With the growing demand for device-free and privacy-preserving sensing solutions, Wi-Fi sensing has emerged as a promising approach for human pose estimation (HPE). However, existing methods often process vast amounts of channel state information (CSI) data directly, ultimately straining networking resources. This paper introduces TinySense, an efficient compression framework that enhances the scalability of Wi-Fi-based human sensing. Our approach is based on a new vector quantization-based generative adversarial network (VQ-GAN). Specifically, by leveraging a VQGAN-learned codebook, TinySense significantly reduces CSI data while maintaining the accuracy required for reliable HPE. To optimize compression, we employ the K-means algorithm to dynamically adjust compression bitrates to cluster a large-scale pre-trained codebook into smaller subsets. Furthermore, a Transformer model is incorporated to mitigate bitrate loss, enhancing robustness in unreliable networking conditions. We prototype TinySense on an experimental testbed using Jetson Nano and Raspberry Pi to measure latency and network resource use. Extensive results demonstrate that TinySense significantly outperforms state-of-the-art compression schemes, achieving up to 1.5 × higher HPE accuracy score (PCK20) under the same compression rate. It also reduces latency and networking overhead, respectively, by up to 5× and 2.5×. The code repository is available online at https://github.com/icclabo/CloudSense.

The evolution from fifth-generation (5G) to sixth-generation (6G) networks is driving an unprecedented demand for advanced machine learning (ML) solutions. Deep learning has already demonstrated significant impact across mobile networking and communication systems, enabling intelligent services such as smart healthcare, smart grids, autonomous vehicles, aerial platforms, digital twins, and the metaverse. At the same time, the rapid proliferation of resource-constrained Internet-of-Things (IoT) devices has accelerated the adoption of tiny machine learning (TinyML) for efficient on-device intelligence, while large machine learning (LargeML) models continue to require substantial computational resources to support large-scale IoT services and ML-generated content. These trends highlight the need for a unified framework that integrates TinyML and LargeML to achieve seamless connectivity, scalable intelligence, and efficient resource management in future 6G systems. This survey provides a comprehensive review of recent advances enabling the integration of TinyML and LargeML in next-generation wireless networks. In particular, we(i)provide an overview of TinyML and LargeML,(ii)analyze the motivations and requirements for unifying these paradigms within the 6G context,(iii)examine efficient bidirectional integration approaches,(iv)review state-of-the-art solutions and their applicability to emerging 6G services, and(v)identify key challenges related to performance optimization, deployment feasibility, resource orchestration, and security. Finally, we outline promising research directions to guide the holistic integration of TinyML and LargeML for intelligent, scalable, and energy-efficient 6G networks and beyond.

Automatic modulation classification (AMC) is a critical task in modern communication systems, particularly under diverse signal conditions and limited data scenarios. Existing transformer-based AMC models often rely on single-stream architectures and uniform input formats, which limit their effectiveness in capturing rich signal features. To address these limitations, we propose DTNet, a novel transformer-based dual-stream network designed for efficient and accurate modulation classification. DTNet introduces two key innovations: (1) a scale feature and extension (SFE) block that applies a scaling function to transform signals into a structured output map, followed by an extension module that reconstructs the signal into a square matrix and integrates a response map using adaptive filters; and (2) a convolutional stream that extracts discriminative features from multi-scale signal representations. Furthermore, a modified feature embedding to leverage the transformer architecture is introduced to capture global dependencies and contextual information from the input signal, thereby enhancing the modulation classification accuracy. Experimental results show that DTNet achieves superior performance on benchmark datasets, reaching classification accuracies of 93.4% on RML2016.10A and 94.4% on RML2016.10B, outperforming state-of-the-art deep learning methods while maintaining lower computational complexity. The source code is available at https://github.com/daothanh2011/DTNet .

An intelligent Real-Time Sensing (RTS) system must continuously acquire, update, integrate, and apply knowledge to adapt to real-world dynamics. Managing distributed intelligence in this context requires Federated Continual Learning (FCL). However, effectively capturing the diverse characteristics of RTS data in FCL systems poses significant challenges, including severely impacting computational and communication resources, escalating energy costs, and ultimately degrading overall system performance. To overcome these challenges, we investigate how the data distribution shift from ideal to practical RTS scenarios affects Artificial Intelligence (AI) model performance by leveraging thegeneralization gapconcept. In this way, we can analyze how sampling time in RTS correlates with the decline in AI performance, computation cost, and communication efficiency. Based on this observation, we develop a novel Sample-driven Control for Federated Continual Learning (SCFL) technique, specifically designed for mobile edge networks with RTS capabilities. In particular, SCFL is an optimization problem that harnesses the sampling process to concurrently minimize the generalization gap and improve overall accuracy while upholding the energy efficiency of the FCL framework. To solve the highly complex and time-varying optimization problem, we introduce a new soft actor-critic algorithm with explicit and implicit constraints (A2C-EI). Our empirical experiments reveal that we can achieve higher efficiency compared to other DRL baselines. Notably, SCFL can significantly reduce energy consumption up to 85% while maintaining FL convergence and timely data transmission.

In real-world applications, Federated Learning (FL) faces two challenges: (1) scalability and (2) heterogeneous data. To address the first problem, we design a novel FL framework named Full-stack FL (F2L). More specifically, F2L provides a hierarchical network architecture, making extending the FL network accessible without reconstructing the whole network system. Moreover, leveraging the advantages of hierarchical network design, we propose a new Label-driven Knowledge Distillation (LKD) technique at the centralized server to address the second problem. Unlike the current knowledge distillation techniques, LKD is capable of training a student model, which consists of good knowledge from all teachers' models. Therefore, our proposed algorithm can effectively extract the knowledge of the regions' data distribution (i.e., the regional aggregated models) to reduce the divergence between clients' models when operating under the FL system with non-independent identically distributed data. Extensive experiment results reveal that: (i) our F2L method can significantly improve the overall FL efficiency in all global distillations (i.e., accuracy is$7-20\%$higher in non-IID settings), and (ii) F2L rapidly achieves convergence as global distillation stages occur instead of increasing on each communication cycle.

In this study, we address the gradient-based domain generalization problem, where predictors aim for consistent gradient directions across different domains. Existing methods have two main challenges. First, minimization of gradient empirical distance or gradient inner products (GIP) leads to gradient fluctuations among domains, thereby hindering straightforward learning. Second, the direct application of gradient learning to the joint loss function can incur high computation overheads due to second-order derivative approximation. To tackle these challenges, we propose a new Pareto Optimality Gradient Matching (POGM) method. In contrast to existing methods that add gradient matching as regularization, we leverage gradient trajectories as collected data and apply independent training at the meta-learner. In the meta-update, we maximize GIP while limiting the learned gradient from deviating too far from the empirical risk minimization gradient trajectory. By doing so, the aggregate gradient can incorporate knowledge from all domains without suffering gradient fluctuation towards any particular domain. Experimental evaluations on datasets from DomainBed demonstrate competitive results yielded by POGM against other baselines while achieving computational efficiency. The code is available at https://github.com/skydvn/POGM.

Neural networks unintentionally memorize training data, creating privacy risks in federated learning (FL) systems, such as inference and reconstruction attacks on sensitive data. To mitigate these risks and to comply with privacy regulations, Federated Unlearning (FU) has been introduced to enable participants in FL systems to remove their data’s influence from the global model. However, current FU methods primarily act post-hoc, struggling to efficiently erase information deeply memorized by neural networks. We argue that effective unlearning necessitates a paradigm shift: designing FL systems inherently amenable to forgetting. To this end, we propose a learning-to-unlearn Transformation-guided Federated Unlearning (ToFU) framework that incorporates transformations during the learning process to reduce memorization of specific instances. Our theoretical analysis reveals how transformation composition provably bounds instance-specific information, directly simplifying subsequent unlearning. Crucially, ToFU can work as a plug-and-play framework that improves the performance of existing FU methods. Experiments on CIFAR-10, CIFAR-100, and the MUFAC benchmark show that ToFU outperforms existing FU baselines, enhances performance when integrated with current methods, and reduces unlearning time.

This paper investigates the maximization of the achievable rate for users served by a high-altitude platform (HAP) acting as a flying base station in the downlink of rate-splitting multiple access (RSMA)-based terahertz (THz) communication systems. Considering the dynamic and uncertain environment caused by user mobility and molecular absorption effects, we propose a generative diffusion model (DM)-based deep reinforcement learning approach to address this challenge. The problem is formulated as a Markov decision process, aiming to maximize the long-term achievable rate for all users by jointly optimizing power allocation and the common rate splitting ratio. Moreover, the generative DM significantly improves the decision-making capabilities of a deep reinforcement learning algorithm, namely the deep deterministic policy gradient (DDPG). Experimental simulations demonstrate the effectiveness of the proposed DM-DDPG algorithm compared to alternative schemes.

Cross-silo federated learning (CFL) enables organizations (e.g., hospitals or banks) to collaboratively train artificial intelligence (AI) models while preserving data privacy by keeping data local. While prior work has primarily addressed statistical heterogeneity across organizations, a critical challenge arises from economic competition, where organizations may act as market rivals, making them hesitant to participate in joint training due to potential utility loss (i.e., reduced net benefit). Furthermore, the combined effects of statistical heterogeneity and inter-organizational competition on organizational behavior and system-wide social welfare remain underexplored. In this paper, we propose CoCoGen, a coopetitive-compatible data generation framework, leveraging generative AI (GenAI) and potential game theory to model, analyze, and optimize collaborative learning under heterogeneous and competitive settings. Specifically, CoCoGen characterizes competition and statistical heterogeneity through learning performance and utility-based formulations and models each training round as a weighted potential game. We then derive GenAI-based data generation strategies that maximize social welfare. Experimental results on the Fashion-MNIST dataset reveal how varying heterogeneity and competition levels affect organizational behavior and demonstrate that CoCoGen consistently outperforms baseline methods.

In this paper, we propose a Sustainable Sensing Federated Learning (S2FL) system where Internet-of-Things (IoT) devices and mobile users harvest energy wirelessly to perform data sensing, local Federated Learning (FL) training, and model update transmissions. We formulate a joint optimization problem that considers transmission power, CPU frequency, bandwidth allocation, and time allocation to maximize long-term learning efficiency. To solve this complex and dynamic problem, we develop an algorithm that integrates Deep Reinforcement Learning (DRL) with optimization techniques, leveraging the Deep Deterministic Policy Gradient (DDPG) algorithm for time allocation and a Lagrangian-Based Block Coordinate Descent (BCD) Method for per-slot resource optimization. Simulation results demonstrate that our proposed DDPG-based DRL-S2FL algorithm significantly outperforms benchmark schemes, achieving up to 15 % higher average reward compared to Deep Q-Networks (DQN) and 40 % greater performance than Random strategies. This work highlights the effectiveness of combining advanced optimization techniques with deep reinforcement learning to enhance federated learning performance in dynamic wireless environments.

Critical learning periods (CLPs) in federated learning (FL) represent early stages where low-quality contributions (e.g., sparse training data availability) can permanently impair learning outcomes. Yet, strategies to motivate clients with highquality contributions to join the model training process and share model updates during CLPs remain underexplored. Additionally, existing incentive mechanisms in FL treat all training periods equally, which consequently fails to motivate clients to participate early. In this paper, we propose a time-aware incentive mechanism, called R3T, to encourage client involvement, especially during CLPs in FL. We first characterize the cloud utility as a function of client's time and system capabilities, effort, joining time, and reward. Then, we analytically derive the optimal contract and devise a CLP-aware mechanism to incentivize early participation and efforts while maximizing cloud utility, even under information asymmetry. By providing the right reward at the right time, R3T can attract the highest-quality contributions during CLPs. Simulation studies show that R3T increases cloud utility and is more economically effective than benchmarks.

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) introduces revolutionary capabilities by reaching full space coverage for wireless signals, significantly enhancing the efficiency and reliability of Internet of Things (IoT) networks compared to traditional RIS. In this article, we propose a novel framework that leverages STAR-RIS into wirelessly powered federated learning (FL) networks with a multiantenna access point, aiming to minimize system latency. A multivariable nonconvex optimization problem is formulated to optimize phase shift vectors of STAR-RIS, beamforming matrices, time, power, and computation frequency for each user in all phases of FL. Block coordinate descent (BCD) over the combination of an 1-D search algorithm and interior point method is employed to optimize time, power, computation frequency, phase shift vectors of STAR-RIS, and active beamforming matrix in the uplink transmission phase, while semi-definite relaxation via BCD addresses phase shift vectors of STAR-RIS and beamforming matrices optimization in harvesting and downlink transmission phases. On this basis, the optimized downlink transmission time and power are derived. The convergence of the proposed algorithm and the superiority of its performance compared to benchmark schemes are validated through comprehensive simulations. Our findings indicate the potential of FL, multiantenna aggregation server, and STAR-RIS in ushering in a new era of intelligent and efficient IoT networks.

In wireless communication networks, it is difficult to solve many NP-hard problems owing to computational complexity and high cost. Recently, quantum annealing (QA) based on quantum physics was introduced as a key enabler for solving optimization problems quickly. However, only some studies consider quantum-based approaches in wireless communications. Therefore, we investigate the performance of a QA solution to an optimization problem in wireless networks. Specifically, we aim to maximize the sum rate by jointly optimizing clustering, subchannel assignment, and power allocation in a multiautonomous aerial vehicle-aided wireless network. We formulate the sum rate maximization problem as a combinatorial optimization problem. Then, we divide it into two subproblems: 1) a QA-based clustering and 2) subchannel assignment and power allocation for a given clustering configuration. Subsequently, we obtain an optimized solution for the joint optimization problem by solving these two subproblems. For the first subproblem, we convert the problem into a simplified quadratic unconstrained binary optimization (QUBO) model. As for the second subproblem, we introduce a novel QA algorithm with optimal scaling parameters to address it. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of the sum rate and running time.

This work proposes a novel privacy-preserving cyberattack detection framework for blockchain-based Internet of Things (IoT) systems. In our approach, artificial intelligence (AI)-driven detection modules are strategically deployed at blockchain nodes (BNs) to identify real-time attacks, ensuring high accuracy and minimal delay. To achieve this efficiency, the model training is conducted by a cloud service provider (CSP). Accordingly, BNs send their data to the CSP for training, but to safeguard privacy, the data is encrypted using homomorphic encryption (HE) before transmission. This encryption method allows the CSP to perform computations directly on encrypted data without the need for decryption, preserving data privacy throughout the learning process. To handle the substantial volume of encrypted data, we introduce an innovative packing algorithm in a single-instruction-multiple-data (SIMD) manner, enabling efficient training on HE-encrypted data. Building on this, we develop a novel deep neural network training algorithm optimized for encrypted data. We further propose a privacy-preserving distributed learning approach based on the FedAvg algorithm, which parallelizes the training across multiple workers, significantly improving computation time. Upon completion, the CSP distributes the trained model to the BNs, enabling them to perform real-time, privacy-preserved detection. Our simulation results demonstrate that our proposed method can not only mitigate the training time but also achieve detection accuracy that is approximately identical to the approach without encryption, with a gap of around 0.01%. Additionally, our real implementations on various blockchain consensus algorithms and hardware configurations show that our proposed framework can also be effectively adapted to real-world systems.

The success of artificial intelligence (AI) in multiple disciplines and vertical domains in recent years has promoted the evolution of mobile networking and the future Internet toward an AI-integrated Internet of Things (IoT) era. Nevertheless, most AI techniques rely on data generated by physical devices (e.g., mobile devices and network nodes) or specific applications (e.g., fitness trackers and mobile gaming). Therefore, generative AI (GAI), a.k.a. AI-generated content (AIGC), has emerged as a powerful AI paradigm; thanks to its ability to efficiently learn complex data distributions and generate synthetic data to represent the original data in various forms. This impressive feature is projected to transform the management of mobile networking and diversify the current services and applications provided. On this basis, this work presents a concise tutorial on the role of GAIs in mobile and wireless networking. In particular, this survey first provides the fundamentals of GAI and representative GAI models, serving as an essential preliminary to the understanding of GAI’s applications in mobile and wireless networking. Then, this work provides a comprehensive review of state-of-the-art studies and GAI applications in network management, wireless security, semantic communication, and lessons learned from the open literature. Finally, this work summarizes the current research on GAI for mobile and wireless networking by outlining important challenges that need to be resolved to facilitate the development and applicability of GAI in this edge-cutting area.

With the massive deployment of Internet of Things (IoT) devices in sixth-generation networks, several critical challenges have emerged, such as large communication overhead, coverage limitations, and limited battery lifespan due to high energy consumption. Federated learning (FL), wireless power transfer (WPT), multi-antenna access point (AP), and reconfigurable intelligent surfaces (RIS) can mitigate these challenges by reducing the need for large data transmissions, enabling sustainable energy harvesting, and optimizing the propagation environment. Compared to conventional RIS, simultaneously transmitting and reflecting (STAR)-RIS not only extends coverage from half-space to full-space but also improves energy saving through appropriate mode selection. Motivated by the need for sustainable, low-latency, and energy-efficient communication in large-scale IoT networks, this paper investigates the efficient STAR-RIS mode in the uplink and downlink phases of a WPT-FL multi-antenna AP network with non-orthogonal multiple access to minimize energy consumption, a joint optimization that remains largely unexplored in existing works on RIS or STAR-RIS. We formulate a non-convex energy minimization problem for different STAR-RIS modes, i.e., energy splitting (ES) and time switching (TS), in both uplink and downlink transmission phases, where STAR-RIS phase shift vectors, beamforming matrices, time and power for harvesting, uplink transmission, and downlink transmission, local processing time, and computation frequency for each user are jointly optimized. To tackle the non-convexity, the problem is decoupled into two subproblems: the first subproblem optimizes STAR-RIS phase shift vectors and beamforming matrices across all WPT-FL phases using block coordinate descent over either semi-definite programming or Rayleigh quotient problems, while the second one allocates time, power, and computation frequency via the one-dimensional search algorithms or the bisection algorithm. Simulation results demonstrate that TS STAR-RIS in both uplink and downlink transmissions achieves the lowest energy consumption, outperforming ES and conventional RIS schemes due to its flexible phase shift adaptation and lower interference levels.

This paper comprehensively investigates the performance of downlink multi-user rate-splitting multiple access (RSMA) networks under Nakagami-m fading channels. We first develop the mathematical outage probability (OP) and ergodic capacity (EC) frameworks, deriving exact expressions for both, along with asymptotic analysis in high signal-to-noise ratio (SNR) and low-rate regions, which serve as the foundation for deducing the approximate and maximal energy-reliability and energy-spectral formulas. To enhance system performance, we tackle the non-convex problems of jointly optimizing common and private power allocation (PA) coefficients to minimize the maximal OP performance. Moreover, we also delve into optimizing PA coefficients and rate-splitting factors concurrently to maximize the ergodic sum capacity (ESC). Addressing the influence of modulation schemes on user error rates, we introduce mathematical frameworks for symbol error rate (SER) considering four combined modulation schemes based on binary phase-shift keying (BPSK) and quadrature-phase shift keying (QPSK), the whole cases are quantified in terms of exact and asymptotic manners. Furthermore, we present a straightforward approach to optimize the PA coefficients to minimize the maximal SER performance. Finally, Monte-Carlo simulations are presented to validate our developed frameworks and optimization solutions.

Owing to flexible management and low overhead, wireless physical layer security (PLS) has been applied to support many critical applications (e.g., data dissemination) of unmanned aerial vehicles (UAVs)-based mobile communication networks in emergency scenarios. Although the impact of static network scenarios or receiver mobility on PLS has been well studied, there is no much work that studies the impact of UAV-based transmitter mobility on PLS. To fill this gap, in this paper, we investigate PLS of a scenario, where a random mobile UAV-based transmitter transmits information to a static ground entity under Rayleigh fading channel. More specifically, we consider a communication system, in which a mobile UAV hovers over a region to collect information and then disseminates this information to a static ground network entity in a confidential manner under the presence of an eavesdropper. Because of popularity and practicality, the UAV is assumed to hover following the random way point (RWP) mobility model. We investigate the secrecy characteristics of the UAV under steady running state in terms of ergodic secrecy capacity (ESC), positive secrecy capacity probability (PSCP) and secrecy outage probability (SOP) for the communication between the UAV and the receiver. We then investigate the secrecy performance of the proposed system while considering the pause time of the RWP mobility model adopted by the UAV. We further extend our proposed theoretical model to other realistic scenarios, including the presence of multiple cooperative and non-cooperative eavesdroppers, and study the PSCP and SOP metrics of the corresponding system. Furthermore, we propose three types of secrecy improvement strategies for the considered communication model. We strike a good trade-off between the secrecy improvement and transmit outage probability. Extensive simulations have been conducted to validate our theoretical analysis as well as the effectiveness of the proposed secrecy improvement strategies.

Recent research efforts on Semantic Communication (SemCom) have mostly considered accuracy as a main problem for optimizing goal-oriented communication systems. However, these approaches introduce a paradox: the accuracy of Artificial Intelligence (AI) tasks should naturally emerge through training rather than being dictated by network constraints. Acknowledging this dilemma, this work introduces an innovative approach that leverages the rate distortion theory to analyze distortions induced by communication and compression, thereby analyzing the learning process. Specifically, we examine the distribution shift between the original data and the distorted data, thus assessing its impact on the AI model's performance. Founding upon this analysis, we can preemptively estimate the empirical accuracy of AI tasks, making the goal-oriented SemCom problem feasible. To achieve this objective, we present the theoretical foundation of our approach, accompanied by simulations and experiments that demonstrate its effectiveness. The experimental results indicate that our proposed method enables accurate AI task performance while adhering to network constraints, establishing it as a valuable contribution to the field of signal processing. Furthermore, this work advances research in goal-oriented SemCom and highlights the significance of data-driven approaches in optimizing the performance of intelligent systems.

In this paper, we introduce a novel uplink semanticaware wireless communication system, catering to multiple users by leveraging a shared probability graph between an access point (AP) and a base station (BS). Under imperfect channel state information (CSI), all users transmit data information to the AP through conventional bit transmission and the lower bound of signal estimation error is derived. An edge server equipped with the AP and a cloud server equipped with the BS execute information compression and recovery based on the shared probability graph, respectively. While semantic information compression incurs computational resource consumption, it significantly reduces communication resource usage. This paper addresses the challenge of minimizing system latency through jointly optimizing communication resource allocation, channel truncation threshold, and data compression ratio, considering limited wireless resources, signal estimation error, and the system’s energy budget. To solve the formulated non-convex time minimization problem, we decompose the optimization problem into four subproblems and solve each of them iteratively. In particular, the power allocation subproblem is transformed into a convex time allocation optimization problem. The bandwidth allocation subproblem is proven to be convex. The optimal channel truncation threshold is obtained through a bisection search. The data compression ratio optimization subproblem is a non-convex integer programming problem solved by the Gurobi optimizer. Numerical results show the effectiveness of the proposed algorithm, validating the necessity to consider imperfect CSI and semantic transmission and the superior performance of semantic communication compared to conventional bit transmission.

This paper considers the scenario where multiple Internet-of-Things (IoT) devices collaborate to train a distributed model using federated learning. Wireless power transfer (WPT) is employed to address the issue of limited battery life of IoT devices, while non-orthogonal multiple access (NOMA) is utilized to facilitate data transmission. Besides, an intelligent reflecting surface (IRS) is applied to assist both energy transfer and data transmission. On this basis, a joint resource allocation problem is formulated to minimize the total energy consumption for the considered IRS-aided FL-WPT networks with NOMA. The non-convex problem is first solved by developing a combination of semi-definite programming relaxation (SDR) with a two-dimensional search algorithm. To lower the computational complexity, SDR with a bisection algorithm is further employed by exploiting the inherent structure of the formulated problem. Numerical results not only validate the equivalence of these two algorithms in performance but also unequivocally establish the superior efficiency of the proposed method over benchmark schemes in terms of energy consumption.

The unmanned aerial vehicle (UAV) swarms have shown great potential to serve next-generation communication networks with their extraordinary flexibility, affordability, and the ability to collaboratively and autonomously provide Line-of-Sight (LoS) services. However, autonomous collaboration under wireless dynamics is challenging. Distributed learning (DL) provides a chance for the UAV swarms to operate intelligently under sophisticated dynamics, such that they can be applied to wireless communication service scenarios, as well as applications including multidirectional remote surveillance, and target tracking. In this survey, we first introduce several popular DL frameworks that are capable of managing a UAV swarm, these include federated learning (FL), multiagent reinforcement learning (MARL), distributed inference (DI), and split learning (SL). We also present a comprehensive overview of how these DL frameworks manage UAV swarms in regard to trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite–drone integration. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such as reconfigurable intelligent surfaces (RISs), virtual reality (VR), and semantic communications (SemComs), and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL-enabled UAV swarms. In summary, this survey provides a concise survey of various DL applications for UAV swarms in extensive scenarios.

Satellite Internet of Things (IoT) network plays a crucial role in providing global coverage. To improve the efficient utilization of spectral and hardware resources in the satellite communications, integrated sensing and communication (ISAC) has attracted considerable attention. In addition, the greater broadcast nature of the satellite-terrestrial integrated network makes it extremely vulnerable to illegal eavesdropper (Eve). In this paper, we adopt non-orthogonal multiple access (NOMA) to support more users for the ISAC of a low earth orbit satellite system, with well-designed precoding according to whether the channel state information (CSI) of the Eve is perfect to ensure security. First, a joint precoding optimization problem is proposed to maximize the sum secrecy rate (SSR) of multiple users by considering the perfect CSI of the Eve. Then, we formulate a secure precoding optimization problem based on the imperfect CSI of the Eve, aiming to maximize the SSR of multiple users via artificial jamming. To address the non-convexity of the optimization problems, we transform them into the convex ones based on successive convex approximation, which includes Taylor’s approximation, arithmetic-geometric mean inequality, and linear matrix inequality. In addition, semi-definite relaxation and iterative penalty function methods are respectively used to optimize the secure precoding problems in the two cases: perfect CSI and imperfect CSI. Simulation results show that the proposed NOMA-ISAC scheme improves the SSR compared to the traditional time division multiple access while ensuring the sensing performance.

Federated learning (FL) has found many successes in wireless communications; however, the implementation of FL has been hindered by the energy limitation of mobile devices (MDs) and the availability of training data at MDs. Wireless power transfer (WPT) and mobile crowdsensing (MCS) are promising technologies that can be leveraged to power energy-limited MDs and acquire data for learning tasks. How to integrate WPT and MCS towards sustainable FL solutions is a research topic entirely missing from the open literature. This work for the first time investigates a resource allocation problem in collaborative sensing-assisted sustainable FL (S2FL) networks with the goal of minimizing the total completion time. In particular, we investigate a practical harvesting-sensing-training-transmitting protocol in which energy-limited MDs first harvest energy from RF signals, use it to gain a reward for user participation, sense the training data from the environment, train the local models at MDs, and transmit the model updates to the edge server. The total completion time minimization problem of jointly optimizing power transfer, transmit power allocation, data sensing, bandwidth allocation, local model training, and data transmission is complicated due to the non-convex objective function, highly non-convex constraints, and strongly coupled variables. In order to solve that problem, we apply the decomposition technique and develop a computationally-efficient path-following algorithm to obtain the solution. In particular, inner convex approximations are developed for the resource allocation subproblem, and the subproblems are performed alternatively in an iterative fashion. Simulation results are provided to evaluate the effectiveness of the proposed S2FL algorithm in reducing the completion time up to 21.45% in comparison with other benchmark schemes. Further, we investigate an extension of our work from frequency division multiple access (FDMA) to non-orthogonal multiple access (NOMA) and show that NOMA can speed up the total completion time 8.36% on average of the considered FL system.

This paper proposes a reconfigurable intelligent surface-aided two-way full-duplex communication with non-orthogonal multiple access transmission schemes to improve spectrum utilization. Besides, the joint impact of error phase-shift quantization, imperfect successive interference cancellation, and residual loop interference on the system performance have also been investigated. Under Nakagami-m fading channels, approximate closed-form expressions for the outage performance and the ergodic rate are derived. Through asymptotic analyses, some insights are achieved, including the diversity order, the coding gain, and the ergodic slope. Moreover, three adaptive power allocation optimization problems are formulated aiming to: 1) minimize outage probability, 2) achieve max-min rate fairness, and 3) maximize the user’s sum rate subject to the quality-of-service constraint. Simulation results not only validate the theoretical analyses and the optimal/sub-optimal solutions but also reveal three following observations. First, the considered system outperforms the orthogonal multiple access baseline. Second, increasing the number of control bits for the error phase-shift quantization and/or the number of RIS’s elements can significantly reduce the impact of imperfect successive interference cancellations. Third, employing one of three adaptive power allocation solutions improves the system’s performance significantly.

This paper investigates the performance of downlink rate-splitting multiple access (RSMA)-aided unmanned aerial vehicle (UAV) communication systems, wherein a multi-antenna UAV exploits RSMA to serve multiple ground users. Considering nonline-of-sight environments, double-shadowed scattering channel modeling is adopted to generically characterize the impacts of mobility and shadowing on UAV-to-ground communications, assuming imperfect successive interference cancellation (SIC). Besides, a unified precoder design is proposed to fully capture the benefits of multi-antenna paradigms. Closed-form expressions for the users' outage probability (OP) and ergodic capacity are derived. In addition, asymptotic analysis is carried out to get further insights into the system design, such as the diversity gain and ergodic slope. Numerical results are presented, and it is shown that: 1) the effects of double-shadowed scattering on the system outage performance can be significantly reduced by increasing the number of antennas installed at the UAV; 2) the imperfect SIC error can be minimized by properly optimizing the power allocation of the common stream; and 3) RSMA provides superior users' ergodic capacity compared to its orthogonal and non-orthogonal multiple access counterparts.

In this paper, we present a probability graph-based semantic information compression system for scenarios where the base station (BS) and the user share common background knowledge. We employ probability graphs to represent the shared knowledge between the communicating parties. During the transmission of specific text data, the BS first extracts semantic information from the text, which is represented by a knowledge graph. Subsequently, the BS omits certain relational information based on the shared probability graph to reduce the data size. Upon receiving the compressed semantic data, the user can automatically restore missing information using the shared probability graph and predefined rules. This approach brings additional computational resource consumption while effectively reducing communication resource consumption. Considering the limitations of wireless resources, we address the problem of joint communication and computation resource allocation design, aiming at minimizing the total communication and computation energy consumption of the network while adhering to latency, transmit power, and semantic constraints. Simulation results demonstrate the effectiveness of the proposed system.

Since Facebook officially changed its name to Meta in Oct. 2021, the metaverse has become a new norm of social networks and three-dimensional (3D) virtual worlds. The metaverse aims to bring 3D immersive and personalized experiences to users by leveraging many pertinent technologies. Despite great attention and benefits, a natural question in the metaverse is how to secure its users' digital content and data. In this regard, blockchain is a promising solution owing to its distinct features of decentralization, immutability, and transparency. To better understand the role of blockchain in the metaverse, we aim to provide an extensive survey on the applications of blockchain for the metaverse. We first present a preliminary to blockchain and the metaverse and highlight the motivations behind the use of blockchain for the metaverse. Next, we extensively discuss blockchain-based methods for the metaverse from technical perspectives, such as data acquisition, data storage, data sharing, data interoperability, and data privacy preservation. For each perspective, we first discuss the technical challenges of the metaverse and then highlight how blockchain can help. Moreover, we investigate the impact of blockchain on key-enabling technologies in the metaverse, including Internet-of-Things, digital twins, multi-sensory and immersive applications, artificial intelligence, and big data. We also present some major projects to showcase the role of blockchain in metaverse applications and services. Finally, we present some promising directions to drive further research innovations and developments toward the use of blockchain in the metaverse in the future.

Recent technological advancements have considerably improved healthcare systems to provide various intelligent services, improving life quality. The Metaverse, often described as the next evolution of the Internet, helps the users interact with each other and the environment, thus offering a seamless connection between the virtual and physical worlds. Additionally, the Metaverse, by integrating emerging technologies, such as artificial intelligence (AI), cloud edge computing, Internet of Things (IoT), blockchain, and semantic communications, can potentially transform many vertical domains in general and the healthcare sector (healthcare Metaverse) in particular. The healthcare Metaverse holds huge potential to revolutionize the development of intelligent healthcare systems, thus presenting new opportunities for significant advancements in healthcare delivery, personalized healthcare experiences, medical education, collaborative research, and so on. However, various challenges are associated with the realization of the healthcare Metaverse, such as privacy, interoperability, data management, and security. Federated learning (FL), a new branch of AI, opens up enormous opportunities to deal with the aforementioned challenges in the healthcare Metaverse by exploiting the data and computing resources available at the distributed devices. This motivated us to present a survey on adopting FL for the healthcare Metaverse. Initially, we present the preliminaries of IoT-based healthcare systems, FL in conventional healthcare, and the healthcare Metaverse. Furthermore, the benefits of the FL in the healthcare Metaverse are discussed. Subsequently, we discuss the several applications of FL-enabled healthcare Metaverse, including medical diagnosis, patient monitoring, medical education, infectious disease, and drug discovery. Finally, we highlight the significant challenges and potential solutions toward realizing FL in the healthcare Metaverse.

Integrated communications and sensing (ICS) has recently emerged as an enabling technology for ubiquitous sensing and IoT applications. For ICS application to autonomous vehicles (AVs), optimizing the waveform structure is one of the most challenging tasks due to strong influences between sensing and data communication functions. Specifically, the preamble of a data communication frame is typically leveraged for the sensing function. As such, the higher number of preambles in a coherent processing interval (CPI) is, the greater sensing task’s performance is. In contrast, communication efficiency is inversely proportional to the number of preambles. Moreover, surrounding radio environments are usually dynamic with high uncertainties due to their high mobility, making the ICS’s waveform optimization problem even more challenging. To that end, this article develops a novel ICS framework established on the Markov decision process and recent advanced techniques in deep reinforcement learning. By doing so, without requiring complete knowledge of the surrounding environment in advance, the ICS-AV can adaptively optimize its waveform structure (i.e., number of frames in the CPI) to maximize sensing and data communication performance under the surrounding environment’s dynamic and uncertainty. Extensive simulations show that our proposed approach can improve the joint communication and sensing performance up to 46.26% compared with other baseline methods.

Nonorthogonal multiple access (NOMA) has been viewed as a potential candidate for the upcoming generation of wireless communication systems. Comparing to traditional orthogonal multiple access (OMA), multiplexing users in the same time-frequency resource block can increase the number of served users and improve the efficiency of the systems in terms of spectral efficiency. Nevertheless, from a security viewpoint, when multiple users are utilizing the same time-frequency resource, there may be concerns regarding keeping information confidential. In this context, physical layer security (PLS) has been introduced as a supplement of protection to conventional encryption techniques by making use of the random nature of wireless transmission media for ensuring communication secrecy. The recent years have seen significant interests in PLS being applied to NOMA networks. Numerous scenarios have been investigated to assess the security of NOMA systems, including when active and passive eavesdroppers are present, as well as when these systems, are combined with relay and reconfigurable intelligent surfaces (RISs). Additionally, the security of the ambient backscatter (AmB)-NOMA systems are other issues that have lately drawn a lot of attention. In this article, a thorough analysis of the PLS-assisted NOMA systems research state-of-the-art is presented. In this regard, we begin by outlining the foundations of NOMA and PLS, respectively. Following that, we discuss the PLS performances for NOMA systems in four categories depending on the type of the eavesdropper, the existence of relay, RIS, and AmB systems in different conditions. Finally, a thorough explanation of the most recent PLS-assisted NOMA systems is given.

The Internet of Things (IoT) is a major driving force for future sixth-generation (6G) wireless systems. With the emergence of various novel IoT applications, more data should be collected and transmitted. However, IoT devices are constrained by battery, transmit power, and processing capacity. Featured by line-of-sight communication links, favorable channels, and better coverage, aerial access networks have been proposed to facilitate data transmission from IoT devices. In parallel, by shifting the computing and storage resources from the cloud to the edge of the network, edge computing [e.g., fog and mobile-edge computing (MEC)] can better support various computing-intensive and low-latency IoT applications. The integration of aerial access networks and edge computing, so-called aerial computing, is anticipated to provide not only traditional communication services but also advanced services for the IoT on a global scale.

Federated learning (FL) is a new artificial intelligence concept that enables Internet-of-Things (IoT) devices to learn a collaborative model without sending the raw data to centralized nodes for processing. Despite numerous advantages, low computing resources at IoT devices and high communication costs for exchanging model parameters make applications of FL in massive IoT networks very limited. In this work, we develop a novel compression scheme for FL, calledhigh-compression federated learning (HCFL), for very large scale IoT networks. HCFL can reduce the data load for FL processes without changing their structure and hyperparameters. In this way, we not only can significantly reduce communication costs, but also make intensive learning processes more adaptable on low-computing resource IoT devices. Furthermore, we investigate a relationship between the number of IoT devices and the convergence level of the FL model and thereby better assess the quality of the FL process. We demonstrate our HCFL scheme in both simulations and mathematical analyses. Our proposed theoretical research can be used as a minimum level of satisfaction, proving that the FL process can achieve good performance when a determined configuration is met. Therefore, we show that HCFL is applicable in any FL-integrated networks with numerous IoT devices.

This work proposes a novel framework to address straggling and privacy issues for federated learning (FL)-based mobile application services, considering limited computing/communications resources at mobile users (MUs)/mobile application provider (MAP), privacy cost, the rationality and incentive competition among MUs in contributing data to the MAP. Particularly, the MAP first determines a set of the best MUs for the FL process based on MUs' provided information/features. Then, each selected MU can encrypt part of local data and upload the encrypted data to the MAP for an encrypted training process, in addition to the local training process. For that, the selected MU can propose a contract to the MAP according to its expected local and encrypted data. To find optimal contracts that can maximize utilities while maintaining high learning quality of the system, we develop a multi-principal one-agent contract-based problem considering the MUs' privacy cost, the MAP's limited computing resources, and asymmetric information between the MAP and MUs. Experiments with a real-world dataset show that our framework can speed up training time up to 49% and improve prediction accuracy up to 4.6 times while enhancing network's social welfare up to 114% under the privacy cost consideration compared with those of baseline methods.

The recent expansion of mobile IoT devices (MIoTDs) along with the exposure of many compute-intensive and latency-critical applications, have given a step rise to the mobile edge computing (MEC) platform to process computational microservices at the edge. The paramount importance of designing an effective incentive mechanism is a very important topic for such systems to get a fair amount of resources and provide incentives to MIoDs. Hence, we design a MEC platform with heterogeneous MIoTDs participating in a computational microservice offloading scheme. Here, we propose an incentive approach applying a double auction mechanism to incentivize the involvement of MIoTDs. In practice, the incentive mechanism typically interacts with the demand estimation scheme that estimates the demand profile of MIoTDs. As a result, we design a novel mechanism for microservices –microservice Incentive Service Offloading (mISO), which comprises an incentive approach and a demand estimation scheme. The mISO mechanism holds truthfulness, rationality, and low computational complexity while guaranteeing positive social welfare and generating the optimal demand profiles for MIoTDs. Simulation results showed that mISO provides 18–21$\%$and 25–30$\%$improvements in terms of average latency and resource utilization compared to existing works.

Federated learning (FL) has found numerous applications in wireless and mobile networks thanks to its distinctive features. However, efficient FL networks require to address the energy limitation of FL users. Exploited the flexible deployment and agile mobility of unmanned aerial vehicles (UAVs), this work proposes to dispatch the UAV with edge computing capabilities as an aerial energy source to wirelessly power FL users and as an aerial server for model aggregation. In this regard, we investigate a resource allocation problem that minimizes the energy consumption of FL users and the aerial server. To resolve the nonconvexity of the formulated problem, we propose to decompose the entire set of variables into three blocks, and then develop an iterative algorithm. Simulations results are presented to show that our proposed algorithm significantly outperforms several benchmarks.

In this paper, we propose a deep learning (DL)-based method to automatically identify the modulations of orthogonal frequency-division multiplexing (OFDM) signals in wireless communication systems. In particular, a cost-efficient OFDM modulation classification convolutional neural network (COM-ConvNet) is principally designed with grouped convolutional layers to reduce computing complexity significantly. Remarkably, reconstructing the high-dimensional data array of OFDM signals allows our deep network to learn the underlying sample correlations within every symbol and among different symbols sufficiently. We leverage residual connection and attention connection with element-wise addition and element-wise multiplication layers in specific-designed processing blocks to enhance the pattern learning efficiency. For performance evaluation, we test the proposed method on a synthetic six-modulation OFDM signal dataset under impaired channel conditions and conduct diverse simulations, such as ablation study, parameter investigation, and complexity analysis. COM-ConvNet achieves cost efficiency (i.e., small network size and low computational cost) while maintaining an acceptable accuracy when compared with other DL models.

This paper is an influential attempt to identify and alleviate some of the issues with the recently proposed optimization technique called the Marine Predator Algorithm (MPA). With a visual investigation of its exploratory and exploitative behavior, it is observed that the transition of search from being global to local can be further improved. As an extremely cost-effective method, a set of nonlinear functions is used to change the search patterns of the MPA algorithm. The proposed algorithm, called Nonlinear Marin Predator Algorithm (NMPA), is tested on a set of benchmark functions. A comprehensive comparative study shows the superiority of the proposed method compared to the original MPA and even other recent meta-heuristics. The paper also considers solving a real-world case study around power allocation in non-orthogonal multiple access (NOMA) and visible light communications (VLC) for Beyond 5G (B5G) networks to showcase the applicability of the NMPA algorithm. NMPA algorithm also shows its superiority in solving a wide range of benchmark functions as well as obtaining fair power allocation for multiple users in NOMA-VLC-B5G systems compared with the state-of-the-art algorithms.1

This article proposes a cooperative friendly jamming framework for swarm unmanned aerial vehicle (UAV)-assisted amplify-and-forward (AF) relaying networks with wireless energy harvesting (EH). In particular, we consider a swarm of hovering UAVs that relays information from a terrestrial base station to a distant mobile user and simultaneously generates friendly jamming signals to interfere/obfuscate an eavesdropper. Due to the limited energy of the UAVs, we develop a collaborative time-switching relaying protocol that allows the UAVs to collaborate in harvesting wireless energy, relay information, and jam the eavesdropper. To evaluate the performance, we derive the secrecy outage probability (SOP) for two popular detection techniques at the eavesdropper, i.e., selection combining and maximum-ratio combining. Monte Carlo simulations are then used to validate the theoretical SOP derivation. Using the derived SOP, one can obtain engineering insights to optimize the EH time and the number of UAVs in the swarm to achieve a given secrecy protection level. Furthermore, simulations show the effectiveness of the proposed framework in terms of SOP compared to the conventional AF relaying system. The analytical SOP derived in this work can also be helpful in future UAV secure-communication optimizations (e.g., trajectory and locations of UAVs). As an example, we present a case study to find the optimal corridor to locate the swarm so as to minimize the system SOP. Our proposed framework helps secure communications for various applications that require large coverage, e.g., industrial IoT, smart city, intelligent transportation systems, and critical IoT infrastructures, such as energy and water.

In recent years, blockchain networks have attracted significant attention in many research areas beyond cryptocurrency, one of them being the Edge of Things (EoT) that is enabled by the combination of edge computing and the Internet of Things (IoT). In this context, blockchain networks enabled with unique features, such as decentralization, immutability, and traceability, have the potential to reshape and transform the conventional EoT systems with higher security levels. Particularly, the convergence of blockchain and EoT leads to a new paradigm, calledBEoTthat has been regarded as a promising enabler for future services and applications. In this article, we present a state-of-the-art review of recent developments in the BEoT technology and discover its great opportunities in many application domains. We start our survey by providing an updated introduction to blockchain and EoT along with their recent advances. Subsequently, we discuss the use of BEoT in a wide range of industrial applications, from smart transportation, smart city, smart healthcare to smart home, and smart grid. Security challenges in the BEoT paradigm are also discussed and analyzed, with some key services, such as access authentication, data privacy preservation, attack detection, and trust management. Finally, some key research challenges and future directions are also highlighted to instigate further research in this promising area.

In existing computing systems, such as edge computing and cloud computing, several emerging applications and practical scenarios are mostly unavailable or only partially implemented. To overcome the limitations that restrict such applications, the development of a comprehensive computing paradigm has garnered attention in both academia and industry. However, a gap exists in the literature, owing to the scarce research, and a comprehensive computing paradigm is yet to be systematically designed and reviewed. This study introduces a novel concept, calledaerial computing, via the amalgamation of aerial radio access networks and edge computing, which attempts to bridge the gap. Specifically, first, we propose a novel comprehensive computing architecture that is composed of low-altitude computing (LAC), high-altitude computing (HAC), and satellite computing platforms, along with conventional computing systems. We determine that aerial computing offers several desirable attributes: global computing service, better mobility, higher scalability and availability, and simultaneity. Second, we comprehensively discuss key technologies that facilitate aerial computing, including energy refilling, edge computing, network softwarization, frequency spectrum, multiaccess techniques, artificial intelligence, and big data. In addition, we discuss vertical domain applications (e.g., smart cities, smart vehicles, smart factories, and smart grids) supported by aerial computing. Finally, we highlight several challenges that need to be addressed and their possible solutions.

With the increasing demand for security in many sectors, such as defense and health systems, developing secure Internet of Things (IoT) networks is a matter of great urgency. Looking at a potential solution for secure IoT systems, we investigate the physical layer security of cooperative nonorthogonal multiple access (NOMA) systems. After decoding information signal, the idea that a strong IoT node can serve as a relay node for other weak IoT nodes in enhancing their signal reception reliability, is known as cooperative NOMA. We consider both single-antenna and multiantenna aided transmission scenarios, where the base station (BS) communicates with two IoT nodes of different strengths. In the multiantenna scenario, artificial noise (AN) is generated at the BS and the strong IoT node for improving the security of the system. In order to characterize the secrecy performance, we derive new exact expressions of the security outage probability for both the IoT nodes under both the single-antenna and multiantenna aided scenarios. For the single-antenna scenario, we show that the power optimization at the BS and the strong IoT node can enhance the secrecy performance to some extent. For this case, we further study the secrecy diversity order of the overall system, which is mainly determined by the IoT node with the worse channel condition. For the multiantenna scenario, we derive the asymptotic secrecy outage probability (SOP) when the number of antennas tends to infinity. Extensive simulations have been conducted to verify the accuracy and effectiveness of the proposed analytical derivations. The presented results verify that the security performance of the cooperative NOMA-based IoT network can be improved through an appropriate power control scheme and by generating AN at the BS and the strong IoT node. The simulation results further illustrate that the asymptotic SOP is close to the exact one.

Unmanned aerial vehicles (UAVs) are one of the effective means to provide emergency communication services in post-disaster areas. In this article, we consider data dissemination in post-disaster areas, where all Internet of Things (IoT) nodes may not have data needs all the time. The energy consumption in data dissemination is one of the key metrics to pay attention to since the charging facilities for UAVs may be limited due to the destruction of existing infrastructure. In addition, UAVs have limited endurance or lifetime, so unnecessarily flying over IoT nodes that may not have data is time consuming. Therefore, given the energy budget and data requirements of the IoT nodes, we formulated a data dissemination problem using multiple UAVs in a post-disaster area while optimizing their trajectory, mission completion time, and energy consumption. After time discretization, the formulated problem is a mixed-integer nonconvex problem and thus difficult to solve in general. For this reason, we jointly use the bisection search technique and the block coordinate descent (BCD) method to solve the entire problem while aiming to optimize the trajectory and mission completion time of the considered UAV as well as the overall energy consumption. In each iteration of the BCD method, we solve the user association, trajectory optimization, and power optimization subproblems one after the other in an alternating fashion. To solve each subproblem, we employ the geometric programming (GP)-based optimization technique that transforms the variables and constraints. Regarding the initial trajectory of the UAV, we utilized dynamic programming techniques based on unsaturated data requirements of IoT nodes. We performed extensive simulations in many realistic environments to verify the effectiveness and efficiency of the proposed data dissemination scheme in post-disaster scenarios.

Faced with the rapid increase in smart Internet-of-Things (IoT) devices and the high demand for new business-oriented services in the fifth-generation (5G) and beyond network, the management of mobile networks is getting complex. Thus, traditional Network Management and Orchestration (MANO) approaches cannot keep up with rapidly evolving application requirements. This challenge has motivated the adoption of the Zero-touch network and Service Management (ZSM) concept to adapt the automation into network services management. By automating network and service management, ZSM offers efficiency to control network resources and enhance network performance visibility. The ultimate target of the ZSM concept is to enable an autonomous network system capable of self-configuration, self-monitoring, self-healing, and self-optimization based on service-level policies and rules without human intervention. Thus, the paper focuses on conducting a comprehensive survey of E2E ZSM architecture and solutions for 5G and beyond networks. The article begins by presenting the fundamental ZSM architecture and its essential components and interfaces. Then, a comprehensive review of the state-of-the-art for key technical areas, i.e., ZSM automation, cross-domain E2E service lifecycle management, and security aspects, are presented. Furthermore, the paper contains a summary of recent standardization efforts and research projects towards the ZSM realization in 5G and beyond networks. Finally, several lessons learned from the literature and open research problems related to ZSM realization are also discussed in this paper.

With outstanding features, machine learning (ML) has become the backbone of numerous applications in wireless networks. However, the conventional ML approaches face many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, can impede the effectiveness and applicability of ML in wireless networks. To address these problems, transfer learning (TL) has recently emerged to be a promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks and valuable experiences accumulated from the past to facilitate the learning of new problems. By doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods’ robustness to different wireless environments. This article aims to provide a comprehensive survey on the applications of TL in wireless networks. Particularly, we first provide an overview of TL, including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, signal recognition, security, caching, localization, and human activity recognition, which are all important to next-generation networks, such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.

Federated learning (FL) is a recent development in artificial intelligence, which is typically based on the concept of decentralized data. As cyberattacks are frequently happening in the various applications deployed in real time, most industrialists are hesitating to move forward in adopting the technology of the Internet of Everything. This article aims to provide an extensive study on how FL could be utilized for providing better cybersecurity and prevent various cyberattacks in real time. We present an extensive survey of the various FL models currently developed by researchers for providing authentication, privacy, trust management, and attack detection. We also discuss few real-time use cases that have been deployed recently and how FL is adopted in them for preserving privacy of data and improving the performance of the system. Based on the study, we conclude this article with some prominent challenges and future directions on which the researchers can focus for adopting FL in real-time scenarios.

This paper presents a deep learning-based method to automatically recognize low probability of intercept (LPI) radar waveforms against diversified jamming attacks. Concretely, an efficient convolutional neural network (CNN) architecture, namely Densely-Accumulated Network (DANet), is introduced to learn the time-frequency representation transformed by the Wigner-Ville distribution. Such an architecture has several novel densely-accumulated connection modules specified by various symmetric and asymmetric convolutional layers to enrich diversified features at multiple representational maps. Besides, the skip-connection and dense-connection are leveraged to improve feature learning efficiency and prevent the vanishing gradient when the network goes deeper. Some image processing techniques (e.g., global thresholding and digital filtering) are adopted to enhance the quality of time-frequency image. Relying on simulations, we benchmark the proposed method on a synthetic 13-waveform dataset and also investigate the influence of hyper-parameters (such as image size, number of modules, training data size) on the overall recognition performance. Remarkably, with average accuracy of 98.2% at 0 dB signal-to-noise ratio (SNR), DANet outperforms several backbone CNNs and state-of-the-art networks of LPI waveform recognition while keeping a cost-efficient model.

Being a beneficial service in emergency situations, UAV-aided relay communications have received tremendous attentions in the recent years. In this paper, we consider a three-node UAV-based relay network, in which a mobile UAV establishes communication between two remaining nodes in the system. Despite numerous works available for such a system on the optimization of trajectory and other communication resources, none of these have addressed the delay performance of the system at the granular packet level. Furthermore, it is already established that the equipment of buffer at the relay node provides more flexibility in the delivery of packets through the exploitation of better channel quality. On the other hand, with the continued popularity of multimedia and similar other applications, it is very likely that the source node in the system receives bursty traffic from different external networks. Given that the source and UAV nodes have finite packet-level buffers and the trajectory of the UAV is known, under a bursty traffic model, we aim to study the average end-to-end packet delay and buffer overflow performance of the system. While capturing the predictable channel variation due to the movement of the UAV, we establish a queuing model for the source and UAV nodes based on the stochastic process. Then, from the dynamics of the established queuing model, we derive the average end-to-end packet delay and queue overflow probability of such a system. Through extensive numerical simulation, we justify the accuracy and effectiveness of the proposed analytical model while comparing with three static deployment scenarios of the UAV. Through providing sufficient analytical evidence as well as the numerical results, we exhibit that a mobile relay system outperforms the static relay one in terms of both the delay and buffer overflow metrics.

Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the massive volume of data generated from ubiquitous mobile devices for enabling intelligent services with the help of artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and training in a single entity, e.g., an MEC server, which is now becoming a weak point due to data privacy concerns and high overhead of raw data communications. In this context, federated learning (FL) has been proposed to provide collaborative data training solutions, by coordinating multiple mobile devices to train a shared AI model without directly exposing their underlying data, which enjoys considerable privacy enhancement. To improve the security and scalability of FL implementation, blockchain as a ledger technology is attractive for realizing decentralized FL training without the need for any central server. Particularly, the integration of FL and blockchain leads to a new paradigm, called FLchain, which potentially transforms intelligent MEC networks into decentralized, secure, and privacy-enhancing systems. This article presents an overview of the fundamental concepts and explores the opportunities of FLchain in MEC networks. We identify several main issues in FLchain design, including communication cost, resource allocation, incentive mechanism, security and privacy protection. The key solutions and the lessons learned along with the outlooks are also discussed. Then, we investigate the applications of FLchain in popular MEC domains, such as edge data sharing, edge content caching and edge crowdsensing. Finally, important research challenges and future directions are also highlighted.

With the advancement of the Internet of Things (IoT), different use cases, such as smart factories, smart cities, and smart cars are being developed. Technologies, such as multiaccess edge computing (MEC) and network slicing are being researched to support various vertical use cases. However, simply utilizing network slicing and MEC is insufficient when providing a specific IoT service. Accordingly, there is a need for a method of arranging and operating common service functions (CSFs) used in the IoT platform at the edge of the network. In addition, it is necessary not only to deliver virtual CSFs but also to implement autoscaling policies of these virtual common service resources. This study extends the idea of resource slicing technologies for the IoT service layer. The IoT service layer exposes the virtual IoT CSFs as an IoT slice service to be provided in the form of virtual network functions (VNFs) with MEC applications on top of the network function virtualization infrastructure (NFVI) at the edge of the network. We propose a framework architecture for virtual IoT slice service orchestration, and illustrate how to instantiate the virtual IoT service functions in the existing centralized cloud to the MEC platform. We also propose an elastic computing algorithm of virtual IoT slice services functions(vIoT-SSFs) resources at the NFVI so that the IoT applications and underlying IoT resources can access vIoT-SSFs at the edge of the network.

Reconfigurable intelligent surface (RIS), a recently introduced technology for future wireless communication systems, enhances the spectral and energy efficiency by intelligently adjusting the propagation conditions between base stations (BSs) and mobile equipments (MEs). An RIS consists of many low-cost passive reflecting elements that are optimized to improve the quality of the received signal. In this paper, we study the problem of power control at the BS and RIS optimization for application to physical-layer broadcasting. Our goal is to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BS and the phase shifts of the passive elements at the RIS. Furthermore, to help validate the proposed optimization methods, we derive lower bounds to quantify the average transmit power at the BS as a function of the number of MEs, the number of RIS elements, and the number of antennas at the BS. The simulation results demonstrate that the average transmit power at the BS is close to the lower bound in an RIS-aided system, and is significantly lower than the average transmit power in conventional schemes without an RIS.

Modulation classification, recognized as the intermediate step between signal detection and demodulation, is widely deployed in several modern wireless communication systems. Although many approaches have been studied in the last decades for identifying the modulation format of an incoming signal, they often reveal the obstacle of learning radio characteristics for most traditional machine learning algorithms. To overcome this drawback, we propose an accurate modulation classification method by exploiting deep learning for being compatible with constellation diagram. Particularly, a convolutional neural network is developed for proficiently learning the most relevant radio characteristics of gray-scale constellation image. The deep network is specified by multiple processing blocks, where several grouped and asymmetric convolutional layers in each block are organized by a flow-in-flow structure for feature enrichment. These blocks are connected via skip-connection to prevent the vanishing gradient problem while effectively preserving the information identity throughout the network. Regarding several intensive simulations on the constellation image dataset of eight digital modulations, the proposed deep network achieves the remarkable classification accuracy of approximately 87% at 0 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel and further outperforms some state-of-the-art deep models of constellation-based modulation classification.

Modulation classification, an intermediate process between signal detection and demodulation in a physical layer, is now attracting more interest to the cognitive radio field, wherein the performance is powered by artificial intelligence algorithms. However, most existing conventional approaches pose the obstacle of effectively learning weakly discriminative modulation patterns. This paper proposes a robust modulation classification method by taking advantage of deep learning to capture the meaningful information of modulation signal at multi-scale feature representations. To this end, a novel architecture of convolutional neural network, namely Chain-Net, is developed with various asymmetric kernels organized in two processing flows and associated via depth-wise concatenation and element-wise addition for optimizing feature utilization. The network is evaluated on a big dataset of 14 challenging modulation formats, including analog and high-order digital techniques. The simulation results demonstrate that Chain-Net robustly classifies the modulation of radio signals suffering from a synthetic channel deterioration and further performs better than other deep networks.

As a recently proposed idea for the future wireless systems, intelligent reflecting surface (IRS) can assist communications between entities which do not have high-quality direct channels in between. Specifically, an IRS comprises many low-cost passive elements, each of which reflects the incident signal by incurring a phase change so that the reflected signals add coherently at the receiver. In this paper, for an IRS-aided wireless network, we study the problem of power control at the base station (BS) for physical-layer broadcasting under quality of service constraints, by jointly designing the transmit beamforming at the BS and the phase shifts of the IRS units. Furthermore, we derive a lower bound of the minimum transmit power at the BS to validate the proposed optimization method. Simulation results show that, the transmit power at the BS approaches the lower bound with the increase of the number of IRS units, and is much lower than that of the communication system without the IRS.

The explosive growth of end devices and mobile applications calls for novel schemes that can enable computation‐hungry applications at small end‐devices and meet the massive connectivity requirement. Cognitive radio (CR), non‐orthogonal multiple access (NOMA) and multi‐access edge computing (MEC) are envisioned as the key technologies in fifth‐generation and beyond. In this work, the authors introduce the concept of CR‐NOMA in MEC offloading, where a secondary user (SU) can utilise the spectrum allocated to a primary user (PU) to offload its computation task to the MEC server for remote execution. For the spectrum utilisation, an equation to specify the minimum transmit power that must be allocated to the PU is derived. The authors also develop an algorithm to determine the offloading decision (i.e. offloading or not) and the paired PU (i.e. subcarrier used for computation offloading) for SUs, using one‐to‐one matching game. Moreover, through numerical simulations, the authors demonstrate the superior performance of the proposed algorithm compared with several baseline schemes.

As the integration of unmanned aerial vehicles (UAVs) into visible light communications (VLCs) can offer many benefits for massive-connectivity applications and services in 5G and beyond, this article considers a UAV-assisted VLC using nonorthogonal multiple-access. More specifically, we formulate a joint problem of power allocation and UAV's placement to maximize the sum rate of all users, subject to constraints on power allocation, quality of service of users, and UAV's position. Since the problem is nonconvex and NP-hard in general, it is difficult to be solved optimally. Moreover, the problem is not easy to be solved by conventional approaches, e.g., coordinate descent algorithms, due to channel modeling in VLC. Therefore, we propose using the Harris hawks optimization (HHO) algorithm to solve the formulated problem and obtain an efficient solution. We then use the HHO algorithm together with artificial neural networks to propose a design that can be used in real-time applications and avoid falling into the “local minima” trap in conventional trainers. Numerical results are provided to verify the effectiveness of the proposed algorithm and further demonstrate that the proposed algorithm/HHO trainer is superior to several alternative schemes and existing metaheuristic algorithms.

The stringent requirements of wireless multimedia transmission lead to very high radio spectrum solicitation. Although the radio spectrum is considered as a scarce resource, the issue with spectrum availability is not scarcity, but the inefficient utilization. Unique characteristics of cognitive radio (CR) such as flexibility, adaptability, and interoperability, particularly have contributed to it being the optimum technological candidate to alleviate the issue of spectrum scarcity for multimedia communications. However, multimedia communications over CR networks (MCRNs) as a bandwidth-hungry, delay-sensitive, and loss-tolerant service, exposes several severe challenges specially to guarantee quality of service (QoS) and quality of experience (QoE). As a result, to date, different schemes based on source and channel coding, multicast, and distributed streaming, have been examined to improve the QoS/QoE in MCRNs. In this paper, we survey QoS/QoE provisioning schemes in MCRNs. We first discuss the basic concepts of multimedia communication, CRNs, QoS and QoE. Then, we present the advantages of utilizing CR for multimedia services and outline the stringent QoS and QoE requirements in MCRNs. Next, we classify the critical challenges for QoS/QoE provisioning in MCRNs including spectrum sensing, resource allocation management, network fluctuations management, latency management, and energy consumption management. Then, we survey the corresponding feasible solutions for each challenge highlighting performance issues, strengths, and weaknesses. Furthermore, we discuss several important open research problems and provide some avenues for future research.

A non‐orthogonal multiple access (NOMA) system is now considered as a promising radio access technique for next‐generation networks owing to its benefits, e.g. spectral efficiency improvement. Due to the successive interference cancellation order at receivers, fairness among users in NOMA may not be guaranteed. In this study, the authors focus on ‐fair resource allocation in NOMA. The complexity of the considered problem is then analysed. In particular, the problem is shown to be convex when and , non‐deterministic polynomial‐time (NP)‐hard when , and polynomial‐time solvable when . Finally, simulation results are provided to examine the effects of the fairness degree on the system performance and verify the effectiveness of the proposed algorithms.