Millisecond-precision measurement of smart grid presents elevated demands for 6G sensing and communication capabilities. How to ensure timely, reliable, and green delivery of critical information remains a core challenge. In this paper, we address this issue by studying electric semantic short packet communication from a green integrated sensing and communication (ISAC) perspective. First, a novel information timeliness metric named peak age of incorrect semantics (PAoIS) is developed. It describes the entire lifetime of sensing, compression, encoding, transmission, and decoding. Then, a collaborative problem is formulated to jointly minimize PAoIS and ISAC energy consumption by optimizing sensing frequency and semantic compression ratio. An electric multimodal driven green collaborative optimization algorithm is proposed. It enables dynamical adjustment of sampling ratio of electric multimodal experience samples, enhancing optimization performance under sparse modes. Simulation results verify the effectiveness of the proposed algorithm.

Gesture recognition plays a vital role in human-computer interaction. Most current gesture recognition systems infer gesture categories directly from raw inputs, often missing the semantic information embedded in the gesture execution process. This paper introduces a novel vision-based gesture semantic understanding system, referred as SEGUS. The goal of this system is to retrieve the symbol and path information contained in gestures. It utilizes a side-mounted camera on a wristband or smartwatch to capture video inputs and performs foreground-background separation of the video. The system extract is symbol and path information form the foreground and the background of T and video, respectively. Then, it achieves gesture recognition by comparing sequences of semantic elements. We evaluate the system through extensive experiments. The results indicate an average improvements from 6.68% to 16.39% in recognition accuracy compared to other end-to-end systems. Additionally, scalability tests show that by reorganizing existing semantic elements, the proposed system can accommodate newly added gestures without the need to re-collect sample data. This significantly reduces system overhead, achieving a 64.10% reduction in training time compared to methods requiring a training set for all gestures, with only a modest average accuracy decrease of 6.39%.

As the proportion of renewable energy in the distribution grid continues to rise, the timely transmission of critical state information becomes essential to ensure the balance of energy flow. Existing metrics for information timeliness based on peak age of information (PAoI) and its variants fall short in fully characterizing the intricate influence of information flow on the dynamics of energy distribution. In this paper, a new information timeliness metric named energy dispatch cost-aware PAoI (EPAoI) is introduced from the perspective of integrating information and energy flows. We propose an information-energy flow integrated wireless resource scheduling algorithm based on fuzzy learning to minimize EPAoI. It exceptionally improves learning accuracy by exploiting key features of dual flows to guide resource scheduling optimization. Simulation results validate superior performances of the proposed algorithm in reducing EPAoI and energy dispatch cost.

mmWave CS (chirp sequence) radar is one of the most widely used automotive sensors for ADAS (Advanced Driving Assistance System) and autonomous driving. It offers various advantages, including high resolution, cost-effectiveness, and robustness in all-weather and ambient light environment. However, as mmWave radar density increases, the potential for inter-radar interference rises, which leads to degraded target detection accuracy. Deep learning technologies have shown significant promise in mitigating inter-radar interference. However, this enhancement often comes with the high computational complexity. In this paper, we propose a ConvMixer based interference suppression method, which is primarily composed of patch embedding and depth-wise separatable convolution. The effectiveness of the proposed ConvMixer-based approach is verified and analyzed using simulated data, demonstrating superior performance in terms of SNR, correlation coefficient, phase difference, detection rate, and processing time compared to the state-of-the-art approaches.

Symbiotic Internet of Things (IoT) systems built upon existing 5G infrastructure are increasingly adopted due to their high capacity, low latency, and wide coverage. The small cell architecture inherent in 5G networks enables efficient spectrum utilization but also introduces challenges, such as complex intercell interference, particularly in deployments with low-cost and compact IoT base stations. While analog beamforming is effective for interference management, it incurs high hardware costs and energy consumption due to the requirement for RF power amplifiers and phase shifters (PSs). To address this issue, on–off analog beamforming (OABF) has emerged as a cost-efficient alternative, replacing PSs with simple RF on–off switches. OABF offers key advantages, including affordability, compactness, rapid speed, and most importantly, low power consumption. In this article, we propose an energy-efficient hybrid OABF coordination strategy for multicell multiple-input and single-output (MISO) downlink symbiotic IoT systems by leveraging deep reinforcement learning. The goal is to maximize the overall system energy efficiency (EE) by jointly optimizing antenna element activation and transmit power allocation at each IoT base station. Through extensive simulations, we compare the proposed approach against conventional antenna selection and PS-based analog beamforming schemes under various power constraint models. The simulation results unequivocally demonstrate the superiority of our method, exhibiting higher average EE across diverse network configurations.

Hand gesture applications (HGA) are essential for human-machine interaction. Although the existing solutions achieve good performance in specific tasks, they still face challenges when users navigate through different application contexts, i.e., requiring multi-task ability to support newly arrived HGA tasks. In this paper, we propose a novel wrist-worn multi-task HGA system namedXHGA, which can implement modal-domain combination, data-domain adaptation and label-domain extension to ensure the performance in multi-task scenarios. The system introduces a novel two-stage training strategy, i.e., task-agnostic stage to align cross-modal features from unlabeled arbitrary gestures through contrastive learning, and task-related stage to learn modality contributions with limited labeled data in specific tasks through self-attention mechanism, while achieves multi-objective recognition simultaneously by employing an adaptive loss function weighting method. Extensive experiments demonstrate thatXHGAcan achieve an average accuracy of 92.7% with only using 15 labeled data per gesture under three HGA tasks. Compared with the state-of-the-art multi-modal approach,XHGAreduces 82.7% training time, and 47.7% storage, with about 5% improvements in accuracy. Code is available athttps://github.com/htang0/XHGA.

In magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems, receiver (RX) feedback communication is promising to enhance the capability and efficiency of the system. Although some studies have explored in-band implementations with low overhead costs, it has not been comprehensively investigated. In this paper, we propose Relip, a Reliable layer-level in-band parallel feedback communication mechanism for MIMO MRC-WPT systems, which addresses the impact of RX-RX couplings (i.e., non-negligible interference from strong couplings and positive effects of relay phenomenon), and provides a theoretical analysis of communication reliability. Technically, we first devise an On-Off based two-phase modulation mechanism to achieve RX identification and dependency detection under relay phenomenon. Then, we utilize observed channel decomposability to collect group-level power transfer channel conditions for eliminating the interference caused by strong RX-RX couplings. Furthermore, we perform RX selection to optimize the trade-off between communication reliability and time overhead. We design and implement the Relip prototype and conduct extensive experiments. The results validate the effectiveness of our mechanism, i.e., Relip can provide ≥99% average decoding accuracy for concurrent feedback communication of 14 devices, achieving an 18.31% improvement compared to the state-of-the-art solution.

Magnetic resonant coupling (MRC) enabled wireless power transfer (WPT) systems have shown significant promise in efficiently charging multiple devices simultaneously through beamforming technology. The existing works propose various mechanisms for achieving better charging performance, but they still lack exploration of transmitter (TX) coil orientation adjustment and rarely consider the dynamic deployment of TXs. In this work, we propose the D istributed A daptive E xploration and E xploitation ( DAEE ) algorithm for orientation adjustment in MRC-WPT systems, which includes both hardware and software innovations. The hardware component features a servo motor-based mechanical device that adjusts the TX coil orientation. In the software aspect, we decompose the charging performance optimization problem and devise a distributed orientation control algorithm combining exploration and exploitation mechanisms. We develop a system prototype for the DAEE algorithm and conduct extensive experiments to validate its performance. Specifically, the TX orientation adjustment significantly enhances performance, achieving an average 103% improvement in power-delivered-to-load (PDL) compared to state-of-the-art frequency adjustment-based solutions that do not adjust orientation. Additionally, the combination of exploration and exploitation strategies in the DAEE algorithm proves effective, delivering a 24% performance improvement over the random beamforming (RB)-based exploration method.

In this article, we address the key problem of sensing and communication integrated resource allocation for 6G-empowered distribution grid hierarchical coordinated control. First, we construct a novel information timeliness metric for electric semantic communication, namely, Peak Age of Semantics (PAoS), which covers the entire lifecycle of information sensing, semantic compression, semantic transmission, and semantic decoding. Second, we propose a sensing and semantic communication integrated resource allocation algorithm based on Top-$\text {N}^{2}$and hybrid knowledge–statistic-driven fuzzy reinforcement learning. A deep fuzzy neural network is utilized to build a knowledge model between the grid operating state and decision making. The knowledge is embedded into statistic-driven model of reinforcement learning to enhance accuracy of upper confidence bound (UCB) utility evaluation. Finally, simulations based on realistic application scenarios indicate that compared with two comparison algorithms, the proposed algorithm reduces average PAoS by 4.72% and 9.49%, and the maximum PAoS by 5.76% and 13.57%. Additionally, its end-to-end delay trend and semantic packet decoding success rate align more closely with semantic importance.

A 5G integrated smart distribution power grid brings a new paradigm shift to realize base station (BS) operation cost reduction, efficient renewable energy utilization, and stable energy supply. However, energy scheduling still faces some major challenges, such as coupling between energy sharing and energy trading, dimensionality curse, and intertwinement of social network attributes and BS load. To tackle these challenges, we propose a social-aware learning-based online energy scheduling (SNES) algorithm, which minimizes BS operation cost minimization under the constraints of energy supply stability. SNES leverages a deep neural network (DNN) to learn the action-state value of energy scheduling and intelligently adjusts purchased, sold, and shared energy based on only casual information. Moreover, SNES achieves social awareness by approximating the nonlinear interconnection between energy scheduling and quality of service (QoS) requirements of social network services. Simulation results verify the superior performance of SNES compared with state-of-the-art energy scheduling algorithms.

Federated learning (FL)-assisted model training plays an important role in distributed energy scheduling of smart park. However, the time synchronization error between edge and end sides and the adversarial routing competition cause poor accuracy and high delay of model training. In this paper, we address this challenge and propose a time synchronization-aware edge-end collaborative deep Q network-based routing management algorithm named TSA-RM. TSA-RM minimizes the weighted sum of model training loss function and delay via routing optimization. TSA-RM achieves time synchronization awareness and avoids adversarial competition by incorporating time synchronization related information in state space construction and relay selection related information in penalty function design. Simulation results verify the superior performance of TSA-RM in terms of global loss function, model training delay, and time synchronization error compared with two state-of-the-art algorithms.

Device scheduling plays a key role in federated learning model training for energy management in low-carbon smart park. It is intuitive to achieve high-accuracy and low-latency model training by scheduling devices with smaller local training loss function and better channel condition. However, the adverse impact of model poisoning attack on model training performance and device scheduling adjustment cannot be neglected. The error model parameters uploaded by malicious attackers-controlled devices significantly reduce model training accuracy and convergence speed. To address this challenge, we propose an Endogenous Security-Aware Deep Q Network (ESA-DQN) based device scheduling algorithm. ESA-DQN integrates model poisoning attach detection with DQN networks to actively adjust device scheduling in accordance with estimated attack probability, thereby achieving endogenous security awareness. Numerical results show that ESA-DQN has excellent performances in terms of model training accuracy and delay.

In recent years, receiver (RX) feedback communication has attracted increasing attention to enhance the charging performance for magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems. People prefer to adopt the in-band implementation with minimal overhead costs. However, the influence of RX-RX coupling couldn’t be directly ignored like that in the RFID field, i.e., strong couplings and relay phenomenon. In order to solve these two critical issues, we propose a Robust layer-level in-band parallel communication protocol for MIMO MRC-WPT systems (called Roland). Technically, we first utilize the observed channel decomposability to construct group-level channel relationship graph for eliminating the interference caused by strong RX-RX couplings. Then, we generalize such method to deal with the RX dependency due to relay phenomenon. Finally, we conduct extensive experiments on a prototype testbed to evaluate the effectiveness of the proposed scheme. The results demonstrate that our Roland could provide ≥95% average decoding accuracy for concurrent feedback communication of 14 devices. Compared with the state-of-the-art solution, the proposed protocol Roland can achieve an average decoding accuracy improvement of 20.41%.

Due to its ease of deployment and high mobility, unmanned aerial vehicles (UAVs) have gained great popularity for a variety of applications. To conduct high-level and complicated tasks such as search/rescue missions and target identification, deep learning functions at UAVs are required. To this end, distributed learning methods such as federated learning (FL) and split learning (SL) have been proposed. In this paper, we investigate the SL assisted image classification task in a multi-UAV system for applications such as area exploration and object detection. Specifically, the whole deep learning model is cut into the UAV-side model and BS (base station)-side model. Each UAV performs forward propagation on UAV-side model by using the locally gathered images, and sends the smashed data to the BS. The BS performs forward and backward propagation based on the smashed data, and sends back the gradients of the cut layer to the UAVs, which is used for the backward propagation of the UAV-side model. The performance was evaluated using an aerial perspective geographic dataset, and the effectiveness of the proposed system was validated by comparing with FL-based and centralized learning methods. It was found that SL can significantly reduce computation time at UAV compared with FL, and is particularly effective with non-IID (independent and identically distributed) dataset. SL also requires less data during the training initial phase and has a faster convergence speed compared to centralized learning.

The security of Low-Power Wide-Area Network (LPWAN) mainly relies on encryption for ensuring packet integrity and confidentiality. Unfortunately, the latest LPWAN specifications refrain from specifying how to distribute keys for encryption. In this paper, we tackle this problem via physical layer security, which exploits the channel characteristics to generate secret keys at the physical layer. In order to generate consistent keys from noisy feature sources and to achieve high reconciliation success rate, we propose FLoRa, a physical layer key generation system for LPWAN based on sequential fuzzy extractor. An adaptive multi-bit quantization algorithm is first proposed to generate the initial key, which accelerates the bit generation rate at the start-up procedure. We then design a novel fuzzy extractor by sequentially slicing the initial key, which improves the reconciliation success rate, as well as reduces the key reconciliation time. We implement FLoRa in a LoRaWAN based network prototype and evaluate it by conducting extensive indoor and outdoor experiments. Experimental results reveal that FLoRa is capable of generating consistent secret keys with high key generation performance in both static and dynamic network environments.

Space-assisted vehicular networks (SAVN) provide seamless coverage and on-demand data processing services for user vehicles (UVs). However, ultra-reliable and low-latency communication (URLLC) demands imposed by emerging vehicular applications are hard to be satisfied in SAVN by existing computation offloading techniques. Traditional deep reinforcement learning algorithms are unsuitable for highly dynamic SAVN due to the underutilization of environment observations. An AsynchronouS federaTed deep Q-learning (DQN)-basEd and URLLC-aware cOmputatIon offloaDing algorithm (ASTEROID) is presented in this paper to achieve throughput maximization considering the long-term URLLC constraints. Specifically, we first establish an extreme value theory-based URLLC constraint model. Second, the task offloading and computation resource allocation are decomposed by employing Lyapunov optimization. Finally, an asynchronous federated DQN-based (AF-DQN) algorithm is presented to address the UV-side task offloading problem. The server-side computation resource allocation is settled by an queue backlog-aware algorithm. Simulation results verify that ASTEROID achieves superior throughput and URLLC performances.

Dispatching and control information freshness conducts an important impact on the training accuracy of distributed energy dispatching and control model. Poor information freshness will increase the loss function of the training model, and reduce the reliability and economy of dispatching and control. Simplified power internet of things can provide plug-and-play and multi- mode fusion communication support, but it still faces challenges of the coupling of model training and data transmission as well as the difficulty in guaranteeing dispatching and control information freshness. In this paper, a semi-distributed federated learning- based framework for dispatching and control model training decision-making is proposed, and a dispatChing and control informAtion fReshness-aware batch size Optimization aLgorithm (CAROL) is presented. CAROL leverages deep Q network and dispatching and control information freshness awareness to learn the batch size optimization strategy. CAROL can minimize model loss function while guaranteeing long-term dispatching and control information freshness constraints. Compared with existing feder- ated learning algorithms, CAROL achieves superior performance in global loss function and information freshness.

Multi-mode power internet of things (PIoT) provides spatio-temporal coverage for low-carbon operation in smart park through combining various communication media. Heterogeneous resources are dynamically and intelligently managed to improve resource utilization and achieve anti-eavesdropping. However, resource management in multi-mode power IoT confronts challenges such as the mutual contradiction in joint communication and security quality of service (QoS) guarantee and the inadaptability to low-carbon services. In this paper, we propose an Adaptive learNing-based secure and enerGy-awarE resource management aLgorithm (ANGEL) to optimize multi-mode channel selection and power splitting for artificial noise (AN)-based anti-eavesdropping. Based on deep actor-critic (DAC) and “win or learn fast (WoLF)” mechanism, ANGEL can realize multi-attribute QoS guarantee, adaptive resource management, and security enhancement. Simulation results demonstrate its superior performance in energy consumption, secrecy capacity, and adaptability to differentiated low-carbon services.

The low-carbon operation of smart park requires to deploy massive internet of things (IoT) devices to provide real-time monitoring and control services. Digital twin (DT) provides accurate guidance for communication network resource management in low-carbon smart park by establishing a digital representation of physical entities. Facing the strict requirements of DT on delay and accuracy, as well as the constraints of access priority and energy consumption, we propose a federated learning-based DT framework and a Latency-awarE diGital twIn assisted resOurce maNagement algorithm (LEGION). LEGION can achieve a well tradeoff between delay and accuracy performances under the long-term constraints of access priority and energy consumption. Compared with existing algorithms, LEGION has superior performance in average iteration delay, DT loss function, energy consumption, and access priority deficit.

Hand motion capture is essential for emerged augmented reality (AR) and virtual reality (VR) applications. We propose MHMC, a real-time hand motion capture system using millimeter-wave (mmWave) radar with the advantages of lighting independence, privacy protection and good user experience. When implementing such a system, we design a novel vertical neural network architecture to infer complex hand motion from limited resolution data of mmWave radar, a vision-based labeling method to acquire adequate labeled data, and effective loss functions to avoid abnormal output. The experimental results demonstrate that MHMC achieves similar accuracy as visionbased methods, along with fast processing speed, i.e., 7. 3ms per frame, for supporting real-time motion capture.

WiFi-based gait recognition is an attractive method for device-free user identification, but path-sensitive Channel State Information (CSI) hinders its application in multi-path environments, which exacerbates sampling and deployment costs (i.e., large number of samples and multiple specially placed devices). On the other hand, although video-based ideal CSI generation is promising for dramatically reducing samples, the missing environment-related information in the ideal CSI makes it unsuitable for general indoor scenarios with multiple walking paths.In this paper, we propose WiVi, a WiFi-video cross-modal fusion based multi-path gait recognition system which needs fewer samples and fewer devices simultaneously. When the subject walks naturally in the room, we determine whether he/she is walking on the predefined judgment paths with a K-Nearest Neighbors (KNN) classifier working on the WiFi-based human localization results. For each judgment path, we generate the ideal CSI through video-based simulation to decrease the number of needed samples, and adopt two separated neural networks (NNs) to fulfill environment-aware comparison among the ideal and measured CSIs. The first network is supervised by measured CSI samples, and learns to obtain the semi-ideal CSI features which contain the room-specific ‘accent’, i.e., the long-term environment influence normally caused by room layout. The second network is trained for similarity evaluation between the semi-ideal and measured features, with the existence of short-term environment influence such as channel variation or noises.We implement the prototype system and conduct extensive experiments to evaluate the performance. Experimental results show that WiVi’s recognition accuracy ranges from 85.4% for a 6-person group to 98.0% for a 3-person group. As compared with single-path gait recognition systems, we achieve average 113.8% performance improvement. As compared with the other multi-path gait recognition systems, we achieve similar or even better performance with needed samples being reduced by 57.1-93.7%

In recent years, multiple-input multiple-output (MIMO) technology has been imported into magnetic resonance coupled (MRC) enabled wireless power transfer (WPT) systems for concurrent charging of multiple devices. Besides the traditional performance optimization methods (e.g., TX current scheduling, system frequency adjustment, etc.), receiver (RX) grouping will also severely influence the achieved power- delivered-to-load (PDL). In this paper, we investigate the optimal RX grouping issue to maximize the proportional fairness of RX achieved PDL, which is a joint optimization problem involving RX grouping and time-slice allocation among groups. By decoupling the problem, we solve the group generation sub-problem with a impedance-matching based greedy algorithm to generate potential RX group candidates, and we further solve the time slice allocation sub-problem with a genetic algorithm to distribute resources among group candidates. We prototype the proposed system, denoted as IMRG, and conduct extensive experiments to evaluate the performance. The experimental results validate the effectiveness of the proposed algorithm, e.g., IMRG achieves average 59.6% PDL improvement through RX grouping compared to the simultaneous charging scheme.

Analog beamforming with low-resolution phase shifters is a key technique for 5G networks due to its superior hardware complexity and power consumption advantages. However, the optimal beamforming coordination is an extremely challenging issue in a downlink multi-antenna base station and single-antenna user equipment scenario. To avoid using global channel state information and reduce the communication overhead, in this paper, we propose a deep reinforcement learning based distributed analog beamforming approach to improve the energy efficiency for a downlink multiple-input and single-output (MISO) system. Specifically, each base station trains a neural network to steer its beamformer by phase shifters according to its local and obtained neighbouring information, with the purpose of maximizing its own energy efficiency and minimizing the negative impacts to its neighbouring cells. We evaluate the performance of the proposed approach by simulations, and validate its superiority by comparing with baseline schemes.

Collaborative vehicular networks (CVNs) provide on-demand data processing via computation offloading empowered by edge and fog computing. However, intelligent computation offloading in CVNs still faces several challenges such as long-term quality of service (QoS) guarantee, inefficient utilization of environmental information, and asynchronous information exchange. In this paper, we aim at maximizing the throughput under long-term QoS constraints such as queuing delay. The problem of server-side computation resource allocation and user vehicle (UV)-side task offloading is decoupled by Lyapunov optimization. Firstly, we propose an asynchronous federated deep Q-learning network based task offloading (AF-DQN) algorithm to solve the task offloading subproblem by exploring the semi-distributed learning framework. Secondly, we develop a heuristic queue backlog-aware algorithm to solve the computation resource allocation subproblem. Simulation results demonstrate that the proposed algorithm effectively reduces end-to-end queuing delay.

As one of the most successful industrial realizations of Internet of Things, a smart grid is a smart IoT system that deploys widespread smart meters to capture fine-grained data on residential power usage. Unfortunately, it always suffers diverse privacy attacks, which seriously increases the risk of violating the privacy of customers. Although some solutions have been proposed to address this privacy issue, most of them mainly rely on a trusted party and focus on the sanitization of metering masurements. Moreover, these solutions are vulnerable to advanced attacks. In this paper, we propose a decentralized mechanism for privacy-preserving computation in smart grid called DDP, which leaverages the differential privacy and extends the data sanitization from the value domain to the time domain. Specifically, we inject Laplace noise to the measurements at the end of each customer in a distributed manner, and then use a random permutation algorithm to shuffle the power measurement sequence, thereby enforcing differential privacy after aggregation and preventing the sensitive power usage mode informaton of the customers from being inferred by other parties. Extensive experiments demonstrate that DDP shows an outstanding performance in terms of privacy from the non-intrusive load monitoring (NILM) attacks and utility by using two different error analysis.

The integration of air-ground electricity internet of things (AGE-IoT) and machine learning, enables flexible network coverage and intelligent task offloading. However, dynamics of AGE-IoT networks, incomplete information, and resource allocation coupling are still major challenges in achieving intelligent AGE-IoT. In this paper, we investigate a joint multi-timescale task offloading and power control optimization problem to minimize the queuing delay of all the EIoT devices under the long-term constraint of energy consumption. We firstly decompose the joint optimization problem and transform it to large-timescale task offloading optimization and small-timescale power control optimization. Then, we propose a fed-erated deep actor-critic-based task offloading algorithm (FDAC) with two actor-critic networks for multi-timescale optimization. Numerical results show that FDAC has excellent performances in queuing delay and energy consumption compared with existing algorithms.

Collaborative vehicular network is a key enabler to meet the stringent communication and computing requirements of user vehicles (UVs). A UV dynamically optimizes task offloading by exploiting its collaborations with edge servers and vehicular fog servers (VFSs). However, the optimization of task offloading in highly dynamic collaborative vehicular networks faces several challenges such as queuing delay guaranteeing, incomplete information, and dimensionality curse. In this paper, a Deep Reinforcement lEarning-based queue-Aware task offloading algorithM named DREAM is proposed to maximize the throughput of the UVs while satisfying the long-term queuing delay constraints in a best-effort way. Compared with existing task offloading algorithms, DREAM achieves superior performance in throughput, convergence, and queuing delay.

Microservice system is a web application architecture that divides a single application into a suite of service nodes running as separate processes and communicating with lightweight message mechanisms. Although microservice can improve the abstraction, modularity and extensibility of web applications, it makes the anomaly detection and fault root cause localization more challenging for operational staff. To this end, in this paper, we first introduce the concept of service dependency graph (SDG) to depict the complex calling relationship between nodes and then develop an anomaly detection and root cause localization framework called TraceModel which consists of TraceVAE and ModelCoder. TraceVAE divides user requests into different request classes according to well-constructed trace and analysis them separately with variational autoencoder(VAE) to figures out abnormal requests. Based on the anomaly detection results of TraceVAE, ModelCoder localizes the root cause of unknown faults by comparing their fault features with the predefined fault models. By evaluating TraceModel on a realworld microservice system monitoring data set spanning 15 days, it is revealed that TraceModel can detect the anomaly and localize the fault root cause nodes within 110 seconds on average. Furthermore, it improves the root cause localization accuracy (to 97%) by 17.5% compared with the state-of-the-art root cause localization algorithm.

WiFi-based gait recognition is an appealing device-free user identification method, but the environment-sensitive WiFi signal hinders it from easy deployment for a new environment. On the other hand, the Inertial Measurement Unit (IMU) based method could obtain environment-independent gait features, however, it suffers from uncomfortable experiences due to device wearing. In this paper, we propose IMFi, a novel cross-modal gait recognition system to achieve device-free and easy deployment at the same time. We carefully choose the torso and foot speed curves as common features for cross-modal matching. In the enrollment phase, we extract and store the environment-independent IMU-based gait features with two IMU devices attached to the waist and ankle, respectively. In the recognition phase, we retrieve environment-related CSI-based gait features for user identification, along with the environment adaptive Principal Component Analysis (PCA) selection method for better noise reduction. We perform cross-modal matching between IMU and CSI-based features through a simple Convolution Neural Network (CNN) with a limited number of trained environments. The effectiveness of the proposed system is verified via extensive experiments. The results demonstrate that IMFi could be easily deployed to the new environment without the need for retraining. Specifically, our proposed system achieves 85% binary classification accuracy and 96% top-3 multi-class classification accuracy in the new environment.

5G NR (New Radio) employs OFDMA (Orthogonal Frequency Division Multiple Access) for both uplink and downlink where sub-carrier spacing can be modified. To enable flexible uplink access for IoT (Internet of Things) in beyond 5G, it is required to accommodate various types of mobile terminals from low to high-speed transmission. When sub-channel spacing is different and/or transmission timing is asynchronous in adjacent channels, OFDMA signals will not be orthogonal with each other and channel filtering is required to reduce ACI (Adjacent channel interference). Supposing joint use of OW-DFTs-OFDM (Overlap-Windowed Discrete Fourier Transform spreading OFDM) signals for low PAPR (Peak to Average Power Ratio) and FFT (Fast Fourier Transform) based filter-bank for flexible channel filtering, this paper discusses FFT-based frequency domain filter design for receiving multi-channel OW-DFTs-OFDM signals to avoid performance degradation due to ACI and ISI (Inter-Symbol Interference).

Existing task offloading mechanisms are developed on some single and rigid quality of service (QoS) performance metrics, which is widely apart from satisfying the true intent of a user vehicle (UV), thereby resulting in low quality of experience (QoE), large queuing latency, and poor reliability. There is an unprecedented demand for an intent-aware task offloading strategy that provides improved QoE and guarantees reliability. In this paper, we develop a novel task offloading framework for air-ground integrated vehicular edge computing (AGI-VEC), which is called the learning-based Intent-aware Upper Confidence Bound (IUCB) algorithm. IUCB enables a UV to learn the long-term optimal task offloading strategy while satisfying the long-term ultra-reliable low-latency communication (URLLC) constraints in a best effort way under information uncertainty. IUCB can achieve three-dimension intent awareness including QoE awareness, URLLC awareness, and trajectory similarity awareness. Simulation results demonstrate that IUCB significantly outperforms existing EMM, sleeping-UCB, and UCB mechanisms in terms of QoE, end-to-end delay, queuing delay, throughput, and times of task offloading failure.

In recent years, to cope with the rapid growth in mobile data traffic, increasing the capacity of cellular networks is receiving more and more attention. To this end, offloading the current LTE‐advanced or 5G system’s data traffic from licensed spectrum to the unlicensed spectrum that is used by WiFi systems, i.e., LTE‐Licensed‐Assisted‐Access (LTE‐LAA), has been extensively investigated. In the current LTE‐LAA system, a Listen‐Before‐Talk (LBT) approach is implemented, which requires the LTE user also perform carrier sense before the transmission. However, fair LTE‐WiFi coexistence is still hard to guarantee due to their unbalanced frame sizes and traffic loads. In the LTE‐LAA system, the optimal channel selection and subframe number adjustment are the keys to realize efficient spectrum utilization and fair system coexistence. To this end, in this paper, we propose a reinforcement learning‐based joint channel/subframe selection scheme for LTE‐LAA. The proposed approach is implemented at the LTE access points with zero knowledge of the WiFi systems. The results of extensive simulations verify that the proposed approach can significantly improve the fairness and packet loss rate compared with baseline schemes.

After a disaster occurred, it is extremely important to reconstruct the network and provide the communication services to the victims immediately. Deploying MDRU (Movable and Deployable Resource Unit) in the disaster area, along with multiple access points to extend the service area of MDRU is a very promising solution. In this kind of heterogeneous disaster response networks, it is of great importance to minimize the packet delay from user terminals by performing optimal radio access control. In this paper, we propose a deep reinforcement learning based radio access control mechanism, which enables the smart relay selection and transmitting power control. We evaluate the performance by extensive simulations, and validate the superiority of the proposed mechanism by comparing with baseline schemes.

By integrating both energy harvesting and backscatter communication technologies, so-called `battery-free tag' emerges as a promising solution to the energy related issues in IoT. However, such tags present new challenges due to the unstable energy supply and excitation signals, which are critical to realize successful backscatter communications. In this paper, we present a novel system, denoted as ecUWB, which enables robust communication for battery-free tags where unstable WiFi signals act as the unified source for both energy supply and excitation signals. At tag side, we propose a charging-transmission division scheme to achieve better signal utilization, and introduce a re-transmission mechanism for possible excitation signal interruption. At receiver side, we implement a simply method which is based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to predict the uncontrollable signals, and propose an energy-centric tag scheduling method according to the tag energy estimation results. Extensive experiments are carried out on customized tags and NI USRP platform. The results show that ecUWB outperforms the existing ones in terms of performance and efficiency under the unstable WiFi signals.

Mobile Ad hoc Network (MANET) is an emerging technology that allows users to transmit data without any physical infrastructure. Among those MANET protocols, Temporally Ordered Routing Algorithm (TORA) is an on-demand MANET routing protocol that attempts to find routes according to the directed acyclic graph (DAG). However, the TORA protocol requires strict synchronization. The routing overhead of TORA will increase linearly with the packet transmission rate. Motivated by Apple Wireless Direct Link (AWDL), we propose an ad hoc link-layer protocol called TORA-based Wireless Protocol (TWP) in this article. TWP can be deployed on embedded devices with Linux-kernel systems. Besides, it has unique frame structures and mechanisms. Also, it can implement synchronization and a TORA-like routing function at the link layer. We analyze the performance of TWP via experiments on Raspberry Pis. The results show that TWP can perform routing and data transmission successfully. It performs well in synchronization and can effectively reduce the routing overhead during the process of network routing.

In recent years, to cope with the rapid growth in mobile data traffic, increasing the capacity of cellular networks is receiving much attention. To this end, offloading the current LTE-advance or the future 5G system's data traffic from licensed spectrum to unlicensed spectrum that used by WiFi system has been proposed. In the current LTE-WiFi coexistence standard, a Listen-Before-Talk (LBT) approach is adopted to make the LTE system senses the medium before a transmission. However, the channel selection and subframe adjustment issues are still open to realize fair coexistence between co-located LTE and WiFi networks. In this paper, we propose a reinforcement learning based joint channel/subframe selection scheme for fair LTE-WiFi coexistence. The proposed approach is distributedly implemented at LTE Access Points (APs) with zero knowledge of the WiFi systems. Extensive simulations have been performed, and the results verified that the proposed approach can achieve better fairness and packet loss rate compared with baseline schemes.

Parameter Server (PS) has been widely used to train a large amount of data on multiple machines in parallel. In parameter server, a critical problem is how to effectively schedule multiple training jobs to minimize the job completion time. Some existing work has proposed methods of setting the number of concurrent workers. However, they do not effectively consider the topology of GPU placement which affects the efficiency of communication. This paper proposes a novel resource-to-time model based on the number of workers and the topology of GPU placement. According to the model, we propose an algorithm called TOPO-PS particularly for topology problem in parameter servers. The algorithm achieves the placement strategy based on graph mapping algorithm. Evaluation under various algorithms evidences the superiority of our algorithm. TOPO-PS yields shorter job completion, by up to 53.48% of that of FIFO and 88.77% of OASIS.

Falls are the second leading cause of injury deaths worldwide, inducing over 0.6 million accidental deaths per year. Among various prevention strategies, fall-related research has been prioritized. However, conventional fall detection solutions rely on computer vision or wearable sensors embody several inherent limitations such as scalability, coverage, and privacy issues. To this end, we present FallSense, a transparent and real-time fall sensing system driven by wireless channel data. FallSense is built on a Dynamic Template Matching (DTM) algorithm, which can start with a light training set and keep updating on usage. FallSense has been realized on commodity WiFi devices and evaluated in real environments. Experimental results show that FallSense outperforms another state-of-the-art approach WiFall in terms of detection precision, false alarm rate and complexity.

Constructing Radio Environment Map (REM) accurately and cost-efficiently is of great importance to realize dynamic spectrum access. Two kinds of approaches are widely investigated recently, i.e., radio propagation model based approaches and sensor monitoring based approaches. However, these existing approaches are suffering from either inaccurate spectrum availability or high deployment cost. To this end, outsourcing the spectrum sensing task to mobile users that are outfitted with spectrum sensors could greatly reduce the operator's expenditure, and meanwhile, achieve a satisfactory accuracy. The key of crowdsourced REM construction is to attract user participation. In this paper, we propose a novel online incentive mechanism for constructing a fine-grained REM with crowdsourcing in a realistic scenario, where the mobile users arrive and leave in an online manner. The proposed mechanism is proven to satisfy the truthfulness, individual rationality, computational efficiency and consumer sovereignty. Evaluation results demonstrate that the proposed mechanism outperforms the baseline schemes substantially.

Low-Power Wide Area Networks (LPWAN) has emerged as the dominant open specification in recent years due to its ability to offer affordable connectivity to the low-power devices distributed over large geographical areas. However, security issues have not been fully addressed in LPWAN specifications, especially, key distribution and key management. Physical layer key generation, which exploits wireless channel reciprocity and randomness to generate secure keys, has attracted considerable attention in recent years. In this paper, we exploit the physical layer key generation problem in LPWAN communication and present a distributed and lightweight key generation scheme for Long Range (LoRa) based network. It explores the shared randomness extracted from measured RSSI (Received Signal Strength Indicator) as consensus information to generate secure keys. To negotiate the RSSI signal as a bidirectional consistent key sequence, we propose a novel level-crossing quantization algorithm with an improved Cascade key agreement protocol to improve the key generation rate, as well as to avoid information leakage during transmission. We implement the proposed physical layer key generation scheme in a LoRa network prototype. Then we conduct extensive experiments in stationary and mobile indoor environments to evaluate the efficiency of the proposed key generation scheme. Experimental results show that its achievable key rate can reach 29.5% in stationary scenario and 35.5% in mobile scenario. Its key generation rate can exceed 1 bit/s with a lightweight implementation on the LoRa network prototype. For the 128-bit key sequence, it passes the the NIST suite of statistical tests.

Broadband wireless systems need to achieve high spectrum efficiency as well as high power efficiency of HPA (High Power Amplifier). When HPA is used with low output back-off (OBO), increased ACLP (Adjacent Channel Leakage Power) results in significant ACI (Adjacent Channel Interference) especially in TDD (Time Division Duplexing) wireless systems due to a near-far problem. In order to solve this ACLP problem of HPA at a mobile terminal, this paper proposes a novel HPA linearization system using amplitude reference pilot signal which is used to measure input-output characteristics of HPA of a mobile terminal at a base station. It describes design and performance of amplitude reference pilot signal and shows ACLP improvement to confirm the feasibility of the proposed HPA linearization system.

Emotion is a unique feature of human beings. Recent research in emotion sensing has already revealed its potentials in enhancing our living experiences through applications like emotion companion and autism treatment. However, existing solutions exploring audiovisual clues or psychological sensors have several critical concerns such as the availability (specialized hardware), reliability (illumination and line-of-sight constraints) and privacy issues (being watched). To this end, we present EmoSense, a first-of-its-kind WiFi-based emotion sensing system leveraging the temporal and frequency fingerprints on the wireless channel data induced by the physical expression of emotion. EmoSense has been prototyped with off- the-shelf WiFi devices and evaluated by comparing with the main-stream sensor-based approach in real environments. Experimental results demonstrate its effectiveness and robustness. Considering that EmoSense is compatible with existing WiFi infrastructures, it constitutes a low-cost yet promising solution for emotion sensing.

Recently, multi-band WLAN becomes a promising solution to increase the spectral efficiency, where 60GHz band provides ultra-high speed transmission and 2.4/5GHz band is used for maintaining the connectivity. For multi-band WLAN end-users, in order to detect the service area of different bands, their RF units need to be turned on all the time, which leads to substantial energy consumption overhead. To solve this problem, this paper proposes an energy efficient learning-based 60GHz band coverage prediction approach by taking into consideration the strong reflected waves in indoor environment. The simulation results demonstrate that the proposed approach could greatly improve the reliability of the prediction compared to the existing approaches.

As IoT (Internet of Things) is one of the most promising applications in 5G and beyond, a new waveform to enable flexible uplink access is strongly required to accommodate various IoT applications. OFDMA (Orthogonal Frequency Division Multiple Access) based approaches such as FBMC (Filter Bank Multi Carrier) are proposed to achieve low ACLP (Adjacent Channel Leakage Power) however, they have some drawbacks such as high PAPR (Peak to Average Power Ratio) and heavy signal processing for channel filtering. This paper proposes overlap-windowed DFTs-OFDM (Discrete Fourier Transform spreading-OFDM) with overlap FFT filter-bank for flexible uplink access in 5G and beyond. The proposed scheme employs overlap windowing at the transmitter side to achieve low ACLP and overlap FFT filter-bank on the receiver side to reduce ACI (Adjacent Channel Interference) even in the case of asynchronous uplink access. This paper also describes performance evaluation results of the proposed scheme.

Sleep is a major event of our daily lives. Its quality constitutes a critical indicator of people's health conditions, both mentally and physically. Existing sleep monitoring systems either are obstructive to use or fail to provide adequate coverage. To overcome these shortages, we propose Sleepy, an adaptive and noninvasive sleep monitoring system leveraging channel response in the commercial WiFi devices. Sleepy needs no calibrations or target-dependent training to recognize posture changes during sleep. To achieve that, a Gaussian Mixture Model (GMM) based foreground extraction method has been designed to adaptively distinguish motions like rollovers (foreground) from background (stationary postures). We prototype Sleepy and evaluate it in two real environments. In the short-term controlled experiments, Sleepy achieves 95.04% detection accuracy and 4.07% false negative rate. In the 60-minute real sleep studies, Sleepy demonstrates strong stability. Considering that Sleepy is compatible with existing WiFi infrastructures, it constitutes a low-cost yet promising solution for sleep monitoring.

5G systems are expected to employ C/U (Control/User)-plane split and massive deployment of small cells for high frequency reuse at SHF bands such as 28GHz band in conjunction with a macro cell using traditional UHF bands to meet increasing demand for higher capacity. In 5G multi-band cellular networks, an energy efficient SHF band cell discovery technique is required since SHF band cells will be deployed on a hot-spot basis. This paper proposes Kriging-based RSSI (Received signal strength indication) prediction for cell coverage discovery using spectrum database in 5G multi-band cellular networks. This paper also describes performance evaluation results of the Kriging-based RSSI prediction using ray-tracing simulation and demonstrates the feasibility of the proposed RSSI prediction method.

In spectrum sharing system, an exclusion zone is defined to protect both primary and secondary users from interference. Reducing the size of exclusion zone is critical for efficiently utilizing the fallow spectrum. In this paper, we propose a novel crowdsourcing augmented exclusion zone refinement framework. In our framework, a barter-like exchange model using spectrum access right is employed to incentivize the secondary users (SUs) to participate in the crowdsourcing. We further design a truthful auction mechanism to select the SUs and determine their access time in a computationally efficient way. We perform simulations to validate the proposed mechanism, and compare it with two baseline schemes.

When a large-scale disaster happens, efficient network connection and communication becomes difficult due to serious damage of existing network infrastructures. Meantime, people have strong demands of information sharing with each other for evacuation and disaster-relief activities in such a disaster environment. To serve these heavy communication demands, establishing local area networks (LANs) consisting of portable servers has been considered as one of the most promising solutions. Based on the established LANs, people can share disaster-related information in covered area. However, due to the lack of stable Internet connection, these LANs are isolated and cannot be synchronized in real time. To tackle this problem, in this paper, we propose an intermittent data synchronization scheme by introducing moving vehicles as relays to exchange data between isolated data servers after disasters. With the objective of maximizing the synchronized weighted data volume under the capability constraints of the mobile relay, we formulate a stochastic programming problem for trajectory planning. We leverage queueing theory and the Lyapunov-drift technique to solve this problem in an online setting, which is practical for a real disaster environment. Our theoretical analysis shows that the performance gap of our proposed online algorithm is (1/V) of the optimum. Additionally, extensive simulations and comparisons with other algorithms are conducted to show the superior performance of our proposed online algorithm.

To improve the spectrum utilization efficiency, radio propagation model based spectrum database is widely investigated recently. However, it is prone to offer inaccurate and stale spectrum availability since the empirical models do not count for local environment details. One promising solution is to incorporate real- time spectrum measurement into the quasi-static spectrum database. In this paper, we propose a novel fine-grained incentive mechanism for sensing augmented spectrum database. We first present a reverse auction framework, which minimizes the operator's total expenditure subject to the quality requirement of each spot that needs to be augmented. Then we propose a practical incentive mechanism to solve the auction problem, which is proven to be truthful, individual rational and computationally efficient. Simulation results demonstrate that the proposed mechanism could save noticeable expenditure compared to two baseline schemes.

Network disaster recovery is one of the greatest concerns for Mobile Network Operators (MNOs) and first responders during large-scale natural disasters such as earth- quakes. In many recent studies, wireless multi-hop networking has been demonstrated as an effective technique to quickly and efficiently extend the network coverage during disasters. In this paper, we specifically address the network deployment problem by proposing the Population-Aware Relay Placement (PARP) solution, which seeks the efficient deployment of a limited number of relays such that population coverage is maximized in the scenario of network disaster recovery. We provide a graph-based modeling and prove its NP-hardness accordingly. In order to efficiently solve this problem, we propose a heuristic solution, which is constructed in two steps. We first design a simple algorithm based on a disk graph to determine the Steiner locations, which is the biggest challenge in this problem. Then, we formulate the problem as an integer programming problem, which is inspired by the formulation of Prize-Collecting Steiner Tree (PCST). Thus, the integer problem is solved by exploring the similarity of the existing algorithm for PCST. To evaluate the proposed solution extensively, we present numerical results on both real-world and random scenarios, which validate the effectiveness of the proposed solution and show substantial improvement by comparing to the previous one.

Human activity recognition based on wireless signals emerges as a research hotspot recently. Though tremendous efforts have been devoted and significant progresses have been achieved, one fundamental issue still remains open, i.e., theoretical modeling between signal dynamics and human activities. This paper fills in the blank by addressing several theoretical issues and providing insightful mathematical analysis including a signal-activity model. To validate such analysis, a prototype system has been built, where a series of real-world experiments has been conducted. Empirical results have justified our theoretical findings. Moreover, important hands-on experiences on the system implementation and parameter settings have been offered.

Motion is a critical indicator of human presence and activities. Recent developments in the field of indoor motion detection have the potential to enhance various aspects of our daily experiences like intrusion detection and sleep monitoring. Existing indoor motion detection solutions either are obstructive to use or fail to provide adequate coverage. To overcome these shortages, a noninvasive and cost-effective motion detection system (MoSense) is proposed for periodically detecting motions by exploring channel response in the commodity WiFi devices. The central idea is that signals in a ``silence'' environment serve well as a ``golden'' benchmark for recognizing motions that lead to signal fluctuations. A prototype of MoSense is realized and evaluated in real environments. By comparing MoSense with another state-of-the-art method, i.e., FIMD, we have shown that MoSense outperforms FIMD in terms of computational complexity, detection accuracy and false alarm rate. Considering that MoSense is compatible with existing WiFi infrastructure, it constitutes a low-cost yet promising solution for motion detection.

Interference management is one of the most important issues in heterogeneous cellular networks with multiple macro and pico cells. The enhanced inter cell interference coordination (eICIC) has been proposed to protect downlink pico cell transmissions by mitigating interference from neighboring macro cells. Therefore, the adaptive eICIC configuration problem is critical, which adjusts the parameters including the ratio of almost blank subframes (ABS) and the bias of cell range expansion (RE). This problem is challenging especially for the scenario with multiple coexisting network services, since different services have different user scheduling strategies and different evaluation metrics. By using a general service model, we formulate the eICIC configuration problem with multiple coexisting services as a general form consensus problem with regularization and solve the problem by proposing an efficient optimization algorithm based on the alternating direction method of multipliers. In particular, we perform local RE bias adaptation at service layer, local ABS ratio adaptation at BS layer, and coordination among local solutions for a global solution at a network layer. To provide the service scalability, we encapsulate the service details into the local RE bias adaptation subproblem, which is isolated from the other parts of the algorithm, and we also introduce some implementation examples of the subproblem for different services. The extensive simulation results demonstrate the efficiency of the proposed algorithm and verify the convergence property.

Heterogeneous networks (HetNets) are intended to offer wide area coverage and high data rate transmission by deploying small cells besides macrocells. Device-to-device (D2D) communication as an underlay of cellular network enriches local service and offloads base station. In this paper, we target the video transmission demanded by picocell edge users (PEUEs), who suffer from low quality channel due to the inter-cell interference from macrocell and long physical distance from picocell. Moreover, the wireless channels are burst-loss prone for upper layer applications such as video on demand (VoD), which makes the traditional channel coding such as forward error correction (FEC) insufficient. In this paper, we address these issues and propose a cooperative video transmission scheme to improve PEUEs' received video quality by constructing two transmission paths from picocell to each PEUE. The two transmission paths are the direct transmission from pico-eNB (i.e., base station) to PEUE and a relay-assisted path by means of D2D communication for frame recovery, respectively. Reference frame selection and unequal error protection are adopted to further improve the overall performance. Extensive simulations are conducted and results demonstrate that the proposed scheme outperforms state-of-the-art scheme in Config.4b scenarios defined by 3GPP.

Natural disasters can result in severe damage to communication infrastructure, which leads to further chaos to the damaged area. After the disaster strikes, most of the victims would gather at the evacuation sites for food supplies and other necessities. Having a good communication network is very important to help the victims. In this paper, we aim at recovering the network from the still-alive mobile base stations to the out-of-service evacuation sites by using multi-hop relaying technique. We propose to reconstruct the post-disaster network in a capacity-aware way based on prize collecting Steiner tree. The purpose of the proposed scheme is to achieve high capacity connectivity ratio in a cost efficient way. To provide more accurate evaluation results, we evaluate the proposed scheme by using the real evacuation site and base station data in Tokyo area, and utilizing the big data analysis based post-disaster service availability model.

Dynamic spectrum reallocation, under which the spectrum owners temporarily share the underutilized spectrum to secondary users for economic profit, is an important approach to improve the spectrum utilization ratio. Auction is believed to be a natural marketing tool to incentivize the spectrum owners, and thus redistribute the idle spectrum efficiently. Extensive researches have been done in the problem of truthful spectrum auction, in which the bidders bid based on their true valuations of the spectrum. The true valuation of the individual bidder, however, is a private information which should be protected against exposure. In this paper, we propose a privacy preserving truthful spectrum auction scheme by utilizing homomorphic encryption. The proposed scheme reveals the group bids but hides the users' bids even from the auctioneer. The evaluation results show that the proposed scheme achieves good spectrum utilization efficiency with low communication and computation overheads.

Network virtualization (NV) is a promising solution for higher resource utilization, improved system performance, and lower investment capitals for network operators. Spectrum sharing is an important issue for NV in the wireless networks. Meanwhile, the scheme of Almost Blank Subframe (ABS) causes new challenge for NV in the heterogeneous cellular networks (HetNet). This paper aims at investigating the joint optimization problem of spectrum sharing and ABS adaptation, and the optimization target is represented through general utility functions of logical virtual operators (LVOs). We formulate the problem, and decouple it into two subproblems. We propose a dynamic programming based algorithm for the spectrum sharing subproblem, and an alternating direction method of multipliers (ADMM) based algorithm for the ABS adaptation subproblem. The simulation results demonstrate the efficiency of the proposed algorithm.

Interference management is one of the most important issues in the heterogeneous cellular networks (HetNet) with macro and pico cells. The enhanced inter cell interference coordination (eICIC) has been proposed to protect downlink pico cell transmissions by mitigating interference from neighboring macro cells. The adaptive eICIC configuration problem is studied in this paper to adjust the parameters including the ratio of Almost Blank Subframes (ABS) and the bias of cell range expansion (RE). We formulate the problem as a general form consensus problem with regularization, and solve the problem by providing an efficient distributed optimization framework. Our algorithm is based on the alternating direction method of multipliers (ADMM) in which the solutions to local subproblems on each macro cell and pico cell are coordinated to find a solution to the global problem for the whole network. We also propose the dynamic programming based algorithms to solve the local subproblems on macro cell or pico cell. The simulation results demonstrate the efficiency of the proposed algorithm compared with existing approaches, and verify the convergence properties of the proposed algorithm.

This paper investigates the secure information transfer issue for cognitive radio networks that have multiple non-altruistic primary users, secondary users and eavesdroppers. The design objective is to improve the secrecy rates of the primary users, and create the transmission opportunities for the secondary users. To achieve this goal, we propose to incentivize the non-altruistic users to cooperate by a barter-like exchange. Specifically, the primary users leverage the assist of the secondary users in the form of cooperative transmitting or friendly jamming, and in return, yield certain licensed spectrum accessing time to the aided secondary users. We propose a truthful Double Auction mechanism for Secure Information transfer in cognitive radio networks, namely DASI, to jointly formulate the cooperator/jammer assignment and the corresponding resource allocation problems. We prove that DASI preserves nice economic properties that are critical for the auction design, including truthfulness, individual rationality and budget balance. We also evaluate DASI in terms of aggregated throughput and spectrum utilization ratio by simulations.

This paper addresses the spectrum leasing issue in cognitive radio networks by exploiting the primary user's cooperative ARQ (automatic repeated-request). To incentivize the otherwise non-cooperative users, we propose a novel trading model to foster the cooperation in the context of cooperative retransmitting. By formulating the network as a Stackelberg game, we maximize the utilities of both primary and secondary users in terms of transmission rates and revenues. We analyze the existence of the unique Nash equilibrium of the game, and give the optimal solutions with corresponding constraints. Numerical results demonstrate the efficiency of the proposed framework, under which the performance of the whole system could be substantially improved.

Cooperative communication, which utilizes nearby terminals to relay the overhearing information to achieve the diversity gains, has a great potential to improve the transmitting efficiency in wireless networks. To deal with the complicated medium access interactions induced by relaying and leverage the benefits of such cooperation, an efficient Cooperative Medium Access Control (CMAC) protocol is needed. In this paper, we propose a novel cross-layer distributed energy-adaptive location-based CMAC protocol, namely DEL-CMAC, for Mobile Ad-hoc NETworks (MANETs). The design objective of DEL-CMAC is to improve the performance of the MANETs in terms of network lifetime and energy efficiency. A practical energy consumption model is utilized in this paper, which takes the energy consumption on both transceiver circuitry and transmit amplifier into account. A distributed utility-based best relay selection strategy is incorporated, which selects the best relay based on location information and residual energy. Furthermore, with the purpose of enhancing the spatial reuse, an innovative network allocation vector setting is provided to deal with the varying transmitting power of the source and relay terminals. We show that the proposed DEL-CMAC significantly prolongs the network lifetime under various circumstances even for high circuitry energy consumption cases by comprehensive simulation study.

Scalable video coding (SVC) is attractive technology for multicasting video to users with different available transmission capacities. In this paper, we investigate the joint optimization of resource allocation and user association problems for SVC multicast over heterogeous cellular networks (HetNet) employing the schemes of cell range expansion (RE) and almost blank subframe (ABS). We solve the joint optimization problem by decoupling it into two problems, namely, resource allocation (RA) subproblem and user association (UA) master problem. For the RA subproblem, we propose a dynamic programming based algorithm to optimally set the transmission profile. For the UA master problem, we propose a similarity-based negotiation protocol (SBNP) based algorithm to obtain the Pareto-optimal range expansion bias. The simulation results demonstrate the efficiency of these algorithms.

This paper deals with the retransmission protocol design for cognitive radio networks by exploiting the primary hybrid ARQ. In contrast with previous work that focuses on cancellation based retransmissions, we propose a novel cooperative coding based retransmission protocol for cognitive radio networks. The design objective is to improve the throughput of the primary user and create the transmission opportunity for the secondary user. By exploiting the primary retransmission appropriately, the knowledge on primary packet which is required by the cooperative coded retransmission can be obtained without any non-causal assumption. Performances on the proposed protocol are analyzed mathematically, and verified by numerical results.

This paper investigates the secure information transfer issue for cognitive radio networks by exploiting the spectrum leasing technique. The design objective is to improve the secrecy rate of the primary user, and meanwhile, create the transmission opportunities for the secondary users. To achieve this goal, we consider a system model where the primary user harnesses the assist of the secondary users in the form of cooperative transmitting. And in return, the primary user provides certain transmission opportunities over licensed spectrum for the cooperating secondary users. We propose an auction-based spectrum leasing scheme to jointly formulate the optimal cooperator selection and resource allocation problems. By analyzing and solving the dominant strategy equilibrium for the proposed scheme, we present reliable predictions for the system behavior and the achievable performances. Simulation results reveal that the proposed scheme could provide substantial gains for both the primary user and the cooperating secondary user.

This paper investigates the joint optimization problem of user scheduling, user association, and resource partition in heterogeneous cellular networks (HetNet) with a general concave utility function used as the performance metric. We formulate the joint optimization problem, and decouple the problem into three sub problems. After proving the sub problems belong to the set of problems that maximizes a monotone sub modular set function with mastoid constraint, we solve them by the proposed greedy based algorithms with theoretical approximation factors. Extensive simulation results demonstrate the efficiency of the proposed algorithms in terms of system utility. In addition, we evaluate some assumptions and results in the related work to show their impacts and correctness.

Cooperative communication, which utilizes neighboring nodes to relay the overhearing information, has been employed as an effective technique to deal with the channel fading and to improve the network performances. Network coding, which combines several packets together for transmission, is very helpful to reduce the redundancy at the network and to increase the overall throughput. Introducing network coding into the cooperative retransmission process enables the relay node to assist other nodes while serving its own traffic simultaneously. To leverage the benefits brought by both of them, an efficient Medium Access Control (MAC) protocol is needed. In this paper, we propose a novel network coding aware cooperative MAC protocol, namely NCAC-MAC, for wireless ad hoc networks. The design objective of NCAC-MAC is to increase the throughput and reduce the delay. Simulation results reveal that NCAC-MAC can improve the network performance under general circumstances comparing with two benchmarks.

Cooperative communication, which utilizes neighboring nodes to relay the overhearing information, has been employed as an effective technique to deal with the channel fading and to improve the network performances. And network coding, which combines several packets together for transmission, is very helpful to reduce the redundancy at the network and to increase the overall throughput. Introducing network coding into the cooperative retransmission process, enables the relay node to assist other nodes while serving its own traffic simultaneously. To leverage the benefits brought by both of them, an efficient Medium Access Control (MAC) protocol is needed. In this paper, we propose a novel network coding aware cooperative MAC protocol, namely NCAC-MAC, for wireless networks. The design objective of NCAC-MAC is to increase the throughput and reduce the delay of the network. Simulation results reveal that our NCAC-MAC can improve the network performance under general circumstances.

This paper addresses the secure data aggregation for wireless sensor networks (WSNs) with both static tree architecture and dynamic cluster-based architecture. For WSNs with static tree architecture, we propose the Leaf Node Representation (LNR) scheme to solve the Id problem and make the key stream-based encrypted data aggregation feasible and practical for large scale networks. For WSNs with dynamic cluster-based architectures, we propose the Delayed Hop-by-hop Authentication (DHA) scheme to provide hop-by-hop data integrity and data freshness only using individual keys. Analytical results show that the proposed scheme can reduce the communication overhead significantly compared to a well known existing scheme.

This paper studies the precision-constraint data aggregation problem for dynamic cluster-based wireless sensor networks. The goal is to extend the network lifetime while keeping reasonable data quality. To achieve the target, we propose the dynamical precision allocation algorithm, which splits the application error bound which users can tolerate into individual local error bounds. We differentiate the sensor nodes in clustering architecture to cluster-heads and leaf nodes, arrange the error bounds to the nodes that can really reduce their transmitting messages. In order to reduce the overhead, our algorithm is merged to the cluster-head reelection process. Experimental results show that our scheme significantly improves the network lifetime compared to the existing methods.

This paper constructs an energy efficient secure data aggregation framework for wireless networks, especially for wireless sensor networks with femtocells. We propose the Leaf Node Representation (LNR) scheme and the Hop-by-hop MAC Authentication (HMA) scheme, in order to provide a balance between the security and the communication cost. The ideas in this paper are not restricted to wireless sensor network, it can be used in other kind of wireless network after modification. Under the proposed scheme, keystream-based encrypted data aggregation is feasible and practical for large scale implementations. Analytical results show that the proposed scheme can reduce the communication overhead significantly compared to existing approaches.