This paper proposes FDALLM-Small, a unified and lightweight RF sensing framework that integrates Functional Data Analysis (FDA) with parameter efficiently tuned small language models. By transforming raw RF measurements into smooth and structured functional embeddings and encoding them into standardized functional prompts, the framework enables compact LLMs to perform classification and localization tasks with strong accuracy and robustness. Through LoRA based fine tuning, small LLMs effectively learn discriminative RF patterns while updating only a tiny fraction of model parameters, making the approach highly efficient and suitable for on device deployment. Experiments on the XRF55 and AdaRF datasets demonstrate that the FDA–prompting pipeline substantially boosts model performance, allowing small LLMs to surpass conventional deep learning baselines and approach the accuracy of large API based LLMs without relying on cloud computation. A scaling study further shows that smaller models consistently offer the best performance–efficiency trade offs, highlighting the intrinsic compatibility between FDA representations and compact architectures. These results confirm the practicality of FDALLM-Small as an edge friendly and computationally efficient solution for real world RF sensing applications.

WiFi-based human activity recognition (HAR) has emerged as a focal point within the Internet of Things landscape, owing to its non-intrusive sensing capabilities and inherent privacy-preserving advantages. In existing WiFi-based HAR research, channel state information (CSI) is primarily utilized to capture activity-related features and enable recognition. However, CSI-based cross-domain HAR remains challenged by issues such as redundant subcarriers, limited samples in the target domain, and high sensitivity of CSI to environmental variations. To address these challenges, this paper proposes Wi-DMAR, a WiFi-based cross-domain HAR framework that integrates three key modules. First, an adaptive subcarrier selection module computes the correlation between each subcarrier and the principal components, identifies subcarriers with high contribution, preserves essential activity-related features, reduces data dimensionality, and lowers computational overhead. Second, a conditional diffusion–based data augmentation module employs a Transformer-based feature extractor to capture domain-specific representations of target-domain data, and optimizes domain consistency loss and domain-guided diffusion loss to generate pseudo samples that resemble the target-domain distribution, thereby mitigating sample scarcity. Third, an activity recognition module based on sample similarity learning reformulates the traditional label classification problem into a sample comparison task, by quantifying similarity between samples, it performs activity recognition and enhances cross-domain generalization. Experimental results demonstrate that Wi-DMAR achieves superior recognition accuracy compared with state-of-the-art cross-domain HAR methods such as DiffAR and MetaAct. Ablation studies further confirm that each core component contributes positively to performance improvements.

Despite significant advancements in terrestrial networks, inherent limitations persist in providing reliable coverage to remote areas and maintaining resilience during natural disasters. Multi-tier networks with low Earth orbit (LEO) satellites and high-altitude platforms (HAPs) offer promising solutions, but face challenges from high mobility and dynamic channel conditions that cause unstable connections and frequent handovers. In this paper, we design a three-tier network architecture that integrates LEO satellites, HAPs, and ground terminals with hybrid free-space optical (FSO) and radio frequency (RF) links to maximize coverage while maintaining connectivity reliability. This hybrid approach leverages the high bandwidth of FSO for satellite-to-HAP links and the weather resilience of RF for HAP-to-ground links. We formulate a joint optimization problem to simultaneously balance downlink transmission rate and handover frequency by optimizing network configuration and satellite handover decisions. The problem is highly dynamic and non-convex with time-coupled constraints. To address these challenges, we propose a novel large language model (LLM)-guided truncated quantile critics algorithm with dynamic action masking (LTQC-DAM) that utilizes dynamic action masking to eliminate unnecessary exploration and employs LLMs to adaptively tune hyperparameters. Simulation results demonstrate that the proposed LTQC-DAM algorithm outperforms baseline algorithms in terms of convergence, downlink transmission rate, and handover frequency. We also reveal that compared to other state-of-the-art LLMs, DeepSeek delivers the best performance through gradual, contextually-aware parameter adjustments.

Integrated sensing and communication systems face critical challenges, including limited bandwidth, power constraints, and varying communication conditions, which demand efficient data transmission and processing strategies. This paper introduces, ByteTrans, a novel joint optimization framework that integrates byte-level predictive modeling with adaptive model scheduling to maximize data transmission efficiency while adhering to communication and computational constraints. The proposed framework employs Transformer-based models to predict and compress data packets losslessly, leveraging the inherent redundancy in multi-modal network data. Such a unified data compression approach predicts occurring byte probabilities, encodes them as ranks using lossless entropy coding, and efficiently reduces data size and entropy across diverse modalities. Then, a dynamic adaptation strategy selects the optimal compression model based on packet characteristics and channel conditions, ensuring efficient operation across heterogeneous sensor environments. Experimental results validate that our scheme achieves compression rates exceeding 50%, while showcasing substantial reductions in communication time and bandwidth usage under both normal and adverse channel conditions. Furthermore, we effectively implement these models across various real-world edge sensors and servers, showcasing their practicality and efficiency in various network applications. By addressing the trade-offs between achieving lower compression ratios and limiting computational and energy consumption, this work establishes a scalable and robust solution for data management in multi-modal communication systems.

Integrated sensing and communication (ISAC) uses the same software and hardware resources to achieve both communication and sensing functionalities. Thus, it stands as one of the core technologies of 6G and has garnered significant attention in recent years. In ISAC systems, a variety of machine learning models are trained to analyze and identify signal patterns, thereby ensuring reliable sensing and communications. However, considering factors such as communication rates, costs, and privacy, collecting sufficient training data from various ISAC scenarios for these models is impractical. Hence, this paper introduces a generative AI (GenAI) enabled robust data augmentation scheme. The scheme first employs a conditioned diffusion model trained on a limited amount of collected CSI data to generate new samples, thereby enhancing the sample quantity. Building on this, the scheme further utilizes another diffusion model to enhance the sample quality, thereby facilitating the data augmentation in scenarios where the original sensing data is insufficient and unevenly distributed. Moreover, we propose a novel algorithm to estimate the acceleration and jerk of signal propagation path length changes from CSI. We then use the proposed scheme to enhance the estimated parameters and detect the number of targets based on the enhanced data. The evaluation reveals that our scheme improves the detection performance by up to 70%, demonstrating reliability and robustness, which supports the deployment and practical use of the ISAC network.

The increasing saturation of terrestrial resources has driven the exploration of low-altitude applications such as air taxis. Low altitude wireless networks (LAWNs) serve as the foundation for these applications, and integrated sensing and communication (ISAC) constitutes one of the core technologies within LAWNs. However, the open nature of low-altitude airspace makes LAWNs vulnerable to malicious channel access attacks, which degrade the ISAC performance. Therefore, this paper develops a game-based framework to mitigate the influence of the attacks on LAWNs. Concretely, we first derive expressions of communication data’s signal-to-interference-plus-noise ratio and the age of information of sensing data under attack conditions, which serve as quality of service metrics. Then, we formulate the ISAC performance optimization problem as a Stackelberg game, where the attacker acts as the leader, and the legitimate drone and the ground ISAC base station act as second and first followers, respectively. On this basis, we design a backward induction algorithm that achieves the Stackelberg equilibrium while maximizing the utilities of all participants, thereby mitigating the attack-induced degradation of ISAC performance in LAWNs. We further prove the existence of the equilibrium. Simulation results show that the proposed algorithm outperforms existing baselines and a static Nash equilibrium benchmark, ensuring that LAWNs can provide reliable service for low-altitude applications.

Leveraging flexible deployment, extensive coverage, and reliable communication links, unmanned aerial vehicle (UAV)-assisted mobile edge computing offers new opportunities to support mobile devices with heavy computational tasks. Considering the energy constraint of UAVs and delay requirement of tasks, many efforts should be devoted to pursuing lower service latency, which however is under explored in this innovational architecture. In this paper, with the purpose of minimizing the overall network service duration, departing from traditional serial task execution, we first design a multi-task parallel execution paradigm, and then investigate a joint optimization problem encompassing content caching, computation offloading, and channel allocation. To address this intractable problem involving large state and action spaces, we decompose it into two subproblems, i.e., an intra content caching and computation offloading optimization of each UAV, and an inter channel allocation of all UAVs. We then propose a reinforcement learning-based two-layer optimization scheme that integrates the efficient representation of DQN learning and the comprehensive exploration of regret minimization learning. Specifically, in the lower layer, a DQN-based algorithm is developed to solve the intra subproblem, and in the upper layer, a regret minimization-based algorithm is designed to tackle the inter subproblem. Through nested optimization between the two layers, optimal strategies for content caching, computation offloading and channel allocation can be achieved. Numerical results demonstrate that the proposed scheme significantly reduces service latency compared to various baseline methods.

User devices (UDs) with non-independent and identically distributed (non-IID) data will worsen accuracy performance of the global model in federated learning (FL). Therefore, the implementation of diffusion strategies in machine learning (ML) models can enhance the effectiveness of federated learning with non-IID data. However, in a device-to-device (D2D) wireless federated learning (WFL) system, limited wireless resources and severe wireless channel interference become the important bottleneck to restrict the diffusion performance and model aggregation so as the global model of WFL with non-IID suffers from the weight divergence challenge. Thus, we propose a novel joint over-the-air computation (OAC) aggregation and diffusion framework by using a graph neural network (GNN) for WFL, termed an OAC-GNN-Dif framework. By integrating the OAC with message passing neural network (MPNN) of GNN, we further develop the OAC-MPNN-Dif algorithm based on the OAC-GNN-Dif framework. To further reduce communication costs, we designed an OAC message recurrent neural network (OAC-MPRNN-Dif) algorithm, where each UD propagates local models via D2D communications to refresh the graph embedding in the current frame based on the graph feature extraction and localization state of the previous frame to reduce communication costs. Additionally, we introduce dynamic time-varying MPNN for federated diffusion within evolving D2D network topologies. The experimental results indicate that our approach significantly performs well in communication overhead, with a 30%-60% decreasing in wireless resources overhead and 1.2-3.5 times decreasing in the number of model transfers compared to the FedDif methods. Moreover, our approach also improves the global model test accuracy, which is about 2.7% higher than the existing communication diffusion FL with non-IID characteristics.

We consider decentralized federated learning (DFL) in mobile computing networks (MCNs), where dynamically changing neighborhood sets among devices arise from mobility and environmental perturbations. The time-varying topology coupled with inherent system heterogeneity poses significant challenges to achieve stable and efficient convergence in DFL. However, existing studies rarely consider both dynamic connectivity and statistical heterogeneity. To close this gap, this paper proposes a novel DFL framework enhanced with topology learning (DFL-TL) to mitigate the spatio-temporal volatility induced by MCNs, where each mobile device faces coupled constraints on its temporal windows for local updates and spatial scopes for model interaction. We introduce a new bounded neighborhood heterogeneity to jointly measure and constrain both the inter-device heterogeneity and the spectral properties of the topologies. Additionally, we formulate a mixed-integer nonlinear programming (MINLP) problem to jointly optimize learning costs and neighborhood heterogeneity. Through problem decomposition, DFL-TL efficiently identifies optimal resource allocations and adaptive mixing matrices, thereby enabling the selection of optimal training time windows while reducing the adverse effects of dynamic topologies in heterogeneous networks. Furthermore, we establish the iteration complexity of DFL-TL under non-convex settings and show that solving the proposed MINLP formulation leads to a tighter convergence bound. Extensive experiments demonstrate that DFL-TL achieves a faster convergence performance and reduces the wall-clock training time compared to the state-of-the-art baselines.

The integration of satellite and autonomous aerial vehicle (AAV) communications has become essential for the scenarios requiring both wide coverage and rapid deployment, particularly in remote or disaster-stricken areas where the terrestrial infrastructure is unavailable. Furthermore, emerging applications increasingly demand simultaneous mobile edge computing (MEC) and data collection (DC) capabilities within the same aerial network. However, jointly optimizing these operations in heterogeneous satellite-AAV systems presents significant challenges due to limited on-board resources and competing demands under dynamic channel conditions. In this work, we investigate a satellite-AAV-enabled joint MEC-DC system where these platforms collaborate to serve ground devices (GDs). Specifically, we formulate a joint optimization problem to minimize the average MEC end-to-end delay and AAV energy consumption while maximizing the collected data. Since the formulated optimization problem is a non-convex mixed-integer nonlinear programming (MINLP) problem, we propose a Q-weighted variational policy optimization-based joint AAV movement control, GD association, offloading decision, and bandwidth allocation (QAGOB) approach. Specifically, we reformulate the optimization problem as an action space-transformed Markov decision process to adapt the variable action dimensions and hybrid action space. Subsequently, QAGOB leverages the multi-modal generation capacities of diffusion models to optimize policies and can achieve better sample efficiency while controlling the diffusion costs during training. Simulation results show that QAGOB outperforms five other benchmarks, including traditional DRL and diffusion-based DRL algorithms. Furthermore, the MEC-DC joint optimization achieves significant advantages when compared to the separate optimization of MEC and DC.

In this paper, a secure and communication-efficient clustered federated learning (CFL) design is proposed. In our model, several base stations (BSs) with heterogeneous task-handling capabilities and multiple users with non-independent and identically distributed (non-IID) data jointly perform CFL training incorporating differential privacy (DP) techniques. Since each BS can process only a subset of the learning tasks and has limited wireless resource blocks (RBs) to allocate to users for federated learning (FL) model parameter transmission, it is necessary to jointly optimize RB allocation and user scheduling for CFL performance optimization. Meanwhile, our considered CFL method requires devices to use their limited data and FL model information to determine their task identities, which may introduce additional communication overhead. We formulate an optimization problem whose goal is to minimize the training loss of all learning tasks while considering device clustering, RB allocation, DP noise, and FL model transmission delay. To solve the problem, we propose a novel dynamic penalty function assisted value decomposed multi-agent reinforcement learning (DPVD-MARL) algorithm that enables distributed BSs to independently determine their connected users, RBs, and DP noise of the connected users but jointly minimize the training loss of all learning tasks across all BSs. Different from the existing MARL methods that assign a large penalty for infeasible actions, we propose a novel penalty assignment scheme that assigns penalty depending on the number of devices that cannot meet communication constraints (e.g., delay), which can guide the MARL scheme to quickly find valid actions, thus improving the convergence speed. Simulation results show that the DPVD-MARL can improve the convergence rate by up to 20% and the ultimate accumulated rewards by 15% compared to independent Q-learning.

With the rise of the Tactile Internet, delivering real-time and high-fidelity tactile feedback is crucial for enhancing immersion in multimedia services. However, existing tactile coding methods fail to simultaneously adapt to the diverse delay requirements of multimedia services and the time-varying network bandwidth. To address these challenges, this paper proposes a Scalable Tactile Coding (STC) method, which provides flexible coding delays and bitrates across multiple levels while ensuring human perceptual quality. Specifically, we first propose a tactile coding framework based on a non-stationary autoencoder for efficient compression, which features both delay-scalable and rate-scalable advantages. Second, by trading the balance between delay and computation, we design a sliding window approach that utilizes overlapping coding to reduce buffer delay. This approach provides multiple delay options to accommodate diverse delay requirements, thus realizing the desired delay-scalable effect. Third, inspired by the base-enhancement strategy of scalable video coding, we design a non-uniform quantization method to compress residual signals, which can be leveraged to dynamically enhance tactile signal fidelity. By adjusting the quantization bit-width, this method provides multiple levels of bitrates, thereby achieving a rate-scalable effect. Extensive experimental results demonstrate that STC achieves a bitrate reduction of over 92.3 % while maintaining satisfactory perceptual quality. Additionally, STC further supports flexible bitrate adjustment and satisfy various delay requirements.

Edge intelligence in space-air-ground integrated networks (SAGINs) can enable worldwide network coverage beyond geographical limitations for users to access ubiquitous and low-latency intelligence services. Facing global coverage and complex environments in SAGINs, edge intelligence can provision large language models (LLMs) agents for users via edge servers at ground base stations (BSs) or cloud data centers relayed by satellites. As LLMs with billions of parameters are pretrained on vast datasets, LLM agents have few-shot learning capabilities, e.g., chain-of-thought (CoT) prompting for complex tasks, which raises a new trade-off between resource consumption and performance in SAGINs. In this paper, we propose a joint caching and inference framework for edge intelligence to provision sustainable and ubiquitous LLM agents in SAGINs. We introduce “cached model-as-a-resource” for offering LLMs with limited context windows and propose a novel optimization framework, i.e., joint model caching and inference, to utilize cached model resources for provisioning LLM agent services along with communication, computing, and storage resources.We design “age of thought” (AoT) considering the CoT prompting of LLMs, and propose a least AoT cached model replacement algorithm for optimizing the provisioning cost. We propose a deep Q-network-based modified second-bid (DQMSB) auction to incentivize satellite/ground network operators in real-time, which can enhance allocation efficiency by 23% while guaranteeing strategy-proofness and being free from adverse selection.

In this paper, we propose a tri-hybrid beamforming (THBF) architecture based on the radiation-center (RC) reconfigurable antenna array (RCRAA), including the digital beamforming, analog beamforming, and electromagnetic (EM) beamforming, where the EM beamformer design is modeled as RC selection. Aiming at spectral efficiency (SE) maximization subject to the hardware and power consumption constraints, we propose a tri-loop alternating optimization (TLAO) scheme for the THBF design, where the digital and analog beamformers are optimized based on the penalty dual decomposition in the inner and middle loops, and the RC selection is determined through the coordinate descent method in the outer loop. Aiming at energy-efficiency (EE) maximization, we develop a dual quadratic transform-based fractional programming (DQTFP) scheme, where the TLAO scheme is readily used for the THBF design. To reduce the computational complexity, we propose the Lagrange dual transform-based fractional programming (LDTFP) scheme, where each iteration has a closed-form solution. Simulation results demonstrate the great potential of the RCRAA in improving both SE and EE. Compared to the DQTFP scheme, the LDTFP scheme significantly reduces the computational complexity with only minor performance loss.

In this paper, we investigate the low-complexity distributed combining scheme design for near-field cell-free extremely large-scale multiple-input-multiple-output (CF XL-MIMO) systems. Firstly, we construct the uplink spectral efficiency (SE) performance analysis framework for CF XL-MIMO systems over centralized and distributed processing schemes. Notably, we derive the centralized minimum mean-square error (CMMSE) and local minimum mean-square error (LMMSE) combining schemes over arbitrary channel estimators. Then, focusing on the CMMSE and LMMSE combining schemes, we propose five low-complexity distributed combining schemes based on the matrix approximation methodology or the symmetric successive over relaxation (SSOR) algorithm. More specifically, we propose two matrix approximation methodology-aided combining schemes: Global Statistics & Local Instantaneous information-based MMSE (GSLI-MMSE) and Statistics matrix Inversion-based LMMSE (SI-LMMSE). These two schemes are derived by approximating the global instantaneous information in the CMMSE combining and the local instantaneous information in the LMMSE combining with the global and local statistics information by asymptotic analysis and matrix expectation approximation, respectively. Moreover, by applying the low-complexity SSOR algorithm to iteratively solve the matrix inversion in the LMMSE combining, we derive three distributed SSOR-based LMMSE combining schemes, distinguished from the applied information and initial values.

Next-generation Wi-Fi 8 (IEEE 802.11bn) targets ultra-high reliability (UHR) by introducing coordinated beamforming (CoBF). In dense networks with multiple access points (APs), simultaneous downlink (DL) multi-user MIMO (MU-MIMO) transmissions from multiple APs can cause severe intra-basic service set (intra-BSS) and inter-BSS interference. CoBF aided by only partial channel state information (CSI) feedback through medium access control (MAC) layer frame exchange is envisioned to support concurrent DL transmission with mitigated physical-(PHY-)layer interference. To improve the network throughput, not only the interference mitigation algorithm design requires careful design but also the selection of optimal AP CoBF clusters is crucial for dense AP deployments. This paper presents a cross-layer solution combining PHY and MAC layer design to optimize AP cluster formation for Wi-Fi 8 CoBF. At the PHY layer, we introduce two beamforming nulling strategies: full nulling, which completely cancels all intra-BSS and inter-BSS interference when sufficient spatial degrees of freedom are available, and partial nulling, which is used under limited degrees of freedom to reduce interference as much as possible. Based on this, we formulate the cross-layer problem that aims to optimize the network throughput, to which we propose an exact linear programming (LP) optimization to determine the optimal AP cluster formation. A greedy clustering algorithm is proposed as a low-complexity alternate. Simulation results demonstrate that the proposed CoBF approach significantly mitigates interference and achieves substantial throughput gains in dense AP scenarios. Furthermore, the LP-optimized AP clustering yields the higher network throughput than the greedy heuristic and mixed integer linear programming (MILP) by up to 12% and 26%, highlighting the benefits of global optimization in terms of performance and time complexity.

This paper investigates the beamforming for the reconfigurable intelligent surface (RIS)-aided millimeter wave integrated sensing and communication system. We propose a fractional programming (FP) and alternating optimization-based hybrid beamforming (HBF) scheme. The weighted sum of the signal-to-clutter-and-noise-ratio at the radar receiver and the smallest signal-to-interference-plus-noise ratio among all communication users is maximized under the hardware constraints. Since it is difficult to directly obtain a solution for this non-convex FP problem, it is divided into three sub-problems that are alternately solved. Two sub-problems optimizing the digital transceiving beamforming at the base station (BS) are transformed into typical convex quadratic constraint quadratic programming ones using quadratic transformation. The other sub-problem optimizing the RIS passive beamforming is transformed into a manifold optimization one using Dinkelbach transformation. In addition, we consider the HBF structure at the BS through substituting the fully digital beamformer by the digital and analog ones. To reduce the computational complexity, a low-complexity HBF scheme based on Rayleigh quotient, zero-forcing and discrete Fourier transform codewords is proposed with closed-form expressions. Simulation results verify the effectiveness of two proposed schemes.

Gesture-based interaction is vital for human-robot communication in settings where verbal cues are constrained, such as aircraft ramps, industrial sites, and healthcare settings like eldercare facilities, rehabilitation centers, and operating rooms. Robots often rely on gestures due to factors like noise, communication barriers, or the need for sterile, hands-free interaction. These dynamic settings demand not only robust gesture recognition but also lifelong adaptability to new and evolving gesture vocabularies. A key challenge in such scenarios is catastrophic forgetting in Class-Incremental Learning (CIL), where the robot forgets previously learned signals when new ones are introduced. We present Momentary Experience Replay (MoER), a novel framework that enhances experience replay strategies to prioritize recently acquired knowledge, which is more vulnerable to degradation. MoER introduces a Momentary Buffer that dynamically increases replay frequency for recently learned gesture signals. Evaluated on a 12-class gesture recognition task using the public NATOPS dataset in a CIL experimental setting, MoER achieves 92% accuracy, outperforming baseline methods (ER and DER++) by up to 6%. MoER also shows improvements in backward transfer and retention stability, supporting its role in continual signal recognition in dynamic environments. This work lays the foundation for lifelong learning in healthcare robotics, where continual adaptation to new tasks and user-specific gestures is critical for effective and trustworthy assistive interaction.

This paper presents TF-Diff, a novel training-free diffusion framework for cross-domain radio frequency (RF)-based human activity recognition (HAR) system. Traditional diffusion models for RF sensing require extensive retraining and large target-domain datasets, making them impractical for real-world deployment scenarios. To address this challenge, TF-Diff couples an enhanced temporal-aware score estimator with brief few-shot feature adaptation via reconstruction, and performing generation through an accelerated Langevin sampler. This enables effective adaptation with minimal target-domain data. Our approach introduces a generative-AI-based method explicitly designed for rapid cross-domain RF-based classification with strong performance and significantly reduced computational cost and memory requirements. Extensive evaluations on two cross-domain RFID datasets demonstrate that TF-Diff achieves superior classification accuracy (e.g., 74.14%) with just five adaptation samples per class, which significantly outperforms several baseline methods, highlighting its practicality and effectiveness for resource-constrained deployments and dynamic scenarios.

This paper studies the joint beamforming and power allocation optimization in Low Earth Orbit (LEO) satellite networks with Rate-Splitting Multiple Access (RSMA), where dynamic channels and limited channel state information significantly degrade the performance of conventional optimization methods. Specifically, we formulate a sum-rate maximization problem under RSMA constraints. The decision variables include the transmit power allocated to the common and private streams, which are subject to total power and minimum user rate constraints. To solve this challenging problem, we propose STELLAR, a novel framework that employs a Large Language Model (LLM) as an intelligent decision-maker to directly generate feasible transmission strategies without requiring repeated model training. Specifically, STELLAR combines model-driven beamforming initialization with prompt-based evolutionary refinement and population updates, enabling rapid adaptation to varying channel conditions. Simulation results show that STELLAR outperforms baseline approaches, achieving superior spectral efficiency and converging within 30 iterations in a system with a 16-antenna LEO satellite and four ground stations.

In dense RFID systems, where numerous readers and tags operate simultaneously in close proximity, securing reader-to-reader communication is essential to prevent attacks such as eavesdropping and spoofing. Existing protocols focus primarily on reader-to-tag communication and use static security mechanisms that may be inadequate in dynamic conditions. We propose DCA-KEAE, a Dynamic Context-Aware Key Exchange and Adaptive Encryption framework for RFID systems. DCAKEAE adapts security protocols in real-time based on factors such as reader proximity, system load, and threat levels: it employs lightweight symmetric keys for low-risk scenarios and escalates to stronger protocols like ECDH and AES-256 in highrisk environments. Evaluations with up to 10,000 readers show that DCA-KEAE reduces latency, optimizes encryption, and improves system throughput, offering a scalable and efficient solution for RFID networks, with applications extending to the Internet of Things (IoT), industrial automation, and smart grids.

Dense RFID environments pose significant challenges, such as reader collisions, tag interference, and scalability issues, which degrade system performance and reliability. This paper introduces RFIDNet, a novel protocol designed to address these challenges by dynamically coordinating reader activities and optimizing network resource utilization. The proposed RFIDNet is an innovative framework of advanced mechanisms that include a Carrier Sense Multiple Access with Reader Arbitration (CSMARA) scheme for efficient reader coordination, Dynamic Frequency Hopping (DFH) for interference mitigation, and merging Frequency and Time Division Multiple Access (F/TDMA) with Reduce Coverage Control (RCC) to handle unresolved contention. Experimental validations using a Universal Software Radio Peripheral (USRP) testbed and MATLAB simulations demonstrate that RFIDNet improves the overall system performance compared to the baseline. This confirms RFIDNet's robustness and scalability, making it a viable solution for realworld, dense RFID deployments.

To address the growing challenges of road safety, efficiency, and environmental sustainability, Cooperative, Connected, and Automated Mobility (CCAM) leverages advanced communication and automation technologies across road networks. In Europe, Cooperative Intelligent Transportation Systems (C-ITS) facilitate communication among vehicles, infrastructure, and other entities, enhancing situational awareness and safety. Cooperative Awareness Messages (CAMs) provide continuous status information-such as vehicle position, speed, and direction-to nearby vehicles and infrastructure. Vehicles send CAMs to other vehicles at high frequencies ($1-10 \text{Hz}$), which, especially in dense networks, risks overloading communication channels. This may degrade network performance and potentially reduce the effectiveness of road safety applications. In this paper, we propose a new deep learning-based communication mechanism to reduce the risk of channel overload in C-ITS. Our approach enables nearby vehicles to form a temporary trust group valid for a specified period. During this period, vehicles initially send CAMs at a frequency of 1 Hz. Then, each vehicle predicts the subsequent messages of its neighbors using a variational autoencoder. Next, vehicles periodically verify the predicted CAMs by sending actual CAMs. This process ensures prediction reliability and reduces channel load by allowing vehicles to decrease CAM transmissions. We validated our approach in a simulation environment (using Omnet++, Sumo, and Artery) demonstrating its effectiveness in maintaining road safety, while reducing communication overhead.

In communication network management, mobile traffic prediction is vital for ensuring efficient system operation. Despite considerable progresses in applying neural networks for traffic prediction, traditional models often struggle to handle high-dimensional and time-dependent data. This paper addresses these challenges by proposing a novel framework that constructs prompts to enhance the predictive ability of large language models (LLMs) and their understanding of traffic data. Specifically, we leverage functional data analysis (FDA), a superior technique to traditional methods, to preprocess traffic data and extract features. Through extensive experiments on various LLMs with a real-world dataset, we validate the effectiveness and scalability of our proposed method, with performance improvements of up to 23.53 % and 21.34 % in mean squared error (MSE) and mean absolute error (MAE), respectively. Our results indicate a significant advance in predictive performance, providing a promising approach for future traffic data analysis.

Recent advancements in intelligent reflecting surfaces (IRS) and mobile edge computing (MEC) offer new opportunities to enhance the performance of vehicular networks. However, meeting the computation-intensive and latency-sensitive demands of vehicles remains challenging due to the energy constraints and dynamic environments. To address this issue, we study an IRS-assisted MEC architecture for vehicular networks. We formulate a multi-objective optimization problem aimed at minimizing the total task completion delay and total energy consumption by jointly optimizing task offloading, IRS phase shift vector, and computation resource allocation. Given the mixed-integer nonlinear programming (MINLP) and NP-hard nature of the problem, we propose a generative diffusion model (GDM)-based Stackelberg game (GDMSG) approach. Specifically, the problem is reformulated within a Stackelberg game framework, where generative GDM is integrated to capture complex dynamics to efficiently derive optimal solutions. Simulation results indicate that the proposed GDMSG achieves outstanding performance compared to the benchmark approaches.

In this paper, a secure and communication-efficient clustered federated learning (CFL) design is investigated. In our model, several base stations (BSs) with heterogeneous task-handling capabilities and multiple users with non-independent and identically distributed (non-IID) data jointly perform CFL training using differential privacy (DP) techniques. Since each BS can process only a subset of learning tasks and has limited wireless resource blocks to allocate to users for federated learning (FL) model parameter transmission, it is necessary to jointly optimize resource block (RB) allocation and user scheduling for CFL performance optimization. Meanwhile, our considered CFL requires devices to use their limited data and FL model information to determine their task identities, which may introduce additional communication overhead. This problem is formulated as an optimization problem whose goal is to minimize the training loss of all learning tasks while considering device clustering, RB allocation, noise, and FL model transmission delay. To solve this, we propose a novel value decomposed multi-agent reinforcement learning (VD-MARL) algorithm that enables distributed BSs to independently determine their connected users, the RBs, and DP noise of the connected users but jointly minimize the training loss of all learning tasks across all BSs. Different from the existing MARL methods that assign a large penalty for invalid actions, we propose a novel penalty assignment scheme that assigns penalty depending on the number of devices that cannot meet communication constraints (e.g., delay), which can guide the MARL scheme to quickly find valid actions thus improving the convergence speed. Simulation results show that the VD-MARL can improve the convergence rate by up to 35% and the ultimate accumulated rewards by 27% compared to independent Q-learning.

In this work, we present a novel approach to enhance the robustness of autonomous robotic Radio Frequency Identification (RFID) inventory systems using Conformal Prediction (CP). Recent AI-driven approaches, especially deep-learning models, have made significant advances in performing inventory strategies and action planning. However, these models lack the capability to measure uncertainty during the prediction process, which can result in accumulated errors and lead to catastrophic failures. To address the above challenge, we propose a confidenceguaranteed policy using CP to ensure reliable predictions in RFID inventory tasks. Our method focuses on managing the uncertainty in sub-goal estimation for a trained model, ensuring that predictions can meet or exceed a user-specific confidence level. We conduct extensive experiments to assess the proposed method by regulating an existing model and evaluate its effectiveness in identifying uncertain predictions. The experimental results demonstrate the effectiveness of our approach in improving both the reliability and efficiency of RFID inventory tasks, ensuring consistent and trustworthy operation.

With the increasing demand for secure communication in 5G and beyond, authentication of wireless devices has become a crucial task for communication security. Radio frequency fingerprint identification (RFFI) leverages the hardware-specific features in radio frequency (RF) signals, known as radio frequency fingerprints (RFF), to achieve highprecision device identification. However, the dependence of RFFI on the physical characteristics of devices makes it vulnerable to physical spoofing attacks. This paper proposes an innovative physical spoofing attack framework that combines spoofed transmitter and legitimate transmitter models. It performs RFF modeling, RFF concealment (RFFC), and RFF spoofing (RFFS) sequentially to achieve precise spoofing of the original baseband signal. We validate the effectiveness of the proposed physical spoofing mechanism through simulations of seven types of transmitters using MATLAB Simulink. The performance is further evaluated on an RFFI model based on complexvalued convolutional neural networks (CVCNN). Experimental results show that neural networks (NN) significantly outperform the generalized memory polynomial (GMP) model in nonlinear data fitting and temporal relationship modeling. Consequently, NN-based physical spoofing methods exhibit superior attack effectiveness. Specifically, under the signal-to-noise ratio (SNR) of 15 dB, the NN-based physical spoofing method achieves a target attack success rate (TSR) as high as 98%, which is superior to adversarial attack methods. NN-based methods also enhanced performance in terms of stealthiness metrics.

Radio frequency (RF) fingerprinting is a technique used to identify a wireless device based on its specific and unique hardware characteristics. In recent years, deep learning has been utilized for RF fingerprinting due to its superiority in feature extraction and higher classification accuracy. However, one major challenge of deep learning-based RF fingerprinting is that wireless signals are highly sensitive to environmental conditions, causing the device fingerprints captured in one environment to not transfer well to another. Hence, deep learning models are found to perform well in the same condition but lose their ability to classify devices in the new condition. In this paper, we examine three transfer learning techniques to mitigate the domain shift problem in RF fingerprinting and compare them with two well-defined baselines. The three RF fingerprinting datasets under various scenarios are examined to explore how environmental factors impact RF fingerprinting, such as transmitter locations, transmitter distance, and device configurations. We identify the most challenging scenarios and study how environmental factors lead to model deterioration through t-SNE visualization.

Federated Temporal Difference (FTD) learning has emerged as a promising framework for collaboratively evaluating policies without sharing raw data. Despite its potential, existing approaches often yield biased convergence results due to the inherent challenges of federated reinforcement learning, such as multiple local updates and environment heterogeneity. In response, we investigate federated temporal difference (TD) learning, focusing on collaborative policy evaluation with linear function approximation among agents operating in heterogeneous environments. We devise a heterogeneous federated temporal difference (HFTD) algorithm which iteratively aggregates agents' local stochastic gradients for TD learning. The HFTD algorithm involves two major novel contributions: 1) it aims to find the optimal value function model for the mixture environment which is the environment randomly drawn from agents' heterogeneous environments, using the local gradients of agents' mean squared Bellman errors (MSBEs) for their respective environments; 2) it allows agents to perform different numbers of local iterations for TD learning based on their heterogeneous computational capabilities. We analyze the finite-time convergence of the HFTD algorithm for the scenarios of IID sampling and Markovian sampling respectively. By characterizing bounds on the convergence error, we show that the HFTD algorithm can exactly converge to the optimal model and also achieves linear speedups as the number of agents increases.

Wireless signals are widely used for human sensing, but they require devices and targets to remain stationary, especially for fine-grained motions like respiration. To enable vital sign monitoring under motion using RFID, we employ dual tags to create a relative coordinate system that reduces motion interference. We also propose physics-informed generative models with frequency domain constraints to improve noise reduction, capturing both time and frequency features. Our method, tested across dynamic scenarios including walking, treadmill exercises, and driving and validated using real patient data, demonstrates superior performance compared to traditional approaches in accurately matching real respiratory signals and exhibits robustness against time shifts.

Decentralized federated learning (DFL) can greatly reduce communication costs due to its decentralized communication structure compared to traditional centralized federated learning (FL). Existing works on FL with partial client participation often considered idealized scenarios (such as all clients participate in a round with the same probability), or required using clients' past gradient/model information which can be too costly to implement, or focused on centralized FL. In this paper, we study lightweight decentralized federated learning that does not use any client's past gradient/model information. We first present a novel sample-path-based cyclic convergence analysis for lightweight DFL with arbitrary client participation for the non-convex objectives case. The cyclic convergence analysis bounds clients' local model drifts due to partial participation over multiple rounds within a cycle and the cyclic consensus error via a per-cycle descent approach, while capturing the effect of client participation through a single unified term. By analyzing this term, we propose Cyclic Decentralized Federated Learning (CDFL), which enables general cyclic client participation by requiring only that each client performs the same total number of local updates per cycle. Our results show that CDFL achieves a convergence rate that matches existing benchmarks. We further propose a cyclic control framework that is both training-round and energy efficient to adaptively select participating clients and determine their number of local updates. Numerical experiments using real-world datasets verify our theoretical results and demonstrate the effectiveness of CDFL and the adaptive cyclic control framework.

Connected and Automated Mobility (CCAM) is undergoing a paradigm shift, with safety and efficiency increasingly dependent on connectivity. Cooperative Intelligent Transport Systems (C-ITS) support this transformation by enabling the exchange of Cooperative Awareness Messages (CAMs) between vehicles and roadside infrastructure. These messages, transmitted periodically at$\text{1 - 1 0 ~ H z}$, must be digitally signed in compliance with ETSI standards using Pseudonym Certificates (PCs). However, this security process introduces a significant overhead, as the size of the security data can be up to three times larger than the CAM payload, thereby consuming a considerable portion of the communication channel bandwidth. In this paper we propose a new authentication scheme based on deep learning. Instead of exchanging signed CAMs every time, the vehicles will authenticate each other once to establish cluster-based trust relationships, and then they will exchange only unsigned CAMs during the cluster lifetime. To ensure security within the cluster, an unsupervised variational autoencoder analyzes vehicle behavior to detect anomalies and confirm that each vehicle remains the same entity originally authenticated. Through simulations using OMNeT++, SUMO, and Artery, our method achieved a$\text{48.9 \%}$reduction in the volume of messages exchanged between vehicles, significantly decreasing communication channel overhead.

ABSTRACT Dense radio frequency identification (RFID) networks suffer from severe reader collisions, redundant reads, and inefficient resource utilization, particularly in large‐scale deployments. Existing approaches, including centralized and hybrid scheduling schemes, fail to scale effectively due to their reliance on global coordination and static allocation mechanisms. This paper presents NEMO (neighbourhood‐aware efficient management and optimization), a fully decentralized neighbourhood‐aware RFID network framework that dynamically optimizes scheduling, power control, and frequency allocation without requiring global coordination. NEMO leverages a novel adaptive scheduling mechanism to mitigate collisions while ensuring fair and efficient tag interrogation. Extensive universal software radio peripheral‐based hardware experiments and large‐scale simulations with up to 5000 readers and 1,000,000 tags demonstrate that NEMO outperforms state‐of‐the‐art protocols by achieving 25% higher throughput, 30% fewer collisions, 40% reduction in redundant reads, and improved energy efficiency by 18%. Additionally, NEMO exhibits scalability and robustness under extreme network congestion by maintaining high performance even as the numbers of readers and tags increase. The proposed framework is highly applicable to real‐world RFID deployments in warehouses, logistics, smart retail, Internet of Things, and industrial automation, where dense RFID environments demand efficient, adaptive, and decentralized resource management.

In communication network management, prediction of mobile network traffic is essential to ensure efficient system operation. Although significant progress has been made in the application of neural networks to traffic prediction tasks, traditional models still face considerable challenges when handling high-dimensional and highly time-dependent data. To address these issues, this paper proposes a new prediction framework that leverages large language models (LLMs), by constructing efficient prompts to enhance the ability of large language models (LLMs) in traffic prediction and improve their understanding of complex traffic patterns. Specifically, we introduce functional data analysis (FDA), a technique that offers superior capabilities compared to traditional methods in processing continuous and high-dimensional data structures, to preprocess traffic data and extract key features. Extensive experiments conducted on multiple LLMs using a real-world dataset validate the effectiveness and scalability of the proposed method. The experimental results demonstrate that the framework achieves significant improvements in predictive performance, providing a promising and efficient solution for traffic data analysis in future communication networks.

With ultra-high-definition (UHD) video services developing in B5G networks, such as an UHD video surveillance system with a resolution of$7680\times 4320$p, that generates video data 24/7, the high-service overhead caused by the bandwidth resource bottleneck will greatly affect the performance of video services. Network slice provider (NSP) focuses on the overall revenue. However, network slice user (NSU) emphasizes task requirements and cost. A crucial challenge to find a desirable tradeoff between NSPs and NSUs since the objective of NSPs’ Quality of Experience (QoE) is partially in conflict with the objective of NSUs’ QoE. In order to investigate the performance of data transmission for UHD video from the perspective of QoE, a Stackelberg game model is leveraged to achieve the optimization goals after constructed a novel QoE model. After analyzing the game process, Nash equilibrium can be achieved that indicates a relatively optimal state. A problem of maximizing the overall effective QoE is formulated by jointly optimizing NSPs’ QoE, NSUs’ QoE and the bandwidth resource allocation. To tackle the nonconvex formulated problem, a QoE-Aware Game-theoretic Band-width Resource Allocation Strategy for UHD Video Services named “QAGBRAS” is proposed. Extensive experiments are conducted to evaluate performance of the proposed approach against the state-of-the art solutions in terms of network congestion control, bandwidth utilization, and QoE factors, including NSP’s revenue, NSU’s task requirements, and cost. Simulation results show that our proposed approach can effectively achieve the optimal solution in the context of QoE.

Radio frequency (RF) sensing has been considered as an effective approach to human perception of nonintrusive and high-privacy scenarios. However, the existing wireless sensing techniques mostly rely on extensive labeled RF sensing data for offline training, while wireless sensory data collection is highly time consuming and costly. To ridge this gap, we investigate the problem of generalized dataset augmentation with an artificial intelligence (AI) generated content (AIGC) approach, termed RF-AIGC, for wireless sensing, which can not only purposefully generate new RF sensing data but reduce the data collection cost by augmenting a limited training dataset with synthesized RF data. We propose a conditional recurrent generative adversarial network (termed RF-CRGAN) to generate labeled synthetic RF data for specified human activities for multiple wireless sensing platforms, such as WiFi, radio-frequency identification (RFID), and millimeter wave (mmWave) radar. We also propose a holistic quantitative method to help evaluate and explain the effects of the synthesized data. The experimental results demonstrate that the proposed approach can effectively enhance the diversity of training data and achieve similar performance as real data.

With advancements in AI, smart home gyms are becoming increasingly popular for providing fitness assistance in indoor environments. In this research, we propose a layer-by-layer framework, called Wi-Fitness, which bridges video perception with Wi-Fi sensing for smart fitness. At the data preprocessing layer, the singular value decomposition-based channel state information denoising mechanism is leveraged to do the Wi-Fi data calibration. Diverse and high-quality training samples are generated by a random quantization-based data augmentation method. At the bimodal fusion layer, the heterogeneity between the Wi-Fi and video is mitigated by the local attention mechanism and the bimodal feature integration mechanism. For the video modality, the attention-based spatio-temporal graph convolutional network (AST-GCN Net) is proposed to refine spatial information. The spatio-temporal semantic alignment module is proposed to transfer spatial information from video to Wi-Fi and maintain temporal consistency across modalities. The fitness assessment layer provides exercise visualization. The generalization of Wi-Fitness is enhanced by layer-by-layer collaboration. Wi-Fitness demonstrates its effectiveness by achieving an average F1-Score of 92.68% in three typical indoor environments.

An increasing number of field robots would be used for mission-critical tasks in remote or post-disaster areas. Due to the limited individual abilities, these robots usually require an edge information hub (EIH), with not only communication but also sensing and computing functions. Such EIH could be deployed on a flexibly-dispatched unmanned aerial vehicle (UAV). Different from traditional aerial base stations or mobile edge computing (MEC), the EIH would direct the operations of robots via sensing-communication-computing-control ($\textbf {SC}^{3}$) closed-loop orchestration. This paper aims to optimize the closed-loop control performance of multiple$\textbf {SC}^{3}$loops, with constraints on satellite-backhaul rate, computing capability, and on-board energy. Specifically, the linear quadratic regulator (LQR) control cost is used to measure the closed-loop utility, and a sum LQR cost minimization problem is formulated to jointly optimize the splitting of sensor data and allocation of communication and computing resources. We first derive the optimal splitting ratio of sensor data, and then recast the problem to a more tractable form. An iterative algorithm is finally proposed to provide a sub-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm. We also uncover the influence of$\textbf {SC}^{3}$parameters on closed-loop controls, highlighting more systematic understanding.

Due to the exponential increase in wireless devices and a diversification of network services, unprecedented challenges, such as managing heterogeneous data traffic and massive access demands, have arisen in next-generation wireless networks. To address these challenges, there is a pressing need for the evolution of multiple access schemes with advanced transceivers. Millimeter-wave (mmWave) communication emerges as a promising solution by offering substantial bandwidth and accommodating massive connectivities. Nevertheless, the inherent signaling directionality and susceptibility to blockages pose significant challenges for deploying multiple transceivers with narrow antenna beams. Consequently, beam management becomes imperative for practical network implementations to identify and track the optimal transceiver beam pairs, ensuring maximum received power and maintaining high-quality access service. In this context, we propose a Contextual Combinatorial Beam Management (CCBM) framework tailored for mmWave wireless networks. By leveraging advanced online probing techniques and integrating predicted contextual information, such as dynamic link qualities in spatial-temporal domain, CCBM aims to jointly optimize transceiver pairing and beam selection while balancing the network load. This approach not only facilitates multiple access effectively but also enhances bandwidth utilization and reduces computational overheads for real-time applications. Theoretical analysis establishes the asymptotically optimality of the proposed approach, complemented by extensive evaluation results showcasing the superiority of our framework over other state-of-the-art schemes in multiple dimensions.

As an important component of the space-air-ground integrated network, aerial base station (AeBS) systems have gained significant attention for their flexibility in mobility and cost-effective construction. Nevertheless, the scarce spectrum resources and difficulty in accessing global information bring necessity and challenges to the deployment and resource allocation of AeBSs. In this paper, we propose a practical two-timescale framework to solve the resource allocation and deployment optimization problem in multi-AeBS networks. Specifically, the subcarrier allocation problem is first transformed into a many-to-one matching game coupled with power allocation and solved in a small timescale. Then, in a large timescale, the AeBS deployment subproblem is transformed into a distributed partially observable Markov decision process (Dec-POMDP), and then a novel multi-agent hypergraph convolutional deep reinforcement learning (MAHGCDRL) is proposed to solve this problem. The proposed MAHGCDRL extracts features of neighboring AeBSs through hypergraph convolutional networks, enabling AeBS agents to achieve better coordination in a distributed manner. Simulation results show that our proposed approach can attain a higher sum rate, and the proposed MAHGCDRL algorithm achieves better learning performance compared to the existing benchmarks in the literature.

Integrated sensing and communications (ISAC) is one of the crucial technologies for 6G, and channel state information (CSI) based sensing serves as an essential part of ISAC. However, current research on ISAC focuses mainly on improving sensing performance, overlooking security issues, particularly the unauthorized sensing of users. Hence, this paper proposes a diffusion model based secure sensing system (DFSS). Specifically, we first propose a discrete conditional diffusion model to generate graphs with nodes and edges, which guides the ISAC system to appropriately activate wireless links and nodes, ensuring the sensing performance while minimizing the operation cost. Using the activated links and nodes, DFSS then employs the continuous conditional diffusion model to generate safeguarding signals, which are next modulated onto the pilot at the transmitter to mask fluctuations caused by user activities. As such, only authorized ISAC devices with the safeguarding signals can extract the true CSI for sensing, while unauthorized devices are unable to perform the effective sensing. Experiment results demonstrate that DFSS can reduce the activity recognition accuracy of the unauthorized devices by approximately 70%, effectively shield the user from the illegitimate surveillance.

Vehicle Twins (VTs) as digital representations of vehicles can provide users with immersive experiences in vehicular metaverse applications, e.g., Augmented Reality (AR) navigation and embodied intelligence. VT migration is an effective way that migrates the VT when the locations of physical entities keep changing to maintain seamless immersive VT services. However, an efficient VT migration is challenging due to the rapid movement of vehicles, dynamic workloads of Roadside Units (RSUs), and heterogeneous resources of the RSUs. To achieve efficient migration decisions and a minimum latency for the VT migration, we propose a multi-agent split Deep Reinforcement Learning (DRL) framework combined with spatio-temporal trajectory generation. In this framework, multiple split DRL agents utilize split architecture to efficiently determine VT migration decisions. Furthermore, we propose a spatio-temporal trajectory generation algorithm based on trajectory datasets and road network data to simulate vehicle trajectories, enhancing the generalization of the proposed scheme for managing VT migration in dynamic network environments. Finally, experimental results demonstrate that the proposed scheme not only enhances the Quality of Experience (QoE) by 29% but also reduces the computational parameter count by approximately 25% while maintaining similar performances, enhancing users' immersive experiences in vehicular metaverses.

Autonomous aerial vehicles (AAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a AAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a AAV-enabled virtual antenna array to improve transmission performance from the AAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the AAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances.

Generative diffusion models like Stable Diffusion are at the forefront of the thriving field of generative models today, celebrated for their robust training methodologies and high-quality photorealistic generation capabilities. These models excel in producing rich content, establishing them as essential tools in the industry. Building on this foundation, the field has seen the rise ofpersonalized content synthesisas a particularly exciting application. However, the large model sizes and iterative nature of inference make it difficult to deploy personalized diffusion models broadly on local devices with heterogeneous computational power. To address this, we propose a novel framework for efficient multi-user offloading of personalized diffusion models. This framework accommodates a variable number of users, each with different computational capabilities, and adapts to the fluctuating computational resources available on edge servers. To enhance computational efficiency and alleviate the storage burden on edge servers, we propose a tailored multi-user hybrid inference approach. This method splits the inference process for each user into two phases, with an optimizable split point. Initially, a cluster-wide model processes low-level semantic information for each user's prompt using batching techniques. Subsequently, users employ their personalized models to refine these details during the later phase of inference. Given the constraints on edge server computational resources and users' preferences for low latency and high accuracy, we model the joint optimization of each user's offloading request handling and split point as an extension of the Generalized Quadratic Assignment Problem (GQAP). Our objective is to maximize a comprehensive metric that balances both latency and accuracy across all users. To solve this NP-hard problem, we transform the GQAP into an adaptive decision sequence, model it as a Markov decision process, and develop a hybrid solution combining deep reinforcement learning with convex optimization techniques. Simulation results validate the effectiveness of our framework, demonstrating superior optimality and low complexity compared to traditional methods. All related code, datasets, and fine-tuned models are available athttps://github.com/wty2011jl/E-MOPDM.

Satellite-terrestrial integrated network (STIN) is a full-scale communication paradigm, which can support joint information processing and seamless service provision by leveraging satellites' wide coverage and terrestrial networks' high capacity. The existing STIN operates with insufficient synergy in transmission scheduling, impacting resource allocation efficiency and transmission delay optimization, particularly in complex transmission scenarios. In this paper, we designDeterministic STIN (DetSTIN), a novel architecture for STIN, along with two algorithms tailored for transmission scheduling to collaboratively optimize resource adaptation and service flow scheduling. Specifically, the DetSTIN enables the smooth interconnection and integration of heterogeneous networks by providing layered deterministic services. Besides, a genetic-based resource adaptation algorithm is designed for fixed-mobile-satellite heterogeneous networks to reduce resource allocation overhead while maintaining the network performance. Furthermore, we propose a deep reinforcement learning-based differentiated scheduling algorithm to solve the routing-queue two-dimensional decision problem to differentially optimize transmission delay of service flows, thus obtaining higher transmission scheduling benefit. By addressing resource adaptation and differentiated scheduling synergistically, the proposed solution achieves reduced resource allocation overhead and increased transmission scheduling benefit, ultimately leading to increased network operation revenue of the DetSTIN. Simulation results demonstrate that the proposed solution delivers effective performance across various flow proportions, and as the number of flows increases, the network operation revenue exhibits a noticeable improvement, compared with benchmark algorithms.

Despite the proven capabilities of deep neural networks (DNNs) in identifying devices through radio frequency (RF) fingerprinting, the security vulnerabilities of these deep learning models have been largely overlooked. While the threat of backdoor attacks is well-studied in the image domain, few works have explored this threat in the context of RF signals. In this paper, we thoroughly analyze the susceptibility of DNN-based RF fingerprinting to backdoor attacks, focusing on a more practical scenario where attackers lack access to control model gradients and training processes. We propose leveraging explainable machine learning techniques and autoencoders to guide the selection of trigger positions and values, allowing for the creation of effective backdoor triggers in a model-agnostic manner. To comprehensively evaluate this backdoor attack, we employ four diverse datasets with two protocols (Wi-Fi and LoRa) across various DNN architectures. Given that RF signals are often transformed into the frequency or time-frequency domains, this study also assesses attack efficacy in the time-frequency domain. Furthermore, we experiment with potential detection and defense methods, demonstrating the difficulty of fully safeguarding against our proposed backdoor attack. Additionally, we consider the attack performance in the domain shift case.

In the era of the sixth generation (6G) and industrial Internet of Things (IIoT), an industrial cyber-physical system (ICPS) drives the proliferation of sensor devices. To address the limited resources of IIoT sensor devices, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution, providing flexible and cost-effective services in close proximity of IIoT sensor devices (ISDs). However, leveraging aerial MEC to meet the delay-sensitive and computation-intensive requirements of the ISDs could face several challenges, including the limited communication, computation and caching (3C) resources, stringent offloading requirements for 3C services, and constrained on-board energy of UAVs. To address these issues, we first present a collaborative aerial MEC-assisted ICPS architecture by incorporating the computing capabilities of the macro base station (MBS) and UAVs. We then formulate a service delay minimization optimization problem (SDMOP). Since the SDMOP is proved to be an NP-hard problem, we propose ajointcomputation offloading,caching,communication resource allocation,computation resource allocation, and UAV trajectorycontrolapproach (J$\rm{C}^{5}$A). Specifically, J$\rm{C}^{5}$A consists of a block successive upper bound minimization method of multipliers (BSUMM) for computation offloading and service caching, a convex optimization-based method for communication and computation resource allocation, and a successive convex approximation (SCA)-based method for UAV trajectory control. Moreover, we theoretically prove the convergence and polynomial complexity of J$\rm{C}^{5}$A. Simulation results demonstrate that the proposed approach can achieve superior system performance compared to the benchmark approaches and algorithms.

Human activity recognition (HAR) based on wearable devices has become an active research direction in the field of ubiquitous computing, and has a wide range of Internet of Things (IoT) applications. Unfortunately, it is challenging to obtain large amounts of labeled sensing data, and manual annotation is time-consuming and labor-intensive, making it impossible for the extensive deployment of HAR systems. Consequently, self-supervised learning has emerged to address this challenge by training on unlabeled data. However, traditional contrastive learning fails to simulate more sample diversity problems caused by environmental heterogeneity and sensor heterogeneity. In this paper, we propose a multi-positive sample quantum contrastive learning (MPSQCL) framework. By increasing the positive samples for contrastive learning and leveraging the advantages of quantum machine learning (QML) techniques, the richer features of input samples are extracted to improve the robustness and generalization of the model. Moreover, we design a new contrastive loss function to adapt to multiple positive sample contrastive learning scenarios. Finally, we validate the effectiveness of the proposed framework on several publicly available HAR datasets.

Recently, there has been a surge in cyberattacks targeting the Internet of Health Things (IoHT), increasing the urgency for advancing network intrusion detection systems (IDS). Machine learning techniques, especially deep neural networks (DNNs), are demonstrating potential in improving the precision of detection methods. Despite their advantages, the complexity of DNNs can obscure their decision-making process, impacting their acceptance in security-critical environments. To improve the transparency of DNN models, we propose a novel post-hoc interpretation method that applies a perturbation-based approach with an optimized mask applied to an autoencoder model for IDS. More importantly, we leverage contrastive learning to maintain perturbed samples within the original feature space, reducing the risk of misclassification due to sample drift and ensuring a clear interpretation of the mask. We validate our approach using the NSL-KDD and UNSW15 datasets, showing that it provides clearer and more robust explanations compared to existing methods. This enhancement in interpretability is pivotal for healthcare cybersecurity experts to gain insights into the decision-making processes of black-box models.

With the advances of Virtual Reality (VR) / Augmented Reality (AR), there arises a compelling need for transmission of point clouds over lossy channels (e.g., a 5G millimeter wave (mmWave) link that tends to be easily blocked). In this paper, we revisit the traditional Multiple Description Coding (MDC) concept and propose a simple point cloud MDC scheme that takes advantage of voxelization and is built upon a typical geometric point cloud compression codec. Our simulation study demonstrates the efficacy of the proposed scheme, as well as the tradeoff between compression efficiency and point cloud quality gain offered by MDC.

Although great advances have been made in machine learning (ML) based wireless communications and networking, the performance of most ML-based schemes is heavily dependent on the availability of large amounts of high quality radio frequency (RF) data, which are more challenging and costly to obtain than other forms of data. To address this challenge, we propose to leverage diffusion models to generate high quality RF data, and develop a novel lightweight AIGC model for RF sensing, termed RFID-ACCDM (Activity Class Conditional Diffusion Model). RFID-ACCDM can synthesize large amounts of RF data at low cost, conditioned on a particular activity class. The high quality of RFID-ACCDM generated data is demonstrated by metrics of Structural Similarity Index (SSIM) and Frechet Inception Distance (FID), as well as a representative downstream task of human activity recognition (HAR), where the model trained with sufficient synthesized data outperforms the model trained by real data.

Recent years have witnessed rapid development of Wi-Fi sensing applications. However, the domain shift problem is still an open problem. Variations in environment, time, and detected objects can undermine the effectiveness of cross-domain sensing. This paper proposes a few-shot learning framework for Wi-Fi sensing that enables generalization to unseen domains given only a few samples. To better extract stable features, functional data analysis (FDA) is first employed as a preprocessing technique. We thoroughly evaluate our approach to different Wi-Fi sensing tasks: gesture recognition, and activity recognition. Our experimental results demonstrate that FDA assisted system improves cross-domain accuracy by 14%, 10%, and 8% on the respective tasks with five samples per class.

Heartbeat signals are useful to disease prediction, sub-health diagnosis, fatigue warning, and even emotion estimation. There is a compelling need for contactless, easy-to-deploy, and long-term heartbeat monitoring. This paper presents a contactless Radio Frequency Identification (RFID) based system for heartbeat monitoring that leverages the insight that RFID signal fluctuations induced by chest motion are synchronous with both respiration and heartbeat. The proposed system collects the temporal phase information from the tag pair on the body to extract heartbeat signals using a sequence of signal processing techniques. We propose a signal separation method based on empirical mode decomposition (EMD) to obtain heart rate after preprocessing. Furthermore, the estimated signal is input to an enhanced variational autoencoder (VAE) model to recover the heartbeat waveform. Implemented with commercial off-the-shelf (COTS) RFID devices, the system achieves accurate heart rate monitoring with less than 3% relative errors. The detected waveform exhibits a median cosine similarity of 0.83 as compared with the ground truth, which validate the system’s wide applicability and high reliability for fine-grained, contactless heartbeat monitoring.

In this paper, we propose a cross-domain, scalable, and interpretable radio frequency (RF) fingerprinting system using a modified prototypical network (PTN) and an explanation-guided data augmentation across various domains and datasets with only a few samples. Specifically, a convolutional neural network is employed as the feature extractor of the PTN to extract RF fingerprint features. The predictions are made by comparing the similarity between prototypes and feature embedding vectors. To further improve the system performance, we design a customized loss function and deploy an eXplainable Artificial Intelligence (XAI) method to guide data augmentation during fine-tuning. To evaluate the effectiveness of our system in addressing domain shift and scalability problems, we conducted extensive experiments in both cross-domain and novel-device scenarios. Our study shows that our approach achieves exceptional performance in the cross-domain case, exhibiting an accuracy improvement of approximately 80% compared to convolutional neural networks in the best case. Furthermore, our approach demonstrates promising results in the novel-device case across different datasets. Our customized loss function and XAI-guided data augmentation can further improve authentication accuracy to a certain degree.

Despite the proven capabilities of deep neural networks (DNNs) for radio frequency (RF) fingerprinting, their security vulnerabilities have been largely overlooked. Unlike the extensively studied image domain, few works have explored the threat of backdoor attacks on RF signals. In this paper, we analyze the susceptibility of DNN-based RF fingerprinting to backdoor attacks, focusing on a more practical scenario where attackers lack access to control model gradients and training processes. We propose leveraging explainable machine learning techniques and autoencoders to guide the selection of positions and values, enabling the creation of effective backdoor triggers in a model-agnostic manner. To comprehensively evaluate our backdoor attack, we employ four diverse datasets with two protocols (Wi-Fi and LoRa) across various DNN architectures. Given that RF signals are often transformed into the frequency or time-frequency domains, this study also assesses attack efficacy in the time-frequency domain. Furthermore, we experiment with potential defenses, demonstrating the difficulty of fully safeguarding against our attacks.

In this paper, we propose a novel approach to cross-domain unmanned aerial vehicle (UAV) authentication using radio frequency (RF) fingerprinting based on prototypical networks (PTNs). UAVs present a unique challenge for RF fingerprinting due to their hovering motion, which creates more diverse signal domains compared to other RF devices like Wi-Fi. This results in a severe domain shift problem, where well-trained models struggle to generalize to unseen domains. To address this issue without incurring significant costs in data collection and model retraining, we employ PTNs, a few-shot learning paradigm that enhances cross-domain performance and system viability. We further improve our method's effectiveness by incorporating fine-tuning with data augmentation, maintaining system viability while improving performance. Comprehensive experimental results demonstrate that our approach significantly mitigates domain shift, achieving up to a 20% improvement in cross-domain accuracy for UAV fingerprinting.

In this paper, the problem of vehicle service mode selection (sensing, communication, or both) and vehicle connections within terahertz (THz) enabled joint sensing and communications over vehicular networks is studied. The considered network consists of several service provider vehicles (SPVs) that can provide: 1) only sensing service, 2) only communication service, and 3) both services, sensing service request vehicles, and communication service request vehicles. Based on the vehicle network topology and their service accessibility, SPVs strategically select service request vehicles to provide sensing, communication, or both services. This problem is formulated as an optimization problem, aiming to maximize the number of successfully served vehicles by jointly determining the service mode of each SPV and its associated vehicles. To solve this problem, we propose a dynamic graph neural network (GNN) model that selects appropriate graph information aggregation functions according to the vehicle network topology, thus extracting more vehicle network information compared to traditional static GNNs that use fixed aggregation functions for different vehicle network topologies. Using the extracted vehicle network information, the service mode of each SPV and its served service request vehicles will be determined. Simulation results show that the proposed dynamic GNN based scheme can improve the number of successfully served vehicles by up to 17% compared to a GNN based algorithm with a fixed neural network model.

Driven by the great advances in metaverse and edge computing technologies, vehicular edge metaverses are expected to disrupt the current paradigm of intelligent transportation systems. As highly computerized avatars of Vehicular Metaverse Users (VMUs), the Vehicle Twins (VTs) deployed in edge servers can provide valuable metaverse services to improve driving safety and on-board satisfaction for their VMUs throughout journeys. To maintain uninterrupted metaverse experiences, VTs must be migrated among edge servers following the movements of vehicles. This can raise concerns about privacy breaches during the dynamic communications among vehicular edge metaverses. To address these concerns and safeguard location privacy, pseudonyms as temporary identifiers can be leveraged by both VMUs and VTs to realize anonymous communications in the physical space and virtual spaces. However, existing pseudonym management methods fall short in meeting the extensive pseudonym demands in vehicular edge metaverses, thus dramatically diminishing the performance of privacy preservation. To this end, we present a cross-metaverse empowered dual pseudonym management framework. We utilize cross-chain technology to enhance management efficiency and data security for pseudonyms. Furthermore, we propose a metric to assess the privacy level and employ a Multi-Agent Deep Reinforcement Learning (MADRL) approach to obtain an optimal pseudonym generating strategy. Numerical results demonstrate that our proposed schemes are high-efficiency and cost-effective, showcasing their promising applications in vehicular edge metaverses.

The Internet of Things (IoT) ecosystem is undergoing a significant evolution through its integration with satellite networks, empowering remote and computation-intensive IoT tasks to leverage computing services via satellite links. Current research in this field predominantly focuses on minimizing latency and energy consumption in computation offloading, yet overlooks the substantial costs incurred by satellite resource utilization. To address this oversight, we introduce a cost-effective hybrid computation offloading (CE-HCO) paradigm in satellite-terrestrial integrated networks (STINs) in this article. First, we propose the 5G-based system framework facilitates gNB and user plane function functionalities on satellites and fosters collaboration between public cloud providers and satellite operators. The framework is in line with the latest 3GPP activities and business models in satellite computing. Then, we formulate the CE-HCO problem, aiming to minimize total computation offloading costs while satisfying diverse user latency requirements and adhering to satellite energy constraints. To tackle this NP-hard problem, we develop an algorithm employing the penalty method and successive convex approximation to simplify the complex mixed-integer nonlinear programming into tractable convex iterations. Simulation results show that our approach outperforms existing baselines in balancing performance and cost, and offer guidance on pricing policies for satellite computing services to promote future commercial growth.

The emergence of new applications has led to a high demand for mobile-edge computing (MEC), which is a promising paradigm with a cloud-like architecture deployed at the network edge to provide computation and storage services to mobile users (MUs). Since MEC servers have limited resources compared to the remote cloud, it is crucial to optimize resource allocation in MEC systems and balance the load among cooperating MEC servers. Caching application data for different types of computing services (CSs) at MEC servers can also be highly beneficial. In this article, we investigate the problem of hierarchical joint caching and resource allocation in a cooperative MEC system, which is formulated as an infinite-horizon cost minimization Markov decision process (MDP). To deal with the large state and action spaces, we decompose the problem into two coupled subproblems and develop a hierarchical reinforcement learning (HRL)-based solution. The lower layer uses the deep$Q$network (DQN) to obtain service caching and workload offloading decisions, while the upper layer leverages DQN to obtain load balancing decisions among cooperative MEC servers. The feasibility and effectiveness of our proposed schemes are validated by our evaluation results.

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

Metaverse can obscure the boundary between the physical and virtual worlds. Specifically, for the human-centric Metaverse in vehicular networks, i.e., the vehicular Metaverse, vehicles are no longer isolated physical spaces but interfaces that extend the virtual worlds to the physical world. Accessing the human-centric Metaverse via autonomous vehicles (AVs), drivers and passengers can immerse in and interact with 3D virtual objects overlaying views of streets on head-up displays (HUD) via augmented reality (AR). The seamless, immersive, and interactive experience rather relies on real-time multi-dimensional data synchronization between physical entities, i.e., AVs, and virtual entities, i.e., Metaverse billboard providers (MBPs). However, mechanisms to allocate and match synchronizing AV and MBP pairs to roadside units (RSUs) in a synchronization service market, which consists of the physical and virtual submarkets, are vulnerable to adverse selection. In this paper, we propose an enhanced second-score auction-based mechanism, named EPViSA, to allocate physical and virtual entities in the synchronization service market of the vehicular Metaverse. The EPViSA mechanism can determine synchronizing AV and MBP pairs simultaneously while protecting participants from adverse selection and thus achieving high total social welfare. We propose a synchronization scoring rule to eliminate the external effects from the virtual submarkets. Then, a price scaling factor is introduced to enhance the allocation of synchronizing virtual entities in the virtual submarkets. Finally, rigorous analysis and extensive experiments demonstrate EPViSA can achieve at least 96% of the social welfare compared to the omniscient benchmark while ensuring strategy-proof and adverse selection free through a simulation testbed.

Positioning and sensing have long been an important area of research. Recently, this field has attracted more attention due to the rapid deployment of emerging applications and next-generation communication networks. On the one hand, emerging applications like extended reality (XR) and autonomous vehicle systems need to precisely “see” the physical world, thus greatly increasing the demands on positioning and sensing technologies. Moreover, these applications also require data rate communication links, and thus technologies like cellular networks and WiFi are excellent for supporting these applications. On the other hand, with the evolution of wireless networks, positioning, and sensing have also been considered important functions of future wireless networks that can further enhance communication performance. Although existing wireless communication has achieved significant success in the past several decades, achieving satisfying positioning and sensing performance for these emerging applications remains a challenge due to the complexity of the wireless environment and the stringent performance requirements.

Positioning has recently received considerable attention as a key enabler in emerging applications such as extended reality, unmanned aerial vehicles, and smart environments. These applications require both data communication and high-precision positioning, and thus they are particularly well-suited to be offered in wireless networks (WNs). The purpose of this paper is to provide a comprehensive overview of existing works and new trends in the field of positioning techniques from both academic and standard perspectives. The paper provides a comprehensive overview of indoor positioning in WNs, covering the background, applications, measurements, state-of-the-art technologies, and future challenges. The paper outlines the applications of positioning from the perspectives of public facilities, enterprises, and individual users. We investigate the key performance indicators and measurements of positioning systems, followed by the review of the key enabler techniques such as artificial intelligence/large models and adaptive systems. Next, we discuss a number of typical wireless positioning technologies. We extend our overview beyond the academic progress, to include the standardization efforts, and finally, we provide insight into the challenges that remain. The comprehensive overview of existing efforts and new trends in the field of indoor positioning from both academic and standardization perspectives would be a useful reference to researchers in the field.

This is Part II of the double-part Special Issue (SI) on Positioning and Sensing Over Wireless Networks. The two-part SI aims to bring cutting-edge and novel contributions on positioning and sensing over wireless networks for future and emerging applications. The accepted 51 papers are arranged into eight groups: 1) fundamental performance analysis and optimization; 2) positioning and sensing with cellular networks; 3) positioning and sensing with WiFi networks; 4) positioning and sensing with emerging communication technologies; 5) positioning and sensing applications; 6) cooperative positioning and sensing; 7) reconfigurable intelligent surfaces (RIS)-assisted positioning and sensing; and 8) privacy and security. The contributions made by the papers in Part II are summarized as follows, which correspond to the last four paper groups.

Radio frequency fingerprint identification (RFFI) is a promising physical layer authentication technique that utilizes the unique impairments within the analog front-end of transmitters as distinct identifiers. State-of-the-art RFFI systems are frequently powered by deep learning, which requires extensive training data to ensure satisfactory performance. However, current RFFI studies suffer from a severe lack of training data, which poses challenges in achieving high identification accuracy. In this paper, we propose a federated RFFI system that is particularly suitable for Internet of Things (IoT) networks, which holds a high potential to address the data scarcity challenge in RFFI development. Specifically, all the receivers in an IoT network can pre-train a deep learning-driven feature extractor in a federated and unsupervised manner. Subsequently, a new client can perform fine-tuning on the basis of the pre-trained feature extractor to activate its RFFI functionality. Extensive experimental evaluation was carried out, involving 60 commercial off-the-shelf (COTS) LoRa transmitters and six software-defined radio (SDR) receivers. The experimental results demonstrate that the federated RFFI protocol can effectively improve the identification accuracy from 63% to 95%, and is robust to receiver hardware and location variations.

Behavior-based Wi-Fi user authentication has gained popularity in user-centered smart systems. However, its wide adoption has been hindered by certain critical issues, including significant performance degradation when the environment changes, the inability to handle unknown activities, and weak security due to basing authentication on the recognition of a single, one-off activity. In this paper, we propose Wi-Dist, which authenticates a user using a behavior password, i.e. a pre-chosen sequence of activities. Wi-Dist addressed the previously mentioned technical challenges through a cross-layer joint optimization framework. In particular, we address environment dependency by incorporating adversarial learning and optimizing both the signal layer and the domain adaptation layer. This enhances the performance of the learned model across various environments. To effectively handle unknown behaviors, we utilize an adversarial learning-based network. This network establishes a pseudo-decision boundary between samples from known and unknown sources, ensuring robust authentication. Additionally, for authentication using continuous activities, we employ double-sliding windows activity monitoring. This approach, coupled with activity state correction, partitions activities for accurate recognition. We also conducted extensive experiments in indoor environments to demonstrate that Wi-Dist is effective and robust.

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at:https://github.com/Lizonghang/AGOD.

Employing massive Mobile AI-Generated Content (AIGC) Service Providers (MASPs) with powerful models, high-quality AIGC services become accessible for resource-constrained end users. However, this advancement, referred to as mobile AIGC, also introduces a significant challenge: users should download large AIGC outputs from the MASPs, leading to substantial bandwidth consumption and potential transmission failures. In this paper, we apply cross-modalGenerativeSemanticCommunications (G-SemCom) in mobile AIGC to overcome wireless bandwidth constraints. Specifically, we utilize cross-modal attention maps to indicate the correlation between user prompts and each part of AIGC outputs. In this way, the MASP can analyze the prompt context and filter the most semantically important content efficiently. Only semantic information is transmitted, with which users can recover the entire AIGC output with high quality while saving mobile bandwidth. Since the transmitted information not only preserves the semantics but also prompts the recovery, we formulate a joint semantic encoding and prompt engineering problem to optimize the bandwidth allocation among users. Particularly, we present a human-perceptual metric named Joint Perceptual Similarity and Quality (JPSQ), which is fused by two learning-based measurements regarding semantic similarity and aesthetic quality, respectively. Furthermore, we develop the Attention-aware Deep Diffusion (ADD) algorithm, which learns attention maps and leverages the diffusion process to enhance the environment exploration ability of traditional deep reinforcement learning (DRL). Extensive experiments demonstrate that our proposal can reduce the bandwidth consumption of mobile users by 49.4% on average, with almost no perceptual difference in AIGC output quality. Moreover, the ADD algorithm shows superior performance over baseline DRL methods, with 1.74× higher overall reward.

With the significant advancements in artificial intelligence (AI) technologies and computational capabilities, generative AI (GAI) has become a pivotal digital content generation technique for offering superior digital services. However, due to the inherent instability of AI models, directing GAI towards the desired output remains a challenging task. Therefore, in this paper, we design a novel framework that utilizeswirelessperception to guideGAI(WiPe-GAI) in delivering AI-generated content (AIGC) service, within resource-constrained mobile edge networks. Specifically, we first propose a new sequential multi-scale perception (SMSP) algorithm to predict user skeleton based on the channel state information (CSI) extracted from wireless signals. This prediction then guides GAI to provide users with AIGC, i.e., virtual character generation. To ensure the efficient operation of the proposed framework in resource constrained networks, we further design a pricing-based incentive mechanism and propose a diffusion model based approach to generate an optimal pricing strategy for the service provisioning. The strategy maximizes the user's utility while incentivizing the participation of the virtual service provider (VSP) in AIGC provision. The experimental results demonstrate the effectiveness of the designed framework in terms of skeleton prediction and optimal pricing strategy generation, outperforming other existing solutions.

The multi-access edge computing (MEC) and ultra-dense network (UDN) are regarded as essential and complementary technologies in the age of Internet of Things (IoT). Deploying MEC servers at the macro-cell and small-cell stations can significantly improve user experience as well as increase network capacity. Nevertheless, there still remain many obstacles in practical MEC-enabled UDNs. Among them, a unique challenge is how to coordinate computing and networking to fit the diverse offloading demands of IoT applications in dynamic network environments. To this end, this paper first investigates a distributed delay-constrained computation offloading methodology based on computing and networking coordination in the UDN. An extended game-theoretic approach based on the Lyapunov optimization theory is designed to achieve adaptive task offloading and computing power management in time-varying environments. Furthermore, considering the uncertainty in users' mobility and limited edge resources, distributed two-stage and multi-stage stochastic programming algorithms under various uncertainties are proposed. The proposed algorithms take posterior recourse actions to compensate for inaccurate predicted network information. Extensive simulations validate the effectiveness and rationality of the proposed algorithms and their superior performance over several benchmark schemes.

Recent years have witnessed the great success of vision transformer (ViT), which has achieved state-of-the-art performance on multiple computer vision benchmarks. However, ViT models suffer from vast amounts of parameters and high computation cost, leading to difficult deployment on resource-constrained edge devices. Existing solutions mostly compress ViT models to a compact model but still cannot achieve real-time inference. To tackle this issue, we propose to explore the divisibility of transformer structure, and decompose the large ViT into multiple small models for collaborative inference at edge devices. Our objective is to achieve fast and energy-efficient collaborative inference while maintaining comparable accuracy compared with large ViTs. To this end, we first propose a collaborative inference framework termedDeViTto facilitate edge deployment by decomposing large ViTs. Subsequently, we design a decomposition-and-ensemble algorithm based on knowledge distillation, termed DEKD, to fuse multiple small decomposed models while dramatically reducing communication overheads, and handle heterogeneous models by developing a feature matching module to promote the imitations of decomposed models from the large ViT. Extensive experiments for three representative ViT backbones on four widely-used datasets demonstrate our method achieves efficient collaborative inference for ViTs and outperforms existing lightweight ViTs, striking a good trade-off between efficiency and accuracy. For example, our DeViTs improves end-to-end latency by 2.89× with only 1.65% accuracy sacrifice using CIFAR-100 compared to the large ViT, ViT-L/16, on the GPU server. DeDeiTs surpasses the recent efficient ViT, MobileViT-S, by 3.54% in accuracy on ImageNet-1 K, while running 1.72× faster and requiring 55.28% lower energy consumption on the edge device.

Unmanned aerial vehicles (UAVs) have a significant potential for sensing applications in further cellular networks due to their extensive coverage and flexible deployment. In this paper, we consider a multi-cell cellular network with a cellular-connected UAV, which senses data with onboard sensors and uploads sensory data to the ground base stations (BSs). To evaluate the freshness of sensory data, we employ the concept of age of information (AoI), which is defined as the time elapsed since the latest successful transmission of sensory data. A lower AoI implies fresher sensory data, which may lead to the increase of UAV operation time. To balance such tradeoff, we aim to minimize the weighted sum of operation time and total AoI for the UAV by jointly optimizing transmission scheduling, BS association, as well as UAV trajectory. The problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is difficult to solve due to the time-varying propagation channels. To this end, we first characterize the average communication performance with statistic channel information, and then develop a search algorithm to obtain the optimal solution via employing the optimal structure as well as convex optimization techniques, while a low-complexity Double Graph based Algorithm (DGA) is developed to obtain a suboptimal solution. Then, by taking into account the site-specific performance and making fast decisions online, we propose a Deep reinforcement Learning Algorithm (DLA). Compared to DGA, DLA can adapt to the specific local environment and obtain a solution more rapidly once the training process is completed. Simulation results show that the proposed algorithms outperform the benchmarks about 30%, and achieve flexible tradeoff between operation time and AoI of UAV sensing, which is not available by considering just one objective.

The surging demand for high-definition video streaming services and large neural network models implies a tremendous explosion of Internet traffic. To mitigate the traffic pressure, architectures with in-network storage have been proposed to cache popular contents at devices in closer proximity to users. Correspondingly, in order to maximize caching utilization, it becomes essential to devise an effective popularity prediction method. In that regard, predicting popularity with dynamic graph neural network (DGNN) models achieves remarkable performance. However, DGNN models still suffer from tackling sparse datasets where most users are inactive. Therefore, we propose a reformative temporal graph network, named semantics-enhanced temporal graph network (STGN), which attaches extra semantic information into the user-content bipartite graph and could better leverage implicit relationships behind the superficial topology structure. On top of that, we customize its temporal and structural learning modules to further boost the prediction performance. Specifically, in order to efficiently aggregate the diversified semantics that a content might possess, we design a user-specific attention (UsAttn) mechanism for the temporal learning. Unlike the attention mechanism that only analyzes the influence of genres on content, UsAttn also considers the attraction of semantic information to a specific user. Meanwhile, as for the structural learning, we introduce the concept of positional encoding into our attention-based graph learning and novelly adopt a semantic positional encoding (SPE) function, which effectively boost the performance of lightweight algorithms. Finally, extensive simulations verify the superiority of our models and demonstrate their effectiveness in content caching.

Recently, wireless sensing techniques have been widely used for Internet of Things (IoT) applications. Unlike traditional device-based sensing, wireless sensing is contactless, pervasive, low cost, and non-invasive, making it highly suitable for relevant IoT applications. However, most existing methods are highly dependent on high-quality datasets, and the minority class will not achieve a satisfactory performance when suffering from a class imbalance problem. In this article, we propose a time–frequency semantic generative adversarial network framework (i.e., TFSemantic) to address the imbalanced classification problem in human activity recognition using radio frequency (RF) signals. Specifically, the TFSemantic framework can learn semantic features from the minority classes and then generate high-quality signals to restore data balance. It includes a data pre-processing module, a semantic extraction module, a semantic distribution module, and a data augmenter module. In the data pre-processing module, we process four different RF datasets (i.e., WiFi, RFID, UWB, and mmWave). We also develop Fourier semantic feature convolution and attention semantic feature embedding methods for the semantic extraction module. A discrete wavelet transform is utilized for reconstructed RF samples in the semantic distribution module. In data augmenter module, we design an associated loss function to achieve effective adversarial training. Finally, we validate the effectiveness of the proposed TFSemantic framework using different RF datasets, which outperforms several state-of-the-art methods.

In this paper, the problem of vehicle service mode selection (sensing, communication, or both) and vehicle connections within terahertz (THz) enabled joint sensing and communications over vehicular networks is studied. The considered network consists of several service provider vehicles (SPVs) that can provide: 1) only sensing service, 2) only communication service, and 3) both services, sensing service request vehicles, and communication service request vehicles. Based on the vehicle network topology and their service accessibility, SPVs strategically select service request vehicles to provide sensing, communication, or both services. This problem is formulated as an optimization problem, aiming to maximize the number of successfully served vehicles by jointly determining the service mode of each SPV and its associated vehicles. To solve this problem, we propose a dynamic graph neural network (GNN) model that selects appropriate graph information aggregation functions according to the vehicle network topology, thus extracting more vehicle network information compared to traditional static GNNs that use fixed aggregation functions for different vehicle network topologies. Using the extracted vehicle network information, the service mode of each SPV and its served service request vehicles will be determined. Simulation results show that the proposed dynamic GNN based method can improve the number of successfully served vehicles by up to 17% and 28% compared to a GNN based algorithm with a fixed neural network model and a conventional optimization algorithm without using GNNs.

Automatic modulation recognition (AMR) technology enables receivers to automatically recognize the modulation type of the received signal for correct demodulation of the received data, but there are still many shortcomings to be addressed. To achieve accurate and efficient AMR, this paper proposes a data augmentation method for AMR, which can increase the amount of data by seven times and solve the problem of a small sample size more effectively than the existing methods. In addition, this paper proposes a concept of microscale, rationalizes the underwater acoustic signal into time series, and proposes a temporal feature extractor named One2Three block, which can extract temporal features of signals from three microscales. Finally, a spatial feature extractor named the Dual-Stream squeeze-and-excitation (SE) block is designed to abstract and synthesize more advanced spatial features for AMR. The recognition accuracy of the proposed method is verified with eight commonly used modulation modes in underwater acoustic communications on the datasets collected in the South China Sea and the Yellow Sea. The results show that the proposed method can achieve a recognition accuracy of 99% with a lower time and space complexity, and has high robustness to noisy data.

Since hybrid beamforming (HBF) can approach the performance of fully-digital beamforming (FDBF) with much lower hardware complexity, we investigate the HBF design for beam-hopping (BH) low earth orbit (LEO) satellite communications (SatComs). Aiming at maximizing the sum-rate of totally illuminated beam positions during the whole BH period, we consider joint beamforming and illumination pattern design subject to the HBF constraints and sum-rate requirements. To address the non-convexity of the HBF constraints, we temporarily replace the HBF constraints with the FDBF constraints. Then we propose an FDBF and illumination pattern random search (FDBF-IPRS) scheme to optimize illumination patterns and fully-digital beamformers using constrained random search and fractional programming methods. To further reduce the computational complexity, we propose an FDBF and illumination pattern alternating optimization (FDBF-IPAO) scheme, where we relax the integer illumination pattern to continuous variables and after finishing all the iterations we quantize the continuous variables into integer ones. Based on the fully-digital beamformers designed by the FDBF-IPRS or FDBF-IPAO scheme, we propose an HBF alternating minimization algorithm to design the hybrid beamformers. Simulation results show that the proposed schemes can achieve satisfactory sum-rate performance for BH LEO SatComs.

Aerial video streaming is a promising application of unmanned aerial vehicles (UAVs), which extends video service from ground to three-dimensional (3D) airspaces. However, high data rates and smooth transmission are required along with ubiquitous and environment-aware communications. To this end, we study the quality of experience (QoE) maximization problem in this paper for aerial video streaming over 3D cellular networks in urban environments with building avoidance. Different from the typical channel model based optimization in prior works, we tackle the joint design of 3D UAV trajectory and transmission scheduling as well as playback rate adaption with an environment and channel knowledge map (ECKM) approach, which provides rich information about the location-specific channel for enabling environment-aware communications. Specifically, we first consider the scenario with perfect ECKM, and propose efficient algorithms to obtain suboptimal solutions by utilizing two graph models and the iterative parameter-enabled block coordinate descent method. For the scenario without such map information, we propose a dueling Deep Q-learning (DQL) solution with map construction such that the learning process can be facilitated for path planning. Simulation results are provided to demonstrate the improvement in QoE by the proposed solutions over baseline schemes, as well as a tradeoff between video quality and rate variation.

Reconfigurable Intelligent Surface (RIS) is an emerging technology that can improve the spectrum and energy efficiency of next-generation wireless networks. However, attaining accurate channel state information (CSI) for the cascaded RIS channel is particularly challenging. Imperfect CSI is a major bottleneck to achieving the spectral efficiency benefit of RIS-assisted networks. Rate splitting (RS), a promising multiple access technology, has been shown to be able to achieve an improved spectrum efficiency and be robust to channel uncertainties. This paper investigates the interplay between RIS and RS by considering a RIS-assisted RS beamforming problem. Active beamforming at the base station (BS) and passive beamforming at the RIS are jointly considered to maximize the minimum user rate under both the perfect CSI case and the imperfect CSI case. A block coordinate descent (BCD) algorithm is developed to solve this non-convex problem. Compared with the conventional semi-definite relaxation (SDR) approach, the proposed method does not require that the covariance matrix of the common beamforming vector be rank-one. Moreover, we present theoretical results to help reveal the impact of the system parameters and explain the performance gain resulting from the integration of RIS and RS. Extensive simulation results are also provided to show that RIS-assisted RS can improve the max-min rate performance significantly compared with conventional multiple access technologies, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) with/without RIS, especially in overloaded systems. With the proposed method, RS shows great potential in combating realistic CSI errors in RIS-assisted networks.

Radio frequency fingerprint (RFF) has been widely used in wireless transceivers as an additional physical security layer. Most of the existing RFF extraction methods rely on a large number of labeled signal samples for model training. However, in real communication environments, it is usually necessary to process timely received signal samples, which are limited in quantity and are difficult to obtain labels, the performance of most RFF methods is generally poor. To effectively extract features from the limited and unlabeled signal samples, we propose an efficient RFF extraction method using an asymmetric masked auto-encoder (AMAE). Specifically, we design an asymmetric extractor-decoder, where the extractor is used to learn the latent representation of the masked signals and the decoder as light as a convolution layer reconstructs the unmasked signal from the latent representation. Using commercial off-the-shelf LoRa datasets and WiFi datasets, we show that the proposed AMAE-based RFF extraction method achieves the best performance compared with four advanced unsupervised methods whether in the case of large data size or small data size, or under line of sight (LOS) and non line of sight (NLOS) channel scenarios. The codes of this paper can be downloaded from Github: https://github.com/YZS666/AnEfficient-RFF-Extraction-Method.

Open radio access network (O-RAN) slicing allows the flexible control of network components and resources to satisfy the ever increasing demand of mobile applications. To optimize service provisioning, efficient management of limited radio resources is challenging due to the orchestration among network slices in the long-timescale and the slice configurations according to the mobile user (MU) statistics in the short-timescale. In this paper, we first propose a novel meta Markov decision process framework to mathematically formulate the problem of two-timescale radio resource management (RRM) in O-RAN slicing. The original RRM problem is then decoupled into a long-timescale master problem and a short-timescale subproblem, which are solved by a hierarchical reinforcement learning (RL) mechanism. Our proposed hierarchical RL mechanism includes a deep RL algorithm, solving the optimal long-timescale RRM policy, and a linear-decomposition based meta-RL algorithm, solving the optimal short-timescale RRM policy. Numerical experiments verify the theoretical analysis and show that our proposed hierarchical RL mechanism outperforms the most representative state-of-the-art baselines.

Deep learning has significantly advanced wireless sensing technology by leveraging substantial amounts of high-quality training data. However, collecting wireless sensing data encounters diverse challenges, including unavoidable data noise, limited data scale due to significant collection overhead, and the necessity to reacquire data in new environments. Taking inspiration from the achievements of AI-generated content, this paper introduces a signal generation method that achieves data denoising, augmentation, and synthesis by disentangling distinct attributes within the signal, such as individual and environment. The approach encompasses two pivotal modules: structured signal selection and signal disentanglement generation. Structured signal selection establishes a minimal signal set with the target attributes for subsequent attribute disentanglement. Signal disentanglement generation disentangles the target attributes and reassembles them to generate novel signals. Extensive experimental results demonstrate that the proposed method can generate data that closely resembles real-world data on two wireless sensing datasets, exhibiting state-of-the-art performance. Our approach presents a robust framework for comprehending and manipulating attribute-specific information in wireless sensing.

Large scale deployment of the Internet of Things (IOT) technology has produced a disruptive effect in many fields in the recent past. Continuous connectivity combined with relatively low physical implementation cost has produced a Big Data paradigm shift, especially in industrial contexts. Unfortunately, the ability to process and adequately make use of this data has not kept pace with deployment. Specifically, models in use today lack the ability to perform well with data from a variety of sources. For instance, many models are trained using only one type of data. Even models trained on multi-modal data lack the ability to predict on different combinations of this data. Sensor deployment on identical machines is often different depending on context, leading to the need for multiple models created for the same machine. The data in question has the ability to radically shift how equipment failure is predicted and when maintenance is completed; when processed correctly. The cost savings on large industrial machines and potential saved downtime could be enormous. This research proposes investigation of a new unsupervised, technology agnostic anomaly detection framework that can be utilized on any combination of data modes for a given machine. This framework is then tested on a real-world anomaly dataset, with results achieved that are significantly better than prior approaches.

To implement ubiquitous wireless sensing, the domain shift problem (e.g., different environments, users, devices) for machine learning based approaches should be addressed. Some existing methods are proven to be effective, such as transfer learning and domain adaptation. Meanwhile, quantum machine learning, a combination of quantum computing and machine learning, has attracted much attention. More importantly, quantum transfer learning (QTL) has been successful for certain applications, e.g., image classification. In this paper, we explore a classical to quantum (C2Q) framework to address the domain shift problem in wireless sensing by exploiting the great potential of QTL. Specifically, we first analyze the data shift problem in various types of wireless datasets by calculating the Kullback-Leibler (KL) divergence of different domains. Then, a QTL framework is designed to introduce importance weighting and adversarial strategies. We finally evaluate the proposed framework using the representative human activity recognition task on three wireless sensing datasets. Experimental results demonstrate the feasibility of the framework and its great potential for solving the domain shift problem in wireless sensing.

The fast development in Deep Learning (DL) has made it a promising technique for various autonomous robotic systems. Recently, researchers have explored deploying DL models, such as Reinforcement Learning and Imitation Learning, to enable robots for Radio-frequency Identification (RFID) based inventory tasks. However, the existing methods are either focused on a single field or need tremendous data and time to train. To address these problems, this paper presents a Cross-Modal Reasoning Model (CMRM), which is designed to extract high-dimension information from multiple sensors and learn to reason from spatial and historical features for latent cross-modal relations. Furthermore, CMRM aligns the learned tasking policy to high-level features to offer zero-shot generalization to unseen environments. We conduct extensive experiments in several virtual environments as well as in indoor settings with robots for RFID inventory. The experimental results demonstrate that the proposed CMRM can significantly improve learning efficiency by around 20 times. It also demonstrates a robust zero-shot generalization for deploying a learned policy in unseen environments to perform RFID inventory tasks successfully.

With the rapid development of artificial general intelligence (AGI), various multimedia services based on pretrained foundation models (PFMs) need to be effectively deployed. With edge servers that have cloud-level computing power, edge intelligence can extend the capabilities of AGI to mobile edge networks. However, compared with cloud data centers, resource-limited edge servers can only cache and execute a small number of PFMs, which typically consist of billions of parameters and require intensive computing power and GPU memory during inference. To address this challenge, in this paper, we propose a joint foundation model caching and inference framework that aims to balance the tradeoff among inference latency, accuracy, and resource consumption by managing cached PFMs and user requests efficiently during the provisioning of generative AI services. Specifically, considering the in-context learning ability of PFMs, a new metric named the Age of Context (AoC), is proposed to model the freshness and relevance between examples in past demonstrations and current service requests. Based on the AoC, we propose a least context caching algorithm to manage cached PFMs at edge servers with historical prompts and inference results. The numerical results demonstrate that the proposed algorithm can reduce system costs compared with existing baselines by effectively utilizing contextual information.

Millimeter wave (mmWave) communications and massive MIMO play crucial roles in the development of future wireless systems. In addition to offering high data rates, these technologies enable the realization of high-precision localization systems, especially in complicated indoor rich multi-path environments without GPS coverage. While deep neural networks (DNNs) enable high accuracy in fingerprint-based indoor localization, their implementations also introduce security problems. In the field of computer vision, backdoor attacks have proven to be able to effectively deceive models using specific or imperceptible triggers. In this paper, we study the impact of backdoor attacks on 5G massive MIMO localization systems in both indoor and outdoor environments. Two different triggers are investigated: the one-pixel trigger (visible) and the random noise trigger (invisible). We evaluate the localization systems using a public dataset and demonstrate that DNN-based localization systems are vulnerable to backdoor attacks.

Cooperative Intelligent Transport Systems (C-ITS) are very important in our daily lives. They ensure road safety through the exchange of data between vehicles and road side units (RSU). Due to the sensitivity of the exchanged data between different entities, C-ITS systems are vulnerable to Cyber-attacks, they require high protection. In order to guarantee the integrity and the authentication of the exchanged messages, the European Telecommunications Standards Institute (ETSI), has specified specific procedures to manage certificates and signatures of all sent messages. Each vehicle periodically sends signed CAMs. Then, the integration of the signature and certificate in each transmitted CAM has a considerable impact on the communication channel load and bandwidth. In this study, we propose a new lightweight authentication mechanism which considers that vehicles on road are composed of a set of clusters having different sizes. The clusters are dynamic and change continuously. In each cluster, we implement some procedures in order to reach a trusted environment where vehicles communicate with unsigned messages when they trust their neighbours. When the trust is not guaranteed, vehicles switch to the standard communication until trust recovery. In order to reach the trust, each vehicle computes its own prediction of neighbours behavior. based on trajectory, speed. The prediction is performed using an auto-encoder running the LTSM algorithm. We have implemented this mechanisms on the OMNET++ environment and we have concluded that our mechanisms reduce the overhead generated by the authentication algorithms around 34% of the size of exchanged messages.

In this paper, the problem of joint sensing and communications is studied over terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles provide either communication service or sensing service to communication target vehicles or sensing target vehicles, respectively. Therefore, it is necessary to determine the service mode (i.e., providing sensing or communication service) for each service provider vehicle and the subset of target vehicles that each service provider vehicle will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of all communication target vehicles while satisfying the sensing service requirements of all sensing target vehicles by determining the service mode and the user association for each service provider vehicle. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle's graph information related to its location, connection, and communication interference. Using the extracted graph information, the joint service mode selection and user association strategy will be determined. Simulation results show that the proposed GNN-based scheme can achieve 94% of the sum rate produced by the optimal solution, and yield up to 3.95% and 36.16% improvements in sum rate, respectively, compared to a homogeneous GNN-based algorithm and the conventional optimization algorithm without using GNNs.

The enormous amount of network equipment and users implies a tremendous growth of Internet traffic for multi-media services. To mitigate the traffic pressure, architectures with in-network storage have been proposed to cache popular content at devices in close proximity to users in order to decrease the number of backhaul hops. Meanwhile, the reduced transmission distance also contributes to energy saving. However, due to limited storage, only a fraction of the content can be cached, while caching the most popular content is cost-effective. Correspondingly, it becomes essential to devise an effective popularity prediction method. In this regard, some existing efforts manifest the effectiveness of dynamic graph neural network (DGNN) models, but it remains challenging to tackle sparse datasets. Herein, we first propose a reformative temporal graph network, named STGN, to address the challenge and improve prediction performance. Specifically, the STGN model leverages extra semantic messages to help establish implicit paths within the sparse interaction graph and enhance the temporal and structural learning of a DGNN model. Furthermore, we devise a user-specific attention mechanism to aggregate various semantics in a fine-grained manner. Finally, extensive simulations verify the superiority of our STGN models and demonstrate the potential in terms of energy-saving.

Federated learning (FL) has recently emerged as a promising paradigm that trains machine learning (ML) models on clients' devices in a distributed manner without the need of transmitting clients' data to the FL server. In many applications of ML (e.g., image classification), the labels of training data need to be generated manually by human agents (e.g., recognizing and annotating objects in an image), which are usually costly and error-prone. In this paper, we study FL with crowdsourced data labeling where the local data of each participating client of FL are labeled manually by the client. We consider the strategic behavior of clients who may not make desired effort in their local data labeling and local model computation (quantified by the mini-batch size used in the stochastic gradient computation), and may misreport their local models to the FL server. We first characterize the performance bounds on the training loss as a function of clients' data labeling effort, local computation effort, and reported local models, which reveal the impacts of these factors on the training loss. With these insights, we devise Labeling and Computation Effort and local Model Elicitation (LCEME) mechanisms which incentivize strategic clients to make truthful efforts as desired by the server in local data labeling and local model computation, and also report true local models to the server. The truthful design of the LCEME mechanism exploits the non-trivial dependence of the training loss on clients' hidden efforts and private local models, and overcomes the intricate coupling in the joint elicitation of clients' efforts and local models. Under the LCEME mechanism, we characterize the server’s optimal local computation effort assignments and analyze their performance. We evaluate the proposed FL algorithms with crowdsourced data labeling and the LCEME mechanism for the MNIST-based hand-written digit classification. The results corroborate the improved learning accuracy and cost-effectiveness of the proposed approaches.

Deep learning (DL)-based specific emitter identification (SEI) is a potential physical layer authentication technique for Industrial Internet-of-Things (IIoT) Security, which detects the individual emitter according to its unique signal features resulting from transmitter hardware impairments. The success of DL-based SEI often depends on sufficient training samples and the integrity of samples’ labels. The extensive deployment of wireless devices generates a huge amount of signals, but signals labeling is quite difficult and expensive with the high demand for expertise. In this article, we present an SEI method based on dual consistency regularization (DCR), which enables feature extraction and identification using a few labeled samples and a large number of unlabeled samples. With the help of pseudo labeling, we leverage consistency between the predicted class distribution of weakly augmented unlabeled training samples and that of strongly augmented training unlabeled samples, and consistency between semantic feature distribution of labeled samples and that of pseudo-labeled samples, which takes the unlabeled samples into account to model parameter tuning for a more accurate emitter identification. Extensive numerical results demonstrate that compared with well-known semi-supervised learning-based SEI methods, our method obtains 99.77% identification accuracy on a WiFi data set and 90.10% identification accuracy on an automatic dependent surveillance-broadcast (ADS-B) data set when only 10% of training samples are labeled, and improves the identification accuracy on the WiFi data set and the ADS-B data set by more than 19.07% and 5.30%, respectively. Our codes are available athttps://github.com/lovelymimola/DCR-Based-SemiSEI.

Radio frequency fingerprint (RFF), which comes from the imperfect hardware, is a potential feature to ensure the security of communication. With the development of deep learning (DL), DL-based RFF identification methods have made excellent and promising achievements. However, on one hand, existing DL-based methods require a large amount of samples for model training. On the other hand, the RFF identification method is generally less effective with limited amount of samples, while the auxiliary dataset and the target dataset often needs to have similar data distribution. To address the data-hungry problems in the absence of auxiliary datasets, in this paper, we propose a supervised contrastive learning (SCL)-based RFF identification method using data augmentation and virtual adversarial training (VAT), which is called “SCACNN”. First, we analyze the causes of RFF, and model the RFF identification problem with augmented dataset. A non-auxiliary data augmentation method is proposed to acquire an extended dataset, which consists of rotation, flipping, adding Gaussian noise, and shifting. Second, a novel similarity radio frequency fingerprinting encoder (SimRFE) is used to map the RFF signal to the feature coding space, which is based on the convolution, long-short-term-memory, and a fully connected deep neural network (CLDNN). Finally, several secondary classifiers are employed to identify the RFF feature coding. The simulation results show that the proposed SCACNN has greater identification ratio than the other classical RFF identification methods. Moreover, the identification ratio of the proposed SCACNN achieves an accuracy of 92.68% with only 5% samples.

With the growing demand for location-based services, fingerprint has become a hot topic in the area of Internet of Things (IoT). However, the performance of fingerprinting-based indoor localization systems is usually affected by the quality and granularity of fingerprints. In this article, we present MapLoc, a long short-term memory (LSTM)-based indoor localization system that takes advantage of the continuous indoor uncertainty maps created using both earth magnetic field readings and WiFi received signal strengths (RSSs). A deep Gaussian process (DGP) model is trained to create indoor radio maps with confidence intervals, which are referred as uncertainty maps. Utilizing the uncertainty maps, an LSTM-based location prediction model is pretrained with artificial trajectory data sampled from the uncertainty maps, and then fine-tuned with the signal measurements collected in the field. In the training process, auxiliary outputs are implemented to overcome overfitting and improve the robustness of the system. Our extensive experiments demonstrate the outstanding performance of the proposed MapLoc system.

Graphic-pattern-based implicit authentication has been successfully exploited to elevate the security of smartphones. On-screen pressure is one of the key features in such an approach since it can reveal users’ touch pattern. However, state-of-the-art approaches rely on a system API to obtain on-screen pressure, which is not adequately accurate and cannot meet the demands of robust implicit authentication. To bridge this gap, we propose PresSafe, a novel implicit authentication system that utilizes the smartphone’s built-in barometer sensor to measure pressure during the unlocking process, and to utilize the pressure data in authentication. A key technical challenge in utilizing barometer sensing, however, is to understand the user activity through measured pressure. To overcome this challenge, PresSafe leverages barometer data along with data from other conventional but heterogeneous ambient sensors to produce accurate and robust user activity descriptions. PresSafe utilizes a transfer-learning-based hybrid workflow to integrate user activity representation learning with a lightweight classical authentication algorithm to obtain a unified model. This approach offloads the computational cost from the terminal and addresses privacy concerns. To ensure applicability of our approach despite data heterogeneity and insufficient training data, we utilize a channel-adaptive data processing mechanism. Extensive experiments utilizing more than 70000 records from 23 volunteers in six different locations show that PresSafe achieves an FAR of 0.45%, an FRR of 0.49%, and an EER of 0.47%, which clearly demonstrate its superiority over several existing solutions.

Malware traffic classification (MTC) plays an important role for securing the Internet of Things (IoT). Many machine learning (ML) and deep learning (DL)-based MTC methods have been proposed in recent years. However, the former still requires human intervention, while the latter incurs considerable computation overheads. To address these problems, we propose a broad learning (BL)-aided MTC method (BL-MTC), which is a lightweight and graphics processing unit-free solution with good performance and extremely low cost. The simulation results show that the proposed BL-MTC method not only achieves superior results on the USTC-TFC2016 data set but also exhibits an exponential advantage in computation overhead.

Emerging wireless networks may suffer severe security threats due to the ubiquitous access of massive wireless devices. Specific emitter identification (SEI) is considered as one of the important techniques to protect wireless networks, which aims to identifying legal or illegal devices through the radio frequency (RF) fingerprints contained in RF signals. Existing SEI methods are implemented with either traditional machine learning or deep learning. The former relies on manual feature extraction which is usually inefficient, while the latter relies on the powerful graphics processing unit (GPU) computing power but with limited applications and high cost. To solve these problems, in this article, we propose a GPU-free SEI method using a signal feature embedded broad learning network (SFEBLN), for efficient emitter identification based on a single-layer forward propagation network on the central processing unit (CPU) platform. With this method, the original RF data is first preprocessed through external signal processing nodes, and then processed to generate mapped feature nodes and enhancement nodes by nonlinear transformation. Next, we design the internal signal processing nodes to extract effective features from the processed RF signals. The final input layer consists of mapped feature nodes, enhancement nodes, and internal signal processing nodes. Then, the network weight parameters are obtained by solving the pseudo inverse problem. Experiments are conducted over the CPU platform and the results show that our proposed SEI method using SFEBLN achieves a superior identification performance and robustness under various scenarios.

In this paper, the problem of joint communication and sensing is studied in the context of terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles (SPVs) provide either communication service or sensing service to target vehicles, where it is essential to determine 1) the service mode (i.e., providing either communication or sensing service) for each SPV and 2) the subset of target vehicles that each SPV will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of the communication target vehicles, while satisfying the sensing service requirements of the sensing target vehicles, by determining the service mode and the target vehicle association for each SPV. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle’s graph information related to its location, connection, and communication interference. Using this extracted graph information, a joint service mode selection and target vehicle association strategy is then determined to adapt to the dynamic vehicle topology with various vehicle types (e.g., target vehicles and service provider vehicles). Simulation results show that the proposed GNN-based scheme can achieve 93.66% of the sum rate achieved by the optimal solution, and yield up to 3.16% and 31.86% improvements in sum rate, respectively, over a homogeneous GNN-based algorithm and a conventional optimization algorithm without using GNNs.

With the rapid development towards smart Internet of Things (IoT), detection of human activity has become essential in a variety of applications. Various radio-frequency (RF) sensing technologies, such as WiFi, Radio-Frequency Identification (RFID), and Frequency-Modulated Continuous Wave (FMCW) radar, have been utilized for non-invasive human activity recognition (HAR). It will be highly desirable to develop a HAR solution that can work with different types of RF technologies, such that the cost and the barrier of wide deployment can both be greatly reduced, and more robust performance can be achieved by utilizing the complementary RF sensory data. In this paper, we propose a technology-agnostic approach for RF-based HAR, termed TARF, which works with several different RF sensing technologies. A novel data generalization technique is proposed to mitigate the disparity in measured data from different RF devices. A domain adversarial neural network is proposed to combat the interference from various RF sensing technologies. The performance of the proposed system is evaluated with experiments using four different RF sensing technologies. TARF is shown to outperform the state-of-the-art Convolutional Neural Network (CNN)-based solution with considerable gains.

One of the crucial challenges in networked Unmanned Aerial Vehicles (UAVs) is to configure them to serve as aerial base stations (BSs) for collecting data from distributed Internet of Things (IoT) devices in a region devoid of backbone connectivity. To address this challenge, it is required to compute optimized trajectories of UAVs to collect data while considering the different activation patterns of IoT devices. We propose a scheme to optimize the trade-off between the number of covered IoT devices and travel time of UAVs. The formulated cost minimization problem is known as the capacitated single depot vehicle routing problem (CSDVRP), which is NP-hard. We propose a solution scheme, named Cost-Effective Data Aggregation for UAV-Enabled IoT Networks (CEDAN), which operates in four steps. First, it determines the optimized hovering locations (HLs) for UAVs. Subsequently, CEDAN determines the optimized route adopting the Christofides's approximation algorithm for Travelling Salesman Problem (TSP). Further, a split function produces the optimized trajectories for all UAVs. Finally, a route adjustment algorithm applies the cost function and rearranges the order of visiting each HL. Extensive simulation results depict that the CEDAN outperforms than Clarke-Wright (CW) savings heuristics, CEDAN without route adjustment (CWRA), and Zhan et al., respectively.

Recently, deploying deep neural network (DNN) models via collaborative inference, which splits a pre-trained model into two parts and executes them on user equipment (UE) and edge server respectively, becomes attractive. However, the large intermediate feature of DNN impedes flexible decoupling, and existing approaches either focus on the single UE scenario or simply define tasks considering the required CPU cycles, but ignore the indivisibility of a single DNN layer. In this article, we study the multi-agent collaborative inference scenario, where a single edge server coordinates the inference of multiple UEs. Our goal is to achieve fast and energy-efficient inference for all UEs. To achieve this goal, we design a lightweight autoencoder-based method to compress the large intermediate feature at first. Then we define tasks according to the inference overhead of DNNs and formulate the problem as a Markov decision process (MDP). Finally, we propose a multi-agent hybrid proximal policy optimization (MAHPPO) algorithm to solve the optimization problem with a hybrid action space. We conduct extensive experiments with different types of networks, and the results show that our method can reduce up to 56% of inference latency and save up to 72% of energy consumption.

At Presently, it is still a great challenge to achieve online classification of traffic flows due to the highly varying network environments, e.g., unpredictable new traffic classes, network noise, and congestion. Traditional classification methods work well in stable network environments, but may not exhibit their performance in dynamic environments. To address online classification issues, a granular computing-based classification model (GCCM) is developed, where the spatial and temporal flow granules are defined to make GCCM robust against variations and less sensitive to noise, and the correlations among flow granules are explored to establish the granular relation matrix (GRM). The inherent burst features between packets indicated by GRM prompt GCCM to achieve fine classification in unstable network environments. GCCM analyzes the burst features of packets without inspecting the payload information, and thus can be used to classify encrypted traffic as well as unencrypted traffic at a fast speed. In addition, the GCCM model, depending on difference measurement$D(\cdot)$, is a threshold-based classification, and therefore can be used to distinguish between time-varying classes. The validity of GCCM for online traffic classification is examined through theoretical results. The experimental evaluation of classification for fine and varied classes under dynamic network environments with noise and congestion also demonstrates its superiority in terms of classification accuracy and real-time performance with the state-of-the-art.

IoT devices have been widely utilized to detect state transition in the surrounding environment and transmit status updates to the base station for system operations. To guarantee the accuracy of system control, age of information (AoI) is introduced to quantify the freshness of the sensory data and meet the stringent timeliness requirement. Due to the limited computing resources, the status update can be offloaded to the mobile edge computing (MEC) server for execution. Since status updates generated by insufficient sensing operations may be invalid and lead to additional processing time, a joint data sensing and processing optimization problem needs to be considered. Therefore, this work formulates an NP-hard problem that considers the freshness of the status updates and energy consumption of the IoT devices. Subsequently, the problem is decomposed into sampling, sensing, and computation offloading optimization problems. To optimize the system overhead, a multi-variable iterative system cost minimization algorithm is proposed. Simulation results illustrate the efficacy of our method in decreasing the system cost, and indicate the influence of sensing and processing under different scenarios.

The developments in reinforcement learning provide a powerful and efficient learning framework for autonomous robotic systems. However, prior works rarely embed historical observations due to the exponentially increasing complexity, which may not perform well for large-scale long-horizon tasks that might require hundreds and thousands of steps to complete. In this paper, we propose Recurrent Imitation and Reinforcement Learning (RIRL) to address the challenges and enable robots for such tasks. The proposed RIRL incorporates a long short-term memory (LSTM) network to retain long-term memories, which could be an effective and efficient method to tackle the long dependency problem raised in long-horizon robotic tasks. To assess the performance of the RIRL, we test it with an optimized path planning problem for a robot to perform a Radiofrequency identification (RFID) inventory in dynamic and previously unknown environments. We experimentally validate RIRL’s feasibility and effectiveness in a visual game-based simulation platform, where the proposed RIRL model outperforms three baseline schemes with considerable gains.

With the rapid development of the cognitive radio networks, the number of terminal devices has exploded. Massive devices generate a large amount of privacy-sensitive data, typically WiFi signals. This paper proposes a method for Radio frequency (RF) fingerprinting identification of WiFi signals based on federated learning, which trains a cooperative model to complete RF fingerprinting identification without transmitting privacy-sensitive data. The experimental findings on a real-world dataset validate that the strategy described in this study increases the RF fingerprinting identification accuracy in a variety of size circumstances, and ensures that data privacy will not be compromised.

Artificial intelligence (AI) has emerged as the most promising solution expected to overcome the high degree of abstraction of radio signals and achieve accurate automatic modulation classification (AMC). To further improve the classification performance of the AMC model and enhance its interpretability, the network output layer is modeled as a decision space into which the input data is projected. In this paper, we expand the inter-class angle between the classes with the largest confusion rate to increase the decision space. In addition, we extend the perspective to the softmax layer and evaluate the negative impact of the output distribution range on the confidence difference in the AMC problem. We further propose constraining the norm of the input data to the output layer in combination with prior knowledge of the distribution of modulation signal data. Combining the above two aspects, a Maximum Focal Inter-Class Angular Loss with Norm Constraint (MFICAL-NC) scheme is proposed. The experimental results show that the method can guide the model to obtain a better fitting state and a stronger generalization ability.

With the wide deployment of 5G communication systems, 5G massive multiple-input multiple-output (MIMO) has been shown effective not only to improve the spectrum efficiency and energy efficiency, but also provides location-based service (LBS) such as outdoor vehicle localization and indoor user localization. Recently, deep convolutional neural network (DCNN) has been applied for massive MIMO localization using channel state information (CSI) or angle-delay profile (ADP). However, the robustness of the DCNN model has not been explored in massive MIMO localization. In this paper, we study the impact of adversarial attack and defense (i.e., adversarial training) on massive MIMO localization using DCNN and the neural ordinary differential equation (ODE) model. We first introduce the massive MIMO system with respect to the channel model and ADP fingerprints, and then present the DCNN model and the neural ODE model for massive MIMO localization, as well as three types of white-box adversarial attacks and adversarial training. Finally, our experimental results validate that the proposed neural ODE with adversarial training could effectively improve the robustness of massive MIMO localization in indoor and outdoor environments.

With outbreak of the COVID-19 pandemic, contact tracing has become an important problem. It has been proven that maintaining social distance and isolating affected people are highly beneficial for curbing the spread of COVID-19, which all depend on identifying people’s trajectories. However, the current interview-based approach is costly, and the existing mobile app-based schemes rely on complete and accurate data. In this paper, we propose a transformer encoder-based approach with spatial position embedding extracted using a graph Combinatorial Laplacian matrix to interpolate incomplete human trajectories. To model human trajectory, we propose a graphical embedded module to extract spatial features based on predefined location clusters. The incomplete trajectory sequences are first preprocessed into matrices and then used to train a deep transformer encoder network for trajectory completion. Our experiments using a real world Bluetooth Low Energy (BLE) dataset validate the efficacy of our proposed approach, which outperforms several baseline methods.

Driven by the fast increase of food demand world wide, the safety of grain storage becomes increasingly important. TWo key factors, i.e., temperature and moisture, greatly influence the safety of stored grain. The traditional methods of detecting grain temperature and moisture are time-consuming, expensive, and inconvenient to use. In this paper, we develop a TagSense system for robust wheat moisture and temperature sensing using cheap commercial-off-the-shelf (COTS) RFID devices. We first validate the feasibility of using tag impedance for robust moisture and temperature sensing. We then propose a distance- free algorithm and an angle-agnostic method to mitigate the impact of different measurement distances and angles. Our experimental results demonstrate that the TagSense system can achieve satisfactory sensing accuracy of wheat moisture and temperature in different rotation angles and at different distances.

Surveillance drones are unmanned aerial vehicles (UAVs) that are utilized to collect video recordings of targets. In this paper, we propose a novel design framework for aerial video surveillance in urban areas, where a cellular-connected UAV captures and transmits videos to the cellular network that services users. Fundamental challenges arise due to the limited onboard energy and quality of service (QoS) requirements over environment-dependent air-to-ground cellular links, where UAVs are usually served by the sidelobes of base stations (BSs). We aim to minimize the energy consumption of the UAV by jointly optimizing the mission completion time and UAV trajectory as well as transmission scheduling and association, subject to QoS constraints. The problem is formulated as a mixed-integer nonlinear programming (MINLP) problem by taking into account building blockage and BS antenna patterns. We first consider the average performance for uncertain local environments, and obtain an efficient sub-optimal solution by employing graph theory and convex optimization techniques. Next, we investigate the site-specific performance for specific urban local environments. By reformulating the problem as a Markov decision process (MDP), a deep reinforcement learning (DRL) algorithm is proposed by employing a dueling deep Q-network (DQN) neural network model with only local observations of sampled rate measurements. Simulation results show that the proposed solutions achieve significant performance gains over baseline schemes.

Radio frequency (RF) fingerprinting is a hardware feature used in Internet of Things (IoT) applications to identify wireless devices. In this paper, we propose few-shot learning (FSL) and prototypical networks (PTNs) to create a new model that can adapt to a new domain with very few labeled examples. The proposed model can mitigate the domain shift caused by changing RF environments. Experimental results show the proposed method can improve the performance of RF fingerprinting over different domains.

Interest in Radio Frequency (RF) based 3D human pose tracking has skyrocketed in the age of Artificial Intelligence of Things (AIoT). Compared to Computer Vision (CV) based methods, RF-based approaches are more resilient to lighting and non-line-of-sight conditions, and can better preserve user privacy. However, the majority of the current RF-based methods rely on a vision-aided multi-modal learning approach. An extensive amount of paired training data, i.e., Radio-Frequency Identification (RFID) data and vision data, must be collected, to achieve an adequate performance with the supervised-learning network. In order to mitigate such time-consuming and costly tasks, we propose a data augmentation method based on Generative Adversarial Network (GAN), named RFPose-GAN, to generate synthesized RFID data to alleviate the complications of using commodity RFID tags and receivers. In this paper, a forward kinematic layer is incorporated to generate simulated vision pose data, thus eliminating the need of using a Kinect 2.0 device in RFPose-GAN. Experiments conducted demonstrate that the synthesized data achieves accurate pose estimation performance.

In this paper, we present a Swin Transformer based indoor localization framework that employs RF hologram tensors to locate multiple ultra-high frequency (UHF) passive Radiofrequency identification (RFID) tags. The RF hologram tensor captures the strong relationship between RFID measurements and spatial location, and helps to improve the robustness of the system in dynamic environments. We develop a Swin Transformer-based hologram filter network to clean the fake peaks in hologram tensors caused by multipath propagation and phase wrapping, exploring the spatial relationship between tags. In contrast to fingerprinting-based localization systems that use deep networks as classifier, the proposed network treats localization as a regression problem. An intuitive peak finding algorithm is introduced for location estimation using the sanitized hologram tensors. We prototype the proposed system using commodity RFID devices and conduct extensive experiments to evaluate its performance.

With the rapid development of the information technology, the number of devices in the Internet of Things (IoT) is increasing explosively, which makes device identification a great challenge. Deep neural networks (DNNs) have been used for device identification in IoT due to their superior learning ability. However, DNNs are susceptible to adversarial attacks, which can greatly degrade the accuracy of deep learning (DL) models for device identification. The adversarial attack is one of the fundamental security concerns for DNNs, and it is of great importance to study the generation of adversarial examples and to examine the attack effects for the design of robust DNN-based device identification schemes. In this article, we examine the effects of nontargeted and targeted adversarial attacks on convolutional neural network (CNN)-based device identification and propose combined evaluation indicators of logits to enrich the evaluation criteria. Our experimental results demonstrate that the identification accuracy degrades with the increase of the perturbation level and iteration step size, and the proposed combined evaluation indicators are effective to show the individual device signal differences. The insights from this study will be useful for the design of robust DL-based IoT systems.

Mobile/wireless virtual reality (VR) services, especially immersive 360° VR videos, have advanced unprecedentedly in recent years. However, the high bandwidth requirement of VR services has compounded the burden on wireless networks. Multicast is a high potential technique for alleviating the bandwidth requirement of 360° VR video streaming, but the multicast capacity is still constrained by the users with poor channel conditions, and it vanishes when the number of users increases while the number of the base station (BS) antennas is fixed. To overcome the drawbacks of multicast, sidelink, which is an adaptation of the core LTE standard that allows the device-to-device (D2D) communications without going through a BS, can be utilized. In this article, two sidelink-aided multicast scenarios (i.e., independent decoding and joint decoding) are studied for multiquality tiled 360° VR video transmission. We propose a utility model for each scenario, and quality level selection, sidelink sender/receiver selection, and transmission resource allocation are optimized to maximize the total utility of all users under the bandwidth constraints as well as the quality smoothness constraints for multiquality tiles. We then develop an iterative two-stage algorithm to obtain suboptimal solutions to the formulated mixed-integer nonlinear programming (MINLP) problems. Simulation results demonstrate the advantage of the proposed solutions over several baseline schemes.

The integration of the blockchain and Internet of Things (IoT) systems can effectively guarantee data security in IoT applications. To facilitate the use of blockchain on resource-constrained IoT end devices, we propose RAFT+ with a new leader selection scheme in this article, which is based on the distributed consensus algorithm RAFT. The design of RAFT+ aims at mitigating the imparities between different types of IoT end devices and enabling these devices to allow different types of IoT end devices to participate in block consensus, thus maintaining strong consistency of the blockchain network. The leader selection scheme is generated by a deep${Q}$-Network (DQN), which can make the optimal selection of the leader under various conditions by leveraging the limited system resources as well as balancing the load of the consensus mechanism on multiple IoT end devices. Simulation results show that RAFT+ can enhance the system performance while maintaining the security of the system under high load conditions.

Nonintrusive load monitoring (NILM) is to obtain individual appliance’s electricity consumption from aggregated smart meter data. In this article, we propose a middle window transformer model, termedMidformer, for NILM. Existing models are limited by high computational complexity, dependency on data, and poor transferability. In Midformer, we first exploit patchwise embedding to shorten the input length, and then reduce the size of queries in the attention layer by only using global attention on a few selected input locations at the center of the window to capture the global context. The cyclically shifted window technique is used to preserve connection across patches. We also follow the pretraining and fine-tuning paradigm to relieve the dependency on data, reduce the computation in modeling training, and enhance transferability of the model to unknown tasks and domains. Our experimental study using two real-world data sets demonstrates the superior performance and transferability of Midformer over three baseline models.

Fingerprinting-based indoor localization has been a research focus for GPS denied areas. The development of neural networks has greatly promoted its application in indoor localization systems. However, recent studies showed that the machine learning models, including state-of-the-art neural networks, are vulnerable to adversarial examples, and thus, neural network-based indoor localization systems are also under the threat of adversarial attacks. To investigate the effect of adversarial attacks on indoor localization systems and to make such systems resilient to adversarial attacks, we propose AdvLoc, an adversarial deep learning for indoor localization system. With the proposed AdvLoc system, the effect of adversarial attacks on indoor localization is studied under six types of adversarial attack methods in both black-box attack and white-box attack scenarios. Furthermore, adversarial training is utilized in offline training of the proposed AdvLoc system, which is effective against first-order adversarial attacks. The proposed AdvLoc system is implemented with commodity WiFi devices and evaluated with extensive experiments in two representative indoor environments. The experimental results verify the robustness of the proposed system against first-order adversarial attacks in representative indoor environments.

With the rapid growth of the Internet of Things (IoT), the radio-frequency identification (RFID) technology has been recognized as an effective and low-cost solution for many IoT applications. In this article, we study the problem of utilizing a sparse RFID tag array for backscatter indoor localization. We first theoretically and experimentally validate the feasibility of using sparse tag arrays for the direction of arrival (DOA) estimation. We then present the SparseTag system, which leverages a novel sparse tag array for high-precision backscatter indoor localization. The SparseTag system includes sparse array processing, difference co-array design, DOA estimation using a spatial smoothing-based method, and a localization method. A robust channel selection method based on the RFID tag array is adopted for mitigating the multipath effect. The SparseTag system is implemented with commodity RFID devices. Its superior performance is validated in two different environments with extensive experiments and comparison to baseline schemes.

Practicing gymnastics activities at home with online resources has become an increasingly popular choice due to its convenience and accessibility. However, without face-to-face guidance by a trainer, a major challenge is how to assess the quality of performed gymnastics activities, effectively and fairly. Existing intrusive assessing approaches usually require live cameras or wearable sensors, which usually generate privacy and feasibility concerns. There is a lacking of accurate approaches to assess the quality of the activities. To address these challenges, a gymnastics activity assessment approach is proposed in this article, and Wi-Gym, an effective first-of-its-kind gymnastics activity assessment system is developed utilizing commodity Wi-Fi. Wi-Gym is designed to compare the activity-induced channel state information (CSI) dynamics by an exerciser and that of a trainer utilizing dynamic time warping (DTW). The comparison results are provided by a fuzzy inference system (FIS). To make Wi-Gym robust to the changes in the environment, domain adaptation is leveraged to mitigate the data distribution imbalance caused by the environment changes. Extensive experimental studies have been conducted using Wi-Gym, acoustic, and video-based sensing systems. The experimental results validate the effectiveness and robustness of the proposed approach.

Deploying small cell base stations (SBS) under the coverage area of a macro base station (MBS), and caching popular contents at the SBSs in advance, are effective means to provide high-speed and low-latency services in next generation mobile communication networks. In this paper, we investigate the problem of content caching (CC) and user association (UA) for edge computing. A joint CC and UA optimization problem is formulated to minimize the content download latency. We prove that the joint CC and UA optimization problem is NP-hard. Then, we propose a CC and UA algorithm (JCC-UA) to reduce the content download latency. JCC-UA includes a smart content caching policy (SCCP) and dynamic user association (DUA). SCCP utilizes the exponential smoothing method to predict content popularity and cache contents according to prediction results. DUA includes a rapid association (RA) method and a delayed association (DA) method. Simulation results demonstrate that the proposed JCC-UA algorithm can effectively reduce the latency of user content downloading and improve the hit rates of contents cached at the BSs as compared to several baseline schemes.

In this paper, we consider adaptive user equipments (UE) link selection and user association in millimeter-wave (mmWave) networks. We formulate a joint optimization of link selection, resource allocation, and user association, aiming to maximize the sum logarithmic rate of all UEs. The formulated problem is solved by decomposing it into two levels of subproblems. The lower-level subproblem is link selection and resource allocation with a given user association, which is solved by a three-stage process. In the first stage, we establish the D2D relaying architecture by assuming that all UEs are served via D2D relaying. Based on the relaying architecture, we derive the optimal resource allocation in the second stage. Finally, an adaptive link selection algorithm is proposed in the third stage to determine the set of UEs that switch from D2D relaying to multi-beam reflection. The high-level subproblem is user association, for which we solve it with a dual decomposition-based approach. Simulation results indicate that compared to benchmark schemes, the average data rate achieved by the proposed scheme is significantly higher than the benchmark schemes and is close to an upper bound. Besides, the proposed scheme achieves a good tradeoff between system performance and fairness.

It is a huge challenge for the receiver to correctly identify the modulation types due to the complex underwater channel environment and severe noise interference. Additionally, the real-time communications have strict requirements in terms of time. In order to solve this well-known issue, in this work, we combine the automatic feature extraction and learning ability of recurrent neural network (RNN) and convolutional neural network (CNN) for designing a modulation recognition model for underwater acoustic signals. The proposed model is based on deep hybrid neural networks called recurrent and convolutional neural network (R&CNN). As compared with the traditional modulation recognition techniques, this method achieves higher recognition accuracy without manual feature extraction. The experimental results show that the validation accuracy of the proposed R&CNN’s on the Trestle data set is 98.21%. Similarly, the validation accuracy of the proposed R&CNN’s on the South China Sea data set is 99.38%. The average recognition time is 7.164ms. As compared with the conventional deep learning methods, the proposed R&CNN not only has a higher recognition accuracy, but also greatly reduces the recognition time.

Human pose tracking has attracted great interest re-cently. Considerable efforts have been made in Radio-Frequency (RF) sensing techniques for human pose tracking without using a video camera. Although the existing RF based schemes can well protect user privacy, they are usually sensitive to the RF environment and are hard to generalize to new environments. In this paper, we analyze the challenges of generalization of Radio-Frequency Identification (RFID) based human pose tracking systems. We then present an RFID based 3D human pose tracking system, termed Meta-Pose, which incorporates meta-learning and few-shot fine-tuning to achieve high adaptability to new environments. The proposed system is implemented with commodity RFID devices and extensive experiments are conducted for performance evaluation. The experiment results validate the superior human pose tracking performance and high adaptability of the proposed Meta-Pose system.

Rather than considering only one legitimate user as in the existing works, we investigate multiple legitimate users served by Alice using multicast millimeter wave communications in this paper. Hybrid beamformers for the max-min fairness problem are designed to maximize the minimum covert rate between Alice and the legitimate users subject to the power constraint for confidential signal (CS) and the covertness constraint. In particular, the fully-digital beamformers for the CS and jamming signal are designed by temporarily neglecting the hardware constraints from the constant envelop for phase shifters and the limited number of RF chains, where a semi-definite programming-based method and a successive convex approximation (SCA)-based method are proposed. To approach the fully-digital beamformers, hybrid beamformers are designed subject to the hardware constraints, where an alternating minimization method is proposed to iteratively optimize the analog and digital beamformers. Simulation results show that the proposed methods can achieve better covert communication performance than the existing methods.

With development of the photovoltaic industry, solar power generation forecasting using weather data has become an important problem. Various machine learning (ML) algorithms have been proposed to handle the random and massive weather data, with considerable recent interest on deep neural networks (DNN). Recent studies show that DNNs are vulnerable to adversarial examples, but most prior work has focused on their impact on the classification problem. In this paper, we investigate the problem of adversarial attacks on solar power generation forecasting, which is a regression problem. We examine the impact of adversarial attacks on both the DNN model and a LASSO-based statistical model proposed in our prior work. Both white-box attack and black-box attack are examined, along with the effect of adversarial training.

Millimeter Wave (mmWave) communications, as a core technique of 5G, can be leveraged for outdoor localization because of its large bandwidth and massive antenna array. Fingerprinting based mmWave outdoor localization methods using deep learning are highly suitable for non-line-of-sight (NLOS) environments. In this paper, we propose a deep convolutional Gaussian process (DCGP) based regression approach to achieve high robustness for fingerprinting-based mmWave outdoor localization, which exploits the convolutional structure for deep Gaussian process to allow uncertainty estimation on location predictions. Specially, we present a system architecture of mmWave based outdoor localization, including beamforming image construction and DCGP training, where DCGP model can effectively learn the location features from mmWave beamforming images. Our experimental results show that the proposed DCGP method can achieve higher outdoor localization accuracy than a CNN-based baseline method.

In this paper, we present MulTLoc, a deep learning based indoor localization system for localizing multiple ultra-high frequency (UHF) passive RFID tags with RF hologram tensor filtering and upscaling. The proposed system leverages the RF hologram tensor as the input of the deep convolutional networks. The RF hologram tensor exhibits a strong relationship between the observation and the spatial location, which enhances the robustness of the system to the dynamic environment and equipment. To sanitize the RF hologram tensor, two architectures of deep networks are newly proposed. The hologram filter network suppresses the fake peaks resulting from the multipath and phase wrapping by leveraging the spatial relationship between tags. The tensor upscaling network recovers the high resolution hologram tensor from the output of the previous network, which enhances the localization accuracy of the system further. Comparing with the fingerprinting based localization systems using deep networks as the classifier, the networks in the MulTLoc system treat the localization problem as the regression problem, in which the ambiguity between fingerprints is reserved. To avoid the inherent errors in the fingerprinting based localization systems, the location estimation is given by intuitive peak finding algorithms using the recovered RF hologram tensor. We implement the proposed MulTLoc system with commodity RFID devices and verify its performance with extensive experiments.

Vital sign (e.g., respiration rate) monitoring has become increasingly more important because it offers useful clues about medical conditions such as sleep disorders. There is a compelling need for technologies that enable contact-free and easy deployment of vital sign monitoring over an extended period of time for healthcare. In this article, we present a SonarBeat system to leverage a phase-based active sonar to monitor respiration rates with smartphones. We provide a sonar phase analysis and discuss the technical challenges for respiration rate estimation utilizing an inaudible sound signal. Moreover, we design and implement the SonarBeat system, with components including signal generation, data extraction, received signal preprocessing, and breathing rate estimation with Android smartphones. Our extensive experimental results validate the superior performance of SonarBeat in different indoor environment settings.

The development of Internet-of-Things (IoT) technology promotes the advances of grain condition detection and analysis systems. Temperature monitoring is a main element to maintain grain quality, and effective control of grain temperature is crucial to safe storage of grain. In this article, an encoder–decoder model with attention mechanism is proposed to accurately forecast the temperature of stored grain. Considering that the points on the gradient direction of the temperature surface have a great influence on the temperature of the target point, the Sobel operator is used to extract the local characteristics of the target point. In addition, considering the correlation structure in the sensory data, the attention mechanism is used to extract the global features of the target point. The extracted spatial features are fed into long short-term memory (LSTM) networks to obtain the long-term state information of spatial factors. LSTM unit and convolutional neural network are used to encode the spatial features of the target points. Taking meteorological factors as the external input of the decoder, temporal attention mechanism and LSTM unit are used to complete the decoding process and realize the prediction of grain temperature in the future. The results with real grain storage data show that the proposed model outperforms several schemes, including Kalman-modified the least absolute shrinkage and selection operator (Kalman-modified LASSO), temporal graph convolutional network (T-GCN), LSTM, CNN-LSTM, and convolutional LSTM (Conv-LSTM), with considerable gains.

In this article, we present a Q-learning-enabled safe navigation system-S-Nav-that recommends routes in a road network by minimizing traveling through categorically demarcated COVID-19 hotspots. S-Nav takes the source and destination as inputs from the commuters and recommends a safe path for traveling. The S-Nav system dodges hotspots and ensures minimal passage through them in unavoidable situations. This feature of S-Nav reduces the commuter's risk of getting exposed to these contaminated zones and contracting the virus. To achieve this, we formulate the reward function for the reinforcement learning model by imposing zone-based penalties and demonstrate that S-Nav achieves convergence under all conditions. To ensure real-time results, we propose an Internet of Things (IoT)-based architecture by incorporating the cloud and fog computing paradigms. While the cloud is responsible for training on large road networks, the geographically aware fog nodes take the results from the cloud and retrain them based on smaller road networks. Through extensive implementation and experiments, we observe that S-Nav recommends reliable paths in near real time. In contrast to state-of-the-art techniques, S-Nav limits passage through red/orange zones to almost 2% and close to 100% through green zones. However, we observe 18% additional travel distances compared to precarious shortest paths.

Wi-Fi-based indoor fingerprinting is attracting increasing interest in the research community due to the ubiquitous access in indoor environments. In this article, we propose ResLoc, a deep residual sharing learning-based system for indoor fingerprinting using bimodal channel state information (CSI) tensor data. The proposed ResLoc system employs CSI tensor data, including the angle of arrival and amplitude, collected from a small set of training locations with known coordinates to train the proposed dual-channel deep residual sharing learning model. The proposed new model extends the traditional deep residual learning model by incorporating two or more channels and let the channels exchange their residual signals after each residual block. Unlike prior deep-learning-based fingerprinting schemes, ResLoc only requires for training one group of weights for all the training locations. The proposed ResLoc system is implemented with commodity Wi-Fi devices and evaluated with extensive experiments in three representative indoor environments. The experimental results validate that the proposed ResLoc system can achieve high localization accuracy using a single Wi-Fi access point in indoor environments.

Unmanned-aerial-vehicle (UAV)-enabled mobile-edge computing (MEC) has emerged as a promising paradigm to extend the coverage of computation service for Internet of Things (IoT) applications, which are usually time sensitive and computation intensive. In this article, a novel design framework is proposed for a multi-UAV-enabled MEC system, where edge servers are equipped on multiple UAVs to provide flexible computation assistance to IoT devices with hard deadlines. The aim is to maximize the number of served IoT devices through jointly optimizing UAV trajectory and service indicator as well as resource allocation and computation offloading, where the chosen IoT devices will complete their computation tasks on time under given energy budgets and co-channel interference is taken into account. We formulate the optimization problem as a mixed integer nonlinear programming (MINLP), which is challenging to solve directly. The problem is first reformulated to a more mathematically tractable form by adding a penalty term to the objective function. We then decouple the problem into two subproblems and develop an iterative algorithm by solving the two subproblems with alternating optimization and successive convex approximation techniques, where the proposed algorithm converges to a Karush–Kuhn–Tucker (KKT) solution. In addition, an efficient initialization scheme is proposed based on multiple traveling salesman problem with time windows (m-TSPTWs) method. Finally, simulation results are provided to demonstrate that the proposed joint design achieves significant performance gains over baseline schemes.

To improve driving safety and avoid accidents caused by driving fatigue, drowsiness detection aims to alarm the driver before he/she falls asleep. Since breathing rate is a key indicator of the drowsy state, respiration monitoring in the noisy driving environment is critical for developing an effective driving fatigue detection system. In this paper, we propose, for the first time, an RFID based respiration monitoring system for driving environments. The system estimates the respiration rate of a driver based on phase values sampled from multiple RFID tags attached to the seat belt, while exploiting the tag diversity to combat the strong noise in the driving environment. Both tensor completion and tensor Canonical Polyadic Decomposition (CPD) are applied to process the phase values, to overcome the influence of frequency hopping, random sampling, vehicle vibration, and other environmental movements. The proposed system is analyzed and implemented with commodity RFID devices. Its accurate and robust performance is demonstrated with extensive experiments conducted in a real driving car.

The new frontier research era and convergence of cognitive data science methods and models with reference to the Internet of Things (IoT) and big data systems have brought about various challenges in industrial systems that need to be addressed in the current scenario. Cognitive science will lead to a high level of fluidity to analytics. This special section aims to explore the domain knowledge and reasoning of data science technologies and cognitive methods with the IoT over the big data systems. Data science techniques have been adopted to improve the IoT in terms of data throughput, optimization, and management, and to have a major impact on the future of IoT networking systems. The main focus is the design of best cognitive embedded data science technologies to process and analyze the large amount of data collected through industrial IoT systems and help for good decision making.

In recent years, human pose tracking has become an important topic in computer vision (CV). To improve the privacy of human pose tracking, there is considerable interest in techniques without using a video camera. To this end, radio-frequency identification (RFID) tags, as a low-cost wearable sensor, provide an effective solution for 3-D human pose tracking. In this article, we propose RFID-Pose, a vision-aided realtime 3-D human pose estimation system, which is based on deep learning assisted by CV. The RFID phase data are calibrated to effectively mitigate the severe phase distortion, and high accuracy low rank tensor completion is employed to impute the missing RFID data. The system then estimates the spatial rotation angle of each human limb, and utilizes the rotation angles to reconstruct human pose in realtime with the forward kinematic technique. A prototype is developed with commodity RFID devices. High pose estimation accuracy and realtime operation of RFID-Pose are demonstrated in our experiments using Kinect 2.0 as a benchmark.

Traffic flows with different requirements of quality of service (QoS requirements) are aggregated into different QoS classes to provide differentiated services (Diffserv) and better quality of experience (QoE) for users. The existing aggregation approaches/QoS mapping methods are based on quantitative QoS requirements and static QoS classes. However, they are typically qualitative and time-varying at the edge of the beyond fifth generation (B5G) networks. Therefore, the artificial intelligence technology of preference logic is applied in this paper to achieve an intelligent method for edge computing, called the preference logic based aggregation model (PLM), which effectively groups flows with qualitative requirements into dynamic classes. First, PLM uses preferences to describe QoS requirements of flows, and thus can deal with both quantitative and qualitative cases. Next, the potential conflicts in these preferences are eliminated. According to the preferences, traffic flows are finally mapped into dynamic QoS classes by logic reasoning. The experimental results show that PLM presents better performance in terms of QoE satisfaction compared with the existing aggregation methods. Utilizing preference logic to group flows, PLM implements a novel way of edge intelligence to deal with dynamic classes and improves the Diffserv for massive B5G traffic with quantitative and qualitative requirements.

With the rapid development and convergence of the mobile Internet and the Internet of Things (IoT), computing-intensive and delay-sensitive IoT applications (APPs) are proliferating with an unprecedented speed in recent years. Mobile edge computing (MEC) and energy harvesting (EH) technologies can significantly improve the user experience by offloading computation tasks to edge-cloud servers as well as achieving green and durable operation. Traditional centralized strategies require precise information of system states, which may not be feasible in the era of big data and artificial intelligence. To this end, how to allocate limited edge-cloud computing resource on demand, and how to develop heterogeneous task offloading strategies with EH in a more flexible manner are remaining challenges. In this paper, we investigate an EH-enabled MEC offloading system, and propose an online distributed optimization algorithm based on game theory and perturbed Lyapunov optimization theory. The proposed algorithm works online and jointly determines heterogeneous task offloading, on-demand computing resource allocation, and battery energy management. Furthermore, to reduce the unnecessary communication overhead and improve the processing efficiency, an offloading pre-screening criterion is designed by balancing battery energy level, latency, and revenue. Extensive simulations are carried out to validate the effectiveness and rationality of the proposed approach.

This paper aims at predicting accurate outdoor and indoor locations using deep neural networks, for the data collected using the Long-Range Wide-Area Network (LoRaWAN) communication protocol. First, we propose an interpolation aided fingerprinting-based localization system architecture. We propose a deep autoencoder method to effectively deal with the large number of missing samples/outliers caused by the large size and wide coverage of LoRa networks. We also leverage three different deep learning models, i.e., the Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN), for fingerprinting based location regression. The superior localization performance of the proposed system is validated by our experimental study using a publicly available outdoor dataset and an indoor LoRa testbed.

Radio access network (RAN) in 5G is expected to satisfy the stringent delay requirements of a variety of applications. The packet scheduler plays an important role by allocating spectrum resources to user equipments (UEs) at each transmit time interval (TTI). In this paper, we show that optimal scheduling is a challenging combinatorial optimization problem, which is hard to solve within the channel coherence time with conventional optimization methods. Rule-based scheduling methods, on the other hand, are hard to adapt to the time-varying wireless channel conditions and various data request patterns of UEs. Recently, integrating artificial intelligence (AI) into wireless networks has drawn great interest from both academia and industry. In this paper, we incorporate deep reinforcement learning (DRL) into the design of cellular packet scheduling. A delay-aware cell traffic scheduling algorithm is developed to map the observed system state to scheduling decision. Due to the huge state space, a recurrent neural network (RNN) is utilized to approximate the optimal action-policy function. Different from conventional rule-based scheduling methods, the proposed scheme can learn from the interactions with the environment and adaptively choosing the best scheduling decision at each TTI. Simulation results show that the DRL-based packet scheduling can achieve the lowest average delay compared with several conventional approaches. Meanwhile, the UEs' average queue lengths can also be significantly reduced. The developed method also exhibits great potential in real-time scheduling in delay-sensitive scenarios.

With the development of the Internet of Things (IoT) technology and the rapid deployment of 5G wireless, more and more radiation devices are appearing in the increasingly complex electromagnetic environment. To be able to manage these devices in a unified manner, accurate identification of the devices has become a top priority. Specific emitter identification (SEI) is to effectively solve this problem. In this paper, both power spectral density (PSD) and fractional Fourier transform (FrFT) methods are used to extract the characteristics of transient signals. The characteristics of steady-state signals are analyzed by the bispectrum method. The SEI system model in this paper is constructed based on these techniques. Our experiments results show that when the SNR is 16dB, the SEI system can achieve a recognition accuracy of over 97% by exploiting the characteristics of the transient signal. Since the characteristics of the steady-state signal can better suppress noise, the SEI system can achieve a nearly 90% classification recognition accuracy under extremely low SNR.

Temperature is one of the major ecological factors that affect the safe storage of grain. In this paper, we propose a deep spatio-temporal attention mode to predict stored grain temperature, which exploits the historical temperature data of stored grain and the meteorological data of the region. In this proposed model, we use the Sobel operator to extract the local spatial factors, and leverage the attention mechanism to obtain the global spatial factors of grain temperature data and temporal information. In addition, a convolutional neural network (CNN) is used to learn features of external meteorological factors. Finally, the spatial factors of grain pile and external meteorological factors are combined to predict future grain temperature using long short-term memory (LSTM) based encoder and decoder models. Experiment results show that the proposed model achieves higher predication accuracy compared with the traditional methods.

With the emergence of the information age, mobile data has become more random, heterogeneous and massive. Thanks to its many advantages, deep learning is increasingly applied in communication fields such as modulation recognition. However, recent studies show that the deep neural networks (DNN) is vulnerable to adversarial examples, where subtle perturbations deliberately designed by an attacker can fool a classifier model into making mistakes. From the perspective of an attacker, this study adds elaborate adversarial examples to the modulation signal, and explores the threats and impacts of adversarial attacks on the DNN-based modulation recognition in different environments. The results show that, regardless of a white-box or a black-box model, the adversarial attack can reduce the accuracy of the target model. Among them, the performance of the iterative attack is superior to the one-step attack in most scenarios. In order to ensure the invisibility of the attack (the waveform being consistent before and after the perturbations), an appropriate perturbation level is found without losing the attack effect. Finally, it is attested that the signal confidence level is inversely proportional to the attack success rate, and several groups of signals with high robustness are obtained.

With the rapid development of computer vision, human pose tracking has attracted increasing attention in recent years. To address the privacy concerns, it is desirable to develop techniques without using a video camera. To this end, RFID tags can be used as a low-cost wearable sensor to provide an effective solution for 3D human pose tracking. User adaptability is another big challenge in RF based pose tracking, i.e., how to use a well-trained model for untrained subjects. In this paper, we propose Cycle-Pose, a subject-adaptive realtime 3D human pose estimation system, which is based on deep learning and assisted by computer vision for model training. In Cycle-Pose, RFID phase data is calibrated to effectively mitigate the severe phase distortion, and High Accuracy LowRank Tensor Completion (HaLRTC) is employed to impute missing RFID data. A cycle kinematic network is proposed to remove the restriction on paired RFID and vision data for model training. The resulting system is subject-adaptive, achieved by learning to transform the RFID data into a human skeleton for different subjects. A prototype system is developed with commodity RFID tags/devices and evaluated with experiments. Compared with a traditional system RFIDPose, higher pose estimation accuracy and subject adaptability are demonstrated by Cycle-Pose in our experiments using Kinect 2.0 data as ground truth.

Radio Frequency (RF) based human pose estimation techniques have been proposed to generate human pose without using a camera, so people will no longer worry about their privacy. Compared with other RF sensing based systems, Radio Frequency Identification (RFID) provides a promising solution for RF based human pose estimation. RFID tags can be used as wearable sensors because of their small size. The interference caused by the multipath effect is much smaller in the RFID system. The cost of RFID systems is also lower than the advanced radar based systems such as FMCW radar. Thus, we propose the RFID-Pose system for tracking the movements of multiple human limbs in realtime [1]. In the proposed system, RFID tags are attached to the target human joints. The movement of the tags are captured by the phase variations in the responses from each tag. The human pose is reconstructed by estimating rotation angles from RFID data and the initial human skeleton. The vision data will not be needed anymore in the testing phase, so the user's privacy can be well protected.

Vital signs, such as respiration and heartbeat, are useful for health monitoring because such signals provide important clues of medical conditions. Effective solutions are needed to provide contact-free, easy deployment, low-cost, and long-term vital sign monitoring. In this article, we present PhaseBeat to exploit channel state information, in particular, phase difference data to monitor breathing and heart rates with commodity WiFi devices. We provide a rigorous analysis of channel state information phase difference with respect to its stability and periodicity. Based on the analysis, we design and implement the PhaseBeat system with off-the-shelf WiFi devices and conduct an extensive experimental study to validate its performance. Our experimental results demonstrate the superior performance of PhaseBeat over existing approaches in various indoor environments.

Due to the popularity of onboard geographic devices, a large number of spatial-textual objects are generated in the Internet of Vehicles (IoV). This development calls for approximate spatial keyword queries with numeric attributes in IoV (A2SKIV), which takes into account the locations, textual descriptions, and numeric attributes of spatial-textual objects. Considering large amounts of objects involved in the query processing, this article comes up with the idea of utilizing vehicles as fog-computing resource and proposes the network structure called FCV, and based on which the fog-based top-k A2SKIV query is explored and formulated. In order to effectively support network distance pruning, textual semantic pruning, and numerical attribute pruning, simultaneously, a two-level spatial-textual hybrid index STAG-tree is designed. Based on STAG-tree, an efficient top-k A2SKIV query processing algorithm is presented. The simulation results show that our STAG-based approach is about 1.87× (17.1×, resp.) faster in search time than the compared ILM (DBM, resp.) method, and our approach is scalable.

With the increasing demand for location-based service, WiFi-based localization has become one of the most popular methods due to the wide deployment of WiFi and its low cost. To improve this technology, we propose DeepMap, a deep Gaussian process for indoor radio map construction and location estimation. Received signal strength (RSS) samples are used in DeepMap to generate accurate and fine-grained radio maps. A two-layer deep Gaussian process model is designed to determine the relationship between the location and RSS samples, while the model parameters are optimized with an offline Bayesian training method. To identify the location of a mobile device, a Bayesian fusion method is proposed, which leverages RSS samples from multiple access points (APs) to achieve high location estimation accuracy. We conduct comprehensive experiments to verify the performance of DeepMap in two indoor settings. DeepMap's robustness is validated using limited training data.

In this article, a hierarchical online distributed algorithm (HODA) is developed to achieve optimal energy management in interconnected microgrids (IMG). The energy management objectives include maximizing users' utility, optimizing the output power of controllable generators, and keeping the system operating in an economic manner. We formulate the problem as an online least absolute shrinkage and selection operator (LASSO) problem, considering both reactive power and system operation characteristics. We then employ averaging fixed horizon control (AFHC) to solve the formulated problem under some mild assumptions on the uncertainties in renewable power generation and load demand. The alternating direction method of multipliers (ADMM) is adopted to decouple the coupled constraints. The proposed online algorithm is asymptotically optimal, since its solution converges to the offline optimal solution. The performance of the proposed algorithm is validated using data traces obtained from a real-world IMG system.

With the popularity of wearable devices, along with the development of clouds and cloudlet technology, there has been increasing need to provide better medical care. The processing chain of medical data mainly includes data collection, data storage and data sharing, etc. Traditional healthcare system often requires the delivery of medical data to the cloud, which involves users' sensitive information and causes communication energy consumption. Practically, medical data sharing is a critical and challenging issue. Thus in this paper, we build up a novel healthcare system by utilizing the flexibility of cloudlet. The functions of cloudlet include privacy protection, data sharing and intrusion detection. In the stage of data collection, we first utilize Number Theory Research Unit (NTRU) method to encrypt user's body data collected by wearable devices. Those data will be transmitted to nearby cloudlet in an energy efficient fashion. Second, we present a new trust model to help users to select trustable partners who want to share stored data in the cloudlet. The trust model also helps similar patients to communicate with each other about their diseases. Third, we divide users' medical data stored in remote cloud of hospital into three parts, and give them proper protection. Finally, in order to protect the healthcare system from malicious attacks, we develop a novel collaborative intrusion detection system (IDS) method based on cloudlet mesh, which can effectively prevent the remote healthcare big data cloud from attacks. Our experiments demonstrate the effectiveness of the proposed scheme.

With the development of virtual reality (VR) technology, the future of VR systems is evolving from single-user wired connections to multi-user wireless connections. However, wireless online rendering and transmission incur extra processing and transmission latency, as well as higher bandwidth requirements. To meet the requirements of wireless VR applications and enhance the quality of the VR user experience, this paper designs a view synthesis-based 360° VR caching system over Cloud Radio Access Network (C-RAN), where both mobile edge computing (MEC) and hierarchical caching are supported. In the system, an MEC-Cache Server is deployed in the pooled Base band Units (BBU pool) and used for view synthesis and caching. In addition, the remote radio heads (RRHs) can also cache some video contents. If the requested content of a specific view is cached in the BBU pool or RRHs, or can be synthesized with the aid of the cached adjacent views, it is unnecessary to request the content from the remote VR video source server. Therefore, the transmission latency and backhaul traffic load for VR services can be decreased. We formulate a hierarchical collaborative caching problem aiming to minimize the transmission latency, which is proved NP-hard. To address the impractical expenses of the offline optimal method, an online MaxMinDistance caching algorithm with low complexity is proposed. Numerical simulation results demonstrate that the proposed caching strategy provides significantly improved cache hit rate, backhaul traffic load, transmission latency, and Quality of Experience (QoE) performances relative to conventional caching strategies.

Internet video traffic exhibits considerable variation as new video services continue to emerge. Some videos require strict real-time performance, while others may aim for a minimal packet loss rate or sufficient bandwidth. Therefore, it is important to develop fine-grained classification mechanisms to realize effective resource management and quality of service (QoS) provisioning. However, the existing methods for classifying video traffic always suffer from two problems: payload inspection and feature selection. In this paper, we propose a novel method that uses fractal characteristics to achieve traffic classification at a fine-grained level. This method requires neither payload signatures nor statistical features. Through rigorous analysis, we prove the feasibility of employing fractal characteristics for video traffic classification and further develop a theoretical framework for the proposed scheme. For the specific scenario of video flow classification, we improve the theory of fractals in terms of estimated spectrum, core domain, segmentation, and threshold setting. The results of an extensive experimental study on several real-world video traffic datasets show that the classification accuracy of the proposed scheme is higher than that of existing methods.

In this paper, the problem of effective and robust delivery of Dynamic Adaptive Streaming over HTTP (DASH) videos over an orthogonal frequency-division multiplexing access (OFDMA) network is studied. Motivated by a measurement study, we propose to explore the request interval and robust rate prediction for DASH over OFDMA. We first formulate an offline cross-layer optimization problem based on a novel quality of experience (QoE) model. Then the online reformulation is derived and proved to be asymptotically optimal. After analyzing the structure of the online problem, we propose a decomposition approach to obtain a user equipment (UE) rate adaptation problem and a BS resource allocation problem. We introduce stochastic model predictive control (SMPC) to achieve high robustness on video rate adaption and consider the request interval for more efficient resource allocation. Extensive simulations show that the proposed scheme can achieve a better QoE performance compared with other variations and a benchmark algorithm, which is mainly due to its lower rebuffering ratio and more stable bitrate choices.

In this paper, we propose a real-time, nondestructive, and low-cost wheat mildew detection system using commodity WiFi devices, which is a new application of the Internet of Things (IoT) to agriculture applications. We first introduce wheat mildew and validate the feasibility of wheat mildew detection using WiFi Channel State Information (CSI) amplitude data. We then present the MiFi system design, including CSI sensing, preprocessing, radial basis function (RBF) neural network based detection modeling, and mildew detection. Our experimental results validate the effectiveness of the proposed MiFi system. The average detection accuracy of the MiFi system is over 90% under both line-of-sight (LOS) and non-line-of-sign (NLOS) scenarios.

With the growth of the number of vehicles and car accidents, driving safety is becoming increasingly important. There is a compelling need for an effective, low-cost driving fatigue detection system. In this paper, we propose an RFID based system, termed NodTrack, to detect the nodding movements of drivers, which is a key indicator of fatigue and one of the most dangerous motions during drowsy driving. The NodTrack system utilizes the phase difference between two RFID tags mounted on the back of a hat worn by the driver, to extract nodding features. We propose an effective technique to mitigate the cumulative error caused by frequency hopping in most FCC-compliant RFID systems, as well as a long short-term memory (LSTM) autoencoder model to learn the nodding features from calibrated data. The highly accurate detection performance of the proposed system is validated by our experimental study.

It has been recognized that fixed spectrum and channel allocation will lead to waste of spectrum resources when multiple agents communicate at the same time. Dynamic allocation of channels is proposed to maximize the utilization of spectrum resources. In the environment of multiple unmanned aerial vehicles (UAVs), it is necessary to ensure that each UAV can communicate successfully without interfering with other UAVs. Dynamic allocation of channels plays an important role in such systems. In this paper, we propose a dynamic channel allocation scheme based on deep reinforcement learning for multi-UAV systems. A slotted time system is used by all the UAVs. Di2642erent from the traditional method, the occupancy of each channel is scanned first in each time slot. Then a channel will be selected for data transmission, with feedback from the environment when the transmission is over. The proposed channel allocation scheme incorporates a long short-term memory (LSTM) into the deep reinforcement learning framework, to better learn from the past experience and better adapt to the the highly dynamic environment in a multi-UAV system. The experimental results show that compared with the traditional reinforcement learning method (Q- learning and Deep Q Network (DQN)), the proposed method achieves faster convergence and better performance with respect to average collision rate, average reward, and average successful communication rate.

In this paper, we study the problem of utilizing a sparse RFID tag array for backscatter indoor localization. We first theoretically and experimentally validate the feasibility of using RFID tag array for direction of arrival (DOA) estimation. We then present the SparseTag system, which leverages a novel sparse RFID tag array for high-precision backscatter indoor localization. The SparseTag system includes sparse array processing, difference co-array design, DOA estimation using a spatial smoothing based method, and a localization method, while a robust channel selection method based on the RFID tag array is proposed for mitigating the indoor multipath effect. The SparseTag system is implemented with commodity RFID devices. Its superior performance is validated in two different environments with extensive experiments.

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. This paper considers MEC for a representative mobile user in an ultradense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modeled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between mobile user and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN)-based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to a novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies.

Fog radio access networks (F-RANs) are seen as potential architectures to support services of Internet of Things by leveraging edge caching and edge computing. However, current works studying resource management in F-RANs mainly consider a static system with only one communication mode. Given network dynamics, resource diversity, and the coupling of resource management with mode selection, resource management in F-RANs becomes very challenging. Motivated by the recent development of artificial intelligence, a deep reinforcement learning (DRL)-based joint mode selection and resource management approach is proposed. Each user equipment (UE) can operate either in cloud RAN (C-RAN) mode or in device-to-device mode, and the resource managed includes both radio resource and computing resource. The core idea is that the network controller makes intelligent decisions on UE communication modes and processors' on-off states with precoding for UEs in C-RAN mode optimized subsequently, aiming at minimizing long-term system power consumption under the dynamics of edge cache states. By simulations, the impacts of several parameters, such as learning rate and edge caching service capability, on system performance are demonstrated, and meanwhile the proposal is compared with other different schemes to show its effectiveness. Moreover, transfer learning is integrated with DRL to accelerate learning process.

As a special form of the Internet of Things, smart grid is an Internet of both power and information, in which energy management is critical for making the best use of the power from renewable energy resources, such as solar and wind, while efficient energy management is hinged upon precise forecasting of power generation from renewable energy resources. In this paper, we propose a novel least absolute shrinkage and selection operator (LASSO) and long short term memory (LSTM) integrated forecasting model for precise short-term prediction of solar intensity based on meteorological data. It is a fusion of a basic time series model, data clustering, a statistical model, and machine learning. The proposed scheme first clusters data using k -means++. For each cluster, a distinctive forecasting model is then constructed by applying LSTM, which learns the nonlinear relationships and LASSO, which captures the linear relationship within the data. Simulation results with open-source datasets demonstrate the effectiveness and accuracy of the proposed model in short-term forecasting of solar intensity.

With the fast-growing adoption of the radio-frequency identification (RFID) technology, RFID-based sensors have attracted great interest. Due to the limitation of RFID tags, most existing RFID-based temperature sensing works rely on hardware modification, which increases the cost and hampers its deployment. In this article, we propose RFThermometer, a remote temperature sensing system with commercial ultra high frequency (UHF) RFID tags. We first investigate the effect of temperature on RFID phases. To alleviate the precision deterioration caused by missing phase measurements, a tensor completion method is proposed to restore missing phases and a Gaussian process model is leveraged to construct a phase-temperature map in the offline stage. In the online stage, the unknown temperature is estimated by a dynamic time warping (DTW)-based greedy method. Extensive experimental results are presented to validate the performance of RFThermometer with off-the-shelf RFID devices.

The optimal power energy scheduling of a microgrid (MG) is not only related to the active power of distributed generators but also dependent on the reactive power and system operational constraints. It is essential to manage the active and reactive power simultaneously in optimal energy distribution. This paper is focused on developing an online algorithm to optimize the real-time power energy distribution in the MG, considering both reactive power and system operational constraints. The goal is to provide high quality electricity usage for users in the MG and maximize users' utility. The output power of controllable generators are also optimized. The proposed online algorithm is asymptotically optimal, since its solution converges to the offline optimal solution. The effectiveness of the proposed algorithm is validated using data traces obtained from a real-world MG.

In this paper, the concept of crowdsourcing is applied to the medical field and a health monitoring mechanism based on photo crowdsourcing is proposed. Specifically, with photo crowdsourcing by many participators, the routine circumstances of users may be represented. However, these photos may include other people than the user, such as the visibility requestor, the invisibility requestor, and the passerby. The visibility and invisibility requestor are the participators in the system, whose identity can be set as visible or invisible, while the passerbys do not participate in the system. Hence, a privacy protection mechanism is proposed for this system, which includes two categories: i) The image fuzzy processing is provided for the invisibility requestor, while the original image is reserved for the visibility requestor. ii) The passerby's image is directly fuzzy processed for privacy protection.

In this paper, we present DeepMap, a deep Gaussian process for indoor radio map construction and location estimation. To address the shortcomings of existing Gaussian process based approaches, we present a DeepMap system, which employs deep Gaussian process for constructing received signal strength (RSS) radio maps and a Bayesian algorithm for online localization. We design a two-layer deep Gaussian process model to capture the relationship between the RSS space and the location space and provide an offline Bayesian training method to determine model parameters. A Bayesian fusion method using multiple APs is proposed for accurate location estimation. Experimental results verify the performances of DeepMap in a large indoor environment and validate its robustness with moderate training data.

With the growth of smart healthcare in the Internet of Things (IoT), breathing monitoring and apnea detection are of increasing importance. In this paper, we propose AutoTag, a recurrent variational autoencoder model for breathing and apnea detection with commodity RFID Tags. The AutoTag system consists of signal extraction, calibration, and respiration monitoring modules. We propose a novel method to mitigate the frequency hopping offset with realtime calibration for FCC complaint RFID systems, and a new recurrent variational autoencoder method for apnea and breathing detection. Experimental results demonstrate the effectiveness of the proposed AutoTag system in two different environments.

With the fast increasing demands of location-based service and proliferation of smartphones and other mobile devices, accurate indoor localization has attracted great interest. In this paper, we present DeepML, a deep long short-term memory (LSTM) based system for indoor localization using the smartphone magnetic and light sensors. We verify the feasibility of using bimodal magnetic and light data for indoor localization through experiments. We then design the DeepML system, which first builds bimodal images by data preprocessing, and then trains a deep LSTM network to extract the location features. Newly received magnetic field and light intensity data is then exploited for estimating the location of the mobile device using an improved probabilistic method. Our extensive experiments verify the effectiveness of the proposed DeepML system.

In this paper, we present a non-destructive and economic wheat moisture detection system with commodity WiFi. First, we experimentally validate the feasibility of wheat moisture detection by using CSI amplitude and phase difference data. We then design Wi-Wheat system, where data preprocessing, feature extraction and support vector machine (SVM) classification are implemented for CSI processing module. For data preprocessing, we employ outlier detection, data normalization and eliminating noise for obtaining clear CSI amplitude and phase difference data. Then, we consider principal component analysis (PCA) based feature extraction for Wi-Wheat system. For SVM classification, Gaussian radial basis function (RBF) is used as the kernel function for wheat moisture detection. The experimental results show the Wi-Wheat system can achieve higher classification accuracy for LOS and NLOS scenarios.

Moisture content of cereal grains is a highly important factor in safe storage and food processing. The existing detection methods are either time-consuming, sensitive to the environment, or have a high cost. In this paper, we propose DeepWMD, a deep LSTM network based system for multi-class wheat moisture detection. We first collect CSI amplitude and phase difference data to detect wheat moisture content. Then, we design the DeepWMD system with commodity Wi-Fi devices in the 5GHz band, including data preprocessing of collected CSI data, offline training, and online testing. Our experimental results verify the efficacy of the proposed DeepWMD system, and demonstrates that DeepWDM can achieve high-precision multi-class wheat moisture detection in different indoor storage environments.

With the increasing commercial prospect of personal Unmanned Aerial Vehicle (UAV), human and UAV interaction has been a compelling and challenging task. In this paper, we present the RFHUI, a human and UAV interaction system based on passive radio-frequency identification (RFID) technology which provides a remote control function. Three or more Ultra high frequency (UHF) RFID tags are attached on a board to create a hand-held controller. A COTS (Commercial Off-The-Shelf) RFID reader with multiple antennas is deployed to collect the observations of the tags. According to the phase measurement from the RFID reader, we leverage a Bayesian filter based method to localize the position of all tags in a global coordinate. From the estimated position of the attached tags, a 6 DOF (Degrees of Freedom) pose of the controller can be obtained. Therefore, when the user moves the controller, its pose will be precisely tracked in a real-time manner. Then, the flying commands, which are generated from the estimated pose of the controller, are sent to the UAV for navigation. We implemented a prototype of the RFHUI, and the experiment results show that it provides precise poses with 0.045 m error in position and 2.5° error in orientation for the controller. It therefore enables the controller to precisely and intuitively instruct the UAV's navigation in an indoor environment.

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile device or to offload a task for cloud execution should adapt to the environmental dynamics in a smarter manner. In this paper, we consider MEC for a representative mobile user in an ultra dense network, where one of multiple base stations (BSs) can be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to minimize the long-term cost and an offloading decision is made based on the channel qualities between the mobile user and the BSs, the energy queue state as well as the task queue state. To break the curse of high dimensionality in state space, we propose a deep Q-network-based strategic computation offloading algorithm to learn the optimal policy without having a priori knowledge of the dynamic statistics. Numerical experiments provided in this paper show that our proposed algorithm achieves a significant improvement in average cost compared with baseline policies.

Fog computing system is an emergent architecture for providing computing, storage, control, and networking capabilities for realizing Internet of Things. In the fog computing system, the mobile devices (MDs) can offload its data or computational expensive tasks to the fog node within its proximity, instead of distant cloud. Although offloading can reduce energy consumption at the MDs, it may also incur a larger execution delay including transmission time between the MDs and the fog/cloud servers, and waiting and execution time at the servers. Therefore, how to balance the energy consumption and delay performance is of research importance. Moreover, based on the energy consumption and delay, how to design a cost model for the MDs to enjoy the fog and cloud services is also important. In this paper, we utilize queuing theory to bring a thorough study on the energy consumption, execution delay, and payment cost of offloading processes in a fog computing system. Specifically, three queuing models are applied, respectively, to the MD, fog, and cloud centers, and the data rate and power consumption of the wireless link are explicitly considered. Based on the theoretical analysis, a multiobjective optimization problem is formulated with a joint objective to minimize the energy consumption, execution delay, and payment cost by finding the optimal offloading probability and transmit power for each MD. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme and the superior performance over several existed schemes are observed.

The smart grid (SG) has emerged as an important form of the Internet of Things. Despite the high promises of renewable energy in the SG, it brings about great challenges to the existing power grid due to its nature of intermittent and uncontrollable generation. In order to fully harvest its potential, accurate forecasting of renewable power generation is indispensable for effective power management. In this paper, we propose a least absolute shrinkage and selection operator (LASSO)-based forecasting model and algorithm for solar power generation forecasting. We compare the proposed scheme with two representative schemes with three real world datasets. We find that the LASSO-based algorithm achieves a considerably higher accuracy comparing to the existing methods, using fewer training data, and being robust to anomaly data points in the training data, and its variable selection capability also offers a convenient tradeoff between complexity and accuracy, which all make the proposed LASSO-based approach a highly competitive solution to forecasting of solar power generation.

Energy management is indispensable in the smart grid, which integrates more renewable energy resources, such as solar and wind. Because of the intermittent power generation from these resources, precise power forecasting has become crucial to achieve efficient energy management. In this paper, we propose a novel adaptive learning hybrid model (ALHM) for precise solar intensity forecasting based on meteorological data. We first present a time-varying multiple linear model (TMLM) to capture the linear and dynamic property of the data. We then construct simultaneous confidence bands for variable selection. Next, we apply the genetic algorithm back propagation neural network (GABP) to learn the nonlinear relationships in the data. We further propose ALHM by integrating TMLM, GABP, and the adaptive learning online hybrid algorithm. The proposed ALHM captures the linear, temporal, and nonlinear relationships in the data, and keeps improving the predicting performance adaptively online as more data are collected. Simulation results show that ALHM outperforms several benchmarks in both short-term and long-term solar intensity forecasting.

The support for high data rate applications with the cognitive radio technology necessitates wideband spectrum sensing. However, it is costly to apply long-term wideband sensing and is especially difficult in the presence of uncertainty, such as high noise, interference, outliers, and channel fading. In this work, we propose scheduling of sequential compressed spectrum sensing which jointly exploits compressed sensing (CS) and sequential periodic detection techniques to achieve more accurate and timely wideband sensing. Instead of invoking CS to reconstruct the signal in each period, our proposed scheme performs backward grouped-compressed-data sequential probability ratio test (backward GCD-SPRT) using compressed data samples in sequential detection, while CS recovery is only pursued when needed. This method on one hand significantly reduces the CS recovery overhead, and on the other takes advantage of sequential detection to improve the sensing quality. Furthermore, we propose (a) an in-depth sensing scheme to accelerate sensing decision-making when a change in channel status is suspected, (b) a block-sparse CS reconstruction algorithm to exploit the block sparsity properties of wide spectrum, and (c) a set of schemes to fuse results from the recovered spectrum signals to further improve the overall sensing accuracy. Extensive performance evaluation results show that our proposed schemes can significantly outperform peer schemes under sufficiently low SNR settings.

The cognitive radio (CR) technology holds promise to significantly increase spectrum availability and wireless network capacity. With more spectrum bands opened up for CR use, it is critical yet challenging to perform efficient wideband sensing. We propose an integrated sequential wideband sensing scheduling framework that concurrently exploits sequential detection and compressed sensing (CS) techniques for more accurate and lower-cost spectrum sensing. First, to ensure more timely detection without incurring high overhead involved in periodic recovery of CS signals, we propose smart scheduling of a CS-based sequential wideband detection scheme to effectively detect the PU activities in the wideband of interest. Second, to further help users under severe channel conditions identify the occupied sub-channels, we develop two collaborative strategies, namely, joint reconstruction of the signals among neighboring users and wideband sensing-map fusion. Third, to achieve robust wideband sensing, we propose the use of anomaly detection in our framework. Extensive simulations demonstrate that our approach outperforms peer schemes significantly in terms of sensing delay, accuracy and overhead.

In this paper, we investigate the problem of frame design, resource allocation, and user association in a massive multiple input multiple output (MIMO) heterogeneous network (HetNet) with wireless backhaul (WB) and linear processing. The objective is to maximize the sum downlink rate of all users, subject to constraints on data rates of WBs and fairness-aware constraints. Such a problem is formulated as an integer programming problem with both coupled variables and coupled constraints. We first develop a centralized scheme in which we decompose the original problem into two subproblems and iteratively solve them until convergence to achieve a near-optimal solution. We then propose a distributed scheme by formulating a repeated game among all users and prove that the game converges to a Nash Equilibrium. Simulation studies show that the proposed schemes are adaptive to different network scenarios and traffic patterns, and achieve considerable gains over several benchmark schemes.

Despite the high promises of renewable energy, it brings great challenges to the existing power grid due to its nature of intermittent and uncontrollable generation. In order to fully harvest its potential, accurate forecasting of renewable power generation is indispensable for effective power management. In this paper, we propose a LASSO- based forecasting model and algorithm for solar power generation forecasting. We compare the proposed scheme with two representative schemes with a real world dataset. We find that the LASSO-based algorithm achieves a considerably higher accuracy comparing to the existing methods, using fewer training data and being robust to anomaly data points in the training data. Its variable selection capability also offers a trade-off between complexity and accuracy, which all make it a highly competitive solution to forecasting of solar power generation.

Vital signs, such as breathing rate, can provide useful information for personal healthcare. In this paper, we present ResBeat, a commodity 5GHz WiFi based system to exploit bimodal channel state information (CSI), including amplitude and phase difference, for realtime, long- term, and contact-free breathing monitoring. We first present an analysis of breathing signal anomaly based on bimodal CSI data. We then describe the data preprocessing, adaptive signal selection, and breathing signal monitoring modules of ResBeat, and employ peak detection to estimate breathing rates. We conduct extensive experiments under three different environments, where superior performance over two alternative methods is validated.

With the increasing demand of location-based services, Wi-Fi based localization has attracted great interest because it provides ubiquitous access in indoor environments. In this paper, we propose CiFi, deep convolutional neural networks (DCNN) for indoor localization with commodity 5GHz WiFi. First, by leveraging a modified device driver, we extract phase data of channel state information (CSI), which is used to estimate angle of arriving (AOA). We then create estimated AOA images as input to the DCNN, to train the weights in the offline phase. The location of mobile device is predicted based on the trained DCNN and new CSI AOA images. We implement the proposed CiFi system with commodity Wi-Fi devices in the 5GHz band and verify its performance with extensive experiments in two representative indoor environments.

Vital sign (e.g., breathing rate) monitoring has become increasingly more important because it can offer useful clues to medical conditions such as sleep disorders or anomalies. There is a compelling need for technologies that enable contact-free, easy deployment, and long-term vital sign monitoring for healthcare. In this paper, we present a SonarBeat system to leverage a phase based active sonar to monitor breathing rates with smartphones. We design and implement the SonarBeat system, with components including signal generation, data extraction, received signal preprocessing, and breathing rate estimation, with Andriod smartphones. Our experimental results validate the superior performance of SonarBeat in different indoor environment settings.

We investigate the resource allocation problem, including time slot allocation, channel allocation, and power adaptation, in a millimeter Wave (mmWave) network with multiple transmission links, multiple channels, and a PicoNet Coordinator (PNC). Each link has a video session to transmit from the transmitter to the receiver. The objective is to minimize the number of time slots to finish the video sessions of all links by jointly optimizing channel allocation and time slot allocation for links, while considering the possible interference between different links on the same channel. The optimal solution for the formulated problem is computationally prohibitive to obtain due to the exponential complexity. We developed a column generation based method to reformulate the original problem into a main problem along with a series of sub-problems, with greatly reduced complexity. We prove that the optimal solution for the reformulated problem converges to the optimal solution of the original problem, and we derived a lower bound for the performance of the reformulated problem at each iteration, which will finally converge to the global optimal solution. The proposed scheme is validated with simulations with its superior performance over existing work is observed.

The directional neighbor discovery problem, i.e., spatial rendezvous, is a fundamental problem in millimeter wave (mmWave) networks. The challenge is how to let the transmitter and receiver beams meet in space under deafness caused by directional transmission and reception. In this paper, we present a Hunting-based Directional Neighbor Discovery (HDND) scheme, where a node continuously rotates its directional beam to scan its neighborhood for neighbors. Through a rigorous analysis, we derive the conditions for ensured neighbor discovery, as well as a bound for the worst case discovery time. We validate the analysis with extensive simulations, and demonstrate the superior performance of the proposed scheme over two benchmark schemes.

Vital signs, such as respiration and heartbeat, are useful to health monitoring since such signals provide important clues of medical conditions. Effective solutions are needed to provide contact-free, easy deployment, low-cost, and long-term vital sign monitoring. In this paper, we present PhaseBeat to exploit channel state information (CSI) phase difference data to monitor breathing and heartbeat with commodity WiFi devices. We provide a rigorous analysis of the CSI phase difference data with respect to its stability and periodicity. Based on the analysis, we design and implement the PhaseBeat system with off-the-shelf WiFi devices, and conduct an extensive experimental study to validate its performance. Our experimental results demonstrate the superior performance of PhaseBeat over existing approaches in various indoor environments.

Extending LTE to unlicensed bands (LTE-U) is gaining increasing interest recently. However, its success faces great challenges due to the inherent lack of compatibility between LTE and Wi-Fi. In this paper, we address the problem of harmonious coexistence and efficient spectrum sharing for LTE-U and Wi-Fi. We develop an analysis framework for the Carrier Sensing Adaptive Transmission (CSAT) mechanism, which leads to a Listen-Before-Talk (LBT) enhanced CSAT scheme that achieves capacity gains for both LTE-U and Wi-Fi, and provides useful guidelines on achieving fairness. For efficient spectrum sharing among LTE-U Pico Evolved NodeBs (PeNBs), we propose an Inter-Cell Interference Coordination (ICIC) based spectrum share mechanism incorporated into a spectrum auction framework. Both the analysis and the superior performance of the proposed scheme are validated with simulations and comparison with several benchmarks.

Device to device (D2D) relaying and multi-beam reflection are two effective approaches to deal with the blockage problem in millimeter-wave (mmWave) communication, each with its own limitations when serving a large number of user equipments (UE). A combination of D2D relaying and multibeam reflection is expected to enhance the performance, but the selection of UEs to be served by each approache remains a challenge. In this paper, we consider adaptive mode selection between D2D relaying and multi-beam reflection in a time division duplex (TDD) mmWave network. We formulate a joint mode selection and resource sharing problem with the objective of maximizing the sum logarithm rate, and propose a two-stage solution algorithm. In the first stage, we derive the optimal resource sharing solution under the case that all UEs are served by D2D relaying. In the second stage, an adaptive algorithm is proposed to determine the set of UEs that switch from D2D relaying to multi-beam reflection. Simulation results demonstrate that the proposed scheme achieves considerable performance gain compared to several benchmark schemes.

Wi-Fi based indoor localization has attracted great interest due to its ubiquitous access in many indoor environments. In this paper, we propose ResLoc, a deep residual sharing learning based system for indoor localization with channel state information (CSI) tensor data. We first introduce CSI data in wireless systems and show how to build CSI tensors for indoor localization. Then, we present the design of ResLoc, which employs dual-channel, bi-modal CSI tensor data to train the deep network using the proposed deep residual sharing learning in the offline phase. In the online test phase, we use newly received CSI tensor data to estimate the location of the mobile device based on an enhanced probabilistic method. The experimental results show that the proposed ResLoc system can obtain submeter level accuracy with a single access point.

In this paper, we investigate the problem of pilot optimization, resource allocation, and user association in a massive MIMO heterogeneous network (HetNet) with wireless backhaul (WB) and linear processing. The objective is to maximize the sum downlink rate of all users, subject to constraints on data rate of WB and fairness-aware constraints. Such a problem is formulated as an integer programming problem with both coupled variables and coupled constraints. We first develop a centralized scheme in which we decompose the original problem into two subproblems and iteratively solve them until convergence to achieve a near-optimal solution. We then propose a distributed scheme by formulating a repeated game among all users and prove that the game converges to a Nash Equilibrium (NE). Simulation studies show that the proposed schemes are adaptive to different network scenarios and traffic patterns, and achieve considerable gains over several benchmark schemes.

Vital sign (e.g., breathing rate) monitoring has become increasingly more important because it offers useful clues of medical conditions such as sleep disorders or anomalies. It is necessary to provide contact-free, easy deployment, and long-term vital sign monitoring for healthcare. In this demo, we present SonarBeat to leverage a phase based active sonar to monitor breathing rates with smartphones.

Although full-duplex transmission can be helpful for enhancing wireless link capacity, it may require extra energy to overcome the residual self-interference. In this paper, we investigate the tradeoff between energy consumption and delay in a multichannel full-duplex wireless LAN. The goal is to minimize the energy consumption while keeping the traffic queues stable. With Lyapunov optimization, we develop a throughput optimal online scheme to achieve the goals with optimized channel assignment, transmission scheduling, and transmission mode selection. We study the influence of full-duplex transmissions on the network capacity region, prove the optimality of the proposed algorithm, and derive upper bounds for the average queue length and energy consumption, which demonstrate the energy-delay tradeoff in such systems. The proposed algorithm and the capacity region analysis are validated with simulations.

Cloud radio access network (Cloud-RAN) is a promising wireless network architecture that can satisfy the fast growing mobile data traffic and improve the performance of Internet of Things. In this paper, we propose an energy-efficient resource allocation scheme based on heterogeneous Cloud-RAN jointly considering the remote radio head (RRH) antenna resource with baseband unit (BBU) computation resource. We formulate our joint resource allocation problem and decompose it into two subproblems. The first subproblem is a network-wide beamforming vectors optimization problem, and it is solved by weighted minimum mean square error approach. Based on the optimized beamforming vector, we propose an algorithm to get the RRH-user equipment clusters. The second subproblem is a BBU scheduling problem, and we reformulate it as a bin packing problem which aims to minimize the number of BBUs in working model to save more energy. Compared to some existed works which form the BBU scheduling problem as a bin packing problem, we propose a bin packing algorithm based on the best-fit-decreasing method, which has better performance. With simulation results and detailed analysis, the system performance of our proposed joint resource allocation scheme is verified, which is more energy-efficient than other existing schemes.

Breathing signal monitoring can provide important clues for health problems. Compared to existing techniques that require wearable devices and special equipment, a more desirable approach is to provide contact-free and long-term breathing rate monitoring by exploiting wireless signals. In this article, we propose TensorBeat, a system to employ channel state information (CSI) phase difference data to intelligently estimate breathing rates for multiple persons with commodity WiFi devices. The main idea is to leverage the tensor decomposition technique to handle the CSI phase difference data. The proposed TensorBeat scheme first obtains CSI phase difference data between pairs of antennas at the WiFi receiver to create CSI tensors. Then canonical polyadic (CP) decomposition is applied to obtain the desired breathing signals. A stable signal matching algorithm is developed to identify the decomposed signal pairs, and a peak detection method is applied to estimate the breathing rates for multiple persons. Our experimental study shows that TensorBeat can achieve high accuracy under different environments for multiperson breathing rate monitoring.

The papers in this special issue focus on the deployment of video over future networks. The past decade has seen how major improvements in broadband and mobile networks have led to widespread popularity of video streaming applications, and how the latter now becomes the major driving force behind exponentially growing Internet traffic. This special issue seeks to investigate these future Internet technologies through the prism of its most prevalent application, that of video communications. video.

We investigate the problem of base station (BS) ON-OFF switching, user association, and power control in a heterogeneous network (HetNet) with massive multiple input multiple output (MIMO), aiming to turn OFF under-utilized BS's and maximize the system energy efficiency. With a mixed integer programming problem formulation, we first develop a centralized scheme to derive the near optimal BS ON-OFF switching, which is an iterative framework with proven convergence. We further propose two distributed schemes based on game theory, with a bidding game between users and BS's, and a pricing game between wireless service provider and users. Both games are proven to achieve a Nash Equilibrium. Simulation studies demonstrate the efficacy of the proposed schemes.

In this paper, we investigate the problem of robust congestion control in infrastructure-based cognitive radio networks (CRN). We develop an active queue management algorithm, termed MAQ, which is based on multiple model predictive control. The goal is to stabilize the TCP queue at the base station under disturbances from the time-varying service capacity for secondary users. The proposed MAQ scheme is validated with extensive simulation studies under various types of background traffic and system/network configurations. It outperforms two benchmark schemes with considerable gains in all the scenarios considered.

With the ever-growing number of users enjoying online video service in mobile environments, video streaming services have been dominating the mobile traffic. It can be predicted that a small improvement in the user's watching experience will cause a substantial leap in profitability in terms of content providers and distributors, network operators and service providers for mobile videos. Though recent years have witnessed effective efforts to improve a user's video quality of experience (QoE) by the use of big data for analyzing users' viewing behaviors based on large-scale, video- viewing history datasets, it is very challenging to precisely analyze users' hidden intents and feelings when they are watching online videos. In addition to obtain a better video QoE, we propose to introduce user's emotional reactions into QoE assessment. In this scheme, first, the user's mood is detected in a real time fashion via emotion detection networking. Then, a mood matching process is performed to gain the similarity of the user's intent and the video content property in terms of emotion design. Finally, a novel, decision tree-based adjustment model is proposed to characterize the relationship between QoE and various factors, including buffer ratio, average bitrate, and the user's emotions. Our study opens a road for improving video QoE based on emotion detection networking.

The nested array, which is implemented by nonuniform antenna placement, is an effective approach to achieve O(N2) degrees of freedom (DoF) with an antenna array of N antennas. Such DoF refers to the number of directions of incoming signals that can be resolved. With the increased number of DoF, an important application of nested array is to nullify the interference signals from multiple directions. In this paper, we apply nested array in a massive MIMO heterogeneous network (HetNet) for interference management. With nested array based interference nulling, each base station (BS) can nullify a certain number of interference signals. A key design issue is to select the interference sources to be nullified at each BS. We formulate this problem as an integer programming problem. The objective is to maximize the sum rate of all users, subject to BS DoF constraints. We propose an approximation scheme to solve this problem and derive a performance upper bound. Simulation results show that the proposed scheme effectively improves the sum rate and achieves a near optimal performance.

In this paper, we focus on the energy efficient joint resource scheduling scheme in time varying Cloud-RAN with delay sensitive traffic. We jointly consider the computation resources provided by baseband units (BBUs), which are modeled as the data processing rate of each virtual machine (VM) provided by the BBUs, and the antenna resources provided by the remote radio heads (RRHs), which are modeled as the beamforming vectors for each user equipment (UE) considering the limited fronthaul capacity and per-UE QoS requirement. Based on the Lyapunov optimization method, we divide the original problem into two subproblems, i.e., the BBU processing rate scheduling problem and the network-wide beamforming strategy problem. The first subproblem can be formulated as a convex programming problem, and we can solve the second subproblem with a weighted minimum mean square error (WMMSE) approach. With the detailed theoretical analysis and simulation results, it is clear that we can achieve a trade- off between energy efficiency and traffic delay, which can be controlled by the control parameter V.

In this paper, we investigate the problem of robust congestion control in infrastructure-based cognitive radio networks (CRN). We develop an active queue management (AQM) algorithm, termed MAQ, based on multiple model predictive control (MMPC). The goal is to stabilize the TCP queue at the base station (BS) under disturbances from the varying service capacity for secondary users (SU). The proposed MAQ scheme is validated with extensive simulation studies under various types of background traffic and system/network parameters. It outperforms two benchmark schemes with considerable gains in all the scenarios considered.

In this paper, we study the problem of video delivery over Orthogonal Frequency Division Multiple Access (OFDMA) networks using the Dynamic Adaptive Streaming over HTTP (DASH) framework. The goal is to integrate these two principal technologies to enable effective wireless video delivery. Based on a comprehensive QoE model, we develop a formulation to maximize the user QoE with joint OFDM resource allocation and DASH rate adaptation. The formulated problem is decomposed into a BS resource allocation problem and a user rate adaptation problem, which are then solved with effective algorithms. Our simulation study validates the efficacy of the proposed scheme.

In this paper, we investigate the problem of optimal base station (BS) ON-OFF switching and user association in a heterogeneous network (HetNet) with massive MIMO, with the objective to maximize the system energy efficiency (EE). The joint BS ON-OFF switching and user association problem is formulated as an integer programming problem. We first develop a centralized scheme, in which we relax the integer constraints and employ a series of Lagrangian dual methods that transform the original problem into a standard linear programming (LP) problem. Due to the special structure of the LP, we prove that the optimal solution to the relaxed LP is also feasible and optimal to the original problem. We then propose a distributed scheme by formulating a repeated bidding game for users and BS's, and prove that the game converges to a Nash Equilibrium (NE). Simulation studies demonstrate that the proposed schemes can achieve considerable gains in EE over several benchmark schemes in all the scenarios considered.

We investigate the scheduling problem in a centralized dual-hop 60 GHz network with multiple Source-Destination (SD) pairs, relays, and a PicoNet Coordinator (PNC). The objective is to minimize the Maximum Expected Delivery Time (MEDT) among all SD pairs by jointly optimizing relay and link selection, while exploiting reflected mmWave transmissions and considering link blockage dynamics. We develop a Decomposition Principle to transform this problem into two sub-problems, one for link selection and the other for relay assignment when there is enough replays. We prove that the proposed scheme can achieve an optimality gap of just 1 time slot at greatly reduced complexity. We also develop a heuristic scheme to handle the case when there is no enough relays. The proposed schemes are validated with simulations, where their superior performance is observed.

In this paper, we address an important problem in the wireless monitoring, i.e., how to choose channels with best (or worst) qualities timely and accurately. We consider both scenarios of one or more sniffers simultaneously monitoring multiple channels in the same area. Since the channel information is initially unknown to the sniffers, we shall adopt learning methods during the monitoring to predict the channel condition by a short time of observation. We formulate this problem as a novel branch of the classic multi-armed bandit (MAB) problem, named exploration bandit problem, to achieve a trade-off between monitoring time/resource budget and the channel selection accuracy. In the multiple sniffer cases, including partly-distributed (with limited communications) and fully-distributed (without any communications) scenarios, we take communication costs and interference costs into account, and analyze how these costs affect the accuracy of channel selection. Extensive simulations are conducted and the results show that the proposed algorithms could achieve higher channel selection accuracy than other exploration bandit approaches, hence it proves the advantages of the proposed algorithms.

With the increasing demand of location-based services, indoor localization based on fingerprinting has become an increasingly important technique due to its high accuracy and low hardware requirement. In this paper, we propose PhaseFi, a fingerprinting system for indoor localization with calibrated channel state information (CSI) phase information. In PhaseFi, the raw phase information is first extracted from the multiple antennas and multiple subcarriers of the IEEE 802.11n network interface card by accessing the modified device driver. Then a linear transformation is applied to extract the calibrated phase information, which we prove to have a bounded variance. For the offline stage, we design a deep network with three hidden layers to train the calibrated phase data, and employ the weights of the deep network to represent fingerprints. A greedy learning algorithm is incorporated to train the weights layer-by-layer to reduce computational complexity, where a subnetwork between two consecutive layers forms a restricted Boltzmann machine. In the online stage, we use a probabilistic method based on the radial basis function for online location estimation. The proposed PhaseFi scheme is implemented and validated with extensive experiments in two representation indoor environments. It is shown to outperform three benchmark schemes based on CSI or received signal strength in both scenarios.

To provide ubiquitous Internet access under the explosive increase of applications and data traffic, the current network architecture has become highly heterogeneous and complex, making network management a challenging task. To this end, software-defined networking (SDN) has been proposed as a promising solution. In the SDN architecture, the control plane and the data plane are decoupled, and the network infrastructures are abstracted and managed by a centralized controller. With SDN, efficient and flexible network control can be achieved, which potentially enhances network performance. To harvest the benefits of SDN in wireless networks, the software-defined wireless network (SDWN) architecture has been recently considered. In this paper, we first analyze the applications of SDN to different types of wireless networks. We then discuss several important technical aspects of performance enhancement in SDN-based wireless networks. Finally, we present possible future research directions of SDWN.

We investigate the problem of streaming multi-user videos over the downlink of a cognitive radio network (CRN), where each cognitive user (CU) can access one channel at a time. We first consider the case where each CU can sense one channel at a time slot at most. To make the problem tractable, we tackle the optimal spectrum sensing and access problems separately and develop matching-based optimal algorithms to the subproblems, which yield an overall suboptimal solution. We then consider the case where each CU can sense multiple channels. We show that under the assumption that all the spectrum sensors work on the same operating point, a two-step approach can derive the optimal spectrum sensing and access policies that maximize the quality of experience (QoE) of the streaming videos. The superior performance of the proposed approaches is validated with simulations and comparisons with benchmark schemes, where a performance gain from 25% to 30% is demonstrated.

We tackle the challenging problem of streaming multiuser videos over the downlink of a cellular cognitive radio network (CRN), where each cognitive user (CU) can sense and access multiple channels at a time. Spectrum sensing, channel assignment, and power allocation strategies are jointly optimized to maximize the quality of service (QoS) for the CUs. We show that the formulated mixed integer nonlinear programming (MINLP) problem can be decomposed into two subproblems: 1) SP1 for the optimal spectrum sensing strategy and 2) SP2 for the optimal channel assignment and power allocation, without sacrificing optimality. We show that SP1 can be optimally solved if there is no restriction on the sensing capability for each CU, and develop a column generation (CG)-based algorithm to solve SP2 iteratively in a distributed manner. We also develop a heuristic algorithm for spectrum sensing with greatly reduced requirement on CU hardware, while still achieving a highly competitive sensing performance. We analyze the proposed algorithms with respect to complexity and time efficiency, and derive a performance upper bound. The proposed algorithms are validated with simulations.

We study the problem of minimizing the scheduling time length to serve users' traffic demand by link scheduling in multi-hop mmWave wireless networks. We formulate a constrained Binary Integer Programming (BIP) problem incorporating a flexible interference model for directional transmissions and a Markov chain based blockage model. Since the problem is NP hard, we propose a heuristic algorithm with greatly reduced complexity, which first finds the optimal streaming path for each data flow and then maximizes the instant network throughput by optimizing the link scheduling at each time slot. The performance of the heuristic algorithm is validated with simulations.

Cloud offloading is considered a promising approach to energy conservation and storage/computation enhancement for resource limited mobile devices. In this paper, we present a Lyapunov optimization based scheme for cloud offloading scheduling, as well as download scheduling for cloud execution output, for multiple applications running in a mobile device with a multi-core CPU. We derive an online algorithm and prove performance bounds for the proposed algorithm with respect to average power consumption and average queue length, which is indicative of delay, and reveal the fundamental trade-off between the two optimization goals.

In this paper, we propose a novel additive cancellation signal (ACS) method for sidelobe suppression in non-contiguous orthogonal frequency division multiplexing (NC-OFDM) based CR systems. The key idea of the proposed method is to dynamically add several additive cancellation symbols on both the primary user (PU) subcarriers and the secondary user (SU) subcarriers, to generate the additive cancellation signals for suppressing the sidelobe power of NC-OFDM signals. Moreover, the ACS method formulates the problem of sidelobe suppression as a quadratically constrained quadratic program (QCQP), and the optimal additive cancellation signal can be obtained by the standard interior-point method. Simulation results show that the proposed ACS method can provide significant sidelobe suppression performance.

With the increasing demand of location-based services, indoor localization based on fingerprinting has become an increasingly important technique due to its high accuracy and low hardware requirement. In this paper, we propose PhaseFi, a fingerprinting system for indoor localization with calibrated channel state information (CSI) phase information. In PhaseFi, the raw phase information is first extracted from the multiple antennas and multiple subcarriers of the IEEE 802.11n network interface card (NIC) by accessing the modified driver. Then a linear transform is used to extract the calibrated phase information, which is proven to have a bounded variance. For the offline stage, we design a deep network with three hidden layers to train the calibrated phase data, and employ weights to represent fingerprints. A greedy learning algorithm is incorporated to train the weights layer-by-layer to reduce computational complexity, where a sub-network between two continuous layers forms a Restricted Boltzmann Machine (RBM). In the online stage, we use a probabilistic method based on the radial basis function (RBF) for online location estimation. The proposed PhaseFi scheme is implemented and validated with intensive experiments in two representation indoor environments. It outperforms other three benchmark schemes based on CSI or RSS in both scenarios.

In this paper, we investigate the problem of incorporating full-duplex (FD) transmission in cognitive femtocell networks (CFN) to achieve higher spectrum utilization. We aim to maximize the sum rate of a full-duplex cognitive femtocell network (FDCFN) as well as guaranteeing the quality of service (QoS) of users in the form of a required signal to interference plus noise ratios (SINR). We propose a duplex mode selection strategy based on stable roommate matching, as well as a greedy channel allocation algorithm with a proven performance bound. Numerical results show that the proposed schemes effectively improve the sum rate of the FDCFN.

In this paper we tackle the problem of minimum time length link scheduling in 60GHz wireless networks, under both traffic demand and Signal to Interference and Noise Ratio (SINR) constraints. A constrained Binary Integer Programming (BIP) problem is formulated by incorporating a flexible interference model for directional transmissions and a Markov chain based blockage model. We then propose two effective solution algorithms, including a Greedy Algorithm (GA) that finds the maximum instant throughput for each time slot, and a Column Generation based algorithm (CG) that iteratively improves the current link schedule. The performance of the proposed algorithms is validated with simulations.

With the fast growing demand of location-based services in indoor environments, indoor positioning based on fingerprinting has attracted a lot of interest due to its high accuracy. In this paper, we present a novel deep learning based indoor fingerprinting system using Channel State Information (CSI), which is termed DeepFi. Based on three hypotheses on CSI, the DeepFi system architecture includes an off-line training phase and an on-line localization phase. In the off-line training phase, deep learning is utilized to train all the weights as fingerprints. Moreover, a greedy learning algorithm is used to train all the weights layer-by-layer to reduce complexity. In the on-line localization phase, we use a probabilistic method based on the radial basis function to obtain the estimated location. Experimental results are presented to confirm that DeepFi can effectively reduce location error compared with three existing methods in two representative indoor environments.

In this paper, we consider the problem of distributed power control in a full duplex (FD) wireless network consisting of multiple pairs of nodes, within which each node needs to communicate with its corresponding node. We aim to find the optimal transmit powers for the FD transmitters such that the network-wide capacity is maximized. Based on the high signal-to-interference-plus-noise ratio (SINR) approximation and a more general approximation method for logarithm functions, we develop effective distributed power control algorithms with the dual decomposition approach. The proposed algorithms are validated with simulation studies.

With the proliferation of mobile devices such as smartphones, an interesting problem is how to make use them to improve the accuracy of localization in indoor environments. In this paper, we develop a novel cooperative localization scheme exploiting mobility in the indoor environment. The problem is formulated as a semidefinite program (SDP) using Linear Matrix Inequality (LMI). With the proposed approach, mobile users utilize their top RSS measurements for distance estimation and to mitigate the the shadowing effect found in indoor environments. In addition, we utilize the estimated position for a user from the last time slot as a virtual access point (AP) to obtain the next position estimation, by utilizing the inertial measurement unit (IMU) data from smartphones. To better take advantage of the moving direction and velocity information provided by the smartphones, we next apply Kalman filter to further mitigate the errors in estimated positions. Simulation results confirm that both the mean error and variance can be effectively reduced by exploiting IMU data and Kalman filter.

Although considerable advances have been made in single microgrid (MG) systems, the problem of cooperation among MGs and the macrogrid has attracted considerable interest only recently. As in wireless communications systems, exploiting the temporal, spatial, and technological diversities in multiple cooperative MGs could bring about more efficient power generation and distribution. This paper investigates a hierarchical power scheduling approach to optimally manage power trading, storage, and distribution in a smart power grid with a macrogrid and cooperative MGs. We first formulate the problem as a convex optimization problem and then decompose it into a two-tier formulation. The first-tier problem jointly considers user utility, transmission cost, and grid load variance, while the second-tier problem minimizes the power generation and transmission cost, and exploits distributed storage in the MGs. We develop an effective online algorithm to solve the first-tier problem and prove its asymptotic optimality, as well as a distributed optimal algorithm for solving the second-tier problem. The proposed algorithms are evaluated with trace-driven simulations and are shown to outperform several existing schemes with considerable gains.

We consider the problem of streaming multi-user videos over the downlink of a Cognitive Radio Network (CRN), where each Cognitive User (CU) can access one channel at a time. Motivated by the prior work that establishes a separation principle for the joint design of spectrum sensor, sensing, and access polices, we first model cooperative spectrum sensing as an Integer Programming problem (IP) and develop a Greedy Poly-matching scheme to solve it for the optimal sensing strategies. We then formulate the problem of CU Quality of Experience (QoE) maximization as a maximum weight matching problem and solve it with the Hungarian Method for optimal channel assignments. The proposed spectrum sensing and channel assignment algorithms are compared with benchmark schemes in simulations, and are found to outperform the benchmark schemes in terms of available channels discovered and CU QoE achieved.

In this paper, we consider the design of user grouping and scheduling for large-scale multiple-input multiple-output (MIMO) frequency-division-duplexing (FDD) systems. Based on a recently proposed two-stage precoding framework, we first propose an improved K-means user grouping scheme which allocates the users to different pre-beamforming groups using the second-order channel statistics, and then a user grouping scheme that considers both load balancing and precoding design. After user groups are so determined, we present a dynamic user scheduling scheme where second-stage precoding is designed based on instantaneous channel conditions. We demonstrate the efficacy of the proposed schemes through simulations.

We tackle the challenging problem of streaming multi-user videos over the downlink of a cellular Cognitive Radio Network (CRN), where each Cognitive User (CU) can sense and access multiple channels at a time. Spectrum sensing, channel assignment, and power allocation strategies are jointly optimized to maximize the Quality of Service (QoS) for the CUs. We show that the formulated Mixed Integer NonLinear Programming (MINLP) problem can be decomposed into two sub-problems without sacrificing optimality: SP1 for the optimal spectrum sensing strategy, and SP2 for optimal channel assignment and power allocation. We show that SP1 can be optimally solved and then develop a Column Generation (CG) based algorithm to solve SP2 iteratively in a distributed manner. We also develop a heuristic algorithm for spectrum sensing with greatly reduced requirement on CU hardware, but with a highly competitive sensing performance. We analyze the proposed algorithms with respect to complexity and derive a performance upper bound. The proposed algorithms are validated with extensive simulations.

As advances are made in single Microgrid (MG) systems, the problem of cooperation among MGs and the Macrogrid has attracted considerable interest only recently. As in wireless communications systems, exploiting the temporal, spatial, and technological diversities in multiple cooperative MGs could bring about more efficient power generation and distribution. This paper investigates a hierarchical power scheduling approach to optimally manage the power trading, storage and distribution in a smart power grid with a Macrogrid and cooperative MGs. We first present a two-tier formulation: the first-tier problem jointly considers user utility, transmission cost, and grid load variance, while the second-tier problem minimizes the power generation and transmission cost and exploits distributed storage in the MGs. We develop an effective online algorithm to solve the first-tier problem and prove its asymptotic optimality, and develop a distributed optimal algorithm for solving the second-tier problem. The proposed hierarchical power scheduling algorithms are evaluated with trace-driven simulations and are shown to outperform several existing schemes with considerable gains.

Attracting increasing attention in recent years, the Free Space Optics (FSO) technology has been recognized as a cost-effective wireless access technology for multi-Gigabit rate wireless networks. Radio over Free Space Optics (RoFSO) provides a promising enhancement to optical fiber systems. In an RoFSO system using wavelength-division multiplexing (WDM), it is possible to concurrently transmit multiple data streams consisting of various wireless services at very high rate. In this paper, we develop power allocation schemes for adaptive WDM transmissions to combat the effect of weather turbulence in FSO networks. The problem of optical power allocation under power budget and eye safety constraints is investigated for adaptive WDM transmission in RoFSO networks. Simulation results show that WDM RoFSO can support high data rates even over long distance or under bad weather conditions with an adequate system design.

In recent years, the smart grid has been recognized as an important form of the Internet of Things (IoT). The two-way energy and information flows in a smart gird, together with the smart devices, bring about new perspectives to energy management. This paper investigates a distributed online algorithm for electricity distribution in a smart grid environment. We first present a formulation that captures the key design factors such as user's utility, grid load smoothing, and energy provisioning cost. The problem is shown to be convex and can be solved with a centralized online algorithm that only requires present information about users and the grid in our prior work. In this paper, we develop a distributed online algorithm that decomposes and solves the online problem in a distributed manner, and prove that the distributed online solution is asymptotically optimal. The proposed distributed online algorithm is also practical and mitigates the user privacy issue by not sharing user utility functions. It is evaluated with trace-driven simulations and shown to outperform a benchmark scheme.

Due to the drastic increase in wireless video traffic, the capacity of the existing and future wireless networks will be greatly stressed, while interference will become the dominant capacity-limiting factor. In this paper, we investigate relay-assisted downlink multiuser video streaming in a cognitive radio (CR) cellular network. We incorporate zero-forcing precoding to allow transmitters collaboratively send encoded (mixed) signals to all CR users, such that undesired signals will be canceled and the desired signal can be decoded at each CR user. We present a stochastic programming formulation of the problem, as well as a problem reformulation that greatly reduces computational complexity. In the cases of a single licensed channel and multiple licensed channels with channel bonding, we develop an optimal distributed algorithm with proven convergence and convergence speed. In the case of multiple channels without channel bonding, we develop a greedy algorithm with a proven performance bound. The algorithms are evaluated with simulations and are shown to achieve considerable gains over two heuristic schemes.

With the widely deployed cloud services, data center networks are evolving toward large-scale and multi-path networks, which cannot be supported by conventional routing methods, such as OSPF and RIP. To alleviate this issue, some new routing methods, such as PortLand and BSR, are proposed for data center networks. However, these routing methods are typically designed for a specific network architecture, and thus lacking adaptability while complex in fault-tolerance. To address this issue, this paper proposes a generic routing method, named fault-avoidance routing (FAR), for data center networks that have regular topologies. FAR simplifies route learning by leveraging the regularity in a topology. FAR also greatly reduces the size of routing tables by introducing a novel negative routing table (NRT) at routers. The operations of FAR is illustrated by an example Fat-tree network and the performance of FAR is analyzed in detail. The advantages of FAR are verified through extensive OPNET simulations.

Femtocells are small, low power cellular base stations (BS) with high potential for coverage extension and offloading voice and wireless data. In this paper, we study the problem of joint access control and spectrum resource allocation in a two-tier femtocell network with one macro base station (MBS) and multiple Femto Access Points (FAP). The objective is to maximize the overall network capacity, while guaranteeing the quality of service (QoS) requirement of all User Equipments (UE). We develop an access scheme for Macro User Equipments (MUE) and a spectrum allocation mechanism for the FAPs. Spectrum allocation is employed as an incentive mechanism to encourage FAPs to serve more MUEs. We also derive an upper bound of the network-wide capacity through a reformulation of the problem. The proposed algorithms are validated and the upper bound is shown to be quite accurate in the simulation study.

The two-way energy and information flows in a smart gird, together with the smart devices, bring new perspectives to energy management and demand response. This paper investigates a distributed online algorithm for electricity energy distribution in a smart grid environment. We first present a formulation that captures the key design factors such as user utility, grid load smoothing, and energy provisioning cost. The problem is shown to be convex and can be solved with an online algorithm that only requires present information about users and the grid in our prior work. In this paper, we develop a distributed online algorithm which decomposes and solves the online problem in a distributed manner, and prove that the distributed online solution is asymptotically optimal. The proposed distributed online algorithm is also practical and effective for user privacy protection. It is evaluated with trace-driven simulations and shown to outperform a benchmark scheme.

Cooperative diversity is considered as an effective means for combating weather turbulence in FSO networks. We investigate the problem of maximizing the FSO network-wide throughput under constraint of a given power budget and a number of FSO transceivers. The problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) problem. We propose both centralized and distributed algorithms using bipartite matching and convex optimization to obtain highly competitive solutions. The proposed algorithms are shown to outperform the non-cooperative scheme and an existing relay selection protocol with considerable gains through simulations.

Free space optical (FSO) networking is an attractive technology with applications ranging from high capacity military communications to “Last-mile” broadband access solutions. Topology control is an important problem in FSO networks. The problem of building a spanning tree when number of transceivers on each base station is limited, in FSO networks, is NP-hard. What makes the problem even more challenging is to maximize the algebraic connectivity of the spanning tree. In this paper, we develop an initially configuring, or bootstrapping, algorithm which produces a degree constrained spanning tree with high algebraic connectivity and also high average edge weight, where the edge weight in the graph represents the FSO link reliability. We also develop a fast reconfiguration algorithm when one or more links fail in the FSO network. Our algorithms outperform alternative schemes in improving both algebraic connectivity and average edge weight. The robustness of the resulting topology of FSO networks is significantly improved.

In this paper, we investigate the problem of incorporating two advanced physical layer technologies, i.e., multiple-input and multiple- output (MIMO) and distributed interference alignment, in cognitive radio (CR) networks. We present a cooperative spectrum leasing scheme for primary and secondary users to trade off between data transmission and revenue collection/payment. A Stackelberg game is formulated, where the primary user is the leader and the secondary users are followers. With backward induction, we derive the unique Stackelberg Equilibrium, where no player can gain by unilaterally changing strategy, as well as the optimal strategies. We find spectrum leasing is always beneficial to enhance the utilities of primary and secondary users. The proposed scheme outperforms a no-spectrum-leasing scheme and a cooperative scheme presented in the literature with considerable gains, which demonstrate the benefits of spectrum leasing and distributed interference alignment and validate the efficacy of the proposed scheme.

Microgrid (MG) is a promising component for future smart grid (SG) deployment. The balance of supply and demand of electric energy is one of the most important requirements of MG management. In this paper, we present a novel framework for smart energy management based on the concept of quality-of-service in electricity (QoSE). Specifically, the resident electricity demand is classified into basic usage and quality usage. The basic usage is always guaranteed by the MG, while the quality usage is controlled based on the MG state. The microgrid control center (MGCC) aims to minimize the MG operation cost and maintain the outage probability of quality usage, i.e., QoSE, below a target value, by scheduling electricity among renewable energy resources, energy storage systems, and macrogrid. The problem is formulated as a constrained stochastic programming problem. The Lyapunov optimization technique is then applied to derive an adaptive electricity scheduling algorithm by introducing the QoSE virtual queues and energy storage virtual queues. The proposed algorithm is an online algorithm since it does not require any statistics and future knowledge of the electricity supply, demand and price processes. We derive several "hard" performance bounds for the proposed algorithm, and evaluate its performance with trace-driven simulations. The simulation results demonstrate the efficacy of the proposed electricity scheduling algorithm.

Although the technology of femtocells is highly promising, many challenging problems should be addressed before fully harvesting its potential. In this paper, we investigate the problem of cell association and handover management in femtocell networks. Two extreme cases for cell association are first discussed and analyzed. Then we propose our algorithm to maximize network capacity while achieving fairness among users. Based on this algorithm, we further develop a handover algorithm to reduce the number of unnecessary handovers using Bayesian estimation. The proposed handover algorithm is demonstrated to outperform a heuristic scheme with considerable gains in our simulation study.

ABSTRACT In this paper, we study the problem of power allocation for streaming multiple variable‐bit‐rate (VBR) videos in the downlink of a cellular network. We consider a deterministic model for VBR video traffic and finite playout buffer at the mobile users. The objective is to derive the optimal downlink power allocation for the VBR video sessions, such that the video data can be delivered in a timely fashion without causing playout buffer overflow and underflow. The formulated problem is a nonlinear nonconvex optimization problem. We analyze the convexity conditions for the formulated problem and propose a two‐step greedy approach to solve the problem. We also develop a distributed algorithm based on the dual decomposition technique, which can be incorporated into the two‐step solution procedure. The performance of the proposed algorithms is validated with simulations using VBR video traces under realistic scenarios. Copyright © 2012 John Wiley & Sons, Ltd.

We investigate the problem of downlink power control for streaming multiple variable bit rate (VBR) videos in a multicell wireless network, where downlink capacities are limited by inter-cell interference. We adopt a deterministic model for VBR video traffic that considers video frame sizes and playout buffers at the mobile users. The problem is to find the optimal transmit powers for the base stations, such that VBR video data can be delivered to mobile users without causing playout buffer underflow or overflow. We formulate a nonlinear nonconvex optimization problem and prove the condition for the existence of feasible solutions. A centralized branch-and-bound algorithm is then developed, which incorporates the Reformulation-Linearization Technique and can produce (1-ε)-optimal solutions. We also propose a low-complexity distributed algorithm with fast convergence as an alternative to the centralized algorithm. Through simulations with VBR video traces under fading channels, we find the distributed algorithm can achieve a performance very close to that of the centralized algorithm.

Compressed sensing (CS) refers to the process of reconstructing a signal that is supposed to be sparse or compressible. CS has wide applications, such as in cognitive radio networks. In this paper, we investigate effective CS schemes for balancing energy efficiency and estimation error. We propose an enhancement to a Bayesian estimation approach and an enhancement to the isotonic regression approach that is based on nearly isotonic regression. We also show how to compute the routing matrix for selecting active sensor nodes. The proposed enhancements are evaluated with trace-driven simulations. Considerable gaps are observed between the original approaches and the proposed enhancements in the simulation results. The near isotonic regression method achieves the best performance among all the CS schemes examined in this paper.

Microgrid (MG) is a key component for future smart grid (SG) deployment with high potentials. Balancing the supply and demand of energy is one of the most important goals of MG management. In this paper, we explore effective schemes for quality-of-usage (QoU) guarantees for local residents in an MG, under randomness in both electricity supply and demand. The microgrid control center (MGCC) aims to maintain the QoU blocking probability around a target value by serving or blocking QoU requests. The problem is formulated as a queue stability problem by introducing the concept of a QoU blocking virtual queue. The Lyapunov optimization technique is then applied to derive an adaptive QoU algorithm with complexity O(1). Furthermore, the proposed algorithm is an online algorithm since it does not require any future knowledge of the system. The stability of the proposed algorithm is proven, and its performance is evaluated with trace-driven simulations under random QoU requests. The simulation results demonstrate the efficacy and robustness of the proposed algorithm.

Multi-user Orthogonal Frequency Division Multiplexing (OFDM) have been widely adopted to combat the detrimental effects of wireless channels and enhance system throughput. Recently, interference alignment is proposed to exploit interference to enable concurrent transmissions of multiple signals. In this paper, we investigate how to incorporate interference alignment in multi-user OFDM systems. We first reveal the unique characteristics and challenges brought about by using interference alignment in diagonal channels. We then derive a performance bound for the multi-user OFDM/interference alignment system under practical constraints (i.e., a finite number of subcarriers), and show how to achieve this bound with a decomposition approach. The superior performance of the proposed scheme is validated with simulations.

A femtocell base station (FBS) is designed to cater for the demand of ever-increasing wireless data traffic, typically in the indoor environment. Among the many technical problems, interference management is particularly a challenging one for fully harvesting the high potential of femtocell networks. In this paper, we address the interference management problem with an iterative multi-user detection approach, and propose to adopt Interleave Division Multiple Access (IDMA) for the uplink of two-tier femtocell networks by exploiting the processing capability of FBS. We consider three IDMA-based schemes, namely, FBS Decode, FBS Forward, and FBS Select, and evaluate their performance with simulations. Numerical results show that the proposed schemes achieve considerable throughput gain over traditional techniques and are highly suited for the uplink of two-tier femtocell networks.

Due to the drastic increase in wireless video traffic, the capacity of existing and future wireless networks will be greatly stressed, while interference will become the dominant capacity limiting factor. In this paper, we investigate cooperative relay in CR networks using video as a reference application. We incorporate interference alignment to allow transmitters collaboratively send encoded signals to all CR users, such that undesired signals will be canceled and the desired signal can be decoded at each CR user. We present a stochastic programming formulation, as well as a reformulation that greatly reduces computational complexity. In the cases of a single licensed channel and multiple licensed channels with channel bonding, we develop an optimal distributed algorithm with proven convergence and convergence speed. In the case of multiple channels without channel bonding, we develop a greedy algorithm with a proven performance bound. The algorithms are evaluated with simulations and are shown to achieve considerable gains over two heuristic schemes that do not consider interference alignment.

Millimeter wave (mmWave) communications in the 60 GHz band can provide multi-gigabit rates for emerging bandwidth-intensive applications, and has thus gained considerable interest recently. In this paper, we investigate the problem of efficient scheduling in mmWave wireless personal area networks (WPAN). We develop a frame-based scheduling directional MAC protocol, termed FDMAC, to achieve the goal of leveraging collision-free concurrent transmissions to fully exploit spatial reuse in mmWave WPANs. The high efficiency of FDMAC is achieved by amortizing the scheduling overhead over multiple concurrent, back-to-back transmissions in a row. The core of FDMAC is a graph coloring-based scheduling algorithm, termed greedy coloring (GC) algorithm, that can compute near-optimal schedules with respect to the total transmission time with low complexity. The proposed FDMAC is analyzed and evaluated under various traffic models and patterns. Its superior performance is validated with extensive simulations.

Femtocells are shown highly effective on improving network coverage and capacity by bringing base stations closer to mobile users. In this paper, we investigate the problem of streaming scalable videos in femtocell cognitive radio (CR) networks. This is a challenging problem due to the stringent QoS requirements of real-time videos and the new dimensions of network dynamics and uncertainties in CR networks. We develop a framework that captures the key design issues and trade-offs with a stochastic programming problem formulation. In the case of a single FBS, we develop an optimum-achieving distributed algorithm, which is shown also optimal for the case of multiple non-interfering FBS's. In the case of interfering FBS's, we develop a greedy algorithm that can compute near-optimal solutions, and prove a closed-form lower bound on its performance. The proposed algorithms are evaluated with simulations, and are shown to outperform three alternative schemes with considerable margins.

A femtocell is a small cellular base station (BS), typically used for serving approved users within a small coverage. In this paper, we investigate the problem of data multicast in femtocell networks that incorporates superposition coding (SC) and successive interference cancellation (SIC). The problem is to decide the transmission schedule for each BS, as well as the power allocation for the SC layers, to achieve a sufficiently large SNR for each layer to be decodable with SIC. The objective is to minimize the total BS power consumption. We formulate a Mixed Integer Nonlinear Programming (MINLP) problem, which is NP-hard in general. We then reformulate the problem into a simpler form, and derive upper and lower performance bounds. Finally, we consider three typical connection scenarios in the femtocell network, and develop optimal and near-optimal algorithms for the three scenarios. The proposed algorithms have low computational complexity, and outperform a heuristic scheme with considerable gains in our simulation study.

In this paper, we investigate the problem of optimal power control for multiuser variable bit rate (VBR) video streaming in a cellular network with orthogonal channels. We adopt a deterministic model for VBR video traffic that incorporates video frame and playout buffer characteristics, and formulate a constrained stochastic optimization problem. We then develop a majorization-based solution approach. For the case of a single VBR video session with relaxed peak power constraint, we develop a power optimal algorithm with low complexity. We prove the power optimality of the proposed algorithm and the uniqueness of the global optimum, and demonstrate that the proposed algorithm is also smoothness optimal. For the case of multiuser VBR video streaming, we develop a heuristic algorithm that selectively suspends some video sessions when the peak power constraint is violated. The proposed algorithms are evaluated with trace-driven simulations, and are shown to achieve considerable power savings and improved video quality over a conventional "lazy" scheme.

In this paper, we investigate the problem of multi-user spatial division multiple access (MU SDMA) operation in mmWave wireless networks, within which directional antennas are used to combat the high path loss incurred in the 60GHz band. We study the feasibility of MU SDMA in mmWave networks and propose two MAC protocols to support CSMA/CA based uplink and downlink MU SDMA transmissions. The proposed protocols adopt virtual carrier sensing and allows multiple users to communicate with an access point (AP) simultaneously. Performance analysis and simulation results both show that the proposed protocols can achieve considerable performance improvements over a system that supports only single user (SU) operation.

Femtocells are shown highly effective on improving network coverage and capacity by bringing base stations closer to mobile users. In this paper, we investigate the problem of streaming scalable videos in femtocell cognitive radio (CR) networks. This is a challenging problem due to the stringent QoS requirements of real-time videos and the new dimensions of network dynamics and uncertainties in CR networks. We develop a framework that captures the key design issues and trade-offs with a stochastic programming problem formulation. In the case of a single FBS, we develop an optimum-achieving distributed algorithm, which is shown also optimal for the case of multiple non-interfering FBS's. In the case of interfering FBS's, we develop a greedy algorithm that can compute near-optimal solutions, and prove a closed-form lower bound for its performance. The proposed algorithms are evaluated with simulations, and are shown to outperform two alternative schemes with considerable margins.

We investigate the problem of downlink power control for streaming multiple variable bit rate (VBR) videos in a multicell wireless network, where downlink capacities are limited by inter-cell interference. We adopt a deterministic model for VBR traffic that considers video frame sizes and playout buffers at the mobile users. The problem is to find the optimal transmit powers for the base stations, such that VBR video data can be delivered to mobile users without causing playout buffer underflow or overflow. We formulate a nonlinear nonconvex optimization problem and prove the condition for the existence of feasible solutions. We then develop a centralized branch-and-bound algorithm incorporating the Reformulation-Linearization Technique, which can produce (1-ε)-optimal solutions. We also propose a low-complexity distributed algorithm with fast convergence. Through simulations with VBR video traces under fading channels, we find the distributed algorithm can achieve a performance very close to that of the centralized algorithm.

Abstract There has been increasing interest in deploying a team of robots, or robot swarms, to fulfill certain complicate tasks such as surveillance. Since robot swarms may move to areas of far distance, it is important to have a pervasive networking environment for communications among robots, administrators, and mobile users. In this paper, we first propose a pervasive architecture to integrate wireless mesh networks and robot swarm networks to build a robot swarm communication network within the areas of special interest. Under the proposed architecture, one or more robots can get connected with a nearby mesh router and access the remote server, while a self‐organizing mobilead hocnetwork is formed within each swarm for communications among the robots. We then address and analyze many important issues and challenges. Finally, we describe our work to enable this architecture through a scalable algorithm for autonomous swarm deployment and ROBOTRAK, a socket‐based‐swarm monitoring and control toolkit. Extensive simulation results and demonstrations are presented to show the desirable features of the proposed algorithm and toolkit. Copyright © 2009 John Wiley & Sons, Ltd.

In recent years, the millimeter wave (mmWave) technology has gained considerable interest due to the huge unlicensed bandwidth (i.e., up to 7GHz) available in the 60GHz band in most part of world. In this paper, we investigate the problem of medium access control (MAC) in mmWave wireless networks, within which directional antennas are used to combat the high path loss incurred in the 60GHz band. We extend a directional CSMA/CA protocol presented in our prior work by exploiting spatial reuse. The proposed protocol adopts virtual carrier sensing and allows non-interfering links to communicate simultaneously. We present a performance analysis as well as simulations to evaluate the proposed protocol. Our results show that the directional MAC with spatial reuse can achieve considerable performance improvements over the 802.11 MAC and the protocol proposed in our prior work. It introduces low protocol overhead and has robust performance even when the network is heavily congested.

Multiple-input multiple-output (MIMO) is one form of the smart antenna technology that uses multiple antennas at both the transmitter and receiver to improve communication performance. In this paper, we investigate the problem of medium access control in wireless local area networks (WLANs) with downlink multi-user MIMO (DL MU MIMO) capability. We propose a CSMA/CA MAC protocol with three response mechanisms for DL MU MIMO and compare the performance of DL MU MIMO with the beam-forming (BF) based approach. A novel per-station weighted queuing mechanism is proposed to mitigate the hidden node problem in the network. Performance analysis and simulation study both show that the proposed DL MU MIMO mechanism incurs low overhead and provides significant throughput performance gain over BF based approach in high SNR scenarios.

Cognitive radios (CR) and cooperative communications represent new paradigms that both can effectively improve the spectrum efficiency of future wireless networks. In this paper, we investigate the problem of cooperative relay in CR networks for further improved network performance. The objective is to provide an analysis for the comparison of two representative cooperative relay strategies, decode and forward (DF) and amplify and forward (AF), in the context of CR networks. We consider optimal spectrum sensing and p-Persistent CSMA for spectrum access, and derive closed-form expressions for network-wide throughput achieved by DF and AF. Our analysis is validated by simulations. We find each of the strategies performs better in a certain parameter range; there is no case of dominance for the two strategies. The considerable gaps between the cooperative relay results and the direct link results exemplify the diversity gain achieved by cooperative relays in CR networks.

We investigate the challenging problem of joint topology design and load balancing in FSO networks. Important factors such as FSO link characteristics, cost constraints, traffic characteristics, traffic demand, and QoS requirements are considered in the problem formulation, along with objective functions of network-wide average traffic load and delay. We develop a fast heuristic algorithm to provide highly competitive solutions. The heuristic algorithm iteratively perturbs the current topology and computes network flows for the new topology, thus progressively improving the configuration and load balancing of the FSO network. Our simulation results show that the heuristic algorithm can achieve an optimality gap close to that of a branch-and-bound algorithm developed in our prior work, with significantly reduced computation time. The heuristic algorithm is complementary to the branch-and-bound algorithm. Jointly applying the algorithms can make the FSO network dynamically reconfigurable and adaptive to events occurring at both large and small timescales.

Free space optical networks have emerged as a viable technology for broadband wireless backbone networks. In this paper, we investigate the challenging problem of joint topology design and load balancing in FSO networks. We consider FSO link characteristics, cost constraints, traffic characteristics, traffic demand, and QoS requirements in the formulation, along with various objective functions including network-wide average load and delay. We apply the Reformulation-Linearization Technique (RLT) to obtain linear programming (LP) relaxations of the original complex problem, and then incorporate the LP relaxations into a branch-and-bound framework. The proposed algorithm can produce highly competitive solutions with performance guarantees in the form of bounded optimality gap. The RLT-based branch-and-bound algorithm is evaluated with extensive simulations and is shown to be highly suitable for jointly optimizing topology and load balancing in FSO networks.

Although having high potential for broadband wireless access, wireless mesh networks are known to suffer from throughput and fairness problems, and are thus hard to scale to large size. To this end, hierarchical architectures provide a solution to this scalability problem. In this paper, we address the problem of design and optimization of a tiered wireless access network. At the lower tier, mesh routers are clustered based on traffic demands and delay requirements. The cluster heads are equipped with wireless optical transceivers and form the upper tier free space optical (FSO) network. We first present a plane sweeping and clustering algorithm aiming to minimize the number of clusters. PSC sweeps the network area and captures cluster members under delay and traffic load constraints. We then present an algebraic connectivity-based formulation for FSO network topology optimization and develop a greedy edge-appending algorithm that iteratively inserts edges to maximize algebraic connectivity. The proposed algorithms are analyzed and evaluated via simulations, and are shown to be highly effective as compared to the performance bounds derived in this paper.

In this paper, we investigate the training problem of wireless local area networks (WLANs) with downlink multi-user multiple input multiple output (DL MU MIMO) capability. We extend the 802.11 MAC protocol and propose a few training protocols at the MAC layer to support DL MU MIMO. We provide a capacity analysis based on measurement results from an 802.11n systems, evaluate the overhead of these training protocols, and compare the performance of DL MU MIMO with that of a beam-forming (BF) based approach. Through simulation studies, we find that at high SNR and with implicit training, the DL MU MIMO mechanism provides significant performance gain over the BF based approach. Furthermore, our capacity analysis and simulation study also show that it is critical to define appropriate training intervals based on antenna configuration, channel mobility, and CSI feedback overhead.

In this paper, we investigate the problem of medium access control in mmWave wireless personal area networks (WPAN), within which directional antennas are used to combat the high path loss incurred in the 60GHz frequency band. The conventional CSMA/CA protocol does not work well with directional antennas due to impaired carrier sensing at the transmitters. We explain why existing directional MAC protocols do not work well at 60GHz and propose a novel directional CSMA/CA protocol designed specifically for 60GHz WPANs. Instead of relying on physical carrier sensing, the proposed protocol adopts virtual carrier sensing and relies on a central coordinator to distribute network allocation vector (NAV) information. Both performance analysis and simulation study show that the proposed mechanism incurs low overhead and has robust performance even when the network is heavily congested.

We investigate the problem of scalable video multicast in emerging cognitive radio (CR) networks. Although considerable advances have been made in CR research, such important problems have not been well studied. Naturally, 'bandwidth hungry' multimedia applications are excellent candidates for fully capitalizing the potential of CRs. We propose a crosslayer optimization approach to multicast video in CR networks. Specifically, we consider an infrastructure-based CR network collocated with N primary networks and model CR video multicast over the N channels as a mixed integer nonlinear programming (MINLP) problem. The objective is three-fold: to optimize the overall received video quality; to achieve proportional fairness among multicast users; and to keep the interference to primary users below a prescribed threshold. We propose a sequential fixing algorithm and a greedy algorithm to solve the MINLP, while the latter has low complexity and proven optimality gap. Our simulations with MPEG-4 fine grained scalability (FGS) video demonstrate the efficacy and superior performance of the proposed algorithms.

We investigate the problem of streaming multiple videos over multi-hop cognitive radio (CR) networks. Fine-Granularity-Scalability (FGS) and Medium-Grain-Scalable (MGS) videos are adopted to accommodate the heterogeneity among channel availabilities and dynamic network conditions. We obtain a mixed integer nonlinear programming (MINLP) problem formulation, with objectives to maximize the overall received video quality and to achieve fairness among the video sessions, while bounding the collision rate with primary users under the presence of spectrum sensing errors. We first solve the MINLP problem using a centralized sequential fixing algorithm, and derive upper and lower bounds for the objective value. We then apply dual decomposition to develop a distributed algorithm and prove its optimality and convergence conditions. The proposed algorithms are evaluated with simulations and are shown to be effective in supporting concurrent scalable video sessions in multi-hop CR networks.

In this paper, we present a spectrum sensing error-aware MAC protocol for a cognitive radio (CR) network collocated with multiple primary networks. We explicitly consider sensing errors in the CR MAC design, since such errors are inevitable for practical spectrum sensors. Two spectrum sensing polices are presented with which secondary users collaboratively sense the licensed channels. The sensing policies are then incorporated into p-Persistent CSMA to coordinate dynamic spectrum access for CR network users. We present an analysis of the interference and throughput performance of the proposed CR MAC, and find the analysis highly accurate in our simulation studies. The proposed sensing error-aware CR MAC protocol outperforms two existing approaches with considerable margins in our simulations, which justify the importance of considering spectrum sensing errors in CR MAC design.

In this paper, we study the problem of rate control for streaming videos by jointly considering encoder rate control and network congestion control. We adopt a control-theoretic approach that models video streaming as a feedback control system. Based on a properly chosen operating point, the model is linearized and a proportional-integral (PI) controller is designed to stabilize the streaming video quality. We derive the guidelines for choosing parameters for the proposed PI rate controller and prove its stability properties. We also show that the PI rate controller is highly robust to fluctuations in the bottleneck link capacity. Our simulation results verify the accuracy of the analysis and demonstrate the efficacy of the proposed control-theoretic approach.

Compared to conventional wireless sensor networks (WSNs) that are operated based on the client-server computing model, mobile agent (MA) systems provide new capabilities for energy-efficient data dissemination by flexibly planning its itinerary for facilitating agent based data collection and aggregation. It has been known that finding the optimal itinerary is NP-hard and is still an open area of research. In this paper, we consider the impact of both data aggregation and energy- efficiency in sensor networks itinerary selection, We propose an itinerary energy minimum for first-source-selection (IEMF) algorithm, as well as the itinerary energy minimum algorithm (IEMA), the iterative version of IEMF. Our simulation experiments show that IEMF provides higher energy efficiency and lower delay compared to existing solutions, and IEMA outperforms IEMF with some moderate increase in computation complexity.

We investigate the challenging problem of enabling multicast video service in emerging cognitive radio (CR) networks. We propose a cross-layer optimization approach to multicast video in CR networks. Specifically, we model CR video multicast as an optimization problem, while considering important design factors including scalable video coding, video rate control, spectrum sensing, dynamic spectrum access, modulation, scheduling, retransmission, and primary user protection. The objective is to optimize the overall received video quality as well as achieving proportional fairness among multicast users, while keeping the interference to primary users below a prescribed threshold. Although the problem can be solved using advanced optimization techniques, we propose a sequential fixing algorithm and a greedy algorithm with low complexity and proven optimality gap. Our simulations using MPEG-4 fine grained scalability (FGS) demonstrate the efficacy and superior performance of the proposed approach as compared with an alternative equal allocation scheme.

Performance evaluation of cognitive radio (CR) networks is an important problem but has received relatively limited attention from the CR community. Unlike traditional radios, a cognitive radio may change its objectives as radio scenarios vary. Because of the dynamic pairing of objectives and contexts, it is imperative for cognitive radio network designers to have a firm understanding of the interrelationships among goals, performance metrics, utility functions, link/network performance, and operating environments. In this paper, we first overview various performance metrics at the node, network, and application levels. From a game-theoretic viewpoint, we then show that the performance evaluation of cognitive radio networks exhibits the interdependent nature of actions, goals, decisions, observations, and context. We discuss the interrelationships among metrics, utility functions, cognitive engine algorithms, and achieved performance, as well as various testing scenarios. We propose the radio environment map-based scenario-driven testing (REM-SDT) for thorough performance evaluation of cognitive radios. An IEEE 802.22 WRAN cognitive engine testbed is presented to provide further insights into this important problem area.

As streaming videos are becoming increasingly popular, it is important to understand the end-to-end streaming system and to develop effective algorithms for quality control. In this paper, we address the problem of rate control for streaming videos with a control-theoretic approach. Among the various control knobs, video bit rate is one of the most effective in the sense that it has a direct impact on the interaction between the video coder and network system. While increasing rate reduces the coder-induced distortion, it may also cause congestion at a bottleneck link. The packet loss due to congestion will, then, increase the distortion of the decoded video. We model end-to-end video steaming as a feedback control system, taking into account video codec and sequence characteristics, rate control, active queue management, and receiver feedback. We then develop effective proportional (P) controllers to stabilize the received video quality as well as the bottleneck link queue, for both homogeneous and heterogeneous video systems. Simulation results are presented to demonstrate the efficacy of thePcontrollers and the viability of the proposed control-theoretic approach.

Abstract Routing protocols in wireless sensor networks (WSNs) typically employ a transmitter‐oriented approach in which the next hop node is selected based on neighbor or network information. This approach incurs a large overhead when the accurate neighbor information is needed for efficient and reliable routing. In this paper, a novel receiver‐oriented load‐balancing and reliable routing (RLRR) protocol is proposed. In RLRR, an intermediate node solicits next hop candidates, each of which is to respond with its own backoff time dubbed a temporal gradient (TG). In this way, the next hop is selected without any central coordination on a packet‐by‐packet basis. Thus, each node needs not maintain any neighbor information. The remaining energy level used to determine the TG is always accurate and up‐to‐date. Furthermore, neighbor nodes whose hop count is less than the soliciting node participate in the next‐hop selection process with loop‐free operation guarantee. Comprehensive simulations are carried out to show that RLRR achieves relatively longer network lifetime and higher reliability than other existing schemes. Copyright © 2007 John Wiley & Sons, Ltd.

The combination of multi-radio nodes in conjunction with a suitably structured mesh architecture has the potential to solve some of the key limitations of present day mesh networks. We propose and evaluate two practical and self-stabilizing channel assignment algorithms for multi-channel dual-radio mesh backhauls. The objective is to find a channel assignment that maximizes network capacity through balancing traffic load and/or co-channel interference on available channels. Through simulations, we show that the proposed scheme can effectively improve the capacity of wireless mesh networks.

In this paper, we study the exposed terminal problem in multi-hop wireless networks with log-normal shadowing channels. Assuming that location information is known, we first calculate the success probability for the concurrent transmissions from exposed nodes. We then propose a new MAC protocol which schedules concurrent transmissions in the presence of log- normal shadowing, thus mitigating the exposed terminal problem and increasing network throughput. The performance of the proposed protocol is evaluated with ns-2 simulations, and it is shown to achieve considerable improvements in both end-to-end throughput and delay over the IEEE 802.11 MAC.

Though data redundancy can be eliminated at aggregation point to reduce the amount of sensory data transmissions, it introduces new challenges due to multiple flows competing for the limited bandwidth in the vicinity of the aggregation point. On the other hand, waiting for multiple flows to arrive a

This paper addresses the problem of real-time video streaming over a bandwidth and energy constrained wireless sensor network (WSN). Considering the compressed video bit stream is extremely sensitive to transmission errors, and the constraints in bandwidth and energy in WSNs and delay in video delivery, we exploit the construction of an application-specific number of multiple disjoint paths to enlarge the aggregate bandwidth and facilitate load balancing and fast packet delivery. For efficient multi-path routing of real-time video frames, we propose a path priority scheduling algorithm to satisfy the delay constraint of video frames while balancing energy and bandwidth usage among all the available paths. In the case that the aggregate bandwidth is still not enough to satisfy the required coding rate, we further exploit a cross-layer technique for adaptive coding according to path status. The effectiveness of the proposed scheme is evaluated and demonstrated by simulations.

It has been shown in the literature that many MAC protocols for wireless networks have a considerable control overhead, which limits their achievable throughput and delay performance. In this paper, we study the problem of improving the efficiency of MAC protocols. We first analyze the popular p- Persistent CSMA scheme and show that it does not achieve 100% throughput.Motivated by insights from polling system theory, we then present three polling service-based MAC schemes, termed PSMACs, for improved performance. The main idea is to serve multiple data frames after a successful contention resolution, thus amortizing the high control overhead and making the protocols more efficient. We present analysis and simulation studies of the proposed schemes. Our results show that PSMAC can effectively improve the throughput and delay performance of p-Persistent CSMA, as well as providing energy savings. We also observe that PSMAC is more efficient for handling the more general and challenging bursty traffic and outperforms p-Persistent CSMA with respect to fairness.

It has been shown in prior work that when used in multi-hop wireless networks, the 802.11 MAC suffers low throughput performance, especially when the number of hops is large. This paper clarifies the relation between exposed node and interference range, and proposes a location-assisted MAC protocol that schedules concurrent transmissions in a multi-hop wireless network. In the proposed algorithm, after identifying a node as an exposed node, a simple procedure is executed to validate the concurrent transmission of the exposed node (called scheduled transmission). Based on location information, the scheduled transmission is allowed if the current and scheduled transmitters are out of the interference range of each other's target receiver. Simulation results show that the proposed algorithm can effectively improve the throughput of multi-hop wireless networks.

In this paper, we consider the problem of optimal multipath routing for providing application performance guarantees in multi-hop wireless networks, using multiple description video streaming as our target application. We address this problem, which is shown to be NP-hard, with a novel reformulation-linearization technique (RLT) and branch-and-bound-based approach, and develop an algorithm that produces a pair of paths within the (1 - epsiv) range of the global optimum. The proposed algorithm is computationally efficient and this (1 - epsiv) optimal algorithm provides an elegant tradeoff between optimality and computational complexity.

Traditionally, routing is considered solely as a network layer problem and has been decoupled from application layer objectives. Although such an approach offers simplicity in the design of the protocol stack, it does not offer good performance for certain applications such as video. In this paper, we explore the problem of how to perform routing with the objective of optimizing application layer performance. Specifically, we consider how to perform multipath routing for multiple description (MD) video in a multi-hop wireless network. We formulate this problem into an optimization problem with application performance metric as the objective function and routing and link layer considerations as constraints. We develop a formal branch-and-bound framework and exploit the so-called reformulation-linearization technique (RLT) in the solution procedure. We show that this solution procedure is able to produce a set of routes whose objective value is within (1 - e) of the optimum. We use simulation results to substantiate the efficacy of the solution procedure and compare the performance with that under non-cross-layer approach.

Current expectations on sensor node in terms of size, cost, and energy efficiency have led to a severely limited design space on hardware and software. In this paper, we explore capabilities at the network edge for sensor networks, aiming to reduce the hardware and software complexity of a sensor node without sacrificing network performance. We present a novel edge-based routing protocol, nicknamed BeamStar, for wireless sensor networks. Under BeamStar, the base station exploits some nice properties associated with directional antenna and power control at the base station. We devise a simple protocol so that each sensor node can determine its location information passively with minimum control overhead. We also show how to design a robust routing protocol based on the location information at each sensor node. Under the proposed protocol, sensor nodes are relieved of the activities (or burdens) that are associated with control and routing, thus enabling much simpler hardware and software implementation at sensor nodes. Simulation results demonstrate that BeamStar achieves high reliability at comparable energy consumptions as compared with prior work. It is a viable approach to pursue size and cost reduction for future sensor node design.

For media streaming in ad hoc networks, service replication has been demonstrated to be a quite effective countermeasure to streaming interruptions caused by fragile paths and dynamic topology. In this paper, we study the problem of joint routing and server selection for double description (DD) video streaming in ad hoc networks. We formulate the task as a combinatorial optimization problem and present tight lower and upper bounds for the achievable distortion. The upper bound provides a feasible solution to the formulated problem. Our extensive numerical results show that the bounds are very close to each other for all the cases studied, indicating the near-global optimality of the derived upper bounding solution. Moreover, we observe significant gains in video quality achieved by the proposed approach over existing server selection schemes. This justifies the importance of jointly considering routing and server selection for optimal MD video streaming

Video communication is an important application area for a multihop wireless network. This paper studies the problem of finding the optimal encoding rates for a number of video sessions in such network. The objective is to maximize the video quality at the receivers and the optimization space takes into consideration, the interaction among all the active video sessions. A branch-and-bound solution procedure is proposed to solve this nonconvex, non-polynomial programming problem. Using analytical and simulation results, we show that this solution procedure is an effective approach for addressing such complex cross-layer optimization problem

Real-time multimedia transport has stringent quality of service requirements, which are generally not supported by current network architectures. In emerging mobile ad hoc networks, frequent topology changes and link failures cause severe packet losses, which degrade the quality of received media. However, in such mesh networks, there usually exist multiple paths between any source and destination nodes. Such path diversity has been demonstrated to be effective in combating congestion and link failures for improved media quality. In this paper, we present a new protocol to facilitate multipath transport of real-time multimedia data. The proposed protocol, the multiflow real-time transport protocol (MRTP), provides a convenient vehicle for real-time applications to partition and transmit data using multiple flows. We demonstrate through analysis that data partitioning, which is an essential function of MRTP, can effectively reduce the short-range dependence of multimedia data, thus improving its queueing performance in underlying networks. Furthermore, we show that a few flows are sufficient for MRTP to exploit most of the benefits of multipath transport. Finally, we present a comprehensive simulation study on the performance of MRTP under a mobile ad hoc network. We show that with one additional path, MRTP outperformed single-flow RTP by a significant margin.

We study the problem of multipath routing for double description (DD) video in wireless ad hoc networks. We follow an application-centric cross-layer approach and formulate an optimal routing problem that minimizes the application layer video distortion. We show that the optimization problem has a highly complex objective function and an exact analytic solution is not obtainable. However, we find that a meta-heuristic approach such as genetic algorithms (GAs) is eminently effective in addressing this type of complex cross-layer optimization problems. We provide a detailed solution procedure for the GA-based approach. Simulation results demonstrate the superior performance of the GA-based approach versus several other approaches. Our efforts in this work provide an important methodology for addressing complex cross-layer optimization problems, particularly those involved in the application and network layers

Multimedia traffic is becoming an increasing portion of today's Internet traffic due to the flourishing of multimedia applications such as music/video streaming, video teleconferencing, IP telephony, and distance learning. In this paper, we study the problem of supporting multimedia traffic using a generalized processor sharing (GPS) server. By examining the sample path behavior and exploring the inherent feasible ordering of the classes, we derive tight performance bounds on backlog and delay for regulated multimedia traffic classes in a GPS system. Our approach is quite general since we do not assume any arriving traffic model or any specific traffic regulator, other than that each traffic flow is deterministically regulated. Such deterministic regulators, as well as approximations of the GPS server, are widely implemented in commercial routers. In addition, our analysis is very accurate and achieves a high utilization of the server capacity, since we exploit the independence among the traffic flows for higher statistical multiplexing gains. Numerical examples and simulation results are presented to demonstrate the accuracy and merits of our approach, which is practical and well suited for supporting multimedia applications in the Internet

To improve the capacity of wireless ad hoc networks by exploiting multiple available channels, we propose a combined proactive routing and multi-channel medium access control (MAC) protocol. The multi-channel MAC protocol is compatible with IEEE 802.11 MAC and imposes the minimum system requirement among existing multi-channel MAC protocols. Because a proactive routing protocol allows each node to have complete topology information of the network, channel assignment can be closely coupled with a proactive routing protocol. The proposed channel assignment protocol is shown to require fewer channels and exhibit significantly lower communication, computation, and storage complexity than existing channel assignment schemes. We prove the correctness of the proposed channel assignment protocol. In addition, through a performance study, we show that the proposed protocol, by effectively increasing capacity, substantially increases throughput and decreases delay compared to the IEEE 802.11 MAC protocol.

Although it has been an active research area for a number of years, distributed channel assignment remains a challenging problem and existing protocols tend to be complex and usually not suitable for practical implementation. In this paper, we propose three principles that facilitate the design of efficient distributed channel assignment protocols in wireless ad hoc networks. Protocols that implement these design principles are shown to require fewer channels and exhibit significantly lower communication, computation, and storage complexity, compared with existing approaches. As examples, we present two such protocols built on the ad-hoc on-demand distance vector (AODV) routing protocol. In addition, we prove the correctness of the algorithms and derive an upper bound on the number of channels required to both resolve collisions and mitigate interference. Simulation results show that, in many cases, the performance of the proposed protocols can approach that of centralized near-optimal algorithms while maintaining low control overhead.

Recent research shows that animals can be guided remotely by stimulating regions of the brain. Therefore, it is possible to set up an animal mobile sensor network for search and rescue operations. Applications of such an animal sensor network have great importance to society, including natural disaster recovery, homeland security and military operations. In this paper, the system architecture and operation is introduced, and major challenges and issues are discussed. Because of its unique challenge, a simple and efficient routing scheme is devised for this special ad hoc network. Each animal needs to carry a backpack to perform sensing and network communications. The implementation of a backpack prototype is presented, and a simple network is set up to capture and transfer video sensor data.

Multiple description (MD) coding has a great potential for multimedia communications in wireless ad hoc networks. In this paper, we study the important problem of joint routing and server selection for MD video in ad hoc networks. We take a cross-layer approach to formulate the task as a combinatorial optimization problem and present tight lower and upper bounds for the achievable distortion. The upper bound also provides a feasible solution to the formulated problem. Our extensive numerical results show that the bounds are very close to each other for all the cases studied, indicating the near-global optimality of the derived upper bounding solution. Moreover, we observe significant gains in video quality achieved by the proposed approach over existing server selection schemes. This justifies the importance of jointly considering routing and server selection for optimal MD video streaming in wireless ad hoc networks. The proposed algorithms are computationally efficient and can be easily incorporated into existing routing protocols.

Real-time multimedia communication is an important service that should be supported in wireless ad hoc networks, In this paper we consider the problem of how to optimally support multiple concurrent video communication sessions in an ad hoc network. Our problem formulation follows an application-centric cross-layer approach with the objective of minimizing the average distortion for all video sessions via finding optimal paths for each session. Since this network-wide optimization problem is shown to be NP-complete, we pursue to develop competitive heuristic algorithms to address this problem. We find that genetic algorithms (GA) are eminently efficient in solving such cross-layer problems with complex objective functions and constraints. We describe a detailed solution procedure based on the GA approach and use numerical results to demonstrate its superior performance over other conventional approaches. Our efforts in this work provide an important methodology for addressing cross-layer network-wide optimal routing problems for video applications.

Multimedia traffic is becoming an increasing portion of today's Internet traffic due to the flourish of multimedia applications such as music/video streaming, video teleconferencing, IP telephony, and distance learning. In this paper, we study the problem of supporting multimedia traffic using a generalized processor sharing (GPS) server. We derive tight performance bounds on the backlog and delay for regulated multimedia traffic classes in a GPS system. Our approach is quite general since we do not assume any arriving traffic model or any specific traffic regulator, other than that each traffic flow is deterministically regulated. Such deterministic regulators, as well as approximations of the GPS server, are amenable to implementation and are widely implemented in commercial routers. In addition, our analysis is very accurate and has a much high utilization of the server capacity, since we exploit the independence among the traffic flows for higher statistical multiplexing gains. Numerical examples and simulation results are presented to demonstrate the accuracy and merits of our approach, which is practical and well suited for supporting multimedia applications in the Internet.

As developments in wireless ad hoc networks continue, there is an increasing expectation with regard to supporting content-rich multimedia communications (e.g., video) in such networks, in addition to simple data communications. The recent advances in multiple description (MD) video coding have made it highly suitable for multimedia applications in such networks. In this paper, we study the important problem of multipath routing for MD video in wireless ad hoc networks. We follow an application-centric cross-layer approach and formulate an optimal routing problem that minimizes the application layer video distortion. We show that the optimization problem has a highly complex objective function and an exact analytic solution is not obtainable. However, we find that a meta-heuristic approach such as genetic algorithms (GAs) is eminently effective in addressing this type of complex cross-layer optimization problems. We provide a detailed solution procedure for the GA-based approach, as well as a tight lower bound for video distortion. We use numerical results to demonstrate the superior performance of the GA-based approach and compare it to several other approaches. Our efforts in this work provide an important methodology for addressing complex cross-layer optimization problems, particularly those involving application and network layers.

Multipath transport provides higher usable bandwidth for a session. It has also been shown to provide load balancing and error resilience for end-to-end multimedia sessions. Two key issues in the use of multiple paths are (1) how to minimize the end-to-end delay, which now includes the delay along the paths and the resequencing delay at the receiver, and (2) how to select paths. In this paper, we present an analytical framework for the optimal partitioning of realtime multimedia traffic that minimizes the total end-to-end delay. Specifically, we formulate optimal traffic partitioning as a constrained optimization problem using deterministic network calculus, and derive its closed form solution. Compared with previous work, our scheme is simpler to implement and enforce. This analysis also greatly simplifies the solution to the path selection problem as compared to previous efforts. Analytical results show that for a given flow and a set of paths, we can choose a minimal subset to achieve the minimum end-to-end delay with O(N) time, where N is the number of available paths. The selected path set is optimal in the sense that adding any rejected path to the set will only increase the end-to-end delay.

Real-time multimedia transport has stringent bandwidth, delay, and loss requirements. Supporting this application in current wireless ad hoc networks is a challenge. Such networks are characterized with frequent link failures, as well as congestion. Consequently, data packets are dropped when a link fails or congestion occurs, resulting in low received quality. In addition, a realtime multimedia service may be unavailable when a particular server is unreachable. In this article, we make the case for using multipath transport for realtime multimedia services in wireless ad hoc networks, which provides a unified solution to the above problems. We review existing work on multipath multimedia transport, and discuss the advantages, as well as related issues, of using multipath transport for realtime multimedia transport.

We consider the problem of multicasting multiple description (MD) video in wireless ad hoc networks. We follow an application-centric, cross-layer approach with the objective of minimizing video distortion. The contribution of this paper is twofold. First, we propose a practical MD video multicast scheme that uses multiple trees to achieve an improved error resilience performance. The proposed scheme also takes into account highly diverse wireless link bandwidths by using scalable coding for each description, thus further improving the overall video quality. Second, we formulate the optimized multicast routing as a combinatorial optimization problem and propose an efficient genetic algorithm (GA)-based metaheuristic solution procedure. Performance comparison with existing approaches show significant gains for a wide range of network operating conditions.

In this paper, we present a base station-assisted, location-aware routing protocol, which we call BeamStar, for wireless sensor networks. We make a major paradigm change by shifting computational intensive and energy consuming routing control overhead from sensor nodes to base stations. In BeamStar, each base station uses a directional antenna with power control. We show that these two capabilities are sufficient for each sensor node to determine its location, and the local location information is sufficient for power-efficient routing. Therefore, sensors are relieved of control and routing burdens, such as maintaining clusters and exchanging control information, yielding substantial energy savings. In addition, each data packet is forwarded in a constrained, loop-free mesh towards the base station, making data delivery robust to sensor failures and transmission errors. The proposed routing scheme is suitable for large-scale, dense sensor networks monitoring rare events.

Enabling video transport over ad hoc networks is more challenging than over other wireless networks. The wireless links in an ad hoc network are highly error prone and can go down frequently because of node mobility, interference, channel fading, and the lack of infrastructure. However, the mesh topology of ad hoc networks implies that it is possible to establish multiple paths between a source and a destination. Indeed, multipath transport provides an extra degree of freedom in designing error resilient video coding and transport schemes. In this paper, we propose to combine multistream coding with multipath transport, to show that, in addition to traditional error control techniques, path diversity provides an effective means to combat transmission error in ad hoc networks. The schemes that we have examined are: 1) feedback based reference picture selection; 2) layered coding with selective automatic repeat request; and 3) multiple description motion compensation coding. All these techniques are based on the motion compensated prediction technique found in modern video coding standards. We studied the performance of these three schemes via extensive simulations using both Markov channel models and OPNET Modeler. To further validate the viability and performance advantages of these schemes, we implemented an ad hoc multiple path video streaming testbed using notebook computers and IEEE 802.11b cards. The results show that great improvement in video quality can be achieved over the standard schemes with limited additional cost. Each of these three video coding/transport techniques is best suited for a particular environment, depending on the availability of a feedback channel, the end-to-end delay constraint, and the error characteristics of the paths.

Typical video applications may need a higher bandwidth and/or higher reliability connection than that provided by a single link in current or emerging wireless networks. We propose to employ path diversity to provide higher bandwidth and more robust end-to-end connections than that affordable by a single path. Under this transport environment, two viable strategies for video coding are multiple description coding (MDC) and layered coding (LC). MDC is more effective when the underlying application has a very stringent delay constraint and the round trip time on each path is relatively long. LC can be a good alternative when limited retransmission of the base layer is acceptable and when it is feasible to apply unequal error protection over different paths. The paper describes the general issues involved in integrating MDC/LC with multiple path transport, and compares the performances of MDC and LC, under different path conditions.

Generalized processor sharing (GPS) is an important scheduling discipline because it enables bandwidth sharing with work conservation and traffic isolation properties. While Markov modulated fluid processes (MMFP) capture the dynamics of the sources, the analysis of such sources with a GPS server is difficult because of the large state space. We study a multi-queue GPS system with MMFP classes and propose a scalable, low complexity algorithm for the tail distributions of the logical queues. The effective bandwidth of the classes and a simple connection admission control (CAC) scheme are derived. Numerical results illustrate the efficiency and accuracy of the technique. The application to an example system of classes consisting of voice and variable bit rate (VBR) video traffic is included.

We introduce a statistical method to analyze multiplexing of multiple sessions sharing link bandwidth using generalized processor sharing (GPS) scheduling. Our method is shown to substantially improve previous upper bounds for GPS scheduling of Markov modulated fluid processes (MMFP) sessions, especially as the number of sessions increases. Application of analytical results to admission control indicate that by sharing bandwidth using GPS among traffic classes there are significant gains over systems that statically segregate the link bandwidth. This effect is quantified in several experiments where various combinations of source types are used. The gains are pronounced when bursty sources with stricter QoS requirements are used.

Enabling video transmission over ad-hoc networks is more challenging than over conventional mobile networks because a connection path in an ad-hoc network is highly error-prone and the path can go down frequently. On the other hand, it is possible to establish multiple paths between a source and a destination, which provides an extra degree of freedom in coding algorithm design. This paper presents a feedback-based reference picture selection scheme for video transmission over ad-hoc networks. Encoded video streams are transmitted over multiple paths and the reference frames for motion compensated prediction are selected according to the feedback information about the paths' condition. Simulations under the two paths scenario have shown significant improvement over two standard techniques, layered coding and video redundancy coding, which do not use feedback. A novel statistical model for the ad-hoc multi-path environment is also proposed and used in our simulation of transmission loss.

The increase in the bandwidth of the wireless channels and the computing power of the mobile devices makes it possible to offer video service for wireless networks in the near future. In an ad-hoc network, strong error protection is required because of the lack of a fixed infrastructure. On the other hand, the mesh structure of an ad-hoc network implies that there may be multiple paths existing between a source and destination, which can be used to enhance video transmissions. We propose a simple but robust scheme for reliable transmission of video in bandwidth limited ad-hoc networks. In our scheme, a video stream is layer coded. The base layer (BL) packets and the enhancement layer (EL) packets are transmitted separately on two disjoint paths using multipath transport (MPT). BL packets are protected by automatic repeat request (ARQ), and a lost BL packet is retransmitted through the path where EL packets are transmitted. An EL packet has lower priority than a retransmitted BL packet and may be dropped at the sender when congestion occurs. Simulation results show that this scheme can guarantee a graceful video quality in adverse channel conditions. It is effective for video transmission over the high loss environment found in ad-hoc networks.

Bit rate justification is a key technique in digital multiplexing. SDH adopts positive justification for C-4 mapping and positive/zero/negative justification for other mappings. The mapping of C-4 has the highest system frequency in all bit rate justifications of SDH. This demands highly on the technology and power consumption in the ASIC design. This paper puts forward a novel technique of positive justification with parallel processing and solves the problem caused by high speed. This method is very useful for implementing the C-4 mapping with CMOS gate array technology. The design has been implemented with FPGAs of Xilinx Inc. The paper ends with the mapping jitter test results, which are quite satisfied.