The quest for higher wireless carrier frequencies spanning the millimeter-wave (mmWave) and Terahertz (THz) bands heralds substantial enhancements in data throughput and spectral efficiency for next-generation wireless networks. However, these gains come at the cost of severe path loss and a heightened risk of beam misalignment due to user mobility, especially pronounced in near-field communication. Traditional solutions rely on extremely directional beamforming and frequent beam updates via beam management, but such techniques impose formidable computational and signaling overhead. In response, we propose a novel approach termed trajectory-adaptive beam shaping (TABS) that eliminates the need for real-time beam management by shaping the electromagnetic wavefront to follow the user's predefined trajectory. Drawing inspiration from self-accelerating beams in optics, TABS concentrates energy along pre-defined curved paths corresponding to the user's motion without requiring real-time beam reconfiguration. We further introduce a dedicated quantitative metric to characterize performance under the TABS framework. Comprehensive simulations substantiate the superiority of TABS in terms of link performance, overhead reduction, and implementation complexity.

Dynamic Security Assessment (DSA) is critical for maintaining stability in large-scale smart grids, especially with the growing integration of renewable energy sources and the inherent uncertainties. Traditional model-based analytical methods are increasingly inadequate under these complex conditions. To address these challenges, we propose a pioneering Quantum Federated Learning-based DSA (QFLDSA) method by combining hybrid quantum-classical machine learning and federated learning. QFLDSA offers an effective way to deal with high-dimensional data and uncertainties inherent in the grid. Moreover, QFLDSA leverages the unique capabilities of quantum computing to enhance the processing of differential-algebraic equations that underpin grid stability. This paper demonstrates through extensive simulations that QFLDSA significantly outperforms traditional methods, achieving the highest average F1-score performance at 97.94%, while maintaining 97.67$\pm$0.17% prediction accuracy on both classical and quantum computing devices only with fewer transmitted model parameters (reducing up to$\sim$1000X). These enhancements enable more reliable and rapid deployment of preventive stability control measures across smart grids. Our results underscore QFLDSA’s potential as a robust solution for the dynamic security challenges of modern smart grids, paving the way for future innovations in grid management technology.Note to Practitioners—In the rapidly evolving world of smart cyber-physical grids, ensuring the stability of electric power systems is paramount. Failures in these systems can lead to catastrophic blackouts, affecting countless homes and businesses. Traditional DSA methods to assess and ensure this stability, while effective, are becoming increasingly complex and vulnerable to single points of failure or cyberattacks. Enter the QFLDSA method, a novel approach we introduce in this paper. In simple terms, this method combines the strengths of quantum machine learning and federated learning to analyze data efficiently across a distributed system. Here’s why these matters: 1) Localized Analysis: Instead of relying on a central hub to analyze all data, QFLDSA allows for localized data analysis. This means that if one part of the system fails, it does not bring down the entire grid’s analysis capabilities. It is akin to having multiple control rooms instead of one, ensuring that a problem in one room does not halt the entire operation. 2) Future-Ready: As we move towards a future where quantum computing becomes more prevalent, QFLDSA is designed to work seamlessly with both today’s classical devices and tomorrow’s quantum devices. This ensures that as technology evolves, our method remains relevant and efficient. 3) Proven Performance: We have not just introduced a new method; we have rigorously tested it. Our theoretical proofs and practical tests confirm that QFLDSA offers accurate and efficient data analysis for smart grids. For industry professionals, the takeaway is clear: if looking for a resilient, future-ready, and proven method to ensure the stability of smart grid, QFLDSA offers a compelling solution.

Multi orbit low earth orbit (LEO) satellites communication is envisioned as a key infrastructure to deliver global coverage, enabling future services from space air ground integrated networks.However, the optimized design of LEO which jointly addresses satellite selection, association control, and resource scheduling while accounting for dynamic visibility in multi orbit constellations still remains open. Satellites moving along distinct orbital planes yield phase shifted ground tracks and heterogeneous, time varying coverage patterns that significantly complicate the optimization.To bridge the gap, we propose a dynamic visibility aware multi orbit satellite selection framework which can determine the optimal serving satellites across orbital layers. The framework is built upon Markov approximation and matching game theory. Specifically, we formulate a combinatorial optimization problem that maximizes the sum rate under per satellite power budgets. The problem is NP hard , combining discrete user association (UA) decisions with continuous power allocation, and an inherently non convex sum rate maximization objective. We address it through a problem specific Markov approximation. Moreover, we alternately solve UA or bandwidth allocation via a matching game and power allocation via a Lagrangian dual program, which together form a block coordinate descent method tailored to this problem. Simulation results show that the proposed algorithm converges to a suboptimal solution across all scenarios. Extensive experiments against four state of the art baselines further demonstrate that our algorithm achieves, on average, approximately 7.85% higher sum rate than the best performing baseline.

LEO satellites are key to global coverage in 6G wireless communications. However, efficiently selecting service satellites in multi-orbit systems remains a challenge due to dynamic topologies and limited resources. To address this challenge, this paper proposes a joint optimization framework for satellite selection, user association, and resource allocation in Space-AirGround Integrated Networks (SAGINs). We design a computationally efficient algorithm that leverages Markov approximation for satellite selection and employs matching game theory for user association and resource allocation. Our simulation results show that the proposed algorithms outperform benchmark methods.

Integrated Sensing and Communication (ISAC) systems promise to revolutionize wireless networks by concurrently supporting high-resolution sensing and high-performance communication. This paper presents a novel radio access technology (RAT) selection framework that capitalizes on vision sensing from base station (BS) cameras to optimize both communication and perception capabilities within the ISAC system. Our framework strategically employs two distinct RATs, LTE and millimeter wave (mmWave), to enhance system performance. We propose a vision-based user localization method that employs a 3D detection technique to capture the spatial distribution of users within the surrounding environment. This is followed by geometric calculations to accurately determine the state of mmWave communication links between the BS and individual users. Additionally, we integrate the SlowFast model to recognize user activities, facilitating adaptive transmission rate allocation based on observed behaviors. We develop a Deep Deterministic Policy Gradient (DDPG)-based algorithm, utilizing the joint distribution of users and their activities, designed to maximize the total transmission rate for all users through joint RAT selection and precoding optimization, while adhering to constraints on sensing mutual information and minimum transmission rates. Numerical simulation results demonstrate the effectiveness of the proposed framework in dynamically adjusting resource allocation, ensuring high-quality communication under challenging conditions.

In the era of smart cyber-physical grid, dynamic insecurity risk has become a significant concern due to the increasing integration of renewable energy sources and the inherent uncertainties in smart grid. Dynamic security assessment (DSA) has been adopted to hedge against such risks by estimating the stability of large-scale smart grids. Existing DSA approaches often involve complex high dimensional models which incur high communication and computational costs, hindering their practical adoption. In this paper, we address these limitations with the Quantum Federated Ensembled Variational Adaptive Learning (QFEVAL) approach for smart grid DSA. QFEVAL is designed to combine quantum machine learning and federated learning to handle the differential-algebraic equations that describe smart grid stability, providing an efficient way to deal with high-dimensional data and uncertainties. QFEVAL enables the training of the hybrid quantum-classical neural networks on distributed DSA datasets located at different nodes in smart grids, without requiring large numbers of parameters to be transmitted. QFEVAL accurately predicts the stability of the smart grid under various conditions, enabling the implementation of preventive stability control measures. Through extensive experiments, we demonstrate that QFEVAL achieves comparable performance to 9 state-of-the-art DSA approaches with more than 2 orders of magnitude fewer model parameter transmissions. QFEVAL paves the way for reliable, secure, and continuous electricity supply, offering a robust solution to the challenges of DSA in smart grids.

In smart healthcare, health monitoring utilizes diverse tools and technologies to analyze patients' real-time biosignal data, enabling immediate actions and interventions. Existing monitoring approaches were designed on the premise that medical devices track several health metrics concurrently, tailored to their designated functional scope. This means that they report all relevant health values within that scope, which can result in excess resource use and the gathering of extraneous data due to monitoring irrelevant health metrics. In this context, we propose a Dynamic Activity-Aware Health Monitoring strategy (DActAHM), as a novel framework based on Deep Reinforcement Learning (DRL) and SlowFast Model, for striking a balance between optimal monitoring performance and cost efficiency while ensuring precise monitoring based on users' activities. Specifically, with the SlowFast Model, DActAHM efficiently identifies individual activities and captures these results for enhanced processing. Subsequently, DActAHM refines health metric monitoring in response to the identified activity by incorporating a DRL framework. Extensive experiments comparing DActAHM against three state-of-the-art approaches demonstrate it achieves 27.3% higher gain than the best-performing baseline that fixes monitoring actions over timeline.

Federated learning (FL) addresses data privacy concerns by enabling collaborative training of AI models across distributed data owners. Wide adoption of FL faces the fundamental challenges of data heterogeneity and the large scale of data owners involved. In this paper, we investigate the prospect of Foundation Model (e.g., transformers)-based FL for achieving generalization and personalization in this setting. Different from existing research efforts which mostly focus on studying Transformer-based FL on small scales, we conduct extensive comparative experiments involving FL with Transformers, ResNet, and personalized ResNet-based FL approaches under various large-scale scenarios. These experiments consider varying numbers of data owners to demonstrate Transformers’ advantages over deep neural networks in large-scale heterogeneous FL tasks. In addition, we analyze the superior performance of Transformers by comparing the Centered Kernel Alignment (CKA) representation similarity across different layers and FL models to gain insight into the reasons behind their promising capabilities.

Incentivizing data owners to contribute to federated learning (FL) is crucial to the sustainable operation of an FL ecosystem. Existing incentive mechanisms are designed assuming that all data owners are known to FL task publishers, which may not always hold in practical scenarios. As the domain of auction-based FL (AFL) continues to grow in importance, we are rethinking this assumption to better suit the realities of Federated Auctioning Networks (FANs). We propose an incentive mechanism named FIM-FAN, which is designed to perform data owner selection based on Lyapunov optimization through referrals among data owners in FANs, without relying on the existence of an entity that has information about all data owners. In FIM-FAN, a data owner can perform the dual roles of a worker and a referrer. Both activities can be incentivized monetarily. To solve the data owner selection problem, an online greedy client selection algorithm is proposed considering reputation, bidding price, and fairness. Reputation is also involved in the compensation calculation process to encourage honest behaviors in FAN. Theoretical analysis shows that FIM-FAN satisfies budget feasibility and individual rationality. Extensive experiments on MNIST and CIFAR-10 datasets against 10 baselines demonstrate that, on average, FIM-FAN outperforms them by 6.54% and 13.63% in terms of test accuracy and fairness, respectively.

Traffic sign recognition systems are crucial for the navigation and situation awareness of autonomous vehicles. They leverage deep learning technologies to swiftly and accurately identify traffic signs, even in the most challenging traffic environments. However, security researchers have uncovered a critical vulnerability in these systems: learning-based TSRs are particularly susceptible to physical-world perturbation attacks. Through subtle modifications (i.e., attaching well-designed patches on traffic signs), attackers can deceive the recognition system into making erroneous judgments, which can further lead to serious traffic accidents. Although several defense mechanisms have been proposed to enhance the security of sign recognition systems, these solutions generally target only specific types of malicious perturbations and thus lack robustness. To address this issue, we present a robust defense mechanism named Shield-U, which restores traffic sign images contaminated by physical patch perturbations, providing credible data for the recognition model. In the process of implementing Shield-U, we first design a feature difference-aware perturbation generator that outputs potential sign contamination patterns. Incorporating generated perturbations during the training phase enables our restoration model to gain sufficient understanding of diverse perturbation types, thus enhancing its ability to repair various perturbed signs. Following this, we build an attention-driven restoration network to repair sign images. Finally, we evaluate the effectiveness of Shield-U using widely used sign recognition models and public datasets. The results demonstrate that our defense mechanism excels in resisting potential perturbations, increasing the average sign recognition accuracy by 50.4%.

The Space-Air-Ground Integrated Network (SAGIN), crucial to the advancement of sixth-generation (6G) technology, plays a key role in ensuring universal connectivity, particularly by addressing the communication needs of remote areas lacking cellular network infrastructure. This paper delves into the role of unmanned aerial vehicles (UAVs) within SAGIN, where they act as a control layer owing to their adaptable deployment capabilities and their intermediary role. Equipped with millimeter-wave (mmWave) radar and vision sensors, these UAVs are capable of acquiring multi-source data, which helps to diminish uncertainty and enhance the accuracy of decision-making. Concurrently, UAVs collect tasks requiring computing resources from their coverage areas, originating from a variety of mobile devices moving at different speeds. These tasks are then allocated to ground base stations (BSs), low-earth-orbit (LEO) satellite, and local processing units to improve processing efficiency. Amidst this framework, our study concentrates on devising dynamic strategies for facilitating task hosting between mobile devices and UAVs, offloading computations, managing associations between UAVs and BSs, and allocating computing resources. The objective is to minimize the time-averaged network cost, considering the uncertainty of device locations, speeds, and even types. To tackle these complexities, we propose a deep reinforcement learning and perception-aided online approach (DRL-and-Perception-aided Approach) for this joint optimization in SAGIN, tailored for an environment filled with uncertainties. The effectiveness of our proposed approach is validated through extensive numerical simulations, which quantify its performance relative to various network parameters.

This paper investigates the issue of resource allocation for unmanned aerial vehicle and reconfigurable intelligent surface (UAV-RIS) assisted vehicular communication systems. To adapt the high dynamics of vehicular networks, we conceive a digital twin-based system over RIS-embedded environment towards environmental-aware communications. Specifically, a digital twin system can leverage data-driven models to predict the large-scale fading of future stages, while RIS is capable of controlling the propagation environments in real time, which can be utilized to mitigate prediction errors imposed by the small-scale fading. Using the capabilities of “prediction" and “reconfiguration", we expect to comprehensively foresee the dynamic changes in vehicular networks. In particular, the above-mentioned issue is formulated as a multi-slot total power consumption minimization problem under the quality of service (QoS) and energy constraints. Considering the finite battery energy of the UAV and the circuit power of the RIS, the transmit power of the UAV and the number of active reflecting elements (REs) are jointly scheduled for a finite time horizon. To tackle this mixed integer non-linear programming (MINLP) problem, we transform the original model into a discrete-time dynamic system. According to whether the dynamics of radio environments are predictable or not, the optimal offline and online policies are derived by using the deterministic and stochastic dynamic programming algorithms, respectively. To reduce the computation complexity, we further propose a novel online policy based on the idea of double-strategy selection. Finally, numerical results demonstrate that the proposed online policy exhibits near-optimal performances and outperforms other benchmarks in terms of transmission failure probability and effective power consumption.

This paper proposes an optimal dynamic transmission power allocation mechanism for multiple users to maximize mobile ad hoc networks (MANETs) energy efficiency. With the widespread adoption of the internet of things (IoT), energy efficiency is a crucial metric in MANETs constrained by the battery capacity of portable devices. Specifically, a practical scheme needs to be explored that ensures the quality of service (QoS) while minimizing battery costs. Previous studies mainly focus on designing power-manageable components and achieving predictive mechanisms at the circuit or system layer. In contrast to the traditional approaches, we jointly consider power management and multiuser cooperative mobility for energy efficiency maximization. On the one hand, we formulate the dynamic power allocation schemes with multiuser cooperative mobility strategies into the infinite-horizon time average problems. On the other hand, an algorithm based on Lyapunov optimization is developed to obtain the optimum, i.e., dynamic power allocation based on the multiuser cooperative mobility (DPA-MCM) algorithm. Numerical simulations show that the proposed algorithm improves the energy efficiency by up to 27.84 % compared to the previous method, demonstrating the effectiveness.

K-degree anonymity is one of the main techniques for data privacy and has gained attention in academia, industry, and government. Many social network data publishing algorithms based on K-anonymity techniques have been proposed, but most studies focus on static social networks. Compared to static social networks, dynamic social networks suffer from problems such as higher information loss and lower data utility. To address the existing problem of dynamic social networks, we propose a K-degree anonymity dynamic data publishing algorithm based on reinforcement learning. The algorithm ends with two phases: anonymization sequence and graph modification. In the anonymous sequence phase, this paper combines the idea of reinforcement learning and the characteristics of dynamic data change to build a reinforcement learning model for anonymous sequences. In this way, an ideal anonymous sequence can be created. We also propose a new strategy for graph modification, which selects edges according to degree centrality to generate anonymous graphs. Finally, experiments on real datasets show the effectiveness of our algorithm.

Federated learning (FL), which addresses data privacy issues by training models on resource-constrained mobile devices in a distributed manner, has attracted significant research attention. However, the problem of optimizing FL client selection in mobile federated learning networks (MFLNs), where devices move in and out of each others’ coverage and no FL server knows all the data owners, remains open. To bridge this gap, we propose a first-of-its-kind Socially-aware Federated Client Selection (SocFedCS) approach to minimize costs and train high-quality FL models. SocFedCS enriches the candidate FL client pool by enabling data owners to propagate FL task information through their local networks of trust, even as devices are moving into and out of each others’ coverage. Based on Lyapunov optimization, we first transform this time-coupled problem into a step-by-step optimization problem. Then, we design a method based on alternating minimization and self-adaptive global best harmony search to solve this mixed-integer optimization problem. Extensive experiments comparing SocFedCS against five state-of-the-art approaches based on four real-world multimedia datasets demonstrate that it achieves 2.06% higher test accuracy and 12.24% lower cost on average than the best-performing baseline.

As healthcare becomes increasingly data-driven, integrating hybrid mobile edge-quantum computing (MEQC) into smart healthcare systems emerges as a promising solution for handling growing computational demand, especially for latency-sensitive tasks. Therefore, this paper proposes a deep reinforcement learning (DRL)-based Lyapunov approach for schedule computation offloading, aiming to minimize the total latency in hybrid MEQC-based smart healthcare systems. In this framework, a sustainable computation offloading strategy is obtained while guaranteeing the individual latency constraints and the required success ratio for each computation task. More precisely, the original latency minimization problem is transformed into a stepwise mixed-integer non-convex optimization problem using Lyapunov techniques. Subsequently, a Deep Q-Network (DQN) is adopted for computation offloading mode selection. The effectiveness of the proposed approach and its dependency on various system parameters are validated and assessed through numerical simulations.

In smart ocean Internet of Things (IoT) systems, autonomous underwater vehicles (AUVs) are responsible for underwater information collection. Due to the nature of the medium, the acoustic communications for AUVs are of low bandwidth and adverse environmental conditions causing severe transmission problems. In order to realize cross-media transmission from AUVs to the offshore platform, unmanned surface vehicles (USVs) have been suggested to forward the collected information in a coordinated manner. Against this backdrop, this contribution develops a cooperative USV-to-USV (U2U) cross-media cooperative communications scheme. Then, we formulate a rate maximization problem with the objective of optimizing the reputation-aware USV selection strategy. Furthermore, to characterize the impact of the mobility of AUVs/USVs, a long-term dynamic process is constructed. Meanwhile, we also develop an efficient algorithm which transforms the reputation-aided dynamic USVs selection problem into the infinite-time horizon average one restricted by time average rate constraints in the collection of penalty processes with the help of the Lyapunov optimization framework and drift-plus-penalty method. Finally, numerical results are presented to validate the convergence behavior and the performance for the designed dynamic USVs selection algorithm.

This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL) system. Each mobile device in the system engages the model training process within the specified area and allocates its computation and communication resources for deriving and uploading parameters, respectively, to minimize the objective of system subject to the computation/communication budget and a target latency requirement. In particular, mobile devices are connect via wireless TCP/IP architectures. Exploiting the optimization problem structure, the problem can be decomposed to two convex sub-problems. Drawing on the Lagrangian dual and harmony search techniques, we characterize the global optimal solution by the closed-form solutions to all sub-problems, which give qualitative insights to multi-resource tradeoff. Numerical simulations are used to validate the analysis and assess the performance of the proposed algorithm.

In this paper, we investigate the stability and effectiveness of intelligent reflecting surface (IRS) aided systems in the context of mobile multi-users and time-varying channel status. Different from the previous researches in the IRS-aided communication mostly based on one or more independent channel realization, we consider dynamic channel status varying with the mobility of users. Specifically, a dynamic problem as maximizing the time-average rate of all users is formulated. A fractional programming method based on Lagrangian dual theory is proposed as a solution. Simulation results demonstrate that the IRS can be more efficient than amplified forward (AF) relay in adapting the dynamically changing channels stably.

In this contribution, we consider the construction of the communication link between two terminals working on different media as underwater acoustics and traditional microwave, with the aid of a cross-media bidirectional relay. Our goal is to develop an optimal power allocation algorithm in order to minimize the outage probability. Through theoretical analysis and simulation results, we demonstrate the effectiveness of the above-mentioned cross-media structure and power optimization scheme, so as to adapt the scenario of hybrid underwater acoustic communication (UAC) and radio frequency (RF) transmission.

With the evolution of mobile terminals and the development of user demands, the existing mobile system is facing with serious challenges, such as the nearly saturated spectrum, limited processing capability of mobile terminals, and the high complexity of emerging technologies. To deal with these challenges, 6G is attracting extensive attentions. In this paper, we firstly investigate the potential technologies for 6G from the view of network design and resource management, then highlight the potential challenges for resource allocation, imposed for satisfying multi-demands in future hyper-heterogeneous networks with massive different devices and applications. In addition, the existing criteria for resource allocation are summarized for their common forms and limitations when dealing with multi-demands. Considering the limitations of the existing criteria, a novel quality-of-experience (QoE)-aware criterion on demand side is introduced toward efficient resource allocation in 6G, including its modeling, the potential application forms and typical scenarios, the future directions and challenges.

Abstract The hybrid visible light communication (VLC)/radio frequency (RF) communications are investigated with the aid of reconfigurable intelligent surfaces (RISs) in dynamic wireless networks, where the RIS access selection processes of VLC/RF users are updated depending on the channel quality dynamically. Specifically, a dynamic optimization problem due to the mobility of users and time‐varying selection strategy is formulated. Under the constraints of the average minimum rate for VLC/RF users and the maximum transmit power constraints for VLC/RF access points (APs), the target is to minimize the average long‐term power consumption, by jointly considering the active beamforming at APs and the passive beamforming at RISs. Based on the Lyapunov optimization framework and the drift‐plus‐penalty (DPP) algorithm, the original optimization problem is transformed into corresponding short‐term problems at each frame. Furthermore, the closed form solutions with active‐passive beamforming are derived using the fractional programming method based on the Lagrangian dual theory. Finally, numerical results demonstrate the convergence and effectiveness of the proposed optimization algorithm.

Abstract The design of dynamic wireless networks assisted by reconfigurable intelligent surfaces (RIS) mounted on aerial platforms (RIS‐APs) is conceived, where the connection status among users and RIS‐APs are selected according to the average channel quality dynamically and timely. Taking into account the time‐varying selection status and the mobility of users, we construct a long‐term dynamic process. The goal is to minimize the time‐averaged power consumption under the requirements of the time‐averaged minimum rate for users as well as the constraint of the maximum transmit power for the base station (BS), via jointly optimizing the active beamforming at the BS and passive beamforming at RIS‐APs. With the aid of Lyapunov concept‐based drift‐plus‐penalty (DPP) algorithm, the long‐term optimization problem is transformed into short‐term sub‐problems related to each other at each frame. Subsequently, the fractional programming method based on Lagrangian dual theory is applied to derive the solutions for active‐passive beamforming in a closed form. Finally, simulation results validate the convergence and effectiveness of the proposed algorithm.

A novel reconfigurable intelligent surface (RIS)-aided hybrid reflection/transmitter design is proposed for achieving information exchange in cross-media communications. In pursuit of the balance between energy efficiency and low-cost implementations, the cloud-management transmission protocol is adopted in the integrated multi-media system. Specifically, the messages of devices using heterogeneous propagation media, are firstly transmitted to the medium-matched AP, with the aid of the RIS-based dual-hop transmission. After the operation of intermediate frequency conversion, the access point (AP) uploads the received signals to the cloud for further demodulating and decoding process. Based on time division multiple access (TDMA), the cloud is able to distinguish the downlink data transmitted to different devices and transforms them into the input of the RIS controller via the dedicated control channel. Thereby, the RIS can passively reflect the incident carrier back into the original receiver with the exchanged information during the preallocated slots, following the idea of an index modulation-based transmitter. Moreover, the iterative optimization algorithm is utilized for optimizing the RIS phase, transmit rate and time allocation jointly in the delay-constrained cross-media communication model. Our simulation results demonstrate that the proposed RIS-based scheme can improve the end-to-end throughput than that of the AP-based transmission, the equal time allocation, the random and the discrete phase adjustment benchmarks.

In this paper, we conceive the design of dynamic wireless networks assisted by multiple intelligent reflecting surfaces (IRSs), where the connection states between users and IRSs are capable of being updated timely. Taking into account the time-varying states of the system, we further construct a long-term dynamic process. Our goal is to maximize the time average sum-rate of the dynamic system under the time average rate and power constraints of users, via jointly optimizing the power allocation at users and the reflecting coefficients at IRSs. With the aid of Lyapunov concept-based drift-plus-penalty (DPP) algorithm, the long-term optimization problem is formulated as an infinite-horizon time-average one. Subsequently, the fractional programming method based on Lagrangian dual transform is applied to optimize power allocation and reflecting coefficients in an iterative manner, and the closed-form solutions of power and reflecting coefficients can be obtained at each iteration. Finally, simulation results demonstrate the convergence and effectiveness of the proposed algorithm. Further performance comparisons indicate that the proposed algorithm can maintain a balance between supply and demand for resource allocation and improve the fairness of users.

This paper investigates the long-term effectiveness and stability of an integrated unmanned aerial vehicles (UAV)-intelligent reflecting surface (IRS) relaying dynamic system in the context of time-varying system states. Consequently, a dynamic optimization problem is constructed to minimize the frame-average transmit power by joint active beamforming at the base station (BS) and passive beamforming at the IRS under frame-average rate constraints. The original problem as an infinite-horizon time-average one can be solved by introducing the drift-plus-penalty (DPP) algorithm and then the optimal active beamforming and passive beamforming can be obtained in an iterative manner. Simulation results demonstrate the theoretical analysis and assess the performance of the dynamic system.

In the vision of the next generation communications, the wireless devices may work on different transmission media, such as microwave and visible light. In this case, how to bridge these different devices remains an open challenge. Following the framework of [1], we consider a cross-media base station (BS) for supporting devices working on different media, as visible light and radio frequency (RF). Specifically, we conceive two criteria for power allocation toward enhanced performance, by maximizing the sum and minimum rates. Furthermore, we also analyze the impact of the position of BS to the system performance. Finally, the theoretical results are verified by simulations for supporting the effectiveness of the developed power allocation schemes in cross-media communications.

In this paper, the adoption of an intelligent reflecting surface (IRS) for multiple user pairs in two-hop networks is investigated. Different from the existing studies on IRS that mainly focused on tuning the reflection coefficients of all elements, we consider the implementation oftruereflection resource management (RRM) through the identification of the best triggered module subset. More precisely, the implementation oftrueRRM builds on the premise of our proposed modular IRS structure consisting of multiple independent and controllable modules. In the context of modular IRS structure, we investigate the signal-to-interference-plus-noise ratio (SINR)-based max-min problem subject to per source terminals (STs) power budgets and module size constraint, via joint triggered module subset identification, transmit power allocation, and the corresponding passive beamforming. Whereas this problem is NP-hard due to the module size constraint, which can be addressed by the convex sparsity-inducing approximation to the hard module size constraint using mixed$\ell _{1,F}\text {-norm}$, where it yields a suitable semidefinite relaxation. Using techniques from separable convex programming, we provide a two-block alternating direction method of multipliers (ADMM) algorithm for the approximated problem. Numerical simulations are used to validate the analysis and assess the performance of the proposed algorithm as a function of the system parameters. Further energy efficiency (EE) performance comparison demonstrates the necessity and meaningfulness of the introduced modular IRS structure. Specifically, for a given network setting, there is an optimal value of the number of triggered modules for system, when the EE is considered.

It is known that the capacity of the intelligent reflecting surface (IRS) aided cellular network can be effectively improved by reflecting the incident signals from the transmitter in a low-cost passive reflecting way. Nevertheless, in the actual network operation, the base station (BS) and IRS may belong to different operators, consequently, the IRS is reluctant to help the BS without any payment. Therefore, this paper investigates price-based reflection resource (elements) allocation strategies for an IRS-aided multiuser multiple-input and single-output (MISO) downlink communication systems, in which all transmissions over the same frequency band. Assuming that the IRS is composed with multiple modules, each of which is attached with a smart controller, thus, the states (active/idle) of module can be operated by its controller, and all controllers can be communicated with each other via fiber links. A Stackelberg game-based alternating direction method of multipliers (ADMM) is proposed to jointly optimize the transmit beamforming at the BS and the passive beamforming of the active modules. Numerical examples are presented to verify the proposed algorithm. It is shown that the proposed scheme is effective in the utilities of both the BS and IRS.

This paper investigates an intelligent reflecting surface (IRS) aided cooperative communication network, where the IRS exploits large reflecting elements to proactively steer the incident radio-frequency wave towards destination terminals (DTs). As the number of reflecting elements increases, the reflection resource allocation (RRA) will become urgently needed in this context, which is due to the non-ignorable energy consumption. The goal of this paper, therefore, is to realize the RRA besides the active-passive beamforming design, where RRA is based on the introduced modular IRS architecture. The modular IRS consists with multiple modules, each of which has multiple reflecting elements and is equipped with a smart controller, all the controllers can communicate with each other in a point-to-point fashion via fiber links. Consequently, an optimization problem is formulated to maximize the minimum SINR at DTs, subject to the module size constraint and both individual source terminal (ST) transmit power and the reflecting coefficients constraints. Whereas this problem is NP-hard due to the module size constraint, we develop an approximate solution by introducing the mixed row block l1,F-norm to transform it into a suitable semidefinite relaxation. Finally, numerical results demonstrate the meaningfulness of the introduced modular IRS architecture.

This paper investigates the spectral efficiency (SE) in reconfigurable intelligent surface (RIS)-aided multiuser multiple-input single-output (MISO) systems, where RIS can reconFigure the propagation environment via a large number of controllable and intelligent phase shifters. In order to explore the SE performance with user proportional fairness for such a system, an optimization problem is formulated to maximize the SE by jointly considering the power allocation at the base station (BS) and phase shift at the RIS, under nonlinear proportional rate fairness constraints. To solve the non-convex optimization problem, an effective solution is developed, which capitalizes on an iterative algorithm with closed-form expressions, i.e., alternatively optimizing the transmit power at the BS and the reflecting phase shift at the RIS. Numerical simulations are provided to validate the theoretical analysis and assess the performance of the proposed alternative algorithm.

A novel social-tie motivated relay selection scheme is proposed for dynamic device-to-device (D2D) communications overlaying cellular networks. Using the non-edge cellular users to forward data, the proposed relay selection scheme can improve the transmission performance of the cell-edge users as an explicit benefit of D2D relays. Meanwhile, the effects of both the physical layer and social layer on the relay selection are jointly considered, where social ties are regarded as not only the motivation of relay services, but also the metric of security performance. Moreover, a generalized satisfaction index is introduced for designing a uniform quality of experience (QoE) criterion that can map different quality of service (QoS) metrics such as rate, throughput, delay, into a unified metric, and hence, is beneficial for the tradeoff between QoE and resource efficiency of relay selection. Furthermore, a dynamic optimization process is constructed for analyzing the effects of both the mobility of users and the randomness of channel on the relay selection, with the aid of the Lyapunov framework and drift-plus-penalty (DPP) algorithm. Finally, numerical results validate the effects of the proposed relay selection scheme.

In this paper, we propose a blockchain (BC)-enabled relay selection method in distributed cooperative communication networks, where non-cell-edge users (NCEUs) consume transmit power to relay cell edge users (CEUs) for uplink transmission in exchange for payments from CEUs. The proposed BC-enabled relay selection method aims at eliminating the failure of cooperative device to device (D2D) communication while maintaining privacy protection. By exploiting BC in the probe-reply phase, both CEU request and NCEU reply messages can be recorded in a verifiable manner. Once the feedback messages are received, the next step is decision making, which can be implemented by a two-sided matching game, in which the players include the CEUs party and the NCEUs one. In addition, the information recorded on the BC contains not only the probe-reply messages but also the optimal matching profile (e.g., transmission power sequence of NCEUs and the corresponding payment sequence of CEUs) in the second phase. The simulation results show that the proposed method is improved compared with the traditional matching scheme.

Internet of Things (IoT) as a prospective platform to develop mobile applications, is facing with significant challenges posed by the tension between resource-constrained mobile smart devices and low-latency demanding applications. Recently, mobile edge computing (MEC) is emerging as a cornerstone technology to address such challenges in IoT. In this paper, by leveraging social ties in human social networks, we investigate the optimal dynamic computation offloading mode selection to jointly minimize the total tasks' execution latency and the mobile smart devices' energy consumption in MEC-aided low-latency IoT. Different from the previous studies, which mostly focus on how to exploit social tie structure among mobile smart device users to construct the permutation of all the feasible modes, we consider dynamic computation offloading mode selection with social awareness-aided network resource assignment, involving both the computing resources and transmit power from heterogeneous mobile smart devices. On the one hand, we formulate the dynamic computation offloading mode selection into the infinite-horizon time-average renewal-reward problems subject to time average latency constraints on a collection of penalty processes. On the other hand, an efficient solution is also developed, which elaborates on a Lyapunov optimization-based approach, i.e., drift-plus-penalty (DPP) algorithm. Numerical simulations are provided to validate the theoretical analysis and assess the performance of the proposed dynamic social-aware computation offloading mode selection method considering different configurations of the IoT network parameters.

In this paper, we investigate the optimal dynamic social-aware peer selection with spectrum-power trading to maximize the average sum energy efficiency (EE) of cellular users (CUs) for uplink transmission for an orthogonal frequency division multiple access cellular network with device-to-device (D2D) communications. Different from the previous studies, which mostly focus on how to exploit social ties in human social networks to construct the permutation of all the feasible peers, we consider dynamic peer selection with social awareness-aided spectrum-power trading in D2D overlaying communications. Specifically, the amount of transmit power from the D2D transmitters to relay the CUs for uplink transmission is determined by their social trust levels. Likewise, the D2D transmitters can gain the corresponding amount of spectrum from the CUs for D2D pair link communications, which can be regarded as the compensation of the power consumption for relaying CUs. We formulate the dynamic peer selection problems with social awareness-aided spectrum-power trading in cooperative D2D communications into the infinite-horizon time-average renewal-reward problems subject to time average constraints on a collection of penalty processes. And the Lyapunov optimization concepts-based drift-plus-penalty algorithms are proposed to solve them. The simulation results demonstrate the effectiveness of the proposed dynamic peer selection algorithms. And further performance comparison indicates that the proposed dynamic peer selection algorithms not only maximize the average EE of CUs but also guarantee higher privacy protection.

We investigate the energy-efficient power allocation for downlink transmission in orthogonal frequency division multiple access-based long term evolution systems. Aiming at realizing on-demand power allocation in cellular networks, we explore the available coordination between the base station and multiple users, and propose a new performance merit, namely, the demand side coordination energy efficiency (DSC-EE), which captures the system normalized EE and the demand side information. The proposed DSC-EE is designed to exploit individual disparities from both the entire system and the individual own expected utility perspectives. Our goal is to maximize the DSC-EE of the system via power allocation with a constraint on the maximum transmit power. Specifically, the objective function of DSC-EE maximization problem in a fractional form can be transformed into a subtractive form that is more tractable based on the fractional programming theory. The convergence property of the proposed algorithms and the meaningfulness of the proposed performance merit related to the EE are demonstrated by simulations. The comparison of four EE metrics, the EE and the rate fairness, global-EE, Sum-EE, and DSC-EE, shows that the DSC-EE maximization tends to achieve high implementation level of on-demand power allocation while ensuring the EE of the system. In addition, when the minimum rate replaces the expected rate in the DSC-EE, further performance comparison indicates the necessity and impact of the expected rate in the tolerable quality of service bias function.

In frequency hopping (FH) wireless communications, finding an effective transmission strategy to properly mitigate jamming has been recently considered as a critical issue, due to the inherent broadcast nature of wireless communications. Recently, game theory has been proposed as a powerful tool for dealing with the jamming problem, which can be considered as a player (jammer) playing against a user (transmitter). Different from existing results, in this paper, a bimatrix game framework is developed for modeling the interaction process between the transmitter and the jammer, and the sufficient and necessary conditions for Nash equilibrium (NE) strategy of the game are obtained under the linear constraints. Furthermore, the relationship between the NE solution and the global optimal solution of the corresponding quadratic programming is presented. In addition, a special analysis case is developed based on the continuous game framework in which each player has a continuum of strategies. Finally, we show that the performance can be improved based on our game theoretic framework, which is verified by numerical investigations.