL. Our framework is experimentally evaluated, and its effectiveness in terms of security, efficiency, and accuracy is demonstrated.

Wireless sensor networks (WSNs) in smart grid (SG) applications are greatly affected by the detrimental impact of bursty impulsive noise (IN) generated by various partial discharges from aging power equipments in Smart Grid’s power substations. Additionally, the deleterious impact of fading on the radio frequency (RF) links between the sensor nodes further deteriorates the performance. To alleviate the problem of fading on the RF links, in this paper, we propose to exploit reconfigurable intelligent surfaces (RISs). In addition, to deal with the bursty IN, we consider the maximal a posteriori (MAP) decoding of the received symbols using the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. The performance of the considered system is evaluated by deriving a novel closed-form expression for the bit error rate (BER). Furthermore, an asymptotic BER analysis is presented in order to highlight the diversity order achieved by the considered RIS-aided system. Finally, extensive numerical results are provided to demonstrate the significant gain that can be achieved through the proposed RIS-aided architecture for WSNs.

Deep learning has been adopted for a wide range of wireless communication tasks, including modulation classification, because of its great classification capability. However, deep learning models have been shown to also introduce risks and vulnerabilities. For instance, adversarial attacks craft and introduce imperceptible perturbations that compromise the accuracy of deep learning-based modulation classifiers on wireless receivers. Therefore, in this article, we propose a novel wireless receiver architecture that enhances deep learning-based modulation classifiers to defend them against adversarial attacks. Our experimental results show that our defense technique significantly diminishes the accuracy reduction that is caused by adversarial attacks by protecting modulation classifiers at least 18% more than existing defense techniques.

The traditional healthcare is overwhelmed by the processing and storage of massive medical data. The emergence and gradual maturation of Internet-of-Things (IoT) technologies bring the traditional healthcare an excellent opportunity to evolve into the IoT-enabled healthcare of massive data storage and extraordinary data processing capability. However, in IoT-enabled healthcare, sensitive medical data are subject to both privacy leakage and data tampering caused by unauthorized users. In this article, an attribute-based secure access control mechanism, coined (SACM), is proposed for IoT-Health utilizing the federated deep learning (FDL). Specifically, we manage to discover the relationship between users’ social attributes and their trusts, which is the trustworthiness of users rely on their social influences. By applying graph convolutional networks to the social graph with the susceptible–infected–recovered model-based loss function, users’ influences are obtained and then are transformed to their trusts. For each occupation, users’ trusts allow them to access specific medical data only if their trusts are higher than the corresponding threshold. Then, the FDL is applied to obtain the optimal threshold and relevant access control parameters for the improvement of access control accuracy and the enhancement of privacy preservation. The experimental results show that the proposed SACM achieves accurate access control in IoT-enabled healthcare with high data integrity and low privacy leakage.

Artificial Intelligence of Things (AIoT), as a fusion of artificial intelligence (AI) and Internet of Things (IoT), has become a new trend to realize the intelligentization of industry 4.0 and the data privacy and security is the key to its successful implementation. To enhance data privacy protection, the federated learning has been introduced in AIoT, which allows participants to jointly train AI models without sharing private data. However, in federated learning, malicious participants might provide malicious models by launching the poisoning attack, which will jeopardize the convergence and accuracy of the global model. To solve this problem, we propose a malicious model detection mechanism based on the isolation forest (iforest), named D2MIF, for the federated learning-empowered AIoT. In D2MIF, an iforest is constructed to compute the malicious score for each model uploaded by the corresponding participant, and then, the models will be filtered if their malicious scores are higher than the threshold, which is dynamically adjusted using reinforcement learning (RL). The validation experiment is conducted on two public data sets Mnist and Fashion_Mnist. The experimental results show that the proposed D2MIF can effectively detect malicious models and significantly improve the global model accuracy in federated learning-empowered AIoT.

Fog radio access network (F-RAN) is a promising architecture that leverages edge computing and caching to improve devices’ latency and quality of service. However, interference, which arises when multiple devices are concurrently scheduled on the same radio resource block (RRB), limits the performance of a dense F-RAN. This article considers a multi-cell F-RAN in which the devices of each small cell receive data from the associated fog access point (F-AP) over the same RRB(s). The F-APs transmit data to the associated devices using rate-splitting multiple access (RSMA) schemes to manage co-channel interference within the small cells efficiently. A transmit power control scheme is proposed to maximize the network’s spectral efficiency (SE) while considering the devices’ hardware impairments (HWIs). The considered transmit power control scheme is an NP-hard problem, which is highly challenging to solve using the legacy optimization approach. To address this challenge, we propose a distributed deep-reinforcement-learning (DRL)-based power allocation (DDPA) scheme that takes the time-varying dynamics of the network and the HWIs of devices into account. Each F-AP in the proposed framework is equipped with a DRL agent that collects signal-to-interference-plus-noise ratio and channel state information from connected devices and adapts the transmit power allocation each scheduling interval. In addition, the ensemble learning framework is exploited to further improve the proposed DDPA scheme’s performance. We use extensive simulations to demonstrate that the DDPA scheme achieves greater SE than contemporary transmit power control schemes. In particular, the proposed DDPA scheme is, especially, suited to scenarios with non-negligible HWIs-induced distortion.

Conventional anti-jamming methods mainly focus on preventing single jammer attacks with an invariant jamming policy or jamming attacks from multiple jammers with similar jamming policies. These anti-jamming methods are ineffective against a single jammer following several different jamming policies or multiple jammers with distinct policies. Therefore, this article proposes an anti-jamming method that can adapt its policy to the current jamming attack. Moreover, for the multiple jammers scenario, an anti-jamming method that estimates the future occupied channels using the jammers’ occupied channels in previous time slots is proposed. In both single and multiple jammers scenarios, the interaction between the users and jammers is modeled using recurrent neural networks (RNNs). The performance of the proposed anti-jamming methods is evaluated by calculating the users’ successful transmission rate (STR) and ergodic rate (ER), and compared to a baseline based on deep$Q$-learning (DQL). Simulation results show that for the single jammer scenario, all the considered jamming policies are perfectly detected and a high STR and ER are maintained. Moreover, when 70% of the spectrum is under jamming attacks from multiple jammers, the proposed method achieves an STR and ER greater than 75% and 80%, respectively. These values reach 90% when 30% of the spectrum is under jamming attacks. In addition, the proposed anti-jamming methods significantly outperform the DQL method for all the considered jamming scenarios.

This paper proposes an energy buffer and reconfigurable intelligent surface (RIS) aided differential chaos-shift-keying (DCSK) wireless-powered communication (WPC) system, which includes an access point (AP), a RIS, and a source node equipped with an energy buffer. In such system, the source node first harvests and stores radio-frequency energy from the AP in the downlink, then uses the stored energy to transmit information to the AP in the uplink. A simple time-switching scheme for the proposed system is designed. Two different energy management policies at the source node are considered, i.e., best-effort policy (BEP) and on-off policy (OOP). The bit-error-rate (BER) expressions of the proposed system with BEP and OOP are derived over multipath Rayleigh fading channels. Results demonstrate that the proposed system is able to achieve better BER performance than the conventional DCSK-WPC system and the RIS aided DCSK-WPC buffer-less system, while keeping the non-coherent feature of the DCSK system.

This paper proposes a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided wireless-powered non-orthogonal multiple access (NOMA) system, which includes an access point (AP), a STAR-RIS, and two non-orthogonal users located at both sides of the STAR-RIS. In this system, the users first harvest the radio-frequency energy from the AP in the downlink, then adopt the harvested energy to transmit information to the AP in the uplink concurrently. Two policies are considered for the proposed system. The first one assumes that the time-switching protocol is used in the downlink while the energy-splitting protocol is adopted in the uplink, which is referred to as time-switching and energy-splitting policy (TEP). The second one assumes that the energy-splitting protocol is utilized in both the downlink and uplink, which is referred to as double energy-splitting policy (EEP). The outage probability, sum throughput, and average age of information (AoI) of the proposed system with TEP and EEP are investigated over Nakagami-$m$fading channels. In addition, we also analyze the outage probability, sum throughput, and average AoI of the STAR-RIS aided wireless-powered time-division-multiple-access (TDMA) system. Simulation and numerical results demonstrate that the proposed system with TEP and EEP yields better performance than baseline schemes. Although the proposed system sacrifices the outage probability and average AoI performance compared to STAR-RIS aided wireless-powered TDMA systems, it significantly enhances the sum throughput. Furthermore, we design a genetic-algorithm based time allocation and power allocation (GA-TAPA) algorithm to maximize the sum throughput and ensure a certain average AoI. Simulation results indicate that the proposed GA-TAPA algorithm can further improve the sum throughput of the proposed system.

The rapid growth of electric vehicle (EV) penetration has led to more flexible and reliable vehicle-to-grid-enabled cyber-physical systems (V2G-CPSs). However, the increasing system complexity also makes them more vulnerable to cyber-physical threats. Coordinated cyber attacks (CCAs) have emerged as a major concern, requiring effective detection and mitigation strategies within V2G-CPSs. Digital twin (DT) technologies have shown promise in mitigating system complexity and providing diverse functionalities for complex tasks such as system monitoring, analysis, and optimal control. This paper presents a resilient and secure framework for CCA detection and mitigation in V2G-CPSs, leveraging a smart DT-enabled approach. The framework introduces a smarter DT orchestrator that utilizes long short-term memory (LSTM) based actor-critic deep reinforcement learning (LSTM-DRL) in the DT virtual replica. The LSTM algorithm estimates the system states, which are then used by the DRL network to detect CCAs and take appropriate actions to minimize their impact. To validate the effectiveness and practicality of the proposed smart DT framework, case studies are conducted on an IEEE 30 bus system-based V2G-CPS, considering different CCA types such as malicious V2G node or control command attacks. The results demonstrate that the framework is capable of accurately estimating system states, detecting various CCAs, and mitigating the impact of attacks within 5 seconds.

The communication among smart meters (SMs) and neighborhood area network (NAN) gateways is a fundamental requisite for managing the energy consumption at the consumer site. The bidirectional communication among SMs and NANs over the insecure public channel is vulnerable to impersonation, SM traceability, and SM physical capturing attacks. Many existing schemes’ insecurities and/or inefficiencies call for an efficient and secure authentication scheme for smart grid infrastructure. In this article, we present a privacy preserving and lightweight authentication scheme for smart grid (LAS-SG) using elliptic curve cryptography. The proposedLAS-SGis proved as secure under the standard model. Moreover, the efficiency of the LAS-SG is extracted through a real-time experiment, which attests that proposedLAS-SGcompletes a round of authentication in 20.331 ms by exchanging only two messages and 192 B. Due to the adequate efficiency and ample security, the proposedLAS-SGis more appropriate for SG environments.

Many IoT-based applications have inherited the artificial intelligence of things (AIoT) techniques to explore new services and benefits of smart recording and monitoring generated information. However, hundreds of hacking incidents caused by highly sophisticated attackers have generated serious risks, where they compromised various IoT sensors for their benefits, impeding the growth of AIoT. Various security schemes have been proposed in the literature; however, it is critical to determine the legitimacy of AIoT devices in real-time scenarios during the initial deployment of the network. Therefore, this article aims to provide a secure, reliable, and trusted decision-making scheme using multiattribute methods in collaborative AIoT. The proposed system uses backpropagation and Bayesian’s rule to ensure a fast and accurate decision. In addition, agent-based modeling and population-based modeling trust schemes are used to compute the legitimacy of the communicating model. Further, the proposed system is validated over various security measures against the various decision-based conventional methods such as Fuzzy c-means, REPTree, and random tree in terms of time, accuracy, replay attack, data falsification attack, recall, region of convergence, and F-Measure. The proposed mechanism achieves 93% improvement over accuracy and attack identification against existing mechanisms.

Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using internet of medical things (IoMT) devices. However, due to the centralized training approach of artificial intelligence (AI), mobile and wearable IoMT devices raise privacy issues concerning the information communicated between hospitals and end-users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm, has opened up new opportunities for privacy preservation in IoMT without accessing the confidential data of the participants. Further, FL provides privacy to end-users as only gradients are shared during training. For these specific properties of FL, in this paper, we present privacy-related issues in IoMT. Afterwards, we present the role of FL in IoMT networks for privacy preservation and introduce some advanced FL architectures by incorporating deep reinforcement learning (DRL), digital twin, and generative adversarial networks (GANs) for detecting privacy threats. Moreover, we present some practical opportunities for FL in IoMT. In the end, we conclude this survey by discussing open research issues and challenges while using FL in future smart healthcare systems.

The optimal transmit power that maximizes energy efficiency (EE) in Longe Range (LoRa) networks is investigated by using the deep learning (DL) approach. Particularly, the proposed artificial neural network (ANN) is trained two times; in the first phase, the ANN is trained by the model-based data which are generated from the simplified system model while in the second phase, the pre-trained ANN is re-trained by the practical data. Numerical results show that the proposed approach outperforms the conventional one which directly trains with the practical data. Moreover, the performance of the proposed ANN under both partial and full optimum architecture are studied. The results depict that the gap between these architectures is negligible. Finally, our findings also illustrate that instead of fully re-trained the ANN in the second training phase, freezing some layers is also feasible since it does not significantly decrease the performance of the ANN.

The fifth-generation (5G) wireless communication technology enables high-reliability and low-latency communications for the Intelligent Transportation System (ITS). However, the growingly sophisticated attacks against 5G-enabled ITS (5G-ITS) might cause serious damages to the valuable data generated by various ITS applications. Therefore, establishing a secure 5G-ITS through trust evaluation against potential threats has become a key objective. Furthermore, as a distributed shared ledger and database, Blockchain has the characteristics of non-tampering, traceability, openness and transparency, can support both trust storage and trust verification for trust evaluation. In this paper, we propose a heterogeneous Blockchain based Hierarchical Trust Evaluation strategy, named BHTE, utilizing the federated deep learning technology for 5G-ITS. Specifically, the trusts of ITS users and task distributers are evaluated using the federated deep learning and hierarchical incentive mechanisms are designed for reasonable and fair rewards and punishments. Moreover, the trusts of ITS users and task distributers are stored on heterogeneous and hierarchical blockchains for trust verification. The extensive experiment results show that: (i) the proposed BHTE can achieve reasonable and fair trust evaluations on both ITS users and task distributers; (ii) the BHTE performs excellently with high system throughput and low latency.

IEEE 802.11ax uses the concept of primary and secondary channels, leading to theDynamic Bandwidth Channel Access (DBCA)mechanism. By applying DBCA, a wireless station can select a wider channel bandwidth, such as$40/80/160$MHz, by applying the channel bonding feature. However, during channel bonding, inappropriate bandwidth selection can cause collisions. Therefore, to avoid collisions, a well-developed media access control (MAC) protocol is crucial to effectively utilize the channel bonding mechanism. In this paper, we address a collision scenario, calledOutside Warning Range Problem (OWRP), that may occur during DBCA when a wireless station interferes with another wireless station after channel bonding is performed. Therefore, we propose a MAC layer mechanism,Intelligent Bonding Avoiding Collision (IBAC), that adapts the channel bonding level in DBCA in order to avoid the OWRP. We first design a theoretical model based on Markov chains for DBCA while avoiding the OWRP. Based on this model, we design aThompson samplingbased Bayesian approach to select the best possible channel bonding level intelligently. We analyze the performance of the IBAC through simulations where it is observed that, comparing to other competing mechanisms, the proposed approach can enhance the network performance significantly while avoiding the OWRP.

The 5th Generation (5G) New Radio (NR) and beyond technologies will support enhanced mobile broadband, very low latency communications, and huge numbers of mobile devices. Therefore, for very high speed users, seamless mobility needs to be maintained during the migration from one cell to another in the handover. Due to the presence of a massive number of mobile devices, the management of the high mobility of a dense network becomes crucial. Moreover, a dynamic adaptation is required for the Time-to-Trigger (TTT) and hysteresis margin, which significantly impact the handover latency and overall throughput. Therefore, in this paper, we propose an online learning-based mechanism, known asLearning-basedIntelligentMobilityManagement (LIM2), for mobility management in 5G and beyond, with an intelligent adaptation of the TTT and hysteresis values. LIM2 uses a Kalman filter to predict the future signal quality of the serving and neighbor cells, selects the target cell for the handover usingstate-action-reward-state-action (SARSA)-based reinforcement learning, and adapts the TTT and hysteresis using the$\epsilon$-greedypolicy. We implement a prototype of the LIM2 in NS-3 and extensively analyze its performance, where it is observed that the LIM2 algorithm can significantly improve the handover operation in very high speed mobility scenarios.

Fifth-generation (5G) networks provide connectivity to a massive number of devices and boost a plethora of applications in several different domains. However, the large adoption of connected devices increases attack surfaces and introduces several security threats that can severely damage physical objects and risk people’s lives. Despite existing intrusion detection systems (IDSs), there are still several challenges to be addressed in the detection of cyber-attacks. For instance, while unsupervised IDSs are required to detect zero-day attacks, they usually present high false positive rates. Moreover, most existing IDSs rely on long short-term memory (LSTM) networks to consider time-dependencies among data. However, LSTM networks have recently been shown to present several drawbacks and limitations, which put into question their performance on sequence modeling tasks. Thus, in this paper, we investigate generative adversarial networks (GANs), a promising unsupervised approach to detecting attacks by implicitly modeling systems, and alternatives to LSTM networks to consider temporal dependencies among data. We propose a novel unsupervised GAN-based IDS that uses temporal convolutional networks (TCNs) and self-attention to detect cyber-attacks. The proposed IDS leverages edge computing and is proposed for edge servers, which bring computation resources closer to end nodes. Experiment results show that our proposed IDS can be configured to satisfy different detection rate and detection time requirements. Moreover, they show that our IDS is more accurate and at least 3.8 times faster than two state-of-the-art GAN-based IDSs that are used as baselines.

The growing demands of the Internet of Things (IoT) require a platform that supports real-time interactions and high availability of services to devices. In this context, the fog computing paradigm has emerged as an attractive solution for processing the data of IoT applications. Owing to the unpredictable traffic demands and resource heterogeneity in the fog environment, a smart workload distribution is essential to achieve high resource utilization and computing efficiency. To this end, this paper considers a joint link and server load balancing problem with multiple cooperative access points, (APs), in a combined edge-fog-cloud environment. The joint optimization problem is formulated as a stochastic game, and an actor-critic reinforcement learning framework, called FLoadNet, is proposed to optimize the joint policy of the multi-agents. FLoadNet consists of a centralized critic network, with parameter sharing and distributed individual actor networks in all the APs. Due to the learning dynamics and partially observable environment, we propose an extended critic network model, where cooperative APs learn to communicate among themselves while evaluating the value function. Unlike previous studies, the proposed critic network is designed to train both value and message functions, which is shown to significantly reduce the computational cost. The main goal of this work is to advance the development of efficient edge learning and the application of distributed learning algorithms specifically to fog network load balancing. The experimental results show that FLoadNet outperforms baseline load balancing methods.

In this paper, we propose a federated learning (FL)-based jammer detection and waveform classification algorithm for distributed tactical wireless networks (TWNs). More specifically, we consider a distributed TWN with multiple clusters and various types of waveforms used in the presence of a mobile jammer. We analyze the frequency domain of the waveforms received on local servers to extract the unique cyclic frequency from each waveform’s spectral correlation function (SCF). The method is used to detect the peak values in the frequency-cyclic frequency plane. The primary signal’s SCF exhibits peaks at the unique cyclic frequency and the zero cyclic frequency. These features are then used to train local convolutional neural networks (CNNs) to detect the jamming attacks and classify the waveforms. Moreover, a practical distributed TWN is considered in which each cluster head has a partial observation of the TWN with insufficient data samples, and the proposed algorithm exploits the distributed learning feature of FL, i.e., global learning aggregation to detect the existence of jammers and distinguish the types of waveforms received throughout the TWN. We implement a rigorous TWN simulation using MATLAB toolboxes and our proposed algorithm in TensorFlow Federated. The numerical results show that our proposed algorithm outperforms the standalone local SCF-CNN algorithm. We further demonstrate that the SCF feature yields more accuracy than the In-phase and Quadrature features.

Due to the privacy breach risks and data aggregation of traditional centralized machine learning (ML) approaches, applications, data and computing power are being pushed from centralized data centers to network edge nodes. Federated Learning (FL) is an emerging privacy-preserving distributed ML paradigm suitable for edge network applications, which is able to address the above two issues of traditional ML. However, the current FL methods cannot flexibly deal with the challenges of model personalization and communication overhead in the network applications. Inspired by the mixture of global and local models, we proposed a Communication-Efficient Personalized Federated Meta-Learning algorithm to obtain a novel personalized model by introducing the personalization parameter. We can improve model accuracy and accelerate its convergence by adjusting the size of the personalized parameter. Further, the local model to be uploaded is transformed into the latent space through autoencoder, thereby reducing the amount of communication data, and further reducing communication overhead. And local and task-global differential privacy are applied to provide privacy protection for model generation. Simulation experiments demonstrate that our method can obtain better personalized models at a lower communication overhead for edge network applications, while compared with several other algorithms.

By leveraging massive low-cost reconfigurable reflect array elements, intelligent reflecting surface (IRS) is recently proposed to exhibit the favorable wireless propagation environment of Internet of Things (IoT) systems. In this article, we provide a spectrum-efficiency approach by exploiting non-orthogonal multiple access (NOMA) as well as cognitive radio (CR) to form NOMA IRS-assisted CR system. In this IRS-based IoT, the active access point in secondary network transmits beamforming and the passive IRS elements are jointly operated to achieve different performance relying on demands of IoT devices, while maintaining the normal operation of primary network. Then, the exact closed-form formulas are introduced to evaluate outage probability at each IoT device. Moreover, to provide more insights of the system, a diversity order is considered aiming to look at limitation of outage performance when the system tries to increase average signal to noise ratio (SNR) at the secondary transmitter. Finally, we conduct numerical simulations to verify the superior performance of IoT systems with higher meta-surface elements at IRS over the necessary comparisons in practical scenarios.

Wireless communication based on Unmanned aerial vehicle (UAV) is one of the important technologies in the future communication system. It is necessary to establish an accurate air-to-ground (A2G) wireless channel model. In this paper, a A2G channel model with UAV three-dimensional (3D) wobbles (pitch, roll, and yaw) is proposed. The internal vibration of the UAV is modeled as a sinusoidal random process, and the UAV wobble caused by the random air fluctuations is modeled as the uniform distribution random process. We derive the A2G channel temporal auto-correlation function (ACF) with UAV 3D wobbles, analyze the variation of the temporal ACF with different time instants, carrier frequencies, and amplitudes of the wobble angles. It is found that, even if the UAV wobbles slightly, the channel temporal correlation will be significantly affected. Numerical results show that the channel ACF will decrease rapidly with the increase of the amplitudes of the wobble angles and the carrier frequency. This work contributes to the establishment of the next generation wireless channel model and the design of communication system.

Reconfigurable intelligent surface (RIS) recently emerged as a promising technology for beyond-5thGeneration (B5G)/6thGeneration (6G) wireless networks. In this paper, considering random waypoint (RWP) mobility model, we evaluate the performance of RIS-aided mobile destination node in the presence of mobile co-channel interferers (CCI). It is assumed that the source-RIS and RIS-destination links follow Rayleigh distributions. The links between the interferers and the destination node also follow Rayleigh distributions. The performance of the considered system is evaluated by deriving novel closed-form expressions for the outage probability (OP), bit error rate (BER), and average channel capacity (ACC) based on the cumulative distribution function (CDF) of the received signal-to-interference plus noise ratio (SINR). It is shown through numerical results that the presence of mobile interferers significantly degrades the overall performance of the considered system.

Smart industries enabling automation and data exchange in manufacturing technologies demanding real-time processing, nearby storage, and reliability, all of which can be satisfied by the fog computing architecture. With the emergence of smart devices coupled with a diverse range of application requirements, it is essential to have an intelligent fog network where intelligence is spread across all network segments, taking network nodes self-aware and self-decision making. In fog networks, an optimal distribution decision faces challenges due to uncertainties associated with user workload and available resources at the fog nodes and also the wide range of node’s computing power. Given this challenge, a computational offloading and CPU resource scheduling method for minimizing energy consumption is proposed. To investigate the characteristics for offloading and optimizing their allocation, we consider two types of tasks, namely, offloadable and nonoffloadable tasks. The independent fog nodes adopt the same strategy without prior knowledge of the dynamic statistics and global observations, aiming to maximize a common goal with cooperative behaviors. Then, the deep recurrent$Q$-network (DRQN) is applied to deal with the partial-observability from limited information. The proposed DRQN-based method requires comparatively less computational complexity than the conventional$Q$-learning algorithm. The simulation results show that the proposed method can effectively deal with both transmission and CPU energy consumptions while guaranteeing convergence in a limited time.

Interference management is a key challenge for cellular-connected Internet of Drones (IoD) networks that employ multiple cellular-connected hovering drones for data acquisition in surveillance and monitoring applications. This article proposes a novel resource optimization framework for managing interference in cellular-connected IoD networks. Specifically, the envisioned system divides the set of transmitting drones into distinct drone pairs, where the paired drones simultaneously transmit over the same radio resource blocks (RRBs). Each drone pair is assigned a set of orthogonal RRBs for data transmission, where these RRBs are shared with the terrestrial cellular network as well. An uplink rate-splitting multiple access scheme is employed to mitigate the interdrone interference at the drone pairs, and an RRB pricing method is exploited to control the interference between the aerial and cellular communication links. Our goal is to maximize the uplink capacity of the IoD network while reducing interference over the shared RRBs between the IoD and cellular networks. Toward this goal, a joint optimization of the drones’ transmit power allocation, drone pairing, and RRB scheduling among the drone pairs is presented. In order to obtain an efficient suboptimal solution, an iterative optimization is devised. Particularly, the presented joint optimization problem is decomposed into three subproblems for transmit power allocation, drone pairing and RRB scheduling, and RRB price update. By solving theses subproblems iteratively, a convergent rate-splitting-empowered resource allocation and clustering for interference management (REACT) algorithm is proposed. Extensive simulations are conducted to verify the effectiveness of the proposed REACT algorithm over several benchmark schemes.

Massive Internet of Things (IoT) connectivity requires addressing spectrum congestion caused by spectrum scarcity in wireless communications. Over the past decade, cognitive radio (CR) has been proposed as a promising solution to utilize the licensed spectrum efficiently. Conventional spectrum-sensing approaches are complex and require statistical information about the behavior of licensed users, which is impractical. To overcome this limitation, several reinforcement-learning (RL)-based spectrum-sensing approaches have been proposed that are also highly adaptable to the dynamics of IoT environments. Additionally, cooperative-RL-based spectrum-sensing approaches have been widely used because they are more accurate than noncooperative approaches. However, the advantage comes at the cost of scalability due to increased information-sharing overhead. Furthermore, cooperative spectrum-sensing (CSS) approaches suffer from attacks on the network, such as sensing data falsification (SDF) attacks, which deteriorate sensing accuracy dramatically. In this article, we present a scalable, partially CSS algorithm that is highly resilient to SDF attacks. The novelty of the proposed algorithm lies in partial cooperation through coalition formation, which reduces sensing and information sharing overhead while improving sensing accuracy. Moreover, the algorithm learns to adapt the sensing participation percentage and selects the most rewarding channel for sensing to maximize rewards while minimizing energy consumption. The proposed algorithm outperforms state-of-the-art CSS algorithms in terms of sensing accuracy and overheard. Contrary to centralized CSS algorithms, the proposed algorithm’s performance is directly proportional to the number of devices; hence, it is suitable for massive connectivity.

Underwater optical wireless communications (UOWCs) have recently appeared as an attractive solution for many applications, such as remote controlling and sensing due to its advantages, such as high transmission rate, ultrawide bandwidth, and low latency. However, due to the harsh underwater conditions, UOWC faces challenges, such as water absorption, scattering, and pointing-acquisition-and-tracking (PAT) problems. This is mainly due to the dynamicity existing underwater. Consequently, this leads to packet loss and hence deteriorates the reliability and link quality of such networks. Such a problem can affect the degree of connectivity and end-to-end (E2E) performance of the communication system. The existing solutions in the literature are based on predefined models, assuming full knowledge of the environment. However, such models do not optimally treat the dynamicity existing underwater. This article proposes novel beam adaptation methods based on reinforcement learning (RL) for point-to-point UOWC. The first method aims to optimize the light beamwidth; the second method focuses on adapting the beam orientation, whereas the last one optimizes both the light’s beamwidth and beam orientation. Our proposed RL-based solutions yield optimal positioning and beamwidth of the light source and improve the considered communication link’s success rate. They also guarantee better link quality in terms of signal-to-noise ratio (SNR) compared to the uncertainty disk static method for four different underwater environments, including pure seawater, clean ocean, coastal ocean, and turbid harbor.

The Industrial Internet of Things (IIoT) is an emerging technology that can promote the development of industrial intelligence, improve production efficiency, and reduce manufacturing costs. However, anomalies of IIoT devices might expose sensitive data about users of high authenticity and validity, resulting in security and privacy threats to the IIoT applications. That suggests the significance of anomaly detection executed by proper authorities. To address these problems, in this paper, we propose a reliable anomaly detection strategy for IIoT using federated learning. Specifically, we apply the federated learning technique to build a universal anomaly detection model with each local model trained by the deep reinforcement learning (DRL) algorithm. Since local data sets are not required during the federated learning, the chance of privacy leakage is reduced. In addition, by introducing privacy leakage degree and action relation to anomaly detection design, we can greatly improve the detection accuracy. The validation experiments indicate that the proposed strategy achieves high throughput, low latency, and high anomaly detection accuracy for privacy preservation in various IIoT scenarios.

With the rapid development of the Internet of Things (IoT), more and more data are generated by smart devices to support various edge services. Since these data may contain sensitive information, security and privacy of data aggregation has become a key challenge in IoT. To tackle this problem, a blockchain-based secure data aggregation strategy, namely (BSDA), is proposed for edge computing empowered IoT. Specifically, in order to restrict task receivers [i.e., mobile data collectors (MDCs)] to search and accept tasks, the block header is intergraded with a security label including task security level (SL) and task completion requirement. Accordingly, new block generation rules are developed to improve system performance in throughput and transaction latency. Furthermore, BSDA decomposes both sensitive tasks and task receivers into groups against privacy disclosure. On the other hand, a deep reinforcement learning method, the improved self-adaptive double bootstrapped deep deterministic policy gradient (IDDPG), is developed to design energy-efficient MDC routes under the constrains that the SLs of MDCs should be higher than the SLs of data aggregation tasks. Simulation results indicate that 1) as a privacy-preserving strategy, BSDA obtains high throughput and low transaction latency and 2) BSDA outperforms certain contemporary strategies in aggregation ratio and energy cost.

A joint energy and correlation detection aided orthogonal frequency division multiplexing differential chaos shift keying (JECD-OFDM-DCSK) system is presented in this paper, where the reference and information-bearing signals of JECD-OFDM-DCSK are superimposed in the same time slot so that the phase offset between the reference and information-bearing signals is addressed. In JECD-OFDM-DCSK, only some subcarriers are activated and additional information bits are transmitted by the indices of these activated subcarriers, thereby improving the data rate while reducing the energy consumption. The energy efficiency, peak to average power ratio (PAPR) performance and system complexity of JECD-OFDM-DCSK are analyzed and then compared to other systems. The comparison results show JECD-OFDM-DCSK can obtain higher energy efficiency and better PAPR performance compared to its competitors at the cost of higher system complexity. Furthermore, the bit error rate (BER) expressions of JECD-OFDM-DCSK are derived in additive white Gaussian noise (AWGN) and multipath fading channels. Simulation results show that the BER performance of JECD-OFDM-DCSK outperforms that of other OFDM-based DCSK systems. Finally, JECD-OFDM-DCSK performs much better than other systems over the underwater acoustic (UWA) channel, which validates the robustness of JECD-OFDM-DCSK.

Information theoretic learning (ITL) criteria have emerged useful for mitigating degradations caused by unknown non-Gaussian noise processes in future wireless communication systems. Specifically, the reproducing kernel Hilbert space (RKHS) based approaches relying on ITL based learning criteria are envisioned to provide near-optimal mitigation of unknown hardware impairments and non-Gaussian noises. Among several ITL criteria, the recent works find the minimum error entropy with fiducial points (MEEF) promising due to its guarantee of unbiased estimation and generalization over generic noise distributions. However, MEEF based learning approaches are known to depend on an accurate kernel-width initialization. Also, the optimal value of this kernel-width is well-known to vary temporally and across deployment scenarios. To remove the dependency on kernel-width, a hyperparameter-free MEEF based adaptive algorithm is derived using random-Fourier features with sampled kernel widths (RFF-SKW). In addition, a detailed convergence analysis is presented for the proposed hyperparameter-free MEEF, which promises a near-optimal error-floor independent of step-size and guarantees convergence for a wide range of step sizes. The promised hyperparameter-independence and improved convergence for the proposed hyperparameter-free MEEF are validated by computer simulations considering different case studies.

In this paper, we aim to improve the connectivity, scalability, and energy efficiency of machine-type communication (MTC) networks with different types of MTC devices (MTCDs), namely Type-I and Type-II MTCDs, which have different communication purposes. To this end, we propose two transmission schemes called connectivity-oriented machine-type communication (CoM) and quality-oriented machine-type communication (QoM), which take into account the stochastic geometry-based deployment and the random active/inactive status of MTCDs. Specifically, in the proposed schemes, the active Type-I MTCDs operate using a novel Bernoulli random process-based simultaneous wireless information and power transfer (SWIPT) architecture. Next, utilizing multi-user power-domain non-orthogonal multiple access (PD-NOMA), each active Type-I MTCD can simultaneously communicate with another Type-I MTCD and a scalable number of Type-II MTCDs. In the performance analysis of the proposed schemes, we prove that the true distribution of the received power at a Type-II MTCD in the QoM scheme can be approximated by the Singh-Maddala distribution. Exploiting this unique statistical finding, we derive approximate closed-form expressions for the outage probability (OP) and sum-throughput of massive MTC (mMTC) networks. Through numerical results, we show that the proposed schemes provide a considerable sum-throughput gain over conventional mMTC networks.

Reactive jammers, which start attacking upon sensing legitimate transmissions, are serious threats to wireless communications. Conventional anti-jamming methods such as frequency hopping-based anti-jamming schemes are not effective against reactive jammers, especially the agile ones that jam immediately after sensing transmissions. Deceiving-based anti-jamming methods have a great potential in harnessing reactive jammers and securing communication channels for legitimate users. However, in deceiving-based anti-jamming methods, reaching the optimal power and channel allocation is complicated due to the unavailability of the jammers’ channel information. In this paper, we propose deceiving-based anti-jamming schemes against reactive jammers employing reinforcement learning. Moreover, we consider both cases where a reactive jammer can jam a channel or all the channels utilized by legitimate users. In the latter case, we model the interaction between users and the jammer as a non-cooperative Stackelberg game and prove equilibrium. In addition, we study different scenarios where the interacting environment is static or dynamic in terms of channel gains. Simulation results show that in static environments, the proposed methods achieve the optimal values of the total received power and Signal-to-interference-plus-noise ratio with an accuracy of 95%. Moreover, in dynamic environments, the proposed methods provide high performance in terms of the considered evaluation metrics.

In the upcoming sixth-generation (6G) networks, ultra-reliable and low-latency communication (URLLC) is considered as an essential service that will empower real-time wireless systems, smart grids, and industrial applications. In this context, URLLC traffic relies on short blocklength packets to reduce the latency, which poses a daunting challenge for network operators and system designers since classical communication systems are designed based on the classical Shannon’s capacity formula. Therefore, to tackle this challenge, this article considers an unmanned aerial vehicle (UAV) acting as a decode-and-forward relay to communicate short URLLC control packets between a controller and multiple-mobile robots in a cell to enable a use-case of Agriculture 4.0. Moreover, this article employs perturbation theory and studies the quasi-optimization of the UAV’s location, height, beamwidth, and resource allocation, including time-varying power and blocklength for the two phases of transmission from the controller to UAV and from UAV to robots. In this regard, we propose an iterative optimization method to find the optimal UAV’s height and location, the antenna beamwidth, and the variable power and blocklength allocated to each robot inside the circular cell to minimize the average overall decoding error. It is demonstrated that the proposed algorithm outperforms other benchmark algorithms based on fixed parameters and performs nearly as well as the smart exhaustive search. Lastly, our results emphasize the need to jointly optimize all of the abovementioned UAV’s system parameters and resource allocation for the two phases of transmission to achieve URLLC for multiple-mobile robots.

The development and maturity of the fifth-generation (5G) wireless communication technology provides the industrial Internet of Things (IIoT) with ultra-reliable and low-latency communications and massive machine-type communications, and forms a novel IIoT architecture, 5G-IIoT. However, massive data transfer between interconnecting industrial devices also brings new challenges for the 5G-IIoT routing process in terms of latency, load balancing, and data privacy, which affect the development of 5G-IIoT applications. Moreover, the existing research works on IIoT routing mostly focus on the latency and the reliability of the routing, disregarding the privacy security in the routing process. To solve these problems, in this article, we propose a quality of service (QoS) and data privacy-aware routing protocol, named QoSPR, for 5G-IIoT. Specifically, we improve the community detection algorithm info-map to divide the routing area into optimal subdomains, based on which the deep reinforcement learning algorithm is applied to build the gateway deployment model for latency reduction and load-balancing improvement. To eliminate areal differences, while considering the privacy preservation of the routing data, the federated reinforcement learning is applied to obtain the universal gateway deployment model. Then, based on the gateway deployment, the QoS and data privacy-aware routing is accomplished by establishing communications along the load-balancing routes of the minimum latencies. The validation experiment is conducted on real datasets. The experiment results show that as a data privacy-aware routing protocol, the QoSPR can significantly reduce both average latency and maximum latency, while maintaining excellent load balancing in 5G-IIoT.

5G communication technologies and networks help researchers and engineers look into intelligent transportation systems (ITS) with a new eye, including vehicular ad hoc networks (VANET) application. Network function virtualization (NFV) and network slicing (NS) are accepted as two most promising technologies towards the agile and elastic network architecture of 5G and beyond 5G (B5G). However, previous researchers studied NFV and NS separately. In addition, learning technologies, such as reinforcement leaning (RL), graph-based learning, emerge so as to enhance the network intelligence and resource allocation in recent years. Inspired from these, we jointly explore intelligent resource allocation issue within B5G-enabled VANETs. At first, the novel virtual resource allocation framework supporting NFV and NS for providing quality of service (QoS)-guaranteed slices is constructed. Then, we formulate the virtual resource allocation of slices as the optimization problem, having the goals of providing guaranteed QoS performance and maximizing the net profit. Considering the non convex attributes of the formulated optimization problem, we propose one intelligent and feasible algorithm instead, including the details of the proposed intelligent algorithm. We record the results in order to validate the feasibility and highlights of our proposed algorithm. For example, our intelligent algorithm has the slice acceptance advantage of 5%, comparing with the best existing work.

Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attractive in intelligent transportation systems. However, it is difficult for the existing DL-OD schemes to realize the responsible, cost-saving, and energy-efficient autonomous vehicle systems due to low their inherent defects of low timeliness and high energy consumption. In this paper, we propose an object detection (OD) system based on edge-cloud cooperation and reconstructive convolutional neural networks, which is called Edge YOLO. This system can effectively avoid the excessive dependence on computing power and uneven distribution of cloud computing resources. Specifically, it is a lightweight OD framework realized by combining pruning feature extraction network and compression feature fusion network to enhance the efficiency of multi-scale prediction to the largest extent. In addition, we developed an autonomous driving platform equipped with NVIDIA Jetson for system-level verification. We experimentally demonstrate the reliability and efficiency of Edge YOLO on COCO2017 and KITTI data sets, respectively. According to COCO2017 standard datasets with a speed of 26.6 frames per second (FPS), the results show that the number of parameters in the entire network is only 25.67 MB, while the accuracy (mAP) is up to 47.3%.

Underwater optical wireless communications (UOWCs) are promising and potential wireless carriers to envisage underwater phenomenal activities for various applications towards the futuristic 5G and beyond (5GB) wireless systems. The main challenges to deploy underwater applications are the physicochemical properties and strong turbulence channel conditions. In this regard, the end-to-end (E2E) performance analysis of a dual-hop mixed FSO/UVLC system under the intensity modulation/direct detection (IM/DD) technique in consideration of pulse amplitude modulation (PAM) scheme is investigated. Throughout this study, to tackle the issues of moderate-to-strong turbulence channel conditions, this work deploys the Gamma-Gamma (GG) distribution fading model and the links are designed by unifying plane wave models in the corresponding links, respectively. This investigation outperforms higher achievable data rate with minimal delay response and enhance network connectivity in real-time monitoring scenarios as compared with the traditional underwater wireless communication technologies. In more contrast, the probability distribution function (PDF), cumulative distribution function (CDF), and closed-form expression of the system are derived and presented in terms of Meijer-G function as well as Extended Generalized Bivariate Meijer-G Function (EGBMGF). The significant E2E performance metrics are obtained by employing the decode-and-forward (DF) relay protocol in hostile channel conditions. In aggregating this work, we combine the analytical expressions that present an efficient tool to depict the impact of channel parameters on the system. The simulation results are plausible of the system performance metrics as average BER (ABER) and outage probability$(P_{out})$in the presence of pointing and without pointing error events. Finally, in this work, we use the Monte-Carlo approach for the best fitting curves and validate the numerical expression yields simulation results.

The Internet of Things (IoT) networks are obstructed by security vulnerabilities that hackers can leverage to operate intrusions in many environments, such as smart homes, smart factories, and smart healthcare systems. To overcome this obstruction, researchers have come up with different intrusion detection and prevention systems (IDPSs). Out of all the implemented technologies, Machine Learning (ML) has emerged as the most promising approach. Therefore, to improve the detection accuracy, most ML-based intrusion detection solutions focus only on investigating appropriate ML algorithms. Yet, the limitations in terms of detection accuracy in various attacks are often caused by lack of appropriate detection features. Moreover, the majority of the previous works lack intrusion prevention mechanisms and deployment architectures. Thus, in this research, we study the properties of different smart home security attacks and the quality of the features that can be brought out and employed in ML algorithms to detect each of these attacks efficiently. Furthermore, this research proposes effective intrusion prevention mechanisms and a Software-Defined Networking (SDN) based deployment architecture of the IDPSs within home networks. Experimental evaluations of the proposed solution are provided using different feature sets and various ML models. The contributions and advancements discussed in this paper will upgrade future research and engineering works on IDPSs for IoT.

In this paper, we propose and investigate an aerial reconfigurable intelligent surface (aerial-RIS)-aided wireless communication system. Specifically, considering practical composite fading channels, we characterize the air-to-ground (A2G) links by Namkagami-m small-scale fading and inverse-Gamma large-scale shadowing. To investigate the delay-limited performance of the proposed system, we derive a tight approximate closed-form expression for the end-to-end outage probability (OP). Next, considering a mobile environment, where performance analysis is intractable, we rely on machine learning-based performance prediction to evaluate the performance of the mobile aerial-RIS-aided system. Specifically, taking into account the three-dimensional (3D) spatial movement of the aerial-RIS, we build a deep neural network (DNN) to accurately predict the OP. We show that: (i) fading and shadowing conditions have strong impact on the OP, (ii) as the number of reflecting elements increases, aerial-RIS achieves higher energy efficiency (EE), and (iii) the aerial-RIS-aided system outperforms conventional relaying systems.

Reconfigurable intelligent surface (RIS) is an emerging technology that can result in a dynamically controllable radio propagation environment by smartly tuning the signal reflections via a large number of low-cost passive reflecting elements. In this paper, we investigate the effect of RIS-aided jammer on distributed RIS-based dual-hop mixed free-space optical (FSO)-radio frequency (RF) communication systems. The channel distribution of the FSO link is assumed to follow a Gamma-Gamma (GG) distributed atmospheric turbulence (AT) model with pointing errors (PEs). The relay-RIS and RIS-destination links are assumed to follow Rayleigh distributions. The received signal at the destination node is corrupted by a RIS-aided jammer. It is assumed that the jammer-RIS and RIS-destination links follow a Rayleigh distribution. Novel closed-form expressions for the outage probability (OP) and bit error rate (BER) of the considered system are derived considering two types of detection schemes, namely intensity modulation/direct detection (IM/DD) and heterodyne detection (HD). Through numerical results, it is shown that the presence of the RIS-aided jammer severely degrades the performance of distributed RIS-based dual-hop mixed FSO-RF systems. Finally, the improvement in performance of dual-hop mixed FSO-RF systems with the use of distributed RIS is also validated through results.

Cyber-attacks cyber-physical systems (CPSs) can lead to sensing and actuation misbehavior, severe damages to physical objects, and safety risks. Machine learning algorithms have been proposed for hindering cyber-attacks on CPSs, but the absence of labeled data from novel attacks makes their detection quite challenging. In this context, generative adversarial networks (GANs) are a promising unsupervised approach to detect cyber-attacks by implicitly modeling the system. However, the detection of cyber-attacks on CPSs has strict latency requirements, since the attacks need to be stopped before the system is compromised. In this article, we propose FID-GAN, a novel fog-based, unsupervised intrusion detection system (IDS) for CPSs using GANs. The IDS is proposed for a fog architecture, which brings computation resources closer to the end nodes and thus contributes to meeting low-latency requirements. In order to achieve higher detection rates, the proposed architecture computes a reconstruction loss based on the reconstruction of data samples mapped to the latent space. Other works that follow a similar approach struggle with the time required to compute the reconstruction loss, which renders them impractical for latency constrained applications. We address this problem by training an encoder that accelerates the reconstruction loss computation. Experiments show that the proposed solution achieves higher detection rates and is at least 5.5 times faster than a baseline approach in the three studied data sets.

As wireless services and applications become more sophisticated and require faster and higher capacity networks, there is a need for an efficient management of the execution of increasingly complex tasks based on the requirements of each application. In this regard, fog computing enables the integration of virtualized servers into networks and brings cloud services closer to end devices. In contrast to the cloud server, the computing capacity of fog nodes is limited and thus a single fog node might not be capable of computing-intensive tasks. In this context, task offloading can be particularly useful at the fog nodes by selecting the suitable nodes and proper resource management while guaranteeing the Quality-of-Service (QoS) requirements of the users. This article studies the design of a joint task offloading and resource allocation control for heterogeneous service tasks in multifog nodes systems. This problem is formulated as a partially observable stochastic game, in which each fog node cooperates to maximize the aggregated local rewards while the nodes only have access to local observations. To deal with partial observability, we apply a deep recurrent Q-network (DRQN) approach to approximate the optimal value functions. The solution is then compared to a deep Q-network (DQN) and deep convolutional Q-network (DCQN) approach to evaluate the performance of different neural networks. Moreover, to guarantee the convergence and accuracy of the neural network, an adjusted exploration-exploitation method is adopted. Provided numerical results show that the proposed algorithm can achieve a higher average success rate and lower average overflow than baseline methods.

The fifth-generation wireless technology (5G) has been developed with an aim to provide ubiquitous and scalable connectivity for Internet-of-Things (IoT) nodes. Likewise, the cloud radio access network (C-RAN) architecture can be exploited to enable efficient network access to IoT nodes. Nevertheless, the 5G C-RAN architecture is based on large data centers geographically located far apart, which introduces an inevitable overhead. Therefore, to supply real-time data services near by the data terminals, fog computing emerges as a promising solution. However, constrained physical fog resources and delay-sensitive services hinder the application of new virtualization technologies in the baseband unit (BBU) task allocation management of the fog network. To tackle these challenges, a task allocation framework for hierarchical software-defined fog virtual radio access networks (v-RANs) is proposed in this article. Precisely, we apply an enhanced ant colony optimization (ACO) in combination with a max-min algorithm to efficiently determine the optimal path for BBU task allocation management, while minimizing the transmission time for parallel task execution scheduling. Experimental results demonstrate that the queue delay in our approach is 98.38% and 98.82% lower than the round-robin (RR) algorithm and least connection technique (LCT), respectively.

Efficient resource allocation can maximize power efficiency, which is an important performance metric in future fifth-generation (5G) communications. The minimization of sum uplink power in order to enable green communications while concurrently fulfilling the strict demands of ultrareliability for short packets is an essential and central challenge that needs to be addressed in the design of 5G and subsequent wireless communication systems. To address this challenge, this article analyzes the joint optimization of various unmanned aerial vehicle (UAV) systems parameters, including the UAV’s position, height, beamwidth, and the resource allocation for uplink communications between ground Internet-of-Things (IoT) devices and a UAV employing short ultrareliable and low-latency (URLLC) data packets. Toward achieving the aforesaid task, we proposed a perturbation-based iterative optimization to minimize the sum uplink power in order to determine the optimal position for the UAV, its height, beamwidth of its antenna, and the blocklength allocated for each IoT device. It is shown that the proposed algorithm has lower time complexity, yields better performance than other benchmark algorithms, and achieves similar performance to exhaustive search. Moreover, the results also demonstrate that Shannon’s formula is not an optimum choice for modeling sum power for short packets as it can significantly underestimate the sum power, where our calculations show that there is an average difference of 47.51% for the given parameters between our proposed approach and Shannon’s formula. Finally, our results confirm that the proposed algorithm allows ultrahigh reliability for all the users and converges rapidly.

Upcoming fifth-generation (5G) networks need to support novel ultrareliable and low-latency (URLLC) traffic that utilizes short packets. This requires a paradigm shift as traditional communication systems are designed to transmit only long data packets based on Shannon's capacity formula, which poses a challenge for system designers. To address this challenge, this article relies on an unmanned aerial vehicle (UAV) and a reconfigurable intelligent surface (RIS) to deliver short URLLC instruction packets between ground Internet-of-Things (IoT) devices. In this context, we perform passive beamforming of RIS antenna elements as well as nonlinear and nonconvex optimization to minimize the total decoding error rate and find the UAV's optimal position and blocklength. In this article, a novel, polytope-based method from the class of direct search methods (DSMs) named Nelder-Mead simplex (NMS) is used to solve the optimization problem based on its computational efficiency; in terms of lesser number of required iterations to evaluate objective function. The proposed approach yields better convergence performance than the traditional gradient-descent optimization algorithm and a lower computation time and equivalent performance for the blocklength variable as the exhaustive search. Moreover, the proposed approach allows ultrahigh reliability, which can be attained by increasing the number of antenna elements in RIS as well as increasing the allocated blocklengths. Simulations demonstrate the RIS's performance gain and conclusively show that the UAV's position is crucial for achieving ultrahigh reliability in short packet transmission.

Mobile-edge crowdsensing is capable of providing a large amount of data via pervasive mobile terminals for Industrial Internet of Things (IIoT). However, the generated data often contain users' sensitive information, which suggests the significance of privacy preserving in data aggregation and analysis for IIoT. Privacy preserving in mobile-edge crowdsensing have conflicting objectives, i.e., the edge fusion center (FC) requires data of better quality for data fusion with higher accuracy whereas participatory users (PUs) desire better privacy preserving by larger noise injection. Therefore, how to select proper noises to achieve the tradeoff between accuracy and privacy is a challenging problem. In addition, FC is subject to data tempering due to the lack of data reliability validations and incentive mechanisms. To tackle these problems, we propose a novel privacy-preserving mobile-edge crowdsensing strategy (PPCS) for IIoT. Specifically, PPCS provides a Kullback-Leibler privacy-preserving data aggregation using a reputation-based incentive mechanism. On the other hand, PPCS offers hypothesis test-based data reliability validation and PU's reputation update, which collaborate to ease the impact of tampered data. Meanwhile, a reinforcement learning algorithm, the expected Sarsa, is applied to obtain the optimal test threshold. Theoretical analysis and experimental results show that PPCS is an energy-efficient strategy and the data provided by PPCS has a better aggregation accuracy than certain baseline strategies.

In the context of outdoor localization over systems impaired by non-line of sight (NLoS), the minimum error entropy with fiducial points (MEE-FP) based methods have emerged as promising due to their excellent generalization and independence to statistics of NLoS. However, the performance of these approaches are well-known to depend on hyperparameters, such as, the spread parameter of the MEE-FP criterion. To enable MEEFP based hyperparameter-free localization, we propose a modified Gauss-Newton based localization algorithm based on sampled kernel-widths. Next, analytical results are derived to demonstrate the asymptotic equivalence of the proposed kernel-width sampling based localization algorithm to its ideal fixed kernel width based counterpart. This equivalence is validated through computer simulations assuming typical non-Gaussian NLoS distributions, which motivates the hyperparameter-independence of the proposed localization algorithm and its generalization to different NLoS statistics.

In this paper, we study the statistical characterization and modeling of distributed multi-reconfigurable intelligent surface (RIS)-aided wireless systems. Specifically, we consider a practical system model where the RISs with different geometric sizes are distributively deployed, and wireless channels associated to different RISs are assumed to be independent but not identically distributed (i.n.i.d.). We propose two purpose-oriented multi-RIS-aided schemes, namely, the exhaustive RIS-aided (ERA) and opportunistic RIS-aided (ORA) schemes. A mathematical framework, which relies on the method of moments, is proposed to statistically characterize the end-to-end (e2e) channels of these schemes. It is shown that either a Gamma distribution or a Log-Normal distribution can be used to approximate the distribution of the magnitude of the e2e channel coefficients in both schemes. With these findings, we evaluate the performance of the two schemes in terms of outage probability (OP) and ergodic capacity (EC), where tight approximate closed-form expressions for the OP and EC are derived. Representative results show that the ERA scheme outperforms the ORA scheme in terms of OP and EC. In addition, under i.n.i.d. fading channels, the reflecting element settings and location settings of RISs have a significant impact on the system performance of both the ERA or ORA schemes.

Nonlinear device characteristics present a severe performance-bottleneck for several upcoming next-generation wireless communication systems and prevent them from delivering high data-rates to the end-users. In this context, reproducing kernel Hilbert space (RKHS) based signal processing methods have gained widespread deployment and have been found to outperform classical polynomial-filtering-based solutions significantly. Furthermore, recent RKHS based techniques that rely on explicit feature-maps called random Fourier features (RFF) have emerged. These techniques alleviate the dependence on learning a dictionary and avoid the computations and errors incurred in dictionary-based learning. However, the performance of existing RKHS based solutions depends on choosing a suitable kernel-width. For the widely-used Gaussian kernel, we propose a methodology of assigning kernel-bandwidths that capitalizes on a stochastic sampling of kernel-widths using an ensemble drawn from a pre-designed probability density function. The technique is found to deliver a comparable convergence/error-rate performance to the scenario when the kernel-width is chosen by brute-force trial and error for tuning it for best performance. The desirable properties of the proposed kernel-sampling technique are supported by analytical proofs and are further highlighted by computer-simulations presented in the form of case studies in the context of next-generation communication systems.

Conventional anti-jamming methods mostly rely on frequency hopping to hide or escape from jammers. These approaches are not efficient in terms of bandwidth usage and can also result in a high probability of jamming. Different from existing works, in this article, a novel anti-jamming strategy is proposed based on the idea of deceiving the jammer into attacking a victim channel while maintaining the communications of legitimate users in safe channels. Since the jammer's channel information is not known to the users, an optimal channel selection scheme and a sub-optimal power allocation algorithm are proposed using reinforcement learning (RL). The performance of the proposed anti-jamming technique is evaluated by deriving the statistical lower bound of the total received power (TRP). Analytical results show that, for a given access point, over 50% of the highest achievable TRP, i.e. in the absence of jammers, is achieved for the case of a single user and three frequency channels. Moreover, this value increases with the number of users and available channels. The obtained results are compared with two existing RL based anti-jamming techniques, and a random channel allocation strategy without any jamming attacks. Simulation results show that the proposed anti-jamming method outperforms the compared RL based anti-jamming methods and the random search method, and yields near optimal achievable TRP.

Sixth generation (6G) network is a revolutionary technology to satisfy the ever-growing demands from the sustainable development of emerging industrial applications and services. Due to its high flexibility, convenient and rapid deployment, self-organization capability, and outstanding expansibility, network-in-box (NIB) represents a promising approach for future networks. The integration of NIB with 6G can lead to many new applications in geoscience, robotics, and industrial automation. For 6G-enabled NIB, services are deployed directly on the NIB, which increases the fault tolerance and reduces the traffic volume on the backhaul link. As more and more data are processed and shared in industrial applications and services, the security of data aggregation becomes a key challenge for 6G-enabled NIB. To address this challenge, in this article, we propose a blockchain based privacy-aware distributed collection (BPDC) oriented strategy for data aggregation. In BPDC, an improved blockchain with a new block header structure and two different block generation rules are designed and introduced, which restricts the task receivers to search and receive the tasks beyond their levels of security permission. While guaranteeing the data aggregation performance, BPDC can also achieve privacy protection by decomposing sensitive tasks and task receivers into multiple groups. Validation experiments show that the BPDC accomplishes low overhead, high throughput, and privacy preservation in various industrial applications.

The industrial Internet-of-Things (IIoT) is a powerful Internet of Things (IoT) application that enables industrial growth by ensuring transparent communication among the various entities of a company such as the manufacturing locations, design hubs, and packaging units. However, current industrial architectures are unable to efficiently deal with advanced security issues that come with this communication due to the distributed and expandable nature of IIoT networks. Furthermore, from a security perspective, malicious devices with the objective of modifying data from within the premises of the network pose a high risk for the IIoT. Therefore, introducing intelligent decision-making models to the IIoT can enhance our ability to examine any collected data in a more structured, efficient, and secure manner. In this article, we provide a decision-making model for securing IIoT data. The proposed model, based on the Technique for Order Preference by Similarity to the Ideal Solution, can provide secure information transmission and recording/storage using various communicating parameters. The degree of trust of the IoT devices is analyzed using these parameters. Simple additive weighting is integrated into the proposed model to remove inefficient and ill-structured parameters. The proposed model is validated using various spectrum sensing and security parameters against a baseline method for the IIoT. Simulation results show that the proposed model is approximately 85% more efficient in identifying malicious nodes and denial-of-service threats compared to the baseline method.

The ever-growing trend of making the traditional power grids smarter than before has resulted in their gradual evolution to more sophisticated grids, referred to as Smart Grids (SGs) Cyber-Physical Systems with complex networking technologies. The integration of Information and Communication Technologies with power grids fosters seamless data sharing between different SG entities, which supports effective and smart governance in terms of demand response management, frequency support, and voltage stabilization. Nonetheless, this integration opens up several security and privacy concerns, namely, electricity theft, power loss, battery exhaustion, infrastructure mapping, etc. These issues become even more important with the addition of distributed energy sources, e.g. electric vehicles (EVs), battery energy storage systems, and renewable energy sources, into the SGs. We present a framework based on Software Defined Networking (SDN) and BlockChain (BC) to address two challenging issues of EV-aided SG ecosystems, namely, privacy assurance and power security. We leverage the capabilities of SDN to handle the complex interactions between different subsystems of the SG. Furthermore, we also employ BC and smart contracts' properties to secure energy transactions and data communications. We design a secure and efficient mutual authentication protocol based on Elliptic Curve Cryptography (ECC) and BC for privacy preservation during smart energy trading. We also proposed a BC-based smart contract for effective Demand Response Management (DRM) during bidirectional energy transfer between EVs and SG. Finally, we present experimental evaluations to validate the proposed framework's performance. The results obtained demonstrate the improved performance of the proposed scheme compared with current state-of-the-art approaches. The mutual authentication protocol designed is not only secure against major attack vectors (namely, session key security, message integrity, anonymity, forward secrecy, and so on), but it is also cost-efficient in terms of communication and computational costs. Additionally, the SC designed assures power security and maintains an adequate balance between demand and supply.

Owing to its benefits such as flexibility, scalability, and interoperability, Software-Defined Networking (SDN) has been incorporated into Internet of Vehicles (IoV) to cope with the increasing demands of vehicular applications. The integration of SDN and IoV, namely SDN-IoV, can enrich many new applications for intelligent transportation such as traffic monitoring, smart navigation, and self-driving. The spatial crowdsourcing technology has been adopted as an effective data collection and processing method that is the premise of various SDN-IoV applications. However, as huge amounts of data are generated in spatial crowdsourcing services, the data privacy and security has become a key challenge for SDN-IoV. To overcome abovementioned challenge, a Deep Reinforcement Learning (DRL) and Blockchain empowered Spatial Crowdsourcing System (DB-SCS) is proposed. In DB-SCS, we design an improved multi-blockchain structure and a blockchain-based hierarchical task management method, which divide the spatial tasks into different categories according to the privacy requirements and the areas of the task and then decompose different categories of tasks and task receivers into sub-blockchains. While guaranteeing the data privacy, DB-SCS can also enhance the spatial crowdsourcing performance by using the proposed DRL-based management strategy to dynamically select the consensus algorithm, block size, and block generation rule. Extensive simulation experiments demonstrate that the DB-SCS can obtain high throughput, low overhead, and data privacy under various SDN-IoV scenarios.

Energy consumption minimization of cloud data centers (DCs) has attracted much attention from the research community in the recent years; particularly due to the increasing dependence of emerging Cyber-Physical Systems on them. An effective way to improve the energy efficiency of DCs is by using efficient job scheduling strategies. However, the most challenging issue in selection of efficient job scheduling strategy is to ensure service-level agreement (SLA) bindings of the scheduled tasks. Hence, an energy-aware and SLA-driven job scheduling framework based on MapReduce is presented in this article. The primary aim of the proposed framework is to explore task-to-slot/container mapping problem as a special case of energy-aware scheduling in deadline-constrained scenario. Thus, this problem can be viewed as a complex multi-objective problem comprised of different constraints. To address this problem efficiently, it is segregated into three major subproblems (SPs), namely, deadline segregation, map and reduce phase energy-aware scheduling. These SPs are individually formulated using Integer Linear Programming. To solve these SPs effectively, heuristics based on Greedy strategy along with classical Hungarian algorithm for serial and serial-parallel systems are used. Moreover, the proposed scheme also explores the potential of splitting Map/Reduce phase(s) into multiple stages to achieve higher energy reductions. This is achieved by leveraging the concepts of classical Greedy approach and priority queues. The proposed scheme has been validated using real-time data traces acquired from OpenCloud. Moreover, the performance of the proposed scheme is compared with the existing schemes using different evaluation metrics, namely, number of stages, total energy consumption, total makespan, and SLA violated. The results obtained prove the efficacy of the proposed scheme in comparison to the other schemes under different workload scenarios.

Adopting OFDMA and MU-MIMO techniques for both downlink and uplink IEEE 802.11ax will help next-generation WLANs efficiently cope with large numbers of devices but will also raise some research challenges. One of these is how to optimize the channelization, resource allocation, beamforming design, and MCS selection jointly for IEEE 802.11ax-based WLANs. In this paper, this technical requirement is formulated as a mixed-integer non-linear programming problem maximizing the total system throughput for the WLANs consisting of unicast users with multicast groups. A novel two-stage solution approach is proposed to solve this challenging problem. The first stage aims to determine the precoding vectors under unit-power constraints. These temporary precoders help re-form the main problem into a joint power and radio resource allocation one. Then, two low-complexity algorithms are proposed to cope with the new problem in stage two. The first is developed based on the well-known compressed sensing method while the second seeks to optimize each of the optimizing variables alternatively until reaching converged outcomes. The outcomes corresponding to the two stages are then integrated to achieve the complete solution. Numerical results are provided to confirm the superior performance of the proposed algorithms over benchmarks.

In the smart healthcare domain, a number of mutual authentication and key agreement protocols have been suggested by the research fraternity. However, the majority of the existing protocols fail to provide the required level of security and fall for different attack vectors. Thus, in this paper, a robust, secure, and lightweight authentication and key agreement protocol is presented. The designed protocol exploits the enhanced security and reduced key size features of Elliptic Curve Cryptography (ECC) to establish mutual trust between the patients (equipped with mobile devices/sensors) and the central servers; followed by settlement on a common session key for further communication. Furthermore, the designed protocol also exploits one of the crucial features of the blockchain technology, i.e., maintaining the hash of the previous transaction. This feature, in turn, instills greater security and prevents impersonation attacks to a much larger extent. The formal and informal security assessments of the proposed protocol establish the fact that it more secure and resilient against different attack vectors than its existing counterpart. In addition to this, comparative evaluation in terms of communication and computational overhead also indicate the lightweight attribute of the proposed protocol.

In this paper, we propose an algorithm for channel sensing, collaboration, and transmission for networks of Tactical Communication Systems that are facing intrusions from hostile Jammers. Members of the network begin by scanning the spectrum for Jammers, sharing this information with neighboring nodes, and merging their respective sets of observation data into a decision vector containing the believed occupancy of each channel. A user can then use this vector to find a vacant channel for its transmission. We introduce the concept of nodes sharing these vectors with their peers, who then merge them into super-decision vectors, allowing each node to better identify and select transmission channels. We consider fading scenarios that substantially limit the reliability of the users' observations, unless they cooperate to increase the trustworthiness of their sensing data. We propose a pseudo-random channel selection algorithm that strikes a balance between sensing reliability with the number of channels being sensed. Simulation results show that the proposed system improves the network's overall knowledge of the spectrum and the rate of Jammer-free transmissions while limiting added computational complexity to the nodes of the network, despite the Jammers' unpredictable nature.

In this work, we present a multi-objective Virtual Data Center Embedding (VDCE) scheme for multi-domain cloud computing setups. The primary focus of the proposed scheme is on -Energy minimization, SLA assurance, and reduced energy Prices; and is named as ESP-driven VDCE. In the preliminary phase of this work, we formulate the proposed scheme as an optimization problem. However, due to the intractability of the formulated problem, its is remodelled and divided into three sub-problems (SP), i.e., data center identification, virtual machine mapping, and virtual link embedding. The output of one SP serves as an input to the next SP, such that the search space can be significantly narrowed. Finally, the proposed approach for VDCE is extensively validated against other algorithms. The obtained results indicate that the proposed ESP-driven VDCE approach achieves almost 7.6% more energy-aware embeddings with 11.5% higher SLA levels and approximately 23% lower energy expenses.

With the recent advancements in wireless communications, Tactile Internet (TI) has witnessed a major blow. TI is considered the next big evolution that will provide real-time control in industrial setups, particularly in the domain of tele-surgery. However, in remote-surgery ecosystems the transmission of data is prone to different attack vectors. Thus, to realize the true potential of secure tele-surgery under the umbrella of TI, it is required to design a secure authentication and key agreement protocol for tele-surgery. In this paper, we present an effective and secure mutual authentication and session establishment protocol for TI-driven remote surgery setups. The designed protocol enables secure communications between the surgeon, robotic arm, and the trusted authority (TA); where the protocol leverages the advantages of Elliptic Curve Cryptography (ECC) and biometrics. The protocol operates along the following three phases: i) setup phase, ii) registration phase, and iii) mutual authentication and key agreement phase. During the third phase, the surgeon and the robotic arm mutually authenticate each other with the help of the TA. Further, the security features of the designed protocol have been established using formal and informal means. The obtained results indicate the resiliency of the protocol against offline password guessing attacks, replay attacks, impersonation attacks, man-in-the-middle attacks, denial of service attacks, etc.

Impulsive noise is a common impediment in many wireless, power line communication (PLC), and smart grid communication systems that prevents the system from achieving error-free transmission. To overcome the detrimental effects of such impulsive interference, knowledge of impulsive noise parameters is generally required by the available mitigation techniques. This work considers a machine learning perspective for the estimation of the impulsive noise parameters in communication systems under the influence of Middleton class-A noise. Precisely, we consider a deep learning approach and design a deep neural network (DNN) that classifies a set of received symbols according to the parameters of the impulsive noise affecting them. It is sown that the classification accuracy greatly depends on the number of symbols fed into the neural network as well as the number of considered states in the classification, where the proposed approach can reach a testing accuracy of more than 99%.

In this paper, we consider full duplex amplify and forward (AF) relay networks in vehicle-to-vehicle (V2V) applications. In this context, taking into account the self-interference (SI) at the relay and assuming independent and not necessarily identically distributed (i.n.i.d) generalized L-Nakagami-m fading channels, we derive novel expressions for the probability density function (PDF) and cumulative distribution function (CDF) of the signal-to-interference-plus-noise ratio at the relay. Capitalizing on this, a lower bound to the end-to-end outage probability is derived. Monte-Carlo simulation results are presented to corroborate the derived analytical results. Our results show that the channel cascading significantly impacts the end-to-end outage probability of full duplex (FD) relaying V2V systems, where the cascading of the source-relay link is shown to be more significant than that of the relay-destination link.

In 5G networks, the Cloud Radio Access Network (C-RAN) is considered a promising future architecture in terms of minimizing energy consumption and allocating resources efficiently by providing real-time cloud infrastructures, cooperative radio, and centralized data processing. Recently, given their vulnerability to malicious attacks, the security of C-RAN networks has attracted significant attention. Among various anomaly-based intrusion detection techniques, the most promising one is the machine learning-based intrusion detection as it learns without human assistance and adjusts actions accordingly. In this direction, many solutions have been proposed, but they show either low accuracy in terms of attack classification or they offer just a single layer of attack detection. This research focuses on deploying a multi-stage machine learning-based intrusion detection (ML-IDS) in 5G C-RAN that can detect and classify four types of jamming attacks: constant jamming, random jamming, deceptive jamming, and reactive jamming. This deployment enhances security by minimizing the false negatives in C-RAN architectures. The experimental evaluation of the proposed solution is carried out using WSN-DS (Wireless Sensor Networks DataSet), which is a dedicated wireless dataset for intrusion detection. The final classification accuracy of attacks is 94.51% with a 7.84% false negative rate.

The Internet of Things (IoT) has many applications, including Industry 4.0. There are a number of challenges when deploying IoT devices in the Industry 4.0 setting, partly due to the low-cost IoT devices/nodes with limited capacity to run/support security solutions. Hence, there is a need for a lightweight and efficient security solution to protect the environment. Thus, in this article, we present a robust, lightweight, and provably secure authentication and key agreement protocol specifically for the IoT environment based on a hierarchical approach. The proposed protocol relies on lightweight operations, such as elliptic curve cryptography, physically unclonable functions, hash functions, concatenation, and XOR operations. We then evaluate the security of the designed protocol, including the widely used automated validation of Internet security protocols and applications (AVISPA), and demonstrate that it supports mutual authentication between IoT nodes and server, and is resilient against a number of common security attacks [denial of service (DoS), replay, spoofing, etc.]. The computational and communication overhead analysis shows that the proposed protocol is comparatively less expensive than three other recently published, competing protocols.

With the rapid explosion of Industrial Internet of Things (IIoT), the need for real-time data processing with enhanced flexibility and scalability has increased manifold. However, the newly evolved containerization technology offers lucrative advantages in comparison to the conventional virtual machines. However, management of these light-weight containers is a tedious task, but Google Kubernetes offers a consolidated container management and scheduling for successful execution of various lightweight containers. Nevertheless, the existing Kubernetes solutions fall short in efficiently handling the “interference” and “energy minimization” challenges in IIoT set-up. Hence, in this article, we present a competent controller, named Kubernetes-based energy and interference driven scheduler (KEIDS), for container management on edge-cloud nodes taking into account the emission of carbon footprints, interference, and energy consumption. The problem of task scheduling has been formulated using integer linear programming based on multiobjective optimization problem. In detail, KEIDS minimizes the energy utilization of edge-cloud nodes in IIoT for optimal green energy utilization. Henceforth, the applications are scheduled on the available nodes in less time with minimum interference from other applications, which in turn guarantees an optimal performance to the end-users. An extensive evaluation of the proposed KEIDS scheduler in comparison to the existing state-of-the-art schemes indicates its superior performance on real-time data acquired from Google compute cluster.

The Internet of Things (IoT) has emerged as a revolution for the design of smart applications like intelligent transportation systems, smart grid, healthcare 4.0, Industry 4.0, and many more. These smart applications are dependent on the faster delivery of data which can be used to extract their inherent patterns for further decision making. However, the enormous data generated by IoT devices are sufficient to choke the entire underlying network infrastructure. Most of the data attributes present little or no relevance to the prospective relationships and associations with the projected benefits foreseen. Therefore, order-based generalization mechanisms, known as tensors, can be used to represent these multidimensional data, thereby minimizing the flow table (FT) lookup time and reducing the storage occupancy. So, a novel IoT-train-deep approach for intelligent software-defined networking is designed in this article. The proposed approach works in four phases: 1) tensor representation; 2) deep Boltzmann machine-based classification; 3) subtensor-based flow matching process; and 4) incremental tensor train network for FT synchronization. The proposed model has been extensively tested, and it illustrates significant improvements with respect to delay, throughput, storage space, and accuracy.

In this article, we propose an index modulation system suitable for optical communications, where the intensity of the light is modulated along the time, space and frequency dimensions, building a Frequency-Hopping Spatial Multi-Pulse Position Modulation (FH-SMPPM). We analyze its performance from the point of view of its efficiency in power and spectrum, and its implementation complexity and required receiver latency. We analyze its error probability in the case of the non-turbulent free-space optical (FSO) channel. We derive formulas for the average symbol and bit error probabilities, and the simulation results show that they are tight enough for a wide range of signal-to-noise ratios and system parameters. We compare FH-SMPPM with other proposed index modulated systems of the same nature, and we highlight its distinctive advantages, like the flexibility to build an appropriate waveform under different constraints and objectives. Moreover, FH-SMPPM shows to be better performing in BER/SER and/or offer advantages in efficiency with respect to those alternatives, thus offering the possibility to be adopted for a variety of contexts.

With the advent of 5G, technologies such as Software-Defined Networks (SDNs) and Network Function Virtualization (NFV) have been developed to facilitate simple programmable control of Wireless Sensor Networks (WSNs). However, WSNs are typically deployed in potentially untrusted environments. Therefore, it is imperative to address the security challenges before they can be implemented. In this paper, we propose a software-defined security framework that combines intrusion prevention in conjunction with a collaborative anomaly detection systems. Initially, an IPS-based authentication process is designed to provide a lightweight intrusion prevention scheme in the data plane. Subsequently, a collaborative anomaly detection system is leveraged with the aim of supplying a cost-effective intrusion detection solution near the data plane. Moreover, to correlate the true positive alerts raised by the sensor nodes in the network edge, a Smart Monitoring System (SMS) is exploited in the control plane. The performance of the proposed model is evaluated under different security scenarios as well as compared with other methods, where the model's high security and reduction of false alarms are demonstrated.

In this article, a secure and lightweight authentication scheme, which provides trust, anonymity, and mutual authentication, with reduced energy, communicational, and computational overheads, is proposed for resource-constrained smart meters (SMs). The designed mutual authentication-based key agreement protocol leverages the advantages of fully hashed menezes-qu-vanstone key exchange mechanism along with Elliptic curve cryptography and one-way hash functions. Moreover, it allows to securely establish and verify the trust between the two communicating parties, i.e., SMs and neighbourhood area network gateway. These entities communicate over the insecure channel and form an important component of the smart metering infrastructure. Furthermore, extensive performance evaluation validates the supremacy of the designed protocol over the state-of-the-art in furnishing higher security features with minimal communicational and computational overheads. The obtained results also reflect that the proposed protocol is fit for implementation on resource-constrained SMs as it leads to minimal energy consumption.

The rapidly evolving industry standards and transformative advances in the field of Internet of Things are expected to create a tsunami of Big Data shortly. This, in turn, will demand real-time data analysis and processing from cloud computing platforms. A substantial part of the computing infrastructure is supported by large-scale and geographically distributed data centers (DCs). Nevertheless, these DCs impose a substantial cost in terms of rapidly growing energy consumption, which in turn adversely affects the environment. In this context, efficient resource utilization is seen as a potential candidate to enhance energy efficiency and minimize the load on the power sector. Nevertheless, in the majority of the public clouds, the resources are idle most of the time (i.e., under-utilized) as the load of the servers is unpredictable; thereby leading to a lofty increase in the energy utilization index and wastage of resources. Thus, it is highly essential to devise a precise and efficient resource management technique. Therefore, in this article, we leverage the advantages of software defined data centers (SDDCs) to minimize energy utilization levels. Precisely, SDDC refers to the process of programmatically abstracting the logical computing, network, and storage resources; and configuring them in real-time based on workload demands. In detail, we demonstrate the possibility of 1) designing a consolidated SDDC-based model to jointly optimize the process of virtual machine (VM) deployment and network bandwidth allocation for reduced energy consumption and guaranteed quality of service (QoS), particularly for heterogeneous computing infrastructures; 2) formulating a multiobjective optimization problem to deduce the optimal allocation of resources for both critical and noncritical applications; and 3) designing an efficient scheme based on heuristics to provide suboptimal results for the formulated multiobjective optimization problem. The proposed article presents a suboptimal approach based on first fit decreasing algorithm. Further, our empirical evaluations suggest that the proposed framework leads to almost 27.9% savings in terms of energy consumptions against the existing schemes with negligible QoS violations (approximately 0.33).

Pressure ulcer prevention is a vital procedure for patients undergoing long-term hospitalization. A human body lying posture (HBLP) monitoring system is essential to reschedule posture change for patients. Video surveillance, the conventional method of HBLP monitoring, suffers from various limitations, such as subject's privacy, and field-of-view obstruction. We propose an autonomous method for classifying the four state-of-the-art HBLPs in healthy adults subjects: supine, prone, left and right lateral, with no sensors or cables attached on the body and no constraints imposed on the subject. Experiments have been conducted on 12 healthy adults (age 27.35 ± 5.39 years) using a collection of textile pressure sensors embedded in a cover placed under the bed sheet. Histogram of oriented gradients and local binary patterns were extracted and fed to a supervised artificial neural network classification model. The model was trained based on the scaled conjugate gradient backpropagation. A nested cross validation with an exhaustive outer validation loop was performed to validate the classification's generalization performance. A high testing prediction accuracy of 97.9% with a Cohen's Kappa coefficient of 97.2% has been interestingly obtained. Prone and supine postures were successfully separated in the classification, in contrast to the majority of previous similar works. We found that using the information of body weight distribution along with the shape and edges contributes to a better classification performance and the ability to separate supine and prone postures. The results are satisfactorily promising toward unobtrusively monitoring posture for ulcer prevention. The method can be used in sleep studies, post-surgical procedures, or applications requiring HBLP identification.

In this paper, we consider a three-node free space optical (FSO) cooperative network with an energy harvesting (EH) decode-and-forward (DF) relay. The relay is equipped with an energy buffer and implements the harvest-store-use (HSU) architecture where the harvested energy is accumulated and used for forwarding the information from the source node to the destination node. We propose a novel HSU-based relaying strategy based on the simultaneous lightwave information and power transfer (SLIPT) concept where the relay harvests energy from the optical signal transmitted by the source. Firstly, we analyze the performance of the considered system by modeling the energy buffer as a continuous-space Markov chain (MC) for the sake of deriving the limiting distribution of the stored energy. Secondly, we discretize the state space and use the resulting discrete-space MC to derive the steady-state distribution of the buffer content. Thirdly, we propose an approximate discrete-space MC for capturing the energy buffer dynamics in a simple manner. The third approach is useful for relating the outage probability to the channel coefficients and average amounts of harvested energy in a simple closed-form manner. Simulation results validate the presented analytical evaluation and demonstrate the performance improvement that is achieved by the proposed scheme.

Smart metering infrastructure (SMI) is the core component of the smart grid (SG) which enables two-way communication between consumers and utility companies to control, monitor, and manage the energy consumption data. Despite their salient features, SMIs equipped with information and communication technology are associated with new threats due to their dependency on public communication networks. Therefore, the security of SMI communications raises the need for robust authentication and key agreement primitives that can satisfy the security requirements of the SG. Thus, in order to realize the aforementioned issues, this paper introduces a lightweight and secure authentication protocol, "LiSA", primarily to secure SMIs in SG setups. The protocol employs Elliptic Curve Cryptography at its core to provide various security features such as mutual authentication, anonymity, replay protection, session key security, and resistance against various attacks. Precisely, LiSA exploits the hardness of the Elliptic Curve Qu Vanstone (EVQV) certificate mechanism along with Elliptic Curve Diffie Hellman Problem (ECDHP) and Elliptic Curve Discrete Logarithm Problem (ECDLP). Additionally, LiSA is designed to provide the highest level of security relative to the existing schemes with least computational and communicational overheads. For instance, LiSA incurred barely 11.826 ms and 0.992 ms for executing different passes across the smart meter and the service providers. Further, it required a total of 544 bits for message transmission during each session.

With the advent of the Internet-of-Things (IoT), vehicular networks and cyber-physical systems, the need for real-time data processing and analysis has emerged as an essential pre-requite for customers' satisfaction. In this direction, Mobile Edge Computing (MEC) provides seamless services with reduced latency, enhanced mobility, and improved location awareness. Since MEC has evolved from Cloud Computing, it inherited numerous security and privacy issues from the latter. Further, decentralized architectures and diversified deployment environments used in MEC platforms also aggravate the problem; causing great concerns for the research fraternity. Thus, in this paper, we propose an efficient and lightweight mutual authentication protocol for MEC environments; based on Elliptic Curve Cryptography (ECC), one-way hash functions and concatenation operations. The designed protocol also leverages the advantages of discrete logarithm problems, computational Diffie- Hellman, random numbers and time-stamps to resist various attacks namely-impersonation attacks, replay attacks, man-in-the-middle attacks, etc. The paper also presents a comparative assessment of the proposed scheme relative to the current state-of-the-art schemes. The obtained results demonstrate that the proposed scheme incurs relatively less communication and computational overheads, and is appropriate to be adopted in resource constraint MEC environments.

The powerful paradigm of Fog computing is currently receiving major interest, as it provides the possibility to integrate virtualized servers into networks and brings cloud service closer to end devices. To support this distributed intelligent platform, Software-Defined Network (SDN) has emerged as a viable network technology in the Fog computing environment. However, uncertainties related to task demands and the different computing capacities of Fog nodes, inquire an effective load balancing algorithm. In this paper, the load balancing problem has been addressed under the constraint of achieving the minimum latency in Fog networks. To handle this problem, a reinforcement learning based decision-making process has been proposed to find the optimal offloading decision with unknown reward and transition functions. The proposed process allows Fog nodes to offload an optimal number of tasks among incoming tasks by selecting an available neighboring Fog node under their respective resource capabilities with the aim to minimize the processing time and the overall overloading probability. Compared with the traditional approaches, the proposed scheme not only simplifies the algorithmic framework without imposing any specific assumption on the network model but also guarantees convergence in polynomial time. The results show that, during average delays, the proposed reinforcement learning-based offloading method achieves significant performance improvements over the variation of service rate and traffic arrival rate. The proposed algorithm achieves 1.17%, 1.02%, and 3.21% lower overload probability relative to random, least-queue and nearest offloading selection schemes, respectively.

MAC layer protocol design in a WSN is crucial due to the limitations on processing capacities and power of wireless sensors. The latest version of the IEEE 802.15.4, referenced to as IEEE 802.15.4e, was released by IEEE and outlines the mechanism of the Time Slotted Channel Hopping (TSCH). Hence, 6TiSCH working group has released a distributed algorithm for neighbour nodes to agree on a communication pattern driven by a minimal scheduling function. A slotframe contains a specific number of time slots, which are scheduled based on the application requirements and the routing topology. Sensors nodes use the schedule to determine when to transmit or to receive data. However, IEEE 802.15.4e TSCH does not address the specifics on planning time slot scheduling. In this paper, we propose a distributed Enhanced Minimal Scheduling Function (EMSF) based on the minimal scheduling function, which is compliant with 802.15.4e TSCH. In this vein, we introduce a distributed algorithm based on a Poisson process to predict the following schedule requirements. Consequently, the negotiation operations between pairs of nodes to agree about the schedule will be reduced. As a result, EMSF decreases the exchanged overhead, the end-to-end latency and the packet queue length significantly. Preliminary simulation results have confirmed that EMSF outperforms the 802.15.4e TSCH MSF scheduling algorithm.

The growing interest in the Internet of Things (IoT) applications is associated with an augmented volume of security threats. In this vein, the Intrusion detection systems (IDS) have emerged as a viable solution for the detection and prevention of malicious activities. Unlike the signature-based detection approaches, machine learning-based solutions are a promising means for detecting unknown attacks. However, the machine learning models need to be accurate enough to reduce the number of false alarms. More importantly, they need to be trained and evaluated on realistic datasets such that their efficacy can be validated on real-time deployments. Many solutions proposed in the literature are reported to have high accuracy but are ineffective in real applications due to the non-representativity of the dataset used for training and evaluation of the underlying models. On the other hand, some of the existing solutions overcome these challenges but yield low accuracy which hampers their implementation for commercial tools. These solutions are majorly based on single learners and are therefore directly affected by the intrinsic limitations of each learning algorithm. The novelty of this paper is to use the most realistic dataset available for intrusion detection called NSL-KDD, and combine multiple learners to build ensemble learners that increase the accuracy of the detection. Furthermore, a deployment architecture in a fog-to-things environment that employs two levels of classifications is proposed. In such architecture, the first level performs an anomaly detection which reduces the latency of the classification substantially, while the second level, executes attack classifications, enabling precise prevention measures. Finally, the experimental results demonstrate the effectiveness of the proposed IDS in comparison with the other state-of-the-arts on the NSL-KDD dataset.

In this study, the authors investigate the physical layer security performance over Rayleigh fading channels in the presence of impulsive noise, as encountered, for instance in smart grid environments. For this scheme, secrecy performance metrics are considered with and without destination assisted jamming at the eavesdropper's side. Specifically, they derive analytical expressions for the secrecy outage probability (SOP), at the legitimate receiver. Finally, numerical results are provided to verify the accuracy of the authors' derivations. From the obtained results, it is verified that the SOP, without destination assisted jamming, is flooring at high signal‐to‐noise‐ratio values and that it can be significantly improved with the use of jamming.

This paper investigates two pulse-based Optical Spatial Modulation (OSM) schemes as cost-efficient solutions for multi-aperture Free-Space Optical (FSO) communications with Intensity-Modulation and Direct-Detection (IM/DD). Namely, we consider Optical Space Shift Keying (OSSK) where information is encoded in the index of the pulsed optical source and Spatial Pulse Position Modulation (SPPM) where additional bits determine the position of the transmitted optical pulse resulting in higher transmission rates. A performance analysis is carried out over gamma-gamma channels with the exact Poisson photon-counting detection model. Exact Symbol Error Probability (SEP) expressions, simple upper bounds and the achievable transmit diversity orders are derived for both the open-loop and closed-loop scenarios. Based on the presented performance analysis, a transmit aperture selection scheme capable of maximizing the transmit diversity order is proposed for OSSK and SPPM in the closed-loop case. Results show that for open-loop OSSK, open-loop SPPM and closed-loop OSSK, the transmit diversity order does not depend on the severity of scintillation unlike the closed-loop SPPM case.

This paper investigates a hybrid radio frequency (RF)/visible light communication (VLC) ultra-small cell network consisting of multiple optical angle-diversity transmitters, one multi-antenna RF access point (AP) and multiple terminal devices. In the network, the optical transmitters play the primary role and are responsible for delivering information and power over the visible light while the RF AP acts as a complementary power transfer system. Thus, we propose a novel collaborative RF and lightwave resource allocation scheme for hybrid RF/VLC ultra-small cell networks. The proposed scheme aims to maximize the communication quality-of-service provided by the VLC under a constraint of total RF and light energy harvesting performance while keeping illumination constant and ensuring health safety. This scheme leads to the formulation of two optimization problems that correspond to the resource allocation at the optical transmitters and the RF AP. Both problems are optimally solved by appropriate algorithms. Moreover, we propose a closed-form suboptimal solution with high accuracy to tackle the optical transmitters' resource allocation problem, as well as an efficient semi-decentralized method. Finally, simulation results illustrate the achievable performance of the investigated system and the effectiveness of the proposed solutions.

With the emergence of the Internet-of-Things (IoT) and seamless Internet connectivity, the need to process streaming data on real-time basis has become essential. However, the existing data stream management systems are not efficient in analyzing the network log big data for real-time anomaly detection. Further, the existing anomaly detection approaches are not proficient because they cannot be applied to networks, are computationally complex, and suffer from high false positives. Thus, in this paper a hybrid data processing model for network anomaly detection is proposed that leverages grey wolf optimization (GWO) and convolutional neural network (CNN). To enhance the capabilities of the proposed model, GWO and CNN learning approaches were enhanced with: 1) improved exploration, exploitation, and initial population generation abilities and 2) revamped dropout functionality, respectively. These extended variants are referred to as Improved-GWO (ImGWO) and Improved-CNN (ImCNN). The proposed model works in two phases for efficient network anomaly detection. In the first phase, ImGWO is used for feature selection in order to obtain an optimal trade-off between two objectives, i.e., reduced error rate and feature-set minimization. In the second phase, ImCNN is used for network anomaly classification. The efficacy of the proposed model is validated on benchmark (DARPA'98 and KDD'99) and synthetic datasets. The results obtained demonstrate that the proposed cloud-based anomaly detection model is superior in comparison to the other state-of-the-art models (used for network anomaly detection), in terms of accuracy, detection rate, false positive rate, and F-score. In average, the proposed model exhibits an overall improvement of 8.25%, 4.08%, and 3.62% in terms of detection rate, false positives, and accuracy, respectively; relative to standard GWO with CNN.

In this work, we consider a sparse code multiple access uplink system, where J users simultaneously transmit data over K subcarriers, such that J > K, with a constraint on the power transmitted by each user. To jointly optimize the subcarrier assignment and the transmitted power per subcarrier, two new iterative algorithms are proposed, the first one aims to maximize the sum-rate (Max-SR) of the network, while the second aims to maximize the fairness (Max-Min). In both cases, the optimization problem is of the mixed-integer nonlinear programming (MINLP) type, with non-convex objective functions, which are generally not tractable. We prove that both joint allocation problems are NP-hard. To address these issues, we employ a variant of the block successive upper-bound minimization (BSUM) framework, obtaining polynomial-time approximation algorithms to the original problem. Moreover, we evaluate the algorithms' robustness against outdated channel state information (CSI), present an analysis of the convergence of the algorithms, and a comparison of the sum-rate and Jain's fairness index of the novel algorithms with three other algorithms proposed in the literature. The Max-SR algorithm outperforms the others in the sum-rate sense, while the Max-Min outperforms them in the fairness sense.

This paper investigates the problem of secure and reliable communications for cognitive radio networks. More specifically, we consider a single input multiple output cognitive model where the secondary user (SU) faces an eavesdropping attack while being subject to the normal interference constraint imposed by the primary user (PU). Thus, the SU must have a suitable power allocation policy which does not only satisfy the constraints of the PU but also the security constraints such that it obtains a reasonable performance for the SU, without exposing information to the eavesdropper. We derive four power allocation policies for different scenarios corresponding to whether or not the channel state information of the PU and the eavesdropper are available at the SU. Further, we introduce the concept secure and reliable communication probability (SRCP) as a performance metric to evaluate the considered system, as well as the efficiency of the four power allocation policies. Finally, we present numerical examples to illustrate the power allocation polices, and the impact of these policies on the SRCP of the SU.

Creating a secure environment for communications is becoming a significantly challenging task in 5G Heterogeneous Networks (HetNets) given the stringent latency and high capacity requirements of 5G networks. This is particularly factual knowing that the infrastructure tends to be highly diversified especially with the continuous deployment of small cells. In fact, frequent handovers in these cells introduce unnecessarily recurring authentications leading to increased latency. In this paper, we propose a software-defined wireless network (SDWN)- enabled fast cross- authentication scheme which combines non- cryptographic and cryptographic algorithms to address the challenges of latency and weak security. Initially, the received radio signal strength vectors at the mobile terminal (MT) is used as a fingerprinting source to generate an unpredictable secret key. Subsequently, a cryptographic mechanism based upon the authentication and key agreement protocol by employing the generated secret key is performed in order to improve the confidentiality and integrity of the authentication handover. Further, we propose a radio trusted zone database aiming to enhance the frequent authentication of radio devices which are present in the network. In order to reduce recurring authentications, a given covered area is divided into trusted zones where each zone contains more than one small cell, thus permitting the MT to initiate a single authentication request per zone, even if it keeps roaming between different cells. Accordingly, once the RSS vectors and the encrypted mobile identification are received by the authentication slice (AS), this latter builds the authentication vector using the k- nearest neighborhood technique to estimate the kdh fingerprint distribution which is compared to the radio trusted zones database to prove the legitimacy of the MT and the network slice (NS). Cross-layer authentication protocol is consequently executed. The proposed scheme is analyzed under different attack scenarios and its complexity is compared with cryptographic and non-cryptographic approaches to demonstrate its security resilience and computational efficiency.

This paper investigates the upper and lower bounds on achievable secrecy rate of multiple-input multiple-output (MIMO) wiretap channels by employing zero-forcing (ZF) detectors at, both the legitimate and malicious receivers' sides. Particularly, both large (i.e., gamma distribution) and small scale (i.e., Rayleigh distribution) fadings are taken into considerationwith the purpose of better simulating some practical scenarios. Motivated by previous works, bounds of achievable secrecy rate for MIMO wiretap channels over uncorrelated and min semi-correlated Rayleigh fading are derived with closed-form expressions. Furthermore, an asymptotic scenario in which the number of antennas tends to infinity is evaluated. Finally, thecloseness of our analytical expressions is corroborated through simulation results.

This paper studies a new concept so-called green cell-less radio frequency (RF) wireless power transfer (WPT) networks. We consider a scenario in which multiple indoor access points (APs) equipped with outdoor energy harvesters are connected with a central control unit via backhaul links. Further, such APs exploit the harnessed green energy to recharge wirelessly indoor devices under the coordination of the control unit. Considering the network, we focus on AP selection and beamforming optimization to maximize the total energy harvesting (EH) rate. The resulting mathematical problem has the form of mixed-integer optimization that is intractable to solve. Thus, we propose an algorithm to tackle this difficulty. Through numerical results, we show the advantages of the cell-less design over the conventional small-cell one to validate our ideas. In particular, the issue on safety requirements of human exposure to RF radiation is discussed. Finally, potential future research is provided.

This paper proposes a new communication protocol for a cooperative non-orthogonal multiple access (NOMA) system. In this system, based on users' channel conditions, each two NOMA users are paired to reduce system complexity. In this concern, the user with a better channel condition decodes and then forwards messages received from the source to the user with a worse channel condition. In particular, the direct link between the paired users is assumed to be unavailable due to the weak transmission conditions. To overcome this issue, we propose a new cooperative NOMA protocol in which an amplify-and-forward (AF) relay is employed to help the user-to-user communications. To evaluate the proposed protocol, the exact closed-form expressions of outage probability (OP) at the two paired users are derived. Based on the analysis of the OP, we further examine the system throughput in a delay-sensitive transmission mode. Finally, our analytical results verified by Monte-Carlo simulation show that the proposed protocol is efficient in enhancing the performance of NOMA system when the user-to-user communications is limited.

We address the distributed estimation of a scalar Gaussian source in wireless sensor networks. The sensor nodes transmit their noisy observations, using the amplify-and-forward relaying strategy through coherent multiple access channel to the fusion center (FC) that reconstructs the source parameter. In this letter, we assume that the received signal at the FC is corrupted by impulsive noise and channel fading, as encountered for instance within power substations. Over Rayleigh fading channel and in presence of Middleton class-A impulsive noise, we derive the minimum mean square error (MMSE) optimal Bayesian estimator along with its mean square error performance bounds. From the obtained results, we conclude that the proposed optimal MMSE estimator outperforms the linear MMSE estimator developed for Gaussian noise scenario.

To cope up with the explosive growth of mobile data demand, the fifth-generation mobile network intents to exploit the available spectrum in millimeter-wave (mmWave) band to boost the communication capacity. However, for a potential mmWave communication to happen, challenges, as propagation losses and blockages, have to be dealt with. Abundant literature illustrating techniques to circumvent these challenges and increase the cellular capacity can be found. Among the approaches used to overcome the challenges in mmWave networks, optimal transmit precoding design, spatial reuse of mmWave base stations (BSs), and mmWave-overlaid microwave (μWave) cellular networks are employed. This paper focuses on the performance analysis of mmWave-overlaid microwave cellular networks, from security perspective. We particularly developed a mathematical framework to analyze the connection outage probability, the secrecy outage probability, and the achievable average secrecy rate of the hybrid mmWave network, while taking fading and the impact of blockages into consideration. Moreover, based on the received signal strength, we formulated a scheme for a generic mobile user to be associated with either the mmWave or μWave network. The exact average secrecy rate of mmWave networks is also formulated using moment generating and Laplace functions as a tool. The derived analytic expressions are validated via simulation results; for different antenna gain, eavesdropper density, BS, and blockage density.

Vehicular Ad-Hoc networks (VANETs) have become an interesting research area especially with growing need to improve the quality of service and the performance of vehicle-to-vehicle (V2V) wireless communication systems. This paper analyses a peer-to-peer V2V wireless communication system in the presence of interference. In this vein, the performance metrics of the proposed system are investigated over Nakagami-m fading channels where no interference cancellation is assumed at the receiver side. In particular, the mathematical expression of the probability density function (PDF) of the resultant signal-to-interference-plus-noise ratio (SINR) of the system is derived. The closed-form expression of the PDF derived here lays the foundation for deriving analytical expressions for the moment generating function and the probability of the average bit error rate of the system under consideration. Finally, the closeness of our analytical expressions are corroborated through Monte Carlo simulation results.

Technological advances and innovative business models led to the modernization of the cyber-physical concept with the realization of the Internet of Things (IoT). While IoT envisions a plethora of high impact benefits in both, the consumer as well as the control automation markets, unfortunately, security concerns continue to be an afterthought. Several technical challenges impede addressing such security requirements, including, lack of empirical data related to various IoT devices in addition to the shortage of actionable attack signatures. In this paper, we present what we believe is a first attempt ever to comprehend the severity of IoT maliciousness by empirically characterizing the magnitude of Internet-scale IoT exploitations. We draw upon unique and extensive darknet (passive) data and develop an algorithm to infer unsolicited IoT devices which have been compromised and are attempting to exploit other Internet hosts. We further perform correlations by leveraging active Internet-wide scanning to identify and report on such IoT devices and their hosting environments. The generated results indicate a staggering 11 thousand exploited IoT devices that are currently in the wild. Moreover, the outcome pinpoints that IoT devices embedded deep in operational Cyber-Physical Systems (CPS) such as manufacturing plants and power utilities are the most compromised. We concur that such results highlight the wide-spread insecurities of the IoT paradigm, while the actionable generated inferences are postulated to be leveraged for prompt mitigation as well as to facilitate IoT forensic investigations using real empirical data.

Coping with the limited amount of available spectrum, time division duplexing (TDD) system is considered as an attractive duplexing method due to exploiting channel reciprocity as well as flexible resource management. The conventional scheduling scheme is based on the channel quality indicator (CQI) reported from the user equipment (UE) to estimate instantaneous data rates for the scheduling metric calculation. However, CQI is insufficient to reflect the state of the channel variation in terms of frequency and time. Based on the channel reciprocity of TDD systems, we utilize uplink sounding reference signal (SRS) to estimate downlink channel status. However the received SRS power is a result of uplink power control where power control effect should be compensated to estimate channel status in downlink scheduling. In order to solve this problem, we propose the SRS path loss estimation method based on the power headroom report. By using this scheme, the base station (BS) can obtain the compensated signal-to-interference-plus-noise-ratio (SINR) and determine the scheduling metric based on its calculated SINR instead of reported CQI from UE. Simulation results show that the proposed scheduling algorithm outperforms the conventional scheme in total throughput, whereas the fairness index experienced by the proposed algorithm is lesser than of the conventional scheme based on CQI.

In this paper, we analyze the association of a user terminal in a two-tier network (i.e., macrocells and millimeter wave small cells). We assumed a decoupled wireless access where a user terminal has the liberty to choose different base stations (BSs) in uplink and downlink based on the received power and the channel quality. A practical blockage model where a human body is a blockage to millimeter wave (mmW) is considered. An in-depth simulation study is done to explore the effectiveness of decoupled wireless access in a crowded environment. In addition to that, a detailed analysis on the intuitiveness and the mathematical tractability of the blockage model used is also provided. In the end, few research questions on the efficacy of decoupled wireless access are raised in this paper.

This paper presents an investigation of the secrecy capacity in device-to-device (D2D) enabled cellular network under the collusion of eavesdroppers. We provide the closed-form expressions for the probability of achieving non-zero secrecy capacity of the downlink channel between cellular base-station (BS) and user in the presence of D2D nodes and eavesdroppers. Furthermore, two techniques are used for the simple yet exact evaluation of the average secrecy rate using moment generating functions with and without interference. The impact of transmission factors, path loss exponents, and node density is analysed in numerical results. In contrast to the general notion of interference-aided secure communication, it has been shown that the interference does not impact the secrecy rate under the collusion of eavesdroppers.

Recently, a short reference differential chaos shift keying system (SR-DCSK) has been proposed to overcome the dominant drawbacks related to low data rate and energy efficiency fondness of conventional DCSK systems. The fact that terminals on a network have a limited battery capacity and are in desperate need to high energy efficiency transmission schemes compels us to tackle these crucial challenges. In this paper, we propose an SR-DCSK system that performs simultaneous wireless information and power transfer (SWIPT). This promising design exploits the saved time gained from the fact that reference signal duration of SR-DCSK scheme occupies less than half of the bit duration to transmit a signal. The aim of this system is to allow receivers to perform without being equipped with any external power supply. Furthermore, at the receiver side, an RF-to-dc conversion is first performed, followed by data recovery without the need to any channel estimator. Closed-form expressions of multiple-input single-output SR-DCSK SWIPT system, such as ergodic rate, harvesting time, energy shortage, and data outage as well as exact and approximate bit error rate probabilities are derived under Rayleigh fading channel and are validated via simulation. Our results show that the proposed solution saves energy without sacrificing the non-coherent fashion of the system or reducing the rate compared to conventional DCSK, while keeping the design simple.

In this paper, we propose an Internet of Things (IoT) system application for remote medical monitoring. The body pressure distribution is acquired through a pressure sensing mattress under the person's body, data is sent to a computer workstation for processing, and results are communicated for monitoring and diagnosis. The area of application of such system is large in the medical domain making the system convenient for clinical use such as in sleep studies, non or partial anesthetic surgical procedures, medical-imaging techniques, and other areas involving the determination of the body-posture on a mattress. In this vein, a novel method for human body posture recognition that consists in providing an optimal combination of signal acquisition, processing, and data storage to perform the recognition task in a quasi-real-time basis. A supervised learning approach was used to build a model using a robust synthetic data. The data has been generated beforehand, in a way to enhance and generalize the recognition capability while maintaining both geometrical and spatial performance. Low-cost and fast computation per sample processing along with autonomy, make the system suitable for long-term operation and IoT applications. The recognition results with a Cohen's Kappa coefficient κ = 0.866 was satisfactorily encouraging for further investigation in this field.

Providing from the robustness of differential chaos shift keying (DCSK) against linear and non-linear channel distortions, in this paper, we propose using this non-coherent modulation with blanking device as a potential candidate for implementation in power-line communications. The performance of this system is analyzed in the general case where background, impulsive and phase noises exist. We derive the probability of blanking error as well as the bit error rate with and without blanking. Moreover, the proposed system is compared to differential phase shift keying (DPSK) modulation to highlight the robustness of DCSK scheme against phase noise imperfection.

In this paper, we deal with the problem of maximum likelihood (ML) estimation of the signal-to-noise ratio (SNR) parameter for frequency modulated differential chaos shift key (FM-DCSK) system over multipath Rayleigh fading channels. The ML estimators are derived for various scenarios including data-aided (DA), non-data aided (NDA) and joint DA-NDA estimation by using both the data and pilot symbols. For comparison purposes, the Cramér- Rao lower bounds (CRLBs) for the SNR estimators are derived. The performance of the estimators is evaluated by simulations and comparing with CRLBs in terms of the mean-square-error. Simulated results show that for a large spreading factor the proposed scheme performs well over a wide SNR range in comparisons with CRLBs

Security abides a tremendous key requirement in the context of Internet of Things (IoT). IoT connects multiple objects together through wired and wireless connections in the aim of enabling ubiquitous interaction where any components can communicate with each other without any constraint. One of the most important elements in the IoT concept is Wireless Sensor Network (WSN). Due to their unattended and shared nature of radio for communication, security becomes an important issue. Wireless sensor nodes are vulnerable to radio jamming. When the jammer has the ability to interpret data link layer protocols, it becomes as energy-efficient as legitimate nodes. This paper presents a comprehensive survey on different sophisticated jamming attacks based on MAC layer. Techniques used to defeat each one of the intelligent jammers are classified based on the knowledge capacity of MAC protocols rules. The concepts behind existing protocols, that are dedicated by design to defeat such type of jammers, are presented. We conclude by a recapitulative table summarizing jamming attacks and proposed MAC-based solutions, and highlight open research directions.

In this paper, we investigate the secrecy performance of the multiple-input multiple-output (MIMO) wiretap channels in the presence of an active full-duplex eavesdropper with consideration of channel estimation error at the legitimate destination and eavesdropper. For this purpose, the probability density functions (PDFs) and cumulative density functions (CDFs) of the receive signal-to-interference-plus-noise ratio (SINR) at the destination and eavesdropper are given by conducting the singular value decomposition (SVD) on the estimated channel coefficient matrices. Consequently, the closed- form expressions for the probability of positive secrecy capacity and secrecy outage probability over Rayleigh fading channels are derived. Finally, the Monte-Carlo simulation results are presented to validate the accuracy of our theoretical analysis.

This paper presents wireless sensor network unmanned aerial vehicle (WSN-UAV) system for military remote monitoring and surveillance. Large scale WSN is deployed in a battlefield or wide hostile region to collect information of interest and send it to a UAV. Collaborative beamforming (CB) is used to achieve the ground-to-air transmissions. An automatic dependent surveillance-broadcast (ADS-B) based multilateration is used to obtain the UAV location and tracking information. It is found that a minimum distance between the UAV and the WSN is required for proper operation of the CB due to the precision of the multilateration and the movement of the UAV.

A novel spectrum sensing approach based on distribution discontinuity estimation facilitated by change-point (CP) detection algorithm has been presented in this work. Specifically, a Markov Chain Monte Carlo (MCMC) based CP detection algorithm to estimate the variations in the distribution over the received signal is developed. Primary User (PU) activity, autonomous classification of modulation and detection of PU emulation attempts are detected using the CP- Maximum Likelihood Estimation (MLE) framework. Specifically, the CP based approach facilitates PU activity detection and initiates the MLE based mechanism to discern or reveal the underlying modulation scheme within the received signal. Thus, the proposed joint CP-MLE framework not only aims at detecting the discontinuity or the variations in the underlying distribution over the sensed signal, but also helps in attributing those variations to distinct modulation schemes, in an effort to identify PU emulation attempts. This CP- MLE framework has been extensively validated using the Universal Software Radio Peripheral (USRP) devices for various types of scenarios involving real-time modulation classification and identification of PU emulation type attacks.

This paper analyzes a novel multiple-input single- output (MISO) simultaneous wireless information and power transfer (SWIPT) system model in the presence of an active eavesdropper over the frequency-selective fading channel. In this model, a transmitter applies the time reversal (TR) beamforming technique to combat the fading effects whereas a legitimate user employs a power splitter to jointly receive information and energy, and the active eavesdropper jams the user. Given the system model, the system performance in terms of symbol error rate (SER) and energy harvesting (EH) is analyzed. In particular, we devise a SER-energy region, instead of the conventional rate-energy region, to evaluate system performance of the SWIPT model. A moment generating function (MGF)-based method is presented to exactly derive the average SER analysis. Moreover, the closed-form expression of average effective harvested energy is then provided to complete SERenergy region analysis. Finally, analytical results confirmed by numerical simulations show that the TR technique can support the SWIPT system to notably improve the SER performance whereas the jamming signal can enhance the EH performance.

In this paper, we investigate the performance of an amplify-and-forward two-way relay network, in which the relay can harvest the energy from the radio frequency and all channels are subject to Nakagami-m fading. In particular, we assume that the relay has a single antenna, while two sources are equipped with multiple antennas. Also, two sources use the maximal ratio transmission (MRT) and maximal ratio combining (MRC) techniques to process the transmit and received signals. Moreover, the multiple access broadcast (MABC) protocol, which can improve the spectrum efficiency, is employed to coordinate the bidirectional communication of the two-way relay network. Given these settings, we derive analytical expressions for the throughput under the delay-limited and delay-tolerant transmission modes and the outage probability. Finally, Monte-Carlo simulations are provided to verify our calculation.

In this paper, we consider the performance analysis of a decode-and-forward (DF) cooperative relaying (CR) scheme over channels impaired by bursty impulsive noise. Although the Middleton class-A model and the Bernoulli-Gaussian model give good results to generate a sample distribution of impulsive noise, they fail in replicating the bursty behavior of impulsive noise, as encountered for instance within power substations. To deal with that, we adopt a two-state Markov-Gaussian process for the noise distribution. For this channel, we evaluate the bit error rate performance of direct transmission (DT) and a DF relaying scheme using M-ary phase shift keying (M-PSK) modulation in the presence of Rayleigh fading with a maximum a posteriori (MAP) receiver. From the obtained results, it is seen that the DF CR scheme in bursty impulsive noise channel still achieves the space diversity and performs significantly better than DT under the same power consumption. Moreover, the proposed MAP receiver attains the lower bound derived for DF CR scheme and leads to large performance gains compared with the conventional receiving criteria, which were optimized for additive white Gaussian noise channel and memoryless impulsive noise channel.

Conventional differential chaos-shift-keying systems (DCSK) are not the most suitable for supporting continuous-mobility scenarios. Therefore, in this paper an improved continuous-mobility differential chaos-shift-keying system (CM-DCSK) is presented that provides greater agility and improved performance in fast fading channels without accurate channel estimation while still being simple compared to a conventional DCSK system. A new DCSK frame signal is designed to reach this goal. In our new frame design, each reference sample is followed by a data carrier sample. This modification of the system design reduces the hardware complexity of DCSK because it requires a shorter wideband delay line and significantly improves the performance over fast fading channels while keeping the non-coherent nature of the transmission system. Once the design is explained, the bit error rate performance is computed over a multipath fast fading channel and compared to the conventional DCSK system. Simulation results confirm the advantages of this new noncoherent spread-spectrum design that can support mobility.

In this paper, a multiuser OFDM-based chaos shift keying (MU OFDM-DCSK) modulation is presented. In this system, the spreading operation is performed in time domain over the multicarrier frequencies. To allow the multiple access scenario without using excessive bandwidth, each user has NP predefined private frequencies from the N available frequencies to transmit its reference signal and share with the other users the remaining frequencies to transmit its M spread bits. In this new design, NP duplicated chaotic reference signals are used to transmit M bits instead of using M different chaotic reference signals as done in DCSK systems. Moreover, given that NP<;<; M, the MU OFDMDCSK scheme increases spectral efficiency, uses less energy and allows multiple-access scenario. Therefore, the use of OFDM technique reduces the integration complexity of the system where the parallel low pass filters are no longer needed to recover the transmitted data as in multicarrier DCSK scheme. Finally, the bit error rate performance is investigated under multipath Rayleigh fading channels, in the presence of multiuser and additive white Gaussian noise interferences. Simulation results confirm the accuracy of our analysis and show the advantages of this new hybrid design.

Data rate and energy efficiency decrement caused by the transmission of reference and data carrier signals in equal portions constitute the major drawback of differential chaos shift keying (DCSK) systems. To overcome this dominant drawback, a short reference DCSK system (SR-DCSK) is proposed. In SRDCSK, the number of chaotic samples that constitute the reference signal is shortened to R such that it occupies less than half of the bit duration. To build the transmitted data signal, P concatenated replicas of R are used to spread the data. This operation increases data rate and enhances energy efficiency without imposing extra complexity onto the system structure. The receiver uses its knowledge of the integers R and P to recover the data. The proposed system is analytically studied and the enhanced data rate and bit energy saving percentages are computed. Furthermore, theoretical performance for AWGN and multipath fading channels are derived and validated via simulation. In addition, optimising the length of the reference signal R is exposed to detailed discussion and analysis. Finally, the application of the proposed short reference technique to the majority of transmit reference systems such as DCSK, multicarrier DCSK, and quadratic chaos shift keying enhances the overall performance of this class of chaotic modulations and is, therefore, promising.

A cognitive detect and avoid radar system based on chaotic UWB-MIMO waveform design to enable autonomous UAV navigation is presented. A Dirichlet-process-mixture-model (DPMM)-based Bayesian clustering approach to discriminate extended targets and a change-point (CP) detection algorithm are applied for the autonomous tracking and identification of potential collision threats. A DPMM-based clustering mechanism does not rely upon any a priori target scene assumptions and facilitates online multivariate data clustering/classification for an arbitrary number of targets. Furthermore, this radar system utilizes a cognitive mechanism to select efficient chaotic waveforms to facilitate enhanced target detection and discrimination. We formulate the CP mechanism for the online tracking of target trajectories, which present a collision threat to the UAV navigation; thus, we supplement the conventional Kalman-filter-based tracking. Simulation results demonstrate a significant performance improvement for the DPMM-CP-assisted detection as compared with direct generalized likelihood-ratio-based detection. Specifically, we observe a 4-dB performance gain in target detection over conventional fixed UWB waveforms and superior collision avoidance capability offered by the joint DPMM-CP mechanism.

Chaos-shift-keying (CSK) and differential CSK (DCSK) are the two popular coherent and non-coherent modulation schemes for ultra wide-band (UWB) communications. However, security of these schemes has never been studied formally from the information-theoretic perspective. In this paper, we investigate the physical layer security of CSK and DCSK modulation schemes over AWGN and Rayleigh fading channels from the information-theoretic manner. For this aim, the average secrecy capacity and outage probability are computed and analyzed by considering the variation of bit energy Eb coming from the use of chaotic signal to convey information. Our results show that CSK has better or close secrecy capacity and outage probability compared with DCSK and the conventional spread-spectrum modulation. Additionally, these metrics favor Rayleigh fading channels over AWGN channels. Finally, we conclude that the non-constant bit energy is useful to enhance the physical layer security.

Based on the capability of harvesting the energy of all paths in the multipath environment, time-reversal (TR) transmission technique offers a great potential of low-complexity energy-efficient communications for future wireless network. The TR-employed systems benefit from a significantly reduced leakage of message-bearing signal to unintended users or eavesdroppers. In this paper, we propose a term so-called average effective secrecy signal-to-interference-plus-noise ratio (SINR) which represents the secrecy performance of TR-employed systems. Furthermore, we also consider: (i) the channel correlation between transmit antennas, and (ii) the channel correlation between a legitimate user and an eavesdropper. Accordingly, we derive the average effective secrecy-SINR in a closed-form expression of TR-employed systems. The obtained expression is based on the exact power-averaged expressions of the desired signal and the inter-symbol interference (ISI) components at both the legitimate user and eavesdropper side. The analytical results confirmed by numerical simulations show that the average effective secrecy-SINR metric is preferred to the average secrecy-SINR term in the perspective of measuring secrecy performance over correlated multi-path channel.

This paper presents the design and performance analysis of an analog network coding (ANC) scheme for multi-user multi-carrier differential chaos shift keying (MC-DCSK) modulation. The incentives to employ the MC-DCSK system are to achieve a better spectral efficiency and more efficient energy consumption compared to that of a conventional DCSK system. The proposed scheme considers a network comprising L user nodes (L ≥ 2) and a single relay node R. In this scheme, called the ANC-based two-way relay system, all users transmit their signals to the relay in the first time slot, and the relay forwards the superposition of the received signals each of the user nodes in the second time slot. At the receiver end, each user mitigates the overall interference by subtracting its own data signal from the received combined signal and then starts the decoding process. The system design is analytically studied, and the corresponding theoretical bit error rate expression for the multipath fading channel is derived. Additionally, the conventional ANC-DCSK scheme is analyzed and compared to the proposed ANC-based MC-DCSK scheme to show the improvement in the performance of our approach. Finally, to validate the accuracy of the methodology, the simulation results are compared to the related theoretical expressions.

This work presents another look at an analog network coding scheme for multi-user spread-spectrum communication systems. Our proposed system combines coding and cooperation between a relay and users to boost the throughput and to exploit interference. To this end, each pair of users, A and B, that communicate with each other via a relay R shares the same spreading code. The relay has two roles, it synchronizes network transmissions and it broadcasts the combined signals received from users. From user B's point of view, the signal is decoded, and then, the data transmitted by user A is recovered by subtracting user B's own data. We derive the analytical performance of this system for an additive white Gaussian noise channel with the presence of multi-user interference, and we confirm its accuracy by simulation.

The performance analysis of a relaying system implementing modulation diversity is investigated in this work. Our relaying system is quite novel, since, we assume that the relay always transmits during the relaying phase, i.e., is never silent. Whereas in the other related works assume that the relay first decodes its received signal and upon a successful decoding, transmits to the destination, i.e., the relay is silent upon an unsuccessful decoding. Modulation diversity creates diversity by transforming the angle of a classical modulation to create signal points with distinct components, followed by subsequent interleaving over the components. Assuming transmission over independent Rayleigh fading channels and maximum likelihood detection on the reordered signals at the receiver, the analysis starts with finding the probability density function of the end-to-end signal-to-noise (SNR) ratio. Then, a tight upper-bound expression for the error probability is obtained. Following that, we achieve the exact outage probability of the relaying system under-study. Then, exact and upper-bound expressions for the system capacity are presented. Finally, numerical results and comparisons with Monte Carlo simulations are presented.

This study carries out the first‐ever investigation of the analysis of a cooperative decode‐and‐forward (DF) relay network with chaos shift keying (CSK) modulation. The performance analysis of DF‐CSK in this study takes into account the dynamical nature of chaotic signal, which is not similar to a conventional binary modulation performance computation methodology. The expression of a lower bound bit error rate (BER) is derived in order to investigate the performance of the cooperative system under independently and identically distributed Gaussian fading wireless environments. The effect of the non‐periodic nature of chaotic sequence leading to a non‐constant bit energy of the considered modulation is also investigated. A computation approach of the BER expression based on the probability density function of the bit energy of the chaotic sequence, channel distribution and number of relays is presented. Simulation results prove the accuracy of the authors BER computation methodology.

This study proposes a low‐complexity amplify‐and‐forward (AF) relaying scheme for a differential chaos shift keying (DCSK) system. In this scenario, like the conventional AF‐DCSK scheme, a source node communicates with a destination node via one relay node, in a way that the source transmits the reference and the data carrier signal to the destination and the selected relay. This approach being sub‐optimal, presents a new relaying algorithm for the AF‐DCSK scheme to minimise the processing load, the cooperative time and the energy for relaying symbols. Furthermore, it is analytically proven that the new AF scheme benefits from less relaying complexity, and while preserving reasonable power levels, it does not degrade the error performance of the system. Finally, the scheme is evaluated by comparing the transmitted energy, the cooperative time and the error performance gain to the corresponding values in the conventional AF‐DCSK scheme.

In this paper, a multi user Multi-Carrier Differential Chaos Shift Keying (MC-DCSK) modulation is presented. The system endeavors to provide a good trade-off between robustness, energy efficiency and high data rate, while still being simple. In this architecture of MC-DCSK system, for each user, chaotic reference sequence is transmitted over a predefined subcarrier frequency. Multiple modulated data streams are transmitted over the remaining subcarriers allocated for each user. This transmitter structure saves energy and increases the spectral efficiency of the conventional DCSK system.

A new Multi-Carrier Differential Chaos Shift Keying (MC-DCSK) modulation is presented in this paper. The system endeavors to provide a good trade-off between robustness, energy efficiency and high data rate, while still being simple compared to conventional multi-carrier spread spectrum systems. This system can be seen as a parallel extension of the DCSK modulation where one chaotic reference sequence is transmitted over a predefined subcarrier frequency. Multiple modulated data streams are transmitted over the remaining subcarriers. This transmitter structure increases the spectral efficiency of the conventional DCSK system and uses less energy. The receiver design makes this system easy to implement where no radio frequency (RF) delay circuit is needed to demodulate received data. Various system design parameters are discussed throughout the paper, including the number of subcarriers, the spreading factor, and the transmitted energy. Once the design is explained, the bit error rate performance of the MC-DCSK system is computed and compared to the conventional DCSK system under multipath Rayleigh fading and an additive white Gaussian noise (AWGN) channels. Simulation results confirm the advantages of this new hybrid design.

In this paper, implementation of discrete-time chaotic generators widely used in digital communications is studied and evaluated. The study focuses on power consumption, resource usage, and maximum execution frequency of implementations for two common Field Programmable Gate Arrays (FPGAs). While the Bernoulli map ranks first in all three aspects, results show significant ranking differences among the other chaotic generators. Results were obtained by first implementing the chaotic generators in a high level register to transistor level description language and then using tools from FPGA manufacturers to obtain the resource usage as well as estimate the other desired characteristics.

The feasibility of having chaos-based communication in a MIMO system is presented. A promising chaotic symbolic dynamics modulation is chosen, and an Alamouti space-time code scheme is used with 2 transmit and 2 receiver antennas. The diversity technique is combined with chaotic modulation to improve the performance of the system. The performance of the proposed system is evaluated, and then the analytical BER expression is derived.

This study presents a spread-spectrum chaos-based communication system with polarisation diversity in a multipath channel. The propagation model takes into account the random direction angle of arrival and the polarisation orientation assigned to each version of the transmitted signal. To improve the performance of the proposed system, the receiver integrates monopoles with different orientations and no space diversity. Once the number of antennas is defined, many antenna positions are simulated, and then the optimal position is deduced to improve the performance of the system. To demodulate the received signal, a RAKE receiver is used for multi-antenna processing. An analysis is carried out leading to the analytical expression of the system bit error rate (BER). Simulation results show first that our system performance is improved with the use of this new receiver, and the perfect match observed between simulations and analytical BER expressions confirms the exactitude of our computation approach. Finally, the performance of our studied system is compared and discussed to that of a conventional spread-spectrum system using gold codes as spreading sequences.

In this paper, we propose a robust synchronization technique for an asynchronous spread spectrum communication system based on chaotic symbolic dynamics modulation. A back-ward iteration of the chaotic map is used to avoid the problem of sensitivity to initial conditions of the chaotic generator. The proposed system integrates a spread spectrum unit, which is used to increase transmission security and to achieve the transmission in a multi-user case. The synchronization technique is assessed in terms of probability of detection and of probability of false alarm. Simulation results prove that the proposed system can achieve phase synchronization with a low signal-to-noise ratio.

This paper analyzes the performance of chaotic communications in a MIMO system. The robustness of chaos-based communications systems makes Differential Chaos Shift Keying (DCSK) the preferred modulation choice. In order to improve the performance of such a system, the Alamouti space-time code is used for 2 transmit and 2 receive antennas. A new approach for computing the bit-error-rate (BER) performance is provided, and an analytical BER expression is derived. The approach used explores the dynamic properties of chaotic sequences and takes into account the fact that the bit energy varies from one transmitted bit to the next. Simulation results confirm the accuracy of this approach.

This paper carries out the first-ever study of the feasibility of using chaos shift keying (CSK) in a Multiple-Input, Multiple-Output (MIMO) channel. To that end, an Alamouti space time code scheme is combined with the CSK system for 2 transmit and 2 receiver antennas. Once the design of CSK-MIMO system is presented, the performance of the proposed system is analyzed in a mono-user case. An exact computation approach of the BER expression based on the probability density function of the bit energy of the chaotic sequence is presented. Simulation results prove the accuracy of our BER computation methodology.

In this paper, a new way to predict the performance of single user differential chaos shift keying communication system over an m-distributed fading channel is presented. Since the Gaussian approximation used to compute the performance leads to inaccurate results especially for low spreading factor, this new approach based on the chaos bit energy distribution gives accurate results. Computer simulations verify the precision of our approach to compute the performance of this chaos-based communication system.

In this paper, we propose a new approach to compute the exact bit error rate expression for asynchronous chaos-based DS-CDMA system over multipath channel. Perfect estimation of the channel coefficients with the associated delays and chaos synchronization are assumed. A simple RAKE receiver structure is considered. A stationary multipath channel with constant gain is assumed. An asynchronous multi-user chaos-based DS-CDMA system is then defined and the BER is derived in terms of the energy distribution, the number of paths, the noise variances, and the number of users.