Unmanned aerial vehicles (UAVs) have emerged as effective platforms for mobile edge computing (MEC), offering flexible and efficient computational support to ground users (GUs). Many practical applications, such as deep neural network inference tasks, generate subtasks with complex dependencies, significantly complicating scheduling and offloading decisions. In this paper, we study the joint optimization of UAV deployment, UAV-GU associations, and dependent task offloading decisions within a multi-UAV-enabled MECsystem, aiming to minimize the end time of the overall tasks. The tasks generated by GUs are modeled using directed acyclic graphs (DAGs), explicitly capturing subtask dependencies and execution orders. To address the resulting complex optimization problem, we first propose a Joint Successive convex approximation and Penalty dual decomposition-based Optimization (JSPO) algorithm to determine the initial UAV deployment and UAV-GU associations. Next, we formulate the dependent task offloading decision process as a Markov decision process (MDP), which is solved by employing deep reinforcement learning (DRL). To effectively exploit the structural information within DAG tasks, we integrate a graph attention network (GAT) to provide enhanced state representations for DRL. JSPO and the DRL framework were executed in turns to gradually improve the performance. Extensive simulation results verify that our proposed framework significantly reduces the end time compared to existing methods, demonstrating its superiority in multi-UAV MEC systems.

With the thriving artificial intelligence-generated content (AIGC), it is becoming increasingly appealing to exploit generative AI to generate wireless signals for facilitating wireless sensing. However, this is a challenging task, as wireless signals are highly random in general and contain rich physical information. To tackle these challenges, we propose a novel signal disentanglement and generation framework termed SigGen, which is inspired by the Fourier Transform (FT) that converts signals to the frequency domain and accordingly separates objectives by distinct frequency bands. In our proposed framework, we first disentangle the features of objects embedded in the signal and subsequently modify these features to generate the desired signals. Specifically, we devise a neural network based on the vision transformer (ViT) to extract effective features for signal generation. In this neural network, we incorporate both local and global frequency attention modules to adaptively leverage frequency features, and introduce a hybrid patch embedding module to enhance information interaction for the ViT architecture. Furthermore, we propose a novel sequential training method to improve the disentanglement and generation capability of the neural network. Finally, extensive experiments on two benchmark public wireless sensing datasets demonstrate that our framework can effectively decouple wireless signals and generate diverse signals closely resembling real ones, surpassing state-of-the-art methods by 30.83%. A practical case study further demonstrates that our framework can be used as a data augmentation method to improve gesture recognition accuracy by 12.74%.

This paper studies the unmanned aerial vehicle (UAV)-enabled aerial monitoring assisted by simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), in which one UAV aims to monitor a number of moving targets, and one STAR-RIS is installed on a building for assisting the UAV to broadcast the monitored information to both indoor and outdoor users. Due to the randomness of target movements over time, the UAV needs to adaptively adjust its flight trajectory to track them. This thus results in highly dynamic channel conditions and uncertain UAV energy consumption, which accordingly make the efficient aerial monitoring a challenging task. To address these challenges, we propose a STAR-RIS-aided UAV-enabled aerial monitoring framework, which aims to maximize the long-term average throughput for all users, through joint optimization of transmit beamforming, UAV trajectory, and STAR-RIS configuration, while ensuring the monitoring requirements under strict energy constraints. The formulated problem is a multi-stage stochastic optimization problem, due to the randomness of various system parameters. To handle this problem, we apply the Lyapunov optimization technique and introduce a virtual energy queue to transform it into a series of single-slot optimization subproblems that are solvable online. For each subproblem, we develop efficient algorithms to obtain a near-optimal solution, in which a penalty dual decomposition (PDD) approach is used for the transmit beamforming and STAR-RIS configuration optimization, and a sequential parametric convex approximation (SPCA) method is used for UAV trajectory optimization. Extensive simulations demonstrate that the proposed framework significantly outperforms benchmark schemes, effectively maximizing the throughput and energy efficiency under dynamic operational conditions.

We study convex optimization problems under differential privacy (DP). With heavy-tailed gradients, existing works achieve suboptimal rates. The main obstacle is that existing gradient estimators have suboptimal tail property, resulting in a superfluous factor of d in the union bound. In this paper, we explore algorithms achieving optimal rates of DP optimization with heavy-tailed gradients. Our first method is a simple clipping approach. Under bounded p-th order moments of gradients, with n samples, it achieves minimax optimal population risk with epsilon less than 1/d. We then propose an iterative updating method, which is more complex but achieves this rate for all epsilon smaller than 1. The results significantly improve over existing methods. Such improvement relies on a careful treatment of the tail behavior of gradient estimators. Our results match the minimax lower bound, indicating that the theoretical limit of stochastic convex optimization under DP is achievable.

Label differential privacy (DP) is a framework that protects the privacy of labels in training datasets, while the feature vectors are public. Existing approaches protect the privacy of labels by flipping them randomly, and then train a model to make the output approximate the privatized label. However, as the number of classes K increases, stronger randomization is needed, thus the performances of these methods become significantly worse. In this paper, we propose a vector approximation approach for learning with label local differential privacy, which is easy to implement and introduces little additional computational overhead. Instead of flipping each label into a single scalar, our method converts each label into a random vector with K components, whose expectations reflect class conditional probabilities. Intuitively, vector approximation retains more information than scalar labels. A brief theoretical analysis shows that the performance of our method only decays slightly with K. Finally, we conduct experiments on both synthesized and real datasets, which validate our theoretical analysis as well as the practical performance of our method.

Nonparametric contextual bandit is an important model of sequential decision making problems. Under $\alpha$-Tsybakov margin condition, existing research has established a regret bound of $\tilde{O}\left(T^{1-\frac{\alpha+1}{d+2}}\right)$ for bounded supports. However, the optimal regret with unbounded contexts has not been analyzed. The challenge of solving contextual bandit problems with unbounded support is to achieve both exploration-exploitation tradeoff and bias-variance tradeoff simultaneously. In this paper, we solve the nonparametric contextual bandit problem with unbounded contexts. We propose two nearest neighbor methods combined with UCB exploration. The first method uses a fixed $k$. Our analysis shows that this method achieves minimax optimal regret under a weak margin condition and relatively light-tailed context distributions. The second method uses adaptive $k$. By a proper data-driven selection of $k$, this method achieves an expected regret of $\tilde{O}\left(T^{1-\frac{(\alpha+1)\beta}{\alpha+(d+2)\beta}}+T^{1-\beta}\right)$, in which $\beta$ is a parameter describing the tail strength. This bound matches the minimax lower bound up to logarithm factors, indicating that the second method is approximately optimal.

Semantic communication is a novel communication paradigm that demonstrates great potential in information transmission applications, particularly in challenging scenarios characterized by low signal-to-noise ratio (SNR) conditions. It extracts task-relevant semantic information and performs end-to-end optimization of source and channel coding. However, current mainstream architectures do not account for the importance of features in the subsequent reconstruction process. This shortcoming leads to the transmission of all features, resulting in inefficient bandwidth utilization. Furthermore, existing methods reconstruct all images equally, regardless of their differences in complexity, which is inefficient and wastes computational resources. To address these issues, we develop an efficient semantic communication architecture, termed ESC. Specifically, we design feature selection and reconstruction modules to filter out unimportant information, addressing the problem of transmission feature redundancy and improving bandwidth utilization efficiency. In addition, we develop a multi-branch decoder architecture and an adaptive inference strategy to accommodate the varying complexities of images, allowing samples with satisfactory reconstruction results to exit the decoder network early, thus reducing inference costs. By introducing feature selection, our architecture’s reconstruction quality surpasses that of 5G systems and mainstream semantic communication under the same bandwidth on the Kodak and CLIC2021 datasets. Our adaptive inference strategy achieves speed-ups of approximately 1.37 × and 1.43 × respectively, with only minimal degradation in image reconstruction quality.

Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but is vulnerable to Byzantine attacks and data heterogeneity, which can severely degrade performance. Existing Byzantine-robust approaches tackle data heterogeneity, but incur high computational overhead during gradient aggregation, thereby slowing down the training process. To address this issue, we propose a simple yet effective Federated Normalized Gradients Algorithm (Fed-NGA), which performs aggregation by merely computing the weighted mean of the normalized gradients from each client. This approach yields a favorable time complexity of $\mathcal{O}(pM)$, where $p$ is the model dimension and $M$ is the number of clients. We rigorously prove that Fed-NGA is robust to both Byzantine faults and data heterogeneity. For non-convex loss functions, Fed-NGA achieves convergence to a neighborhood of stationary points under general assumptions, and further attains zero optimality gap under some mild conditions, which is an outcome rarely achieved in existing literature. In both cases, the convergence rate is $\mathcal{O}(1/T^{\frac{1}{2} - \delta})$, where $T$ denotes the number of iterations and $\delta \in (0, 1/2)$. Experimental results on benchmark datasets confirm the superior time efficiency and convergence performance of Fed-NGA over existing methods.

Target speaker extraction (TSE) aims to isolate a specific speaker’s voice from multi-talker environments using enrollment data. While current approaches primarily utilize speaker embeddings from enrollment, they often neglect contextual information and the dynamic interactions between the mixture and enrollment. To address this limitation, we propose a novel DualStream Contextual Fusion Network (DCF-Net) that operates in the time-frequency (T-F) domain. Our framework introduces a DualStream Fusion Block (DSFB) that: 1) captures contextual information, 2) models interactions between contextualized enrollment and mixture representations across spatial and channel dimensions, and 3) employs these enriched representations to guide the extraction process. Comprehensive experiments show that DCF-Net achieves state-of-the-art (SOTA) performance with a 21.6 dB improvement in scale-invariant signal-to-distortion ratio (SI-SDR) on benchmark datasets while demonstrating robustness in noisy and reverberant conditions. Notably, our model significantly reduces the wrong extraction rate to just 0.4% when testing on target confusion problem (TCP), underscoring its practical applicability.

Semantic communication is an emerging paradigm with significant potential for image transmission. However, resource-efficient architecture design and resource allocation in this field have not received adequate research attention. This paper proposes a resource-efficient multi-branch semantic communication architecture based on saliency detection, aimed at optimizing computational efficiency in image transmission. The architecture leverages models with varying capacities to process regions of images with different complexities. We further address the problem of multi-user uplink semantic communication and resource allocation, focusing on minimizing the total energy consumption for communication and computation. The optimization problem, subject to user demand, computation, delay, and transmission power constraints, is non-convex due to the coupling of variables, making it challenging to solve. To tackle this, we introduce a two-level decomposition approach. The lower-level problem, given a fixed compression rate, is solved using Karush-Kuhn-Tucker (KKT) conditions to derive closed-form solutions for transmission power and computation frequency. The upper-level problem, which optimizes the compression rate, is reformulated as a monotone optimization problem for efficient solution finding. Numerical results demonstrate that the proposed architecture significantly reduces computational resource usage while maintaining image quality, and the resource allocation strategy effectively minimizes energy consumption, outperforming baseline schemes in terms of energy efficiency.

Low Earth Orbit (LEO) satellites play a crucial role in providing high-speed internet to remote areas and ensuring network resilience during outages. The design of efficient satellite constellations requires optimizing network topology, which is a complex task due to the large solution space and the need for fault tolerance. This paper presents the AlphaSat algorithm, a two-phase approach to improve latency and network robustness in LEO constellations. In the initialization phase, Monte Carlo Tree Search (MCTS) is used to generate an initial topology by selecting links from a vast search space. In the refinement phase, an edge-switching method is applied to enhance network resilience and performance. AlphaSat is evaluated on OneWeb, Starlink, and Telesat mega-constellations, demonstrating superior performance over existing algorithms. The results show significant reductions in latency ranging from 4.7% to 44.5% and improvements in network robustness, increasing by 3.3% to 28.3%. Furthermore, AlphaSat effectively balances network load and optimizes power consumption, offering a promising solution for efficient and resilient LEO satellite network design.

The integration of edge computing into satellite networks offers a promising solution for extending computational services to remote and underserved areas. To effectively provide a variety of computing services, it is essential to cache the corresponding services on satellites. However, challenges exist such as dynamic computing requests that vary over time and space, energy constraints due to restricted power supply, as well as limited storage capacity on satellites and the impracticality of frequently adjusting service deployments. To tackle such challenges, this paper proposes a two-timescale joint optimization framework to minimize energy consumption in satellite edge computing networks while ensuring the delay requirements, by jointly optimizing service placement and task offloading, as well as computation resource and power allocation. On a larger timescale, we optimize service caching placement by strategically deploying services on satellites and ground devices (GDs) based on long-term service request statistics, aiming to minimize the total average delay over each time frame. We develop an efficient iterative algorithm by employing penalty-based methods and Lagrange duality techniques to achieve suboptimal service deployment. On a smaller timescale, we optimize task offloading and resource allocation in shorter time slots, adapting to dynamic traffic fluctuations to minimize energy consumption while meeting delay constraints. We utilize alternating optimization and quadratic transform methods to efficiently allocate resources and schedule tasks. Extensive simulations demonstrate the effectiveness and superiority of our framework over benchmark schemes, revealing significant reductions in delay and energy consumption. The results also highlight the trade-offs between task delay and energy consumption, as well as between transmit power and energy consumption.

In a real federated learning (FL) system, communication overhead for passing model parameters between the clients and the parameter server (PS) is often a bottleneck. Hierarchical federated learning (HFL) that poses multiple edge servers (ESs) between clients and the PS can partially alleviate communication pressure but still needs the aggregation of model parameters from multiple ESs at the PS. To further reduce communication overhead, we remove the central PS, so that each iteration only completes model training by transmitting the global model between two adjacent ES. We call this serial learning method Sequential FL (SFL). For the first time, we introduced SFL into HFL and proposed a novel algorithm adapted to this combined framework, called Fed-CHS. Convergence results are derived for strongly convex and non-convex loss functions under various data heterogeneity setups, which show comparable convergence performance with the algorithms for HFL or SFL solely. Experimental results provide evidence of the superiority of our proposed Fed-CHS on both communication overhead saving and test accuracy over baseline methods.

Cellular-connected Unmanned Aerial Vehicles (UAVs) have significant potential for target tracking in future cellular networks due to their broad coverage and operational flexibility. In this paper, we consider a multi-cell cellular network with a cellular-connected UAV for target tracking, which encounters challenges such as unpredictable flight energy consumption from the stochastic movements of the tracking target and severe uplink interference from ground devices (GDs). To tackle these challenges, we propose a multi-stage stochastic optimization framework focused on energy-efficient target tracking with interference coordination. Our objective is to optimize the long-term average uplink throughput of both aerial users and GDs by jointly optimizing the UAV's trajectory, power allocation, and cell association across multiple orthogonal communication resource blocks (RBs). The formulated stochastic non-convex problem is first transformed into a deterministic problem for each time slot by using the Lyapunov optimization framework. An online optimization strategy is proposed, utilizing the optimal structure, alternative optimization, and successive convex approximation (SCA) techniques. Simulation results show that the proposed approach significantly enhances network throughput and UAV energy queue stability compared to existing baseline schemes.

This paper addresses federated learning (FL) in the context of malicious Byzantine attacks and data heterogeneity. We introduce a novel Robust Average Gradient Algorithm (RAGA), which uses the geometric median for aggregation and allows flexible round number for local updates. Unlike most existing resilient approaches, which base their convergence analysis on strongly-convex loss functions or homogeneously distributed datasets, this work conducts convergence analysis for both strongly-convex and non-convex loss functions over heterogeneous datasets. The theoretical analysis indicates that as long as the fraction of the data from malicious users is less than half, RAGA can achieve convergence at a rate of$\mathcal {O}({1}/{T^{2/3- \delta }})$for non-convex loss functions, where$T$is the iteration number and$\delta \in (0, 2/3)$. For strongly-convex loss functions, the convergence rate is linear. Furthermore, the stationary point or global optimal solution is shown to be attainable as data heterogeneity diminishes. Experimental results validate the robustness of RAGA against Byzantine attacks and demonstrate its superior convergence performance compared to baselines under varying intensities of Byzantine attacks on heterogeneous datasets.

The Multi-Layer Satellite Remote Sensing (SRS) integrates data collection by Low Earth Orbit (LEO) satellites and data processing assistance from Medium Earth Orbit (MEO) satellites, thereby playing a crucial role in scientific exploration. However, effectively controlling access to LEO satellites for processing data, especially considering the frequent handovers caused by speed differences, presents a significant challenge to achieving high energy efficiency services. To address this challenge, we explore cooperative dynamic access control based on efficient communication mechanisms, with the aim of prioritizing processed data volume and meeting energy consumption requirements for satellites. Specifically, we formulate the access control issue as an optimization problem and integrate it into the framework of partially observable Markov decision process (POMDP), considering MEO satellites’ limited observation ability. By employing Multi-agent Deep Reinforcement Learning (MADRL), we propose a novel dynamic access control algorithm named DAC to solve our featured problem. Specifically, for improving performance, communication-efficient cooperation among MEOs is enhanced through modeling decision-relevant information of fellow MEO satellites and maximizing mutual information with their actual data to extract precise awareness and enable the generation of concise message. Finally, we conduct comprehensive experiments and an ablation study spanning the Starlink, OneWeb, and Telesat mega-constellations. The results demonstrate that DAC increases the average system data processing volume by at least 13.5%, while meeting energy consumption constraints and outperforming baseline algorithms.

Federated learning (FL) has emerged as an appealing machine learning approach to deal with massive raw data generated at multiple mobile devices, which needs to aggregate the training parameter of every mobile device at one base station (BS) iteratively. For parameter aggregating in FL, over-the-air computation is a spectrum-efficient solution, which allows all mobile devices to transmit their parameter-mapped signals concurrently to a BS. Due to heterogeneous channel fading and noise, there exists difference between the BS’s received signal and its desired signal, measured as the mean-squared error (MSE). To minimize the MSE, we propose to jointly optimize the signal amplification factors at the BS and the mobile devices as well as the data size (the number of data samples involved in local training) at every mobile device. The formulated problem is difficult to address due to its nonconvexity. To find the optimal solution, we perform cost function simplification and variable transformation, and solve the transformed problem in a two-level structure. Optimal solution of the lower level problem is found by analyzing every candidate solution from the Karush–Kuhn–Tucker (KKT) condition. Optimal solution of the upper level problem is found by exploring its piecewise convexity. Numerical results show that our proposed method can greatly reduce the MSE and can help to enhance the training performance of FL compared with benchmark methods.

The integrated satellite-terrestrial network (ISTN) system has experienced significant growth, offering seamless communication services in remote areas with limited terrestrial infrastructure. However, designing a routing scheme for ISTN is exceedingly difficult, primarily due to the heightened complexity resulting from the inclusion of additional ground stations, along with the requirement to satisfy various constraints related to satellite service quality. To address these challenges, we study packet routing with ground stations and satellites working jointly to transmit packets, while prioritizing fast communication and meeting energy efficiency and packet loss requirements. Specifically, we formulate the problem of packet routing with constraints as a max-min problem using the Lagrange method. Then we propose a novel constrained Multi-Agent reinforcement learning (MARL) dynamic routing algorithm named CMADR, which efficiently balances objective improvement and constraint satisfaction during the updating of policy and Lagrange multipliers. Finally, we conduct extensive experiments and an ablation study using the OneWeb and Telesat mega-constellations. Results demonstrate that CMADR reduces the packet delay by a minimum of 21% and 15%, while meeting stringent energy consumption and packet loss rate constraints, outperforming several baseline algorithms.

This paper investigates how to suppress the Age of Information (AoI) in an opportunistic channel access system, which allows multiple mobile devices to access a base station without central coordination while being aware of instant channel quality. An optimization problem is formulated to minimize the average AoI by optimizing each mobile device’s probability of contending for channel access opportunity and the threshold of offload rate. We derive the exact expression of the average AoI and generate a reformulated optimization problem. Although being non-convex, the reformulated problem is tackled by the following operations. First, we leverage the Dinkelbach method and the block coordinate descent method to convert the reformulated problem into an iterative solving procedure of two non-convex sub-problems, which optimize the contending probability and the threshold of offload rate respectively. Second, for each non-convex sub-problem, we explore the piecewise differential monotonicity for the cost function, and achieve the associated optimal solution by transforming them into standard monotonic optimization problems. Numerical results can verify the effectiveness of the proposed method through the comparison with benchmark methods.

The incorporation of Unmanned Aerial Vehicles (UAVs) into cellular networks opens up new possibilities to enhance their ubiquitous operations and establish superior performance owing to the high probability of line-of-sight (LoS) for air-to-ground channels. However, this also results in the UAV inducing more significant uplink interference to non-associated Base Stations (BSs). This paper explores the online design policy in cellular-connected multiple UAV communications in the absence of channel conditions, focusing on wireless resource allocation and dynamic three-dimensional (3-D) path planning. Our objective is to maximize the minimum uplink throughput for all UAVs while considering the energy constraints of the UAVs. First, we implement an online design utilizing the achievable rate based on the estimated instantaneous channel state information (CSI) for the current time slot, and the expected data rate for future time slots based on channel distribution information (CDI). Our solution employs the exact penalty method along with alternating optimization and successive convex optimization methods. Second, we formulate an online design by merely using the achievable rate based on the estimated instantaneous CSI for the current time slot. We introduce an energy-triggered penalty term to regulate the energy consumption of the UAVs, resulting in a low-complexity solution even if the CDI is unavailable before the flight. Lastly, we conduct extensive simulations to corroborate our findings and provide comprehensive comparisons with other baseline schemes to underline the effectiveness of the proposed designs.

In mobile edge computing (MEC), one of the key issue is to optimize the offloading policy and the allocation of communication and computation resources among multiple mobile users (MUs). For different MUs, deadline of their computation tasks may be heterogeneous. It becomes more challenging as the computation burden of each computation task turns to be a random variable, which may even conform to uncertain probabilistic distribution. To address these issues, this work studies the offloading of asynchronous computation task and resource allocation in a MEC system supporting multiple MUs. Only with the mean and variance about uncertain computation burden, an optimization problem to minimize the weighted sum of energy consumption of multiple MUs is formulated, which is non-deterministic and non-convex, and is hard to solve. To overcome this challenge, we transform it into a deterministic problem, but is still non-convex. In order to solve the non-convex deterministic optimization problem, we decompose the problem into two levels. A heuristic algorithm is proposed for the upper-level to solve an ordering problem and a combination of alternative descend method, successive convex approximation (SCA), and Karush-Kuhn-Tucker (KKT) condition investigating are utilized for the lower-level problem.

Intelligent reflecting surface (IRS) has been recently exploited as a symbiotic radio (SR) technology to improve energy and spectral efficiencies in wireless systems. In this article, we consider a symbiotic IRS-assisted mobile-edge computing (MEC) system that allows edge users to first harvest RF power from a hybrid access point (HAP) and then offload its computational workload to the MEC server associated with the HAP. We aim to minimize the HAP’s energy consumption by jointly optimizing the users’ offloading schemes, the HAP’s active beamforming, and the IRS’s passive beamforming strategies. We propose an optimization-driven hierarchical deep deterministic policy gradient (OH-DDPG) framework to decompose the energy minimization problem into the optimization and the learning subproblems, respectively. The outer loop DDPG learning method adapts the IRS’s passive beamforming strategy, while the inner loop optimization deals with the other control variables with reduced dimensionality. Moreover, to improve the learning efficiency, we extend OH-DDPG to the multiagent scenario. In particular, the HAP first estimates the users’ offloading strategy by the inner-loop optimization and shares it with all user agents. Then, each user agent refines its offloading decision using the DDPG algorithm independently. This can avoid signaling overhead among users and improve the multiuser learning efficiency. Simulation results show that the proposed OH-DDPG and the multiuser extension can achieve significant performance gains compared to the conventional model-free learning algorithms.

In mobile edge computing (MEC) supporting multiple mobile users (MUs), it is essential to optimize the offloading policy and communication and computation resource allocation. A main challenge is that the computation burden of a computation task may be random and even with uncertain probabilistic distribution. To address this challenge, we investigate a multiple-MU MEC system with random computation burden. For the random computation burden of an MU, only the mean and variance are known, but its distribution is unknown. Robustness is provided such that computation outage probabilities (due to uncertain distribution of computation burden) are bounded by a predefined threshold. We minimize the weighted sum of the MUs’ energy consumption. The formulated optimization problem is non-deterministic and non-convex, and thus, is hard to solve. To deal with the challenge, we transform the formulated problem into a deterministic and convex problem by applying the Chebyshev-Cantelli inequality and some mathematical manipulations. We further decompose the convex problem to a lower-level and an upper-level problem. Low-complexity algorithms are developed for the lower-level and upper-level problems. The overall complexity of our proposed method is linear with the number of MUs.

Running intelligent applications on a satellite is in urgent need, which can help to extract useful information from massive surveillance or remote sensing data and return it to ground in time. However, the limited computing ability on a satellite prohibits it from completing the whole application by itself quickly. Within the circumstance of space-air-ground integrated network (SAGIN), we propose to offload part of the computation task from the satellite to the ground station with strong computing ability, through the introduction of airship, which can assist the satellite not only by relaying but also in computing. To save the energy consumption of the satellite and airship, task offloading policy and resource allocation, are investigated for a special task model supporting deep neural network (DNN), which is popular in intelligent application. An optimization problem is formulated, which is difficult to solve. We achieve the global optimal solution through the following operations: 1) Transform the formulated problem into two levels, with every level dealing with discrete or continuous variables exclusively; 2) Explore implicit monotonicity and convexity of concerned functions so as to solve the non-convex lower level problem optimally only with several rounds of bisection or Golden search methods; 3) Solve the upper level problem optimally by enumeration but with polynomial complexity. Numerical results verify the effectiveness of our proposed method.

Incorporating mobile-edge computing (MEC) in the Internet of Things (IoT) enables resource-limited IoT devices to offload their computation tasks to a nearby edge server. In this article, we investigate an IoT system assisted by the MEC technique with its computation task subjected to sequential task dependency, which is critical for video stream processing and other intelligent applications. To minimize energy consumption per IoT device while limiting task processing delay, task offloading strategy, communication resource, and computation resource are optimized jointly under both slow and fast-fading channels. In slow fading channels, an optimization problem is formulated, which is nonconvex and involves one integer variable. To solve this challenging problem, we decompose it as a 1-D search of task offloading decision problem and a nonconvex optimization problem with task offloading decision given. Through mathematical manipulations, the nonconvex problem is transformed to be a convex one, which is shown to be solvable only with the simple Golden search method. In fast-fading channels, optimal online policies depending on the instant channel state are derived even though they are entangled. In addition, it is proved that the derived policy will converge to the offline policy when the channel coherence time is low, which can help save extra computation complexity. Numerical results verify the correctness of our analysis and the effectiveness of our proposed strategies over the existing methods.

The emergence of mobile-edge computing (MEC) makes it possible to run intelligent applications on Internet of Things (IoT) devices. However, due to blockage or deep fading, one IoT device may not have direct link with the edge server. In this case, many surrounding wireless devices can serve as a cooperative node. In this article, we study a cooperative MEC system running sequential task, which is composed of a series of subtasks and can support many intelligent applications. To minimize the energy consumption of the IoT device and cooperative node, a task offloading policy together with the allocation of communication and computation resources is designed jointly. The cases when the cooperative node has no/has private task to complete are investigated, which are denoted as cases I and II, respectively. Although both cases involve the optimization of integer variables, their optimal solutions are achieved. For the first case, the associated problem is simplified equivalently and then decomposed into two levels, with the upper level dealing with integer variables and the lower level handling continuous variables. Bisection search is employed to reach optimality in the lower level and the searching space is compressed in the upper level. For the second case, the associated problem is subdivided into three subproblems. To solve every subproblem optimally, a similar operation like case I is followed, with a semiclosed form solution derived in the lower level. Numerical results verify the effectiveness of our proposed methods compared with benchmark methods and our effort on reducing computation complexity.

The novel reconfigurable intelligent surface (RIS) is an emerging technology which facilitates high spectrum and energy efficiencies in Beyond 5G and 6G wireless communication applications. Against this backdrop, this paper investigates two-way communications via reconfigurable intelligent surfaces (RISs) where two users communicate through a common RIS. We assume that uplink and downlink communication channels between two users and the RIS can be reciprocal. We first obtain the optimal phase adjustment at the RIS. We then derive the exact outage probability and the average throughput in closed-forms for single-element RIS. To evaluate multiple-element RIS, we first introduce a gamma approximation to model a product of Rayleigh random variables, and then derive approximations for the outage probability and the average throughput. For large average signal-to-interference-plus-noise ratio (SINR) $\rho$, asymptotic analXsis also shows that the outage decreases at the rate $(\log(\rho)/\rho)$ where L is the number of elements, whereas the throughput increases with the rate $\log(\rho)$.

In this6study, the channel estimation problem is investigated for a wireless communication system assisted by a reconfigurable intelligent surface (RIS). The RIS thus creates an assistant channel, which has the features of positivity and dominance. Owing to these features, the channel estimation problem is formulated as a constrained residual sum of squares minimisation problem, which differs radically from the traditional channel estimation issue. An efficient Lagrange multiplier and dual ascent‐based estimation scheme is then designed to obtain an iterative solution for the estimator. Moreover, the Cramér–Rao lower bounds are deduced as a performance benchmark. Simulation results show that the authors' designed scheme improves the estimation accuracy up to 33%, compared with the conventional least‐square method in the low signal‐to‐noise ratio regime.

In this paper, we investigate the two-way communication between two users assisted by a reconfigurable intelligent surface (RIS). The scheme that two users communicate simultaneously over Rayleigh fading channels is considered. The channels between the two users and RIS can either be reciprocal or non-reciprocal. For reciprocal channels, we determine the optimal phases at the RIS to maximize the signal-to-interference-plus-noise ratio (SINR). We then derive exact closed-form expressions for the outage probability and spectral efficiency for single-element RIS. By capitalizing the insights obtained from the single-element analysis, we introduce a gamma approximation to model the product of Rayleigh random variables which is useful for the evaluation of the performance metrics in multiple-element RIS. Asymptotic analysis shows that the outage decreases at (log(ρ)/ρ)Lrate where L is the number of elements, whereas the spectral efficiency increases at log(ρ) rate at large average SINR p. For non-reciprocal channels, the minimum user SINR is targeted to be maximized. For single-element RIS, closed-form solution is derived whereas for multiple-element RIS the problem turns out to be non-convex. The latter one is solved through semidefinite programming relaxation and a proposed greedy-iterative method, which can achieve higher performance and lower computational complexity, respectively.

Due to the coverage constraint of a wireless base station, mobile users suffer from the unstable network connection and poor service quality, especially for the prevalent video services. As an alternative solution, an unmanned aerial vehicle (UAV) is able to reach the cell edge and serve ground users (GUs). In this paper, we extend the UAV applications to the more challenging adaptive streaming service over fading channel. First, we decompose the system into different modules, and present mathematical models for each of them, including a trajectory model of the UAV, fading channels between the UAV and GUs, and video streaming utility. Second, we formulate the problem as a non-convex optimization problem by optimizing the UAV trajectory and transmit power allocation, jointly with transmission schedule and rate allocation for multiple users. The objective is to maximize the overall utility while guaranteeing the fairness among multiple users under the UAV energy budget and rate-outage probability constraints. Third, to tackle this problem, we first analyze the relationship between transmission rate and rate-outage probability over the fading channel, and then divide the original problem into three subproblems, which can be solved by leveraging the successive convex approximation technique. Furthermore, an overall iterative algorithm over the three subproblems is proposed to obtain a locally optimal solution by applying the block coordinate descent technique. Finally, through extensive experiments, we demonstrate that the proposed design can achieve almost 30% performance gain in terms of max-min streaming utility for all users, compared with other benchmark schemes.

By allowing a mobile device to offload computation- intensive tasks to a base station, mobile edge computing (MEC) is a promising solution for saving the mobile device's energy. In real applications, the offloading may span multiple fading blocks. In this paper, we investigate energy-efficient offloading over multiple fading blocks with random channel gains. An optimization problem is formulated, to find out how much data should be offloaded such that the mobile device's energy consumption is minimal. Although the formulated optimization problem is non-convex, we prove that the objective function of the problem is piecewise convex, and accordingly develop an optimal solution for the problem. Numerical results verify the correctness of our findings and the effectiveness of our proposed method.

Mobile edge computing (MEC) is a kind of technology which can provide computing service for users at the edge of mobile network. This technology can help mobile devices to save local computing of task and reduce energy consumption. Meanwhile, this technology can help to solve the problem of insufficient computing power of the Internet of things (loT). This paper investigates this topic for the case of multiple base stations, in which one user uses time-division multiple access technology (TDMA) and frequency division multiple access technology (FDMA) transmission mode to offload data. Two systems with single user and several base stations are investigated and two optimization problems are formulated. The optimal optimization problem under TDMA transmission mode is a convex optimization problem, which is easy to solve. The optimization problem under FDMA transmission mode is non-convex. To solve the non-convex optimization problem, we decompose it into two layers. In the upper layer, the time for offloading data is optimized, while all the other parameters are optimized with the time for data offloading is fixed. The simulation result shows that the increase of the amount of base station can contribute to the decrease of energy consumption. TDMA mode is better compared with FDMA mode.

By allowing mobile device to offload all or part of a latency-constrained computational task to a base station at the edge of a network, mobile edge computing (MEC) is a promising technique to help the mobile device save its energy consumption. In this paper, we consider the scenario with one mobile device and multiple edge base stations, which is usual in practice. Nonorthogonal multiple access (NOMA) technique is implemented. An optimization problem is formulated, in which the transmission power to every edge base station and the amount of data to be offloaded for computation is optimized to minimize the total energy consumption of the mobile device. However, the formulated optimization problem is non-convex. To get the global optimal solution, we decompose the formulated optimization problem into two levels. In the lower level, a convex optimization problem is required to be solved. By finding out some special property of the lower level optimization problem, the upper level optimization problem can be formulated as a monotonic optimization problem, whose global optimal solution is achievable. Numerical results verify the effectiveness of our proposed method.

Deploying sub-6-GHz network together with millimeter wave (mm-wave) is a promising solution to simultaneously achieve sufficient coverage and high data rate. In heterogeneous networks, the traditional coupled access, i.e., users are constrained to be associated with the same base station in both downlink and uplink, is no longer optimal, and the concept of downlink and uplink decoupling (DUDe) has recently been proposed. In this paper, we analyze the coverage probability and area throughput for both the downlink and uplink of sub-6-GHz/mm-wave cellular networks with decoupled access. Compared with the existing works, we take uplink power control and mm-wave interference into account. Using the tools from stochastic geometry, the expressions of signal-to-interference-plus-noise ratio coverage probability, user-perceived rate coverage probability and the area throughput are derived. The impact of decoupled access and different small cells (SCells) is investigated. In particular, analytical results reveal that with decoupled access, UEs are more likely to be associated with SCells in uplink when the network is sparse, and the uplink traffic will be offloaded from sub-6-GHz SCells to mm-wave SCells when the network is dense. Moreover, the dense deployment of mm-wave SCells rather than sub-6-GHz SCells is more reasonable, and the DUDe is a key factor in improving the performance of dense cellular networks with multi-band.

In this paper, we consider a point-to-point single antenna communication system where the receiver decodes information and harvests energy at the same time. Two practical schemes, namely power splitting scheme and time switching scheme, are investigated, in which the receiver splits or switches signals for information decoding and energy harvesting. We derive the characteristics of the formulated problem which maximizes outage received power under the outage capacity probability and outage received power probability constraints. Moreover, the numerical results prove the correctness of the characteristics of the optimization problem. In particular, it illustrates that the PS scheme outperforms the TS scheme.

In this paper, we consider a wireless communication pair powered with radio frequency (RF) signal, in which the transmitter first harvest energy from an energy access point and then transmit information to the destination. Optimization problems aiming at maximizing throughput are investigated with a consideration of two types of power constraints at the transmitter: i) short-term power constraint, which corresponds to the case that no battery capacity is assumed at the transmitter and ii) long-term power constraint, which corresponds to the case that battery capacity at the transmitter is large enough. The formulated optimization problems are non-convex but are transformed to be convex ones in the first step. For the optimization problem with short-term power constraint, a fast algorithm with a combination of bisection search and closed-form solution is proposed for the first time. For the optimization problem with long-term power constraint, an online calculation method is designed for the first time. Numerical results prove the effectiveness of the proposed algorithms.

We consider a cognitive radio system, in which a secondary transmitter harvests energy from a primary transmitter's wireless signals. If the achievable rate of the primary transmitter's direct link to the primary receiver is smaller than a target rate, the secondary transmitter can provide decode-and-forward relaying service for the primary system and transmit its own data as well, in which the secondary transmitter uses a time-switching protocol to harvest energy and decode primary transmitter's information. Our target is to achieve maximal secondary throughput by selecting the time portion used for energy harvesting and the percentage of the secondary transmitter's power used for relaying. The formulated problem is nonconvex. To solve the problem, we show that, after some math manipulations, the initial problem can be converted to a problem with the objective function being theoretically proved to be quasiconcave. Then we propose a two-level bisection search algorithm to find the maximal objective function. The efficiency of our proposed method is demonstrated by computer simulation.

In this study, a problem of efficient spectrum sensing and power allocation is studied. A network of cognitive radio adopting the technique of two‐way relay is considered. A relay node, sitting between two secondary users, carries out spectrum sensing and helps the two secondary users to realise bidirectional relay communications. For such a system, an optimisation problem which targets at maximising the average rate between the two secondary users while guaranteeing the detection probability above a predefined threshold, is formulated. Although the problem is shown to be non‐convex which means the global optimal solution is hard to be obtained, the authors solve the formulated problem global optimally by using a combination of bilevel optimisation and monotonic programming. Numerical results are provided to present the effectiveness of the authors’ proposed algorithm.

In this paper, we investigate the energy-efficient power control for device-to-device (D2D) communications underlaying cellular networks, where uplink resource blocks allocated to one cellular user equipment are reused by multiple D2D pairs and co-channel interference caused by resource sharing becomes a significant challenge. We consider both the total energy efficiency (EE) and individual EE optimization problems, which are fractional programming and generalized fractional programming problems, respectively, and are hard to tackle due to their non-concave nature. We first transform them into equivalent optimization problems in parametric subtractive forms, which fit in a class of non-concave optimization methods known as difference of two concave functions programming, and then solve them using Dinkelbach and branch-and-bound methods to give global optimal solutions. Due to the unaffordable complexity of the global optimal solution, we further propose sub-optimal schemes through adding constraints on the interferences to convert the non-concave problems into concave ones and to give sub-optimal solutions with reasonable complexity. The sub-optimal solution gives a tight lower bound on the optimal EE. Simulation results are presented to demonstrate the effectiveness of the proposed schemes.

Ambient backscatter technology that utilizes the ambient radio frequency signals to enable the communications of battery-free devices has attracted much attention recently. In this paper, we study the problem of signal detection for an ambient backscatter communication system that adopts the differential encoding to eliminate the necessity of channel estimation. Specifically, we formulate a new transmission model, design the data detection algorithm, and derive two closed-form detection thresholds. One threshold is used to approximately achieve the minimum sum bit error rate (BER), while the other yields balanced error probabilities for “0” bit and “1” bit. The corresponding BER expressions are derived to fully characterize the detection performance. In addition, the lower and the upper bounds of BER at high signal-to-noise ratio regions are also examined to simplify a performance analysis. Simulation results are then provided to corroborate the theoretical studies.

In this paper, the problem of joint transmit power and bandwidth allocation over multiple channels is investigated for a secondary user in underlay mode, when partial information of interference channel is known. The target is to maximize the capacity of a secondary user under a probabilistic constraint of the interference to the primary user. A robust optimization problem is formulated, which is nondeterministic and cannot be solved directly. We then transform the original optimization problem into an equivalent convex optimization problem. For general case, an optimal solving algorithm, which is a combination of analytical and bisection-search methods, is given. For some special cases, simple and optimal solving algorithms are also devised. Numerical results are presented to show that our proposed optimal algorithm has low computation complexity when the number of channels is not large and can achieve global optimal utility in general case and our proposed simple algorithms have much lower computation complexity and can achieve global optimal utility in special cases.

In this paper, we consider the wire-tap channel model consisting of three users, namely, a transmitter, a legal receiver, and an eavesdropper, where the eavesdropper has possible unlimited centralized or distributed multiple antennas, while the legal user has just a single antenna. With this model, we study the realization of strict positive secrecy rate through Rayleigh flat fading channels using M-ary phase shift keying (PSK) and orthogonal space-time block code (OSTBC) based on channel reciprocal principle. We demonstrate that the information rate at the eavesdropper can be reduced to zero and a positive communication rate at the legal receiver can be realized if the transmitter employs a proper phase precoder based on the channel reciprocal, and uses OSTBC and M-ary PSK modulations for data transmissions. Application examples of space-time codes for positive secrecy rate are illustrated. As we have given the eavesdropper more capabilities than the legal receiver and thus captured a worst case scenario, any positive secrecy rate that is achieved serves as a lower bound on the secrecy capacity.

Abstract This paper investigates the channel selection and slot time configuration in a cognitive radio network with a number of potential channels. Each channel alternates between ON state (i.e., the primary user is using the channel) and OFF state (i.e., the primary user does not use the channel), and the state evolution process is modeled as a continuous‐time Markov process. The traffic parameters (the transition rates) of the Markov process also evolve with time, modeled as a discrete‐time Markov process. A secondary user adopts a slotted structure with dynamic slot length. At each slot, the secondary user needs to determine which channel to sense and, if the channel is sensed idle, how long the slot length should be. Considering both the amount of data that the secondary user can transmit and the duration when the secondary user interferes with primary activities, a reward definition is given. Based on the reward definition, an adaptive channel selection and slot length configuration method is proposed, which includes a reward maximization procedure to maximize the achieved reward and an update procedure for the channel state belief vector and traffic parameter state belief vector. Numerical results are given to demonstrate the effectiveness and features of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we present a novel approach for automatically creating the photo collage that assembles the interest regions of a given group of images naturally. Previous methods on photo collage are generally built upon a well-defined optimization framework, which computes all the geometric parameters and layer indices for input photos on the given canvas by optimizing a unified objective function. The complex nonlinear form of optimization function limits their scalability and efficiency. From the geometric point of view, we recast the generation of collage as a region partition problem such that each image is displayed in its corresponding region partitioned from the canvas. The core of this is an efficient power-diagram-based circle packing algorithm that arranges a series of circles assigned to input photos compactly in the given canvas. To favor important photos, the circles are associated with image importances determined by an image ranking process. A heuristic search process is developed to ensure that salient information of each photo is displayed in the polygonal area resulting from circle packing. With our new formulation, each factor influencing the state of a photo is optimized in an independent stage, and computation of the optimal states for neighboring photos are completely decoupled. This improves the scalability of collage results and ensures their diversity. We also devise a saliency-based image fusion scheme to generate seamless compositive collage. Our approach can generate the collages on nonrectangular canvases and supports interactive collage that allows the user to refine collage results according to his/her personal preferences. We conduct extensive experiments and show the superiority of our algorithm by comparing against previous methods.

In this paper, a new robust routing formulation in a wireless mesh network (WMN) is proposed, which enables the routing strategy to be robust to random traffic fluctuation. Together with resource allocation in link layer, an optimization problem aiming at minimizing the total transmission power in the WMN is formulated and solved optimally. The research assumes only limited knowledge of the traffic distribution, in particular, arbitrary distribution with known mean and variance. Numerical results are presented to illustrate the features of the proposed strategy.

In this paper, optimal multi-channel cooperative sensing strategies in cognitive radio networks are investigated. A cognitive radio network with multiple potential channels is considered. Secondary users cooperatively sense the channels and send the sensing results to a coordinator, in which energy detection with a soft decision rule is employed to estimate whether there are primary activities in the channels. An optimization problem is formulated, which maximizes the throughput of secondary users while keeping detection probability for each channel above a pre-defined threshold. In particular, two sensing modes are investigated: slotted-time sensing mode and continuous-time sensing mode. With a slotted-time sensing mode, the sensing time of each secondary user consists of a number of mini-slots, each of which can be used to sense one channel. The initial optimization problem is shown to be a nonconvex mixed-integer problem. A polynomial-complexity algorithm is proposed to solve the problem optimally. With a continuous-time sensing mode, the sensing time of each secondary user for a channel can be any arbitrary continuous value. The initial nonconvex problem is converted into a convex bilevel problem, which can be successfully solved by existing methods. Numerical results are presented to demonstrate the effectiveness of our proposed algorithms.

Two major challenges exist in the development and deployment of cognitive radio networks: spectrum sensing and hidden terminal problem. In this research, we consider a network structure where the spectrum sensing task is separated from the unlicensed users (secondary users). The service provider for the secondary users needs to place sensing devices within the networks of licensed users (primary users). These sensing devices sense the primary users' activity. The sensing devices also decide whether to admit a secondary user's transmission. A new cognitive cycle is proposed accordingly. The proposed protocol is analyzed using the theory of Lamé curve. The problem of optimally locating sensing devices and the properties of the proposed system are studied for single-user case and multi-user case. For the case without a separate control channel, a lowtemperature handshake technique is proposed for handshakes between the secondary users and the sensing devices. The other advantage of the proposed scheme is from the business model point of view: the expensive sensing devices will be implemented by the cognitive radio service provider, instead of being built in the secondary user devices which are usually consumer products demanding low cost.

This paper investigates the optimal sensing order problem in multi-channel cognitive medium access control with opportunistic transmissions. The scenario in which the availability probability of each channel is known is considered first. In this case, when the potential channels are identical (except for the availability probabilities) and independent, it is shown that, although the intuitive sensing order (i.e., descending order of the channel availability probabilities) is optimal when adaptive modulation is not used, it does not lead to optimality in general with adaptive modulation. Thus, a dynamic programming approach to the search for an optimal sensing order with adaptive modulation is presented. For some special cases, it is proved that a simple optimal sensing order does exist. More complex scenarios are then considered, e.g., in which the availability probability of each channel is unknown. Optimal strategies are developed to address the challenges created by this additional uncertainty. Finally, a scheme is developed to address the issue of sensing errors.

Abstract Localization is essential for wireless sensor networks (WSNs). It is to determine the positions of sensor nodes based on incomplete mutual distance measurements. In this paper, to measure the accuracy of localization algorithms, a ranging error model for time of arrival (TOA) estimation is given, and the Cramer—Rao Bound (CRB) for the model is derived. Then an algorithm is proposed to deal with the case where (1) ranging error accumulation exists, and (2) some anchor nodes broadcast inaccurate/wrong location information. Specifically, we first present a ranging error‐tolerable topology reconstruction method without knowledge of anchor node locations. Then we propose a method to detect anchor nodes whose location information is inaccurate/wrong. Simulations demonstrate the effectiveness of our algorithm. Copyright © 2008 John Wiley & Sons, Ltd.

This paper investigates the problem of maximally utilizing the spectrum opportunities in cognitive radio networks with multiple potential channels. In particular, the optimal sensing order problem in multi-channel cognitive medium access control with opportunistic transmissions is studied. It is first shown that, when the potential channels are identical (except for the availability probabilities) and independent, the intuitive sensing order (i.e., descending order of the channel availability probabilities) does not lead to optimality in general. A dynamic programming approach for the search of optimal sensing order is then presented. Finally, for some special cases, it is proved that a simple optimal sensing order does exist.

Localization is essential for wireless sensor networks (WSNs). It is to determine the positions of sensor nodes based on incomplete mutual distance measurements. In this paper, to measure the accuracy of localization algorithms, a ranging error model for time of arrival (TOA) estimation is given, and the Cramer-Rao bound (CRB) for the model is derived. Then an algorithm is proposed to deal with the case where 1) ranging error accumulation exists, and 2) some anchor nodes broadcast inaccurate/wrong location information. Specifically, we first present a ranging error-tolerable topology reconstruction method without knowledge of anchor node locations. Then we propose a method to detect anchor nodes that have inaccurate/wrong position information. Simulations demonstrate the improvement of our algorithm compared to others.

It is a challenging task for multi-hop wireless networks to support multimedia applications with quality-ofservice (QoS) requirements. This letter presents a joint crosslayer optimization approach, i.e., joint medium access control, routing, and energy distribution. User satisfaction represented by user utility is maximized within the required network lifetime, given the constraints on the total available energy in the network and the minimum user rates. Although the resulting optimization problem is nonlinear and nonconvex, we prove that it is approximately equivalent to a two-step convex problem. Furthermore, we prove that the problem of maximizing network utility within achievable network lifetime is quasiconvex