Recently, large models have shown significant potential for smart healthcare. However, the deployment of Large Vision-Language Models (LVLMs) for clinical services is currently hindered by three critical challenges: a tendency to hallucinate answers not grounded in visual evidence, the inefficiency of fixed-depth reasoning, and the difficulty of multi-institutional collaboration. To address these challenges, in this paper, we develop MedAlign, a novel framework to ensure visually accurate LVLM responses for Medical Visual Question Answering (Med-VQA). Specifically, we first propose a multimodal Direct Preference Optimization (mDPO) objective to explicitly align preference learning with visual context. We then design a Retrieval-Aware Mixture-of-Experts (RA-MoE) architecture that utilizes image and text similarity to route queries to a specialized and context-augmented LVLM (i.e., an expert), thereby mitigating hallucinations in LVLMs. To achieve adaptive reasoning and facilitate multi-institutional collaboration, we propose a federated governance mechanism, where the selected expert, fine-tuned on clinical datasets based on mDPO, locally performs iterative Chain-of-Thought (CoT) reasoning via the local meta-cognitive uncertainty estimator. Extensive experiments on three representative Med-VQA datasets demonstrate that MedAlign achieves state-of-the-art performance, outperforming strong retrieval-augmented baselines by up to 11.85% in F1-score, and simultaneously reducing the average reasoning length by 51.60% compared with fixed-depth CoT approaches.

The rapid development of autonomous driving technologies and the expansion of the Internet of Things (IoT) have intensified the demand for timely and accurate vehicular perception, highlighting the potential of leveraging vehicular edge computing (VEC) systems to support perception tasks. Unmanned aerial vehicles (UAVs), owing to their flexible mobility and line-of-sight advantages, have emerged as promising IoT-enabling aerial platforms to enhance both vehicular perception and computation capabilities in VEC environments. In this paper, we propose an accuracy-oriented and computation-efficient framework for air-ground cooperative sensing and computing, wherein a UAV cooperates with a group of connected and autonomous vehicles (CAVs) to collect sensing data of the objects around them, followed by data fusion and computation for object classification. We formulate a joint optimization problem involving UAV trajectory planning and sensing task placement, aiming to minimize the sensing accuracy error and task processing delay. The joint optimization problem is reformulated as a Markov decision process (MDP), where a penalty term for constraint violations is incorporated into the reward function to ensure feasibility. Furthermore, we develop an improved twin delayed deep deterministic policy gradient (TD3)-based algorithm for UAV-assisted cooperative sensing and computing to derive an efficient UAV trajectory control and subtask placement strategy. Results demonstrate that the proposed algorithm achieves superior performance compared to baselines in terms of convergence speed, training stability, and cost-saving, validating its applicability in dynamic UAV-assisted VEC environments.

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.

Deep joint source-channel coding (JSCC) has emerged as a promising paradigm for semantic communication, delivering significant performance gains over conventional separate coding schemes. However, existing JSCC frameworks remain vulnerable to physical-layer adversarial threats, such as pilot spoofing and subcarrier jamming, compromising semantic fidelity. In this paper, we propose SecDiff, a plug-and-play, diffusion-aided decoding framework that significantly enhances the security and robustness of deep JSCC under adversarial wireless environments. Different from prior diffusion-guided JSCC methods that suffer from high inference latency, SecDiff employs pseudoinverse-guided sampling and adaptive guidance weighting, enabling flexible step-size control and efficient semantic reconstruction. To counter jamming attacks, we introduce a power-based subcarrier masking strategy and recast recovery as a masked inpainting problem, solved via diffusion guidance. For pilot spoofing, we formulate channel estimation as a blind inverse problem and develop an expectation-minimization (EM)-driven reconstruction algorithm, guided jointly by reconstruction loss and a channel operator. Notably, our method alternates between pilot recovery and channel estimation, enabling joint refinement of both variables throughout the diffusion process. Extensive experiments over orthogonal frequency-division multiplexing (OFDM) channels under adversarial conditions show that SecDiff outperforms existing secure and generative JSCC baselines by achieving a favorable trade-off between reconstruction quality and computational cost. This balance makes SecDiff a promising step toward practical, low-latency, and attack-resilient semantic communications.

In this paper, we investigate a Multi-layer Aerial system for GenAI inference at the Edge (MAGE). Therein, ground user equipments (UEs) request image synthesis services from a remote base station (BS) that leverages the Latent Diffusion Model (LDM) for image generation. Multiple Unmanned Aerial Vehicles (UAVs) are deployed to serve the UEs for relaying their images and prompts to the BS. To reduce the communication cost and the computation burden at the BS, the UAVs can partially execute the LDM inference, i.e., an image autoencoder and prompt encoder, and offload the diffusion process task to the BS. In this work, we aim to minimize the BRISQUE scores across all the UEs by jointly optimizing the UAVs' positions, UE-UAV associations, the number of denoising steps at the BS, and offloading strategies of the UAVs. The optimization problem is non-convex, in which the objective function based on BRISQUE scores has no closed-form expression. Due to the fixed exploration strategy of Proximal Policy Optimization (PPO), which limits the policy's adaptability in dynamic environments, this leads to sub-optimal solutions. To address these potential drawbacks, we propose an adaptive exploration strategy that dynamically adjusts the exploration rate based on observed improvements in rewards. Specifically, the exploration capability is controlled by modulating the influence of the entropy bonus according to recent reward gains. Simulations based on the COCO-Stuff datasets show that the proposed scheme outperforms baseline schemes in different terms of BRISQUE score, UAVs' energy consumption, and inference latency. In particular, the proposed scheme reduces the BRISQUE score by up to 20-28.57%, inference energy consumption up to 15.98-30.17%, transmission energy consumption by 15.4-18.5%, and the latency by up to 33.33-43.28% compared to the baseline methods, resulting in higher image quality with a noticeably improved level of perceptual naturalness, improved energy efficiency, as well as substantially faster performance.

Uncrewed aerial vehicle (UAV) networks are increasingly exposed to widespread and various network attacks due to their fully distributed nature and the limited defensive capabilities of individual devices. Existing defense strategies rely on network connectivity and UAV status information, which overlook information of network traffic. Malicious traffic detection offers a promising solution to achieve fine-grained attack detection. However, the dynamic nature and complexity of UAV networks limit the effectiveness of traditional traffic detection methods. Current approaches either fail to fully exploit the raw characteristics of traffic or do not consider the timeliness requirements of UAV networks. To address these challenges, we propose a novel twin graph encoder neural network, which can extract features of raw traffic bytes for efficient traffic detection. First, we propose a decoupled architecture for model training and inference to enable efficient detection of malicious traffic in UAV networks. Second, we propose a novel modeling method that models traffic as the co-occurrence graph and word frequency graph based on raw bytes. Then, we propose TGE-ETD, a Twin Graph Encoder for Encrypted Traffic Detection. TGE-ETD consists of a set of twin graph encoders that effectively extract intrinsic traffic features from graphs constructed from raw bytes. In addition, TGE-ETD employs a global attention pooling mechanism to effectively distinguish the feature contributions of different bytes. Finally, we conducted extensive experiments on a real UAV traffic dataset and four real-world network traffic datasets. TGE-ETD achieved an improvement of 1%-20% over the baseline methods by reducing the number of parameters by 20 times. Tested on multiple UAV hardware devices, TGE-ETD can achieve millisecond-level traffic detection.

There has been a growing trend toward leveraging machine learning (ML) and deep learning (DL) techniques to optimize and enhance the performance of wireless communication systems. However, limited attention has been given to the vulnerabilities of these techniques, particularly in the presence of adversarial attacks. This paper investigates the adversarial attack and defense in distributed multiple reconfigurable intelligent surfaces (RISs)-aided multiple-input multiple-output (MIMO) communication systems-based autoencoder in finite scattering environments. We present the channel propagation model for distributed multiple RIS, including statistical information driven in closed form for the aggregated channel. The symbol error rate (SER) is selected to evaluate the collaborative dynamics between the distributed RISs and MIMO communication in depth. The relationship between the number of RISs and the SER of the proposed system based on an autoencoder, as well as the impact of adversarial attacks on the system’s SER, is analyzed in detail. We also propose a defense mechanism based on adversarial training against the considered attacks to enhance the model’s robustness. Numerical results indicate that increasing the number of RISs effectively reduces the system’s SER but leads to the adversarial attack-based algorithm becoming more destructive in the white-box attack scenario. The proposed defense method demonstrates strong effectiveness by significantly mitigating the attack’s impact. It also substantially reduces the system’s SER in the absence of an attack compared to the original model. Moreover, we extend the phenomenon to include decoder mobility, demonstrating that the proposed method maintains robustness under Doppler-induced channel variations.

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.

Low-altitude economy networking (LAENet) aims to deploy various aerial vehicles to support diverse services, where data collection from edge devices via unmanned aerial vehicles (UAVs) is a critical task. The key challenge lies in jointly optimizing energy consumption and data freshness in spectrum-constrained and eavesdropping-prone low-altitude environments during the data collection process. Although deep reinforcement learning (DRL) has become a viable solution for UAV-assisted data collection, the RL agent still has limited ability to obtain and utilize informative feedback from complex low-altitude environments. In this paper, we propose a large language model (LLM)-enhanced DRL framework for secure data collection in the LAENet, where we leverage an LLM to process environmental feedback for the RL agent. Specifically, we employ the LLM as (i) a state processor to transform basic environmental observations into task-aligned representations, (ii) a reward designer to generate enriched reward signals that guide the agent's actions toward the optimization objective, and (iii) a simulator to construct a virtual LAENet environment for evaluating enhanced state-reward pairs before policy training. Theoretical analysis and numerical results demonstrate that the proposed LLM-enhanced DRL framework achieves faster convergence, improved training stability, and superior performance compared with state-of-the-art baselines.

Low-altitude wireless networks (LAWNs) have been envisioned as flexible and transformative platforms for enabling delay-sensitive control applications in Internet of Things (IoT) systems. In this work, we investigate the real-time wireless control over LAWNs, where an aerial drone is employed to serve multiple mobile automated guided vehicles (AGVs) via finite blocklength (FBL) transmission. Toward this end, we adopt the model predictive control (MPC) to ensure accurate trajectory tracking, while we analyze the communication reliability using the outage probability. Subsequently, we formulate an optimization problem to jointly determine control policy, transmit power allocation, and drone trajectory by accounting for the maximum travel distance and control input constraints. To address the resultant non-convex optimization problem, we first derive the closed-form expression of the outage probability under FBL transmission. Based on this, we reformulate the original problem as a quadratic programming (QP) problem, followed by developing an alternating optimization (AO) framework. Specifically, we employ the projected gradient descent (PGD) method and the successive convex approximation (SCA) technique to achieve computationally efficient sub-optimal solutions. Furthermore, we thoroughly analyze the convergence and computational complexity of the proposed algorithm. Extensive simulations and AirSim-based experiments are conducted to validate the superiority of our proposed approach compared to the baseline schemes in terms of control performance.

Despite the widespread deployment of terrestrial networks, providing reliable communication services to remote areas and maintaining connectivity during emergencies remains challenging. Low Earth orbit (LEO) satellite constellations offer promising solutions with their global coverage capabilities and reduced latency, yet struggle with intermittent coverage and limited communication windows due to orbital dynamics. This paper introduces an age of information (AoI)-aware space-air-ground integrated network (SAGIN) architecture that leverages a high-altitude platform (HAP) as intelligent relay between the LEO satellites and ground terminals. Our three-layer design employs hybrid free-space optical (FSO) links for high-capacity satellite-to-HAP communication and reliable radio frequency (RF) links for HAP-to-ground transmission, and thus addressing the temporal discontinuity in LEO satellite coverage while serving diverse user priorities. Specifically, we formulate a joint optimization problem to simultaneously minimize the AoI and satellite handover frequency through optimal transmit power distribution and satellite selection decisions. This highly dynamic, non-convex problem with time-coupled constraints presents significant computational challenges for traditional approaches. To address these difficulties, we propose a novel diffusion model (DM)-enhanced dueling double deep Q-network withaction decomposition andstate transformer encoder (DD3QN-AS) algorithm that incorporates transformer-based temporal feature extraction and employs a DM-based latent prompt generative module to refine state-action representations through conditional denoising. Simulation results highlight the superior performance of the proposed approach compared with policy-based methods and some other deep reinforcement learning (DRL) benchmarks. Moreover, performance analysis under various system settings verifies the robustness of the proposed approach.

The rapid development of the low-altitude economy (LAE) has significantly increased the utilization of autonomous aerial vehicles (AAVs) in various applications, necessitating efficient and secure communication methods among AAV swarms. In this work, we aim to introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of AAVs when constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives by formulating a multi-objective optimization problem, which is NP-hard and with a large number of decision variables. Accordingly, we design a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead, which contains a conditional variational autoencoder (CVAE)-based generative method and a proposed powerful swarm intelligence algorithm. In this framework, CVAE can collect expert solutions obtained by the swarm intelligence algorithm in other environment states to explore characteristics and patterns, thereby directly generating high-quality initial solutions in new environment factors for the swarm intelligence algorithm to search solution space efficiently. Simulation results show that the proposed swarm intelligence algorithm outperforms other state-of-the-art baseline algorithms, and the GenSI can achieve similar optimization results by using far fewer iterations than the ordinary swarm intelligence algorithm. Experimental tests demonstrate that introducing the CVAE mechanism achieves a 58.7% reduction in execution time, which enables the deployment of GenSI even on AAV platforms with limited computing power.

Due to the massive computational demands of large generative models, AI-Generated Content (AIGC) can organize collaborative Mobile AIGC Service Providers (MASPs) at network edges to provide ubiquitous and customized content generation for resource-constrained users. However, such a paradigm faces two significant challenges: i) raw prompts (i.e., the task description from users) often lead to poor generation quality due to users' lack of experience with specific AIGC models, and ii) static service provisioning fails to efficiently utilize computational and communication resources given the heterogeneity of AIGC tasks. To address these challenges, we propose an intelligent mobile AIGC service scheme. Firstly, we develop an interactive prompt engineering mechanism that leverages a Large Language Model (LLM) to generate customized prompt corpora and employs Inverse Reinforcement Learning (IRL) for policy imitation through small-scale expert demonstrations. Secondly, we formulate a dynamic mobile AIGC service provisioning problem that jointly optimizes the number of inference trials and transmission power allocation. Then, we propose the Diffusion Enhanced Deep Deterministic Policy Gradient (D3PG) algorithm to solve the problem. By incorporating the diffusion process into Deep Reinforcement Learning (DRL) architecture, the environment exploration capability can be improved, thus adapting to varying mobile AIGC scenarios. Extensive experimental results demonstrate that our prompt engineering approach improves single-round generation success probability by 6.3×, while D3PG increases the user service experience by 50.3% compared to baseline DRL approaches.

The vehicular metaverse is a novel physical-virtual fusion realm that aims to disrupt the current transportation paradigm. Within this landscape, the coexistence of moving vehicles and their digital counterparts inevitably brings new privacy concerns. Pseudonym exchange, where vehicles exchange temporary identifiers with neighbors to enhance anonymity, offers an affordable solution to protect the location privacy of vehicles. However, existing pseudonym exchange schemes primarily focus on physical vehicles, limiting their effectiveness across physical and virtual spaces in the vehicular metaverse. Furthermore, studies have shown that many vehicles care little about their location privacy, so incentivizing more vehicles to participate in pseudonym exchanges remains a challenge. Motivated by these issues, we propose a physical-virtual dual pseudonym exchange scheme, incorporating an Attribute-Matched Double Dutch Auction (AMDDA) incentive mechanism to facilitate pseudonym exchange transactions. We use a trajectory prediction model to evaluate vehicle attributes, ensuring pseudonym exchange between vehicles with high trajectory similarity to enhance location privacy preservation. Furthermore, we devise a Generative Diffusion Model (GDM)-based approach to derive the optimal pricing strategy in the AMDDA market. Extensive experiments on real-world datasets demonstrate that the proposed scheme significantly improves both the efficiency and degree of location privacy protection.

Vehicular fog computing (VFC) has emerged as a promising paradigm, which leverages the idle computational resources of nearby fog vehicles (FVs) to complement the computing capabilities of conventional vehicular edge computing. However, utilizing VFC to meet the delay-sensitive and computation-intensive requirements of the FVs poses several challenges. First, the limited resources of road side units (RSUs) struggle to accommodate the growing and diverse demands of vehicles. This limitation is further exacerbated by the information asymmetry between the controller and FVs due to the reluctance of FVs to disclose private information and to share resources voluntarily. This information asymmetry hinders the efficient resource allocation and coordination. Second, the heterogeneity in task requirements and the varying capabilities of RSUs and FVs complicate efficient task offloading, thereby resulting in inefficient resource utilization and potential performance degradation. To address these challenges, we first present a hierarchical VFC architecture that incorporates the computing capabilities of both RSUs and FVs. Then, we formulate a delay minimization optimization problem (DMOP), which is an NP-hard mixed integer nonlinear programming (MINLP) problem. To solve the DMOP, we propose a joint computing resource allocation and task offloading approach (JCRATOA), which comprises the components of computing resource allocation and task offloading. Specifically, we propose a convex optimization-based method for RSU resource allocation and a contract theory-based incentive mechanism for FV resource allocation. Moreover, we present a two-sided matching method for task offloading by employing the matching game. Additionally, we theoretically prove the polynomial complexity of JCRATOA. Simulation results demonstrate that the proposed JCRATOA outperforms the benchmark approaches, achieving at least 7.6%, 6.6%, 6.25%, and 11.9% improvements in terms of the task completion delay, task completion ratio, system throughput, and resource utilization fairness, respectively, while satisfying the energy constraints of task vehicles (TVs), RSUs, and FVs.

Emerging technologies in sixth generation (6G) of wireless communications, such as terahertz communication and ultra-massive multiple-input multiple-output, present promising prospects. Despite the high data rate potential of millimeter wave communications, millimeter wave (mmWave) communications in urban low altitude economy (LAE) environments are constrained by challenges such as signal attenuation and multipath interference. Specially, in urban environments, mmWave communication experiences significant attenuation due to buildings, owing to its short wavelength, which necessitates developing innovative approaches to improve the robustness of such communications in LAE networking. In this paper, we explore the use of an unmanned aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) to support low altitude mmWave communication. Specifically, we consider a typical urban low altitude communication scenario where a UAV-carried IRS establishes a line-of-sight (LoS) channel between the mobile users and a source user (SU) despite the presence of obstacles. Subsequently, we formulate an optimization problem aimed at maximizing the transmission rates and minimizing the energy consumption of the UAV by jointly optimizing phase shifts of the IRS and UAV trajectory. Given the non-convex nature of the problem and its high dynamics, we propose a deep reinforcement learning-based approach incorporating neural episodic control, long short-term memory, and an IRS phase shift control method to enhance the stability and accelerate the convergence. Simulation results show that the proposed algorithm effectively resolves the problem and surpasses other benchmark algorithms in various performances.

In long-distance and long-duration flight missions of unmanned aerial vehicles (UAVs), optimal scheduling of flight speed and energy replenishment is crucial to ensure flight efficiency and safety. This paper focuses on a UAV-based patrol inspection system, where a UAV is scheduled to visit multiple task nodes that are geographically distributed in the communication coverage of a base station (BS). The UAV hovers at each task node, performing data collection and data processing. The BS is equipped with a mobile edge computing (MEC) server and a battery swapping station, offering computation and energy support to the UAV. A decision-making model customized for the UAV is proposed, jointly optimizing flight speed selection, battery swapping, and task offloading to minimize the UAV's total operational cost in its flight. By introducing virtual nodes in the flight network, we construct a unidirectional extended graph, based on which the original nonconvex cost minimization problem is reformulated to a tractable mixed-integer convex problem. Further, a fast heuristic based on analytical target cascading (ATC) is developed to obtain suboptimal solutions to large-scale problems. Results demonstrate that the proposed model can lower the UAV's total operational cost by providing greater flexibility in terms of speed selection and battery swapping, and the proposed heuristic shows high computational efficiency for large-scale network scenarios.

This paper introduces a two-stage generative AI (GenAI) framework tailored for temporal spectrum cartography in low-altitude economy networks (LAENets). LAENets, characterized by diverse aerial devices such as UAVs, rely heavily on wireless communication technologies while facing challenges, including spectrum congestion and dynamic environmental interference. Traditional spectrum cartography methods have limitations in handling the temporal and spatial complexities inherent to these networks. Addressing these challenges, the proposed framework first employs a Reconstructive Masked Autoencoder (RecMAE) capable of accurately reconstructing spectrum maps from sparse and temporally varying sensor data using a novel dual-mask mechanism. This approach significantly enhances the precision of reconstructed radio frequency (RF) power maps. In the second stage, the Multi-agent Diffusion Policy (MADP) method integrates diffusion-based reinforcement learning to optimize the trajectories of dynamic UAV sensors. By leveraging temporal-attention encoding, this method effectively manages spatial exploration and exploitation to minimize cumulative reconstruction errors. Extensive numerical experiments show that this integrated GenAI framework consistently surpasses traditional interpolation and deep learning methods, especially under sparse sensing conditions. The proposed trajectory planner substantially improves spectrum map accuracy, reconstruction stability, and sensor deployment efficiency in dynamically evolving low-altitude environments.

In the rapidly evolving Next-Generation Networking (NGN) era, the adoption of zero-trust architectures has become increasingly crucial to protect security. However, provisioning zero-trust services in NGNs poses significant challenges, primarily due to the environmental complexity and dynamics. Motivated by these challenges, this paper explores efficient zero-trust service provisioning using hierarchical micro-segmentations. Specifically, we model zero-trust networks via hierarchical graphs, thereby jointly considering the resource- and trust-level features to optimize service efficiency. We organize such zero-trust networks through micro-segmentations, which support granular zero-trust policies efficiently. To generate the optimal micro-segmentation, we present the Large Language Model-Enhanced Graph Diffusion (LEGD) algorithm, which leverages the diffusion process to realize a high-quality generation paradigm. Additionally, we utilize gradient ascent and Large Language Models (LLM) to enable LEGD to optimize the generation policy and understand complicated graphical features. Moreover, realizing the unique trustworthiness updates and service upgrades in zero-trust NGN, we further present LEGD-Adaptive Maintenance (LEGD-AM), providing an adaptive way to perform task-oriented fine-tuning on LEGD. Extensive experiments demonstrate that the proposed LEGD achieves 90% higher efficiency in provisioning services compared with other baselines. Moreover, the LEGD-AM can reduce the service outage time by over 50%.

The cell-free integrated sensing and communication (CF-ISAC) system, which effectively mitigates intra-cell interference and provides precise sensing accuracy, is a promising technology for future 6G networks. However, to fully capitalize on the potential of CF-ISAC, accurate time synchronization (TS) between access points (APs) is critical. Due to the limitations of current synchronization technologies, TS errors have become a significant challenge in the development of the CF-ISAC system. In this paper, we propose a novel CF-ISAC architecture based on movable antennas (MAs), which exploits spatial diversity to enhance communication rates, maintain sensing accuracy, and reduce the impact of TS errors. We formulate a worst-case sensing accuracy optimization problem for TS errors to address this challenge, deriving the worst-case Cramér-Rao lower bound (CRLB). Subsequently, we develop a joint optimization framework for AP beamforming and MA positions to satisfy communication rate constraints while improving sensing accuracy. A robust optimization framework is designed for the highly complex and non-convex problem. Specifically, we employ manifold optimization (MO) to solve the worst-case sensing accuracy optimization problem. Then, we propose an MA-enabled meta-reinforcement learning (MA-MetaRL) to design optimization variables while satisfying constraints on MA positions, communication rate, and transmit power, thereby improving sensing accuracy. The simulation results demonstrate that the proposed robust optimization algorithm significantly improves the accuracy of the detection and is strong against TS errors. Moreover, compared to conventional fixed position antenna (FPA) technologies, the proposed MA-aided CF-ISAC architecture achieves higher system capacity, thus validating its effectiveness.

Generative Artificial Intelligence (GenAI) models, with their powerful feature learning capabilities, have been applied in many fields. In mobile wireless communications, GenAI can dynamically optimize the network to enhance the user experience. Especially in signal detection and channel estimation tasks, due to digital signals following a certain random distribution, GenAI models can fully utilize their distribution learning characteristics. For example, diffusion models (DMs) and normalized flow models have been applied to related tasks. However, since the DM cannot guarantee that the generated results are the maximum-likelihood estimation points of the distribution during the data generation process, the successful task completion rate is reduced. Based on this, this paper proposes a Maximum-Likelihood Estimation Inference (MLEI) framework. The framework uses the loss function in the forward diffusion process of the DM to infer the maximum-likelihood estimation points in the discrete space. Then, we present a signal detection task in near-field communication scenarios with unknown noise characteristics. In experiments, numerical results demonstrate that the proposed framework has better performance than state-of-the-art signal estimators.

This article presents a novel reconfigurable intelligent surface (RIS)-integrated base station (BS) by deploying an RIS very close to the base station antennas (BAs), within its radiative near-field range. We propose a practical algorithm to maximize the performance of uplink communications with the proposed RIS-integrated BS while maintaining reasonable complexity. Furthermore, we have implemented a testbed to experiment with the proposed RIS-integrated BS-based uplink multiple-input-multiple-output (MIMO) system. The experimental data validates the performance of the proposed RIS-integrated BS-based uplink MIMO system.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for next-generation wireless communications due to its ability to manipulate electromagnetic (EM) waves. This work introduces a novel RIS direct modulation scheme based on the backscatter communication concept. In this work, the RIS acts as a sole information modulator. Unlike conventional RIS systems that relay incoming data-carrying waves, this work enables RIS to encode its own information into unmodulated waves without requiring any hardware modifications. We propose an adaptive algorithm that maximizes modulation order while meeting the quality of service (QoS) requirements by selecting appropriate constellation points to minimize error probability. The proposed scheme was validated through simulations and experiments using a passive 5.8 GHz RIS prototype. The validations demonstrate consistent performance in both distributed and co-located transmitter-receiver scenarios. These results highlight its feasibility as an alternative approach for RIS-assisted communications and contribute to further exploration of RIS-enabled symbiotic radio (SR) and over-the-air (OTA) modulation systems.

This paper addresses the challenge of optimizing multi-hop routing in IoT-based digital twin systems, referred to as the MOUNTAIN problem. Multi-hop routing is inherently complex due to the need to balance energy consumption and communication reliability across multiple nodes, especially in dynamic and large-scale IoT networks. In MOUNTAIN, multiple IoT devices in the physical network (PN) frequently transmit data to the digital network twin (DNT), managed by a central server. Given the limited energy resources of IoT devices, our approach considers both energy efficiency and communication reliability. We formulate the MOUNTAIN problem as an optimization task aimed at reducing overall energy consumption while maintaining robust data transmission. Moreover, we address the problem with both single-task optimization and multi-task optimization and propose two corresponding evolution-based metaheuristics that utilize well-designed solution representations and genetic operators to obtain near-optimal solutions to the problem. Among them, the proposed Single-task Evolutionary Algorithm (STEA) solves each problem instance independently, while the proposed Multi-task Evolutionary Algorithm (MTEA) solves multiple instances at the same time to take advantage of exchanging useful solution information during parallel solution searches. Extensive experiments on synthetic datasets demonstrate that our proposed algorithms significantly outperform existing methods, reducing energy consumption and improving network stability. This research contributes to the development of sustainable and efficient IoT infrastructures, which are essential for the operational demands of digital twin applications.

An effective traffic management system is crucial to smart city growth. Consequently, the significance of IoT devices is increasing. Numerous IoT devices, including cameras, are commonly positioned along major roads in a smart city. These IoT devices, embedded with computing and transmitting capabilities, collect data from cameras and then relay it to the central traffic controller (CTC) responsible for managing traffic flow. In our study, we introduce a novel framework termed semantic communication (SemCom), which integrates a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network. The SemCom model employs a semantic encoder within each IoT device to extract pertinent information from raw images. This encoded data is transmitted to the CTC as symbols by the transmitter of the IoT device. Subsequently, the CTC’s semantic decoder utilizes this sequence of symbols to predict vehicle counts on respective roads and devise traffic management strategies accordingly. To enhance the quality of experience (QoE), we formulate an optimization problem considering vehicle user safety, IoT device transmission power, prediction accuracy, and semantic entropy. Through numerical analysis, we demonstrate that the SemCom model significantly reduces overhead by 54.42% compared to conventional source encoder/decoder models. Moreover, simulation results showcase the superiority of our proposed model in terms of mean absolute error (MAE) and QoE metrics over existing state-of-the-art approaches. Since vehicle count prediction is pivotal in traffic management, our SemCom framework offers a promising avenue for efficient and accurate vehicle count prediction, contributing to more effective traffic management in smart cities.

Reconfigurable intelligent surface (RIS) has gained significant momentum as a cost-effective and energy-efficient technology to enable the next generation of mobile communications. In this article, we propose an RIS-integrated base station (BS) by deploying an RIS sufficiently close to the base station antennas (BAs), within its radiative near-field range. In the proposed RIS-integrated BS system, we utilize RIS as a passive array to reconfigure incoming signals from user equipments (UEs) without experiencing substantial path loss. The near-field channel model between the RIS and BAs is analyzed and applied to the RIS beam control model. Furthermore, we develop a practical algorithm to maximize the performance of the proposed RIS-integrated BS-based uplink multiple-input-multiple-output (MIMO) system with the aim of maintaining reasonable complexity. This goal is achieved by combining the two proposed algorithms, beam search and path-antenna pairing algorithms. The beam search algorithm identifies the radio paths of all UEs to the RIS by sweeping the beams of the RIS. Then, the path-antenna pairing algorithm allocates the strongest radio path between each UE and the RIS to one of the BAs by controlling an RIS beam to direct the signal from that path to the BA. Finally, we have built a real-time testbed to experiment with the proposed RIS-integrated BS-based uplink MIMO system. Numerical results, including experimental data, validate the effectiveness of the proposed RIS-integrated BS-based uplink MIMO system.

In this work, we consider the integration of energy harvesting (EH) and semantic communication strategies in resource-constrained Internet of Things (IoT) systems. The system empowers IoT devices to harvest energy from a base station, utilizing this harvested energy for the extraction and transmission of semantic information (e.g., scene graphs). To maximize the total transmission of image data or scene graphs to the central station, we formulate a comprehensive problem that jointly optimizes the EH duration, original image selection, transmit power, and channel allocation to IoT devices. The challenges arising from the dynamic environments and uncertain system parameters are effectively tackled by policy-based deep reinforcement learning algorithms, i.e., advantage actor-critic (A2C) and proximal policy optimization (PPO). Simulation results are implemented on the real data set clearly showing the superior performance achieved by our proposed algorithms compared to the baseline schemes. Notably, our approach enables IoT devices to transmit a greater number of original images and scene graphs with increased triplets to the central station, as highlighted in the simulation outcomes. This phenomenon showcases the potential of our strategy to enhance the capabilities of IoT systems in dynamic environments.

Distributed Artificial Intelligence-Generated Content (AIGC) has attracted significant attention, but two key challenges remain: maximizing subjective Quality of Experience (QoE) and improving energy efficiency, which are particularly pronounced in widely adopted Generative Diffusion Model (GDM)-based image generation services. In this paper, we propose a novel user-centric Interactive AI (IAI) approach for service management, with a distributed GDM-based AIGC framework that emphasizes efficient and cooperative deployment. The proposed method restructures the GDM inference process by allowing users with semantically similar prompts to share parts of the denoising chain. Furthermore, to maximize the users' subjective QoE, we propose an IAI approach, i.e., Reinforcement Learning With Large Language Models Interaction (RLLI), which utilizes Large Language Model (LLM)-empowered generative agents to replicate users interactions, providing real-time and subjective QoE feedback aligned with diverse user personalities. Lastly, we present the GDM-based Deep Deterministic Policy Gradient (G-DDPG) algorithm, adapted to the proposed RLLI framework, to allocate communication and computing resources effectively while accounting for subjective user traits and dynamic wireless conditions. Simulation results demonstrate that G-DDPG improves total QoE by 15% compared with the standard DDPG algorithm.

In this paper, we investigate the joint data relay communication and semantic communication in an unmaned aerial vehicle (UAV)-based Metaverse system. Therein, UAVs as relays forward data from ground users to ground data collectors (GDCs). Meanwhile, they capture images of area of interests, and the images can be used to update digital twin (DTs) for Metaverse platforms. As the UAVs and their GDCs may belong to different platforms, they may use the same spectrum at the same time that cause interference to each other. A third party, i.e., a network service provider (NSP), is involved to provide licensed channels in terms of transmission periods to the UAVs. We design auction schemes as incentive mechanisms for trading the transmission periods between the UAVs and the NSP. With a single transmission period, we design a learning auction with neural networks constructed from the Myerson theorem that maximizes the NSP’s revenue while ensuring important economic properties. With multiple transmission periods, we develop a nearly-optimal auction scheme by using attention mechanisms. A semantic communication technique is implemented at each UAV to reduce the size of the original images and cost for using the licensed channels. Extensive experiments shows that the learning auction driven from the Myerson theorem outperform the baseline scheme in terms of NSP’s revenue and truthfulness, while the revenue obtained by the attention-based auction is much higher than the existing learning auction.

We investigate a heterogeneous network (HetNet) including sub-6GHz base stations (BSs), mmWave BSs, and THz BSs to support enhanced mobile broadband (eMBB) users and ultra-reliable low-latency communication (URLLC) users. We particularly investigate a user-centric network in which the users locally and dynamically select and switch among BSs over time to achieve their highest utility. Two types of users have different Quality of Service (QoS) requirements. Thus, we design two types of utility functions specifically for the eMBB users and URLLC users. Then, to model the dynamic selection behavior of the users, we propose to use a fractional game with the power-law memory. The fractional game allows the eMBB users and the URLLC users to incorporate their past strategies into their current selection, thus improving their utility. Furthermore, we consider the case that the BSs communicate the system state with each other, and we model the network selection of the users as a multi-agent problem. Then, we propose to use a multi-agent deep reinforcement learning (MADRL) algorithm that enables the URLLC users and eMBB users to make their network selection decision online to achieve their long-term utility. Various simulation results are provided to demonstrate the scalability and effectiveness of the proposed approaches. Particularly, compared with the classical game, the fractional game is able to achieve a higher utility but incurs a higher network adaptation cost. Moreover, the different types of URLLC users (in terms of latency and reliability requirements) and the number of URLLC users in the network significantly affect the total utility and the network selection strategies of the eMBB users. Importantly, given the full observations, the MADRL outperforms both classical and fractional games in terms of total network utility.

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.

This paper investigates the deep learning based approaches for simultaneous wireless information and power transfer (SWIPT). The quality-of-service (QoS) constrained sumrate maximization problems are, respectively, formulated for power-splitting (PS) receivers and time-switching (TS) receivers and solved by a unified graph neural network (GNN) based model termed SWIPT net (SWIPTNet). To improve the performance of SWIPTNet, we first propose a single-type output method to reduce the learning complexity and facilitate the satisfaction of QoS constraints, and then, utilize the Laplace transform to enhance input features with the structural information. Besides, we adopt the multi-head attention and layer connection to enhance feature extracting. Furthermore, we present the implementation of transfer learning to the SWIPTNet between PS and TS receivers. Ablation studies show the effectiveness of key components in the SWIPTNet. Numerical results also demonstrate the capability of SWIPTNet in achieving nearoptimal performance with millisecond-level inference speed which is much faster than the traditional optimization algorithms. We also show the effectiveness of transfer learning via fast convergence and expressive capability improvement.

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments.

Multi-access edge computing (MEC) is emerging as a promising paradigm to provide flexible computing services close to user devices (UDs). However, meeting the computation-hungry and delay-sensitive demands of UDs faces several challenges, including the resource constraints of MEC servers, inherent dynamic and complex features in the MEC system, and difficulty in dealing with the time-coupled and decision-coupled optimization. In this work, we first present an edge-cloud collaborative MEC architecture, where the MEC servers and cloud collaboratively provide offloading services for UDs. Moreover, we formulate an energy-efficient and delay-aware optimization problem (EEDAOP) to minimize the energy consumption of UDs under the constraints of task deadlines and long-term queuing delays. Since the problem is proved to be non-convex mixed integer nonlinear programming (MINLP), we propose an online joint communication resource allocation and task offloading approach (OJCTA). Specifically, we transform EEDAOP into a real-time optimization problem by employing the Lyapunov optimization framework. Then, to solve the real-time optimization problem, we propose a communication resource allocation and task offloading optimization method by employing the Tammer decomposition mechanism, convex optimization method, bilateral matching mechanism, and dependent rounding method. Simulation results demonstrate that the proposed OJCTA can achieve superior system performance compared to the benchmark approaches.

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.

This paper investigates adaptive transmission strategies in embodied AI-enhanced vehicular networks by integrating vision language models (VLMs) for semantic information extraction and deep reinforcement learning (DRL) for decision-making. The proposed framework aims to optimize both data transmission efficiency and decision accuracy by formulating an optimization problem that incorporates the Weber-Fechner law, serving as a metric for balancing bandwidth utilization and quality of experience (QoE). Specifically, we employ the large language and vision assistant (LLAVA) model to extract critical semantic information from raw image data captured by embodied AI agents (i.e., vehicles), reducing transmission data size by approximately more than 90% while retaining essential content for vehicular communication and decision-making. In the dynamic vehicular environment, we employ a generalized advantage estimation-based proximal policy optimization (GAE-PPO) method to stabilize decision-making under uncertainty. Simulation results show that attention maps from LLAVA highlight the model's focus on relevant image regions, enhancing semantic representation accuracy. Additionally, our proposed transmission strategy improves QoE by up to 36% compared to DDPG and accelerates convergence by reducing required steps by up to 47% compared to pure PPO. Further analysis indicates that adapting semantic symbol length provides an effective trade-off between transmission quality and bandwidth, achieving up to a 61.4% improvement in QoE when scaling from 4 to 8 vehicles.

This paper investigates intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) visible light communication (VLC) networks utilizing the rate-splitting multiple access (RSMA) scheme. In these networks, an eavesdropper (Eve) attempts to eavesdrop on communications intended for legitimate users (LUs). To enhance information security and energy efficiency simultaneously, we formulate a secrecy energy efficiency (SEE) maximization problem by jointly optimizing the beamforming vectors, RSMA common rates, direct current (DC) bias, and IRS alignment matrices. The problem is constrained by total power budget, quality of service (QoS) requirements, linear operating region of light emitting diodes (LEDs), and common information rate allocation. Due to the non-convex and NP-hard nature of the formulated problem, we propose a deep reinforcement learning (DRL)-based dual-sampling proximal policy optimization (DS-PPO) approach. The approach leverages dual sample strategies and generalized advantage estimation (GAE). In addition, the maximum ratio transmission (MRT) and zero-forcing (ZF) are adopted to design the beamforming vectors. Simulation results show that the proposed DS-PPO approach outperforms traditional baseline approaches. Moreover, the implementation of the RSMA scheme and IRS contributes to overall system performance, achieving approximately 19.67% improvement over traditional multiple access schemes and 25.74% improvement over networks without IRS deployment.

Semantic communication (SemCom) holds promise for reducing network resource consumption while achieving the communications goal. However, the computational overheads in jointly training semantic encoders and decoders—and the subsequent deployment in network devices—are overlooked. Recent advances in Generative artificial intelligence (GAI) offer a potential solution. The robust learning abilities of GAI models indicate that semantic decoders can reconstruct source messages using a limited amount of semantic information, e.g., prompts, without joint training with the semantic encoder. A notable challenge, however, is the instability introduced by GAI’s diverse generation ability. This instability, evident in outputs like text-generated images, limits the direct application of GAI in scenarios demanding accurate message recovery, such as face image transmission. To solve the above problems, this paper proposes a GAI-aided SemCom system with multi-model prompts for accurate content decoding. Moreover, in response to security concerns, we introduce the application of covert communications aided by a friendly jammer. The system jointly optimizes the diffusion step, jamming, and transmitting power with the aid of the generative diffusion models, enabling successful and secure transmission of the source messages.

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLMenabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems.

In this paper, we propose a simultaneous wire-less information and power transfer (SWIPT)-enabled Ad hoc network underlay rate-splitting multiple access (RSMA)-based system with intelligent reflecting surface (IRS). Therein, a base station (BS) in a primary network uses RSMA to serve primary users (PUs), and secondary user (SU) pairs constitute an Ad hoc network sharing the spectrum with the primary network. The power splitting (PS)-based SWIPT protocol is used in the Ad hoc network that allow the SU receivers to decode the information and harvest energy simultaneously. An IRS is deployed to further enhance the system performance. We formulate optimization problems that optimize the common data rate allocation and beamformers associated with the common and private messages of RSMA, the beamformers and the PS factor in the Ad hoc network, and reflection coefficients of the IRS to maximize the minimum rate of the PUs while satisfying the requirements of harvested energy and data rate of the SU pairs. The optimization problems are non-convex and challenging to be solved. We propose a low complexity algorithm based on alternating descent techniques. Numerical results demonstrate the effectiveness and improvement of the proposed framework compared with the framework based on existing multiple access schemes, i.e., non-orthogonal multiple access (NOMA).

Vehicle count prediction is an important aspect of smart city traffic management. Most major roads are monitored by cameras with computing and transmitting capabilities. These cameras provide data to the central traffic controller (CTC), which is in charge of traffic control management. In this paper, we propose a joint CNN-LSTM-based semantic communication (SemCom) model in which the semantic encoder of a camera extracts the relevant semantics from raw images. The encoded semantics are then sent to the CTC by the transmitter in the form of symbols. The semantic decoder of the CTC predicts the vehicle count on each road based on the sequence of received symbols and develops a traffic management strategy accordingly. Using numerical results, we show that the proposed SemCom model reduces overhead by 54.42% when compared to source encoder/decoder methods. Also, we demonstrate through simulations that the proposed model outperforms state-of-the-art models in terms of mean absolute error (MAE) and mean-squared error (MSE).

To enable high-tech lifestyles in the near future, trillions of connected low-power internet of things (IoT) devices should perpetually operate to meet the high-demand requirements of the users. Simultaneous wireless information and power transfer (SWIPT) is an indispensable technology for guaranteeing the endurable operation of massive IoT devices. Reconfigurable intelligent surface (RIS) is currently emerging as a cost-effective and energy-efficient solution for controlling wireless communication environments to enhance the quality of service. In this paper, we propose an efficient beam-sharing algorithm (BSA) designed for SWIPT systems that incorporate RIS to realize the coexistence of wireless power and information. The considered RIS-assisted SWIPT system consists of one RIS, one data transmitter (DTx), one power transmitter (PTx), one data user (DU), and one power user (PU). Since the required power for the power transfer is radically higher than that for the information transmission, the high power signal leaked from the PTx can cause fatal damage to data transmission and sensitive electronic components (e.g., LNA) integrated with DU. Hence, we primarily aim to maximize the desired power transfer from PTx to PU while minimizing the leakage power (i.e., interference) delivered to the DU. Additionally, the algorithm maximizes the quality of the information signal transmitted from DTx to DU. We then develop a simulator to verify the effectiveness of the proposed algorithm. We have investigated the performance of the proposed BSA algorithm with various quantization phase shifts (i.e., 1-bit, 2-bit, 3-bit, and continuous phase). A suppression ranging between 15 dB and 38 dB is witnessed in all simulation scenarios, while the DTx-DU power and PTx-PU power are simultaneously maximized in the simulated scenario. For further confirmation, we have built a real-life RIS-assisted SWIPT testbed and validated the proposed BSA algorithm. Experimental results indicate that the proposed BSA algorithm successfully delivers the maximum power/signal from PTx/DTx to PU/DU while limiting the interference signal sent by PTx to the DU to ensure robust and reliable data transmission.

A novel near-field transmission framework is proposed for dynamic metasurface antenna (DMA)-enabled nonorthogonal multiple access (NOMA) networks. The base station (BS) exploits the hybrid beamforming to communicate with multiple near users (NUs) and far users (FUs) using the NOMA principle. Based on this framework, two novel beamforming schemes are proposed. 1) For the case of the grouped users distributed in the same direction, a beam-steering scheme is developed. The metric of beam pattern error (BPE) is introduced for the characterization of the gap between the hybrid beamformers and the desired ideal beamformers, where a two-layer algorithm is proposed to minimize BPE by optimizing hybrid beamformers. Then, the optimal power allocation strategy is obtained to maximize the sum achievable rate of the network. 2) For the case of users randomly distributed, a beam-splitting scheme is proposed, where two subbeamformers are extracted from the single beamformer to serve different users in the same group. An alternating optimization (AO) algorithm is proposed for hybrid beamformer optimization, and the optimal power allocation is also derived. Numerical results validate that: 1) the proposed beamforming schemes exhibit superior performance compared with the existing imperfect-resolution-based beamforming scheme and 2) the communication rate of the proposed transmission framework is sensitive to the imperfect distance knowledge of NUs but not to that of FUs.

In this paper, we address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data to help creating and rendering the digital copy of the physical world in the Metaverse. Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices. Nevertheless, mechanisms to hire sensing IoT devices to share their data with the VSP and then deliver the constructed digital twin to the Metaverse users are vulnerable to adverse selection problem. The adverse selection problem, which is caused by information asymmetry between the system entities, becomes harder to solve when the private information of the different entities are multi-dimensional. We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem. To demonstrate the effectiveness of our algorithm, we conduct extensive simulations and measure several key performance metrics of the contract for the Metaverse. Our results show that our designed iterative contract is able to incentivize the participants to interact truthfully, which maximizes the profit of the VSP with minimal individual rationality (IR) and incentive compatibility (IC) violation rates. Furthermore, the proposed learning-based iterative contract framework has limited access to the private information of the participants, which is to the best of our knowledge, the first of its kind in addressing the problem of adverse selection in incentive mechanisms.

Groundbreaking applications such as ChatGPT have heightened research interest in generative artificial intelligence (GAI). Essentially, GAI excels not only in content generation but also signal processing, offering support for wireless sensing. Hence, we introduce a novel GAI-assisted human flow detection system (G-HFD). Rigorously, G-HFD first uses the channel state information (CSI) to estimate the velocity and acceleration of propagation path length change of the human induced reflection (HIR). Then, given the strong inference ability of the diffusion model, we propose a unified weighted conditional diffusion model (UW-CDM) to denoise the estimation results, enabling detection of the number of targets. Next, we use the CSI obtained by a uniform linear array with wavelength spacing to estimate the HIR’s time of flight and direction of arrival (DoA). In this process, UW-CDM solves the problem of ambiguous DoA spectrum, ensuring accurate DoA estimation. Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i.e., the subflow size. The evaluation based on practical downlink communication signals shows G-HFD’s accuracy of subflow size detection can reach 91%. This validates its effectiveness and underscores the significant potential of GAI in the context of wireless sensing.

In response to the needs of 6G global communications, satellite communication networks have emerged as a key solution. However, the large-scale development of satellite communication networks is constrained by complex system models, whose modeling is challenging for massive users. Moreover, transmission interference between satellites and users seriously affects communication performance. To solve these problems, this paper develops generative artificial intelligence (AI) agents for model formulation and then applies a mixture of experts (MoE) approach to design transmission strategies. Specifically, we leverage large language models (LLMs) to build an interactive modeling paradigm and utilize retrieval-augmented generation (RAG) to extract satellite expert knowledge that supports mathematical modeling. Afterward, by integrating the expertise of multiple specialized components, we propose an MoE-proximal policy optimization (PPO) approach to solve the formulated problem. Each expert can optimize the optimization variables at which it excels through specialized training through its own network and then aggregate them through the gating network to perform joint optimization. The simulation results validate the accuracy and effectiveness of employing a generative agent for problem formulation. Furthermore, the superiority of the proposed MoE-ppo approach over other benchmarks is confirmed in solving the formulated problem. The adaptability of MoE-PPO to various customized modeling problems has also been demonstrated.

Driven by the ever-increasing requirements of ultrahigh spectral efficiency, ultralow latency, and massive connectivity, the forefront of wireless research calls for the design of advanced next-generation multiple access schemes to facilitate the provisioning of these stringent demands. This inspires the embrace of nonorthogonal multiple access (NOMA) in future wireless communication networks. Nevertheless, the support of massive access via NOMA leads to additional security threats due to the open nature of the air interface, the broadcast characteristic of radio propagation, and the intertwined relationship among paired NOMA users. To address this specific challenge, the superimposed transmission of NOMA can be explored as new opportunities for security-aware design; for example, multiuser interference inherent in NOMA can be constructively engineered to benefit communication secrecy and privacy. The purpose of this tutorial is to provide a comprehensive overview of the state-of-the-art physical layer security techniques that guarantee wireless security and privacy for NOMA networks, along with the opportunities, technical challenges, and future research trends.

Wireless information and energy transfer (WIET) represents an emerging paradigm that employs controllable transmission of radio frequency signals for the dual purpose of data communication and wireless charging. As such, WIET is widely regarded as an enabler of envisioned sixth-generation (6G) use cases that rely on energy-sustainable Internet-of-Things (IoT) networks, such as smart cities and smart grids. Meeting the quality-of-service demands of WIET, in terms of both data transfer and power delivery, requires effective codesign of the information and energy signals. In this article, we present the main principles and design aspects of WIET, focusing on its integration in 6G networks. First, we discuss how conventional communication notions, such as resource allocation and waveform design, need to be revisited in the context of WIET. Next, we consider various candidate 6G technologies that can boost WIET efficiency, namely, holographic multiple-input multiple-output, near-field beamforming, terahertz communication, intelligent reflecting surfaces (IRSs), and reconfigurable (fluid) antenna arrays. We introduce respective WIET design methods, analyze the promising performance gains of these WIET systems, and discuss challenges, open issues, and future research directions. Finally, a near-field energy beamforming scheme and a power-based IRS beamforming algorithm are experimentally validated using a wireless energy transfer testbed. The vision of WIET in communication systems has been gaining momentum in recent years, with constant progress with respect to theoretical and also practical aspects. The comprehensive overview of the state of the art of WIET presented in this article highlights the potential of WIET systems and their overall benefits in 6G networks.

Joint radar and communication (JRC) is a promising technique for spectrum re-utilization, which enables radar sensing and data transmission to operate on the same frequencies and the same devices. However, due to the multi-objective property of JRC systems, channel allocation to JRC nodes should be carefully designed to maximize system performance. Additionally, because of the broadcast nature of wireless signals, a watchful adversary, i.e., a warden, can detect ongoing transmissions and attack the system. Thus, we develop a covert JRC system that minimizes the detection probability by wardens, in which friendly jammers are deployed to improve the covertness of the JRC nodes during radar sensing and data transmission operations. Furthermore, we propose a robust multi-item auction design for channel allocation for such a JRC system that considers the uncertainty in bids. The proposed auction mechanism achieves the properties of truthfulness, individual rationality, budget feasibility, and computational efficiency. The simulations clearly show the benefits of our design to support covert JRC systems and to provide incentive to the JRC nodes in obtaining spectrum, in which the auction-based channel allocation mechanism is robust against perturbations in the bids, which is highly effective for JRC nodes working in uncertain environments.

We investigate incentive mechanism designs for edge computing trading between virtual service providers (VSPs) and an edge computing provider (ECP). The VSPs deploy unmanned aerial vehicles (UAVs) to collect sensing data from physical objects for updating their digital twins (DTs). In the case with a single computing unit, we design a deep learning (DL)-based auction constructed from the Myerson theorem to maximize the ECP's revenue and guarantee incentive compatibility (IC) and individual rationality (IR). In the case of multiple computing units, a DL-based auction based on an augmented Lagrangian method is proposed that maximizes the ECP's revenue and guarantees IC, IR, and budget (BG) constraints. A semantic communication (SemCom) technique is employed to reduce the collected data and offloading cost for the VSPs. To train the deep learning algorithms, we use valuations of the computing resources to the VSPs, which particularly are a function of the age of DT, semantic symbol size, and communication time of the UAVs. We provide numerical results showing that the proposed auctions outperform the classical auctions in terms of ECP's revenue, IR, IC, BG, and their ability of preventing the false bid submissions. Also, SemCom reduces the offloading cost for the VSPs.

Image transmission over wireless communications can be used in a variety of applications, such as smart cities, surveillance systems, and Metaverse construction. In this paper, we propose a task-oriented semantic information transmission (SIT) framework with rate-splitting multiple access (RSMA) for image transmission. As such, only the semantic information of interest is transmitted to each user, and RSMA is adopted to improve transmission efficiency. We also design the quality of experience (QoE) for the framework as a performance metric, which can be used for transmission-parameter optimization. Specifically, we first optimize power allocation with the top-Ncommon message selection strategy. To further improve system performance, we jointly optimize power allocation and common message selection. Simulation results show that the proposed task-oriented SIT framework with RSMA outperforms the space-division multiple access (SDMA)-based benchmark, which reflects the effectiveness of the proposed framework. Furthermore, the results show that optimizing power allocation can improve performance significantly as compared with fixing power allocation, and the joint optimization of power allocation and common message selection has an obvious performance gain over optimizing only power allocation, which demonstrates the effectiveness of the designed optimization algorithms.

A vital challenge in the deployment of federated learning (FL) over wireless networks is the high energy consumption incurred for the local computation and model update upload on energy-constrained devices such as IoT sensors. Equipping with energy harvesting (EH) modules is a promising solution that allows the devices to work in a self-sustainable manner. Moreover, quantizing the model updates can further improve the energy efficiency during the upload. In this paper, we propose an EH-enabled FL system with model quantization in which EH devices act as clients and client scheduling, model quantization, and transmit energy are jointly optimized to minimize the training loss while satisfying energy causality constraints and guaranteeing fairness in client selection. We formulate a non-convex mixed-integer nonlinear programming (MINLP) problem for the optimization. Then, by recasting the product of a continuous variable and a 0-1 variable in an equivalent linear form, we transform this non-convex MINLP problem into a convex problem and solve it. We present numerical evaluations on various datasets to show that our proposed system is stable and achieves high performance regardless of whether the loss function is convex or non-convex and whether the data distributions are independent and identically distributed (i.i.d.) or non-i.i.d.

In this paper, we propose a simultaneous wireless information and power transfer (SWIPT)-enabled ad hoc network underlay rate-splitting multiple access (RSMA)-based system with intelligent reflecting surface (IRS). Therein, a base station (BS) in a primary network uses RSMA to serve primary users (PUs), and secondary user (SU) pairs constitute an ad hoc network sharing the spectrum with the primary network. Both power splitting (PS)- and time splitting (TS)-based SWIPT protocols are used in the ad hoc network that allow the SU receivers to decode the information and harvest energy simultaneously. An IRS is deployed to further enhance the system performance. We formulate optimization problems that optimize the common data rate allocation and beamformers associated with the common and private messages of RSMA, the beamformers and the PS/TS factors in the ad hoc network, and reflection coefficients of the IRS to maximize the minimum rate of the PUs while satisfying the requirements of harvested energy and data rate of the SU pairs. The optimization problems are non-convex and challenging to be solved. We propose low complexity algorithms based on alternating descent techniques. Numerical results demonstrate the effectiveness and improvement of the proposed algorithms, especially when combined with the TS-based SWIPT.

In this paper, we investigate an effective energy management in a UAV -assisted Metaverse synchronization system. The UAV s perform the data collection for a virtual service provider (VSP) for the synchronization between the physical objects and digital twins (DTs). The UAVs buy energy resources from an energy service provider (ESP). The key issue is to motivate both the ESP and the UAV s to participate in the energy trading market. For this, we design a deep learning (DL)-based auction scheme that maximizes the revenue of the ESP while guaranteeing individual rationality (IR) and incentive compatibility (IC). We provide numerical results to demonstrate the improvement of the DL-based auction scheme compared to the baseline scheme in terms of revenue, IC, and IR.

In next-generation Internet services, such as Metaverse, the mixed reality (MR) technique plays a vital role. Yet the limited computing capacity of the user-side MR headset-mounted device (HMD) prevents its further application, especially in scenarios that require a lot of computation. One way out of this dilemma is to design an efficient information sharing scheme among users to replace the heavy and repetitive computation. In this paper, we propose a free-space information sharing mechanism based on full-duplex device-to-device (D2D) semantic communications. Specifically, the view images of MR users in the same real-world scenario may be analogous. Therefore, when one user (i.e., a device) completes some computation tasks, the user can send his own calculation results and the semantic features extracted from the user's own view image to nearby users (i.e., other devices). On this basis, other users can use the received semantic features to obtain the spatial matching of the computational results under their own view images without repeating the computation. Using generalized small-scale fading models, we analyze the key performance indicators of full-duplex D2D communications, including channel capacity and bit error probability, which directly affect the transmission of semantic information. Finally, the numerical analysis experiment proves the effectiveness of our proposed methods.

We design an incentive mechanism for edge computing trading between virtual service providers (VSPs) and an edge computing provider (ECP). The VSPs deploy unmanned aerial vehicles (UAVs) to collect sensing data from physical objects to update their digital twins (DTs) to serve their Metaverse users. To process the huge data, the VSP offloads a part of data computation to the ECP. Given the limited computing capacity, we propose a DL-based auction using the augmented Lagrangian method for determining the winning probabilities of the VSPs and their payments. The DL-based auction aims to maximize the ECP's revenue and holds incentive compatibility (IC) and individual rationality (IR) while satisfying budget (BG) constraints. To reduce the offloading cost, a semantic communication (SemCom) technique is deployed at the UAVs of the VSPs. The SemCom technique allows the UAVs to generate and transmit semantic symbols rather than the raw images to their corresponding VSP, which significantly reduces the offloading cost. To train the neural networks used in the DL-based auctions, we use a dataset including valuations of the computing resources to the VSPs, which is a function of the age of DT, the size of the semantic symbol, the sensing time and communication time of the UAVs, and the available computing capacity of the VSP. Simulation results clearly show that the proposed DL-based auction outperforms the classical auctions in terms of ECP's revenue, IR, IC, and BG. The results further show that the use of SemCom reduces the offloading cost for the VSPs.

We consider a rate-splitting multiple access (RSMA)-based communication and radar coexistence (CRC) system. The proposed system allows an RSMA-based communication system to share spectrum with multiple radars. Furthermore, RSMA enables flexible and powerful interference management by splitting messages into common parts and private parts to partially decode interference and partially treat interference as noise. The RSMA-based CRC system thus significantly improves spectral efficiency and quality of service (QoS) of communication users (CUs). The communication network and the radars cause interference to each other, which reduces the signal-to-interference-plus-noise ratio (SINR) of the radars as well as the data rate of the CUs. Therefore, a major problem is to maximize the sum rate of the CUs while guaranteeing their QoS requirements of data transmissions and the SINR requirements of multiple radars. To achieve these objectives, we formulate a problem that optimizes i) the common rate allocation to the CUs, transmit power of common message and transmit power of private messages of the CUs, and ii) transmit power of the radars. We propose an additive approximation scheme (AAS) which solves the problem globally. Simulation results show the improvement of the AAS compared with the sequential quadratic programming (SQP) in terms of sum rate.

Simultaneous wireless information and power transfer (SWIPT) is a key technology for enabling future high-tech lifestyles by guaranteeing the perpetual operation of trillions of low-power IoT devices. Currently, reconfigurable intelligent surface (RIS) is a promising technology for achieving cost- effective and energy-efficient wireless technologies. In this paper, we propose an efficient beam-sharing algorithm for RIS-aided SWIPT systems. The proposed algorithm maximizes the power transferred from the power transmitter (Ptx) to the power user (PU) while minimizing that to the data user (DU) but maximizing the signal from the data transmitter (Dtx) to DU. Simulation results demonstrate that the proposed beam-sharing algorithm effectively delivers power from Ptx to PU while limiting the power sent by Ptx to DU but maximizing the received signal from Dtx.

In this paper, we propose an interest-based semantic information transmission framework with rate splitting multiple access (RSMA), to reduce the amount of transmitted data, thereby reducing the burden of data transmission and data processing. In the framework, only the semantic information of interest is transmitted to each user. In the process of semantic information transmission, RSMA is adopted to improve transmission efficiency. In particular, we adopt maximum ratio transmission and zero-forcing for the precoding of the common and private streams, respectively. We also design the quality of experience (QoE) for the system as a performance metric. Experimental results demonstrate the effectiveness of the proposed framework as compared with the benchmark.

The recent emergence of 6G raises the challenge of increasing the transmission data rate even further in order to break the barrier set by the Shannon limit. Traditional communication methods fall short of the 6G goals, paving the way for Semantic Communication (SemCom) systems. These systems find applications in wide range of fields such as economics, metaverse, autonomous transportation systems, healthcare, smart factories, etc. In SemCom systems, only the relevant information from the data, known as semantic data, is extracted to eliminate unwanted overheads in the raw data and then transmitted after encoding. In this paper, we first use the shared knowledge base to extract the keywords from the dataset. Then, we design an auto-encoder and auto-decoder that only transmit these keywords and, respectively, recover the data using the received keywords and the shared knowledge. We show analytically that the overall semantic distortion function has an upper bound, which is shown in the literature to converge. We numerically compute the accuracy of the reconstructed sentences at the receiver. Using simulations, we show that the proposed methods outperform a state-of-the-art method in terms of the average number of words per sentence.

We study the impact of nonlinear saturation effect and receiver architecture on the performance of multiple-input and multiple-output (MIMO) wireless power transfer (WPT). We propose a generic hybrid combining receiver architecture where the receive antennas are grouped into multiple subarrays, each subarray consisting of multiple energy harvesting (EH) circuits. For efficient receiver reconfiguration, a power splitter is placed in between each antenna subarray and the associated EH circuits. To improve the WPT performance, we formulate an optimization problem for joint transmit and receive beamforming design along with receiver reconfiguration, which appears non-convex fractional programming. To resolve this, the optimization problem is reformulated into equivalent semi-definite programming problem with rank-one relaxation through quadratic transform, which provides a suboptimal solution for the original problem. Then, we propose an iterative algorithm for optimizing the joint beamforming design and receiver reconfiguration. We confirm that the WPT system optimization considering the nonlinearities existing in the proposed generic receiver structure yields better performance than when the nonlinearities and resulting receiver architecture are not taken into account.

This article proposes a sparsity-aware channel estimation scheme for reconfigurable intelligent surface (RIS)-assisted wireless communications. We present an angular domain-channel sparsity model in a closed-form mathematical expression. A comprehensive formulation of the RIS channel estimation problem based on the sparsity analysis and RIS reflection model is also presented in this manuscript. This work aims to achieve a practical RIS beamforming algorithm without requiring excessive training overhead or any sensor deployment. We achieve the goal by proposing two sparsity-aware RIS channel estimation schemes based on the compressive sensing (CS) algorithms, such as the Dantzig selector (DS) and orthogonal matching pursuit (OMP). Differently from the existing works on the CS algorithms for RIS, we consider a fully passive RIS without any active sensor. We validate the theory and algorithm through both simulations and experiments. We have experimented orthogonal frequency-division multiplexing (OFDM) communications on our 5.8-GHz 1-bit RIS testbed with QPSK, 16QAM, 64QAM, and 256QAM modulation schemes. By experiments, it is shown that the proposed scheme is able to adaptively form a beam toward the receiver and improves the quality of the wireless communication to a notable level. Thanks to the properties of the proposed scheme, the wireless channel can be estimated without excessive training time and complexity.

Metaverse, also known as the Internet of 3-D worlds, has recently attracted much attention from both academia and industry. Each virtual subworld, operated by a virtual service provider (VSP), provides a type of virtual service. Digital twins (DTs), namely, digital replicas of physical objects, are key enablers. Generally, a DT belongs to the party that develops it and establishes the communication link between the two worlds. However, in an interoperable metaverse, data-like DTs can be “shared” within the platform. Therefore, one set of DTs can be leveraged by multiple VSPs. As the quality of the shared DTs may not always be satisfying, in this article, we propose an agile solution, i.e., a dynamic hierarchical framework, in which a group of Internet of Things devices in the lower level are incentivized to collectively sense physical objects’ status information and VSPs in the upper level determine synchronization intensities to maximize their payoffs. We adopt an evolutionary game approach to model the devices VSP selections and a simultaneous differential game to model the optimal synchronization intensity control problem. We further extend it as a Stackelberg differential game by considering some VSPs to be first movers. We provide open-loop solutions based on the control theory for both formulations. We theoretically and experimentally show the existence, uniqueness, and stability of the equilibrium to the lower level game and further provide a sensitivity analysis for various system parameters. Experiments show that the proposed dynamic hierarchical game outperforms the baseline.

To provide a dependency-aware application, multiple unmanned aerial vehicles (UAVs) are employed to serve a ground user with a set of interdependent tasks. This leads to a new computing paradigm called as multi-UAV-enabled aerial edge computing (MU-AEC). For the large-scale application of MU-AEC, both the task-centric objective and UAV-centric objective should be simultaneously considered. Thus, we focus on the joint interdependent task scheduling and energy balancing for MU-AEC by using a multiobjective optimization approach, which enables a decision maker to identify the optimal solutions corresponding to the best feasible tradeoffs between the two objectives. A constrained multiobjective optimization problem involving two objectives: 1) the makespan minimization of all tasks and 2) energy balancing among different UAVs, is formulated. In the solution methodology, we propose a constrained decomposition-based multiobjective evolution algorithm. To quickly seek more superior solutions, a local search mechanism by utilizing the objective information, and an improved genetic operator are proposed for remarkable performance improvements. Finally, numerical results demonstrate that compared with the baseline algorithms, our algorithm achieves both advantages in increasing the convergence and diversity of the solutions.

Unmanned aerial vehicles (UAVs) have gained wide research interests due to their technological advancement and high mobility. The UAVs are equipped with increasingly advanced capabilities to run computationally intensive applications enabled by machine learning techniques. However, because of both energy and computation constraints, the UAVs face issues hovering in the sky while performing computation due to weather uncertainty. To overcome the computation constraints, the UAVs can partially or fully offload their computation tasks to the edge servers. In ordinary computation offloading operations, the UAVs can retrieve the result from the returned output. Nevertheless, if the UAVs are unable to retrieve the entire result from the edge servers, i.e., straggling edge servers, this operation will fail. In this article, we propose a coded distributed computing (CDC) approach for computation offloading to mitigate straggling edge servers. The UAVs can retrieve the returned result when the number of returned copies is greater than or equal to the recovery threshold. There is a shortfall if the returned copies are less than the recovery threshold. To minimize the cost of the network, energy consumption by the UAVs, and prevent over and under subscription of the resources, we devise a two-phase stochastic coded offloading scheme (SCOS). In the first phase, the appropriate UAVs are allocated to the charging stations amid weather uncertainty. In the second phase, we use the$z$-stage stochastic integer programming (SIP) to optimize the number of computation subtasks offloaded and computed locally, while taking into account the computation shortfall and demand uncertainty. By using a real data set, the simulation results show that our proposed scheme is fully dynamic and minimizes the cost of the network and UAV energy consumption amid stochastic uncertainties.

Metaverse encapsulates our expectations of the next-generation Internet, while bringing new key performance indicators (KPIs). Although conventional ultra-reliable and low-latency communications (URLLC) can satisfy objective KPIs, it is difficult to provide a personalized immersive experience that is a distinctive feature of the Metaverse. Since the quality of experience (QoE) can be regarded as a comprehensive KPI, the URLLC is evolved towards the next generation URLLC (xURLLC) with a personalized resource allocation scheme to achieve higher QoE. To deploy Metaverse xURLLC services, we study the interaction between the Metaverse service provider (MSP) and the network infrastructure provider (InP), and provide an optimal contract design framework. Specifically, the utility of the MSP, defined as a function of Metaverse users’ QoE, is to be maximized, while ensuring the incentives of the InP. To model the QoE mathematically, we propose a novel metric named Meta-Immersion that incorporates both the objective KPIs and subjective feelings of Metaverse users. Furthermore, we develop an attention-aware rendering capacity allocation scheme to improve QoE in xURLLC. Using a user-object-attention level dataset, we validate that the xURLLC can achieve an average of 20.1% QoE improvement compared to the conventional URLLC with a uniform resource allocation scheme. The code for this paper is available athttps://github.com/HongyangDu/AttentionQoE.

The next generation of Internet services, such as Metaverse, rely on mixed reality (MR) technology to provide immersive user experiences. However, limited computation power of MR headset-mounted devices (HMDs) hinders the deployment of such services. Therefore, we propose an efficient information-sharing scheme based on full-duplex device-to-device (D2D) semantic communications to address this issue. Our approach enables users to avoid heavy and repetitive computational tasks, such as artificial intelligence-generated content (AIGC) in the view images of all MR users. Specifically, a user can transmit the generated content and semantic information extracted from their view image to nearby users, who can then use this information to obtain the spatial matching of computation results under their view images. We analyze the performance of full-duplex D2D communications, including the achievable rate and bit error probability, by using generalized small-scale fading models. To facilitate semantic information sharing among users, we design a contract theoretic AI-generated incentive mechanism. The proposed diffusion model generates the optimal contract design, outperforming two deep reinforcement learning algorithms, i.e., proximal policy optimization and soft actor-critic algorithms. Our numerical analysis experiment proves the effectiveness of our proposed methods. The code for this paper is available athttps://github.com/HongyangDu/SemSharing.

In this paper, we address a dynamic network resource selection problem for mobile users in a rate-splitting multiple access (RSMA)-enabled network by leveraging evolutionary games. Particularly, mobile users are able to locally and dynamically make their selection on orthogonal resource blocks (RBs), which are also considered as network resources (NRs), over time to achieve their desired utilities. Then, RSMA is used for each group of users selecting the same NR. With the use of RSMA, the main goal is to optimize the beamformers of the common and private messages for users in the same group to maximize their sum rate. The resulting problem is generally non-convex, and thus we develop a successive convex approximation (SCA)-based algorithm to efficiently solve it in an iterative fashion. To model the NR adaptation of users, we propose to use two evolutionary games, i.e. a traditional evolutionary game (TEG) and fractional evolutionary game (FEG). The FEG approach enables users to incorporate memory effects (i.e. their past experiences) for their decision-making, which is more realistic than the TEG approach. We then theoretically verify the existence of the equilibrium of the proposed game approaches. Simulation results are provided to validate their consistency with the theoretical analysis and merits of the proposed approaches. They also reveal that, compared with TEG, FEG enables users to leverage past information for their decision-making, resulting in less communication overhead, while still guaranteeing convergence.

Rate Splitting Multiple Access (RSMA) has recently emerged as a promising technique to enhance the transmission rate for multiple access networks. Unlike conventional multiple access schemes, RSMA requires splitting and transmitting messages at different rates. The joint optimization of the power allocation and rate control at the transmitter is challenging given the uncertainty and dynamics of the environment. Furthermore, securing transmissions in RSMA networks is a crucial problem because the messages transmitted can be easily exposed to adversaries. This work first proposes a stochastic optimization framework that allows the transmitter to adaptively adjust its power and transmission rates allocated to users, and thereby maximizing the sum-rate and fairness of the system under the presence of an adversary. We then develop a highly effective learning algorithm that can help the transmitter to find the optimal policy without requiring complete information about the environment in advance. Extensive simulations show that our proposed scheme can achieve non-saturated transmission rates at high SNR values with infinite blocklength. More significantly, our proposed scheme can achieve positive covert transmission rates in the finite blocklength regime, compared with zero-valued covert rates of a conventional multiple access scheme.

Recently, Metaverse has attracted increasing attention from both industry and academia, because of the significant potential to integrate real and digital worlds ever more seam-lessly. By combining advanced wireless communications, edge computing and virtual reality (VR) technologies into Metaverse, a multidimensional, intelligent and powerful wireless edge Meta-verse is created for future human society. In this paper, we design a privacy preserving targeted advertising strategy for the wireless edge Metaverse. Specifically, a Metaverse service provider (MSP) allocates bandwidth to the VR users so that the users can access Metaverse from edge access points. To protect users' privacy, the covert communication technique is used in the downlink. Then, the MSP can offer high-quality access services to earn more profits. Motivated by the concept of “covert”, targeted advertising is used to promote the sale of bandwidth and ensure that the advertising strategy cannot be detected by competitors who may make counter-offer and by attackers who want to disrupt the services. We derive the best advertising strategy in terms of budget input, with the help of the Vidale-Wolfe model and Hamiltonian function. Furthermore, we propose a novel metric named Meta-Immersion to represent the user's experience feelings. The performance evaluation shows that the MSP can boost its revenue with an optimal targeted advertising strategy, especially compared with that without the advertising.

Federated learning (FL) is a distributed machine learning paradigm with a promising future, which can preserve data privacy while training the global model collaboratively. However, FL is still facing model confidentiality issues. Therefore, in this paper, we propose a quantum key distribution (QKD) based secure FL scheme to facilitate FL model encryption against network eavesdropping attacks. Specifically, we introduce a stochastic resource allocation scheme for QKD to support FL networks. In the network, remote FL workers are connected to the server to train an aggregated global model in a distributed manner. However, due to the unpredictable number of workers at each location, the demand for secret-key rates to support secure model transmission to the server is not uniform. The proposed scheme can allocate QKD resources (i.e., wavelengths) in a way that minimizes the total cost given the stochastic demand. We formulate the optimization problem for the proposed scheme as a stochastic programming model. Numerical results demonstrate that the proposed scheme can successfully achieve the cost-minimizing objective while satisfying all uncertain demands and other security constraints.

Unmanned aerial vehicles (UAVs) have attracted a lot of research attention in serving as aerial base stations (BSs). To protect the data privacy without being detected by a warden, we investigate a jammer-aided UAV covert communication system, aiming to maximize the user's covert rate with optimized transmit and jamming power. By considering the general composite fading and shadowing channel models, we derive the closed-form expressions for detection error probability and covert rate. The covert rate maximization problem is formulated as a Nash bargaining game, and the Nash bargaining solution (NBS) is introduced. To solve the NBS, we propose a particle swarm optimization-based power allocation algorithm. The numerical results are presented to verify the theoretical analysis.

Spurred by the severe restrictions on mobility due to the COVID-19 pandemic, there is currently intense interest in developing the Metaverse, to offer virtual services/business online. A key enabler of such virtual service is the digital twin, i.e., a digital replication of real-world entities in the Metaverse, e.g., city twin, avatars, etc. The real-world data collected by IoT devices and sensors are key for synchronizing the two worlds. In this paper, we consider the scenario in which a group of IoT devices are employed by the Metaverse platform to collect such data on behalf of virtual service providers (VSPs). Device owners, who are self-interested, dynamically select a VSP to maximize rewards. We adopt hybrid evolutionary dynamics, in which heterogeneous device owner populations can employ different revision protocols to update their strategies. Extensive simulations demonstrate that a hybrid protocol can lead to evolutionary stable states.

In this paper, we propose a unified simultaneous wireless information and power transfer (SWIPT) signal and its architecture design in order to take advantage of both single tone and multi-tone signaling by adjusting only the power allocation ratio of a unified signal. For this, we design a novel unified and integrated receiver architecture for the proposed unified SWIPT signaling, which consumes low power with an envelope detection. We demonstrate that the proposed unified SWIPT system improves the achievable rate under the self-powering condition for low-power Internet-of-Things (IoT) devices. This will facilitate effective deployment of low-power IoT networks that concurrently supply both information and energy wirelessly to the devices by using the proposed unified SWIPT signaling and architecture.

The Metaverse is regarded as the next-generation Internet paradigm that allows humans to play, work, and socialize in an alternative virtual world with an immersive experience, for instance, via head-mounted displays for Virtual Reality (VR) rendering. With the help of ubiquitous wireless connections and powerful edge computing technologies, VR users in the wireless edge-empowered Metaverse can immerse themselves in the virtual through the access of VR services offered by different providers. However, VR applications are computation- and communication-intensive. The VR service providers (SPs) have to optimize the VR service delivery efficiently and economically given their limited communication and computation resources. An incentive mechanism can be thus applied as an effective tool for managing VR services between providers and users. Therefore, in this paper, we propose a learning-based Incentive Mechanism framework for VR services in the Metaverse. First, we propose the quality of perceptual experience as the metric for VR users immersing in the virtual world. Second, for quick trading of VR services between VR users (i.e., buyers) and VR SPs (i.e., sellers), we design a double Dutch auction mechanism to determine optimal pricing and allocation rules in this market. Third, for auction information exchange cost reduction, we design a deep reinforcement learning-based auctioneer to accelerate this auction process. Experimental results demonstrate that the proposed framework can achieve near-optimal social welfare while reducing at least half of the auction information exchange cost than baseline methods.

Recently, reconfigurable intelligent surface (RIS) has drawn massive attention among researchers and entrepreneurs as a potential technology for next-generation wireless technologies. This paper proposes a beam splitting method to simultaneously deliver power and information to different users (i.e., power user/information user). We use orthogonal training patterns generated by the Hadamard matrix to estimate the end-to-end channel information of each user. Then, a pattern addition (PA) method is applied to split the beam for each user. We implement a real-life testbed of the RIS-aided simultaneous wireless information and power transfer (SWIPT) system to verify the proposed algorithm. By experiment, we show that the proposed method can effectively distribute power and information to the corresponding users.

In this work, we propose a drone-based sensor information gathering system that utilizes an aerial drone to wirelessly transfer an electromagnetic (EM) wave signal toward a battery-less forward-scatter tag device with multiple antennas. The proposed tag device is designed to harvest the EM signal from the drone as a source of power, and at the same time to reuse the EM signal for the data transmission. We propose a beam-rotation forward-scattering algorithm for the data transmission, which creates the main beam rotating along the azimuth plane. In the proposed algorithm, data are transmitted from the tag device to the tag reader by mapping each bit of information to varying beam rotation speeds. A unique hardware design of the forward-scatter tag device is proposed in this article, and it has been fabricated and implemented for wireless power transfer (WPT) and data transmission experiments. By experiment, we have verified that power is successfully harvested by the proposed tag device, with the average power conversion efficiency rated at 57%. The proposed tag device is able to transmit the sensor data to the tag readers without the knowledge of the location of the tag readers. We have tested the proposed beam-rotation forward-scattering communication at up to 500 m communication distance and shown that the sensor data can successfully be delivered.

Recently, coded distributed computing (CDC), with advantages in intensive computation and reduced latency, has attracted a lot of research interest for edge computing, in particular, IoT applications, including IoT data preprocessing and data analytics. Nevertheless, it can be challenging for edge infrastructure providers (EIPs) with limited edge resources to support IoT applications performed in a CDC approach in edge networks, given the additional computational resources required by CDC. In this article, we propose “coded edge federation” (CEF), in which different EIPs collaboratively provide edge resources for CDC tasks. To study the Nash equilibrium, when no EIP has an incentive to unilaterally alter its decision on edge resource allocation, we model the CEF based on the evolutionary game theory. Since the replicator dynamics of the classical evolutionary game are unable to model economic-aware EIPs, which memorize past decisions and utilities, we propose “fractional replicator dynamics” with a power-law fading memory via Caputo fractional derivatives. The proposed dynamics allow us to study a broad spectrum of EIP dynamic behaviors, such as EIP sensitivity and aggressiveness in strategy adaptation, which classical replicator dynamics cannot capture. Theoretical analysis and extensive numerical results justify the existence, uniqueness, and stability of the equilibrium in the fractional evolutionary game. The influence of the content and the length of the memory on the rate of convergence are also investigated.

In this article, we propose a unified simultaneous wireless information and power transfer (SWIPT) signaling and architecture to take advantage of both single tone and multitone signaling by adjusting only the power allocation ratio of a unified signal. Toward this, we design a unified and integrated receiver architecture for the proposed unified SWIPT signaling via an envelope detection with low power consumption. To lower the computational complexity of the receiver, we propose an adaptive control algorithm where the transmitter adjusts the communication mode through temporal convolutional network (TCN)-basedasymmetric processing. To this end, the transmitter optimizes the modulation index and power allocation ratio in short-term scale while updating the mode switching threshold in long-term scale. We demonstrate that the proposed unified SWIPT system improves the achievable rate under the self-powering condition at low-power Internet of Things (IoT) devices. Consequently we will facilitate the effective deployment of low-power IoT networks that concurrently supply both information and energywirelesslyto the devices by using the proposed unified SWIPT and adaptive control algorithm at the transmitter side.

Recently, reconfigurable intelligent surface (RIS) with passive beamforming capability is emerging as a potential technology for wireless power transfer (WPT) applications thanks to its cost-effective and energy-efficient features. This work studies multifocus techniques for RIS-aided WPT systems to simultaneously and adaptively charge multiple Internet of Things (IoT) devices in the Fresnel zone. In particular, we propose three multifocus methods, which are pattern addition (PA), random unit cell interleaving (RUI), and RIS tile division (RTD), for enabling multifocus RIS-aided WPT applications. We elaborate the algorithms for computing the RIS reflection phases for all methods. The proposed methods can balance the power levels of beams focused on multiple receivers by controlling weight factors. Furthermore, we have implemented a real-life RIS-aided WPT testbed to verify these proposed methods. The system consists of a phased antenna array transmitter, two receivers, and a 1-bit RIS with 512 unit cells. The WPT experiments have been performed in several test scenarios to show the effectiveness of the proposed techniques. The experiment results demonstrate that the proposed schemes effectively generate multiple focusing beams with adjustable power levels toward the desired receivers.

Reconfigurable intelligent surface (RIS) is a promising technology for radio-frequency wireless power transfer (WPT) as it is capable of beamforming and beam focusing without using active and power-hungry components. In this article, we propose a multitile RIS beam scanning (MTBS) algorithm for powering up Internet of Things (IoT) devices. Considering the hardware limitations of the IoT devices, the proposed algorithm requires only power information to enable the beam focusing capability of the RIS. Specifically, we first divide the RIS into smaller RIS tiles. Then, all RIS tiles and the phased array transmitter are iteratively scanned and optimized to maximize the receive power. We elaborately analyze the proposed algorithm and build a simulator to verify it. Furthermore, we have built a real-life testbed of RIS-aided WPT systems to validate the algorithm. The experimental results show that the proposed MTBS algorithm can properly control the transmission phase of the transmitter and the reflection phase of the RIS to focus the power at the receiver. Consequently, after executing the algorithm, about 20-dB improvement of the receive power is achieved compared to the case that all unit cells of the RIS are in OFF state. By experiments, we confirm that the RIS with the MTBS algorithm can greatly enhance the power transfer efficiency.

Unmanned aerial vehicles (UAVs) have attracted a lot of research attention because of their high mobility and low cost in serving as temporary aerial base stations (BSs) and providing high data rates for next-generation communication networks. To protect user privacy while avoiding detection by a warden, we investigate a jammer-aided UAV covert communication system, which aims to maximize the user’s covert rate with optimized transmit and jamming power. The UAV is equipped with multi-antennas to serve multi-users simultaneously and enhance the Quality of Service. By considering the general composite fading and shadowing channel models, we derive the exact probability density (PDF) and cumulative distribution functions (CDF) of the signal-to-interference-plus-noise ratio (SINR). The obtained PDF and CDF are used to derive the closed-form expressions for detection error probability and covert rate. Furthermore, the covert rate maximization problem is formulated as a Nash bargaining game, and the Nash bargaining solution (NBS) is introduced to investigate the negotiation among users. To solve the NBS, we propose two algorithms, i.e., particle swarm optimization-based and joint two-stage power allocation algorithms, to achieve covertness and high data rates under the warden’s optimal detection threshold. All formulated problems are proven to be convex, and the complexity is analyzed. The numerical results are presented to verify the theoretical performance analysis and show the effectiveness and success of achieving the covert communication of our algorithms.

Federated Learning (FL) is a privacy-preserving collaborative learning approach that trains artificial intelligence (AI) models without revealing local datasets of the FL workers. While FL ensures the privacy of the FL workers, its performance is limited by several bottlenecks, which become significant given the increasing amounts of data generated and the size of the FL network. One of the main challenges is the straggler effects where the significant computation delays are caused by the slow FL workers. As such, Coded Federated Learning (CFL), which leverages coding techniques to introduce redundant computations to the FL server, has been proposed to reduce the computation latency. In CFL, the FL server helps to compute a subset of the partial gradients based on the composite parity data and aggregates the computed partial gradients with those received from the FL workers. In order to implement the coding schemes over the FL network, incentive mechanisms are important to allocate the resources of the FL workers and data owners efficiently in order to complete the CFL training tasks. In this paper, we consider a two-level incentive mechanism design problem. In the lower level, the data owners are allowed to support the FL training tasks of the FL workers by contributing their data. To model the dynamics of the selection of FL workers by the data owners, an evolutionary game is adopted to achieve an equilibrium solution. In the upper level, a deep learning based auction is proposed to model the competition among the model owners.

As wireless has disrupted communications, wireless will also disrupt the delivery of energy. Future wireless networks will be equipped with (radiative) wireless power transfer (WPT) capability and exploit radio waves to carry both energy and information through unified wireless information and power transfer (WIPT). Such networks will make the best use of the RF spectrum and radiation, as well as the network infrastructure for the dual purpose of communicating and energizing. Consequently, those networks will enable trillions of future low-power devices to sense, compute, connect, and energize anywhere, anytime, and on the move. In this article, we review the foundations of such a future system. We first give an overview of the fundamental theoretical building blocks of WPT and WIPT. Then, we discuss some state-of-the-art experimental setups and prototypes of both WPT and WIPT, and contrast theoretical and experimental results. We draw special attention to how the integration of RF, signal, and system designs in WPT and WIPT leads to new theoretical and experimental design challenges for both microwave and communication engineers and highlight some promising solutions. Topics and experimental testbeds discussed include closed-loop WPT and WIPT architectures with beamforming, waveform, channel acquisition, and single-antenna/multiantenna energy harvester, centralized and distributed WPT, reconfigurable metasurfaces and intelligent surfaces for WPT, transmitter and receiver architecture for WIPT, modulation, and rate–energy tradeoff. Moreover, we highlight important theoretical and experimental research directions to be addressed for WPT and WIPT to become a foundational technology of future wireless networks.

Autonomous Vehicles (AVs) are required to operate safely and efficiently in dynamic environments. For this, the AVs equipped with Joint Radar-Communications (JRC) functions can enhance the driving safety by utilizing both radar detection and data communication functions. However, optimizing the performance of the AV system with two different functions under uncertainty and dynamic of surrounding environments is very challenging. In this work, we first propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions in selecting JRC operation functions under the dynamic and uncertainty of the surrounding environment. We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV without requiring any prior information about surrounding environment. Furthermore, to make our proposed framework more scalable, we develop a Transfer Learning (TL) mechanism that enables the AV to leverage valuable experiences for accelerating the training process when it moves to a new environment. Extensive simulations show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches. With the deep reinforcement learning and transfer learning approaches, our proposed solution can find its applications in a wide range of autonomous driving scenarios from driver assistance to full automation transportation.

In this paper, we consider an integrated data and energy network and D2D communication coexistence (DED2D) system. The DED2D system allows a base station (BS) to transfer data to information-demanded users (IUs) and energy to energy-demanded users (EUs), i.e., using a time-fraction-based information and energy transfer (TFIET) scheme. Furthermore, the DED2D system enables D2D communications to share spectrum with the BS. Therefore, the DED2D system addresses the growth of energy and spectrum demands of the next generation networks. However, the interference caused by the D2D communications and propagation loss of wireless links can significantly degrade the data throughput of IUs. To deal with the issues, we propose to deploy an intelligent reflecting surface (IRS) in the DED2D system. Then, we formulate an optimization problem that aims to optimize the information beamformer for the IUs, energy beamformer for EUs, time fractions of the TFIET, transmit power of D2D transmitters, and reflection coefficients of the IRS to maximize IUs’ worse throughput while satisfying the harvested energy requirement of EUs and D2D rate threshold. The max-min throughput optimization problem is computationally intractable, and we develop an alternating descent algorithm to resolve it with low computational complexity. The simulation results demonstrate the effectiveness of the proposed algorithm.

Joint radar-communications (JRC) has been proposed recently for autonomous vehicles (AVs) to simultaneously perform radar sensing, e.g., detecting distant vehicles and pedestrian, and data transmission, e.g., to edge computing services, all on the same waves. However, due to the high AV density in urban area, the spectrum service provider (SSP) needs to allocate the spectrum resources optimally. In this paper, we consider the social welfare of the network which is defined as the total revenue of the SSP and the utilities of the AV users, and propose an auction-based algorithm to model the competition among the AV users to obtain the spectrum resources and maximize the social welfare. Since some AV users can have critical and useful information about each other (e.g., AV neighbours sharing traffic data), we consider the network effect in our proposed auction mechanism to incentivize more AV users to join the auction. The numerical results demonstrate the effectiveness of our proposed design compared to traditional schemes.

Radar detection and communication are two of several sub-tasks essential for the operation of next-generation autonomous vehicles (AVs). The former is required for sensing and perception, more frequently so under various unfavorable environmental conditions such as heavy precipitation; the latter is needed to transmit time-critical data. Forthcoming proliferation of faster 5G networks utilizing mmWave is likely to lead to interference with automotive radar sensors, which has led to a body of research on the development of Joint Radar Communication (JRC) systems and solutions. This paper considers the problem of time-sharing for JRC, with the additional simultaneous objective of minimizing the average age of information (AoI) transmitted by a JRC-equipped AV. We formulate the problem as a Markov Decision Process (MDP) where the JRC agent determines in a real-time manner when radar detection is necessary, and how to manage a multiclass data queue where each class represents different urgency levels of data packets. Simulations are run with a range of environmental parameters to mimic variations in real-world operation. The results show that deep reinforcement learning allows the agent to obtain good results with minimal a priori knowledge about the environment.

Task offloading has been proposed and studied to overcome the problem of energy and computation constrained terminals. Computationally intensive tasks are often parallelable, and therefore the execution time can be further improved via a coded distributed computing (CDC) approach, as CDC offers robustness against stragglers by introducing redundant computational tasks. In this paper, we study a user-centric task offloading problem, in which the edge performs the of-floaded computation with CDC. Furthermore, the extent of the straggler's effect on servers is also unknown to the user. This requires users to explore server and code settings of the CDC, and “opportunistically” select the best combo to maximize the utility. For simplicity, we refer to this scenario as opportunistic coded distributed computing. We formulate the problem as an evolutionary game in which each user is self-interested. The payoff is calculated based on the monetary cost of CDC-as-a-Service and total delay, weighted by user-defined parameter values. For the game solution, an evolutionary stable equilibrium (ESS) is used, i.e., probabilistic joint selection of server and code configuration. To obtain the ESS, we present an iterative algorithm based on the revision protocol. A theoretical analysis of equilibrium in terms of existence, uniqueness, stationarity, and stability is provided. Numerical simulations are conducted to support the theoretical findings and the adaption of equilibrium states to the hyper-parameters.

We propose a heterogeneously reconfigurable energy harvester which contains multiple energy harvesting (EH) blocks, each comprised of multiple EH circuits in parallel to be optimized for different input power ranges. Practical EH circuits have their own favorable input power range due to turn-on sensitivity and saturation effects. To account for this, we present the necessary conditions for efficient configuration of an energy harvester, and then propose an algorithm for optimal reconfiguration of the energy harvester. Furthermore, adaptive time switching and mode switching problems are formulated in order to maximize the average achievable rate under the self-powering condition. Our proposed energy harvester and algorithm can be applied to low-power Internet-of-Things (IoT) devices with wireless EH capability for self-sustainability, which will be a key enabler for realizing a battery-free IoT network.

Federated Learning (FL) is a promising privacy-preserving distributed machine learning paradigm. However, communication inefficiency remains the key bottleneck that impedes its large-scale implementation. Recently, hierarchical FL (HFL) has been proposed in which data owners, i.e., workers, can first transmit their updated model parameters to edge servers for intermediate aggregation. This reduces the instances of global communication and straggling workers. To enable efficient HFL, it is important to address the issues of edge association and resource allocation in the context of non-cooperative players, i.e., workers, edge servers, and model owner. However, the existing studies merely focus on static approaches and do not consider the dynamic interactions and bounded rationalities of the players. In this paper, we propose a hierarchical game framework to study the dynamics of edge association and resource allocation in self-organizing HFL networks. In the lower-level game, the edge association strategies of the workers are modelled using an evolutionary game. In the upper-level game, a Stackelberg differential game is adopted in which the model owner decides an optimal reward scheme given the expected bandwidth allocation control strategy of the edge server. Finally, we provide numerical results to validate that our proposed framework captures the HFL system dynamics under varying sources of network heterogeneity.

Recently, due to the staggering growth of wireless data traffic, heterogeneous networks have drawn tremendous attention due to the capabilities of enhancing the capacity/coverage and reducing energy consumption for the next generation wireless networks. In this paper, we study a long-run user-centric network selection problem in the 5G heterogeneous network, where the network selection strategies of the users can be investigated dynamically. Unlike the conventional studies on the long-run model, we incorporate the memory effect and consider the fact that the decision-making of the users is affected by their memory, i.e., their past service experience. Namely, the users select the network based on not only their instantaneous achievable service experience but also their past service experience within their memory. Specifically, we model and study the interaction among the users in the framework of fractional evolutionary game based on the classical evolutionary game theory and the concept of the power-law memory. We analytically prove that the equilibrium of the fractional evolutionary game exists, is unique and uniformly stable. We also numerically demonstrate the stability of the fractional evolutionary equilibrium. Extensive simulations have been conducted to evaluate the performance of the fractional evolutionary game. The numerical results have revealed some insightful findings. For example, the user in the fractional evolutionary game with positive memory effect can achieve a higher cumulative utility compared with the user in the fractional evolutionary game with negative memory effect. Moreover, the fractional evolutionary game with positive memory effect can reduce the loss in the user's cumulative utility caused by the small-scale fading.

In traditional machine learning, the central server first collects the data owners' private data together and then trains the model. However, people's concerns about data privacy protection are dramatically increasing. The emerging paradigm of federated learning efficiently builds machine learning models while allowing the private data to be kept at local devices. The success of federated learning requires sufficient data owners to jointly utilize their data, computing and communication resources for model training. In this article, we propose an auction-based market model for incentivizing data owners to participate in federated learning. We design two auction mechanisms for the federated learning platform to maximize the social welfare of the federated learning services market. Specifically, we first design an approximate strategy-proof mechanism which guarantees the truthfulness, individual rationality, and computational efficiency. To improve the social welfare, we develop an automated strategy-proof mechanism based on deep reinforcement learning and graph neural networks. The communication traffic congestion and the unique characteristics of federated learning are particularly considered in the proposed model. Extensive experimental results demonstrate that our proposed auction mechanisms can efficiently maximize the social welfare and provide effective insights and strategies for the platform to organize the federated training.

Improper Gaussian signaling (IGS) has shown its capability of improving the rate of interference-limited networks by exploiting the additional degrees of freedom in signal processing. This article considers a system of a multiple-input single-output (MISO) cellular network coexisting with device-to-device (D2D) communication, where the former employs proper Gaussian signaling (PGS) but the latter employs IGS to improve D2D's rate and also to mitigate interference to the former. Both non-orthogonal and orthogonal bandwidth sharing between cellular users (CUs) and D2D pairs are considered. The problems of joint bandwidth allocation and signal beamforming to maximize the minimum CUs' rate subject to the transmit power budget and D2D's rate threshold are addressed, which pose critical computational challenges. Path-following algorithms of low complexity are developed for computational solutions. Two distinct scenarios, i.e., unmanned aerial vehicle (UAV)-enabled networks, and MISO cellular networks are simulated to give insight into the superiority of using IGS over PGS. Our results reveal that in UAV-enabled networks, orthogonal sharing produces a higher D2D's rate, while non-orthogonal sharing offers better CUs' rate under practical levels of D2D's rate. In MISO systems, IGS is a game-changer, which enables the orthogonal sharing to uniformly outperform the non-orthogonal sharing in terms of CUs' rate.

In this paper, we consider a hybrid satellite-terrestrial network (HSTN) where a multiantenna satellite communicates with a ground user equipment (UE) with the help of multiple amplify-and-forward (AF) three-dimensional (3D) mobile unmanned aerial vehicle (UAV) relays. Herein, we employ a stochastic mixed mobility (MM) model to deploy mobile UAV relays in a 3D cylindrical cell with UE at its ground centre. Taking into account the multiantenna satellite links and the random 3D distances between UAV relays and UE, we analyze the outage probability (OP) of considered system under an opportunistic UAV relay selection policy. We further carry out asymptotic OP analysis to present insights on system diversity order. Moreover, we compare the performance of proposed 3D mobile UAV relaying with the fixed altitude mobile UAV relaying as well as the fixed distance static relaying schemes. The analysis will be verified through simulations.

In this article, we propose a novel simultaneous wireless information and power transfer (SWIPT) scheme for the Internet of Things (IoT). Different from the conventional power splitting (PS) and time switching (TS) schemes, the proposed scheme sends the wireless power via the unmodulated high-power continuous wave (CW) and transmits information by using a small modulated signal in order to reduce the interference and to enhance the power amplifier efficiency. We design a receiver circuit for processing such SWIPT signals, which is designed with the aim of minimizing the circuit complexity and power consumption for information decoding. This goal is achieved by first rectifying the received signal and then splitting the power and information signals. We analyze the proposed receiver circuit and derive the closed-form expression for the energy harvesting efficiency and the frequency response of the communication signal. We have implemented the proposed receiver circuit and built the real-time testbed for experimenting with simultaneous transmission of information and power. By experiments, we have verified the correctness of the receiver circuit analysis and shown the validity of the proposed SWIPT scheme.

Simultaneous wireless information and power transfer (SWIPT) is very promising for various applications with the Internet of Things (IoT). In this article, we study dynamic power splitting for the SWIPT in an ergodic fading channel. Considering nonlinearity of practical energy harvesting (EH) circuits, we adopt the realistic nonlinear EH model rather than the idealistic linear EH model. To characterize the optimal rate-energy (R-E) tradeoff, we consider the problem of maximizing the R-E region, which is nonconvex. We solve this challenging problem for two different cases of the channel state information (CSI): 1) when the CSI is known only at the receiver (the CSIR case) and 2) when the CSI is known at both the transmitter and the receiver (the CSI case). For these two cases, we develop the corresponding optimal dynamic power-splitting schemes. To address the complexity issue, we also propose the suboptimal schemes with low complexities. Comparing the proposed schemes to the existing schemes, we provide various useful insights into the dynamic power splitting with nonlinear EH. Furthermore, we extend the analysis to the scenarios of the partial CSI at the transmitter and the harvested energy maximization. The numerical results demonstrate that the proposed schemes significantly outperform the existing schemes and the proposed suboptimal scheme works very close to the optimal scheme at a much lower complexity.

In this article, we propose a dual-mode simultaneous wireless information and power transfer (SWIPT) system with a deep-learning-based adaptive mode switching (MS) algorithm to exploit both advantages of single-tone and multitone SWIPT. For self-powering of low-energy Internet-of-Things (IoT) devices, a duty-cycling operation is used with nonlinear energy harvesting. For this, we employ a new energy-assisted single-tone modulation which simplifies the receiver structure for information decoding. Considering the symbol-error rate performance, we formulate an adaptive MS problem to maximize the achievable rate under the energy-causality constraint by adjusting the MS threshold. To relieve the computational burden of the receiver, we introduce asymmetric processing for adaptive MS, for which the transmitter adapts the communication mode based on the feedback from the receiver. We invoke deep learning for adaptive MS at the transmitter that iteratively updates the MS threshold in a long-term scale via deep long short-term memory (LSTM) recurrent neural network (RNN) while deciding on the communication mode and modulation index in a short-term scale. We demonstrate the achievable rate improvement under an energy-neutral operation while providing interesting insights into designing the adaptive MS algorithm for the dual-mode SWIPT system.

By enabling revenue sharing between the network operators and the sponsors, the sponsored data has been proven to be a promising solution and is becoming a ubiquitous trend in the fifth generation (5G) networks for improving data connectivity for the users, increasing mobile engagement for the sponsors, and ensuring revenue for the network operators. In this paper, we investigate the data sponsored 5G system on a long-run basis. Compared with the conventional dynamic, i.e., long-run, model, the users in the system are memory-affecting, i.e., the users' decision-making is affected by their past service experience. In the system under our consideration, the users decide on the communication service access by jointly taking into account their instantaneous achievable utility and the history of their service experience, e.g., the past improved utility corresponding to the data sponsorship. The 5G system works as the utility provider for managing the communication service. Specifically, by using the concept of the power-law fading memory and the classical evolutionary game theory, we formulate a population game to model and study the dynamic behaviors of the players in the data sponsored 5G system. In the game, the interaction among the memory-affecting rational users is formulated as a fractional evolutionary game, and the communication service management of the 5G system is formulated as a classical evolutionary game. We analytically prove the existence and uniqueness of the solution to the population game. We both analytically and numerically verify the stability of the solution. The performance evaluation shows some insightful results. For example, the data sponsorship can significantly increase the data consumption for the users when they are heavily memory-affecting. Following this, we study a data sponsorship pricing problem with the objective to maximize the data consumption at the expense of the minimal data sponsorship.

The Internet of Things (IoT) connects mobile and wireless devices, and enables the IoT service providers to deliver IoT services to the mobile users in various applications, e.g., transportation and communications. In this paper, the problem of IoT service delivery management is studied with the consideration of substitutability, complementarity, and externalities of delivering IoT services due to the diversity of different IoT components in mobile systems. The substitutable IoT services have similar functionalities to serve IoT users, and the IoT users can switch to buy service from any IoT service provider. The complementary IoT services have different functionalities to serve IoT users, and the IoT users may request a bundle of IoT services from multiple IoT service providers as their IoT services can be integrated. Externalities represent the situation in which IoT users in the same system can affect the utilities of each other due to the connections and interference among the IoT users, which leads to the presence of network effect and congestion effect. To analyze the impact of these factors on the performance of IoT systems, a multi-leader multi-follower Stackelberg game model is introduced. Therein, the IoT service providers and IoT users make their strategic decisions in terms of pricing and service requests, respectively, toward their individual objectives in a distributed manner. A closed-form equilibrium solution is derived analytically through backward induction.

With the emerging Internet-of-Things services, massive machine-to-machine (M2M) communication will be deployed on top of human-to-human (H2H) communication in the near future. Due to the coexistence of M2M and H2H communications, the performance of M2M (i.e., secondary) networks depends largely on the H2H (i.e., primary) network. In this paper, we propose ambient backscatter communication for the M2M networks which exploits the energy (signal) sources of the H2H network, referring to its traffic applications and popularity. In order to maximize the harvesting and transmission opportunities offered by varying traffic sources of the H2H network, we adopt a Bayesian nonparametric (BNP) learning algorithm to classify traffic applications (patterns) for secondary transmitters (STs). We then analyze the performance of STs using the stochastic geometric approach, based on a criterion for optimal traffic selection. Because of the mathematical intractability of the optimal criterion, we have attained a suboptimal traffic selection criterion which provides more tractable analysis. Results are presented to validate the performance of the proposed BNP classification algorithm and the criterion, as well as the impact of traffic sources and popularity.

We propose backscatter-aided cooperative transmission for wireless-powered heterogeneous networks (WPHetNets). In WPHetNets, where various kinds of nodes such as high-power base station (e.g., TV tower and macro base station) and small-power access point (e.g., WiFi access point) coexist, we aim to increase transmission range and support fair communication through internet-of-things (IoT) device cooperation. For this, we first propose long-range bistatic backscatter (BB)-aided cooperative transmission with two-device cooperation where ambient backscatter (AB) enables short-range information exchange in sequential mode between devices nearby under energy neutral operation, termed `Cooperation mode'. To ensure fairness between devices, we formulate common-throughput maximization problem where an algorithm for time allocation is presented. Compared with `Non-cooperation mode' (i.e., no information exchange via AB) and active RF based cooperation schemes, the proposed scheme is shown to increase both the coverage and fairness between devices for battery-less IoT networks. We then generalize the architecture of backscatter-aided cooperative transmission with multiple-device cooperation where the information exchange is performed in sequential mode and parallel (broadcasting) mode. In the sequential mode, a graph-matching based suboptimal pairing algorithm is proposed whose validity is corroborated through comparison with a heuristic search based optimal pairing, and then we compare the two modes for multiple-device cooperation.

In this paper, we propose a novel decode-and-forward (DF)-based secure 3D mobile unmanned aerial vehicle (UAV) relaying for hybrid satellite-terrestrial networks (HSTNs) in the presence of an aerial eavesdropper lying around a serving UAV relay in a circular plane. Herein, we adopt a stochastic mixed mobility (MM) model for mobilizing the UAV relays in a 3D cylindrical cell with a ground user equipment (UE). We consider the deployment of eavesdropper under the cases: (a) the eavesdropper is located at certain fixed distance around a serving UAV relay; (b) the eavesdropper is located uniformly random around the relay. By considering the opportunistic closest-, uniform-, and maximum-signal-to-noise ratio (maximum-SNR) UAV relay selection (URS) strategies, we analyze the secrecy performance of the considered HSTN in terms of probability of non-zero secrecy capacity (PNZSC) and secrecy outage probability (SOP). In particular, we derive exact PNZSC and SOP for the closest-URS (CURS) and uniform-URS (UURS). While, we derive analytical lower and upper bounds on PNZSC and SOP for maximum-SNR URS (MURS). We further carry out the corresponding asymptotic SOP analysis to assess the secrecy diversity order and coding gain of aforementioned URS strategies. Simulations are performed to corroborate the analysis.

To ensure that the data aggregation, data storage, and data processing are all performed in a decentralized but trusted manner, we propose to use the blockchain with the mining pool to support IoT services based on cognitive radio networks. As such, the secondary user can send its sensing data, i.e., transactions, to the mining pools. After being verified by miners, the transactions are added to the blocks. However, under the dynamics of the primary channel and the uncertainty of the mempool state of the mining pool, it is challenging for the secondary user to determine an optimal transaction transmission policy. In this paper, we propose to use the deep reinforcement learning algorithm to derive an optimal transaction transmission policy for the secondary user. Specifically, we adopt a Double Deep-Q Network (DDQN) that allows the secondary user to learn the optimal policy. The simulation results clearly show that the proposed deep reinforcement learning algorithm outperforms the conventional Q-learning scheme in terms of reward and learning speed.

With the development of decentralized consensus protocols, permissionless blockchains have been envisioned as a promising enabler for the general-purpose transaction-driven, autonomous systems. However, most of the prevalent blockchain networks are built upon the consensus protocols under the crypto-puzzle framework known as proof-of-work. Such protocols face the inherent problem of transaction-processing bottleneck, as the networks achieve the decentralized consensus for transaction confirmation at the cost of very high latency. In this paper, we study the problem of consensus formation in a system of multiple throughput-scalable blockchains with sharded consensus. Specifically, the protocol design of sharded consensus not only enables parallelizing the process of transaction validation with sub-groups of processors, but also introduces the Byzantine consensus protocols for accelerating the consensus processes. By allowing different blockchains to impose different levels of processing fees and to have different transaction-generating rate, we aim to simulate the multi-service provision eco-systems based on blockchains in real world. We focus on the dynamics of blockchain-selection in the condition of a large population of consensus processors. Hence, we model the evolution of blockchain selection by the individual processors as an evolutionary game. Both the theoretical and the numerical analysis are provided regarding the evolutionary equilibria and the stability of the processors' strategies in a general case.

The burgeoning vehicular networks generate a huge amount of sensing data. Data sharing among vehicles enables a number of valuable vehicular applications to improve driving safety and enhance vehicular services. To ensure security and traceability of data sharing, an efficient Delegated Proof-of-Stake (DPoS) consensus algorithm is utilized to establish Blockchain-Enabled VEhicular Networks (BEVENs). Miners in DPoS include active miners and standby miners. The active miners are responsible for block generation and block verification. However, due to the limited number of active miners in DPoS, the compromised active miners may collude with each other to generate maliciously manipulated results of block verification. To prevent the internal collusion among the active miners, a newly generated block can be further verified and audited by the standby miners. To incentivize the participation of the miners in block verification, we adopt the contract theory to model the interactions between active miners and standby miners, where both block verification security and delay are taken into consideration. Numerical results demonstrate the security and efficiency of our schemes for data sharing in BEVENs.

In this paper, we propose backscatter based cooperative transmission for wireless-powered heterogeneous networks (WPHetNets). In WPHetNets, where various kinds of nodes such as high-power base station (e.g., TV tower, macro base station) and small-power access point (e.g., WiFi) coexist, we aim to increase transmission range and support fair communication for battery-free internet-of-things (IoT) devices through user cooperation. For this, we propose long-range bistatic backscatter (BB) based distributed beamforming (DTB) where ambient backscatter (AB) supports short-range information exchange between devices for energy neutral operation, which is termed 'Cooperation mode'. In order to ensure fairness between devices, we formulate common-throughput maximization problem, where an algorithm for optimal time allocation is presented. Comparing with 'Non-Cooperation mode' (i.e., no information exchange via AB) and active RF based cooperation schemes, the proposed scheme is shown to increase both coverage and fairness between IoT devices for low-power wide area network (LP-WAN).

In an RF-powered backscatter cognitive radio network, multiple secondary users communicate with a secondary gateway by backscattering or harvesting energy and actively transmitting their data depending on the primary channel state. To coordinate the transmission of multiple secondary transmitters, the secondary gateway needs to schedule the backscattering time, energy harvesting time, and transmission time among them. However, under the dynamics of the primary channel and the uncertainty of the energy state of the secondary transmitters, it is challenging for the gateway to find a time scheduling mechanism which maximizes the total throughput. In this paper, we propose to use the deep reinforcement learning algorithm to derive an optimal time scheduling policy for the gateway. Specifically, to deal with the problem with large state and action spaces, we adopt a Double Deep-Q Network (DDQN) that enables the gateway to learn the optimal policy. The simulation results clearly show that the proposed deep reinforcement learning algorithm outperforms non-learning schemes in terms of network throughput.

Embedding sensors on wireless devices for collaborative environment sensing has been envisioned as a cost-effective solution for IoT applications. However, existing IoT platforms faces challenges, e.g., unsustainablility due to the limited on-device battery and tremendous cost of deploying middlewares for centralized task dispatching. In this paper, we employ wireless energy transfer and permissionless blockchains to construct a sustainable and decentralized IoT crowdsensing platform. Therein, IoT sensing cloud composed of multiple co-located sensors is wirelessly powered by RF-energy beacons for data sensing and transmission. The data is then forwarded to the blockchain for distributed data/transaction verification and trading. The data users access the crowdsensing service by renting sensors from the sensing clouds. Both the sensing clouds and data users are self-interested and aim to maximize their individual profits. The sensing clouds handle the interference of concurrent wireless transmissions and the on-chain transaction cost. Meanwhile, each user distributes its limited budget over the sensing clouds to optimize the service quality. We formulate a Stackelberg differential game to analyze the interaction among the sensing clouds and data users. Then, we investigate the Stackelberg equilibrium by capitalizing on Pontryagin's maximum principle. Furthermore, we provide a series of insightful numerical results about the Stackelberg equilibrium.

We propose a battery-less location tracking system that enables 3-D positioning of an Internet of Things (IoT) device powered by the radio frequency (RF) wireless power transfer (WPT). In the proposed system, a power beacon is equipped with a phased antenna array that has the dual purposes of high-efficiency WPT and phase-based accurate positioning. In order to enhance the efficiency of the RF WPT, we propose a beam focusing algorithm that dynamically controls the respective phases of antenna elements to place the focal point of the electro-magnetic (EM) wave onto the target IoT device. We also propose a phase-based positioning algorithm that requires only one multi-antenna anchor point for determining the distance as well as the direction from the anchor point. We analyze the Cramer-Rao lower bound (CRLB) of the phase-based positioning with a single multi-antenna anchor point, and show that the distance from the anchor point can be estimated as long as the IoT device lies within the radiative near-field region. We propose a joint location tracking and WPT algorithm that performs 3-D positioning and beam-focused WPT in a unified way. We have built a real-life testbed with a large-scale antenna array with 64 antenna elements for testing the proposed algorithm. The experimental results show the effectiveness of the proposed algorithm in a real environment.

Wireless transmission of information and power has received growing attention in the research community in the past few years. In two consecutive special issues, a total of thirty papers present state-of-the-art results in the broad area of wireless transmission of information and power.

Radio waves carry both energy and information simultaneously. Nevertheless, radio-frequency (RF) transmissions of these quantities have traditionally been treated separately. Currently, the community is experiencing a paradigm shift in wireless network design, namely, unifying wireless transmission of information and power so as to make the best use of the RF spectrum and radiation as well as the network infrastructure for the dual purpose of communicating and energizing. In this paper, we review and discuss recent progress in laying the foundations of the envisioned dual purpose networks by establishing a signal theory and design for wireless information and power transmission (WIPT) and identifying the fundamental tradeoff between conveying information and power wirelessly. We start with an overview of WIPT challenges and technologies, namely, simultaneous WIPT (SWIPT), wirelessly powered communication networks (WPCNs), and wirelessly powered backscatter communication (WPBC). We then characterize energy harvesters and show how WIPT signal and system designs crucially revolve around the underlying energy harvester model. To that end, we highlight three different energy harvester models, namely, one linear model and two nonlinear models, and show how WIPT designs differ for each of them in single-user and multi-user deployments. Topics discussed include rate-energy region characterization, transmitter and receiver architectures, waveform design, modulation, beamforming and input distribution optimizations, resource allocation, and RF spectrum use. We discuss and check the validity of the different energy harvester models and the resulting signal theory and design based on circuit simulations, prototyping, and experimentation. We also point out numerous directions that are promising for future research.

This second of the two issues on wireless transmission of information and power starts with some works on Simultaneous Wireless Information and Power Transfer (SWIPT), then switches to Wirelessly Powered Communication Networks (WPCNs), and finishes with a few works on Wirelessly Powered Backscatter Communication (WPBC).

A novel generalized coordinated multi-point transmission (GCoMP)-enabled non-orthogonal multiple access (NOMA) scheme is proposed. In particular, distributed base stations (BSs) in a network coverage area cooperate on the downlink to serve a set of user equipments (UEs) using the same transmission frequency band. Furthermore, all UEs associated to a BS and using a particular frequency band forms a single NOMA cluster. The number of BSs serving a UE in a particular frequency band is referred to as theorder of clustering(or order of BS cooperation). To evaluate the proposed scheme, we derive a closed-form expression for the probability of outage for a UE with different orders of BS cooperation. To obtain important insights on the performance of the proposed system, approximate (asymptotic) expressions for the probability of outage and outage capacity are derived considering both perfect and imperfect channel state information (CSI) estimation. We observe that improved spectral efficiency with a large number of UEs per NOMA cluster can be achieved by increasing the clustering order (i.e., number of cooperating BSs per UE). Furthermore, an optimal transmission power allocation scheme that jointly allocates transmission power fractions from all cooperating BSs to all connected UEs is developed.

In this paper, we investigate the coverage performance of a reference user equipment (UE) in a finite network of multiple three-dimensional (3D) mobile unmanned aerial vehicles (UAVs). Specifically, following the general theory on mobile ad hoc networks (MANETs), we propose a mixed mobility (MM) model which characterizes the movement process of a UAV in the 3D cylindrical region. In this paper, we invoke the random waypoint mobility (RWPM) and uniform mobility (UM) models to represent the movement of a UAV in vertical and spatial directions, respectively. By employing this MM model, we analyze the coverage probability of a reference UE in a finite network of multiple UAVs under the uniform UAV association and closest UAV association policies in one snapshot. According to the former policy, the UE connects to a random UAV while in the latter policy it connects to the closest UAV. Furthermore, all the non-serving UAVs act as the interferers to UE. To facilitate coverage probability analysis, we determine the serving and interfering UAVs-to-UE distance distributions under two association policies for Nakagami-m fading. We quantify the coverage performance of UE for various system and mobility parameters to exemplify the closest UAV association policy. Moreover, the analytical results are verified through simulations.

This paper proposes a new reconfigurable energy harvester for simultaneous wireless information and power transfer (SWIPT), where the energy harvester has multiple energy harvesting (EH) circuits in parallel and is adaptively reconfigured depending on the received power. Unlike the existing ideal linear EH model, where EH efficiency is a fixed constant regardless of the received power, we consider the practical nonlinear EH model. With the new reconfigurable energy harvester, we study adaptive mode switching between information decoding and EH over the fading channel. We optimize the problem of maximizing the average achievable rate under the energy causality condition, namely an average harvested energy constraint. Then we evaluate the rate-energy tradeoff for the proposed reconfigurable energy harvester. Numerical results demonstrate considerable performance gains through the proposed energy harvester. This implies that the SWIPT receiver can be self-powered effectively with the proposed one while meeting the energy causality safely.

In this paper, we propose a dual mode simultaneous wireless information and power transfer (SWIPT) system in which a sensor node monitors the received power and adaptively controls the communication mode. To this end, we design two types of single and multi-tone waveforms and propose a new transceiver architecture that realizes adaptive power management and information decoding (adaptive PM-&-ID) module. On top of this, we implement adaptive PM-&-ID policy algorithm which reflects non-linear energy harvesting (EH) model for both single and multi-tone waveforms. Our newly designed SWIPT can be applied to Internet-of-Things (IoT) network with RF EH capability for self-powering, leading to battery-free network. Numerical results show that significant performance gain can be achieved by the proposed dual mode SWIPT over the existing SWIPT design.

The wireless power transfer via an electro-magnetic (EM) wave enables far-field power transfer for supplying power to Internet of Things (IoT) devices. However, the power attenuation of the EM wave leads to low end-to-end power transfer efficiency. In this paper, we provide an analytic and experimental study on the distributed wireless power transfer system as a means to overcome the low power transfer efficiency. In the distributed wireless power transfer system, a number of multiantenna power beacons, which are distributed over space, send out wireless power to charge IoT devices. Since each power beacon has a separate local oscillator and controller, it is very challenging to achieve frequency and phase synchronization among power beacons, which is the prerequisite for optimal distributed beamforming. In this paper, we study the performance of the distributed wireless power transfer system with or without the frequency and phase synchronization. Based on the experiment and simulation results, we show that the distributed wireless charging is advantageous in terms of the coverage probability as long as the optimal distributed beamforming is available in the distributed wireless power transfer system.

With the advancement of wireless energy harvesting and transfer techniques, an Internet of Things (IoT) node equipped with a wireless charging facility can request and receive energy from wireless chargers deployed at different locations. This provides more opportunity for the mobile IoT node to replenish its battery and be able to operate without interruption due to shortage of energy supply. In this paper, we develop an optimal energy charging scheme for the mobile IoT node, considering the states of location, traffic generation, and energy storage. We formulate the problem of energy charging as a Markov decision process (MDP) to obtain the mobile IoT node’s optimal policy. The objective is to maximize the expected utility. Furthermore, we prove that the optimal policy of the proposed MDP has a threshold structure. The numerical results show the performances of the mobile IoT node under various scenarios and parameter setting. Furthermore, the proposed MDP-based wireless energy charging scheme outperforms conventional baseline schemes.

This paper considers the problem of dynamic power allocation in the downlink of multi-cell networks, where each cell utilizes non-orthogonal multiple access (NOMA)-based resource allocation. Also, coordinated multi-point (CoMP) transmission is utilized among multiple cells to serve users experiencing severe inter-cell interference (ICI). Under this CoMP- NOMA framework, CoMP transmission is applied to a user experiencing less distinctive channel gain with multiple base stations (BSs)/cells (i.e., severe ICI-prone user) and non-CoMP transmission (i.e., transmission without any coordination among multiple BSs) is applied to a user experiencing dominating channel gain with only one BS/cell, while NOMA is utilized at each BS to schedule CoMP and non-CoMP users over the same transmission resources, i.e., time, spectrum and space. After discussing various CoMP- NOMA models for downlink power allocation in multi-cell networks, we focus on a joint transmission CoMP- NOMA (JT-CoMP-NOMA) model. For the JT-CoMP-NOMA model, an optimal joint power allocation problem is formulated and the solution is derived for each CoMP- set consisting of multiple cooperating BSs (i.e., CoMP BSs). To avoid the huge computational complexity of the joint power optimization approach, we propose a distributed power optimization approach at each cooperating BS whose optimal solution is independent of the solution of other coordinating BSs. The distributed solution for the joint power optimization problem is validated and numerical performance evaluation is carried out for the proposed CoMP- NOMA models including JT-CoMP-NOMA and coordinated scheduling CoMP- NOMA (CS-CoMP-NOMA). The obtained results reveal significant gains in spectral and energy efficiency in comparison with conventional CoMP- orthogonal multiple access (CoMP-OMA) systems.

In this paper, the problem of joint mode switching and power adaptation is studied for simultaneous wireless information and power transfer (SWIPT) over a fading channel. The receiver dynamically switches between information decoding (ID) and energy harvesting (EH) modes while the transmitter dynamically adapts the transmit power. Considering the nonlinearity of practical EH circuits, a realistic nonlinear EH model is adopted rather than the idealistic linear EH model. To characterize the ultimate performance tradeoff between ID and EH, an optimization problem is formulated to maximize the average harvested energy under the constraints on the average achievable rate and the average transmit power, which is a nonconvex and combinatorial problem. To solve this problem, first, the optimal power adaptation scheme for the nonlinear EH receiver that operates only in the EH mode is proposed. Using this scheme, the jointly optimal solution for the mode switching and power adaptation is then derived. By comparing the obtained results to the existing results, various useful and interesting insights into the optimized SWIPT system with nonlinear EH are presented. An important insight into the impact of nonlinear EH is that, to exploit the high energy conversion efficiency of the nonlinear circuit, the EH mode has to be selected only in the moderate range of channel gains. Also, in the EH mode, the power has to be adapted to the short-term power threshold only for the moderate channel gains.

Traffic patterns associated with different primary users (PUs) might provide different spectral access and energy harvesting opportunities to secondary users (SUs) in wireless powered cognitive radio networks (WP-CRNs). Since the traffic applications have their own distinctive patterns, spectral access and energy harvesting opportunities are also expected to be distinctive. In this paper, we propose a novel approach to identify the PU traffic patterns and estimate the energy harvested from each traffic pattern so that SU can maximize its capacity accordingly. More specifically, we propose a theoretical framework based on a variational inference algorithm to cluster various traffic patterns and design a threshold-based SU transmission strategy by taking into account the spectral access and energy harvesting opportunities for each traffic pattern, so as to optimize SU transmission. Through simulations, we demonstrate the effectiveness of the proposed scheme in terms of throughput gains and show the transmission thresholds under various traffic applications (patterns). Further, we illustrate the effects of different collision costs on throughput for different traffic applications using real wireless traces.

In this paper, we investigate a multi-node multi-antenna wireless-powered sensor network (WPSN) comprised of one power beacon and multiple sensor nodes. We have implemented a real-life multi-node multi-antenna WPSN testbed that operates in real time. We propose a beam-splitting beamforming technique that enables a power beacon to split microwave energy beams toward multiple nodes for simultaneous charging. We experimentally demonstrate that the beam-splitting beamforming technique achieves the Pareto optimality. For perpetual operation of the sensor nodes, we adapt an energy neutral control algorithm that keeps a sensor node alive by balancing the harvested and consumed power. The joint beam-splitting and energy neutral control algorithm is designed by means of the Lyapunov optimization technique. In our experiments, the proposed algorithm has successfully kept all sensor nodes alive by optimally splitting energy beams toward multiple sensor nodes.

In this paper, we study rate-energy (R-E) tradeoffs for simultaneous wireless information and power transfer (SWIPT). In the existing literature, by invoking a simplistic and ideal assumption of linear energy harvesting, the R-E tradeoff performance was analyzed only for the four SWIPT schemes: the dynamic power splitting, type-I on-off power splitting (OPS), static power splitting, and time switching. Different from such works, in this work, we consider the realistic and practical scenario of nonlinear energy harvesting. Furthermore, to characterize the R-E tradeoff with nonlinear energy harvesting, we propose a new SWIPT scheme, the generalized OPS (GOPS). As a special case of the proposed GOPS, we also investigate an additional SWIPT scheme, the type-II OPS. Through the analysis based on the realistic nonlinear models reported in the literature, we derive new theoretical results on the R-E tradeoff, which are in sharp contrast to those in the existing literature obtained with linear energy harvesting. Furthermore, we provide various useful insights into the SWIPT system with nonlinear energy harvesting.

The recent advanced wireless energy harvesting technology has enabled wireless-powered communications to accommodate wireless data services in a self-sustainable manner. However, wireless-powered communications rely on active RF signals to communicate and result in high power consumption. On the other hand, ambient backscatter technology that passively reflects existing RF signal sources in the air to communicate has the potential to facilitate an implementation with ultra-low power consumption. In this paper, we introduce a hybrid device-to-device (D2D) communication paradigm by integrating ambient backscattering with wireless-powered communications. The hybrid D2D communications are self-sustainable, as no dedicated external power supply is required. However, since the radio signals for energy harvesting and for backscattering come from the ambient, the performance of the hybrid D2D communications depends largely on environment factors, e.g., distribution, spatial density, and transmission load of ambient energy sources. Therefore, we design two mode selection protocols for the hybrid D2D transmitter, allowing a more flexible adaptation to the environment. We then introduce analytical models to characterize the impacts of the considered environment factors on the hybrid D2D communication performance. Together with extensive simulations, our analysis shows that the communication performance benefits from larger repulsion, transmission load, and density of ambient energy sources. Furthermore, we investigate how different mode selection mechanisms affect the communication performance.

In this paper, we study the transmission strategy adaptation problem in an RF-powered cognitive radio network, in which hybrid secondary users are able to switch between the harvest-then-transmit mode and the ambient backscatter mode for their communication with the secondary gateway. In the network, a monetary incentive is introduced for managing the interference caused by the secondary transmission with imperfect channel sensing. The sensing-pricing-transmitting process of the secondary gateway and the transmitters is modeled as a single-leader-multi-follower Stackelberg game. Furthermore, the follower sub-game among the secondary transmitters is modeled as a generalized Nash equilibrium problem with shared constraints. Based on our theoretical discoveries regarding the properties of equilibria in the follower sub-game and the Stackelberg game, we propose a distributed, iterative strategy searching scheme that guarantees the convergence to the Stackelberg equilibrium. The numerical simulations show that the proposed hybrid transmission scheme always outperforms the schemes with fixed transmission modes. Furthermore, the simulations reveal that the adopted hybrid scheme is able to achieve a higher throughput than the sum of the throughput obtained from the schemes with fixed transmission modes.

In this work, we investigate a system where users are served by an access point that is equipped with energy harvesting and caching mechanism. Focusing on the design of an efficient content delivery scheduling, we propose a joint scheduling and caching scheme. The scheduling problem is formulated as a Markov decision process and solved by an on-line learning algorithm. To deal with large state space, we apply the linear approximation method to the state-action value functions, which significantly reduces the memory space for storing the function values. In addition, the preference learning is incorporated to speed up the convergence when dealing with the requests from users that have obvious content preferences. Simulation results confirm that the proposed scheme outperforms the baseline scheme in terms of convergence and system throughput, especially when the personal preference is concentrated to one or two contents.

Each traffic application follows a unique packet transmission pattern, which can be used to identify traffic applications. Current literature on cognitive radio networks (CRNs) assume that primary user (PU) channel idle and busy time probabilities are predefined and known. However, in practice, those probabilities are application-specific. In this paper, from application-dependent traffic features, we propose a Bayesian nonparametric method to detect and classify primary transmitter's (PT's) applications to estimate the secondary user (SU) spectral access and energy harvesting opportunities related to each application. To this end, the Dirichlet process mixture model (DPMM) is employed and a mean-field variational method is proposed. We demonstrate the effectiveness of the proposed method by both simulations and experiment data obtained from the WiMax networks.

In this paper, we propose a novel network model for RF-powered cognitive radio networks and ambient backscatter communications. In the network under consideration, each secondary transmitter is able to backscatter primary signals to the gateway for data transfer or to harvest energy from the primary signals and then use that energy to transmit data to the gateway. To maximize overall network throughput of the network, we formulate an optimization problem with the aim of finding not only an optimal tradeoff between data backscattering time and energy harvesting time, but also time sharing among multiple secondary transmitters. Through the numerical results, we demonstrate that the solution of the optimization problem always achieves the best performance compared with two other baseline schemes.

In this paper, we study an overlay RF-powered cognitive radio network with ambient backscatter communications. In the network, when the channel is occupied, the secondary transmitter (ST) can perform either energy harvesting or data transmission using ambient backscattering technique to a gateway. We consider the case that the gateway charges the ST a certain price if the ST transmits information. This leads to questions of how to determine the best price for the gateway and how to find the optimal backscatter time. To address this problem, we propose a Stackelberg game in which the gateway is the leader adapting the price to maximize its profit in the first stage. Meanwhile, the ST chooses its backscatter time to maximize its utility in the second stage. To analyze the game, we apply the backward induction technique. We show that the game always has a unique subgame perfect Nash equilibrium. Additionally, our results provide insights on the impact of the competition on the players' profit and utility.

Self-sustainable communications based on advanced energy harvesting technologies have been under rapid development, which facilitate autonomous operation and energy-efficient transmission. Recently, ambient backscattering that leverages existing RF signal resources in the air has been invented to empower data communication among low-power devices. In this paper, we introduce hybrid device-to-device (D2D) communications by integrating ambient backscattering and wireless-powered communications. The hybrid D2D communications are self-sustainable, as no dedicated external power supply is required. However, since the radio signals for energy harvesting and backscattering come from external RF sources, the performance of the hybrid D2D communications needs to be optimized efficiently. As such, we design two mode selection protocols for the hybrid D2D transmitter, allowing a more flexible adaptation to the environment. We then introduce analytical models to characterize the impacts of the considered environment factors, e.g., distribution, spatial density, and transmission load of the ambient transmitters, on the hybrid D2D communications performance. Extensive simulations show that the repulsion factor among the ambient transmitters has a non-trivial impact on the communication performance. Additionally, we reveal how different mode selection protocols affect the performance metrics.

Non-orthogonal multiple access (NOMA) is a promising multiple access technique for 5G wireless technology. In this paper, we first discuss the fundamentals of uplink and downlink NOMA transmissions in a cellular system and outline their key distinctions in terms of implementation complexity, detection and decoding at the SIC receiver(s), and the intra-cell and inter-cell interferences. Later, for both downlink and uplink NOMA, for each individual user in a two-user NOMA cluster, we theoretically derive the NOMA dominant condition, which refers to the condition under which the spectral efficiency gains of NOMA are guaranteed compared to conventional orthogonal multiple access (OMA). The conditions, which are distinct for uplink and downlink as well as for each individual user, provide direct insights into selecting appropriate users in two-user NOMA clusters. Numerical results show the significance of the derived conditions for user selection in uplink/downlink NOMA clusters and provide a comparison to the random user selection. Finally, a brief summary of the recent research investigations is provided which is followed by a discussion on the research challenges and future research directions.

To address the problem of unbalanced received signal-to-interference-and-noise ratio (SINR) at relay and destination nodes in wireless power transfer (WPT)- supported relay system, we propose a novel asymmetric full-duplex (FD) decode-and-forward (DF) WPT relay strategy, where the transmission time slots are not necessarily identical. By introducing asymmetric time slots, higher degree of freedom is obtained than the conventional symmetric WPT relay system. Furthermore, based on the asymmetric strategy, we develop a spectral efficiency (SE)- oriented resource allocation algorithm by jointly designing time slots, transmission power at source and relay. Simulation results show that the proposed asymmetric system demonstrates higher SE than the symmetric WPT-powered FD and the time- switching based FD relay systems. Besides, more energy can be harvested at the relay node by the proposed system benefiting from the enhanced degree of freedom, showing its applicability in WPT-powered relay systems.

In this study, the performance of a dual‐hop amplify‐and‐forward relaying system with beamforming is analysed, where only the source and destination are equipped with multiple antennas and both hops are subject to κ – μ fading channels. The κ – μ fading model is a general fading model that can accurately model practical small scale fading in line‐of‐sight environments and accommodates Rician, Nakagami‐ m , and Rayleigh as special cases. New exact analytical expressions on the outage probability (OP), average symbol error rate (SER), and average capacity are derived. Moreover, asymptotic results for the OP, SER, and average capacity are also derived in simpler forms in terms of basic elementary functions which make it easy to understand the system behaviour and the impact of the channel parameters. These analytical results are general and can emulate different symmetric and asymmetric fading scenarios as special cases such as Rician/Rician, Nakagami‐ m /Nakagami‐ m , Rayleigh/Rayleigh, and mixed κ – μ , Rician, Nakagami‐ m , and Rayleigh fading links.

This paper introduces a new solution to improve the performance for secondary systems in radio frequency (RF) powered cognitive radio networks (CRNs). In a conventional RF-powered CRN, the secondary system works based on the harvest-then-transmit protocol. That is, the secondary transmitter (ST) harvests energy from primary signals and then uses the harvested energy to transmit data to its secondary receiver (SR). However, with this protocol, the performance of the secondary system is much dependent on the amount of harvested energy as well as the primary channel activity, e.g., idle and busy periods. Recently, ambient backscatter communication has been introduced, which enables the ST to transmit data to the SR by backscattering ambient signals. Therefore, it is potential to be adopted in the RF-powered CRN. We investigate the performance of RF-powered CRNs with ambient backscatter communication over two scenarios, i.e., overlay and underlay CRNs. For each scenario, we formulate and solve the optimization problem to maximize the overall transmission rate of the secondary system. Numerical results show that by incorporating such two techniques, the performance of the secondary system can be improved significantly compared with the case when the ST performs either harvest-then-transmit or ambient backscatter technique.

This paper studies the data scheduling and admission control problem for a backscatter sensor network (BSN). In the network, instead of initiating their own transmissions, the sensors can send their data to the gateway just by switching their antenna impedance and reflecting the received RF signals. As such, we can reduce remarkably the complexity, the power consumption, and the implementation cost of sensor nodes. Different sensors may have different functions, and data collected from each sensor may also have a different status, e.g., urgent or normal, and thus we need to take these factors into account. Therefore, in this paper, we first introduce a system model together with a mechanism in order to address the data collection and scheduling problem in the BSN. We then propose an optimization solution using the Markov decision process framework and a reinforcement learning algorithm based on the linear function approximation method, with the aim of finding the optimal data collection policy for the gateway. Through simulation results, we not only show the efficiency of the proposed solution compared with other baseline policies, but also present the analysis for data admission control policy under different classes of sensors as well as different types of data.

In this paper, we study a multi-antenna wireless-powered sensor network (WPSN), in which a power beacon wirelessly transfers electric energy to a sensor node via an electromagnetic wave. We have implemented a real-life multi-antenna WPSN testbed and conducted extensive experiments on the testbed. The key technology for the high-efficiency WPSN is an adaptive energy beamforming scheme that dynamically steers a microwave beam towards a sensor node. We propose a receive power-based channel estimation and energy beamforming algorithm. In addition, an adaptive duty cycle control algorithm is proposed to prevent energy storage of a sensor node from being depleted. The proposed duty cycle control algorithm is designed based on a proportional-integral-derivative controller. These algorithms are all implemented in the multi-antenna WPSN testbed. By experiments, we validate the feasibility of the multi-antenna WPSN, and show the performance of the proposed algorithms.

We investigate self-energy recycling (S-ER)-based RF powered multi-antenna relay channels (RCs) for the coverage extension in sensor networks or small cells. In the S-ER-based RF powered RC, the power used at the relay station for data transmission depends only on the energy from the access point and the recycled energy from its own transmission. We propose an optimal beamforming scheme with associated protocols and study their behaviors for both cases of downlink only and joint up-down link. Strategies for the power allocation between data transmission and RF powering are provided and a comparison with the conventional RF powered RC is made. For the proposed power allocation strategies, the performance of the conventional RC is achieved by the proposed RF powered RC only when infinite RF power is available. A high value of S-ER helps the proposed systems to reach the performance of conventional relay systems. In the joint up-down link protocol, we show that the up-down rate region can simply be constructed with easy power allocation strategy.

In this paper, we propose hybrid backscatter communication for wireless-powered communication networks (WPCNs) to increase transmission range and provide uniform rate distribution in the heterogeneous network (HetNet) environment. In such HetNet, where the TV tower or high-power base station (macrocell) coexists with densely deployed small-power access points (e.g., small-cells or WiFi), users can operate in either bistatic scatter or ambient backscatter, or a hybrid of them, given that the harvested energy from the dedicated or ambient RF signals may not be sufficient enough to support the existing harvest-then-transmit protocol for WPCN, which is extended to the wireless-powered heterogeneous network (WPHetNet). Considering the hybrid and dual mode operation, we formulate a throughput maximization problem depending on the user location, namely Macro-zone or WiFi-zone. After performing the optimal time allocation for the above operation, we show that the proposed hybrid backscatter communication can increase the transmission range of WPHetNet, while achieving uniform rate distribution.

In this paper, the fundamental performance of the simultaneous wireless information and power transfer (SWIPT) system is studied. Unlike any existing works where the codelength was assumed to be infinity, we explicitly consider the case of the finite codelength, which is much more realistic especially for the practical SWIPT system due to its limited power and complexity. For the four well-known SWIPT schemes, we analyze the tradeoff between the rate and energy; then we study the optimality of those SWIPT schemes. Furthermore, to fully characterize the fundamental performance of the SWIPT system in the regime of finite codelength, we propose to additionally use the new tradeoff between the decoding error probability and the harvested energy. In the sense of this new tradeoff, we study the optimality of the four SWIPT schemes. For the analysis of the two types of tradeoffs, we consider two different cases: when the transmit power of symbols is adapted or not. For various scenarios, we provide useful insights into the performance of the SWIPT system in the finite codelength.

In this paper, we consider an amplify-and-forward-based two-way relaying network, in which the relays need to harvest energy from the received radio frequency signals to remain active in the network and assist data exchange between two transceivers. In particular, considering time-switching architecture, we investigate the problem of joint energy harvesting (EH) time allocation and distributed beamforming in the presence of interference. Specifically, assuming that the perfect knowledge of the interfering links is not available, we study three different design approaches. First, we maximize the sum-rate of the network subject to individual EH power constraints at relays. Resorting to the semi-definite relaxation (SDR) and successive upper-bound minimization techniques, we devise an iterative algorithm that efficiently solves such a challenging problem. Next, we minimize the total power consumed by the relays subject to the rate constraints at the transceivers. Finally, we minimize the EH-phase duration subject to the individual EH power constraints at the relays as well as the rate constraints at the transceivers. The rate constraints, however, make both second and third design optimization problems non-convex and complicated. Although no closed-form solutions are available for these approaches, we propose efficient schemes by applying the SDR technique followed by semi-definite programming problems.

In this paper, we introduce a new model for RF-powered cognitive radio networks with the aim to improve the performance for secondary systems. In our proposed model, when the primary channel is busy, the secondary transmitter is able either to backscatter the primary signals to transmit data to the secondary receiver or to harvest RF energy from the channel. The harvested energy then will be used to transmit data to the receiver when the channel becomes idle. We first analyze the tradeoff between backscatter communication and harvest-then-transmit protocol in the network. To maximize the overall transmission rate of the secondary network, we formulate an optimization problem to find time ratio between taking backscatter and harvest-thentransmit modes. Through numerical results, we show that under the proposed model can achieve the overall transmission rate higher than using either the backscatter communication or the harvest-then-transmit protocol.

Big data has been emerging as a new approach in utilizing large datasets to optimize complex system operations. Big data is fueled with Internet-of-Things (IoT) services that generate immense sensory data from numerous sensors and devices. While most current research focus of big data is on machine learning and resource management design, the economic modeling and analysis have been largely overlooked. This paper thus investigates the big data market model and optimal pricing scheme. We first study the utility of data from the data science perspective, i.e., using the machine learning methods. We then introduce the market model and develop an optimal pricing scheme afterward. The case study shows clearly the suitability of the proposed data utility functions. The numerical examples demonstrate that big data and IoT service provider can achieve the maximum profit through the proposed market model.

Wireless caching has been used to improve network performance and reduce bandwidth and energy consumption. In this paper, we study the issue of joint admission control and content caching for wireless access points with energy harvesting capability. Given limited energy supply, the access points, in a competitive environment, aim to maximize their payoff defined in terms of revenue by optimizing their admission control and content caching policy. Moreover, the throughput of the content transmission by the access point has to be maintained above a certain threshold. Thus, we propose a constrained stochastic game to model this competitive caching scenario. The equilibrium policy, which is a mapping from the energy, cache, and demand states to the action, is obtained from the model. From the performance evaluation, the joint admission control and content caching policy can achieve significantly better performance than that of the baseline schemes, especially when the energy harvesting rate becomes constricted.

Caching has shown the success in performance improvement for many wireless communications and networking systems. In this paper, we introduce a caching mechanism for the energy harvesting based Internet of Things (IoT) sensing service. In the service, a sensor harvests energy from an environment. The energy is stored in the battery, and the sensor uses it for sensing and transmitting the reading to the user. A sensing cache can be implemented at a wireless gateway of the sensor to avoid activating the sensor too frequently, hence reducing its energy consumption. We develop an analytical model to investigate the benefit of the proposed caching mechanism. We also introduce the threshold adaptation algorithm that allows the sensing cache dynamically to adjust the parameter of caching to maximize the combined hit rate of the sensing service from multiple sensors. The performance evaluation clearly shows the tradeoff between energy consumption and caching.

A wireless powered communication network is a potential application of wireless energy harvesting to improve convenience and flexibility. However, wireless energy transfer from a wireless energy source has to be scheduled to minimize energy usage while meeting quality of service (QoS) requirements of sensor nodes in the network. In this paper, we consider wireless powered sensor network whose sensor nodes have auxiliary energy sources in addition to dedicated wireless energy transfer. We propose a distributed wireless energy transfer scheduling to achieve the aforementioned objective and meet the requirements. We formulate a constrained stochastic game model to obtain a multi-policy constrained Nash equilibrium of wireless energy transfer request. This equilibrium instructs the sensor node to request for wireless energy transfer based on its local state. The performance evaluation shows that the analytical model is well verified by numerical simulations.

In radio frequency (RF) energy harvesting (EH) cognitive radio network (CRN), the EH secondary users (SUs) access an idle channel to transmit data and an occupied channel to harvest energy. Therefore, the EH SUs can reliably transmit data only if sufficient energy and an idle channel are available. In this paper, we analyze the probability that the EH SUs may completely run out of energy and the achievable throughput of the EH SUs is derived accordingly. To improve the throughput of the SUs, we consider a 2-channel sensing scheme that the EH SUs are allowed to sequentially sense up to 2 channels to further search for the opportunities for data transmission. Consequently, the opportunities for data transmission increase, while less opportunities are available for energy harvesting. To validate the proposed analysis, we use the Monte-Carlo simulation to show an agreement between the analytical and simulated values, and the simulation results turn out to be reasonably acceptable.

Wireless powered sensor networks are composed of multiple sensor nodes with limited energy supply and storage. In this paper, we consider the wireless powered sensor networks, where an energy gateway can supply energy wirelessly to the sensor nodes. The sensor nodes use the energy to transmit their data. We then propose an opportunistic energy scheduling scheme. In this scheme, the energy gateway with limited energy supply transfers energy to sensor nodes based on channel conditions. We formulate an optimization problem based on a Markov decision process to obtain the optimal energy transfer policy. The objective is to maximize the weighted energy received by the sensor nodes. We prove that the optimal policy is a structure policy, in which the numerical studies confirm the result.

In wireless powered cognitive radio networks (WP-CRNs) where secondary users (SUs) access spectral white spaces left by primary users (PUs), there exists a trade-off between SU transmission and energy harvesting. However, the white spaces and the harvested energy depends on the PUs traffic applications, and this fact can be utilized to optimize SU transmissions based on the energy harvested from that traffic application. In this paper, we propose a threshold-based SU transmission strategy under the constraints that take into account the spectral access opportunities and the total energy harvested associated with each traffic application. Furthermore, we propose an algorithm which obtains an optimal threshold with respect to the available harvested energy. In the simulation results, we demonstrate the effectiveness of the proposed scheme in terms of throughput gains and show the transmission thresholds under various traffic applications (patterns). Moreover, we illustrate the effects of different collision costs on throughput for different traffic applications using real wireless traces.

We consider the downlink of a dense multicell network where each cell region is divided into two zones. The users nearby their serving base station (BS) in the inner zone implement simultaneous wireless information and power transfer (SWIPT), thus harvest energy and decode information using the power splitting approach. Further, they try to eavesdrop the information intended for other users within the same cell. The users in the outer zone of each cell only implement information decoding. Our objective is to maximize the minimum user equipment (UE) signal-to-interference-and-noise ratio (SINR) under constraints on the BS transmit power, minimum energy harvesting levels of near-by users, and maximum SINR of eavesdroppers in the presence of multi-cell interference. For such a highly non-convex problem, semidefinite relaxation (SDR) may even fail to locate a feasible solution. We propose two methods to address such a difficult problem. In the spectral optimization, we express the rank-one constraints as a single reverse convex nonsmooth constraint and incorporate it into the optimization objective. In the difference-of-convex-functions iteration method, we directly solve for the beamforming vectors via quadratic programming (QP), avoiding the matrix rank constraints. In each iteration of the proposed algorithms, we only solve one simple convex semidefinite program (SDP) or QP. Our simulation results confirm that the proposed algorithms converge quickly after a few iterations. More importantly, our algorithms yield the performance that is very close to the theoretical bound given by SDP relaxation with comparable computational complexity.

We consider cluster-based routing protocols for energy harvesting wireless sensor networks. Since the energy harvesting process does not match the real energy demand, sensor nodes suffer from occasional energy shortages, especially when they serve as cluster head (CH) nodes. To address this problem, we propose a cluster-based routing protocol referred to as distance-and-energy-aware routing with energy reservation (DEARER). The DEARER protocol encourages nodes with high energy-arrival rate or being close to the sink to serve as CH nodes. Also, DEARER allows non-CH nodes to reserve a portion of the harvested energy for future use. In doing so, the DEARER selects “enabler” nodes as CH nodes and provides them with more energy, thereby mitigating the energy shortage events at CH nodes. By theoretical analysis and numerical experiments, we demonstrate that the DEARER protocol outperforms direct transmission and also approaches the genie-aided routing, where CH nodes are selected based on the real-time energy information of each node.

In this paper, we propose a stochastic optimal control algorithm for the wireless powered communication networks (WPCNs), in which the access point (AP) supplies energy to wireless nodes by means of the RF energy transfer technology. The energy beamforming is used to enhance the RF energy transfer efficiency by concentrating the radiated power on target nodes. Each wireless node is equipped with an energy queue and a data queue. We propose an algorithm that minimizes the expected energy transmission power from the AP while stabilizing the data queues of all nodes. The proposed algorithm is an online algorithm that adaptively decides the beamforming vector, the data scheduling, and the data transmission power, only based on the current state of the energy and the data queues. The proposed algorithm dynamically steers the energy beam to nodes that currently have low energy in the energy queue. We apply the Lyapunov optimization technique to design such an algorithm. We mathematically prove that the proposed algorithm achieves the optimal performance.

In this paper, the fundamental rate-energy (R-E) tradeoffs are studied for simultaneous wireless information and power transfer (SWIPT). Unlike the existing results obtained using Gaussian input given an average power constraint, we focus on the equi-probable arbitrary shaped discrete inputs considering a peak power constraint as well as an average power constraint. For these power constraints, the transmit power strategies are presented and the R-E regions are derived. The optimality of different SWIPT schemes is first studied in terms of the R-E tradeoff for the case of negligible circuit power consumption in the information decoder. The results are extended to the case of non-negligible circuit power consumption in the information decoder. The asymptotic R-E regions are also derived, which give us useful insights into the fundamental R-E tradeoffs. Finally, the R-E regions of Gaussian input and discrete inputs are compared under an average power constraint.

This paper studies the problem of optimal beamforming and power allocation for an amplify-and-forward (AF)-based two-way relaying network in the presence of interference and channel state information (CSI) uncertainty. In particular, we obtain the beamforming vector as well as the users’ transmit powers under two assumptions on the availability of the CSI of the interfering links, namely norm-bounded uncertainty model and the second-order statistics scenario. To do so, we develop two design approaches. The first approach is based on the total transmit power minimization technique. We start with the norm-bounded uncertainty model and derive the optimal solution to the corresponding problem. To reduce the computational complexity, we also develop a low-complexity algorithm which offers performance that is very close to the optimal one. In the second approach, we apply a signal-to-interference-plus-noise ratio (SINR) balancing technique. We propose another low-complexity algorithm based on the SINR balancing criteria. Next, we consider the scenario where the second-order statistics of the CSIs are available. Again we start with the total power minimization method and derive both optimal and suboptimal algorithms. Finally, we apply the SINR balancing technique to this scenario and develop another low-complexity algorithm, which is suitable for practice.

Backscatter radio communication has become a newly emerging technique for low-rate, low-power and large-scale wireless sensor networks. As this promising technology enables a long-range communication for sensors with low power in a distributed area, it is desirable to support wireless powered communication networks (WPCNs) that experience doubly near-far problem. In a backscatter radio based WPCN, users harvest energy from both the signal broadcast by the hybrid access point and the carrier signal transmitted by the carrier emitter in the downlink and transmit their own information in a passive way via the reflection of the carrier signal using frequency-shift keying modulation in the uplink. We characterize the energy-free condition and the signal-to-noise ratio (SNR) outage zone in a backscatter radio based WPCN. Numerical results demonstrate that the backscatter radio based WPCN achieves an increased long-range coverage and a diminished SNR outage zone compared to the active radio based WPCNs.

Internet of Things (IoT) objects and devices, e.g., sensors and actuators, can be accessed as a service to meet the users' demand from various applications. In this paper, we propose an optimal service auction to determine which user to access an IoT device. The auction decision to accept the highest bid is obtained as a policy of a Markov decision process (MDP) with an objective to maximize the reward of the IoT device defined as a function of the revenue from the bid minus the costs from energy replenishment and channel access for data transfer. We consider system dynamics in terms of wireless energy transfer and wireless transmission which can incur different costs. The optimal policy obtained from the MDP shows the adaptability of the IoT device owner to accept the highest bid and to request for wireless energy transfer. The performance evaluation shows clearly that the proposed optimal service auction achieves significantly higher reward than a static scheme.

Massive MIMO will be one of the technologies adopted in 5G cellular networks due to its ability to enhance transmission performance. However, resource management issues remain unsolved, especially with quality of service (QoS) requirements from users. This paper focuses on cell association and antenna allocation problems in such networks. We analyze the competitive situations where users in different classes with different QoS (i.e., data rate) requirement can choose to associate with any cell rationally and independently. Likewise, access points can allocate their antennas to different users. The users and access points are self-interested to maximize their own benefits in terms of data rate and total revenue, respectively. We formulate a hierarchical evolutionary game framework which is composed of the games for cell association and antenna allocation. We apply both deterministic and stochastic approaches to obtain the equilibrium solutions of the game.

In mobile social networks, mobile users can help each other to disseminate and deliver contents utilizing social relationship (e.g., physical contact). In this paper, we consider content delivery in mobile social networks, where a mobile user (i.e., a content source) transfers not only content, but also energy to an intermediate user (i.e., a mobile content messenger). The messenger uses this energy to store, carry, and forward the content to the destination (i.e., a sink). We particularly address the content messenger selection and wireless energy transfer problem of the content source to determine which messenger to deliver the content and the amount of energy to be transferred to the messenger. We formulate a Markov decision process (MDP) to obtain the optimal policy. The numerical results show clearly the improved performance in terms of higher throughput as compared with a baseline static policy.

In wireless energy harvesting communication networks, a user harvests energy from an environment and uses the energy for data transmission. However, the user's data transmission is susceptible to a jamming attack by jammers, which also harvest energy from the environment. To address this problem, therefore we introduce a user's deception mechanism in which the user can transmit fake signals (i.e., blank transmission) to trigger the jammers to perform the attack, wasting their energy. We propose an analysis of the network with the deception mechanism based on a Markov chain. The performance evaluation reveals some interesting results. For example, the user can adjust the number of blank transmissions to achieve the highest throughput. We provide a benchmarking scheme based on an optimization. The benchmarking is useful for developing an effective deception mechanism with minimum complexity and knowledge about the network and jammers.

In wireless powered networks, a user can make a request and use the wireless energy transferred from an energy source for its data transmission. However, due to broadcast nature of wireless energy transfer (e.g., RF energy), a malicious node (i.e., an attacker) can also intercept the energy and use it to perform an attack by jamming the data transmission of the user. We consider such a jamming attack where the user and attacker are aware of each other. We formulate a game theoretic model to analyze the energy request and data transmission policy of the user and the attack policy of the attacker when the user and the attacker both want to maximize their own rewards. We use an iterative algorithm designed based on the best response dynamics to obtain the solution defined in terms of the constrained Nash equilibrium. The numerical results show not only the convergence of the proposed algorithm, but also the optimal reward of the user under different energy cost constraints.

Delivering high-speed mobile social networks requires smart mechanisms that can explore the social relations between users to improve data delivery and content dissemination performance. In this paper, a novel approach for energy sharing in mobile social energy networks is proposed. In this proposed system, pairs of users that have a friendship relationship can share their energy, e.g., from batteries or power banks, to improve the data transmission performance. An analytical model is introduced to derive some important performance measures (e.g., energy outage probability and average transferred energy) of the friend users. Based on this proposed analytical model, it is observed that being friends may not always be beneficial for some of the user. To this end, a friend matching algorithm is proposed to determine the best friendship pairings between users that allow to minimize the energy outage probability. Using the proposed approach, it is shown that that there exist certain regions of system parameters, such as the transmission probability and the capacity of an energy storage, within which the stability of the friend relationship between users can be maintained. Simulation results were used to evaluate the performance of the proposed approach and to gain more insights on the potential of mobile social energy networks.

In wireless energy harvesting communication networks, a user receives wireless energy released by an ambient or dedicated energy source, and uses that energy for delay constrained data transmission. However, such data transmission can be susceptible to a jamming attack from a nearby attacker also harvesting from the wireless energy source. In this paper, we consider such a scenario and present performance analysis. In particular, we develop an analytical model for the network based on a Markov chain to obtain various performance measures for the user including throughput and delay distribution. The performance evaluation shows some interesting results. For example, under the jamming attack, there is a maximum achievable throughput of the user. We also validate the analytical model using simulation.

We present a joint sub-channel and power allocation framework for downlink transmission in an orthogonal frequency-division multiple access (OFDMA)-based cellular network composed of a macrocell overlaid by small cells. In this framework, the resource allocation (RA) problems for both the macrocell and small cells are formulated as optimization problems. For the macrocell, we formulate an RA problem that is aware of the existence of the small cell tier. In this problem, the macrocell performs RA to satisfy the data rate requirements of macro user equipments (MUEs) while maximizing the tolerable interference from the small cell tier on its allocated sub-channels. Although the RA problem for the macrocell is shown to be a mixed integer nonlinear problem (MINLP), we prove that the macrocell can solve another alternate optimization problem that will yield the optimal solution with reduced complexity. For the small cells, following the same idea of tier-awareness, we formulate an optimization problem that accounts for both RA and admission control (AC) and aims at maximizing the number of admitted users while simultaneously minimizing the consumed bandwidth. Similar to the macrocell optimization problem, the small cell problem is shown to be an MINLP. We obtain a sub-optimal solution to the MINLP problem relying on convex relaxation. In addition, we employ the dual decomposition technique to have a distributed solution for the small cell tier. Numerical results confirm the performance gains of our proposed RA formulation for the macrocell over the traditional resource allocation based on minimizing the transmission power. Besides, it is shown that the formulation based on convex relaxation yields a similar behavior to the MINLP formulation. Also, the distributed solution converges to the same solution obtained by solving the corresponding convex optimization problem in a centralized fashion.

We consider beamformer optimization for user equipment (UE) relaying cooperation in heterogeneous cellular networks (HCNs), where the interference from femto cells nearby aggravates the signal-to-interference-plus-noise ratio (SINR) of a macro UE (MUE). A femto UE acts as a relay over device-to-device (D2D) uplink and forward to the MUE, the composite of desired signal and interference to improve the post SINR of the MUE. The beamformers of the UE relay and MUE collaborate to make a balance between the two signals so that the output signal after multistage maximal-ratio combining (MS-MRC) yields a sufficient post SINR for the desired signal. We first consider the case with a single interfering femto access point (FAP) and show that the beamforming for dual-stage MRC can effectively handle the interference. Then, the idea is generalized to the case of two interfering FAPs with a three-stage MRC. A geodesic geometry view allows us to parameterize the BF design with the angles that the involved channel vectors make. This approach makes the optimization of the BF simpler, reduces the feedback overhead, and provides better insight into the problem at hand. Simulations are carried out to validate the performance of the UE relaying cooperation in HCN environments.

This paper studies the symmetric two-user Gaussian interference channel where the transmitters harvest randomly arrived energies and share the harvested energy with each other for energy cooperation. We characterize the achievable average rate region of the interference channel with time-varying available power when the simplified Han-Kobayashi scheme, known as a near-optimal transmission strategy for the conventional two-user Gaussian interference channel, is employed. We prove that each corner point on the average rate region is on a sum rate bound with appropriate power allocation. Based on the proof, we find the optimal strategy of energy cooperation and power allocation between the two transmitters to achieve the boundary points on the average rate region. It is shown that the energy cooperation can enlarge the average rate region compared to that of the conventional interference channel. We also show that the energy cooperation yields almost the same average rate region regardless of the interference channel condition because it enables to effectively change the given interference channel condition into a more favorable one by flexibly controlling the transmit powers.

This paper investigates simultaneous wireless information and power transfer (SWIPT) in energy harvesting (EH) relay systems. Unlike the existing SWIPT schemes requiring the instantaneous channel state information (CSI) for coherent information delivery, we propose a noncoherent SWIPT framework for decode-and-forward (DF) relay systems bypassing the need for the instantaneous CSI and consequently saving energy in the network. The proposed SWIPT framework embraces both the power-splitting noncoherent DF (PS-NcDF) and timeswitching noncoherent DF (TS-NcDF) in a unified form, and supports arbitrary M-ary noncoherent frequency-shift keying (FSK) and differential phase-shift keying (DPSK). The exact (noncoherent) maximum-likelihood detectors (MLDs) for PS-NcDF and TS-NcDF are derived in a unified form, which involves integral evaluations yet serves as the optimum performance benchmark for noncoherent SWIPT. To reduce the computational cost of the exact MLDs, we also propose closed-form approximate MLDs achieving near-optimum performance, thus serving as a practical solution for noncoherent SWIPT. Numerical results demonstrate a performance tradeoff between the first and second hops through the adjustment of time switching or power splitting parameters, whose optimal values minimizing the symbol-error rate (SER) are strictly between 0 and 1. We demonstrate that M-FSK results in a significant energy saving over M-DPSK for M ≥ 8; thus M-FSK may be more suitable for EH relay systems.

In energy harvesting (EH) relay networks, the coherent communication requires accurate estimation/tracking of the instantaneous channel state information (CSI) which consumes extra power. As a remedy, we propose two noncoherent EH relaying protocols based on the amplify-and-forward (AF) relaying, namely, power splitting noncoherent AF (PS-NcAF) and time switching noncoherent AF (TS-NcAF), which do not require any instantaneous CSI. We develop a noncoherent framework of simultaneous wireless information and power transfer (SWIPT), embracing PS-NcAF and TS-NcAF in a unified form. For arbitraryM-ary noncoherent frequency-shift keying (FSK) and differential phase-shift keying (DPSK), we derive maximum-likelihood detectors (MLDs) for PS-NcAF and TS-NcAF in a unified form, which involves integral evaluations yet serves as the optimum performance benchmark. To avoid expensive integral computations, we propose a closed-form detector using the Gauss-Legendre approximation, which achieves almost identical performance as the MLD but at substantially lower complexity. These EH-based noncoherent detectors achieve full diversity in Rayleigh fading. Numerical results demonstrate that our proposed PS-NcAF and TS-NcAF may outperform the conventional grid-powered relay system under the same total power constraint. Various insights which are useful for the design of practical SWIPT relaying systems are obtained. Interestingly, PS-NcAF outperforms TS-NcAF in the single-relay case, whereas TS-NcAF outperforms PS-NcAF in the multi-relay case.

We study the performance optimization problem for a cognitive radio network with radio frequency (RF) energy harvesting capability for secondary users. In such networks, the secondary users are able to not only transmit packets on a channel licensed to a primary user when the channel is idle, but also harvest RF energy from the primary users' transmissions when the channel is busy. Specifically, we propose a system model where the secondary users are able to cooperate to maximize the overall network throughput through sensing a set of common channels. We first consider the case where the secondary users cooperate in a TDMA fashion and propose a novel solution based on a learning algorithm to find optimal channel access policies for the secondary users. Then, we examine the case where the secondary users cooperate in a decentralized manner and we formulate the cooperative decentralized optimization problem as a decentralized partially observable Markov decision process (DEC-POMDP). To solve the cooperative decentralized stochastic optimization problem, we apply a decentralized learning algorithm based on the policy gradient and the Lagrange multiplier method to obtain optimal channel access policies. Extensive performance evaluation is conducted and it shows the efficiency and the convergence of the learning algorithms.

In this paper, we propose a hierarchical power control (PC) algorithm with interference allowance (IA) in two-tier heterogeneous networks. Specifically, we consider a scenario where densely deployed femtocells exhibit on–off activity and a target macrocell base station (BS) is able to dynamically measure/estimate the sum interference from femtocell users in uplink transmission. In such a scenario, to mitigate the aggregate interference (AGGI) from active femtocells, the macrocell BS first decides macrocell user power based on the average uplink power budget, and then the IA per femtocell under hierarchical structure. Femtocell users then allocate their transmit power within the IA to suppress the cross-tier interference in heterogeneous networks. The PC algorithm should reflect the number of active femtocells to effectively control the AGGI, for which we propose a centralized sensing algorithm to estimate the number of active femtocells. Further, to track both the on–off activity and the random variations due to shadowing, we implement an iterative sensing algorithm which does not require cross-tier channel gains, suitable for wireless backhaul with higher latency and lower capacity. The iterative algorithm is shown to outperform the utility-based distributed power adaptation that requires the cross-tier channel gains, in terms of total cell throughput subject to the same outage rate.

We propose a novel cooperative user relaying scheme for a two-user multiple-antenna downlink cellular system where each user has to receive a certain required amount of information. A user who successfully receives its required amount of information is supposed to help the other user in receiving its required amount of information through cooperative user relaying. For the proposed cooperative user relaying scheme, we jointly design linear beamformers at the base station over three transmission phases to minimize the total transmission time required for both users to receive their respective required amounts of information, which turn out to be approximated equivalent to the maximization of the sum throughput. In addition, considering a practical hybrid automatic repeat request (HARQ) protocol with user relaying, we modify the proposed scheme to minimize the required number of retransmissions. Our numerical results show that the proposed cooperative user relaying scheme achieves substantial gains over conventional transmission without user relaying in terms of both the average sum throughput and the transmission failure probability.

This paper characterizes the spectral efficiency of an uplink radio frequency (RF)-powered macrocell network considering harvest-then-transmit protocol such that the macrocell users transmit in the uplink while replenishing the energy from their serving base station (BS) in the downlink. Using the theory of order statistics, a tractable mathematical framework is developed to derive the uplink spectral efficiency and the downlink power consumption resulting due to wireless energy transfer. The framework captures the impact of the locations of the users that are selected for uplink transmission, their channel statistics for information and energy transfer, and different user selection schemes. We first analyze the performance of state-of-the-art greedy and round-robin scheduling schemes in RF-powered cellular networks. Closed-form expressions for the minimum power outage probability (i.e., the probability that the selected user is unable to harvest sufficient power for uplink transmission) are also derived. We then develop modified versions of the conventional user selection schemes that improve the spectral efficiency on a given uplink transmission channel with zero power outage probability (i.e., probability of outage due to insufficient amount of harvested power). The developed schemes are shown to outperform the conventional user scheduling schemes in terms of the throughput and energy harvesting time with a trade-off in fairness among users. The accuracy of the expressions is validated via Monte-Carlo simulations. Numerical results highlight the trade-offs associated with the various user selection schemes as a function of network parameters.

In this paper, we propose a fully-distributed random access protocol for the device-to-device (D2D) communication in a cellular network. The D2D communication can provide a significant capacity gain by enabling a cellular network to offload data traffic to direct communication links between devices (i.e., D2D link). However, a D2D link can generate serious interference to other D2D links as well as cellular devices without any proper interference control mechanism. Compared to centralized resource allocation and power control schemes for the D2D communication, a distributed scheme is advantageous in that it has smaller control overhead and is more responsive to traffic demands. To protect a D2D receiver, the proposed scheme employs a collision avoidance mechanism that creates an exclusion region around the D2D receiver, where interferers are prohibited from transmitting a signal. We analyze the proposed scheme by assuming that the locations of devices follow a Poisson point process. By simulation, we show that the analysis results accurately match the simulation results and that the proposed scheme outperforms a distributed D2D scheme without collision avoidance by a very wide margin.

The cognitive radio (CR) paradigm for designing next‐generation wireless communications systems is becoming increasingly popular, and different aspects of it are being implemented in currently available wireless systems. In the last decade, a significant amount of research efforts has been made to solve CR challenges, and several standards related to CR and dynamic spectrum access have been developed. Also, there have been advances in software‐defined radio platforms to implement the CR systems. In this article, we provide a comprehensive survey on the evolution of CR research covering aspects such as spectrum sensing, measurements and statistical modeling of spectrum usage, physical layer aspects such as waveform and modulation design, multiple access, resource allocation and power control, cognitive learning, adaptation and self‐configuration, multihop transmission and routing, and robustness and security in CR networks. Also, state‐of‐the‐art research on the economics of CR networks, CR simulation tools, testbeds and hardware prototypes, CR applications, and CR standardization efforts is summarized. Emerging trends on CR research and open research challenges related to the cost‐effective and large‐scale deployment of CR systems are outlined.

Radio frequency (RF) energy harvesting capability allows wireless nodes to harvest and convert ambient RF signal into energy supply for their operations and data transmission. Cognitive radio networks can employ such capability that enables secondary users to opportunistically not only transmit data on an idle channel, but also harvest RF energy from primary users' transmission on a busy channel. In this paper, we propose a queueing model to analyze performance of the cognitive radio networks with opportunistic RF energy harvesting. The queueing model captures the channel state and considers multiple secondary users whose transmissions are scheduled using the round-robin policy. Then, we introduce a simple network selection strategy for the secondary users. We also develop an analytical model to analyze the network selection decision at the steady state.

In this paper, we consider a decentralized wireless communication system with wireless energy transfer capability. We aim to minimize the total number of packets waiting at wireless nodes for the whole system. We first formulated the optimization problem as a decentralized partially observable Markov decision process (DEC-POMDP). To solve an optimization problem with constraints, we applied the Lagrangian multiplier and the policy gradient method. In addition, to reduce the complexity of DEC-POMDP, we proposed a decentralized online learning algorithm with minimum communication among the wireless nodes. Under appropriate conditions, we showed that the proposed algorithm converges to a local optimal solution. The simulation results clearly showed the convergence as well as the efficiency of the proposed algorithm.

With RF (radio frequency) energy transfer capability, an access point not only communicates with wireless nodes, but also supplies them with energy for data transmission. In this paper, we consider the wireless network with RF energy transfer. To support quality of service (QoS) in the network, we introduce an admission control policy, which decides whether incoming nodes can be admitted into the network or not. Also, the policy determines the RF energy transfer strategy to maximize the reward of the network, while the performance requirement in terms of node throughput is maintained at the target level. We present optimization and queueing models to obtain an optimal admission control policy and performance measures of the node in the network, respectively. The performance evaluation shows that the admission control policy can successfully achieve the network objective and meet the node constraint on QoS requirement.

Radio frequency (RF) energy harvesting is a promising technique to sustain an operation of wireless networks. In a cognitive radio network, a secondary user can be equipped with RF energy harvesting capability. We consider such a network where the secondary user can select one of the channels to transmit data when it is not occupied by a primary user, and to harvest RF energy when the primary user transmits data. Specifically, we formulate an optimization problem to determine an optimal channel selection policy for the secondary user. The secondary user selects a channel based on the energy level in its battery (i.e., energy queue) and the number of packets in its data queue. The optimization considers complete information and incomplete information cases, where the secondary user has and does not have the knowledge about channel states, respectively. The performance obtained in the complete information case can serve as an upper bound for the secondary user.

This paper proposes a model and a mechanism for cooperative bidding of wireless data transmission and energy transfer in a decentralized wireless communication system. We aim to minimize the total number of packets waiting at wireless nodes and the total bid prices from the nodes. We first formulated the optimization problem as a decentralized partially observable Markov decision process (DEC-POMDP) with constraints for wireless nodes and then proposed a decentralized learning algorithm to obtain an optimal policy for the DEC-POMDP. Through simulations, we showed the convergence as well as the significantly better performance of the proposed algorithm compared with other algorithms.

Radio frequency (RF) energy harvesting is a promising technique to sustain operations of wireless networks. In a cognitive radio network, a secondary user can be equipped with RF energy harvesting capability. In this paper, we consider such a network where the secondary user can perform channel access to transmit a packet or to harvest RF energy when the selected channel is idle or occupied by the primary user, respectively. We present an optimization formulation to obtain the channel access policy for the secondary user to maximize its throughput. Both the case that the secondary user knows the current state of the channels and the case that the secondary knows the idle channel probabilities of channels in advance are considered. However, the optimization requires model parameters (e.g., the probability of successful packet transmission, the probability of successful RF energy harvesting, and the probability of channel to be idle) to obtain the policy. To obviate such a requirement, we apply an online learning algorithm that can observe the environment and adapt the channel access action accordingly without any a prior knowledge about the model parameters. We evaluate both the efficiency and convergence of the learning algorithm. The numerical results show that the policy obtained from the learning algorithm can achieve the performance in terms of throughput close to that obtained from the optimization.

Linear precoding and cooperative jamming for multiuser broadcast channel is studied to enhance the physical layer security. We consider the system where multiple independent data streams are transmitted from the base station to multiple legitimate users with the help of a friendly jammer. It is assumed that a normalized linear precoding matrix is given at the base station, whereas the power allocated to each user is to be determined. The problem is to jointly design the power allocation across different users for linear precoding and the cooperative jamming at the friendly jammer. The goal is to maximize a lower bound of the secrecy rate, provided that a minimum communication rate to the users is guaranteed. The optimal solution is obtained when the number of antennas at the friendly jammer is no less than the total number of antennas at the users and eavesdropper. Moreover, a suboptimal algorithm is proposed, which can be applied for all the scenarios. Numerical results demonstrate that the proposed schemes are effective for secure communications.

Small cells such as femtocells overlaying the macrocells can enhance the coverage and capacity of cellular wireless networks and increase the spectrum efficiency by reusing the frequency spectrum assigned to the macrocells in a universal frequency reuse fashion. However, management of both the cross-tier and co-tier interferences is one of the most critical issues for such a two-tier cellular network. Centralized solutions for interference management in a two-tier cellular network with orthogonal frequency-division multiple access (OFDMA), which yield optimal/near-optimal performance, are impractical due to the computational complexity. Distributed solutions, on the other hand, lack the superiority of centralized schemes. In this paper, we propose a semi-distributed (hierarchical) interference management scheme based on joint clustering and resource allocation for femtocells. The problem is formulated as a mixed integer non-linear program (MINLP). The solution is obtained by dividing the problem into two sub-problems, where the related tasks are shared between the femto gateway (FGW) and femtocells. The FGW is responsible for clustering, where correlation clustering is used as a method for femtocell grouping. In this context, a low-complexity approach for solving the clustering problem is used based on semi-definite programming (SDP). In addition, an algorithm is proposed to reduce the search range for the best cluster configuration. For a given cluster configuration, within each cluster, one femto access point (FAP) is elected as a cluster head (CH) that is responsible for resource allocation among the femtocells in that cluster. The CH performs sub-channel and power allocation in two steps iteratively, where a low-complexity heuristic is proposed for the sub-channel allocation phase. Numerical results show the performance gains due to clustering in comparison to other related schemes. Also, the proposed correlation clustering scheme offers performance, which is close to that of the optimal clustering, with a lower complexity.

Device-to-device (D2D) communication in cellular networks allows direct transmission between two cellular devices with local communication needs. Due to the increasing number of autonomous heterogeneous devices in future mobile networks, an efficient resource allocation scheme is required to maximize network throughput and achieve higher spectral efficiency. In this paper, performance of network-integrated D2D communication under channel uncertainties is investigated where D2D traffic is carried through relay nodes. Considering a multi-user and multi-relay network, we propose a robust distributed solution for resource allocation with a view to maximizing network sum-rate when the interference from other relay nodes and the link gains are uncertain. An optimization problem is formulated for allocating radio resources at the relays to maximize end-to-end rate as well as satisfy the quality-of-service (QoS) requirements for cellular and D2D user equipments under total power constraint. Each of the uncertain parameters is modeled by a bounded distance between its estimated and bounded values. We show that the robust problem is convex and a gradient-aided dual decomposition algorithm is applied to allocate radio resources in a distributed manner. Finally, to reduce the cost of robustness defined as the reduction of achievable sum-rate, we utilize the chance constraint approach to achieve a trade-off between robustness and optimality. The numerical results show that there is a distance threshold beyond which relay-aided D2D communication significantly improves network performance when compared to direct communication between D2D peers.

The effect of imperfect channel state information (CSI) on rate adaptation in time-varying multi-antenna fading channels is studied. In the presence of feedback delay and channel estimation error, we analyze the outage probability (OP), defined as the average probability that the achievable rate in the current channel state is lower than data rate scheduled based on outdated feedback, for maximum ratio combining receivers in single-input multiple-output channels and zero-forcing (ZF) receivers in multiple-input multiple-output (MIMO) channels. In particular, for temporally correlated MIMO channels, the conditional distribution of SNR at the output of ZF detection in the current channel state is unknown; hence we apply a Gaussian approximation to obtain a closed-form expression for the average OP of the MIMO-ZF receiver. Simulation results show that our derivations are well-matched to the actual OP and throughput of multi-antenna rate adaptive systems. Also, based on our analysis results, we propose a simple rate adaptation scheme that maximizes the average throughput under a target OP constraint. The proposed schemes optimize the scheduled rate according to a given CSI condition, and effectively maximize the average outage-constrained throughput.

With the pervasiveness of wireless devices, machine-to-machine (M2M) communications or machine-type-communications (MTC) is emerging to support data transfer among devices without human interaction. In this paper, we introduce a tractable queueing model for performance modeling and analysis for heterogeneous MTC. We then demonstrate versatile applications of the proposed queueing model. Firstly, we use the queueing model to study the coexistence between M2M communications of MTC devices and human-to-human (H2H) communications in the same networks. We also consider more sophisticated settings, where the MTC user equipments (UEs) are able to perform the transmission to the macro Evolved Node B (eNodeB) or small-cell eNodeB, or perform the relay transmission. In addition, we extend our study to analyze the eNodeB selection and coalition formation for relay transmission when MTC UEs coexist with H2H UEs. In this case, we formulate the non-transferable utility (NTU) coalitional game to model the eNodeB selection and coalition formation for relay transmission. The performance evaluation reveals some interesting results. For example, the throughput of MTC UEs can be improved when the MTC UEs spend more time inactive due to lower contention in the network, compared with the case when the MTC UEs are mostly active.

With wireless energy transfer capability, network nodes can rely on the energy supplied wirelessly from a network hub or access point. As a result, there is no need for the nodes to replace or recharge their battery using any wire. This paper considers such a scenario and proposes the performance analysis and optimization framework for the network operating on a TDMA protocol with wireless energy transfer. We first present the analysis and optimization of an individual node in the network. The objective is to maximize the network utility defined in terms of throughput and the number of packets in the queue such that the packet loss probability is maintained below the threshold. We solve the optimization problem to obtain an optimal policy to operate the node (i.e, to be active or inactive). We next formulate the network optimization problem for the network hub. The problem can be solved to determine the amount of wireless energy transfer to meet the quality of service (QoS) requirements of all the nodes in the network. We reveal the special structure of the problem, that we can decompose the network optimization into small subproblems. These subproblems can be solved efficiently using the standard algorithm.

We consider the mobility- and QoS-aware admission control problem for OFDMA femtocell networks. To mitigate strong cross-tier interference in the downlink communication, we assume each macrocell is partitioned into cell center and cell edge zones where femtocells in the edge zone share the same bandwidth with the macrocell while femtocells in the center zone use different bandwidth from that allocated for the macrocell. We propose an admission control algorithm that efficiently associates low-speed and high-speed users with femto and macro BSs (FBS and MBS) to avoid large handoff overhead. In addition, calls from low-speed users that fail to connect with their nearby FBSs are allowed to overflow to the macrocell tier. Then, we develop an analytical model for performance evaluation of the proposed admission control scheme. Finally, numerical results are presented to demonstrate the impacts of different parameters (e.g., bandwidth requirements) and access design (i.e., closed versus hybrid access) on the user blocking probabilities.

A closed-form expression of the capture probability of generalized selection combining (GSC) RAKE receivers was introduced in [1]. The idea behind this new performance metric is to quantify how the remaining set of uncombined paths affects the overall performance both in terms of loss in power and increase in interference levels. In this previous work, the assumption was made that the fading is both independent and identically distributed from path to path. However, the average strength of each path is different in reality. In order to derive a closed-form expression of the capture probability over independent and non-identically distributed (i.n.d.) fading channels, we need to derive the joint statistics of ordered non-identical exponential variates. With this motivation in mind, we first provide in this paper some new order statistics results in terms of both moment generating function (MGF) and probability density function (PDF) expressions under an i.n.d. assumption and then derive a new exact closed-form expression for the capture probability GSC RAKE receivers in this more realistic scenario.

As an effective solution for indoor coverage and service offloading from the conventional cellular networks, femtocells have attracted a lot of attention in recent years. From the perspective of spectral efficiency, the macrocell base station (MBS) and femtocell base stations (FBSs) are usually deployed in the same spectrum. Then the interference problem has become a key obstruction that limits the network performance. In this paper, we study the spectrum reuse in the two-tier LTE femtocell network. In order to improve the network performance, the FBSs are encouraged to provide services to nearby macrocell users, and the MBS releases a fractional spectrum to the FBSs for avoiding cross-tier interference in return. We model this problem as a Stackelberg game where the MBS acts as a leader and the FBSs as the followers. We define the utilities for the MBS and FBSs as the average throughput and the distortion-rate function, respectively. It is worth noting that in our Stackelberg game model, there is no monetary price for the interaction between the leader and followers, which is the significant distinction from previous works. The optimal strategies of spectrum reuse for both MBS and FBSs are proposed by analyzing the Stackelberg game model. The simulation results show that the proposed spectrum reuse scheme can significantly improve the network performance.

We develop closed-form generalized likelihood ratio test (GLRT) sequence detectors for multi-relay amplify-and-forward (AF) cooperative networks employing M-ary differential phase-shift keying (M-DPSK) and noncoherent M-ary frequency-shift keying (M-FSK). The proposed detectors achieve robust performance in a wide range of fading environments where prior knowledge of channels, signal powers, noise variances, and relay functionalities is unavailable to the destination. A comprehensive error probability performance analysis is carried out in Rayleigh fading. Specifically, we derive unified pairwise error probability (PEP) expressions for both detectors in a dual-hop network, which are valid for arbitrary modulation order M and arbitrary sequence length Ns. We also derive unified bit-error probability (BEP) expressions for both detectors employing binary signalings in various networks for Ns= 1. It is further shown that both detectors achieve near full diversity orders. Finally, the superiorities of the proposed detectors over the state-of-the-art noncoherent detectors are justified through extensive comparisons in practical scenarios. For example, in a multi-relay network where the relays are uniformly distributed between the source and destination, the proposed detector for noncoherent FSK with Ns= 1 outperforms the well-known maximum energy selector by almost 15 dB in high signal-to-noise ratios.

Likelihood-based algorithms for the classification of linear digital modulations are systematically investigated for a multiple receive antennas configuration. Existing modulation classification (MC) algorithms are first extended to the case of multiple receive antennas and then a critical problem is identified that the overall performance of the multiple antenna systems is dominated by the worst channel estimate of a particular antenna. To address the performance degradation issue, we propose a new MC algorithm by optimally combining the log likelihood functions (LLFs). Furthermore, to analyze the upper-bound performance of the existing and the proposed MC algorithms, the exact Cramer-Rao Lower Bound (CRLB) expressions of non-data-aided joint estimates of amplitude, phase, and noise variance are derived for general rectangular quadrature amplitude modulation (QAM). Numerical results demonstrate the accuracy of the CRLB expressions and verify that the results reported in the literature for quadrature phase-shift keying (QPSK) and 16-QAM are special cases of our derived expressions. Also, it is demonstrated that the probability of correct classification of the new algorithm approaches the theoretical bounds and a substantial performance improvement is achieved compared to the existing MC algorithm.

We consider a multiple-access relay network where multiple source nodes send independent packets to a common destination with the assistance of multiple relay nodes. We assume that the relay nodes are equipped with multiple antennas and are allowed to choose either spatial multiplexing (SM) or transmit diversity (TD) in sending network coded packets. We verify the performance limit of conventional MIMO network coding and propose two optimization schemes to overcome this limit. To this end, we develop an integrated design methodology that jointly optimizes the redundancy offered by network coding at the relays and channel coding at the sources as well as the spatial redundancy offered by multiple antennas in order to minimize the end-to-end outage probability. We show that such joint optimization can provide a significant energy saving.

In this paper, we apply the concepts of network multiple-input-multiple-output (MIMO) to small cell networks. To do so, the issue of imperfect channel state information (CSI) at the transmitter is considered when frequency-division duplexing is used, for which the feedback channel is limited. We first introduce a regret based learning approach to optimize the transmit beamforming parameters for the cases when the feedback channel is temporarily unavailable during deep fades. We then propose a coalition formation game model to cluster the small cell base stations so that they can perform cluster-wise joint beamforming. We take the \tit{recursive core} as the solution concept of the coalition formation game. To obtain the recursive core, we first consider a typical merge-split algorithm. However, we show that this algorithm can be unstable. Alternatively, we adopt the merge-only algorithm which guarantees the formation stability and show that its outcome belongs to the recursive core. Finally, we analyze the average number and the average size of coalitions that can form during such a coalition formation process. Numerical simulations are given to illustrate the behavior of the coalition formation among small cell base stations.

This paper considers the problem of downlink power allocation in an orthogonal frequency-division multiple access (OFDMA) cellular network with macrocells underlaid with femtocells. The femto-access points (FAPs) and the macro-base stations (MBSs) in the network are assumed to compete with each other to maximize their capacity under power constraints. This competition is captured in the framework of a Stackelberg game with the MBSs as the leaders and the FAPs as the followers. The leaders are assumed to have foresight enough to consider the responses of the followers while formulating their own strategies. The Stackelberg equilibrium is introduced as the solution of the Stackelberg game, and it is shown to exist under some mild assumptions. The game is expressed as a mathematical program with equilibrium constraints (MPEC), and the best response for a one leader-multiple follower game is derived. The best response is also obtained when a quality-of-service constraint is placed on the leader. Orthogonal power allocation between leader and followers is obtained as a special case of this solution under high interference. These results are used to build algorithms to iteratively calculate the Stackelberg equilibrium, and a sufficient condition is given for its convergence. The performance of the system at a Stackelberg equilibrium is found to be much better than that at a Nash equilibrium.

Abstract—We consider the joint resource allocation and admis-sion control problem for Orthogonal Frequency-Division Multi-ple Access (OFDMA)-based femtocell networks. We assume that Macrocell User Equipments (MUEs) can establish connections with Femtocell Base Stations (FBSs) to mitigate the excessive cross-tier interference and achieve better throughput. A cross-layer design model is considered where multiband opportunistic scheduling at the Medium Access Control (MAC) layer and admission control at the network layer working at different time-scales are assumed. We assume that both MUEs and Femtocell User Equipments (FUEs) have minimum average rate constraints, which depend on their geographical locations and their application requirements. In addition, blocking probability constraints are imposed on each FUE so that the connections from MUEs only result in controllable performance degradation for FUEs. We present an optimal design for the admission control problem by using the theory of Semi-Markov Decision Process (SMDP). Moreover, we devise a novel distributed femtocell power adaptation algorithm, which converges to the Nash equilibrium of a corresponding power adaptation game. This power adaptation algorithm reduces energy consumption for femtocells while still maintaining individual cell throughput by adapting the FBS power to the traffic load in the network. Finally, numerical results are presented to demonstrate the desirable operation of the optimal admission control solution, the significant performance gain of the proposed hybrid access strategy with respect to the closed access counterpart, and the great power saving gain achieved by the proposed power adaptation algorithm. Index Terms—Femtocell network, admission control, Markov decision process, blocking probability, channel assignment.

Cooperative jamming for multiuser multiple input multiple output (MIMO) broadcast channel is studied to enhance the physical layer security with the help of a friendly jammer. We assume the base station transmits multiple independent data streams to multiple legitimate users. During the transmission, however, there are multiple eavesdroppers with multiple antennas that have interests in the streams from the base station. In order to wiretap the desired streams, the eavesdroppers may collude or not, and maximize the signal-to-interference-plus-noise ratio (SINR) of the desired streams using receive beamforming. The optimal cooperative jammer is designed to keep the achieved SINR at eavesdroppers below a threshold to guarantee that the transmission from the base station to legitimate users is confidential. One main advantage of the proposed cooperative jamming scheme is that no modification is needed for the existing precoding schemes at the base station and decoding schemes at legitimate users. Thus, any existing practical precoding/decoding schemes for multiuser MIMO broadcast channel can be applied directly with the help of a friendly jammer using the proposed cooperative jamming.

In this paper, we consider the admission control problem for hybrid access in OFDMA-based femtocell networks. We assume that Macrocell User Equipments (MUEs) can establish connections with Femtocell Base Stations (FBSs) to improve their QoSs. Both MUEs and Femtocell User Equipments (FUEs) have minimum rate requirements, which depend on their geographical locations and maybe their running applications. In addition, blocking probability constraints are imposed on each FUE so that connections from MUEs only result in controllable performance degradation for FUEs. We show how to formulate the admission control problem as a Semi-Markov Decision Process (SMDP) and present a Linear Programming (LP) based solution approach. Moreover, we develop a novel femtocell power adaptation algorithm, which can be implemented in a distributed manner jointly with the proposed admission control scheme. This power adaptation algorithm enables to achieve better cell throughput and more energy-efficient operation of the femtocell network considering the heterogeneity of traffic load in the network. Finally, numerical results are presented to illustrate the desirable performance of the optimal admission control solution and the significant throughput and power saving gains of the proposed cross-layer solution.

We design a multiple-access relay network with multiple sources and relays via network coded cooperation where the radio resources in three dimensions: time, space and user are jointly optimized. For this, we derive the decoding error probability of two MIMO modes and develop an optimal MIMO mode selection and power allocation strategy for multiple sources and relays, so as to minimize the decoding error probability. Numerical results show that the optimal MIMO mode selection and power allocation strategy provides a significant gain of 25dB in SNR, and this SNR gain increases as the number of relays increases.

This paper considers the problem of access control in the uplink transmission of an OFDMA femtocell network. An underlying noncooperative power game has been devised, based on which a coalition game is formulated by taking a suitable value function. Only two complementary coalitions are allowed to exist in order to reflect the set of transmitters connected to either the macro base station or the femto access point. The transmitters in the same coalition cooperate by operating on non-interfering subchannels, while those in the complementary coalition are assumed to operate so as to cause maximum jamming. The value of a coalition is obtained as the max-min of utility sum of each individual in the given coalition. In the process, we also examine the optimal jamming strategy of the complementary coalition. Finally, we argue that the obtained value function cannot be super-additive. Since the super-additivity property is required for some of the solutions of cooperative game theory, we resort to the Shapley value solution, for which the super-additivity need not hold, to allocate the payoff to each user in a given coalition. Assuming that the transmitters want to be in the coalition that maximizes their payoff, we form a Markov model to obtain the stable coalition structure. We take these stable coalition structures as the required solution of our access control problem.

A dynamic decode-&-forward (DDF) is redesigned to meet the crucial requirements for type-2 user equipment (UE) relays which are being considered for next-generation cellular standards (e.g., LTE-Advanced). Toward this, the proposed modified DDF (M-DDF) realizes a fast jump-in relaying and sequential decoding where a subframe decoding is employed to yield a combination of energy/information/mixed combining, in conjunction with the random codeset for encoding and re-encoding at source and multiple UE relays, depending on their channel conditions. These additional features existing in the M-DDF circumvent the need of exchanging control messages between multiple relays and end user near cell boundary, which is not allowed for type-2 UE relays. Through comparison among the DDF with single and multiple orthogonal channels and M-DDF, the M-DDF bridges the gap between former relaying protocols with application to type-2 UE relays, offering reliable rate capacity with less channel usage and further improvement, if the early termination of decoding is made available.

Relay selection (RS) has widely been studied for both decode-and-forward (DF) and amplify-and-forward (AF) protocols, and relay ordering (RO) was recently proposed for the AF protocol only . The two strategies, RS and RO, have been individually shown to be very effective to enhance the performance of cooperative relay networks. In this letter, we first discuss the fundamental difference of the two schemes and investigate an important tradeoff in terms of spectral efficiency and energy efficiency between RS and RO. Specifically, RS is more spectrally efficient, whereas RO can be more efficient in energy consumption. Then we demonstrate that indeed RS and RO generally have different outage performance and may complement each other depending on channel conditions. To optimize the outage performance, therefore, we combine the two strategies, RS and RO, and we propose joint RS-RO, followed by exact outage probability derivation of the joint RS-RO in closed-form. Finally, we obtain some interesting insights into the issue of selecting efficient transmission schemes between RS and RO.

In conventional multiuser parallel scheduling schemes, every scheduled user is interfering with every other scheduled user, which limits the capacity and performance of multiuser systems, and the level of interference becomes substantial as the number of scheduled users increases. Based on the above observations, we investigate the trade-off between the system throughput and the number of scheduled users through the exact analysis of the total average sum rate capacity and the average spectral efficiency. Our analytical results can help the system designer to carefully select the appropriate number of scheduled users to maximize the overall throughput while maintaining an acceptable quality of service under certain channel conditions.

This paper proposes distributed joint power and admission control algorithms for the management of interference in two-tier femtocell networks, where the newly-deployed femtocell users (FUEs) share the same frequency band with the existing macrocell users (MUEs) using code-division multiple access (CDMA). As the owner of the licensed radio spectrum, the MUEs possess strictly higher access priority over the FUEs; thus, their quality-of-service (QoS) performance, expressed in terms of the prescribed minimum signal-to-interference-plus-noise ratio (SINR), must be maintained at all times. For the lower-tier FUEs, we explicitly consider two different design objectives, namely, throughput-power tradeoff optimization and soft QoS provisioning. With an effective dynamic pricing scheme combined with admission control to indirectly manage the cross-tier interference, the proposed schemes lend themselves to distributed algorithms that mainly require local information to offer maximized net utility of individual users. The approach employed in this work is particularly attractive, especially in view of practical implementation under the limited backhaul network capacity available for femtocells. It is shown that the proposed algorithms robustly support all the prioritized MUEs with guaranteed QoS requirements whenever feasible, while allowing the FUEs to optimally exploit the remaining network capacity. The convergence of the developed solutions is rigorously analyzed, and extensive numerical results are presented to illustrate their potential advantages.

We consider the uplink of a distributed antenna system (DAS) in the presence of interference and unknown oscillator offsets, which lead to synchronization errors. For this scenario, we develop a linear receiver which maximizes the output signal-to-interference-plus-noise ratio (SINR). Specifically, we first propose a new structured generalized sidelobe canceller (SGSC) formulation for the commonly used minimum-variance receiver, and derive a general framework to improve the robustness of a linear receiver when oscillator offsets exist. Then a new linear receiver is proposed in closed-form based on the proposed robust SGSC and a ridge regression technique. It is shown by simulations that the proposed linear receiver can provide very robust SINR performance and excellent symbol error rate (SER) performance in the existence of interference and unknown oscillator offsets.

In cooperative cellular wireless networks, a user equipment (UE) relay can be a good alternative to rendering an end user by forwarding the signal overheard from the source to the end user. However, due to its practical limitation, it has no cell-specific reference signal. Therefore, different from conventional fixed relay station, the channel state information (CSI) is not available to the UE relay. Since the UE relay cannot estimate relay-to-destination (R-D) channel capacity, there arises the possibility of an outage event in cooperating phase. In this paper, we introduce an outage based rate control under multiple relay networks in which there are one source, multiple UE relays and one end user. To achieve maximal overall transmission rate, resource allocation during the cooperating phase is done at the source in advance by considering both the expected transmission rate and the resulting outage probability. Especially we consider three cases of relaying strategies in such open-loop R-D links and observe that they complement each other depending on the R-D link geometry and channel conditions, leading to optimal relaying strategy for UE relays.

This paper proposes the power control method using a pricing mechanism via emergency message to mitigate the cross-channel interference for downlink transmission in two-tier femtocell networks, where macrocell and overlaid femtocells share the same frequency band (i.e., cochannel deployment). Macrocell users measure the channel gain from base station to user, and computes the achieved SINR. If the macrocell achieved SINR is not satisfied for a target SINR, a macrocell user broadcasts emergency message to close-by femtocell users. When femtocell users receive the emergency message, the femtocell pricing coefficient value is forced to increase, which in turn decreases their power, resulting in reduced cross-channel interference at the macrocell. Consequently, the macrocell user will then satisfy the target SINR faster, but with some modest loss in total throughput.

This paper proposes an opportunistic power control (PC) algorithm to mitigate the aggregate interference (AGGI) from active femtocells in uplink transmission. Macrocell base station (BS) decides the interference allowance per femtocell and femtocell users then allocate their transmit power within the allowance to suppress the cross-tier interference in heterogeneous networks. The PC algorithm should reflect the number of active femtocells per cluster to effectively control the AGGI, and we propose two sensing algorithms, such as centralized and distributed ones, to estimate the number of active femtocells. We compare the two sensing algorithms in terms of the outage and throughput performance. The algorithms exploit large-scale channel information and shadowing variation to guarantee the macrocell uplink channel quality. Consequently, the two sensing algorithms increase total cell throughput.

This paper considers a two-tier cellular network wherein femtocell users, who communicate with their home-owner-deployed base stations, share the same frequency band with macrocell users by code-division multiple access (CDMA) technology. Since macrocell users have strictly higher priority in accessing the available radio spectrum, their quality-of-service (QoS) performance, expressed in terms of the minimum required signal-to-interference-plus-noise ratio (SINR), should be maintained at all times. Femtocell users, on the other hand, are allowed to exploit residual network capacity for their own communications. In this work, we develop a joint power- and admission-control algorithm for interference management in such two-tier networks. Specifically, throughput-power tradeoff optimization is achieved for femtocell users while all macrocell users being supported with guaranteed QoS requirements whenever feasible. Importantly, the proposed algorithm makes power and admission control decisions in an autonomous and distributive manner with minimal coordination signaling, a desirable feature in two-tier networks where only limited exchange of signaling information can be afforded on backhaul links. Under certain practical conditions, the developed scheme is shown to converge to a stable solution. An effective technique is also proposed to improve the efficiency of such equilibrium in lightly-loaded networks. The performance of our proposed algorithm is demonstrated by numerical results.

We propose a novel cooperative spectrum sensing algorithm for a cognitive radio (CR) network to detect a primary user (PU) network that exhibits some degree of randomness in topology (e.g., due to mobility). We model the PU network as a random geometric network that can better describe small-scale mobile PUs. Based on this model, we formulate the random PU network detection problem in which the CR network detects the presence of a PU receiver within a given detection area. To address this problem, we propose a location-aware cooperative sensing algorithm that linearly combines multiple sensing results from secondary users (SUs) according to their geographical locations. In particular, we invoke the Fisher linear discriminant analysis to determine the linear coefficients for combining the sensing results. The simulation results show that the proposed sensing algorithm yields comparable performance to the optimal maximum likelihood (ML) detector and outperforms the existing ones, such as equal coefficient combining, OR-rule-based and AND-rule-based cooperative sensing algorithms, by a very wide margin.

We propose a novel subchannel and transmission power allocation scheme for multi-cell orthogonal frequency-division multiple access (OFDMA) networks with cognitive radio (CR) functionality. The multi-cell CR-OFDMA network not only has to control the interference to the primary users (PUs) but also has to coordinate inter-cell interference in itself. The proposed scheme allocates the subchannels to the cells in a way to maximize the system capacity, while at the same time limiting the transmission power on the subchannels on which the PUs are active. We formulate this joint subchannel and transmission power allocation problem as an optimization problem. To efficiently solve the problem, we divide it into multiple subproblems by using the dual decomposition method, and present the algorithms to solve these subproblems. The resulting scheme efficiently allocates the subchannels and the transmission power in a distributed way. The simulation results show that the proposed scheme provides significant improvement over the traditional fixed subchannel allocation scheme in terms of system throughput.

We propose adaptive threshold based relay selection scheme for type-2 (user equipment) relay that requires only minimum 1-bit feedback, for which each relay reports to the source if its R-D (relay-destination) instantaneous channel gain is above threshold (i.e., available). A source then selects the best relay among those that yields the highest S-R (source-relay) instantaneous gain, provided threshold is adjusted according to a target signal-to-noise ratio (SNR). Exact and upper bound on the symbol-error rate (SER) are derived for M-PSK signaling to show that the proposed scheme provides the SER performance close to that of an optimal scheme . Further, it is shown that the spectral efficiency can be improved without degrading the link quality in terms of the SER, via adaptive selection between relay (S-R-D) link and direct (S-D) link based on their link SNRs.

Recently, single-symbol maximum-likelihood (ML) decodable distributed space-time block coding (DSTBC) has been developed for use in cooperative diversity networks. However, the symbol rate of the DSTBC decreases with the number of relays. This issue can be addressed if the channel phase information (CPI) of the first-hop is exploited, and such code is referred to as DSTBC-CPI. Some complex single-symbol decodable DSTBCs-CPI were reported in the literature. However, no upper-bound on the symbol rate of such DSTBC-CPI has been derived, although it is a fundamental issue. Furthermore, finding a tight (more preferably achievable) upper-bound is essential to check if any developed code is optimum or not. In this letter, we derive an upper-bound on the symbol rate of real single-symbol decodable DSTBC-CPI and show that the bound is independent of the number of relays in the network. Finally, we demonstrate that our derived bound is actually achievable.

Abstract Mobility management is one of the most challenging research issues for vehicular networks to support a variety of intelligent transportation system (ITS) applications. The traditional mobility management schemes for Internet and mobile ad hoc network (MANET) cannot meet the requirements of vehicular networks, and the performance degrades severely due to the unique characteristics of vehicular networks (e.g., high mobility). Therefore, mobility management solutions developed specifically for vehicular networks would be required. This paper presents a comprehensive survey on mobility management for vehicular networks. First, the requirements of mobility management for vehicular networks are identified. Then, classified based on two communication scenarios in vehicular networks, namely, vehicle‐to‐vehicle (V2V) and vehicle‐to‐infrastructure (V2I) communications, the existing mobility management schemes are reviewed. The differences between host‐based and network‐based mobility management are discussed. To this end, several open research issues in mobility management for vehicular networks are outlined. Copyright © 2009 John Wiley & Sons, Ltd.

This paper considers the downlink power allocation problem in a cellular network where a bi-level hierarchy exists. The network is comprised of the macrocells underlaid with femtocells. The objective of each station in the network is to maximize its capacity under power constraints. The problem is formulated as a Stackelberg game with the macrocell base stations as the leaders and the femtocell access points as the followers. The leaders are assumed to have enough information and foresight to consider the response of the followers while formulating their strategies. To characterize such interaction between leaders and followers, Stackelberg equilibrium is introduced; and it is shown to exist under the assumption of continuity of best response function of the leader sub-game. %For the case of Nash games, the relationship between the upper and lower sub-game equilibrium is explored.

In this paper, we study the optimization of an ad-hoc cognitive radio network (CRN) coexisting with a multi-cell primary radio network (PRN) utilizing spectrum underlay. To maximize the weighted sum rate (WSR) of the CRN, in contrast to the exclusive subchannel assignment (ESA) method considered in former results, we design shared subchannel assignment (SSA) method to approach the performance limit assuming discrete-rate modulation. The considered SSA method involves interference-channel-sharing based optimization, and in cognitive radio it becomes more complicated when the CRN-to-PRN sum-interference constraint has to be strictly satisfied. We design fast-convergent SSA duality schemes and use the interior point search to satisfy the various system constraints. Additionally, we design distributed duality schemes for both SSA and ESA schemes which involve only CRN local information exchange, using multichannel parallel dual update and a novel mini-slot competition. Effects of many system parameters are presented via simulation results, which show that our SSA duality scheme can perform significantly better than the near-optimal ESA duality scheme, and that the distributed schemes entail only small overhead and convergence losses.

Coordinated multipoint (CoMP) transmission is being considered in next-generation cellular systems to improve cell-edge throughput. In this paper, we consider open-loop CoMP with L base stations (BSs) where they cooperatively serve a cell-edge user. We first propose antenna selected space-time block code (STBC) to exploit diversity gain for serving the cell-edge user and also support the scenario that cell-edge and cell-center users coexist. To further exploit distinct path losses of cell-edge and cell-center users, we propose pre-cancellation scheme to remove the in-cell interference. Next, we formulate the power allocation problem that maximizes per-cell throughput with minimum throughput constraint. Results show that the proposed antenna selected STBC CoMP has benefit in BER performance with limited feedback constraint, compared to coherent CoMP, and it achieves higher per-cell throughput.

This paper proposes a novel partial decode-and-forward (DF) relaying strategy with multiple antennas. In the first phase, the source broadcasts data streams consisting of non-forwarding and forwarding data streams. In the second phase, a relay node forwards only forwarding data streams to a destination node, and a destination node decodes both non-forwarding and forwarding data streams by successive interference cancellation (SIC). We provide an analytical framework of achievable rate and design a linearly combined precoding matrix for rate maximization. Our results show that the proposed partial DF relaying with a linearly combined precoding matrix achieves substantially higher rate than a conventional DF relaying scheme.

In this letter we propose per antenna superposition coding (PASC) by which partial information can be relayed instead of full information, to exploit the higher capacity of source-relay-destination link. Here, the PASC is designed across antennas, producing basic layer and superposed layer for each data stream per antenna. It is shown that an overall data rate of partial information relaying with PASC can be increased beyond that offered by full information relaying by virtue of fast forwarding of partial information over relatively better link.

In this paper, we study the coexistence and optimization of a multicell cognitive radio network (CRN) which is overlaid with a multicell primary radio network (PRN). We propose a PRN-willingness-based design framework for coexistence and subchannel sharing, and a Lagrange duality based technique to optimize the weighted sum rate (WSR) of secondary users (SUs) over multiple cells. First, to avoid unacceptable SU interference to primary users (PUs), the PRN determines its interference margin based on its target performance metric and channel conditions, and broadcasts this information to the CRN. Second, each CRN cell optimizes its WSR and implements intercell iterative waterfilling (IC-IWF) to control the intercell interference. To account for the interference and transmit power limits at SUs, multilevel waterfilling (M-WF) and direct-power truncation (DPT) duality schemes are developed. Third, we develop a serial dual update technique which enables low-complexity and fast-convergence of the proposed duality schemes. Numerical results demonstrate the effects of multiple parameters, such as the number of SUs per cell, subchannel occupancy probability (SOP), and outage probability of the PUs. Our results show that the proposed duality schemes provide a large performance enhancement than the channel-greedy and access-fairness based resource allocation schemes.

This paper proposes an adaptive multi-node incremental relaying technique in cooperative communications with amplify-and-forward (AF) relays. In order to reduce the excessive burden of MRC with all diversity paths at the destination node, the destination node decides if it combines signals over the first N(<; K) time slots/frames or over all of the K times slots, where K is the number of relay nodes. Our analytical and simulation results show that the proposed adaptive multi-node incremental relaying outperforms the conventional MRC in terms of outage probability in AF based cooperative communications since the proposed scheme effectively reduces the spectral efficiency loss. Our asymptotic analysis also shows that the proposed adaptive multi-node incremental relaying achieves full diversity order K + 1.

In this paper, we combine opportunistic transmission and source/destination cooperation in a decode-and-forward (DF)-based two-hop cooperative diversity network consisting of multiple source-destination pairs and a single relay. Firstly, we consider a special scenario with two source-destination pairs. For this network, there are two possible strategies in each hop: no-cooperation or cooperation. Considering the combination of the two strategies in two cascaded hops, we investigate four different end-to-end transmission strategies, and we find that the four strategies complement one another depending on channel conditions in terms of outage performance. To maximize the mutual information, we propose an optimum joint selection of source-destination pair and end-to-end transmission strategy. Then we show that the optimum joint selection scheme can be simplified without loss of outage performance, and derive its exact outage probability in closed-form. Secondly, we generalize the proposed transmission strategy into a scenario with multiple source-destination pairs. For this network, we first propose an optimum end-to-end transmission strategy to maximize the mutual information, then a suboptimum end-to-end transmission strategy to reduce the signaling overhead and computational complexity. For the suboptimum strategy, we derive the outage probability and diversity order.

A primary ad-hoc network working in parallel with a secondary ad-hoc network is considered. The main challenge in operating cognitive ad-hoc networks is the lack of a centralized controller performing resource allocation for different users in the network. In this paper, a distributed power allocation scheme is considered for secondary users and its performance is analyzed when time average channel gains are substituted for instantaneous channel gains. In this way, it is not necessary to exchange instantaneous channel information; however, users' allocated power will be perturbed. It is of interest to analyze mathematically this perturbation and to show how it affects the network performance. In particular, an upper bound on perturbation of each user's allocated power, rate, and interference caused to a primary receivers by the secondary users is obtained. Then, it is shown that how this perturbation affects the transmission rate and the probability of interference constraint violation by the secondary users.

In this paper, we study coexistence and optimization of an ad hoc cognitive radio network (CRN) coexisting with multicell primary radio networks (PRNs). We assume the PRN cells operate in multiple frequency subbands, and the ad hoc CRN can utilize several subchannels in each PRN subband using spectrum underlay, which maintains that the pre-specified PRN signal-to-interference-plus-noise ratio (SINR) outage probability is not violated. To jointly optimize the throughput of the ad hoc secondary user (SU) links, we utilize the Lagrange duality optimization tool and design fast-convergent weighted sum rate (WSR) maximization schemes under important system and quality of service constraints, including the power spectral mask (PSM), the available transmit power of SUs, the maximum-subchannel-rate, and the minimum-rate per SU link. Both continuous rate (C-rate) and discrete rate (D-rate) modulations are considered. Additionally, we design a distributed access duality scheme which uses the mini-slot competition approach and involves only CRN local information exchange for the dual update, and achieves fast and stable convergence. Effects of many system operating parameters are presented via simulation results, which show that the optimal duality scheme can perform substantially better than the suboptimal duality scheme, and that the performance loss of the distributed scheme is small compared to the centralized scheduling.

Mobile multihop relay (MMR) networks based on the IEEE 802.16J standard are able to extend the service area as well as improve the performance of mobile WiMAX networks. We present an optimization framework for jointly optimizing the placement and bandwidth reservation for a relay station in an MMR network. The objective of this framework is to maximize utility of the MMR network service provider. The decision on the placement of the relay corresponds to finding the best location for the relay station under uncertainty about the number of active users in the extended service area of the MMR network. This uncertainty could be due to random connection initiation and termination by the users or due to the random arrivals and departures of the mobile subscriber stations in the extended service area. However, this decision on relay placement may not achieve the highest utility when the users dynamically adapt their decisions on whether to transmit directly to the base station or transmit through the relay station. In this scenario, optimal decision on bandwidth reservation by the relay station needs to be made (over a relatively shorter period of time) which takes the dynamics of users' decision into account. The placement of the relay station (over a relatively longer period of time) can then be optimized based on the optimal bandwidth reservation. A stochastic programming formulation and a Markov decision process formulation are used to obtain the long-term and short- term optimization solutions, respectively.

We propose a penalized iterative waterfilling to improve the performance of a distributed resource allocation method in terms of quality of service (QoS) requirements in multi-cell and multi-user OFDMA systems. In the distributed resource allocation method, since each selfish user tries to maximize its own throughput (i.e., sum rate) without any information on other users, it is less likely to satisfy QoS requirements from the system point of view. To overcome this problem, we introduce a self-status prediction period (SPP) and user-based penalty function (PF). Each user can measure its own status during SPP. Using this measurement, each user computes its own PF and then reallocates its resource so that the number of users being supported can increase. Simulation results show that the proposed algorithm can greatly increase the number of users satisfying QoS requirements, also providing tradeoff between total power consumption and achieved sum rate.

This paper proposes a novel generalized selection combining (GSC)/maximal ratio combining(MRC) switching technique based on error detection in cooperative communications with amplify-and-forward (AF) relays. In order to reduce the excessive burden of MRC with all diversity paths at the destination node, the destination node decides if it performs GSC with order N(< K) or MRC with order K + 1 based on the error detection, where K is the number of relay nodes. Our analytical and simulation results show that the proposed GSC/MRC switching outperforms the conventional MRC in terms of outage probability in AF based cooperative communications since the proposed scheme effectively reduces the spectral efficiency loss with the help of error detection codes.

In this paper, we investigate a dynamic spectrum sharing problem for centralized uplink cognitive radio networks using orthogonal frequency division multiple access. We formulate average-sense joint rate and power allocation as an optimization problem under quality of service and interference constraints, and suggest admission control to find a feasible solution to the optimization problem. To implement the resource allocation in average sense, we introduce a concept of using the conservative factors and depending on the outage and violation probabilities. Since estimating instantaneous channel gains is costly and requires high complexity, the proposed algorithm pursues a practical and implementation-friendly resource allocation. Simulation results reveal that the proposed average-sense resource allocation incurs a slight loss in system throughput over the instantaneous one, but it achieves the large-scale power adaptation with less sensitivity to variations in shadowing statistics.

A tractable and compact closed-form expression on the channel-averaged signal-to-interference-plus-noise ratio (SINR) is derived for M-ary orthogonal coded/balanced transmitted-reference (BTR) systems, taking into consideration both inter-pulse interference (IPI) and multiple-access interference (MAI) in dense multipath ultra-wideband (UWB) channels. The UWB channel here is a realistic and standard one with lognormal channel gain distribution and double independent Poisson arrival distribution of cluster and ray. Hence, the analytical framework developed here can be applied to typical UWB channel models, especially considering the channel sparseness and cluster overlapping observed in realistic UWB channels. Based on the channel-averaged SINR, the effect of inter-pulse distance (between the reference and data pulses in BTR) on the multiuser performance is fully investigated. A proper selection of user-specific inter-pulse distances in multiuser scenario is then determined to maximize the user capacity for a given UWB channel.

In this paper, near-optimal and suboptimal receivers are proposed for multiuser ultra-wideband (UWB) impulse radio (IR) systems in dense multipath channels, wherenon-Gaussianmultiuser interference (MUI) seen at each finger of a RAKE receiver is taken into account. The receivers exploit two dimensional diversity that is offered by UWB signaling, namely multipath diversity and repetition diversity (i.e., repetition coding). Non-Gaussian modeling of the MUI results in employing a nonlinear process at each finger, jointly considering the desired users, path gain being estimated and the order associated with MUI statistics. For this, the MUI statistics are characterized in terms of their second and fourth order moments, especially considering the channelsparsenessandcluster overlappingobserved in realistic UWB channels, which leads to modeling the MUI by ageneralizedGaussian distribution. It is shown that the MUI statistics exhibit non-Gaussian nature even for relatively large number of users, and the diversity gain against MUI can be significant when relatively small number of users coexist.

In this paper, we propose and study several sum rate maximization algorithms for uplink orthogonal frequency division multiple access (OFDMA). For uplink scheduling without fairness consideration, we propose two Lagrangian duality optimization-based methods to maximize the weighted sum rate, which include a cyclic dual-update algorithm and a per-stage dual-update algorithm. For a low-complexity alternative, we design and analyze the transmit power and signal-to-noise ratio (SNR) product (PSP) based selective multiuser diversity (SMuD) schemes. Next, for fair scheduling, we propose rate maximization schemes under access proportional fairness (APF) and rate proportional fairness (RPF) constraints, respectively. The APF is achieved using normalized channel SNR (n-SNR) ranking-based SMuD for user selection per carrier, and the RPF is realized using dynamical carrier assignment based on the target rate ratios. Analytical throughput and fairness metrics are derived and verified via simulations. Numerical results illustrate the sum rate loss caused by rate fairness and access fairness constraints compared to the duality approach. Also, we show that unlike the downlink case, for uplink OFDMA the correlated frequency channels (carriers) cause significant ergodic sum rate degradation compared to the independent channels. These results provide new insight into the achievable uplink OFDMA performance with and without fairness constraints.

We consider the problem of quality-of-service (QoS) provisioning for multiple traffic classes in a MIMO wireless network. This QoS provisioning is posed as a radio resource management (RRM) problem at a wireless node (e.g., a wireless mesh router) with multiple antennas. We decompose this RRM problem into two tractable subproblems, namely, the antenna assignment and the admission control problems. The objective of antenna assignment is to minimize the weighted packet dropping probability for the different traffic classes under constrained packet delay. The objective of admission control is to maximize the revenue of the wireless node gained from the ongoing connections for different traffic classes under constrained connection blocking probability and average per-connection throughput. The decision of antenna assignment is made in a short-term basis (e.g., for every packet transmission interval) while that of admission control is made in a long-term basis (i.e., when a connection arrives). Constrained Markov decision process (CMDP) models are formulated to obtain the optimal decisions on antenna assignment and admission control. To provide efficient channel utilization, the RRM framework considers adaptive modulation at the physical layer which exploits channel state information. Performance evaluation results show that this joint antenna assignment and admission control framework can provide class-based service differentiation while satisfying both the connection-level and packet-level QoS requirements.

Mobile multihop relay (MMR) networks based on the IEEE 802.16j standard are able to extend the service area as well as improve the performance of mobile WiMAX networks. In this paper, we present a relay-centric hierarchical optimization model for jointly optimizing the radio resource management (RRM) and network planning for the relay stations in MMR networks. We consider an in-band relaying system. For a relay station, the RRM problem deals with optimizing the amount of bandwidth reserved from the base station and admission control for the mobile subscriber stations (MSSs) using relay-based transmissions so that the utility of a relay station is maximized. A Markov decision process (MDP) model is formulated to obtain the short-term optimal action of a relay station. Based on the optimal action of each relay station, the network planning problem is solved for a group of relay stations by optimizing the relay placement and base station selection over a longer period of time considering uncertainties in user mobility and traffic load in the network. A chance-constrained assignment problem (CCAP) is formulated to obtain the optimal decisions to maximize the total utility of relay stations under the probabilistic constraint on the total bandwidth usage of the base stations. Numerical results show that the proposed scheme outperforms a static scheme. The proposed radio resource management and network planning framework will be useful for design and optimization of multihop cellular wireless networks in general.

In this paper, we study the weighted sum rate maximization of multicell cellular cognitive radio networks (CRNs) which are overlaid with multicell primary radio networks (PRNs). We assume each CRN cell is collocated with a PRN cell and has a cellular structure with access point (AP) and multiple secondary users (SUs). We propose a unified framework to determine the operation parameters of the CRNs in the multicell environment. First, to avoid unacceptable interference to primary users (PUs), we propose methods to determine the power spectral masks (PSMs) of SUs and APs in uplink and downlink transmissions at each subchannel based on the target signal-to-interference-plus-noise ratio (SINR) outage probability of PRN base station (BS) receivers. Second, we utilize the duality optimization tool and design weighted sum rate maximization schemes which include the PSM optimally. Third, we accurately model the intercell interferences between CRNs and mutual interferences between the PRNs and CRNs, as a function of multiple system parameters. Our model and approaches provide powerful design tools and deep insights into achievable performance for overlaid CRNs and PRNs.

In this paper a channel-averaged signal-to- interference-plus-noise ratio (SINR) is theoretically derived in closed-form for M-ary orthogonal balanced transmitted-reference (BTR) systems, considering the channel sparseness (i.e., no multipath component in a time bin) and cluster overlapping, often observed in realistic ultra-wideband (UWB) channels. The UWB channel here is a realistic and standard one with lognormal channel gain distribution and double independent Poisson arrival distribution of cluster and ray. The SINR expression derived is also valid when there exists the inter-pulse interference that arises due to a short inter-pulse distance between the reference and data pulses in BTR. Based on the channel-averaged SINR, the effect of the inter-pulse distance on the multiple access performance is fully investigated.

A high-rate, reliable uplink transmission is designed using multistage selective maximum-likelihood (ML) decoding for the multicode (MC) direct-sequence code-division multiple access (DS-CDMA) with precoding. The precoding achieves a constant envelope MC signal by adding some redundancy bits, resulting in rate loss, but multidimensional signaling on top of MC DS-CDMA enables to further increase the date rate. The designed multistage selective ML decoding is shown to be promising for use in uplink high-rate and reliable data transmission.

A new M-ary orthogonal coded signaling is introduced to avoid the inter-frame interference that is especially detrimental to realizing high rate ultra-wideband (UWB) transmitted-reference (TR) systems. To further increase the information rate, the inter-pulse interference by an overlap of multipath-delayed pulses is controlled by integrating the signaling and a pair of balanced matched Alters in a joint manner, so as to permit a shorter time delay between the reference and data pulses in TR systems. To evaluate an achievable information rate increase relative to conventional TR, the symbol error probability (SEP) is theoretically derived for the proposed M-ary orthogonal coded/balanced TR system, considering the realistic IEEE standard UWB channel models. In addition, we consider the issue of receiver complexity and present two alternative low- complexity receiver implementations for the proposed TR system.

In this paper, the system performance of M-ary code shift keying (MCSK) impulse modulation is studied in detail and compared to M-ary pulse position modulation (MPPM) under single- and multi-user scenarios. For that, bounds on the semi- analytic symbol-error rate (SER) expressions are derived and simulation studies are conducted. When practical implementations of MCSK and MPPM are considered, it is shown that MCSK can provide about 2 dB performance gain over MPPM as it reduces the effects of multipath delays on the decision variables by randomizing locations of the transmit pulse.

This paper is concerned with the analysis of scheduling performance in downlink WCDMA networks that employ adaptive modulation and coding (AMC) and fast cell selection (FCS). The scheduling schemes investigated include (i) the round robin (RR) scheme, (ii) the maximum carrier-to-interference ratio (C/I) scheme, (iii) the simplified proportional fair (PF) scheme, and (iv) the conventional PF scheme. The channel model includes large-scale signal attenuation and small-scale fading. By using a form analogous to Shannon's channel capacity formula, a logarithmic relationship between the instantaneous data rate and the C/I with AMC is first established. Using the so-called Poisson scheme in which the probabilities of the occurrence of the events in Bernoulli trials depend on the trial index, FCS implementation is then examined in detail. Finally, a complete set of analytical expressions on the average system throughput, the peak data rate and the fairness for the abovementioned scheduling schemes are derived.

Recently, a novel balanced transmitted-reference (TR) system has been proposed for UWB communications. This TR system is capable of properly eliminating the inter-pulse interference (IPI) between the reference and data pulses in a single user multipath environment. In this paper, we investigate its multiple access (MA) performance by evaluating the second- order moments of noise and interference terms. The analytical framework developed here can also be used to accurately evaluate the MA performance of conventional TR system. It is shown that the proposed balanced TR system can achieve comparable MA capacity as conventional TR, while operating at higher information rate. On the other hand, given a target information rate, the balanced TR system in conjunction withM-ary signaling offers higher MA capacity than conventional TR system. The performance results suggest there exists a trade-off between the inter-pulse distance, thus the achievable information rate, and the MA capacity.

We investigate the dynamic spectrum sharing problem among primary and secondary users in a cognitive radio network. We consider the scenario where primary users exhibit on-off behavior and secondary users are able to dynamically measure/estimate sum interference from primary users at their receiving ends. For such a scenario, we solve the problem of fair spectrum sharing among secondary users subject to their QoS constraints (in terms of minimum SINR and transmission rate) and interference constraints for primary users. Since tracking channel gains instantaneously for dynamic spectrum allocation may be very difficult in practice, we consider the case where only mean channel gains averaged over short-term fading are available. Under such scenarios, we derive outage probabilities for secondary users and interference constraint violation probabilities for primary users. Based on the analysis, we develop a complete framework to perform joint admission control and rate/power allocation for secondary users such that both QoS and interference constraints are only violated within desired limits. Throughput performance of primary and secondary networks is investigated via extensive numerical analysis considering different levels of implementation complexity due to channel estimation.

This paper is concerned with the throughput and fairness analysis in a downlink WCDMA network with adaptive modulation and coding (AMC) and fast cell selection (FCS). The downlink scheduling schemes investigated include (i) the Round Robin scheme, (ii) the maximum carrier-to-interference ratio (C/I) scheme, and (iii) the proportional fair scheme. The channel model is assumed to include path loss, lognormal shadowing and fast Rayleigh fading. By using a form analogous to Shannon's channel capacity equation, a logrithmic relation between the instantaneous data rate and theC/Iwith AMC is first established. Using generalized Bernoulli trial, FCS is then examined in detail. Finally, the throughput and fairness expressions for the abovementioned downlink scheduling schemes with FCS are derived.

In ultra-wideband impulse radio (UWB-IR) systems, multipath-delayed received pulses may overlap if two consecutive multipaths arrive within less than the pulse duration. This condition makes it nontrivial for the Rake receiver to capture enough multipath energies for reliable communications. In this paper, we study in detail the effects of channel models and Rake receiving process on the performance of UWB-IR systems. Specifically, we consider Tc- and T-spaced channel models and different Rake receiver implementations to maximize the captured signal energy, where we discuss their validity for real system considerations. Also, we show that code shift keying (CSK) impulse modulation can achieve about 1 dB performance gain over conventional pulse position modulation (PPM) when there is pulse overlapping, as it randomizes pulse transmit locations as opposed to fixed pulse transmit locations used by PPM.

In this paper, we derive a closed-form expression for channel-averaged signal-to-interference-plus-noise ratio for ultrawideband (UWB) Rake receiving system in an indoor multiuser communication scenario, given that the interference level is fluctuating due to asynchronous transmissions among the users. The indoor wireless channel model adopted here is a standard one recently released by IEEE 802.15 Study Group 3a. We propose a theoretical framework to derive the channel-averaged SINR, considering the lognormal channel gain distribution and the double independent Poisson arrival distribution of cluster and ray, and show that our analysis is well coincident with the simulation results. With this framework, we demonstrate that our analysis can be applied to theoretically determine the optimum integration interval for a UWB transmitted reference system, even in a multiuser scenario.

In this paper, we derive a closed-form expression for channel-averaged signal-to-interference-plus-noise ratio (SINR) for ultra-wideband (UWB) Rake receiving system in an indoor multiuser communication scenario, given that the interference level is fluctuating due to asynchronous transmissions among users. The indoor wireless channel model adopted here is a standard one, recently released by IEEE 802.15 study group 3a . We propose a theoretical framework to derive the channel-averaged SINR considering the lognormal channel gain distribution and the double independent Poisson arrival distribution of cluster and ray, and show that our analysis is well coincident with the simulation results. With this framework, we demonstrate that our analysis can be applied to theoretically determine the optimum integration interval for a UWB transmitted reference system, even in a multiuser scenario.

A novel modulation format is proposed for time-hopping pulse position modulation (TH-PPM) ultra wideband (UWB) communications, where an orthogonal set of user-specific TH codes is employed for M-ary code shift keying (MCSK) to carry additional data. A particular TH code, selected by log2M-bit data, modulates the basic pulses (i.e., determines the location of the pulses), that are additionally time-shifted by binary PPM (BPPM), resulting in combined MCSK and BPPM (i.e., "MCSK/BPPM"). The combined modulation achieves increased data rate with respect to conventional TH-BPPM without affecting pulse shaping and can further improve the bit-error-rate (BER) performance. MCSK, which can also be employed by itself or combined with binary pulse amplitude modulation (PAM), is also shown to outperform TH M-ary PPM in dense multipath as it increases the diversity gain through randomized pulse locations.

This paper analyzes the power spectral density (PSD) characteristics of ultra wideband (UWB) signals modulated by M-ary code shift keying (MCSK) that can be combined with binary pulse position modulation (BPPM) and binary pulse amplitude modulation (BPAM), which we refer to as MCSK based impulse radios (IR). MCSK based IR are modified IR that were designed to increase the data rate of conventional IR by embedding the data on a time hopping (TH) code randomly selected from a set of M distinct TH codes per user. This random selection also results in increased effective TH code period, which- helps smoothing the continuous spectrum and suppressing the discrete spectral components. In combination with the random code selection, design of the TH code set for each user is very important for spectrum shaping and multiple access (MA) capability, and is addressed in detail in the paper.

Fixed guard times considered for the physical layer of the IEEE 802.15.4a standard (low-rate Wireless Personal Area Networks) cause user asynchronism-dependent collisions that affect the instantaneous bit-error-rate (BER) and packet- error-rate (PER) performances in the presence of simultaneously operating piconets (SOPs). Using code shift keying (CSK), the location of the guard times can be randomized yielding the collision event independent of user asynchronism. In this paper, CSK modulation is adopted for the physical layer of the IEEE 802.15.4a standard with a special code design technique, and it is shown that the proposed implementation of CSK stabilizes the instantaneous BER and PER values. Stabilizing the instantaneous BER and PER values prevents worst-case scenarios, where many packets may be lost due to user asynchronism-dependent collisions, and should be carefully considered for real system implementation.

In this paper, we analyze the multiuser performance of a modified version of a newly proposed balanced transmitted-reference (TR) system for ultra-wideband (UWB) communications. The proposed TR system is capable of properly eliminating the inter-pulse interference (IPI) between the reference and data pulses in multipath environments, with a low system complexity. We further investigate the effect of the inter-pulse distance on the multiuser capacity and demonstrate the trade-off between the system's achievable information rate and the number of coexisting users.

This paper investigates the effects of code design on the power spectral density (PSD) characteristics of M-ary code shift keying (MCSK) based impulse radios (IRs) used in ultra wideband (UWB) communications. MCSK based IRs are modified IRs that were designed to increase the data rate by randomly selecting a time hopping (TH) code from a set of M distinct TH codes per user. This selection results in increased effective TH code period, which helps smoothing the continuous spectrum and suppressing the discrete spectral components. In combination with the random code selection, design of the TH code set for each user is very important for spectrum shaping and multiple access (MA) capability, and is studied in detail in the paper. Accordingly, the trade-off between efficient spectrum shaping and MA capability is discussed for different code design criteria, which should be carefully addressed for real system implementation.

A new M-ary orthogonal coded/balanced signaling is proposed to realize high rate UWB transmitted-reference (TR) systems. Especially, the inter-pulse interference by an overlap of multipath-delayed pulses is mitigated by combining the signaling and a pair of balanced matched filters in a joint manner, so as to permit a shorter time delay between the reference and data pulses in TR systems. To evaluate an achievable information rate increase relative to conventional TR, the symbol error probability (SEP) is theoretically derived for the M-ary balanced TR system in dense multipath.

To faciliate high-rate uplink transmission, a novel selective maximum-likelihood (SML) detection is realized on a set of parallel multicode (MC) channels as a result of MC wideband code-division multiple-access (WCDMA) signaling with precoding. Multidimensional (MD) signaling can be combined with MC to further increase the data rate with fixed spreading factor per symbol. In connection with this MDMC signaling, the proposed SML detection achieves significantly improved symbol-error probability, compared with other detection methods, both without soft sequential decoders for a tractable analysis.

This paper is concerned with the throughput and fairness analysis in a downlink WCDMA network. The channel model is assumed to include path loss, lognormal shadowing and fast Rayleigh fading. The scheduling schemes investigated are (i) the round robin scheme, (ii) the maximum carrier-to-interference ratio (C/I) scheme, (iii) the proportional fair scheme, (iv) the maximum instantaneous signal scheme, and (v) the fading-based signal power scheme. By using an approximation of the probability density function of C/I, throughput and fairness expressions are derived, and a performance comparison among the five scheduling schemes is given. The results indicate that throughput and fairness performance of each scheduling scheme depends on mobile users' location. Tn general, the round robin scheme has the worst throughput performance as compared to the other four schemes. The proportional fair scheme and the fading-based signal power scheme can provide relatively better tradeoffs between the throughput and the fairness. The findings presented here are not only of fundamental theoretical value, but are also of practical interest to the designers of third-generation mobile communication systems based on WCDMA technology.

A scheduling scheme to compromise between pure opportunistic (PO) and relative best (RB) is proposed for downlink packet-based data transmission. For this, an instantaneous channel gain is factorized into two channel gain components such as short-term and long-term channel gains, in order to exploit individual characteristics in designing the scheduling scheme. Here, selection diversity offered by short-term gains is used to improve fairness compared to the PO, while multiuser diversity by independent spatial user distribution, resulting in distinct long-term gains, is partially used to yield higher throughput than the RB. The proposed scheme is referred to as selective relative best (SRB) and is shown to provide a balance between fairness and throughput of the system

This paper addresses the problem of dynamic rate and power adaptation for forward link data transmission using high-order modulation and multicode formats in cellular wideband code division multiple access (WCDMA) networks. A novel framework for dynamic joint adaptation of modulation order, number of code channels (hence transmission rate), and transmission power is proposed for downlink data transmission in a cellular WCDMA system where different users have similar frame error rate (FER) requirements. Based on a general downlink signal-to-interference ratio (SIR) model, the problem of optimal dynamic rate and power adaptation is formulated, for which the rate and power allocation can be found by an exhaustive search. Two heuristic-based dynamic rate and power allocation schemes are proposed. Performance of dynamic joint rate and power adaptation under the proposed framework is evaluated for a random micromobility model using computer simulations. Also, an analytical approach to evaluate the throughput performance of dynamic rate and power adaptation using high-order modulation and multicode formats is presented.

In this paper, a novel modulation format is proposed for time-hopping pulse position modulation (TH-PPM) ultra wideband (UWB) communications, where a near-orthogonal set of user-specific TH codes is employed for M-ary code shift keying (MCSK) to carry additional data. A particular TH code, selected by log/sub 2/ M-bit data, modulates the basic pulses that are time-shifted by binary PPM (BPPM), resulting in combined MCSK and BPPM (i.e., MCSK/BPPM). The combined modulation can achieve a high data rate without affecting pulse shaping and further improve the bit-error-rate (BER) performance subject to the same signal energy per bit.

This paper addresses the issue on how the code constraint affects the design of scheduling when multicode CDMA with high-order modulation is adopted for downlink best-effort packet data. For this, two-best user scheduling is proposed for proper trade-off between sum-rate throughput and short-term fairness, while the code and power constraints are effective along with high-order modulation over multicode. To clearly show the trade-off, conventional pure opportunistic, proportional fair, and round robin schedulings are compared with the proposed one, for which a theoretical framework for evaluation of the sum-rate throughput is developed to derive a closed-form formula, taking into account both short-term and long-term fadings.

A new base station receiver is proposed and analyzed for a code-division multiple-access (CDMA) cellular system. The proposed receiver can achieve remarkable diversity gain by increasing diversity order with reasonable cost and complexity. From the numerical results, it is confirmed that the proposed receiver structure can be a practical solution for enhancing reverse-link capacity and improving performance in CDMA cellular system operations. The result in the letter can find its applications to legacy IS-95/cdma2000 1x base stations with simple modifications.

A novel two-dimensional diversity combining is proposed for the uplink in a single cell which employs multidimensional multicode direct-sequence code-division multiple-access signaling and two receive antennas. First, the signaling is combined with precoding to obtain a constant envelope signal that is suitable for the uplink, and the resulting error detection is applied to the diversity combining. Based on the error detection, an adaptive selection-combining/maximal-ratio combining (SC/MRC) is performed, for which initial data detection is made by the SC to avoid the combining loss of the very noisy paths. When the initial SC is unreliable (indicated by the error detection), the MRC is attempted to fully exploit the space and multipath diversity. The adaptive SC/MRC is generalized to further increase the diversity gain over the MRC, offered by the error-detection capability. Through analysis and simulation results, it is shown that the adaptive SC/MRC and its generalized diversity receiver outperforms the other schemes in terms of the symbol-error rate, and also its bit-error rate can be lower than that of the variable spreading factor scheme using a single code.

To improve the spectral efficiency while meeting the radio link level quality of service requirements such as the bit-error-rate (BER) requirements for the different wireless services, transmission rate and power corresponding to the different mobile users can be dynamically varied in a cellular wideband code-division multiple-access (WCDMA) network depending on the variations in channel interference and fading conditions. This paper models and analyzes the performance of transmission control protocol (TCP) under joint rate and power adaptation with constrained BER requirements for downlink data transmission in a cellular variable spreading factor (VSF) WCDMA network. The aim of this multilayer modeling of the WCDMA radio interface is to better understand the interlayer protocol interactions and identify suitable transport and radio link layer mechanisms to improve TCP performance in a wide-area cellular WCDMA network.

High-rate packet transmission is realized for the downlink in cellular multirate code-division multiple-access systems using multicode concatenated signaling, combined with iterative detection and self-interference cancellation. Since an optimum packet transmission is to allocate the maximum allowable rate to the user with the best received signal-to-interference-plus-noise ratio (SINR), a problem with fairness arises, in the sense that only a very small number of users receive at or near the maximum allowable rate, and the rest of the users do not receive at all. To overcome this, a new soft multimodal fairness control is proposed to adjust the latency (or waiting time) between two extreme values. Throughput is analyzed by deriving the probability distribution of the rate allocation, which is based on the maximum received SINR in a slot. For this, statistics on the SINRs are jointly characterized under three-sector cell structure because of their mutual correlation. Traffic variations are also taken into account to formulate the statistics under two algorithms for adaptive base station selection. It is shown that high-rate transmission can be achieved by a substantial reduction in in-cell interference, and the tradeoff between throughput and fairness can be met by the fairness control.

Optimal dynamic rate allocation among mobile stations for variable rate packet data transmission in a cellular wireless network is an NP-complete problem; therefore, suboptimal solutions to this problem are sought for. In this paper, three novel suboptimal dynamic rate adaptation schemes, namely, peak-interference-based rate allocation, sum-interference-based rate allocation, and mean-sense approximation-based rate allocation, are proposed for uplink packet data transmission in cellular variable spreading factor wide-band code division multiple access (WCDMA) networks. The performances of these schemes are compared to the performance of the optimal dynamic link adaptation for which the rate allocation is found by an exhaustive search. The optimality criterion is the maximization of the average number of radio link level frames transmitted per frame time under constrained signal-to-interference-plus-noise ratio (SINR) at the base station receiver. Two different error control alternatives for variable rate packet transmission environment are presented. We demonstrate that the dynamic rate adaptation problem under constrained SINR can be mapped into the radio link level throughput maximization problem with integrated rate and error control. Performance evaluation is carried out under random and directional micromobility models with uncorrelated and correlated long-term fading, respectively, in a cellular WCDMA environment for both the homogeneous (or uniform) and the nonhomogeneous (or nonuniform) traffic load scenarios.

This paper addresses the problem of dynamic link adaptation under multiple quality-of-service (QoS) constraints in cellular wideband code-division multiple-access (WCDMA) systems. A novel dynamic joint rate and power adaptation framework is proposed for downlink data transmission in a multicell variable spreading factor (VSF) WCDMA system where the different classes of users have different signal-to-interference ratio (SIR) requirements. Based on a general downlink SIR model, the problem of optimal dynamic rate and power adaptation under multiple SIR constraints is also formulated, for which the rate and power allocation can be found by an exhaustive search. Two schemes, namely, near-optimal and suboptimal schemes, are proposed for implementation-friendly dynamic rate and power adaptation. Performance of dynamic joint rate and power adaptation under the proposed framework is evaluated under random micro-mobility model with uncorrelated long-term fading and a directional micro-mobility model with correlated long-term fading in a cellular WCDMA environment.

This paper models and analyzes the performance of opportunistic scheduling under constrained frame error rate (FER) requirement for downlink data transmission in cellular CDMA networks using high-order modulation and multicode formats. The effects of multipath-induced interference and imperfect self-interference cancellation on single-transmission are specifically modeled. Based on the proposed framework the performances of the several opportunistic schedulers are evaluated using Monte Carlo simulation considering both bursty and nonbursty downlink data traffic flows.

A novel two-dimensional diversity combining is proposed for the uplink in a single cell that employs multidimensional multicode DS-CDMA signaling and two receive antennas. First, the signaling is combined with preceding to obtain a constant envelope signal that is suitable for the uplink, and the resulting error detection is applied to the diversity combining. Based on the error detection, an adaptive selection combining/maximal-ratio combining (SC/MRC) is performed for which initial data detection is made by the SC to avoid the combining loss of the very noisy paths. Further, this adaptive SC/MRC is generalized to maximize the diversity gain over the MRC, offered by the error detection capability.

This paper addresses the problem of dynamic rate and power adaptation for forward link data transmission using high-order modulation and multicode formats in cellular wideband code division multiple access (WCDMA) networks. A novel framework for dynamic joint adaptation of modulation order, number of code channels (hence transmission rate) and transmission power is proposed for downlink data transmission in a cellular WCDMA system where the different users have similar frame error rate (FER) requirements. Based on a general downlink signal-to-interference ratio (SIR) model, the problem of optimal dynamic rate and power adaptation is formulated, for which the rate and power allocation can be found by an exhaustive search. Two heuristic-based dynamic rate and power allocation schemes are proposed. Performance of dynamic joint rate and power adaptation under the proposed frame-work is evaluated under random micro-mobility model using computer simulations.

A novel dynamic joint rate and power adaptation framework is proposed for downlink data transmission in a multicell variable spreading factor (VSF) WCDMA system where the different classes of users have different signal-to-interference ratio (SIR) requirements. Based on a general downlink SIR model, the problem of optimal dynamic rate and power adaptation under multiple SIR constraints is also formulated, for which the rate and power allocation can be found by an exhaustive search. Performance of the dynamic joint rate and power adaptation under the proposed framework is evaluated under random micro-mobility model with uncorrelated long-term fading and a directional micro-mobility model with correlated long-term fading in a cellular WCDMA environment for both homogeneous (or uniform) and the non-homogeneous (or non-uniform) traffic load scenarios.

Dynamic rate adaptation for uplink data transmission in a cellular multidimensional multicode (MDMC) direct-sequence code-division multiple-access packet data network is modeled and analyzed. An analytical framework is developed to evaluate the performances of radio link level dynamic rate adaptation schemes under multipath fading and log-normal shadowing. The radio link level throughput under optimal dynamic rate adaptation (having exponential computational complexity) and different heuristic-based suboptimal rate adaptation schemes can be assessed under the presented analytical framework. The performance of MDMC signaling is compared with that of the single-code variable spreading factor (VSF) signaling. To this end, based on an equilibrium point analysis of the system in steady-state, a base station-assisted and mobile-controlled dynamic rate adaptation scheme is presented.

This paper proposes a novel dynamic joint rate and power control procedure for downlink data transmission in a multicell variable spreading factor wideband code-division multiple-access (WCDMA) system where the different users have similar quality-of-service requirements in terms of the signal-to-interference ratio (SIR). Two variations of the dynamic joint rate and power allocation procedure, namely, Algorithm-1 and Algorithm-2, are presented. The performances of these two schemes are compared to the performance of the optimal dynamic link adaptation for which the rate and power allocation is found by an exhaustive search. The optimality criterion is the maximization of the total radio link level capacity (or sum-rate capacity) in terms of the average number of radio link level frame transmitted per adaptation interval under constrained SIR and power limit in the base station transmitter. The proposed schemes have linear time complexity as compared to the exponential time complexity of the optimal scheme and achieve better radio link level throughput fairness compared to the optimal link adaptation scheme with a moderate loss in total throughput. Performance evaluation is carried out under random and directional micromobility models with uncorrelated and correlated long-term fading, respectively, in a cellular WCDMA environment for both the homogeneous (or uniform) and the nonhomogeneous (or nonuniform) traffic load scenarios.

We propose a measurement-based dynamic random access (RA) code assignment procedure for prioritized packet data transmission in wideband code-division multiple access (WCDMA) networks. This dynamic adaptation process is based on analytical performance results derived for random packet access under Rayleigh fading in WCDMA networks. The performance of the proposed measurement-based RA code assignment procedure with three different adaptation methods is evaluated by using computer simulations. The performance of the proposed scheme is compared with those of a retransmission control-based and static channel allocation-based prioritized packet access scheme. An integrated (physical layer and link layer) delay-throughput performance model is presented for finite population RA WCDMA systems. The proposed dynamic RA code assignment procedure can be used in an adaptive quality of service (QoS) framework for dynamically adjusting the QoS of prioritized RA data traffic in the evolving WCDMA-based differentiated services wireless Internet protocol networks.

Abstract In this article, a comprehensive survey of the medium access control (MAC) approaches for wireless mobile ad hoc networks is presented. The complexity in MAC design for wireless ad hoc networks arises due to node mobility, radio link vulnerability and the lack of central coordination. A series of studies on MAC design has been conducted in the literature to improve medium access performance in different aspects as identified by the different performance metrics. Tradeoffs among the different performance metrics (such as between throughput and fairness) dictate the design of a suitable MAC protocol. We compare the different proposed MAC approaches, identify their problems and discuss the possible remedies. The interactions among the MAC and the higher layer protocols such as routing and transport layer protocols are discussed and some interesting research issues are also identified. Copyright © 2003 John Wiley & Sons, Ltd.

This paper models and analyzes the performance of TCP (transmission control protocol) under joint rate and power adaptation with constrained BER requirements for downlink data transmission in a multi-cell VSF (variable spreading factor) WCDMA system. The performance of TCP in a wide-area Internet environment is evaluated by using computer simulations considering user mobility, short-term fading (i.e., multipath fading) and long-term fading (i.e., shadowing). The motivation is to explore the inter-layer protocol interactions and to identify suitable transport and radio link layer mechanisms to improve wireless TCP performance in a cellular WCDMA environment.

An algorithm for joint power and rate allocation is proposed to equalize the downlink quality between cells when there exists traffic non-uniformity. Unlike locally SIR-based power control, the global power allocation to keep the SIR constant across cells is performed by an effective three-cell model per sector of 60 degrees. It is shown that the proposed algorithm increases the average-rate capacity further compared to that with uniform traffic.

Multidimensional signaling is newly designed to provide a diversity gain of order 2 using two transmit antennas in uplink transmission of wide-band CDMA (W-CDMA) while achieving high and multiple data rates at the same time. The rate can be easily changed on the slot basis in a frame transmission by adapting the order of multidimensional signaling to the incoming traffic. The multidimensional signaling of order zero simply reduces to conventional multicode scheme, so there exists a tradeoff between rate and complexity. Also, the use of multidimensional signaling results in far reduced envelope variations at the maximum rate. With the transmit diversity, the uplink signal-to-interference ratio (SIR) will be further stabilized to meet the requirements of multimedia traffic. Statistics of interferences are characterized in terms of their second- and fourth-order moments from which diversity gain is theoretically verified. For realistic multipath fading channels, considering both equal and unequal average path powers, the average probability of symbol error is obtained in compact form, in which the two schemes, multidimensional signaling with and without transmit diversity are compared, and then with nonmulticode scheme in view of the bit error rate (BER). Numerical and simulation results show that the multidimensional signal with transmit diversity provides a significant gain over that with no diversity, and furthermore outperforms nonmulticode scheme subject to the same signal energy per bit and chip rate.

A reduced complexity channel estimation for OFDM systems with transmit diversity is proposed by exploiting the correlation of the adjacent subchannel responses. The sizes of the matrix inverse and the FFTs required in the channel estimation at every OFDM data symbol are reduced by half of the existing method for OFDM systems with nonconstant modulus subcarrier symbols or constant modulus subcarrier symbols with some guard tones. The complexity reduction of half FFTs size and some matrix multiplications is still achieved for constant modulus subcarrier symbols with no guard tones. The price for the complexity reduction is a slight BER degradation and for the channels with small relative delay spreads, the BER performance of the reduced complexity method becomes quite comparable to the existing method. An alternative approach for the number of significant taps required in the channel estimation is described which achieves a comparable performance to the case with the known suitable number of significant taps. A simple modification which reduces the lost leakage of the nonsample-spaced channel paths is also proposed. This modification achieves a substantial performance improvement over the existing method without any added complexity.

In this paper, a measurement-based dynamic RA (Random Access) code assignment procedure Is proposed for prioritized packet data transmission in WCDMA (Wideband Code Division Multiple Access) networks. This dynamic assignment process is based on analytical performance results derived for random packet access under Rayleigh fading in WCDMA networks. The performance of the proposed measurement-based RA code assignment procedure with three different adaptation methods is evaluated using computer simulation for bursty data packet arrival patterns. The performance of the proposed scheme is compared to those of a retransmission control-based and static channel allocation-based prioritized packet access schemes. The proposed scheme can be used in an adaptive QoS (Quality of Service) framework for dynamically adjusting the QoS of prioritized random access data traffic In the evolving WCDMA-based DS (Differentiated Services) wireless IP (Internet Protocol) networks.

Optimal dynamic rate allocation among mobile stations for variable rate packet data transmission in a cellular wireless network is an NP-complete problem; therefore, sub-optimal solutions to this problem are sought. Again, interference calculation is non-trivial in the case of a packet-switched cellular CDMA network with heterogeneous traffic load in different cells. In this paper, a sub-optimal two-step dynamic rate selection procedure is proposed for uplink packet data transmission in cellular WCDMA (wideband code division multiple access) networks. A novel 'mean-sense' approach for inter-cell interference calculation is employed assuming homogeneous traffic load in the different cells. Two different error control alternatives for this variable rate packet transmission environment are presented and their performances are analyzed for three different channel models. The performance of the proposed two-step rate selection procedure is fairly close to that of the optimal rate allocation found through exhaustive search.

High rate transmission can be realized using multiple orthogonal codes (MOC), as proposed in the third-generation wide-band code-division multiple-access (W-CDMA) standard. However, the linear sum of MOC channels is no longer constant amplitude, and a highly linear, power-inefficient amplifier may be required for transmission. Recently, a nonlinear block coding technique called precoding is introduced to maintain a constant amplitude signal after superposition of MOC channels. This is achieved by adding redundancy. In this paper, we first describe a multidimensional signaling scheme that recovers some information rate loss by precoding. Second, we propose a self-interference (SI) cancellation scheme resulting from a code diversity between the in-phase and quadrature subchannels among MOC channels. In a typical wireless channel with multipath fading, this type of SI can be detrimental especially when the number of parallel MOC channels is large. Third, we show that the error detection capability of precoding can be combined with code diversity, resulting in a diversity gain. In addition, we show that the diversity gain can be achieved using antenna diversity to assure the degree of freedom in code diversity, and even with the large number of MOC channels, the error performance can be maintained reliably while outperforming the variable spreading factor scheme in W-CDMA.

A novel multidimensional signaling and per-symbol detection are proposed for the uplink of DS-CDMA systems to achieve a constant envelope modulation and coherent demodulation while providing variable and high data rates. Unlike the conventional precoding technique, this scheme increases the data rate by compensating a loss in the information rate incurred by the constant envelope signal. It is shown that an optimum rate of multidimensional signaling can be found for the bit SNRs of interest, and the proposed scheme with 4-parallel multicodes offers significant gain over conventional DS-CDMA.

A novel modulation format is proposed for cellular direct-sequence CDMA systems where a user-specific spreading sequence is binary pulse position and biorthogonally modulated to form a set of biorthogonal spreading sequences. The modulation scheme trades the signal space used for spreading sequences with that for modulation while a global space is fixed. The interference is mainly determined by the cross correlation properties among sequences, but also affected by modulation. The effect is taken into account to evaluate the multi-user performance of the combined modulation. Compared to M-ary orthogonal modulation, the performance is shown to be almost the same while resulting in a simpler receiver structure.

A new modulation format is proposed for cellular code-division multiple-access (CDMA) communications where binary pulse position modulation (PPM) is embedded in the chip waveform and combined with orthogonal modulation using Walsh/Hadamard codes. Compared to the conventional CDMA using orthogonal codes, this scheme allows reduction in receiver complexity by lowering the modulation level for the second-stage orthogonal modulation. The staggered (half-chip) quadrature direct-sequence signaling is adopted to uniformly distribute the transmit power and allow noncoherent detection at the receiver because carrier phase tracking is not feasible because of the binary PPM, suitable for the reverse link in cellular networks. Statistics of inter-user interferences are characterized, and then derive the symbol error probability for the proposed M-ary modulation format. It is shown that the advantage in view of receiver complexity can be achieved without deteriorating the multi-user performance in terms of the number of users affordable at a specified error rate.

A slotted asynchronous (SA) code-division multiple-access communication scheme controlling the time of arrival is proposed for distributed spread-spectrum packet radio networks where the transmission range is limited as in an indoor wireless system. In this scheme, each terminal can send its packet randomly at any one of N/sub w/, possible time instants, equally spaced over one period of direct-sequence spread-spectrum signals. Such transmissions initiated at different time instants can be resolved because of the high time resolution of wide-band signals if the channel delays associated with multipath are small due to limited transmission range. Quasi-synchronous distributed networks are considered to allow timing drift among terminals and also reflect wireless multiple-access channels, in which common-transmitter-based (C-T) and receiver-transmitter-based (R-T) spreading code assignments are adopted to permit a contention mode only for the header portion. The throughput is evaluated under the spread ALOHA assumption on collision events and also by reflecting the effect of the MAI in the header detection process. Theoretical results show that the combination of the SA scheme with C-T assignment results in more significant improvement than the case of R-T assignment, and also the former provides the benefit of the efficient usage of spreading codes in a code-limited environment.

This paper analyzes a decorrelating detector for synchronous packet CDMA communications where a set of quasiorthogonal code waveforms is generated from a common code by assigning distinct initial code phases to all users. In this analysis, we characterize the residual multiple-access interference (MAI) caused by possible timing offsets when synchronous packet transmissions are on the reverse link of centralized networks. Also, to show the feasibility of the decorrelating detector, we further investigate its robustness against the multipath channel. It is shown that the decorrelating detector greatly reduces the residual MAI to the order of N/sup -2/, N number of chips/bit, and yields acceptable performance even faced with the near-far situation.

We address an issue of channel sharing among users by using a random assignment/transmitter-oriented (RA/T) code scheme which permits the contention mode only in the transmission of a header while avoiding collision during the data packet transmission. Once the header is successfully received, the data packet is ready for reception by switching to one of the programmable matched-filters. But the reception may be blocked due to a limited number of matched-filters so that this effect is taken into account in our analysis. We also consider an acknowledgment scheme to notify whether the header is correctly detected and the data packet can be processed continuously, which aims at reducing the interference caused by unwanted data transmission. For a realistic analysis, we integrate the detection performance at the physical level with the channel activity at the link level through a Markov chain model. It is shown that compared to classical code-division multiple-access (CDMA) systems, a reduction in the receiver complexity of a half is allowed by choosing a proper number of RA/T codes without losing the performance quality in view of the normalized throughput.

This paper presents a technique for computing the probability that f of m transmissions will be collision-free at a given receiver in a spread-spectrum multiple-access (SSMA) radio network in which all transmitters employ identical wideband symbol waveforms for signalling. Separation of signals (collision-freedom) is based on the fine time-resolution characteristics of pulse compression receivers operating on wideband waveforms. A taxonomy of collision-free reception events is presented for a low-complexity sampling receiver operating in the absence of significant resolvable multipath, and combinatorial techniques are used to count collision-free reception events. A method for embedding this calculation in receiver performance analyses is given.>

The paper presents a random spreading code assignment scheme for enhancing channel efficiency in centralized DS-SS packet radio networks which employ a multiple-capture receiver for each code channel. Compared to the common code case, this approach requires a modest increase in receiver complexity, but the number of distinct spreading codes being used is considerably less than the number of radios in the network. A general theoretical framework for evaluation of collision-free packet performance in each code channel is described, in which the possibility of collision-free transmission is conservatively estimated using a combinatorial method, and the effects of asynchronous multiple-access interference are characterized in terms of the primary and secondary user interferences. At the link level, the capture and throughput performances are evaluated for a proper set of codes, and compared with the results from the common code scheme. It is shown that the use of a random assignment scheme with more than one code results in a higher performance gain, and most of this gain can be achieved with just two distinct spreading codes.>