Deploying six-dimensional movable antenna (6DMA) systems in Internet-of-Vehicles (IoV) scenarios can greatly enhance spectral efficiency. However, the high mobility of vehicles causes rapid spatio-temporal channel variations, posing a significant challenge to real-time 6DMA optimization. In this work, we pioneer the application of 6DMA in IoV and propose a low-complexity, instantaneous channel state information (CSI)-free dynamic configuration method. By integrating vehicle motion prediction with offline directional response priors, the proposed approach optimizes antenna positions and orientations at each reconfiguration epoch to maximize the average sum rate over a future time window. Simulation results in a typical urban intersection scenario demonstrate that the proposed 6DMA scheme significantly outperforms conventional fixed antenna arrays and simplified 6DMA baseline schemes in terms of total sum rate.

A version of the accepted manuscript is available in arXiv at arXiv:2603.04898v1 [cs.LG] (https://arxiv.org/abs/2603.04898). Comments: This paper has been accepted by infocom. The source code has been released at: https://github.com/qiongwu86/U-Parking . Submission history: From: Qiong Wu: [v1] Thu, 5 Mar 2026 07:38:51 UTC (499 KB).

Uncrewed Aerial Vehicles (UAVs) have become key enabling platforms for low-altitude economic networks, yet achieving efficient and adaptive optimization under resource-constrained and dynamic environments remains challenging. This paper investigates language models for UAV-enabled Wireless Power Transfer (WPT) systems. First, a lightweight small language model (SLM)-based solution is developed using a pre-trained BERT backbone, enhanced UAV embeddings and contextual features, a geometry-aware path decoder, and ensemble inference to achieve low complexity, low latency, and high energy efficiency. Second, an Agentic AI-based framework is designed to exploit the reasoning and interactive capabilities of large language models (LLMs). It integrates four collaborative agents—Initializer, Actor, Critic, and Reflector—to form a closed loop of generation, optimization, evaluation, and reflection for iterative UAV path and energy optimization. Finally, simulations compare the SLM-, LLM-, and Agentic AI-based approaches.

With the advent of 6G communications, intelligent communication systems face multiple challenges, including constrained perception and response capabilities, limited scalability, and low adaptability in dynamic environments. To address these challenges, this tutorial provides a systematic and comprehensive introduction to the principles, design, and applications of Large Artificial Intelligence Models (LAMs) and Agentic AI technologies in intelligent communication systems, aiming to offer researchers an integrated overview of cutting-edge methodologies and practical insights. First, the tutorial outlines the background of 6G communications and reviews the technological evolution from LAMs to Agentic AI. It then systematically examines the key components required for constructing LAMs, classifies various types of LAMs, and analyzes their applicability in communication. A LAM-centric design paradigm tailored for communication systems is subsequently proposed, encompassing dataset construction, internal learning, and external learning approaches. Building upon this foundation, the tutorial develops an LAM-based Agentic AI system for intelligent communications, elaborating on its core components—including agents, world models, planners, knowledge bases, tools, and memory modules— as well as their interaction mechanisms. Finally, it provides an in-depth review of representative applications of LAMs and Agentic AI in communication scenarios, and summarizes the current research challenges and future directions, with the goal of fostering the development of efficient, secure, and sustainable next-generation intelligent communication systems.

Traditional unimodal sensing faces limitations in accuracy and capability, and its decoupled implementation with communication systems increases latency in bandwidth-constrained environments. Additionally, single-task-oriented sensing systems fail to address users’ diverse demands. To overcome these challenges, we propose a semantic-driven integrated multimodal sensing and communication (SIMAC) framework. This framework leverages a joint source-channel coding architecture to achieve simultaneous sensing, decoding, and transmission of sensing results. Specifically, SIMAC first introduces a multimodal semantic fusion (MSF) network, which employs two extractors to extract semantic information from radar signals and images, respectively. MSF then applies cross-attention mechanisms to fuse these unimodal features and generate multimodal semantic representations. Secondly, we present a large language model (LLM)-based semantic encoder (LSE), where relevant communication parameters and multimodal semantics are mapped into a unified latent space and input to the LLM, enabling channel-adaptive semantic encoding. Thirdly, a task-oriented sensing semantic decoder (SSD) is proposed, in which different decoded heads are designed according to the specific needs of tasks. Simultaneously, a multi-task learning strategy is introduced to train the SIMAC framework, achieving diverse sensing services. Finally, experimental simulations demonstrate that the proposed framework achieves diverse and higher-accuracy sensing services.

With the development of surveillance cameras, more bandwidth is required to transmit surveillance videos. Since surveillance videos contain a large amount of redundant information, it causes a waste of bandwidth. Meanwhile, previous video compression methods with the fixed compression standards are unable to handle asymmetric information effectively. To address these problems, we propose Task-driven Semantic Communication with Unsupervised Semantic Segmentation (TSCUSS) for surveillance video assisted by Generative Artificial Intelligence (GAI), to improve efficiency. First, at the transmitter, we segment the videos into the foreground semantic and background models. Second, in the transmission side, we transmit the extracted semantic information in two-stage semantic communication, which greatly reduces redundant information. Third, at the receiver, we merge the foreground and background semantic models through the diffusion model to recover the original semantic content. Finally, our experiment shows that our method not only achieves 78.34% average video compression rate and improves bandwidth utilization, but also dominates in both semantic segmentation accuracy and generative foreground background merge similarity.

The advent of extremely large antenna arrays and high-frequency signaling is expected to enable next-generation integrated sensing and communication (ISAC) networks to predominantly operate in the near-field region. Due to the dual influence of distance and angle on wave propagation characteristics in the near-field region, accurately modeling these characteristics remains a critical challenge. Motivated by the potential of large language models (LLMs) in angle prediction and distance estimation, an LLM-enhanced multi-objective optimization problem (MOOP) is developed to accurately capture the dependence of the channel on both the angular position and distance. The formulated LLM-enhanced MOOP framework is decomposed into a series of sub-problems, which can balance spectral efficiency for communication and localization accuracy for sensing. To overcome the computational and energy challenges associated with LLMs, a gray wolf optimization (GWO)-based algorithm is integrated as black-box search operator with LLM-specific prompt engineering to solve these sub-problems. Numerical results demonstrate that the proposed LLM-GWO scheme achieves an trade-off between communication and sensing performance, outperforming baseline approaches in terms of both Pareto front quality and convergence.

This paper presents a mobile computing-based framework for distributed computing and cooperative control of connected and automated vehicles (CAVs) in ramp merging scenarios under intelligent transportation systems (ITS). A centralized trajectory planning problem is first formulated to optimize merging efficiency and safety. To eliminate reliance on a central controller, a distributed solution is developed using ADMM algorithm based on V2X communication, enabling CAVs to collaboratively compute trajectories in parallel by leveraging their onboard computing power. Building on this, a multi-vehicle model predictive control (MPC) problem is proposed to enhance system stability under strict constraints. To solve it efficiently, a Distributed Cooperative Iterative MPC (DCIMPC) method is introduced, which decomposes and reformulates the problem for real-time distributed execution across CAVs. Together, these methods form a mobile edge computing-driven control framework. Simulations and experiments demonstrate significant improvements in computational efficiency and system performance, highlighting the potential of mobile computing in cooperative CAV control.

Despite the widespread adoption of vision sensors in edge applications, such as surveillance, video transmission consumes substantial spectrum resources. Semantic communication (SC) offers a solution by extracting and compressing information at the semantic level, but traditional SC without sensing capabilities faces inefficiencies due to the repeated transmission of static frames in edge videos. To address this challenge, we propose an SC with computer vision sensing (SCCVS) framework for edge video transmission. The framework first introduces a compression ratio (CR) adaptive SC (CRSC) model, capable of adjusting CR based on whether the frames are static or dynamic, effectively conserving spectrum resources. Simultaneously, we present a knowledge distillation (KD)-based approach to ensure the efficient learning of the CRSC model. Additionally, we implement a computer vision (CV)-based sensing model (CVSM) scheme, which intelligently perceives the scene changes by detecting the movement of the sensing targets. Therefore, CVSM can assess the significance of each frame through in-context analysis and provide CR prompts to the CRSC model based on real-time sensing results. Moreover, both CRSC and CVSM are designed as lightweight models, ensuring compatibility with resource-constrained sensors commonly used in practical edge applications. Experimental results show that SCCVS improves transmission accuracy by approximately 70% and reduces transmission latency by about 89% compared with baselines. We also deploy this framework on an NVIDIA Jetson Orin NX Super, achieving an inference speed of 14 ms per frame with TensorRT acceleration and demonstrating its real-time capability and effectiveness in efficient semantic video transmission.

In this paper, we propose a general digital twin edge computing network comprising multiple vehicles and a server. Each vehicle generates multiple computing tasks within a time slot, leading to queuing challenges when offloading tasks to the server. The study investigates task offloading strategies, queue stability, and resource allocation. Lyapunov optimization is employed to transform long-term constraints into tractable short-term decisions. To solve the resulting problem, an in-context learning approach based on large language model (LLM) is adopted, replacing the conventional multi-agent reinforcement learning (MARL) framework. Experimental results demonstrate that the LLM-based method achieves comparable or even superior performance to MARL.

In this paper, we address the problem of fair access and Age of Information (AoI) optimization in 5G New Radio (NR) Vehicle to Everything (V2X) Mode 2. Specifically, vehicles need to exchange information with the road side unit (RSU). However, due to the varying vehicle speeds leading to different communication durations, the amount of data exchanged between different vehicles and the RSU may vary. This may poses significant safety risks in high-speed environments. To address this, we define a fairness index through tuning the selection window of different vehicles and consider the image semantic communication system to reduce latency. However, adjusting the selection window may affect the communication time, thereby impacting the AoI. Moreover, considering the re-evaluation mechanism in 5G NR, which helps reduce resource collisions, it may lead to an increase in AoI. We analyze the AoI using Stochastic Hybrid System (SHS) and construct a multi-objective optimization problem to achieve fair access and AoI optimization. Sequential Convex Approximation (SCA) is employed to transform the non-convex problem into a convex one, and solve it using convex optimization. We also provide a large language model (LLM) based algorithm. The scheme's effectiveness is validated through numerical simulations.

In this paper, we propose an optimisation framework for deployment of multiple reconfigurable intelligent surfaces (RISs) to meet the wireless coverage demands for smart warehouses. Specifically, we are the first to formulate a unified network optimisation task that jointly considers RIS placement and beamforming to maximize overall network coverage with a deterministic channel model to accurately describe the multipath effect for the warehouse. To address this problem, we design a hybrid optimisation framework composed of three synergistic modules. (1) A Large Language Model (LLM) acts as a semantic planner that generates physically feasible multi-RIS configurations, jointly determining the placement and beamforming directions guided by structured prompts and environment-aware embeddings. (2) A Genetic Algorithm (GA) module performs local numerical refinements to enhance the precision of LLMgenerated solutions under physical constraints. (3) A Diversity Reflection and Correction (DiRect) module evaluates structural similarity among candidate configurations and triggers additional semantic regeneration to maintain exploration diversity. These three modules form an alternating iterative process in which LLM reasoning, GA-based evolution, and DiRect-driven regeneration collectively guide the optimisation toward high-coverage configurations. Extensive simulations validate the effectiveness and robustness of the proposed framework. Compared with traditional heuristics, reinforcement learning methods, and LLMguided baselines, our hybrid framework achieves 10%-15% higher coverage within 10-20 iterations. The performance consistently scales with the number of RISs and element sizes, and remains stable under varying transmitter positions, demonstrating strong adaptability to complex smart warehouse layouts. Overall, the proposed hybrid optimisation framework provides a scalable and physically grounded solution for RIS-assisted network deployment optimisation in realistic in.

We propose EKF-GS, a hybrid optimization framework for 3D Gaussian Splatting that integrates Extended Kalman Filter with stochastic gradient descent. EKF-GS enables faster convergence, uncertainty-guided Gaussian densification, and uncertainty quantification capability. Experiments on public datasets show improved reconstruction quality with fewer iterations and reduced training time.

The Six-Dimensional Movable Antenna (6DMA) system has emerged as a promising technology to enhance wireless capacity by fully exploiting spatial degrees of freedom. However, applying 6DMA to high-mobility Internet of Vehicles (IoV) scenarios faces significant challenges, primarily due to the difficulty of acquiring instantaneous Channel State Information (CSI) and the risk of service interruptions caused by mechanical reconfiguration delays. To address these issues, this paper proposes a low-complexity, CSI-free single-step reconfiguration framework. First, we design a deterministic discrete position generation scheme based on a latitude-longitude grid with inherent topological structures. Leveraging graph theory, we explicitly model and theoretically derive the lower bounds of movement and time costs for antenna reconfiguration. Subsequently, utilizing the directional sparsity of 6DMA channels, we develop an adaptive optimization strategy that fuses offline environmental priors with online historical feedback. Furthermore, a periodic reconfiguration mechanism based on predicted cumulative vehicle distributions is introduced. By strictly restricting antenna adjustments to the first-order spatial neighborhood, the proposed single-step method effectively eliminates service interruptions. Simulation results demonstrate that the proposed scheme significantly outperforms traditional fixed and global-search-based benchmarks in terms of uplink sum rate, while incurring negligible mechanical overhead and latency, thereby validating its feasibility and robustness in highly dynamic vehicular networks.

Severe signal blockage and fast-varying channels in vehicular environments pose critical challenges to reliable semantic communication. To address these, this paper proposes a novel Row-Movable Active Reconfigurable Intelligent Surface (RM-A-RIS) assisted vehicular semantic communication system. This architecture uniquely combines active signal amplification with element mobility to compensate for multiplicative fading and reconstruct channel geometry, thereby enhancing spatial diversity. We formulate a joint optimization problem to maximize Semantic Spectral Efficiency (SSE) by coordinating RIS element positions, active reflection coefficients, and semantic symbol length. An efficient Alternating Optimization (AO) algorithm is developed to tackle the coupled non-convexity. Simulation results demonstrate that the proposed scheme substantially outperforms existing benchmarks, achieving up to 132.9%, 9.2%, and 35.2% improvements in Sum-Semantic Spectral Efficiency (Sum-SSE) compared to the passive RIS, fixed-position active RIS, and QPSO baselines, respectively.

Motion prediction is crucial for autonomous driving, as it enables accurate forecasting of future vehicle trajectories based on historical inputs. This paper introduces Trajectory Mamba, a novel efficient trajectory prediction framework based on the selective state-space model (SSM). Conventional attention-based models face the challenge of computational costs that grow quadratically with the number of targets, hindering their application in highly dynamic environments. In response, we leverage the SSM to redesign the self-attention mechanism in the encoder-decoder architecture, thereby achieving linear time complexity. To address the potential reduction in prediction accuracy resulting from modifications to the attention mechanism, we propose a joint polyline encoding strategy to better capture the associations between static and dynamic contexts, ultimately enhancing prediction accuracy. Additionally, to balance prediction accuracy and inference speed, we adopted the decoder that differs entirely from the encoder. Through cross-state space attention, all target agents share the scene context, allowing the SSM to interact with the shared scene representation during decoding, thus inferring different trajectories over the next prediction steps. Our model achieves state-of-the-art results in terms of inference speed and parameter efficiency on both the Argoverse 1 and Argoverse 2 datasets. It demonstrates a four-fold reduction in FLOPs compared to existing methods and reduces parameter count by over 40% while surpassing the performance of the vast majority of previous methods. These findings validate the effectiveness of Trajectory Mamba in trajectory prediction tasks.

Single-camera markerless systems have emerged as a robust methodology for human motion capture and rehabilitation applications. Traditional methodologies typically necessitate multiple strategically positioned cameras or special equipment, including sensors to capture patient ambulatory motion, requiring preliminary calibration and synchronization procedures, which may incur significant costs. This paper presents a wireless single-camera markerless framework for rehabilitation applications that leverages advanced deep learning (DL) architectures to estimate and extract three-dimensional skeletal coordinates from monocular camera views of ambulatory patients. The extracted skeletal representation is subsequently transmitted across wireless communication channels. Then, the rendering technique has been applied for displaying virtual movement of the patient for privacy enhancement. Simulations demonstrate the effectiveness of the framework while maintaining motion assessment capabilities, presenting opportunities for deployment in remote healthcare monitoring scenarios.

In this work, a novel double-covertness framework is developed for integrated sensing and communication (ISAC) systems, where both the radar and communication signals are protected from being maliciously detected by adversaries. Specifically, a new measurement named joint intercept probability (JIP) is proposed for characterizing the double-covertness performance. Next, an optimal power allocation strategy is designed to minimize the JIP, subject to certain quality-of-service (QoS) requirements for communication and sensing. Simulation results verify the effectiveness of our proposed solution and demonstrate the intrinsic relationship among power allocation, JIP and different QoS requirements.

In this paper, a novel artificial noise (AN)-aided secure and covert integrated sensing and communication (ISAC) framework is established for uncrewed aerial vehicle (UAV) systems, to against malicious radar signal detection and communication eavesdropping. Specifically, we consider that besides the communication and sensing signals, the AN signal, which is used to interfere with the eavesdropper and conceal the existence of radar signal, will be transmitted by the UAV-enabled base station (UBS) with uncertainty on its power level. The closed-form expressions of intercept probability (IP) as well as the minimum detection error probability (M-DEP) are derived. Moreover, an efficient communication and sensing performance maximization strategy is designed by optimizing the beamforming vector of communication, covariance matrix of sensing, and UBS receiver filter jointly, to satisfy the IP, power and M-DEP constraints. Simulation results are provided to verify the effectiveness of our joint design by comparing it to benchmark strategy. Moreover, the impact of AN power uncertainty is examined via simulations.

Modern rescue operations rely on wireless communications for safety reporting, area monitoring, and rescue coordination. However, natural disasters severely damage ground infrastructure, creating significant challenges for emergency rescue and recovery efforts. This paper establishes a disaster response network using Movable and Deployable Resource Units (MDRUs) in disaster-affected areas, to provide timely and reliable message transmission services. Firstly, to ensure a timely and efficient disaster response, we design a post-disaster emergency vehicle network architecture. Secondly, we propose a three-phase emergency relief model to dynamically deploy MDRUs, aiming to maximize their service coverage. Finally, we propose a Trajectory-Based Anycast Routing (TBAR) protocol, which enhances message transmission efficiency by optimizing route selection. Specifically, by facilitating the flexibility of any cast in delivering messages to anyone of the reachable MDRUs, TBAR utilizes multiple copies of messages to reduce end-to-end latency and increase the delivery ratio. Moreover, TBAR adaptively evaluates the message delivery capability of candidate vehicles using a multi-attribute decision-making algorithm, considering link quality, trajectory similarity, and distance cost. Extensive simulation results show that TBAR significantly outperforms other baseline algorithms in multiple aspects.

Terahertz (THz) communication has received much attention recently for its large bandwidth availability, high data-rate transmission and alleviating the spectrum shortage, and it can meet the requirements of Internet of Things with large system capacity and networking capability. In this paper, the performance of multi-antenna THz communication systems with rate-splitting multiple access (RSMA) under the hardware impairments and imperfect successive interference cancelation (SIC) are investigated, where the THz channel is modeled as a composite fading channel including the molecular absorption effects, misalignment fading and small-scale α-μ fading. Taking the hardware impairments and imperfect SIC into account, the probability density function and cumulative distribution function of the effective channel gain are derived. A joint zero-forcing and maximum ratio transmission beamforming design is employed to eliminate the interference among devices. Then, with the performance analysis, the closed-form outage probability (OP) and diversity gain of the system are respectively deduced. By minimizing the OP, a closed-form power allocation (PA) scheme is proposed to adjust the PA coefficients between the common stream and private streams, and resultant lower OP is attained. Moreover, the closed-form expression of the ergodic sum rate (ESR) is derived by means of Fox-H function and the Meijer-G function. With this ESR expression, the asymptotic ESR at high signal to noise ratio (SNR) is also provided to gain further insights. Furthermore, a simple upper bound of the ESR is derived for performance evaluation based on the Jensen’s inequality. Simulation results show that the theoretical analysis is effective, and the proposed PA scheme can obtain lower OP. Besides, the impact of different system and fading parameters on the performance are also analyzed.

Packet losses significantly impact the user experience of content delivery network (CDN) services such as live streaming and data backup-and-archiving. However, our production network measurement studies show that the legacy loss recovery is far from satisfactory due to the wide-area loss characteristics (i.e., dynamics and burstiness) in the wild. In this paper, we propose a sender-side Adaptive ReTransmission scheme, ART, which minimizes the recovery time of lost packets with minimal redundancy cost. Distinguishing itself from forward-error-correction (FEC), which preemptively sends redundant data packets to prevent loss, ART functions as an automatic-repeat-request (ARQ) scheme. It applies redundancy specifically to lost packets instead of unlost packets, thereby addressing the characteristic patterns of wide-area losses in real-world scenarios. We implement ART upon QUIC protocol and evaluate it via both trace-driven emulation and real-world deployment. The results show that ART reduces up to 34% of flow completion time (FCT) for delay-sensitive transmissions, improves up to 26% of goodput for throughput-intensive transmissions, reduces 11.6% video playback rebuffering, and saves up to 90% of redundancy cost.

This paper considers a movable antenna (MA)-aided secure multiple-input multiple-output (MIMO) communication system consisting of a base station (BS), a legitimate information receiver (IR) and an eavesdropper (Eve), where the BS is equipped with MAs to enhance the system’s physical layer security (PLS). Specifically, we aim to maximize the secrecy rate (SR) by jointly optimizing the transmit precoding (TPC) matrix, the artificial noise (AN) covariance matrix and the MAs’ positions under the constraints of the maximum transmit power and the minimum spacing between MAs. To solve this non-convex problem with highly coupled optimization variables, the block coordinate descent (BCD) method is applied to alternately update the variables. Specifically, we first reformulate the SR into a tractable form, and derive the optimal TPC matrix and the AN covariance matrix with fixed MAs’ positions by applying the Lagrangian multiplier method in semi-closed forms. Then, the majorization-minimization (MM) algorithm is employed to iteratively optimize each MA’s position while keeping others fixed. We also extend this work to the more general multicast scenario. Finally, simulation results are provided to demonstrate the effectiveness of the proposed algorithms and the significant advantages of the MAs over conventional fixed position antennas (FPAs) in enhancing system’s security.

Global registration of LiDAR point clouds is a pivotal requirement for autonomous platforms localization, loop closure detection, and map fusion. Despite numerous global registration methods proposed in recent years, their performance is constrained by two primary issues: Low overlap, and large-scale point clouds. These issues become exacerbated when registering partial maps with significant pose discrepancies. To address these issues, we introduce a Branch-and-Bound (BnB)-based method incorporating hierarchical subproblem solution and consistency-based validation, which we term HV-BnB. To mitigate the high time complexity associated with large-scale point cloud registration, our approach reduces the BnB search space by leveraging the Atlanta world assumption and decomposes pose estimation problem into translation estimation and transformation optimization. Hierarchical strategies are also designed to balance between representativeness and accuracy for subproblem solution, thereby enhancing both efficiency and precision. Although BnB algorithm is a full search algorithm, it cannot guarantee global optimal registration under high outlier rate, we propose validation strategy to achieve robust global registration by evaluation the consistency of correspondences. Through verification in both Scan2Scan and Map2Map registration tasks under various scenarios and datasets, our proposed method demonstrates robust and fast global registration performance compared to the state-of-the-art baselines.

In this paper, the joint power allocation (PA), passive beamforming (BF) and hybrid BF (HBF) including digital and analogue BFs are designed for an intelligent reflecting surface (IRS)-assisted millimeter-Wave (mmWave) communication system with imperfect channel state information (CSI) and multiple mobile users to optimize the weighted sum rate (WSR) and energy efficiency (EE). The achievable WSR and EE of the IRS-mmWave system are first derived based on imperfect cascaded CSI for performance optimization. Then, the non-convex constrained problem is formulated to maximize the WSR, where the PA, HBF, phase and amplitude of IRS elements are jointly optimized. Given PA and passive BF (PBF), closed-form suboptimal HBF is obtained for each iteration. Also, given HBF and PBF, using the block coordinate descent (BCD) methods, closed-form PA is derived. Moreover, the phase and amplitude of IRS elements are derived for PBF design during each iteration. With the obtained HBF, the digital and analogue BFs are also derived. Based on this, joint schemes of PA, HBF and PBF are developed. Besides, an efficient iterative algorithm based upon the alternating optimization (AO), weighted minimum mean-square error (WMMSE) and Dinkelbach methods are presented for EE maximization and the suboptimal solution is obtained. Correspondingly, the energy-efficient design for joint PA, HBF and PBF is provided. Simulation results verify the proposed solutions.

Conventional collaborative machine learning (CML) faces significant challenges in resource-constrained environments, such as emergency scenarios with limited power, bandwidth, and computing resources, leading to increased communication delays and energy consumption. To address these issues, this paper introducesAir-CoKD, a novel CML framework designed to reduce resource consumption and training latency while preserving model performance.Air-CoKDleverages knowledge distillation (KD) to minimize data transmission by avoiding the direct sharing of model parameters. It also integrates over-the-air computation (AirComp) to aggregate local logits, optimizing bandwidth utilization. To address the dimensional differences in local logits caused by the unbalanced device data class,Air-CoKDemploys orthogonal frequency division multiplexing (OFDM) to transmitting local logits for different target classes. To handle aggregation errors introduced by AirComp, we conduct a detailed analysis of error bounds. Specifically, we convert the Kullback-Leibler (KL) divergence, used in KD loss function, into a quadratic upper bound for precise error quantification and effective optimization. Based on these insights, we propose a strategy to manage bandwidth constraints, transmission power limits, and device energy budgets withinAir-CoKD. Extensive simulations demonstrate thatAir-CoKDsurpasses state-of-the-art methods, effectively balancing training efficiency and model performance. The framework proves to be a robust solution for CML in resource-constrained networks.

Semantic Communication (SC) has emerged as a novel communication paradigm in recent years. Nevertheless, extant Image Semantic Communication (ISC) systems face several challenges in dynamic environments, including low information density, catastrophic forgetting, and uncertain Signal-to-Noise Ratio (SNR). To address these challenges, we propose a novel Vision-Language Model-based Cross-modal Semantic Communication (VLM-CSC) system. The VLM-CSC comprises three novel components: 1) Cross-modal Knowledge Base (CKB) is used to extract high-density textual semantics from the semantically sparse image at the transmitter and reconstruct the original image based on textual semantics at the receiver. The transmission of high-density semantics contributes to alleviating bandwidth pressure; 2) Memory-assisted Encoder and Decoder (MED) employ a hybrid long/short-term memory mechanism, enabling the semantic encoder and decoder to overcome catastrophic forgetting in dynamic environments when there is a drift in the distribution of semantic features; 3) Noise Attention Module (NAM) employs attention mechanisms to adaptively adjust the semantic coding and the channel coding based on SNR, ensuring the robustness of the CSC system. The experimental simulations validate the effectiveness, adaptability, and robustness of the CSC system.

Over-the-air federated learning (OTA-FL) presents a promising distributed machine learning paradigm that improves the efficiency of local update aggregation by leveraging the superposition property of wireless multiple access channels (MACs). However, it faces significant security and privacy concerns that demand careful consideration. To address these threats associated with OTA-FL, we develop a secure and private over-the-air federated learning (SP-OTA-FL) framework, which can realize the secure and private aggregation for both OTA-FL with unbiased aggregation (UB-OTA-FL) and OTA-FL with biased aggregation (B-OTA-FL). In this framework, a subset of devices participate in training, while another subset functions as jammers, emitting jamming signals to enhance the security and privacy of the OTA-FL process. In particular, we measure the privacy leakage of users’ data using differential privacy (DP) and introduce an innovative application of mean squared error security (MSE-security) to evaluate the security of the OTA-FL system. We conduct convergence analyses for both convex and non-convex loss functions. Building on these analytical results, we separately formulate optimization problems for UB-OTA-FL and B-OTA-FL to enhance the learning performance of SP-OTA-FL by strategically optimizing the scheduling of training participants and jammers. The effectiveness of the proposed schemes is verified through simulations.

Intelligent reflecting surface (IRS) can significantly enhance the performance of data and energy integrated networks (DEIN) by adjusting its amplitude and/or phase. However, there is a lack of comprehensive performance analysis model for realistic DEIN where multiple cells exist rather than only one cell as assumed by most existing work. In this paper, we consider an IRS-assisted multi-cell DEIN. Specifically, in the downlink wireless energy transfer (WET) stage, the hybrid access point (HAP) in each cell broadcasts radio frequency (RF) energy signals to edge user equipments (UEs). Subsequently, during the uplink wireless information transfer (WIT) stage, the edge UEs employ the harvested energy to send their information to the HAP. We first represent the statistical characteristics of the signal-to-interference-plus-noise ratio (SINR) at the edge UE. Then, we derive the closed-form expressions for outage probability, ergodic rate and average symbol error probability of the edge UE in the typical cell. To gain more insights, we obtain the minimum required number of reflection elements and a sub-optimal solution for time allocation coefficients. Finally, extensive numerical results are provided to validate the correctness of the theoretical results.

In this paper, we introduce the healthcare system where Semantic Communication (SC) technology is applied to improve the quality of service for healthcare and medical applications. We first show the concepts and possible architecture of SC. Then, we show different types of SC in the healthcare system. Next, some examples of SC-enhanced healthcare applications are discussed. Finally, we give research challenges and future research directions.

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This paper proposes a framework for integrating sensing, energy, and communication (ISEAC) in a single system. Specifically, the base station (BS) transmits the same signals to the information receivers (IRs), energy harvesting receivers (ERs), and sensing nodes. We aim to optimize the transmission beamforming to minimize the Cramér-Rao bound (CRB) while guaranteeing the performance of the IRs and ERs. We then solve this optimization problem by employing the semidefinite relaxation (SDR) method and Gaussian randomization algorithm. We provide a suboptimal solution with the closed-form solution for the special case with a single IR and a single ER. Finally, simulation results demonstrate the effectiveness of the proposed optimal beamforming design.

Federated learning (FL) has been widely investigated in academic and industrial fields to resolve the issue of data isolation in the distributed Internet of Things (IoT) while maintaining privacy. However, challenges persist in ensuring adequate privacy and security during the aggregation process. In this article, we investigate device scheduling strategies that ensure the security and privacy of wireless FL. Specifically, we measure the privacy leakage of user data using differential privacy (DP) and assess the security level of the system through the mean-square error security (MSE-security). We commence by deriving the analytical results that reveal the impact of the device scheduling on privacy and security protection, as well as on the learning process. Drawing from these analytical findings, we propose three scheduling policies that can achieve secure aggregation of wireless FL under different cases of channel noise. In particular, we formulate an integer nonlinear fractional programming problem to improve the learning performance while guaranteeing privacy and security of wireless FL. We provide an insightful solution in the closed form to the optimization problem when the model has a high dimension. For the general case, we propose a secure and private aggregation (SPA) algorithm based on the branch-and-bound (BnB) method, which can obtain the optimal solution with low complexity. The effectiveness of the proposed schemes for device selection is validated through simulations.

In this paper, the energy consumption (EC) optimization of an aerial high altitude platform station (HAPS) aided mobile edge computing (MEC) network with non-orthogonal multiple access (NOMA) in the presence of imperfect successive interference cancellation is studied. Specifically, joint design schemes of the resource allocation (RA) and the two-dimensional (2D) horizontal position are proposed to minimize the sum EC subject to the different constraint conditions. In particular, we jointly optimize the receive beamforming (BF), the power allocation (PA), HAPS position, the local computation resource, the computation task offload coefficient, and the computation resource allocated for each user via the block coordinate descent method. Namely, given the other optimization parameters, we first optimize a 2D position of HAPS. Then, given the 2D position, by introducing the auxiliary variables, a joint design of BF, PA, offload coefficient and computation resource is solved by an efficient iteration algorithm based on the successive convex approximation method. Additionally, a suboptimal joint design scheme is also developed to lower the complexity. Simulation results show that the proposed two design schemes of the joint RA and position are effective in reducing the EC, and they have a lower EC when compared to benchmark schemes.

This paper considers an intelligent reflecting surface (IRS) aided secure communication with direct communication links where a legitimate receiver (Bob) served by a base station (BS) is overheard by multiple eavesdroppers (Eves), meanwhile the artificial noise (AN) is incorporated to confuse Eves. Since Eves are not legitimate users, their channels cannot be estimated perfectly. We investigate two scenarios with partial channel state information (CSI) error of only cascaded BS-IRS-Eve channel and full CSI errors of both cascaded BS-IRS-Eve channel and direct BS-Eve channel under the statistical CSI error model. To ensure the security performance under CSI errors, the transmit beamforming, AN spatial distribution at the BS, and phase shifts at IRS are jointly optimized to minimize the transmit power constrained by the minimum data rate requirement of Bob and the outage probability of maximum data rate limitation of Eves. In contrast to existing works, the direct link considered in our work makes the optimization of phase shifts at IRS much more challenging, thus we propose a series of novel and artful mathematical manipulations to tackle this issue. Moreover, the proposed algorithm can be applied for both uncorrelated and correlated CSI errors. Simulations confirm the superiority of our proposed algorithm.

This paper develops an optimal design for device scheduling, alignment coefficient, and aggregation rounds within a differentially private over-the-air federated averaging (DP-OTA-FedAvg) system considering a constrained sum power budget. In DP-OTA-FedAvg, gradients are aligned using an alignment coefficient and then aggregated over the air, utilizing channel noise to ensure participant privacy. This study highlights two critical tradeoffs in aligned over-the-air federated learning (OTA-FL) systems with limited power and privacy budgets. Firstly, it reveals the tradeoff between the number of scheduled devices and the alignment coefficient. Secondly, it investigates the balance between aggregation distortion and local training error while adhering to the sum power constraint. Specifically, we measure privacy using differential privacy (DP) and perform convergence analyses for both convex and non-convex loss functions. These analyses provide insights into how device scheduling, the alignment coefficient, and the number of global aggregations affect both privacy preservation and the learning process. Building on these analytical results, we formulate an optimization problem aimed at minimizing the optimality gap of DP-OTA-FedAvg under power and privacy constraints. By specifying the number of aggregation rounds, we derive a closed-form expression describing the relationship between the alignment coefficient and the number of scheduled devices. We then tackle the problem through iterative optimization of scheduling and aggregation rounds. The effectiveness of the proposed policies is verified through simulations, and the performance advantage is particularly pronounced in scenarios where devices have poor channel conditions and limited sum-power budgets.

In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hence, we propose an Industrial Edge Semantic Network to allow IIoT devices to send warnings through Semantic communication (SC). Thus, we should consider 1) data privacy and security; 2) SC model adaptation for heterogeneous devices; 3) explainability of semantics. Therefore, first, we present an eXplainable Semantic Federated Learning (XSFL) to train the SC model, thus ensuring data privacy and security. Then, we present an adaptive client training strategy to provide a specific SC model for each device according to its Fisher information matrix, thus overcoming the heterogeneity. Next, an Explainable SC mechanism is designed, which introduces a leakyReLU-based activation mapping to explain the relationship between the extracted semantics and monitoring data. Finally, simulation results demonstrate the effectiveness of XSFL.

In this article, energy efficiency (EE) is maximized for the reconfigurable intelligent surface (RIS) aided millimeter-Wave (mmWave) networks with non-orthogonal multiple access (NOMA) and multiple mobile devices. To this end, we first propose the EE optimization, under the constraints of maximum power, minimal rate of devices and constant modulus of beamforming (BF) vectors. Then, the joint resource allocation scheme of power allocation (PA) and BF is designed. Specifically, given PA, an effective iterative algorithm based on the majorization-minimization, concave-convex procedure and block coordinate descent (BCD) is presented to obtain closed-form solutions of suboptimal passive BF (PBF) and analog BF (ABF) for each iteration. Then, given PBF and ABF, an effective iterative algorithm based on the successive convex approximation, BCD and Dinkelbach methods is derived to achieve suboptimal closed-form PA for each iteration. By incorporating these two algorithms into the BCD method, a joint optimization algorithm for EE maximization is presented. As a result, joint resource allocation of PA, PBF and ABF is attained. Besides, the convergence and complexity of the algorithms are analyzed. For comparison, the benchmark scheme based on the multidimensional search method and artificial bee colony algorithm is also presented. Simulation results show that the proposed joint scheme is effective and higher EE can be obtained with lower complexity.

Cell-free massive multiple-input multiple-output (CF mMIMO) is known for its ability to provide ubiquitous connectivity. In this paper, we investigate the achievable spectral efficiency (SE) of a user-centric (UC) CF mMIMO system with both multi-antenna APs and users over joint-correlated Rayleigh fading channels. First, we provide a performance analytical framework for the system with the linear decorrelator and study the impact of hardware impairments (HIs) at transceivers on the uplink SE. Based on that, we discuss the local minimum mean-squared error (MMSE) and partial MMSE combining schemes and the partial large-scale fading decoding (LSFD) method from a scalable point of view. Besides, the exact closed-form SE expression is derived with maximum ratio combining (MRC). Then, we study the MMSE-based successive interference cancelation (MMSE-SIC) detector and give an approximate closed-form SE expression with MRC. In the simulations, we compare the linear decorrelator to the MMSE-SIC detector under different hardware-impaired scenarios. Numerical results correspond to the theoretical analyses and show that the impact of HIs can be mitigated by adding the number of receive antennas.

In Cell-Free Massive multi-input multi-output (CF mMIMO), it is challenging to assign regular pilots due to the pre-log pilot overhead on spectral efficiency (SE). This paper explores a superimposed-pilot-(SP)-assisted CF mMIMO system which avoids the separate pilot training duration by superimposing pilot symbols onto data symbols. We consider spatial-correlated Rician fading channels with and without random phase shifts, where linear minimum-mean-square-error (LMMSE) estimators are performed at each access point locally. Then, we derive the closed-form SE expressions with maximal-ratio (MR) combining. To fill the gap, we introduce novel expressions of MMSE combining vectors and compare their SE performance with approximate MMSE combining vectors. Next, a generic model is provided for the optimal large-scale fading decoding (LSFD), and we derive the closed-form suboptimal LSFD solutions with MR combining. A line-of-sight-based combining scheme is proposed based on the approximate analysis, where closed-form SE expressions are derived using MR and MMSE combining and corresponding optimal LSFD coefficients. Numerical results show that the combination of MMSE and optimal LSFD yields almost 200% enhancement over the combination of MR and simple centralized decoding in 95% likely per-user SE without phase shifts, and 111% enhancement when phase shifts exist.

Reconfigurable intelligent surface (RIS) is a promising technology to improve the spectral and energy efficiency of Internet of Things (IoT) systems. In this paper, we investigate an RIS-assisted simultaneous wireless information and power transmission (SWIPT) system by utilizing stochastic geometry. Moreover, we consider not only the case of random phase shift, but also the case where the phase shift of the RIS are aligned to thek-th IoT device. We first derive the closed-form expressions of the uplink outage probability and the average uplink data size for thek-th IoT device under the Rayleigh channel. Then, we extend the performance analysis to the Rician fading channel and multi-antenna scenarios. Finally, extensive numerical results have been carried out to verify the effectiveness of our derived results.

Empowering sensing capabilities is becoming increasingly important in future wireless networks. Meanwhile, intelligent reflecting surface (IRS) and wireless power transfer (WPT) have also received widespread attention as two key technologies to improve network spectrum efficiency and solve device energy shortage issues, respectively. To this end, we investigate an IRS-assisted and WPT-enabled integrated sensing and communication (ISAC) system. Specifically, a base station (BS), with the assistance of the IRS, has the dual functions of radar sensing as well as receiving the data information transmitted by Internet of Things (IoT) devices. IoT devices can charging itself by harvesting the power of radar signals transmitted from the BS. The sensing performance is studied by deriving an exact closed-form expression and an upper bound of the average radar estimation information rate. In addition, we derive an exact expression for the average data information rate to evaluate the communication performance of the system. The simulation results reveal that increasing the number of reflecting elements of the IRS can simultaneously enhance the radar sensing and communication performance.

In this paper, we propose a device scheduling scheme for differentially private over-the-air federated learning (DP-OTA-FL) systems, referred to as S-DPOTAFL, where the privacy of the participants is guaranteed by channel noise. In S-DPOTAFL, the gradients are aligned by the alignment coefficient and aggregated via over-the-air computation (AirComp). The scheme schedules the devices with better channel conditions in the training to avoid the problem that the alignment coefficient is limited by the device with the worst channel condition in the system. We conduct the privacy and convergence analysis to theo-retically demonstrate the impact of device scheduling on privacy protection and learning performance. To improve the learning accuracy, we formulate an optimization problem with the goal to minimize the training loss subjecting to privacy and transmit power constraints. Furthermore, we present the condition that the S-DPOTAFL performs better than the DP-OTA-FL without considering device scheduling (NoS-DPOTAFL). The effectiveness of the S-DPOTAFL is validated through simulations.

To combat the half-space coverage and enhance the flexibility of the reconfigurable intelligent surface (RIS) technology, a simultaneously transmitting and reflecting RIS (STAR-RIS) is applied in the radar-communication co-existence (RCC) system, where the signal through STAR-RIS is transmitted to opposite spaces, and STAR-RIS is utilized to handle the interference from the base station (BS) to the radar. A radar detection probability maximization problem by optimizing the transmit beamforming vector of the BS and the transmission-and reflection-coefficient matrices of the STAR-RIS is formulated, subject to the power constraint of the BS and the communication rate constraints of users. The problem is challenging to solve due to the highly coupled variables. We convert it into two sub-problems and propose an efficient alternating optimization (AO) algorithm to solve this non-convex problem. The simulation results validate the convergence of the proposed algorithm and the performance advantages of using STAR-RIS over conventional RIS.

In this article, we consider an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) and relay communication network consisting of a base station (BS), a UAV, and multiple ground users. The UAV acts as both a wireless power transmission source and an uplink communication relay. Specifically, an entire transmission period of the considered system is divided into two stages. In the first stage, the UAV transfers the power to the ground users along a well-optimized flight trajectory and meanwhile, the users transmit data to the UAV using the harvested energy. Subsequently, in the second stage, the UAV flies to the vicinity of the BS and forwards the data to the BS. For the purpose of minimizing the energy consumed by the UAV, we jointly optimize the time durations of the two stages, the UAV’s transmit powers for WPT and data forwarding, as well as its flight trajectory, subject to the constraints of the Quality of Service (QoS), the information forwarding, the energy causality, and the mobility of the UAV. The involved optimization problem is nonconvex and highly intractable. To this end, we propose an efficient alternating algorithm to iteratively solve the two subproblems with respect to the time durations of the two stages and the UAV’s transmit powers and trajectory, respectively. The first subproblem has a closed-form optimal solution and the second subproblem is handled by addressing a surrogate convex problem based on the technique of successive convex approximation. Finally, the simulation results confirm the superiority of our proposed algorithm.

This article studies a dynamic Mobile Edge Computing (MEC) system assisted by Unmanned Aerial Vehicles (UAVs) and Intelligent Reflective Surfaces (IRSs). We propose a scaleable resource scheduling algorithm to minimize the energy consumption of all UEs and UAVs in the MEC system with a variable number of UAVs. We propose a Multi-tAsk Resource Scheduling (MARS) framework based on Deep Reinforcement Learning (DRL) to solve the problem. First, we present a novel Advantage Actor-Critic (A2C) structure with the state-value critic and entropy-enhanced actor to reduce variance and enhance the policy search of DRL. Then, we present a multi-head agent with three different heads in which a classification head is applied to make offloading decisions and a regression head is presented to allocate computational resources, and a critic head is introduced to estimate the state value of the selected action. Next, we introduce a multi-task controller to adjust the agent to adapt to the varying number of UAVs by loading or unloading a part of weights in the agent. Finally, a Light Wolf Search (LWS) is introduced as the action refinement to enhance the exploration in the dynamic action space. The numerical results demonstrate the feasibility and efficiency of the MARS framework.

This article studies an intelligent reflecting surface (IRS)-aided communication system under the time-varying channels and stochastic data arrivals. In this system, we jointly optimize the phase-shift coefficient and the transmit power in sequential time slots to maximize the long-term energy consumption for all mobile devices while ensuring queue stability. Due to the dynamic environment, it is challenging to ensure queue stability. In addition, making real-time decisions in each short time slot also needs to be considered. To this end, we propose a method (called LETO) that combines Lyapunov optimization with evolutionary transfer optimization (ETO) to solve the above optimization problem. LETO first adopts Lyapunov optimization to decouple the long-term stochastic optimization problem into deterministic optimization problems in sequential time slots. As a result, it can ensure queue stability since the deterministic optimization problem in each time slot does not involve future information. After that, LETO develops an evolutionary transfer method to solve the optimization problem in each time slot. Specifically, we first define a metric to identify the optimization problems in past time slots similar to that in the current time slot, and then transfer their optimal solutions to construct a high-quality initial population in the current time slot. Since ETO effectively accelerates the search, we can make real-time decisions in each short time slot. Experimental studies verify the effectiveness of LETO by comparison with other algorithms.

This paper investigates the two-timescale transmission scheme for reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems, where the beamforming at the base station (BS) is adapted to the rapidly-changing instantaneous channel state information (CSI), while the nearly-passive beamforming at the RIS is adapted to the slowly-changing statistical CSI. Specifically, we first consider a system model with spatially independent Rician fading channels, which leads to tractable expressions and offers analytical insights on the power scaling laws and on the impact of various system parameters. Then, we analyze a more general system model with spatially correlated Rician fading channels and consider the impact of electromagnetic interference (EMI) caused by any uncontrollable sources present in the considered environment. For both case studies, we apply the linear minimum mean square error (LMMSE) estimator to estimate the aggregated channel from the users to the BS, utilize the low-complexity maximal ratio combining (MRC) detector, and derive a closed-form expression for a lower bound of the achievable rate. Besides, an accelerated gradient ascent-based algorithm is proposed for solving the minimum user rate maximization problem. Numerical results show that, in the considered setup, the spatially independent model without EMI is sufficiently accurate when the inter-distance of the RIS elements is sufficiently large and the EMI is mild. In the presence of spatial correlation, we show that an RIS can better tailor the wireless environment. Furthermore, it is shown that deploying an RIS in a massive MIMO network brings significant gains when the RIS is deployed close to the cell-edge users. On the other hand, the gains obtained by the users distributed over a large area are shown to be modest.

Recommendation systems rely heavily on behavioural and preferential data (e.g., ratings and likes) of a user to produce accurate recommendations. However, such unethical data aggregation and analytical practices of Service Providers (SP) causes privacy concerns among users. Local differential privacy (LDP) based perturbation mechanisms address this concern by adding noise to users’ data at the user-side before sending it to the SP. The SP then uses the perturbed data to perform recommendations. Although LDP protects the privacy of users from SP, it causes a substantial decline in recommendation accuracy. We propose an LDP-based Matrix Factorization (MF) with a Gaussian Mixture Model (MoG) to address this problem. The LDP perturbation mechanism, i.e., Bounded Laplace (BLP), regulates the effect of noise by confining the perturbed ratings to a predetermined domain. We derive a sufficient condition of the scale parameter for BLP to satisfy$\varepsilon$-LDP. We use the MoG model at the SP to estimate the noise added locally to the ratings and the MF algorithm to predict missing ratings. Our LDP based recommendation system improves the predictive accuracy without violating LDP principles. We demonstrate that our method offers a substantial increase in recommendation accuracy under a strong privacy guarantee through empirical evaluations on three real-world datasets, i.e., Movielens, Libimseti and Jester.

In this paper, the intelligent reflecting surface (IRS)-aided unmanned aerial vehicle (UAV) communication system is studied, where the UAV is deployed to serve the user equipment (UE) with the assistance of multiple IRSs mounted on several buildings to enhance the communication quality between UAV and UE. We aim to maximize the energy efficiency of the system, including the data rate of UE and the energy consumption of UAV via jointly optimizing the UAV's trajectory and the phase shifts of reflecting elements of IRS, when the UE moves and the selection of IRSs is considered for the energy saving purpose. Since the system is complex and the environment is dynamic, it is challenging to derive low-complexity algorithms by using conventional optimization methods. To address this issue, we first propose a deep Q-network (DQN)-based algorithm by discretizing the trajectory, which has the advantage of training time. Furthermore, we propose a deep deterministic policy gradient (DDPG)-based algorithm to tackle the case with continuous trajectory for achieving better performance. The experimental results show that the proposed algorithms achieve considerable performance compared to other traditional solutions.

In this paper, the millimeter-wave (mmWave) communications and non-orthogonal multiple access (NOMA) are exploited for mobile edge computing (MEC) networks to improve the performance of task offloading. Aiming at improving the computation efficiency (CE) and ensuring the fairness among users, we study the CE optimization for mmWave-MEC with NOMA, where both the analog beamforming (ABF) and hybrid beamforming (HBF) architectures under the partial offloading mode are considered. First, according to the max-min fairness criterion, the CE optimization problem is formulated to jointly optimize the ABF at the base station and the local resource allocation of each user in mmWave-MEC with ABF. An efficient algorithm based on the penalized successive convex approximation is proposed to solve this non-convex problem. Then, the max-min CE optimization problem in mmWave-MEC with HBF is studied, where the joint design of the HBF at the BS and the local resource allocation of each user is carried out. By using the penalty function and the inexact block coordinate descent method, a feasible optimization algorithm is developed to tackle this challenging problem. Simulation results verify the convergence of the proposed algorithms and show that the proposed resource allocation schemes can improve the system CE effectively, and the mmWave-MEC with HBF scheme can obtain higher CE than that with ABF scheme. Besides, the NOMA scheme exhibits superior performance over the conventional orthogonal multiple access scheme in terms of CE.

This paper studies the worst-case robust beamforming design for a reconfigurable intelligent surface (RIS) aided non-orthogonal multiple access (NOMA) network with imperfect channels. We aim to minimize the transmission power while satisfying the requirement of the worst-case quality of service (QoS). With the worst-case QoS constraints, unit-modulus constraints and imperfect channel state information (CSI), this problem is a non-convex optimization problem. To solve this problem, we propose a two-procedure algorithm by applying penalty function and semidefinite relaxation (SDR). Finally, simulation results illustrate that the RIS-aided NOMA system has better performance than the traditional NOMA system.

In this paper, the sum rate is maximized for the intelligent reflective surface (IRS) assisted terahertz (THz) non-orthogonal multiple access (NOMA) communication system. A novel algorithm is proposed to alternatively optimize the IRS phase shift, the sub-band allocation, and power control. To tackle the formulated non-convex problem, we utilize the auxiliary variables to find the feasible initialization solution meanwhile guarantee the individual rate requirements. The decoding order of successive interference cancellation (SIC) is determined according to channel gain maximization, and the IRS phase is further adjusted to improve the sum rate. A long-distance priority (LDP) algorithm is then proposed to compensate for the distance-dependent THz pathloss attenuation, and a blocking pair eliminating (BPE) algorithm is proposed to obtain a stable THz sub-band allocation. Simulation results show that the proposed scheme significantly enhances the sum-rate performance of the IRS-assisted THz NOMA networks.

Unmanned aerial vehicles (UAVs) can be applied in many Internet of Things (IoT) systems, e.g., smart farms, as a data collection platform. However, the UAV-IoT wireless channels may be occasionally blocked by trees or high-rise buildings. An intelligent reflecting surface (IRS) can be applied to improve the wireless channel quality by smartly reflecting the signal via a large number of low-cost passive reflective elements. This article aims to minimize the energy consumption of the system by jointly optimizing the deployment and trajectory of the UAV. The problem is formulated as a mixed-integer-and-nonlinear programming (MINLP), which is challenging to address by the traditional solution, because the solution may easily fall into the local optimal. To address this issue, we propose a joint optimization framework of deployment and trajectory (JOLT), where an adaptive whale optimization algorithm (AWOA) is applied to optimize the deployment of the UAV, and an elastic ring self-organizing map (ERSOM) is introduced to optimize the trajectory of the UAV. Specifically, in AWOA, a variable-length population strategy is applied to find the optimal number of stop points, and a nonlinear parameter$a$and a partial mutation rule are introduced to balance the exploration and exploitation. In ERSOM, a competitive neural network is also introduced to learn the trajectory of the UAV by competitive learning, and a ring structure is presented to avoid the trajectory intersection. Extensive experiments are carried out to show the effectiveness of the proposed JOLT framework.

In large-scale mobile edge computing (MEC) systems, the task latency, and energy consumption are important for massive resource-consuming and delay-sensitive Internet of Things Devices (IoTDs). Against this background, we propose a distributed intelligent resource scheduling (DIRS) framework to minimize the sum of task latency and energy consumption for all IoTDs, which can be formulated as a mixed-integer nonlinear programming. The DIRS framework includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. Specifically, we first introduce a novel multiagent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Second, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Levy flight search. Finally, an imitation acceleration scheme is presented to pretrain all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. The simulation results in three typical scenarios demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes.

In this article, we aim to minimize the total power of all the Internet of Things Devices (IoTDs) by jointly optimizing user association, computational capacity, transmit power, and the location of unmanned aerial vehicles (UAVs) in an UAV-assisted cloud radio access network (C-RAN). In order to solve this nonconvex problem, we propose an effective algorithm by solving four subproblems iteratively. For the user association and the computational capacity subproblems, the nonconvex constraints are relaxed and the optimal solutions are obtained. For the transmit power control and the location planning subproblems, the successive convex approximation (SCA) technique is used to transform the nonconvex constraints into convex ones. Moreover, to obtain the suboptimal solutions, slack variables are also introduced to deal with the feasibility-check problems. The simulation results demonstrate that the proposed algorithm can greatly reduce the total power consumption of IoTDs.

In this article, we investigate multiple unmanned aerial vehicles (UAVs)-enabled data collection system in Internet of Things (IoT) networks with time windows, where multiple rotary-wing UAVs are dispatched to collect data from time-constrained terrestrial IoT devices. We aim to jointly minimize the number and the total operation time of UAVs by optimizing the UAV trajectory and hovering location. To this end, an optimization problem is formulated, considering the energy budget and cache capacity of UAVs as well as the data transmission constraint of IoT devices. To tackle this mix-integer nonconvex problem, we decompose the problem into two subproblems: 1) UAV trajectory and 2) hovering location optimization problems. To solve the first subproblem, an modified ant colony optimization (MACO) algorithm is proposed. For the second subproblem, the successive convex approximation (SCA) technique is applied. Then, an overall algorithm, termed the MACO-based algorithm, is given by leveraging the MACO algorithm and SCA technique. Simulation results demonstrate the superiority of the proposed algorithm.

In this article, we investigate the energy minimization problem of an unmanned-aerial-vehicle (UAV)-assisted data collection sensor network. We jointly optimize the trajectory of the UAV and the power consumption of the sensors for data uploading with the power and energy constraints of sensors. The trajectory design consists of two parts: 1) the serving orders for sensors and 2) the UAV’s hovering positions, where the latter is highly coupled with the power consumption of the sensors. To find the optimal serving orders of sensors, we formulate the problem as a standard traveling salesman problem (TSP), which can be optimally solved by the efficient Cutting-Plane method. To solve the UAV position and sensor uploading power optimization problem, we propose the PSPSCA algorithm that optimizes the transmit power by the pattern search method, while the UAV’s hovering positions are optimized by the successive-convex-approximation (SCA) method in the inner loop. To deal with the high computational complexity of the PSPSCA algorithm, we analyze the analytical relationship between optimal sensor uploading power and the UAV’s hovering positions, based on which we simplify the optimization problem and propose the AQSCA algorithm as an alternative approach. Simulation results have validated that the proposed algorithm outperforms the existing benchmark schemes.

In this paper, the joint design of power allocation (PA), beamforming (BF) and positioning is studied for unmanned-aerial-vehicle (UAV) aided millimeter-Wave (UAV-mmWave) systems, with the objective of maximizing the energy efficiency (EE), under the constraints of maximum transmitting power, minimum data rate from the ground users and positioning range of the UAV. To address the above problem, we first obtain the positioning of the UAV, with the help of approximate beam pattern. Then, near-optimal BF and closed-form PA are derived given the obtained position, with the help of block coordinate descent method. To reduce the complexity, two suboptimal BF schemes with one-loop iteration and closed-form solutions are respectively derived. Furthermore, we propose the simplified algorithms for two special cases, i.e., only line-of-sight (LoS) path and Non-LoS (NLoS) path exist between the users and the UAV. Simulation results verify the effectiveness of the developed joint schemes and show the superior EE performance. Moreover, they can obtain almost the same performance as the existing benchmark schemes but with lower complexity.

To alleviate the negative impact of noise on wireless federated learning (FL), we propose a channel-weighted aggregation scheme of FL (CWA-FL), in which the parameter server (PS) makes aggregation of the gradients according to the channel conditions of devices. In the proposed scheme, the gradients are transmitted to the PS in an uncoded way through an orthogonal multiple access (OMA) channel, which can avoid the synchronization issue among devices faced by over-the-air FL. The convergence analysis of CWA-FL is conducted and the theoretical results show that the scheme can converge with the rate of$\mathcal {O} (\frac{1}{T})$. Simulation results show that the proposed scheme performs better than the equal-weighted aggregation scheme of FL (EWA-FL) and is more robust to noise.

Different from conventional wired line connections, industrial control through wireless transmission is widely regarded as a promising solution due to its reduced cost, increased long-term reliability, and enhanced reliability. However, mission-critical applications impose stringent quality of service (QoS) requirements that entail ultra-reliability low-latency communications (URLLC). The primary feature of URLLC is that the blocklength of channel codes is short, and the conventional Shannon’s Capacity is not applicable. In this paper, we consider the URLLC in a factory automation (FA) scenario. Due to densely deployed equipment in FA, wireless signal are easily blocked by the obstacles. To address this issue, we propose to deploy intelligent reflecting surface (IRS) to create an alternative transmission link, which can enhance the transmission reliability. In this paper, we focus on the performance analysis for IRS-aided URLLC-enabled communications in a FA scenario. Both the average data rate (ADR) and the average decoding error probability (ADEP) are derived under finite channel blocklength for seven cases: 1) Rayleigh fading channel; 2) With direct channel link; 3) Nakagami-m fading channel; 4) Imperfect phase alignment; 5) Multiple-IRS case; 6) Rician fading channel; 7) Correlated channels. Extensive numerical results are provided to verify the accuracy of our derived results.

In this paper, we present the computation-efficient resource allocation (RA) schemes for millimeter-wave mobile edge computing (mmWave-MEC) system with the aid of reconfigurable intelligent surface (RIS), which is used to assist the uplink communication from the users to the base station (BS). By means of the theoretical analysis, the achievable rate and computation efficiency (CE) are derived. Then, the optimization problem for the CE maximization under the constraints of the minimum rate, maximum power consumption and local CPU frequency is formulated, where the joint design of the hybrid beamforming at the BS and the passive beamforming at the RIS as well as the local resource allocation of each user is carried out. An effective iterative algorithm based on the penalized inexact block coordinate descent (BCD) method is proposed to obtain the computation-efficient RA scheme. Next, a low-complexity suboptimal RA scheme based on the BCD method is proposed, and corresponding algorithm is presented. Simulation results show that the proposed schemes are effective, and high CE can be attained. Moreover, the second scheme can achieve the CE performance close to the first scheme but with lower complexity. Besides, it is effective to deploy the RIS scheme in mmWave-MEC system, which can strike a balance between the CE and energy consumption when compared to the conventional relay schemes.

This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system, where the phase shifts of the RIS are designed relying on statistical channel state information (CSI). Considering the complex environment, the general Rician channel model is adopted for both the users-RIS links and RIS-BS links. We first derive the closed-form approximate expressions for the achievable rate which holds for arbitrary numbers of base station (BS) antennas and RIS elements. Then, we utilize the derived expressions to provide some insights, including the asymptotic rate performance, the power scaling laws, and the impacts of various system parameters on the achievable rate. We also tackle the sum-rate maximization and the minimum user rate maximization problems by optimizing the phase shifts at the RIS based on genetic algorithm (GA). Finally, extensive simulations are provided to validate the benefits by integrating RIS into conventional massive MIMO systems. Our simulations also demonstrate the feasibility of deploying large-size but low-resolution RIS in massive MIMO systems.

This article investigates a wireless-powered mobile edge computing (MEC) system, where the service provider (SP) provides the device owner (DO) with both computing resources and energy to execute tasks from Internet-of-Things devices. In this system, SP first sets the prices of computing resources and energy whereas DO then makes the optimal response according to the given prices. In order to jointly optimize the prices of computing resources and energy, we formulate a bilevel optimization problem (BOP), in which the upper level generates the prices of computing resources and energy for SP and then under the given prices, the lower level optimizes the mode selection, broadcast power, and computing resource allocation for DO. This BOP is difficult to address due to the mixed variables at the lower level. To this end, we first derive the relationships between the optimal broadcast power and the mode selection and between the optimal computing resource allocation and the mode selection. After that, it is only necessary to consider the discrete variables (i.e., mode selection) at the lower level. Note, however, that the transformed BOP is still difficult to solve because of the extremely large search space. To solve the transformed BOP, we propose a divide-and-conquer bilevel optimization algorithm (called DACBO). Based on device status, task information, and available resources, DACBO first groups tasks into three independent small-size sets. Afterward, analytical methods are devised for the first two sets. As for the last one, we develop a nested bilevel optimization algorithm that uses differential evolution and variable neighborhood search (VNS) at the upper and lower levels, respectively. In addition, a greedy method is developed to quickly construct a good initial solution for VNS. The effectiveness of DACBO is verified on a set of instances by comparing with other algorithms.

The rapid progress in informatisation and electrification in transportation has gradually transferred public transport junctions such as metro stations into the nexus of pedestrian flows, information flows, computation flows and energy flows. These smart environments that are efficient in handling large volume passenger flows in routine circumstances can become even more vulnerable during emergency situations and amplify the losses in lives and property owing to power outage triggered service degradation and destructive crowed behaviours. On the bright side, the increasingly abundant resources contained in smart environments have enlarged the optimisation space of an evacuation process, yet little research has concentrated on the joint optimal resource allocation between transportation infrastructures and pedestrians. Hence, in the paper, we propose a queueing network based resource allocation model to comprehensively optimise various types of resources during emergency evacuations. Experiments are conducted in a simulated metro station environment with realistic settings. The simulation results show that the proposed model can considerably improve the evacuation efficiency as well as the robustness of the emergency response system during emergency situations.

In this paper, we consider a platform of flying mobile edge computing (F-MEC), where unmanned aerial vehicles (UAVs) serve as equipment providing computation resource, and they enable task offloading from user equipment (UE). We aim to minimize energy consumption of all UEs via optimizing user association, resource allocation and the trajectory of UAVs. To this end, we first propose a Convex optimizAtion based Trajectory control algorithm (CAT), which solves the problem in an iterative way by using block coordinate descent (BCD) method. Then, to make the real-time decision while taking into account the dynamics of the environment (i.e., UAV may take off from different locations), we propose a deep Reinforcement leArning based trajectory control algorithm (RAT). In RAT, we apply the Prioritized Experience Replay (PER) to improve the convergence of the training procedure. Different from the convex optimization based algorithm which may be susceptible to the initial points and requires iterations, RAT can be adapted to any taking off points of the UAVs and can obtain the solution more rapidly than CAT once training process has been completed. Simulation results show that the proposed CAT and RAT achieve the considerable performance and both outperform traditional algorithms.

This paper proposes a new simultaneous terahertz (THz) information and power transfer (STIPT) system, which utilizes a reconfigurable intelligent surface (RIS) for both the data and power transmission. We aim to maximize the information users’ (IUs’) sum data rate while guaranteeing the power harvesting requirements of energy users (EUs) and RIS. To solve the formulated non-convex problem, the block coordinate descent (BCD) based algorithm is adopted to alternately optimize the transmit precoding of IUs, RIS’s reflecting coefficients, and the position of RIS. Additionally, the penalty constrained convex approximation (PCCA) algorithm is proposed to optimize the deployment of the RIS, where the introduced penalties ensure that the solution is always feasible. The simulation results show that the proposed solution outperforms the benchmark schemes, and the proposed BCD algorithm can greatly improve the performance of the STIPT system.

In order to solve the problem of multi-target trajectory correlation in underwater environments, such as the error of underwater measurement and the inconsistency of the targets reported by sensors, we leverage the gradation pre-processing to eliminate the noise and propose the Gaussian mixture model by the topology information between the trajectories. A novel mixed integer nonlinear programming is constructed to determine the relationship between underwater target trajectories, and the correlation bias is reduced by recursing the sensor bias estimation continuously. At the same time, the idea of weighting is introduced to maximize the clustering expectation. The optimal closure solution of the GMM is achieved with the expectation maximization clustering. The corresponding relationship of the trajectory is gotten at the expectation maximization stage, and finally obtains the trajectory correlation result of the underwater targets. The simulation results show that the GMP algorithm has better positive correlation rate and robustness than other algorithms with different target numbers in different dimensions, different sensor angular ranging errors, and different sensor detection probabilities. The GMP algorithm has advantages in the complex underwater environments with multiple noise and high false alarms, and it has accuracy and robustness in underwater multi-target trajectory correlation.

A fundamental challenge for millimeter wave (mmWave) communications lies in its sensitivity to the presence of blockages, which impact the connectivity of the communication links and ultimately the reliability of the entire network. In this paper, we are exploited to deal with the link outage issue caused by a reconfigurable intelligent surface (RIS)-aided mmWave communication system for enhancing the network reliability and connectivity in the presence of random blockages. To enhance the robustness of the beamforming in the presence of random blockages, we formulate a stochastic optimization problem with the aim of minimizing the outage probability. To tackle the proposed optimization problem, we introduce a low-complexity algorithm based on the stochastic majorization-minimization method, which learns sensible blockage patterns without searching for all combinations of potentially blocked links. Numerical results confirm the performance benefits of the proposed algorithm in terms of outage probability and effective data rate.

This paper investigates a UAV-enabled wireless downlink system, where a UAV-enabled base station flies above a group of Internet of things devices (IoTDs) and transmits data to them. In order to ensure the fairness among all IoTDs, we jointly optimize scheduling association and UAV trajectory to maximize the minimal throughput of all IoTDs under the 3dimensional (3-D) channel model. Meanwhile, to ensure the stability of communication, we aim to guarantee a high probability of line-of-sight (LoS) links between the UAV and all IoTDs. The optimization problem is non-convex and we propose an iterative algorithm based on the block coordinate descent and successive convex approximation to solve it. Furthermore, we show the convergence of the algorithm through simulations. It is shown that the proposed algorithm achieves higher data rate than the traditional scheme without guaranteeing the LoS probability.

In this paper, we study the average packet error probability (APEP) and effective throughput (ET) of the control link in unmanned-aerial-vehicle (UAV) communications, where the ground central station (GCS) sends control signals to the UAV that requires ultra-reliable and low-latency communications (URLLC). To ensure the low latency, short packets are adopted for the control signal. As a result, the Shannon capacity theorem cannot be adopted here due to its assumption of infinite channel blocklength. We consider both free space (FS) and 3-Dimensional (3D) channel models by assuming that the locations of the UAV are randomly distributed within a restricted space. We first characterize the statistical characteristics of the signal-to-noise ratio (SNR) for both FS and 3D models. Then, the closed-form analytical expressions of APEP and ET are derived by using Gaussian-Chebyshev quadrature. Also, the lower bounds are derived to obtain more insights. Finally, we obtain the optimal value of packet length with the objective of maximizing the ET by applying one-dimensional search. Our analytical results are verified by the Monte-Carlo simulations.

In this paper, we propose a low-latency virtual reality (VR) delivery system where an unmanned aerial vehicle (UAV) base station (U-BS) is deployed to deliver VR content from a cloud server to multiple ground VR users. Each VR input data requested by the VR users can be either projected at the U-BS before transmission or processed locally at each user. Popular VR input data is cached at the U-BS to further reduce backhaul latency from the cloud server. For this system, we design a low-complexity iterative algorithm to minimize the maximum communications and computing latency among all VR users subject to the computing, caching and transmit power constraints, which is guaranteed to converge. Numerical results indicate that our proposed algorithm can achieve a lower latency compared to other benchmark schemes. Moreover, we observe that the maximum latency mainly comes from communication latency when the bandwidth resource is limited, while it is dominated by computing latency when computing capacity is low. In addition, we find that caching is helpful to reduce latency.

In this paper, we investigate the design of robust and secure transmission in intelligent reflecting surface (IRS) aided wireless communication systems. In particular, a multi-antenna access point (AP) communicates with a single-antenna legitimate receiver in the presence of multiple single-antenna eavesdroppers, where the artificial noise (AN) is transmitted to enhance the security performance. Besides, we assume that the cascaded AP-IRS-user channels are imperfect due to the channel estimation error. To minimize the transmit power, the beamforming vector at the transmitter, the AN covariance matrix, and the IRS phase shifts are jointly optimized subject to the outage rate probability constraints under the statistical cascaded channel state information (CSI) error model. To handle the resulting non-convex optimization problem, we first approximate the outage rate probability constraints by using the Bernstein-type inequality. Then, we develop a suboptimal algorithm based on alternating optimization, the penalty-based and semidefinite relaxation methods. Simulation results reveal that the proposed scheme significantly reduces the transmit power compared to other benchmark schemes.

In this paper, a cooperative task computation framework exploits the computation resource in user equipments (UEs) to accomplish more tasks meanwhile minimizes the power consumption of UEs. The system cost includes the cost of UEs' power consumption and the penalty of unaccomplished tasks, and the system cost is minimized by jointly optimizing binary offloading decisions, the computational frequencies, and the offloading transmit power. To solve the formulated mixed-integer non-linear programming problem, three efficient algorithms are proposed, i.e., integer constraints relaxation-based iterative algorithm (ICRBI), heuristic matching algorithm, and the decentralized algorithm. The ICRBI algorithm achieves the best performance at the cost of the highest complexity, while the heuristic matching algorithm significantly reduces the complexity while still providing reasonable performance. As the previous two algorithms are centralized, the decentralized algorithm is also provided to further reduce the complexity, and it is suitable for the scenarios that cannot provide the central controller. The simulation results are provided to validate the performance gain in terms of the total system cost obtained by the proposed cooperative computation framework.

In this paper, we investigate the robust transmission design for the intelligent reflecting surface (IRS) aided secure wireless communication systems, where a multi-antenna access point (AP) communicates with a single-antenna legitimate receiver in the presence of multiple single-antenna eavesdroppers via IRS. The estimation error of the imperfect cascaded AP-IRS-user channels is considered in the robust beamforming. Specifically, a transmit power minimization problem is formulated subject to the outage rate probability of information leakage to Eves under the statistical cascaded CSI error model, and the beamformer at the transmitter, the covariance matrix of artificial noise (AN), and the IRS phase shifts are jointly optimized. To handle the resulting non-convex optimization problem, we first approximate the rate outage probability constraints by using the Bernstein-type inequality. Then, we develop a suboptimal algorithm based on the alternating optimization, penalty-based and semidefinite relaxation methods. Simulation results reveal that the proposed scheme significantly reduces the transmit power while ensures the system security.

Intelligent reflecting surface (IRS) has been proved to be an efficient technology to improve the spectrum and energy efficiency in cognitive radio (CR) networks. Unfortunately, due to the fact that the primary users (PUs) and the secondary users (SUs) are non-cooperative, it is challenging to obtain the perfect PUs-related channel sate information (CSI). In this paper, we investigate the robust beamforming design based on the statistical CSI error model for PU-related cascaded channels in IRS-aided CR systems. We jointly optimize the transmit precoding (TPC) matrix and phase shifts to minimize the SU's total transmit power, meanwhile subject to the quality of service (QoS) of SUs, the interference imposed on the PU and unit-modulus of the reflective beamforming. The non-convex optimization problems are transformed into two second-order cone programming (SOCP) subproblems and efficient algorithms are proposed for solving these subproblems. Simulation results verify the efficiency of the proposed algorithms and reveal the impacts of CSI uncertainties on ST's transmit power and feasibility rate of the optimization problem.

Intelligent reflection surface (IRS) has recently been recognized as a promising technique to enhance the performance of wireless systems due to its ability of reconfiguring the signal propagation environment. However, the perfect channel state information (CSI) is challenging to obtain at the base station (BS) due to the lack of radio frequency (RF) chains at the IRS. Since most of the existing channel estimation methods were developed to acquire the cascaded BS-IRS-user channels, this paper is the first work to study the robust beamforming based on the imperfect cascaded BS-IRS-user channels at the transmitter (CBIUT). Specifically, the transmit power minimization problems are formulated subject to the rate outage probability constraints under the statistical CSI error model, respectively. After approximating the rate outage probability constraints by using the Bernstein-type inequality, the reformulated problems can be efficiently solved. Numerical results show that the negative impact of the CBIUT error on the system performance is greater than that of the direct CSI error.

Mobile edge computing (MEC) has been proposed in recent years to process resource-intensive and delay-sensitive applications at the edge of mobile networks, which can break the hardware limitations and resource constraints at user equipment (UE). In order to fully use the MEC server resource, how to maximize the number of offloaded tasks is meaningful especially for crowded place or disaster area. In this paper, an optimal partial offloading scheme POSMU (Partial Offloading Strategy Maximizing the User task number) is proposed to obtain the optimal offloading ratio, local computing frequency, transmission power and MEC server computing frequency for each UE. The problem is formulated as a mixed integer nonlinear programming problem (MINLP), which is NP-hard and challenging to solve. As such, we convert the problem into multiple nonlinear programming problems (NLPs) and propose an efficient algorithm to solve them by applying the block coordinate descent (BCD) as well as convex optimization techniques. Besides, we can seamlessly apply POSMU to UAV (Unmanned Aerial Vehicle) enabled MEC system by analyzing the 3D communication model. The optimality of POSMU is illustrated in numerical results, and POSMU can approximately maximize the number of offloaded tasks compared to other schemes.

Unmanned aerial vehicle (UAV)-assisted communication is a promising technology in future wireless communication networks. UAVs can not only help offload data traffic from ground base stations (GBSs) but also improve the Quality of Service (QoS) of cell-edge users (CEUs). In this article, we consider the enhancement of cell-edge communications through a mobile relay, i.e., UAV, in multicell networks. During each transmission period, GBSs first send data to the UAV, and then the UAV forwards its received data to CEUs according to a certain association strategy. In order to maximize the sum rate of all CEUs, we jointly optimize the UAV mobility management, including trajectory, velocity, and acceleration, and association strategy of CEUs to the UAV, subject to minimum rate requirements of CEUs, mobility constraints of the UAV, and causal buffer constraints in practice. To address the mixed-integer nonconvex problem, we transform it into two convex subproblems by applying tight bounds and relaxations. An iterative algorithm is proposed to solve the two subproblems in an alternating manner. Numerical results show that the proposed algorithm achieves higher rates of CEUs as compared with the existing benchmark schemes.

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision, transmission power, and resource allocation in the large-scale mobile-edge computing (MEC) system. Toward this end, a deep reinforcement learning (DRL)-based solution is proposed, which includes the following components. First, a related and regularized stacked autoencoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high-dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Second, we present an adaptive simulated annealing approach (ASA) as the action search method of DRL, in which an adaptive ${h}$ -mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Third, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. The numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks.

In this article, we consider a hybrid mobile edge computing (H-MEC) platform, which includes ground stations (GSs), ground vehicles (GVs), and unmanned aerial vehicles (UAVs), all with the mobile edge cloud installed to enable user equipments (UEs) or Internet of Things (IoT) devices with intensive computing tasks to offload. Our objective is to obtain an online offloading algorithm to minimize the energy consumption of all the UEs, by jointly optimizing the positions of GVs and UAVs, user association and resource allocation in real time, while considering the dynamic environment. To this end, we propose a hybrid deep-learning-based online offloading (H2O) framework where a large-scale path-loss fuzzy c-means (LS-FCM) algorithm is first proposed and used to predict the optimal positions of GVs and UAVs. Second, a fuzzy membership matrix U-based particle swarm optimization (U-PSO) algorithm is applied to solve the mixed-integer nonlinear programming (MINLP) problems and generate the sample data sets for the deep neural network (DNN) where the fuzzy membership matrix can capture the small-scale fading effects and the information of mutual interference. Third, a DNN with the scheduling layer is introduced to provide the user association and computing resource allocation under the practical latency requirement of the tasks and limited available computing resource of H-MEC. In addition, different from the traditional DNN predictor, we only input one UE's information to the DNN at one time, which will be suitable for the scenarios where the number of UE is varying and avoid the curse of dimensionality in DNN.

This article studies an unmanned aerial vehicle (UAV)-enabled edge-cloud system, where UAV acts as a mobile edge computing (MEC) server interplaying with remote central cloud to provide computation services to ground terminals (GTs). The UAV-enabled edge-cloud system implements a virtualized network function, namely, mobile clone (MC), for each GT to help execute their offloaded tasks. Through such network function virtualization (NFV) implemented on top of the UAV-enabled edge-cloud system, GTs can have extended computation capability and prolonged battery lifetime. We aim to jointly optimize the allocation of resource and the UAV trajectory in the 3-D spaces to minimize the overall energy consumption of the UAV. The proposed solution, therefore, can extend the endurance of the UAV and support reliable MC functions for GTs. This article solves the complicated optimization problem through a block coordinate descent algorithm in an iterative way. In each iteration, the allocation of resource is modeled as a multiple constrained optimization problem given predefined UAV trajectory, which can be reformulated into a more tractable convex form and solved by successive convex optimization and Lagrange duality. Second, given the allocated resource, the optimization of the trajectory of rotary-wing/fixed-wing UAV can be formulated into a series of convex quadratically constrained quadratically program (QCQP) problems and solved by the standard convex optimization techniques. After the block coordinate descent algorithm converges to a prescribed accuracy, a high-quality suboptimal solution can be found. According to the simulation, the numerical results verify the effectiveness of our proposed solution in contrast to the baseline solutions.

With the increasing number of electric vehicles (EVs), temporary charging demands grow rapidly. Unlike charging at home or workplace, temporary charging requires less waiting time. In this article, a mobile edge computing (MEC)-enabled charging and discharging networking system algorithm (CDNSA) is proposed to minimize the waiting time for EVs in charging stations (CSs). A software-defined network (SDN) paradigm is adopted to enhance the data transmission efficiency for MEC servers. In CDNSA, the optimization problem is formulated as a mixed-integer nonlinear programming (MINLP). A heuristic algorithm is proposed to solve the optimal CS selection variables for EVs that needs to be charged (EVCs) and EVs that can be discharged (EVDs), and then a remaining problem nonlinear programming (NLP) is obtained. By verifying the convexity of each continuous variable, the NLP is solved by adopting the block coordinate descent (BCD) method. In simulation, the optimality of CDNSA is verified by comparing with the exhaustive algorithm in terms of minimizing maximal waiting time (MMWT) of CSs. We also compare CDNSA with other benchmarks to illustrate its advantage.

An intelligent reflecting surface (IRS) is invoked for enhancing the energy harvesting performance of a simultaneous wireless information and power transfer (SWIPT) aided system. Specifically, an IRS-assisted SWIPT system is considered, where a multi-antenna aided base station (BS) communicates with several multi-antenna assisted information receivers (IRs), while guaranteeing the energy harvesting requirement of the energy receivers (ERs). To maximize the weighted sum rate (WSR) of IRs, the transmit precoding (TPC) matrices of the BS and passive phase shift matrix of the IRS should be jointly optimized. To tackle this challenging optimization problem, we first adopt the classic block coordinate descent (BCD) algorithm for decoupling the original optimization problem into several subproblems and alternately optimize the TPC matrices and the phase shift matrix. For each subproblem, we provide a low-complexity iterative algorithm, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) point of each subproblem. The BCD algorithm is rigorously proved to converge to the KKT point of the original problem. We also conceive a feasibility checking method to study its feasibility. Our extensive simulation results confirm that employing IRSs in SWIPT beneficially enhances the system performance and the proposed BCD algorithm converges rapidly, which is appealing for practical applications.

We study a mobile edge computing system assisted by multiple unmanned aerial vehicles (UAVs), where the UAVs act as edge servers to provide computing services for Internet of Things devices. Our goal is to minimize the energy consumption of this system by planning the trajectories of UAVs. This problem is difficult to address because when planning the trajectories, we need to consider not only the order of stop points (SPs), but also their deployment (including the number and locations) and the association between UAVs and SPs. To tackle this problem, we present an energy-efficient trajectory planning algorithm (TPA) which comprises three phases. In the first phase, a differential evolution algorithm with a variable population size is adopted to update the number and locations of SPs at the same time. In the second phase, the k -means clustering algorithm is employed to group the given SPs into a set of clusters, where the number of clusters is equal to that of UAVs and each cluster contains all SPs visited by the same UAV. In the third phase, to quickly generate the trajectories of UAVs, we propose a low-complexity greedy method to construct the order of SPs in each cluster. Compared with other algorithms, the effectiveness of TPA is verified on a set of instances at different scales.

This article considers an artificial noise (AN)-aided secure MIMO wireless communication system. To enhance the system security performance, the advanced intelligent reflecting surface (IRS) is invoked, and the base station (BS), legitimate information receiver (IR) and eavesdropper (Eve) are equipped with multiple antennas. With the aim for maximizing the secrecy rate (SR), the transmit precoding (TPC) matrix at the BS, covariance matrix of AN and phase shifts at the IRS are jointly optimized subject to constrains of transmit power limit and unit modulus of IRS phase shifts. Then, the secrecy rate maximization (SRM) problem is formulated, which is a non-convex problem with multiple coupled variables. To tackle it, we propose to utilize the block coordinate descent (BCD) algorithm to alternately update the variables while keeping SR non-decreasing. Specifically, the optimal TPC matrix and AN covariance matrix are derived by Lagrangian multiplier method, and the optimal phase shifts are obtained by Majorization-Minimization (MM) algorithm. Since all variables can be calculated in closed form, the proposed algorithm is very efficient. We also extend the SRM problem to the more general multiple-IRs scenario and propose a BCD algorithm to solve it. Simulation results validate the effectiveness of system security enhancement via an IRS.

In this paper, we propose a secure unmanned aerial vehicle (UAV) mobile edge computing (MEC) system where multiple ground users offload large computing tasks to a nearby legitimate UAV in the presence of multiple eavesdropping UAVs with imperfect locations. To enhance security, jamming signals are transmitted from both the full-duplex legitimate UAV and non-offloading ground users. For this system, we design a low-complexity iterative algorithm to maximize the minimum secrecy capacity subject to latency, minimum offloading and total power constraints. Specifically, we jointly optimize the UAV location, users' transmit power, UAV jamming power, offloading ratio, UAV computing capacity, and offloading user association. Numerical results show that our proposed algorithm significantly outperforms baseline strategies over a wide range of UAV self-interference (SI) efficiencies, locations and packet sizes of ground users. Furthermore, we show that there exists a fundamental tradeoff between the security and latency of UAV-enabled MEC systems which depends on the UAV SI efficiency and total UAV power constraints.

Mobile crowdsensing (MCS) is a novel sensing scenario of cyber-physical-social systems. MCS has been widely adopted in smart cities, personal health care, and environment monitor areas. MCS applications recruit participants to obtain sensory data from the target area by allocating reward to them. Reward mechanisms are crucial in stimulating participants to join and provide sensory data. However, while the MCS applications execute the reward mechanisms, sensory data and personal private information can be in great danger because of malicious task initiators/participants and hackers. This article proposes a novel blockchain-based MCS framework that preserves privacy and secures both the sensing process and the incentive mechanism by leveraging the emergent blockchain technology. Moreover, to provide a fair incentive mechanism, this article has considered an MCS scenario as a sensory data market, where the market separates the participants into two categories: monthly-pay participants and instant-pay participants. By analyzing two different kinds of participants and the task initiator, this article proposes an incentive mechanism aided by a three-stage Stackelberg game. Through theoretical analysis and simulation, the evaluation addresses two aspects: the reward mechanism and the performance of the blockchain-based MCS. The proposed reward mechanism achieves up to a 10% improvement of the task initiator's utility compared with a traditional Stackelberg game. It can also maintain the required market share for monthly-pay participants while achieving sustainable sensory data provision. The evaluation of the blockchain-based MCS shows that the latency increases in a tolerable manner as the number of participants grows. Finally, this article discusses the future challenges of blockchain-based MCS.

This paper studies a multiuser cooperative mobile edge computing offloading (called CoMECO) system in a multiuser interference environment, in which delay-sensitive tasks may be executed on local devices, cooperative devices, or the primary MEC server. In this system, we jointly optimize the offloading decision and computation resource allocation for minimizing the total energy consumption of all mobile users under the delay constraint. If this problem is solved directly, the offloading decision and computation resource allocation are generally generated separately at the same time. Note, however, that they are closely coupled. Therefore, under this condition, their dependency is not well considered, thus leading to poor performance. We transform this problem into a bilevel optimization problem, in which the offloading decision is generated in the upper level, and then the optimal allocation of computation resources is obtained in the lower level based on the given offloading decision. In this way, the dependency between the offloading decision and computation resource allocation can be fully taken into account. Subsequently, a bilevel optimization approach, called BiJOR, is proposed. In BiJOR, candidate modes are first pruned to reduce the number of infeasible offloading decisions. Afterward, the upper-level optimization problem is solved by ant colony system (ACS). Furthermore, a sorting strategy is incorporated into ACS to construct feasible offloading decisions with a higher probability and a local search operator is designed in ACS to accelerate the convergence. For the lower-level optimization problem, it is solved by the monotonic optimization method. In addition, BiJOR is extended to deal with a complex scenario with the channel selection. Extensive experiments are carried out to investigate the performance of BiJOR on two sets of instances with up to 400 mobile users. The experimental results demonstrate the effectiveness of BiJOR and the superiority of the CoMECO system.

This article establishes a new multiunmanned aerial vehicle (multi-UAV)-enabled mobile edge computing (MEC) system, where a number of unmanned aerial vehicles (UAVs) are deployed as flying edge clouds for large-scale mobile users. In this system, we need to optimize the deployment of UAVs, by considering their number and locations. At the same time, to provide good services for all mobile users, it is necessary to optimize task scheduling. Specifically, for each mobile user, we need to determine whether its task is executed locally or on a UAV (i.e., offloading decision), and how many resources should be allocated (i.e., resource allocation). This article presents a two-layer optimization method for jointly optimizing the deployment of UAVs and task scheduling, with the aim of minimizing system energy consumption. By analyzing this system, we obtain the following property: the number of UAVs should be as small as possible under the condition that all tasks can be completed. Based on this property, in the upper layer, we propose a differential evolution algorithm with an elimination operator to optimize the deployment of UAVs, in which each individual represents a UAV's location and the entire population represents an entire deployment of UAVs. During the evolution, we first determine the maximum number of UAVs. Subsequently, the elimination operator gradually reduces the number of UAVs until at least one task cannot be executed under delay constraints. This process achieves an adaptive adjustment of the number of UAVs. In the lower layer, based on the given deployment of UAVs, we transform the task scheduling into a 0-1 integer programming problem. Due to the large-scale characteristic of this 0-1 integer programming problem, we propose an efficient greedy algorithm to obtain the near-optimal solution with much less time. The effectiveness of the proposed two-layer optimization method and the established multi-UAV-enabled MEC system is demonstrated on ten instances with up to 1000 mobile users.

With the increase of the number of electric vehicles (EVs), it is of vital importance to develop the efficient and effective charging scheduling schemes for all the EVs. In this article, we aim to maximize the social welfare of all the EVs, charging stations (CSs) and power plant (PP), by taking into account the changing demand of each EV, the changing price, the capacity and the congestion balance between different CSs. To this end, two efficient scheduling algorithms, i.e., Centralized Charging Strategy (CCS) and Distributed Charging Strategy (DCS) are proposed. CCS has a slightly better performance than the DCS, as it takes all the information and make the decision in the central control unit. On the other hand, DCS dose not require the private information from EVs and can make decentralized decision. Extensive simulation are conducted to verify the effectiveness of the proposed algorithms, in terms of the performance, congestion balance, and computing complexity.

Intelligent reflecting surfaces (IRSs) constitute a disruptive wireless communication technique capable of creating a controllable propagation environment. In this paper, we propose to invoke an IRS at the cell boundary of multiple cells to assist the downlink transmission to cell-edge users, whilst mitigating the inter-cell interference, which is a crucial issue in multicell communication systems. We aim for maximizing the weighted sum rate (WSR) of all users through jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS subject to each BS's power constraint and unit modulus constraint. Both the BSs and the users are equipped with multiple antennas, which enhances the spectral efficiency by exploiting the spatial multiplexing gain. Due to the non-convexity of the problem, we first reformulate it into an equivalent one, which is solved by using the block coordinate descent (BCD) algorithm, where the precoding matrices and phase shifts are alternately optimized. The optimal precoding matrices can be obtained in closed form, when fixing the phase shifts. A pair of efficient algorithms are proposed for solving the phase shift optimization problem, namely the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least locally optimal solutions. We also extend the proposed algorithms to the more general multiple-IRS and network MIMO scenarios. Finally, our simulation results confirm the advantages of introducing IRSs in enhancing the cell-edge user performance.

This paper considers a multi-drone enabled data collection system for smart cities, where there are two kinds of drones, i.e., Low Altitude Platforms (LAPs) and a High Altitude Platform (HAP). In the proposed system, the LAPs perform data collection tasks for smart cities and the solar-powered HAP provides energy to the LAPs using wireless laser beams. We aim to minimize the total laser charging energy of the HAP, by jointly optimizing the LAPs' trajectory and the laser charging duration for each LAP, subject to the energy capacity constraints of the LAPs. This problem is formulated as a mixed-integer and non-convex Drones Traveling Problem (DTP), which is a combinatorial optimization problem and NP-hard. We propose an efficient and novel search algorithm named Drones Traveling Algorithm (DTA) to obtain a near-optimal solution. Simulation results show that DTA can deal with the large-scale DTP (i.e., more than 400 data collection points) efficiently. Moreover, the DTA only uses 5 iterations to obtain the near-optimal solution whereas the normal Genetic Algorithm needs nearly 10000 iterations and still fails to obtain an acceptable solution.

In this paper, the user cooperative task computation is explored by sharing the computing capability of the user equipments (UEs) so as to enhance the performance of mobile edge computing (MEC) networks. The number of completed tasks is maximized while minimizing the total power consumption of the UEs by jointly optimizing the user task offloading decision, the computational speed for the offloaded task and the transmit power for task offloading. An iterative algorithm based on the linear programming relaxation is proposed to solve the formulated mixed integer non-linear problem. The simulation results show that the proposed user cooperative computation scheme can achieve a higher completed tasks ratio than the non-cooperative scheme.

In this paper, we study a general Near-Far Computing Enhanced C-RAN (NFC-RAN), in which users can offload the tasks to the near edge cloud (NEC) or the far edge cloud (FEC). We aim to propose a profit-aware task allocation model by maximizing the profit of the edge cloud operators. We first prove that this problem can be transformed to a Multiple-Choice Multi-Dimensional 0-1 Knapsack Problem (MMKP), which is NP-hard. Then, we solve it by using a low complexity heuristic algorithm. The simulation results show that the proposed algorithm achieves a good tradeoff between the performance and the complexity compared with the benchmark algorithm.

In this paper, multi-unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC), i.e., UAVE is studied, where several UAVs are deployed as flying MEC platform to provide computing resource to ground user equipments (UEs). Compared to the traditional fixed location MEC, UAV enabled MEC (i.e., UAVE) is particular useful in case of temporary events, emergency situations and on-demand services, due to its high flexibility, low cost and easy deployment features. However, operation of UAVE faces several challenges, two of which are how to achieve both 1) the association between multiple UEs and UAVs and 2) the resource allocation from UAVs to UEs, while minimizing the energy consumption for all the UEs. To address this, we formulate the above problem into a mixed integer nonlinear programming (MINLP), which is difficult to be solved in general, especially in the large-scale scenario. We then propose a Reinforcement Learning (RL)-based user Association and resource Allocation (RLAA) algorithm to tackle this problem efficiently and effectively. Numerical results show that the proposed RLAA can achieve the optimal performance with comparison to the exhaustive search in small scale, and have considerable performance gain over other typical algorithms in large-scale cases.

In this paper, we consider the sum power minimization problem via jointly optimizing user association, power control, computation capacity allocation, and location planning in a mobile edge computing (MEC) network with multiple unmanned aerial vehicles (UAVs). To solve the nonconvex problem, we propose a low-complexity algorithm with solving three subproblems iteratively. For the user association subproblem, the compressive sensing-based algorithm is accordingly proposed. For the computation capacity allocation subproblem, the optimal solution is obtained in closed form. For the location planning subproblem, the optimal solution is effectively obtained via one-dimensional search method. To obtain a feasible solution for this iterative algorithm, a fuzzy c-means clustering-based algorithm is proposed. The numerical results show that the proposed algorithm achieves better performance than the conventional approaches.

This paper considers the deployment of a hybrid wireless data/power access point in an 802.11- based wireless powered IoT network. The proportionally fair allocation of throughputs across IoT nodes is considered under the constraints of energy neutrality and CPU capability for each device. The joint optimization of wireless powering and data communication resources takes the CSMA/CA random channel access features, e.g. the backoff procedure, collisions, protocol overhead into account. Numerical results show that the optimized solution can effectively balance individual throughput across nodes, and meanwhile proportionally maximize the overall sum throughput under energy constraints.

This paper considers an unmanned aerial vehicle based mobile edge computing (UAV based MEC) system, where we assume there is one UAV, acts as an edge cloud, providing data processing services to the Internet of things devices (IoTDs). We consider the UAV hovers at difference places for different time to receive and process data for IoTDs. We aim to minimize the energy consumption of the UAV, including its hovering energy and computation energy, by optimizing the hovering time, scheduling and resource allocation of the tasks received from IoTDs, subject to the quality of service (QoS) requirement of all the IoTDs and the computing resource available at UAV. This is formulated as a mixed-integer non-convex optimization problem, which is difficult to solve in general. We propose an efficient iterative algorithm to get a high-quality suboptimal solution. Simulation results show that our proposed method has a very good performance compared with the other benchmarks.

With the fast development of mobile edge computing (MEC), user equipments (UEs) can enjoy much higher experience than before by offloading the tasks to its close edge cloud. In this paper, we assume there are several edge clouds, each of which has limited resource. We aim to maximize the number of offloaded tasks and minimize the energy consumption of all the UEs and edge clouds, by selecting the best edge cloud for each UE to offload. We formulate the problem as a mixed-integer non-convex optimization, which is difficult to solve in general. By transforming this problem into a minimum-cost maximum-flow (MCMF) problem, we can solve it efficiently. The simulation shows that our proposed algorithm has better performance and lower complexity than the conventional solutions.

Cloud radio access network (C-RAN) has emerged as a potential candidate of the next generation access network technology to address the increasing mobile traffic, while mobile cloud computing (MCC) offers a prospective solution to the resource-limited mobile user in executing computation intensive tasks. Taking full advantages of above two cloud-based techniques, C-RAN with MCC are presented in this paper to enhance both performance and energy efficiencies. In particular, this paper studies the joint energy minimization and resource allocation in C-RAN with MCC under the time constraints of the given tasks. We first review the energy and time model of the computation and communication. Then, we formulate the joint energy minimization into a non-convex optimization with the constraints of task executing time, transmitting power, computation capacity and fronthaul data rates. This non-convex optimization is then reformulated into an equivalent convex problem based on weighted minimum mean square error (WMMSE). The iterative algorithm is finally given to deal with the joint resource allocation in C-RAN with mobile cloud. Simulation results confirm that the proposed energy minimization and resource allocation solution can improve the system performance and save energy.

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile edge cloud computing (MEC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile devices. But the power consumption has become skyrocketing for MSP and it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MEC separately while less work had considered the integration of C-RAN with MEC. In this paper, we present an unifying framework for the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MEC to maximize the profit of MSP. To achieve this objective, we formulate the resource scheduling issue as a stochastic problem and design a new optimization framework by using an extended Lyapunov technique. Specially, because the standard Lyapunov technique critically assumes that job requests have fixed lengths and can be finished within each decision making interval, it is not suitable for the dynamic situation where the mobile job requests have variable lengths. To solve this problem, we extend the standard Lyapunov technique and design the VariedLen algorithm to make online decisions in consecutive time for job requests with variable lengths. Our proposed algorithm can reach time average profit that is close to the optimum with a diminishing gap (1/V) for the MSP while still maintaining strong system stability and low congestion. With extensive simulations based on a real world trace, we demonstrate the efficacy and optimality of our proposed algorithm.

In this paper, a congestion balanced green charging networks is proposed for the electric vehicles (EVs) in smart grid. Firstly, a problem about the congestion probability balance among the charging stations (CSs) is analyzed and formulated, and then a two-layer optimization model is established based on the profit functions of power plant (PP), CSs and EVs. In the first layer, the optimal generation capacities as well as the charging capacities of CSs are determined, while in the second layer, the sum of each CS's profit and that of the EVs which want to charge at the CS is formulated as a profit maximization problem. The two-layer optimization model solves the congestion probability balance problem in the iterative manner, and finally the congestion balanced smart charging algorithm (CBSCA) is obtained. By comparing with other benchmarks, the results show that CBSCA is converged in an acceptable time, and the congestion probabilities among the CSs are balanced.

With the fast development of the cloud computing and virtualization techniques, computation resources can be allocated more dynamically and scalably on demand. This paper aims to study two types of computing, i.e., service computing and communication computing. We have proposed to have both computing resource in mobile operator's mobile cloud and investigated how to jointly allocate them with the objective of reducing mobile operator's power consumption and meanwhile, improving mobile users' experience. In this paper, we have introduced the computing power minimization problem, which is NP-hard. By applying several transformations and estimations, the problem can be solved by the branch and bound solution. Also, admission control is considered in this paper. Simulation results have shown that the proposed joint resource allocation solution has a very good performance and outperforms the traditional fixed data rate guarantee algorithm.

Cloud radio access network (C-RAN) and mobile edge computing (MEC) have emerged as promising candidates for the next generation access network techniques. Unfortunately, although MEC tries to utilize the highly distributed computing resources in close proximity to user equipments equipments (UE), C-RAN suggests to centralize the baseband processing units (BBU) deployed in radio access networks. To better understand and address such a conflict, this paper closely investigates the MEC task offloading control in C-RAN environments. In particular, we focus on perspective of matching problem. Our model smartly captures the unique features in both MEC and C-RAN with respect to communication and computation efficiency constraints. We divide the cross-layer optimization into the following three stages: (1) matching between remote radio heads (RRH) and UEs, (2) matching between BBUs and UEs, and (3) matching between mobile clones (MC) and UEs. By applying the Gale-Shapley Matching Theory in the duplex matching framework, we propose a multi-stage heuristic to minimize the refusal rate for user's task offloading requests. Trace-based simulation confirms that our solution can successfully achieve near-optimal performance in such a hybrid deployment.

A practical problem facing Infrastructure-as-a-Service (IaaS) cloud users is how to minimize their costs by choosing different pricing options based on their own demands. Recently, cloud brokerage service is introduced to tackle this problem. But due to the perishability of cloud resources, there still exists a large amount of idle resource waste during the reservation period of reserved instances. This idle resource waste problem is challenging cloud broker when buying reserved instances to accommodate users' job requests. To solve this challenge, we find that cloud users always have low priority jobs (e.g., non latency-sensitive jobs) which can be delayed to utilize these idle resources. With considering the priority of jobs, two problems need to be solved. First, how can cloud broker leverage jobs' priorities to reserve resources for profit maximization? Second, how to fairly price users' job requests with different priorities when previous studies either adopt pricing schemes from IaaS clouds or just ignore the pricing issue. To solve these problems, we first design a fair and priority aware pricing scheme, PriorityPricing, for the broker which charges users with different prices based on priorities. Then we propose three dynamic algorithms for the broker to make resource reservations with the objective of maximizing its profit. Experiments show that the broker's profit can be increased up to 2.5× than that without considering priority for offline algorithm, and 3.7× for online algorithm.

Taking full advantages of two cloud-based techniques, i.e., cloud radio access network (C-RAN) and mobile cloud computing (MCC), mobile operators will be able to provide the good service to the mobile user as well as increasing their revenue. This paper aims to minimize the mobile operator's cost while at the same time, meet the task time constraints of the mobile users. In particular, we assume that the mobile cloud first completes the tasks for the mobile user and then transmits the results back to the users through C-RAN. Joint cost-effective resource allocation is proposed between MCC and C-RAN and simulation results confirm that the proposed cost minimization and resource allocation solution outperforms nonoptimal solutions.

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile cloud computing (MCC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile users' devices to provide better quality of service (QoS). But the power consumption has become skyrocketing for MSP as it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MCC separately while less work had considered the integration of C-RAN with MCC. In this paper, we present a unifying framework for optimizing the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MCC to minimize the power consumption of MSP while still guaranteeing the QoS for mobile users. Our objective is to maximize the profit of MSP. To achieve this objective, we first formulate the resource scheduling issue as a stochastic problem and then propose a Resource onlIne sCHeduling (RICH) algorithm using Lyapunov optimization technique to approach a time average profit that is close to the optimum with a diminishing gap (1/V) for MSP while still maintaining strong system stability and low congestion to guarantee the QoS for mobile users. With extensive simulations, we demonstrate that the profit of RICH algorithm is 3.3× (18.4×) higher than that of active (random) algorithm.

Abstract Energy detection is widely used in cognitive radio due to its low complexity. One fundamental challenge is that its performance degrades in the presence of noise uncertainty, which inevitably occurs in practical implementations. In this work, three novel detectors based on uniformly distributed noise uncertainty as the worst‐case scenario are proposed. Numerical results show that the new detectors outperform the conventional energy detector with considerable performance gains. Copyright © 2015 John Wiley & Sons, Ltd.

Bit error rates (BERs) for amplify-and-forward (AF) relaying systems with two different pilot-symbol- aided channel estimation methods, disintegrated channel estimation (DCE) and cascaded channel estimation (CCE), are derived in Rayleigh fading channels. Based on these BERs, the pilot powers at the source and at the relay are optimized when their total transmitting powers are fixed. Numerical results show that the optimized system has a better performance than other conventional nonoptimized allocation systems. They also show that the optimal pilot power in variable gain is nearly the same as that in fixed gain for similar system settings.

The sum of ratios of products of independent 2642 2642α - μ random variables (RVs) is approximated by using the Generalized Gamma ratio approximation (GGRA) with Gamma ratio approximation (GRA) as a special case. The proposed approximation is used to calculate the outage probability of the equal gain combining (EGC) or maximum ratio combining (MRC) receivers for wireless multihop relaying or multiple scattering systems considering interferences. Numerical results show that the newly derived approximation works very well verified by the simulation, while GRA has a slightly worse performance than GGRA when outage probability is below 0.1 but with a more simplified form.

Bit error rate (BER) and outage probability for amplify-and-forward (AF) relaying systems with two different channel estimation methods, disintegrated channel estimation and cascaded channel estimation, using pilot-aided maximum likelihood method in slowly fading Rayleigh channels are derived. Based on the BERs, the optimal values of pilot power under the total transmitting power constraints at the source and the optimal values of pilot power under the total transmitting power constraints at the relay are obtained, separately. Moreover, the optimal power allocation between the pilot power at the source, the pilot power at the relay, the data power at the source and the data power at the relay are obtained when their total transmitting power is fixed. Numerical results show that the derived BER expressions match with the simulation results. They also show that the proposed systems with optimal power allocation outperform the conventional systems without power allocation under the same other conditions. In some cases, the gain could be as large as several dB's in effective signal-to-noise ratio.