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.

With growing complexity in urban transportation systems, multimodal traffic flow prediction is essential for coordinating multiple transport modes, optimizing resource allocation, and enhancing urban mobility efficiency. To improve prediction accuracy, it is essential to model cross-modal correlations effectively. However, existing methods in cross-modal correlation modeling overlook two critical issues: 1) Existing cross-modal graph construction methods fail to uncover the latent relationships between nodes based on the travel patterns across different modes; 2) Existing cross-modal feature extraction methods are limited to a single scale and lack comprehensive spatiotemporal representations, resulting in incomplete modeling of cross-modal dependencies. To enhance cross-modal correlation capture, we propose a novel framework named Multi-Scale Cross-Modal Spatiotemporal Enhanced Network (MCSTEN). Specifically, we design a cross-modal graph generator that utilizes tensor decomposition to uncover latent node correlations from peak-time asynchrony features across modes and constructs a cross-modal graph with rich information. Subsequently, we introduce a multi-scale feature extraction block equipped with a scale transformation mechanism to enhance the modeling of cross-modal correlations at different temporal and spatial scales. Finally, an attention-based fusion strategy integrates these multi-scale correlations, adaptively balancing the contribution of each scale. Experimental results on seven real-world multimodal transportation datasets demonstrate that MCSTEN significantly outperforms state-of-the-art methods in prediction accuracy.

In this paper, we propose a novel generative artificial intelligence (GAI)-aided approach to address the quality of experience (QoE)-aware resource allocation for reconfigurable intelligent surface (RIS)-assisted digital twin (DT) interactions with uncertain evolutions. In the considered system, mobile users interact with a DT model, referring to the high-fidelity and interactive virtual counterpart of a physical entity, hosted by a DT server deployed on a wireless base station via the assistance of an RIS, for gaining DT services, such as real-time monitoring and predictive analytics. Noted that DT interactions involve round-trip communications with both uplink and downlink, and concern not only objective performance but also subjective experience. As such, we formulate an optimization problem for RIS-assisted DT interactions, aiming to maximize the sum of all mobile users' mixed objective and subjective QoE, by jointly determining the phase shift marix, receive/transmit beamforming matrices, feedback signal rendering resolution and computing resource configuration. Further taking into account the DT model's uncertain evolutions and the resulted variations of the DT scene that mobile users engage in, we extend the resource allocation problem to a series of scene-specific ones. To obtain a generalized approach with low complexity, avoiding to re-solve each scene-specific problem whenever the engaged DT scene changes, we develop a GAI-aided approach, called prompt-guided decision transformer integrated with zero-forcing optimization (PG-ZFO). Specifically, in PG-ZFO, we first reformulate each scene-specific problem into a Markov decision process (MDP). Then, we design a “decision-making trajectory” based prompt to capture the scene-specific information and extend the traditional decision transformer to a prompt-guided decision transformer with strong generalization. On top of that, a zero-forcing (ZF)-based optimization algorithm is integrated to help derive high-dimensional decisions, i.e., beamforming matrix, along with the offline training and online execution of PG-ZFO. Simulations show the effectiveness of the proposed approach, and demonstrate its superiority over counterparts, i.e., rigid optimization method and decision transformer without prompt.

Sporadic ambient radio frequency signals can offer opportunistic spectrum and energy sources for backscatter communications, but they also induce unpredictable transmission interruptions in ambient backscatter wireless networks (AmBWNs). Integrating self-carrier-generative active transmissions with backscatter communications could significantly enhance transmission stability but require frequent mode switching to accommodate the ever-changing ambient radio frequency signals. However, this will result in frequent changes in network topology and link capacity, posing significant challenges in solving the network maximum flow problem in hybrid AmBWNs. To address this problem, we design a link-aware online maximum flow (OnMAXFlow) scheme to tackle agile and adaptive flow scheduling and communication mode selection. Specifically, we first employ an online learning framework to dynamically track changes in ambient signal strength and channel states, enabling real-time evaluation of link capacity. We then model the network maximum flow problem as a stochastic multi-armed bandit (MAB) problem and solve it with a Kullback-Leibler upper confidence bound (KL-UCB) algorithm. Our experimental evaluation results reveal that our OnMAXFlow scheme exhibits rapid convergence and superior adaptability against the varying network states, while maintaining spectrum efficiency and latency performance comparable to the Oracle scheme, which always selects the optimal transmission modes and paths.

Digital twin (DT) is envisioned not only to perform the high-fidelity virtual representation of its corresponding physical entity (PE), but also to serve as an active agent delivering diverse types of sophisticated services. This paper studies an end-edge-cloud collaborative DT placement and update framework. Specifically, we consider that DTs are dynamically placed across edge servers (ESs) via migration following their paired PEs' potential mobility, while being supported by real-time data fetched from the cloud center and user ends. On top of this, we emphasize a unique feature that DTs should also be continually updated capturing the uncertain evolutions for both personalized service ability improvement and versatile service ability maintenance, where the personalization is improved by utilizing the experiential knowledge from the cloud center and their corresponding PEs, and the versatility is maintained by integrating pre-stored profiles. To maximize the long-term system-wide average weighted quality-of-service (QoS) in handling all types of PEs' service requests under the stringent system cost constraint, we formulate an online problem to jointly optimize DT migrations, service priorities towards various request types, and all related DT updating strategies. To address underlying difficulties, we propose a novel adversarial bandit learning assisted online optimization approach, called ARBOK. We first leverage the Lyapunov decomposition method to transform the long-term problem into multiple instant ones, each of which is further decoupled into two correlated subproblems. For solving one subproblem with a bilinear structure, we develop a McCormick envelopes based algorithm (MO-EL). Besides, we design an extended adversarial combinatorial multi-armed bandit algorithm (AC-BL) to tackle the other subproblem, which constructs a super arm set to resolve the issue of excessively large decision space and employs a robust scheme to handle the inherent uncertainty and non-stationarity in each super arm's loss function. We integrate both algorithms seamlessly into ARBOK and alternately execute them till the convergence. Theoretical analysis and extensive simulations show the effectiveness of the introduced dynamic DT placement and continual update framework, demonstrating that ARBOK can converge to the asymptotic optimum within a polynomial-time complexity while outperforming counterparts.

Long-range (LoRa) technology has emerged as a promising solution for Internet of Things applications due to its low power consumption and long communication range. However, its pure ALOHA-based MAC layer leads to severe packet collisions as the network scale expands, significantly degrading the system energy efficiency (EE). While careful allocation of transmission parameters such as channel (CH), transmission power (TP), and spreading factor (SF) could mitigate this issue, the complex interference patterns in multi-gateway scenarios and the time-consuming nature of EE evaluation pose significant challenges. Therefore, we propose an analytical model to calculate the system EE while fully considering the impacts of multiple gateways, duty cycling, quasi-orthogonal SFs and capture effects. Based on this model, we formulate a joint CH, SF, and TP allocation problem to optimize the system EE. To solve this NP-hard optimization problem, we decompose it into CH assignment and SF/TP assignment subproblems. A two-phase optimization framework is then designed. In the first phase, a matching-based algorithm is designed for CH assignment. In the second phase, a multi-agent reinforcement learning approach that incorporates a two-stage attention mechanism and graph convolutional networks is proposed for SF/TP assignment, which effectively captures and weights inter-ED interactions in multi-gateway scenarios. Simulation results indicate that the proposed approach well-suited for complex multi-GW LoRa network topologies and outperforms state-of-the-art algorithms.

This paper explores the application of movable antenna (MA), a cutting-edge technology with the capability of altering antenna positions, in a symbiotic radio (SR) system enabled by reconfigurable intelligent surface (RIS). The goal is to fully exploit the capabilities of both MA and RIS, constructing a better transmission environment for the co-existing primary and secondary transmission systems. For both parasitic SR (PSR) and commensal SR (CSR) scenarios with the channel uncertainties experienced by all transmission links, we design a robust transmission scheme with the goal of maximizing the primary rate while ensuring the secondary transmission quality. To address the maximization problem with thorny non-convex characteristics, we propose an alternating optimization framework that utilizes the general S-procedure, general sign-definiteness, successive convex approximation (SCA), and simulated annealing (SA) improved particle swarm optimization (SA-PSO) algorithms. Numerical results validate that the CSR scenario significantly outperforms the PSR scenario in terms of primary rate, and also show that compared to the fixed-position antenna scheme, the proposed MA scheme can increase the primary rate by 1.48 bps/Hz and 1.57 bps/Hz for the PSR and CSR scenarios, respectively.

Multivariate long-term time series forecasting is pivotal across numerous domains, yet precise predictions require a differentiated assessment of historical time segments due to their varying influence on future trends. Patch-based Transformer frameworks show promise for capturing local temporal patterns. However, they face limitations with static patching, which disrupts temporal continuity, fails to adapt to shifts between periodic and volatile patterns, and overlooks dynamic interactions between time segments and variables. To address these limitations, we propose Entropy-Aware Patch Transformer (EAPformer) which dynamically segments time series for differentiated assessments of historical patterns. Specifically, we overcome static patching limitations by leveraging temporal entropy to dynamically adjust patch boundaries through a two-stage policy, achieving interpretable and context-sensitive segmentation. Subsequently, we adapt EAPformer to periodic and volatile dynamics by employing entropy-aware segmentation that captures distinct temporal patterns across diverse segments. Finally, we further capture dynamic interactions across time segments and variables by introducing a multi-dimensional dependency learning architecture. Additionally, a gated fusion mechanism integrates local and global patterns, enhancing robustness. Extensive experiments on eight public benchmarks demonstrate that EAPformer outperforms state-of-the-art models, achieving superior accuracy across all metrics.

In this paper, we consider a large-scale Wi-Fi HaLow heterogeneous network, where numerous stations (STAs) are distributed around an access point (AP) to collect and transmit data using the restricted access window (RAW) mechanism. The AP manages channel access by adjusting and broadcasting RAW Parameter Set (RPS) and grouping messages, which include the duration and slot allocation for each RAW group. We aim to maximize the overall network throughput while ensuring fairness among STAs. Traditional methods struggle with real-time RAW optimization in practical networks. To overcome this challenge, we first divide the RAW groups heuristically according to the STAs' task type and formulate the throughput optimization problem regarding various RPS. Then, we construct a virtual twin network environment to estimate the throughput performance used for RAW optimization. Specifically, the twin environment is built on neural networks and trained by both synthetic data generated from the classic Markov model and the NS-3 simulator. Given the throughput estimate, we devise the reward function of the proximal policy optimization (PPO) algorithm to adapt the optimal RPS, without frequent interaction with the real network environment. Numerical results indicate that the twin-enhanced PPO (TE-PPO) algorithm achieves a comparable performance with the classic PPO algorithm built on the real trace of the NS3 simulator. Particularly, TE-PPO can reduce the time overhead for RPS adaptation to 1/180.

This paper focuses on secure communications in UAV-assisted wireless sensing systems in the presence of a mobile UAV eavesdropper (MUE), without prior information about the traffic demands of ground users (GUs) in the sensing environment. To maximize secrecy performance, we propose a digital-twin enhanced proximal policy optimization (DTEPPO) algorithm that optimizes the GUs' sensing scheduling and the UAVs' trajectory planning and network formation. Unlike traditional reinforcement learning methods that require complete environment-interacted information, we build a digital twin (DT) model using Gaussian process regression (GPR) based on the historical sensing information collected by the UAVs. Moreover, DT model can be dynamically updated by exploiting the PPO algorithm to lean the UAVs' interactions with the environment, continuously enhancing its accuracy and providing a reliable virtual learning environment for fast network adaptation. Numerical simulations demonstrate that the proposed DTEPPO algorithm achieves rapid convergence and improved secure throughput with reduced communication overhead compared to conventional approaches. Moreover, the proposed framework enables simultaneous learning and modeling (SLAM) in an unknown environment, providing a general framework to solve complex network control problems in wireless and mobile systems with high costs of environmental interactions.

The imbalance in network resource allocation and service quality across cities remains a critical issue that demands effective solutions. Accurate city-scale cellular traffic prediction plays a vital role in addressing this challenge. However, existing methods heavily rely on abundant data, limiting their applicability in data-scarce cities. To overcome this limitation, we propose a two-stage Cross-city Transfer Learning framework for cellular traffic Prediction (CTLP). In the first stage, a spatiotemporal feature concatenation network is proposed. This network captures the dynamics of cellular traffic derived from a data-rich city and a data-scarce city with attention mechanisms and then aggregates these dynamics with a CNN. In the second stage, parameters of the first-stage network are transferred from the data-rich cities to the data-scarce cities for addressing the challenge of cellular traffic heterogeneity in cross-city transfer and enabling more accurate predictions. Experiments on a realworld cross-city cellular traffic dataset demonstrate that CTLP significantly outperforms existing methods, effectively solving the problem of cellular traffic prediction in data-scarce cities.

In this paper, we consider a large-scale Wi-Fi HaLow heterogeneous network in a real-world Internet of Things (IoT) environment, where numerous devices are distributed around an access point (AP). These devices collect and transmit data using the restricted access window (RAW) mechanism. They exhibit varying traffic characteristics. Moreover, dependencies among the devices often exist. We aim to maximize the overall throughput by adjusting the RAW grouping decision. The heterogeneity of real network data and dependencies between devices make the RAW grouping process more complicated. To overcome this challenge, we propose a novel traffic-driven RAW grouping approach. Specifically, we build a simulation environment based on real IoT data and NS-3. Then, we analyze the traffic characteristics of each IoT device. This analysis allows us to fully explore the dependencies and cooperation relationships among these devices. Hence, we can aggregate devices with these relationships into clusters. Each cluster is then treated as a supernode which is used as a basic unit for RAW grouping. Then, we use proximal policy optimization (PPO) algorithm to optimize the RAW grouping process via interacting with the environment. Numerical results indicate that the proposed traffic-driven algorithm significantly achieves faster convergence and improves grouping efficiency in large-scale heterogeneous networks compared to baselines.

LoRa-based backscatter communication technology is promising in enabling ubiquitous connectivity for the Internet of Things (IoT) over large distances with extremely low power consumption. In this paper, we design and implement CPLoRa, a high-throughput parallel LoRa backscatter communication system compatible with commodity LoRa receivers. The core idea of CPLoRa is to enable multiple backscatter tags to communicate with remote LoRa receivers simultaneously by generating standard LoRa packets from a common single-tone RF emitter, which can be extracted from ambient LoRa transmitters or generated from dedicated mobile devices. CPLoRa employs a modified low-power direct digital synthesizer (DDS) scheme for precise frequency synthesis, ensuring compatibility with commodity LoRa receivers and enhancing data rates for long-range backscatter transmissions. Each tag is assigned a unique frequency offset in the synthesizer, allowing parallel transmissions and creating orthogonal, independent LoRa channels. Moreover, we design a harmonic-canceling switch network at the RF front end to reduce mutual interference among different tags. Finally, we implement the CPLoRa tag prototype using low-cost circuit components and rigorously tested in outdoor and indoor environments, demonstrating that CPLoRa supports long-range transmissions of up to 1000 meters while achieving a throughput of 9.6 kbps with 10 parallel tags compatible with commodity LoRa receivers.

The adoption of deep learning models in V2X scenarios has boosted computing demands in edge computing, while concept drift requires frequent model retraining, further increasing computing resource consumption. However, few works study computing instance allocation considering dynamic model retraining, especially under varying workloads. In this paper, we study the joint online model retraining and instance allocation problem in edge computing networks considering model performance degradation due to concept drift. Solving the online problem is challenging since it is a quadratic binary programming problem and its instance switching cost is time-coupling. We propose an efficient online algorithm, where we first linearize the quadratic term, then regularize the time-linearize the problem and then regularize the timecoupling switching cost to decouple the problem, and finally round the fractional solution by a randomization method. We prove that our proposed algorithm achieves a bounded optimality gap. Simulations demonstrate that our algorithm can achieve a balance between instance costs and model performance.

Clustering user devices (UDs) with similar traffic flows enables more effective transmission scheduling and resource allocation in wireless networks, especially for Internet of Things (IoT) with dominant demands for machine-to-machine (M2M) data communications. In this paper, we propose an experience-driven spatial-temporal graph attention network (Exp-STGAN) for UDs’ clustering, by exploiting the spatial-temporal correlations from UDs’ historical traffic flows. Considering the UDs’ heterogeneity, we first characterize each UD’s out-going traffic flows by a dynamic radiation pattern, which reflects the UD’s spatial distribution of traffic demands and its targeted receivers in different directions. We aim to explore UDs’ clustering based on traffic flows’ radiation patterns and propose a spatial-temporal graph attention to aggregate information from different UDs correlated in space and time domains. Without true labels for the UDs’ clustering, we formulate a flow similarity metric based on Kullback-Leibler (KL) divergence to quantify the clustering performance. Moreover, to improve the learning efficiency, we integrate salient human experience into the graph attention module and also continuously update the experience during the training process. Experiments demonstrate that the Exp-STGAN framework can effectively cluster similar UDs by their dynamic traffic flows, highlighting the potential for flow-aware network performance maximization in large-scale IoT systems.

This paper investigates an unmanned aerial vehicle (UAV)-assisted semantic communication network. The energy-limited ground users (GUs) provide semantic services to periodically generated raw data and a UAV relays the extracted semantic information to a base station (BS). Semantic extraction enhances data responsiveness and reduces the age-of-information (AoI) by transmitting only the most essential information. However, more complex semantic extraction increases energy consumption, making it easier for the GUs to deplete their energy. Therefore, we introduce a novel energy-efficient AoI (EAoI) metric to capture both information freshness and energy consumption of the GUs. We formulate a time-averaged EAoI minimization problem by jointly optimizing the GUs' scheduling, pre-extraction strategy, semantic control, computing resource allocation, and the UAV's trajectory. We further propose a semantic-aware joint pre-extraction and trajectory planning (Sem-JPT) algorithm to decompose the complex optimization problem into three subproblems, which are solved by a series of approximation methods. Simulation results demonstrate that semantic communication can reduce the overall EAoI by more than 18% compared with conventional bit-based communication. Moreover, the proposed Sem-JPT algorithm can maintain information freshness and prolong the GUs' lifetimes, outperforming existing baselines.

In mobile edge computing systems, base stations (BSs) equipped with edge servers can provide computing services to users to reduce their task execution time. However, there is always a conflict of interest between the BS and users. The BS prices the service programs on the basis of the user demand to maximize its own profit, whereas the users determine their offloading strategies based on the prices to minimize their costs. Moreover, service programs need to be precached to meet immediate computing needs. Due to the limited caching capacity and variations in service program popularity, the BS must dynamically select which service programs to cache. Since service caching and pricing have different needs for adjustment time granularities, we propose a two-time scale framework to jointly optimize service caching, pricing and task offloading. For the large time scale, we propose a game-nested deep reinforcement learning algorithm to dynamically adjust service caching according to the estimated popularity information. For the small time scale, by modeling the interaction between the BS and users as a two-stage game, we prove the existence of the equilibrium under incomplete information and then derive the optimal pricing and offloading strategies. Extensive simulations based on a real-world dataset demonstrate the efficiency of the proposed approach.

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.

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.

In this paper, we investigate hybrid passive and active intelligent reflecting surface (IRS)-assisted secure non-orthogonal multiple access (NOMA) networks. Multiple users concurrently transmit sensitive data to an access point (AP) in the presence of an eavesdropper (Eve). The hybrid IRS is employed to enhance the NOMA users’ sum rates while simultaneously performing jamming beamforming against the Eve by optimizing the communication channels of NOMA users and injecting controllable noise into the Eve’s channel. We formulate a sum secrecy rate maximization problem by jointly optimizing the users’ scheduling policy, the hybrid IRS’s working mode and beamforming, and the AP’s receiving beamforming. To address combinatorial user scheduling and high-dimensional beamforming design, we develop a dual-cycling deep reinforcement learning (DRL) framework. We first determine the NOMA users’ scheduling strategy and the hybrid IRS’s working mode using a proximal policy optimization (PPO)-based learning algorithm. Then, we optimize the AP’s receiving beamforming and hybrid IRS’s beamforming strategies using an alternating optimization (AO) algorithm. The joint beamforming optimization can significantly enhance the DRL’s learning efficiency by limiting its action space. Moreover, we propose a lightweight two-phase algorithm with approximation techniques to reduce computational complexity by eliminating double-nested loops in AO, while maintaining secrecy performance close to optimum. Numerical results demonstrate that the proposed dual-cycling DRL scheme achieves 54.85% gains in the secrecy rate compared to traditional DRL schemes.

Volumetric videos offer an incredibly immersive viewing experience but encounters challenges in maintaining quality of experience (QoE) due to its ultra-high bandwidth requirements. One significant challenge stems from user’s spatial interactions, potentially leading to discrepancies between transmission bitrates and the actual quality of rendered viewports. In this study, we conduct comprehensive measurement experiments to investigate the impact of six degrees of freedom information on received video quality. Our results indicate that the correlation between spatial quality and transmission bitrates is influenced by the user’s viewing distance, exhibiting variability among users. To address this, we propose a spatial quality oriented rate control system, namely sparkle, that aims to satisfy spatial quality requirements while maximizing long-term QoE for volumetric video streaming services. Leveraging richer user interaction information, we devise a tailored learning-based algorithm to enhance long-term QoE. To address the complexity brought by richer state input and precise allocation, we integrate pre-constraints derived from three-dimensional displays to intervene action selection, efficiently reducing the action space and speeding up convergence. Extensive experimental results illustrate that sparkle significantly enhances the averaged QoE by up to 29% under practical network and user tracking scenarios.

Mobile crowdsensing (MCS) has emerged as a prominent trend across various domains. However, ensuring the quality of the sensing data submitted by mobile users (MUs) remains a complex and challenging problem. To address this challenge, an advanced method is needed to detect low-quality sensing data and identify malicious MUs that may disrupt the normal operations of an MCS system. Therefore, this article proposes a prediction- and reputation-based truth discovery (PRBTD) framework, which can separate low-quality data from high-quality data in sensing tasks. First, we apply a correlation-focused spatio-temporal Transformer network that learns from the historical sensing data and predicts the ground truth of the data submitted by MUs. However, due to the noise in historical data for training and the bursty values within sensing data, the prediction results can be inaccurate. To address this issue, we use the implications among the sensing data, which are learned from the prediction results but are stable and less affected by inaccurate predictions, to evaluate the quality of the data. Finally, we design a reputation-based truth discovery (TD) module for identifying low-quality data with their implications. Given the sensing data submitted by MUs, PRBTD can eliminate the data with heavy noise and identify malicious MUs with high accuracy. Extensive experimental results demonstrate that the PRBTD method outperforms existing methods in terms of identification accuracy and data quality enhancement.

In this paper, we employ an unmanned aerial vehicle (UAV) to ensurethe freshness of sensing data, as measured by the age of information (AoI), in Internet of Things (IoT) networks. Specifically, the UAV switches between flying and hovering modes to collect data from widely distributed IoT devices. UAV trajectory planning, which determines the times and moments of data collection, is vital for optimizing the system AoI. Considering the limited UAV onboard energy and mission duration, AoI-optimal trajectory planning is formulated as a mixed-integer nonlinear programming (MINLP) problem. We first decompose the MINLP problem into two subproblems: a UAV time scheduling subproblem and a UAV path planning subproblem. Then, we propose a learning-based iterative (LBI) algorithm that consists of two modules: a successive convex approximation (SCA)-based module for solving the time scheduling subproblem, and a hierarchical asynchronous advantage actor-critic (A3C) module for addressing the path planning subproblem. The numerical results verify that the proposed LBI algorithm outperforms typical baselines in terms of the AoI performance.

This paper investigates an unmanned aerial vehicle (UAV) assisted semantic network where the ground users (GUs) periodically capture and upload the sensing information to a base station (BS) via UAVs’ relaying. Both the GUs and the UAVs can extract semantic information from large-size raw data and transmit it to the BS for recovery. Smaller-size semantic information reduces latency and improves information freshness, while larger-size semantic information enables more accurate data reconstruction at the BS, preserving the value of original information. We introduce a novel semantic-aware age-of-information (SAoI) metric to capture both information freshness and semantic importance, and then formulate a time-averaged SAoI minimization problem by jointly optimizing the UAV-GU association, the semantic extraction, and the UAVs’ trajectories. We decouple the original problem into a series of subproblems via the Lyapunov framework and then use hierarchical deep reinforcement learning (DRL) to solve each subproblem. Specifically, the UAV-GU association is determined by DRL, followed by the optimization module updating the semantic extraction strategy and UAVs’ deployment. Simulation results show that the hierarchical structure improves learning efficiency. Moreover, it achieves low AoI through semantic extraction while ensuring minimal loss of original information, outperforming the existing baselines.

In this paper, we consider an aerial reconfigurable intelligent surface (ARIS)-assisted wireless network, where multiple unmanned aerial vehicles (UAVs) collect data from ground users (GUs) by using the non-orthogonal multiple access (NOMA) method. The ARIS provides enhanced channel controllability to improve the NOMA transmissions and reduce the co-channel interference among UAVs. We also propose a novel dual-mode switching scheme, where each UAV equipped with both an ARIS and a radio frequency (RF) transceiver can adaptively perform passive reflection or active transmission. We aim to maximize the overall network throughput by jointly optimizing the UAVs’ trajectory planning and operating modes, the ARIS’s passive beamforming, and the GUs’ transmission control strategies. We propose an optimization-driven hierarchical deep reinforcement learning (O-HDRL) method to decompose it into a series of subproblems. Specifically, the multi-agent deep deterministic policy gradient (MADDPG) adjusts the UAVs’ trajectory planning and mode switching strategies, while the passive beamforming and transmission control strategies are tackled by the optimization methods. Numerical results reveal that the O-HDRL efficiently improves the learning stability and reward performance compared to the benchmark methods. Meanwhile, the dual-mode switching scheme is verified to achieve a higher throughput performance compared to the fixed ARIS scheme.

In mobile edge computing systems, base station (BS) equipped with edge servers can provide computing services to users to reduce their task durations. The BS prices the service programs based on user demand to maximize its own profits. Additionally, due to limited caching capacity and variations in service programs popularity, the BS has to dynamically select which service programs to cache. To address the conflict between high profits requirement and system instability, we propose a two time-scale framework to optimize service caching, pricing and task offloading. Under the small time scale, by modeling the interaction between the BS and users as a two-stage game, we derive the optimal offloading strategy and pricing algorithm. Then, we deduce the existence of equilibrium points. Under the large time scale, we propose a game-nested deep reinforcement learning algorithm to dynamically adjust the service caching according to estimated popularity information. Extensive data based simulations demonstrate the efficiency of the proposed approach.

LoRa is considered one of the most promising low-power wide-area techniques. Given that end devices (EDs) are typically battery-powered, energy efficiency (EE) is a critical factor to consider. In this paper, we aim to improve the system EE of the LoRa network by jointly allocating transmission parameters such as the channel (CH), transmission power (TP) and spreading factor (SF) for each ED. Owing to the low duty cycle and sporadic traffic of LoRa networks, evaluating the system EE under various parameter settings proves to be time-consuming. Consequently, we propose an analytical model aimed at calculating the system EE while fully considering the impact of multiple gateways, quasi-orthogonal SFs and capture effects. On this basis, we investigate a joint CH, SF and TP allocation problem to optimize the system EE for uplink transmissions. Given the NP-hard complexity of the problem, the original problem is decomposed into two subproblems: CH assignment and SF/TP assignment. First, a matching-based algorithm is introduced to tackle the CH assignment subproblem. Then, an attention-based multiagent reinforcement learning technique is employed to address the SF/TP assignment subproblem for EDs allocated to the same CH. The simulation outcomes indicate that the proposed approach converges quickly and obtains significantly better system EE than baseline algorithms.

Physical layer security issues have attracted significant attention in wireless networks to protect information leakage from illegitimate eavesdroppers. In this paper, we focus on an intelligent reflecting surface (IRS)-assisted secure non-orthogonal multiple access (NOMA) uplink system. Multiple users intend to transmit sensitive data to an access point (AP) considering the existence of a nearby eavesdropper (Eve). The IRS can be used to enhance the NOMA users’ sum rates while concurrently weakening the Eve’s channel condition, suppressing information leakage to the Eve without resorting to a cooperative jammer or the power injection of artificial noise in the system. The users’ scheduling, the IRS’s passive beamforming, and the AP’s receive beamforming are jointly optimized to maximize the secure rate of the IRS-assisted NOMA system. We develop a hierarchical deep reinforcement learning (DRL) framework to iteratively search for an optimal solution considering the combinatorial nature of the NOMA users’ scheduling and the high-dimensional beamforming design. Firstly, we search for the NOMA users’ scheduling strategy by using the PPO-based DRL algorithm. Given the NOMA scheduling strategy, we then optimize the active and passive beamforming strategies by the alternating optimization (AO) algorithm. The inner optimization helps evaluate the quality of the scheduling strategy and thus guides the outer-PPO algorithm to update a better scheduling strategy. Simulation results demonstrate the superiority of the proposed scheme over existing benchmarks, resulting in significant gains in secure rate.

Mobile crowdsensing (MCS) has emerged as a prominent trend across various domains. However, ensuring the quality of the sensing data submitted by mobile users (MUs) remains a complex and challenging problem. To address this challenge, an advanced method is required to detect low-quality sensing data and identify malicious MUs that may disrupt the normal operations of an MCS system. Therefore, this article proposes a prediction- and reputation-based truth discovery (PRBTD) framework, which can separate low-quality data from high-quality data in sensing tasks. First, we apply a correlation-focused spatio-temporal transformer network that learns from the historical sensing data to predict the ground truth of the data submitted by MUs. However, due to the noise in historical data for training and the bursty values within sensing data, the prediction results can be inaccurate. To address this, we utilize the implications among sensing data, which are extracted from the prediction results but are stable and less affected by inaccurate predictions, to evaluate the quality of data. Finally, we design a reputation-based truth discovery (TD) module for identifying low-quality data with their implications. Given sensing data submitted by MUs, PRBTD can eliminate the data with heavy noise and identify malicious MUs with high accuracy. Extensive experimental results demonstrate that PRBTD outperforms the existing methods in terms of identification accuracy and data quality enhancement.

In the rapidly evolving field of Internet of Vehicles technology, Cellular Vehicle-to-Everything (C-V2X) communication has garnered considerable attention due to its superior performance in terms of coverage, latency, and throughput. Efficient resource allocation in C-V2X networks is crucial for ensuring the transmission of safety-critical information in Vehicle-to-Vehicle (V2V) communication. In this paper, we propose Dynamic-aware Graph-based Proximal Policy Optimization (DGPPO), a novel approach that integrates Graph Neural Networks (GNNs) with Deep Reinforcement Learning to effectively tackle the challenges of joint spectrum and power allocation in C-V2X networks. The proposed approach constructs a dynamic graph representation where communication links are modeled as nodes, allowing for flexible modeling of network topology changes. Next, we utilize an adaptive GNN to extract essential low-dimensional global features from the dynamically evolving graph network topology, which is constructed based on local observations. Finally, we propose a multi-agent proximal policy optimization (MAPPO) approach that leverages these extracted features to promote cooperative behavior and adaptability among agents. The simulation results validate the effectiveness of the DGPPO algorithm, outperforming the state-of-the-art algorithms in highly dynamic vehicular network environments.

In this paper, we aim to improve the throughput capacity of a wireless powered network by allowing user devices (UDs) to adapt channel access strategies. A base station (BS) can receive data and provide RF energy for UDs simultaneously in a full duplex mode. Each UD can choose a flexible access mode to transmit its data with the non-orthogonal multiple access technique or assist the other UDs' data transmissions via backscattering when it has less urgent data demands or insufficient energy supply. We maximize the sum throughput by optimizing the UDs' channel access mode, time allocation and beamforming strategies, according to the UDs' channel conditions and energy status. Practically, maximizing throughput is a challenging problem due to the uncertain channel information, the UDs' dynamic traffic demands and limited energy storages. Therefore, we propose a hierarchical learning approach to decompose the access mode selection and transmission control in two steps. We first employ a multi-agent deep reinforcement learning approach to update each UD's channel access strategy by interacting with the uncertain network environment. Then, we efficiently update the UDs' time allocation and the BS's beamforming strategies to further enhance the sum throughput. The simulation results verify a significant improvement in terms of the throughput and the learning efficiency compared to the benchmark methods.

This paper exploits multiple unmanned aerial vehicles (UAVs) to assist energy transfer, data uploading, and transmission in wireless networks, aiming to maximize the network's energy efficiency (EE). The inherent challenge of inaccessible or energy-intensive real-time information exchanges among UAVs results in undesirable delays in acquiring global network information. Such delayed information significantly hinders the transmission control and trajectory planning of the UAV s in multi-UAV-assisted wireless networks. To address this challenge, we propose a delay-tolerant multi-agent deep reinforcement learning (DT-MADRL) algorithm to jointly optimize the UAVs' trajectories and transmission control strategies based on randomly delayed information. In particular, we integrate a delay penalty term in the reward function that forces each UAV to have more regular information exchanges with the base station (BS). This ensures that each UAV can understand the real-time network environment, thereby reducing information delay and fostering more effective multi-agent collaboration. The simulation results reveal that our proposed algorithm reduces the UAVs' average information delay by 68% and improves overall EE by 28% compared to traditional MADRL algorithms.

In this paper, we propose a movable antenna (MA) empowered scheme for symbiotic radio (SR) communication systems. Specifically, multiple antennas at the primary transmitter (PT) can be flexibly moved to favorable locations to boost the channel conditions of the primary and secondary transmissions. The primary transmission is achieved by the active transmission from the PT to the primary user (PU), while the backscatter device (BD) takes a ride over the incident signal from the PT to passively send the secondary signal to the PU. Under this setup, we consider a primary rate maximization problem by jointly optimizing the transmit beamforming and the positions of MAs at the PT under a practical bit error rate constraint on the secondary transmission. Then, an alternating optimization framework with the utilization of the successive convex approximation, semi-definite processing and simulated annealing (SA) modified particle swarm optimization (SA-PSO) methods is proposed to find the solution of the transmit beamforming and MAs' positions. Finally, numerical results are provided to demonstrate the performance improvement provided by the proposed MA empowered scheme and the proposed algorithm.

In this work, we propose the use of energy harvesting and semantic communication for Internet of Things (IoT) systems. The system allows IoT devices to harvest energy from a base station and then uses the harvested energy for extracting and transmitting semantic information, i.e., scene graphs, to the base station. The proposed network thus copes with the energy and network resource constraints of the IoT devices. To maximize the total image data or scene graph transmitted to the base station, we formulate a problem that optimizes the energy harvesting duration, the selection of original image or portions of scene graphs, transmit power, and channel allocation to the IoT devices. Under the high dynamics and uncertainty of the context and size of the collected images as well as the wireless channels and computing resources, we propose two advanced deep reinforcement learning (DRL) algorithms, i.e., advantage Actor-Critic (A2C) and proximal policy optimization (PPO), to solve the problem. Simulation results are implemented on the real dataset clearly showing that the performance achieved by the proposed algorithms is much higher than that achieved by the baseline scheme. This implies that more original images or triplets are transmitted.

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).

In this paper, we consider a multiple unmanned aerial vehicles (UAVs)-assisted wireless sensing network, where low-power ground users (GUs) periodically sense the environmental information and upload the recent sensing information to a base station (BS). The GUs firstly backscatter their information to the UAVs and then the UAVs transmit the information to the BS by the non-orthogonal multiple access (NOMA) transmissions. Our goal is to minimize the long-term age-of-information (AoI) by jointly optimizing the UAV's sensing scheduling, transmission control, and trajectories. To solve this problem, we propose the Lyapunov-driven hierarchical proximal policy optimization framework, named Lya-HPPO, to decouple the multi-stage AoI minimization problem into several control subproblems. In each control subproblem, the UAVs' sensing scheduling and transmission control are firstly determined by the outer-loop deep reinforcement learning (DRL) approach, and then the inner-loop optimization module is to update the UAVs' trajectories. Simulation results verify that the proposed Lya-HPPO framework converges very fast to a stable value and can make online decisions in real time, while guaranteeing the long-term data buffer and AoI stability.

In this paper, 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 the user-centric network in which the users are allowed to locally and dynamically select and switch among the BSs over time to achieve their highest utility. The two types of users have different Quality of Service (QoS) requirements. Thus, we design two types of utility functions specific 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 (PLM). The fractional game allows the eMBB users and URLLC users to incorporate their past strategies into their current selection, thus improving their utility. Simulation results show that the total utility obtained by the users with fractional game is higher than that obtained by the users with classical game. Moreover, the type of URLLC users in the network also affects the total utility obtained by the eMBB users.

In this paper, we employ dual-mode unmanned aerial vehicles (UAVs) equipped with both the active radio frequency (RF) module and aerial reconfigurable intelligent surface (ARIS) to assist ground users (GUs) for both the downlink energy transfer and uplink data transmission in a wireless-powered network. To maximize the GUs' minimum throughput, we propose a collaborative mode switching scheme for the dual-mode UAVs to dynamically switch between the active RF and passive ARIS modes according to the time-varying channel conditions. Besides, we jointly optimize the GUs' transmission control, the UAVs' beamforming, and the trajectory planning strategies. This optimization problem is intractable directly due to the non-convexity in both the objective and constraints. We design an iterative algorithm to first decompose the original problem into several subproblems, and then solve each subproblem individually by approximate optimization methods. Numerical results verify that the UAVs' collaborative mode switching along with their trajectories efficiently improves the transmission performance compared to the benchmarks in which both UAVs are operating in one fixed mode.

Mobile edge computing (MEC) is viewed as a promising technology to address the challenges of intensive computing demands in hotspots (HSs). In this paper, we consider a unmanned aerial vehicle (UAV)-assisted backscattering MEC system. The UAVs can fly from parking aprons to HSs, providing energy to HSs via RF beamforming and collecting data from wireless users in HSs through backscattering. We aim to maximize the long-term utility of all HSs, subject to the stability of the HSs' energy queues. This problem is a joint optimization of the data offloading decision and contract design that should be adaptive to the users' random task demands and the time-varying wireless channel conditions. A deep reinforcement learning based contract incentive (DRLCI) strategy is proposed to solve this problem in two steps. Firstly, we use deep Q-network (DQN) algorithm to update the HSs' offloading decisions according to the changing network environment. Secondly, to motivate the UAVs to participate in resource sharing, a contract specific to each type of UAVs has been designed, utilizing Lagrangian multiplier method to approach the optimal contract. Simulation results show the feasibility and efficiency of the proposed strategy, demonstrating a better performance than the natural DQN and Double-DQN algorithms.

This article introduces a novel lightweight framework using ambient backscattering communications to counter eavesdroppers. In particular, our framework divides an original message into two parts. The first part, i.e., the active-transmit message, is transmitted by the transmitter using conventional RF signals. Simultaneously, the second part, i.e., the backscatter message, is transmitted by an ambient backscatter tag that backscatters upon the active signals emitted by the transmitter. Notably, the backscatter tag does not generate its own signal, making it difficult for an eavesdropper to detect the backscattered signals unless they have prior knowledge of the system. Here, we assume that without decoding/knowing the backscatter message, the eavesdropper is unable to decode the original message. Even in scenarios where the eavesdropper can capture both messages, reconstructing the original message is a complex task without understanding the intricacies of the message-splitting mechanism. A challenge in our proposed framework is to effectively decode the backscattered signals at the receiver, often accomplished using the maximum likelihood (MLK) approach. However, such a method may require a complex mathematical model together with perfect channel state information (CSI). To address this issue, we develop a novel deep meta-learning-based signal detector that can not only effectively decode the weak backscattered signals without requiring perfect CSI but also quickly adapt to a new wireless environment with very little knowledge. Simulation results show that our proposed learning approach, without requiring perfect CSI and complex mathematical model, can achieve a bit error ratio close to that of the MLK-based approach. They also clearly show the efficiency of the proposed approach in dealing with eavesdropping attacks and the lack of training data for deep learning models in practical scenarios.

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.

Long Range (LoRa) wireless technology, characterized by low power consumption and a long communication range, is regarded as one of the enabling technologies for the Industrial Internet of Things (IIoT). However, as the network scale increases, the energy efficiency (EE) of LoRa networks decreases sharply due to severe packet collisions. To address this issue, it is essential to appropriately assign transmission parameters such as the spreading factor and transmission power for each end device (ED). However, due to the sporadic traffic and low duty cycle of LoRa networks, evaluating the system EE performance under different parameter settings is time-consuming. Therefore, we first formulate an analytical model to calculate the system EE. On this basis, we propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) with the aim of maximizing the system EE of LoRa networks. Notably, MALoRa employs an attention mechanism to guide each ED to better learn how much “attention” should be given to the parameter assignments for relevant EDs when seeking to improve the system EE. Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms with an acceptable degradation in packet delivery rate (PDR).

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

Federated learning (FL) can empower Internet-of-Vehicles (IoV) networks by leveraging smart vehicles (SVs) to participate in the learning process with minimum data exchanges and privacy disclosure. The collected data and learned knowledge can help the vehicular service provider (VSP) improve the global model accuracy, e.g., for road safety as well as better profits for both VSP and participating SVs. Nonetheless, there exist major challenges when implementing the FL in IoV networks, such as dynamic activities and diverse quality-of-information (QoI) from a large number of SVs, VSP's limited payment budget, and profit competition among SVs. In this paper, we propose a novel dynamic FL-based economic framework for an IoV network to address these challenges. Specifically, the VSP first implements an SV selection method to determine a set of the best SVs for the FL process according to the significance of their current locations and information history at each learning round. Then, each selected SV can collect on-road information and propose a payment contract to the VSP based on its collected QoI. For that, we develop a multi-principal one-agent contract-based policy to maximize the profits of the VSP and learning SVs under the VSP's limited payment budget and asymmetric information between the VSP and SVs. Through experimental results using real-world on-road datasets, we show that our framework can converge 57% faster (even with only 10% of active SVs in the network) and obtain much higher social welfare of the network (up to 27.2 times) compared with those of other baseline FL methods.

In this paper, we focus on a wireless-powered sensor network coordinated by a multi-antenna access point (AP). Each node can generate sensing information and report the latest information to the AP using the energy harvested from the AP’s signal beamforming. We aim to minimize the average age-of-information (AoI) by adapting the nodes’ scheduling and the transmission control strategies jointly. To reduce the transmission delay, an intelligent reflecting surface (IRS) is used to enhance the channel conditions by controlling the AP’s beamforming strategy and the IRS’s phase shifting matrix. Considering dynamic data arrivals at different sensing nodes, we propose a hierarchical deep reinforcement learning (DRL) framework for AoI minimization in two steps. The users’ transmission scheduling is firstly determined by the outer-loop DRL approach, e.g. the DQN or PPO algorithm, and then the inner-loop optimization is used to adapt either the uplink information transmission or downlink energy transfer to all nodes. A simple and efficient approximation is also proposed to reduce the inner-loop rum time overhead. Numerical results verify that the hierarchical learning framework outperforms typical baselines in terms of the average AoI and proportional fairness among different nodes.

With the accelerated popularization of 5G applications, accurate cellular traffic prediction is becoming increasingly important for efficient network management. Currently, the latest algorithms for cellular traffic prediction generally neglect extraction of the shallow features of cellular traffic and the prediction accuracy is hence limited. Therefore, we propose a global-local spatial-temporal transformer network (GLSTTN) that can fully excavate diverse spatial-temporal characteristics of cellular traffic for accurate cellular traffic prediction. Specifically, GLSTTN achieves this goal by constructing two modules: the global spatial-temporal module and the local spatial-temporal module. In the global spatial-temporal module, GLSTTN captures global correlations using stacked spatial-temporal blocks, where each block is composed of one spatial transformer and one temporal transformer. A skip connection is then used in each block to strengthen feature propagation. In the local spatial-temporal module, GLSTTN fully extracts the local spatial-temporal dependencies hidden in globally encoded features using densely connected convolutional neural networks. Extensive experiments demonstrate that GLSTTN achieves more accurate cellular traffic prediction than existing approaches on a real-world cellular traffic dataset.

In this paper, we propose a robust secure transmission scheme for an active reconfigurable intelligent surface (RIS) enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). In the considered system, the active RIS is adopted to enable the secure transmission of primary signals from the primary transmitter to multiple primary users in a multicasting manner, and simultaneously achieve its own information delivery to the secondary user by riding over the primary signals. Taking into account the imperfect channel state information (CSI) related with Eves, we formulate the system power consumption minimization problem by optimizing the transmit beamforming and reflection beamforming for the bounded and statistical CSI error models, taking the worst-case SNR constraints and the SNR outage probability constraints at the Eves into considerations, respectively. Specifically, the S-Procedure and the Bernstein-Type Inequality are implemented to approximately transform the worst-case SNR and the SNR outage probability constraints into tractable forms, respectively. After that, the formulated problems can be solved by the proposed alternating optimization (AO) algorithm with the semi-definite relaxation and sequential rank-one constraint relaxation techniques. Numerical results show that the proposed active RIS scheme can reduce up to 27.0% system power consumption compared to the passive RIS.

In this paper, we consider a mobile edge computing (MEC) system with multiple unmanned aerial vehicles (UAVs) and stochastic energy harvesting. The UAVs' mobility can help data offloading over a larger geographical area containing multi- hotspots (HSs). If HSs have offloading requests, the dispatch agent (DA) can recruit different types of UAVs to fly close to HSs and help computation. We aim to maximize the long-term utility of all HSs, subject to the stability of energy queue. The proposed problem is a joint optimization problem of offloading strategy and contract design in a dynamic setting over time. We design a deep reinforcement learning based contract incentive (DRLCI) strategy that solves the joint optimization problem in two steps. Firstly, we use an improved deep Q-network (DQN) algorithm to obtain the offloading decision. Secondly, to motivate UAVs to participate in resources sharing, a contract has been designed for asymmetric information scenarios, and Lagrangian multiplier method has been utilized to approach the optimal contract. Simulation results show the feasibility and efficiency of the proposed strategy. It can achieve a very close-to the performance obtained by complete information scenario.

In this paper, we employ multiple unmanned aerial vehicles (UAVs) to assist sensing data transmission from the ground users (GUs) to the remote base station (BS). Each UAV can first cache the sensing data and then report the cached data to the BS. We consider a time-slotted protocol to coordinate the UAVs' data collection and reporting. Only one UAV is allowed to forward its data to the BS in each time slot. We formulate a multi-stage stochastic optimization problem to minimize the longterm age-of-information (AoI) by jointly optimizing the UAVs' trajectories and scheduling strategies. To simplify this problem, we model the dynamics of the UAVs' data buffer and AoI statuses by queueing systems, and propose a novel AoI-aware Adaptation scheme. This scheme allows us to transform the multistage dynamic programming problem into per-slot scheduling and trajectory planning sub-problems by using the Lyapunov optimization framework. Then, in each time slot, we can update the UAVs' scheduling and flying strategies in an iterative manner according to the instant buffer and AoI statuses. Simulation results show that the proposed scheme outperforms baseline schemes in terms of reducing AoI while stabilizing and balancing the UAVs' data queues.

Passive traffic monitoring can be used for network diagnosis and management in wireless networks by deploying wireless sniffers to monitor abnormal data traffic on different channels and locations. This motivates the spatial sniffer-channel assignment (SSCA) problem, i.e., assigning each wireless sniffer a proper operating channel and location to detect the target signals or data packets. The existing approaches for SSCA problems are usually designed for the scenarios where the behavior features of the target users are known. In this paper, we focus on a cognitive monitoring system without information about the users' activities. The wireless sniffers can be deployed to patrol different locations and meet a desirable detection probability requirement. Considering a dynamic network environment with a huge state space, we propose a novel deep reinforcement learning (DRL) approach to adapt the patrolling route for each wireless sniffer. Moreover, we employ Bayesian optimization to help explore the action space and thus improve the learning efficiency. Via numerical simulations, we show that the Bayesian optimization enhanced DRL approach can improve the detection performance and fast adapt the wireless sniffers' actions according to the environmental changes.

In this paper, we consider an IRS-assisted and wireless-powered mobile edge computing (MEC) system that allows both edge users and the IRS to harvest energy from the hybrid access point (HAP), co-located with the MEC server. Each edge user uses the harvested energy to offload its data to the MEC server. The IRS not only assists downlink energy transfer to the edge users, but also improves the users' uplink offloading rates. To minimize the overall energy consumption, we jointly optimize the users' offloading decisions, the HAP's active beamforming, as well as the IRS's energy harvesting and passive beamforming strategies. The energy minimization problem is intractable due to complicated couplings in both the objective function and constraints. We decompose this problem into the downlink energy transfer and the uplink data offloading phases. The uplink phase can be efficiently optimized by the conventional semi-definite relaxation (SDR) method, while the downlink phase depends on the alternating optimization between the users' offloading decisions and the joint active and passive beamforming strategies. Numerical results demonstrate that the proposed offloading scheme can significantly reduce the HAP's energy consumption compared with typical benchmarks.

In this paper, we propose a reconfigurable intelligent surface (RIS) assisted secure transmission scheme for a symbiotic radio multicast system, where the RIS not only assists the confidential information multicasting from a primary transmitter (PT) to multiple primary users (PUs) to against the information interception by eavesdroppers, but also delivers its own signal to a secondary user (SU) by passive reflections. We formulate a signal-to-noise ratio (SNR) maximization problem for the SU by jointly optimizing the active beamforming at the PT, amplitude reflection coefficients and phase shifts of the RIS. To address the non-convexity of the formulated problem, we propose to decompose the original problem into two sub-problems and solve them independently in an iteratively alternating manner. For the first sub-problem, we adopt the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques to design the active beamforming by proving the tightness of SDR. For the second sub-problem, the sequential rank-one constraint relaxation (SROCR) technique is adopted to handle the rank-one constraint for reflection coefficients optimization. Numerical results show that compared to the benchmark schemes, the proposed scheme can achieve up to 68.3% performance gain in terms of SNR.

In this paper, we consider a hybrid computation offloading scheme that allows edge users to offload workloads to the edge servers by using active RF communications and backscatter communications. We aim to maximize the overall energy efficiency by jointly optimizing the beamforming of access point (AP) and the users’ offloading decisions. Considering a dynamic environment, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework to solve this problem. The high-level agent resides in the AP and optimizes the beamforming strategy, while the low-level user agents learn and adapt individuals’ offloading strategies. To further improve the learning efficiency, we propose a novel optimization-driven learning algorithm that allows the AP to estimate the low-level users’ actions by solving an approximate problem efficiently. Then, the action estimation can be shared with all users and drive them to update individuals’ actions independently. Simulation results reveal that our algorithm can improve the reward performance by 50%. The learning efficiency and reliability are also enhanced comparing to the conventional model-free learning methods.

In this paper, we focus on a multi-user wireless network coordinated by a multi-antenna access point (AP). Each user can generate the sensing information randomly and report it to the AP. The freshness of information is measured by the age of information (AoI). We formulate the AoI minimization problem by jointly optimizing the users’ scheduling and transmission control strategies. Moreover, we employ the intelligent reflecting surface (IRS) to enhance the channel conditions and thus reduce the transmission delay by controlling the AP’s beamforming vector and the IRS’s phase shifting matrices. The resulting AoI minimization becomes a mixed-integer program and difficult to solve due to uncertain information of the sensing data arrivals at individual users. By exploiting the problem structure, we devised a hierarchical deep reinforcement learning (DRL) framework to search for optimal solution in two iterative steps. Specifically, the users’ scheduling strategy is firstly determined by the outer-loop DRL approach, and then the inner-loop optimization adapts either the uplink information transmission or downlink energy transfer to all users. Our numerical results verify that the proposed algorithm can outperform typical baselines in terms of the average AoI performance.

In this paper, we employ multiple wireless-powered relays to assist information transmission from a multi-antenna access point to a single-antenna receiver. The wireless relays can operate in either the passive mode via backscatter communications or the active mode via RF communications, depending on their channel conditions and energy states. We aim to maximize the overall throughput by jointly optimizing the transmit beamforming and the relays’ radio modes and operating parameters. Due to the non-convex and combinatorial problem structure, we develop a novel optimization-driven hierarchical deep deterministic policy gradient (H-DDPG) approach to adapt the beamforming and relay strategies. The optimization-driven H-DDPG algorithm firstly decomposes the binary relay mode selection into the outer-loop deep$Q$-network (DQN) algorithm and then optimizes the continuous beamforming and relaying strategies by using the inner-loop DDPG algorithm. Secondly, to improve the learning efficiency, we integrate the model-based optimization into the inner-loop DDPG framework by providing a better-informed target estimation for DNN training. Simulation results reveal that these two special designs ensure a more stable learning performance and achieve a higher reward, up to 20%, compared to the conventional model-free DDPG approach.

Symbiotic radio (SR) has recently emerged as a promising technology to boost spectrum efficiency of wireless communications by allowing reflective communications underlying the active RF communications. In this paper, we leverage SR to boost physical layer security by using an array of passive reflecting elements constituting the intelligent reflecting surface (IRS), which is reconfigurable to induce diverse RF radiation patterns. In particular, by switching the IRS’s phase shifting matrices, we can proactively create dynamic channel conditions, which can be exploited by the transceivers to extract common channel features and thus used to generate secret keys for encrypted data transmissions. As such, we firstly present the design principles for IRS-assisted key generation and verify a performance improvement in terms of the secret key generation rate (KGR). Our analysis reveals that the IRS’s random phase shifting may result in a non-uniform channel distribution that limits the KGR. Therefore, to maximize the KGR, we propose both a heuristic scheme and deep reinforcement learning (DRL) to control the switching of the IRS’s phase shifting matrices. Simulation results show that the DRL approach for IRS-assisted key generation can significantly improve the KGR.

In this paper, we consider IRS-assisted transmissions from a multi-antenna access point (AP) to a receiver with uncertain channel information. By adjusting the magnitude of reflecting coefficients, the IRS can sustain its operations by harvesting energy from the AP's signal beamforming. Considering channel estimation errors, we model both the AP-IRS channel and the AP-IRS-receiver as a cascaded channel by norm-based uncertainty sets. This allows us to formulate a robust optimization problem to minimize the AP's transmit power, subject to the receiver's worst-case data rate requirement and the IRS's worst-case power budget constraint. Instead of using the alternating optimization (AO) method, we firstly propose a heuristic scheme to decompose the IRS's phase shift optimization and the AP's active beamforming. Based on semidefinite relaxations of the worst-case constraints, we further devise an iterative algorithm to optimize the AP's transmit beamforming and the magnitude of the IRS's reflecting coefficients efficiently by solving a set of semidefinite programs. Simulation results reveal that the AP requires a higher transmit power to deal with the channel uncertainty. Moreover, the negative effect of channel uncertainty can be alleviated by using a larger-size IRS.

This paper proposes a hybrid-relaying scheme empowered by a self-sustainable intelligent reflecting surface (IRS) in a wireless powered communication network (WPCN), to simultaneously improve the performance of downlink energy transfer (ET) from a hybrid access point (HAP) to multiple users and uplink information transmission (IT) from users to the HAP. We propose time-switching (TS) and power-splitting (PS) schemes for the IRS, where the IRS can harvest energy from the HAP's signals by switching between energy harvesting and signal reflection in the TS scheme or adjusting its reflection amplitude in the PS scheme. For both the TS and PS schemes, we formulate the sum-rate maximization problems by jointly optimizing the IRS's phase shifts for both ET and IT and network resource allocation. To address each problem's non-convexity, we propose a two-step algorithm to obtain the near-optimal solution with high accuracy. To show the structure of resource allocation, we also investigate the optimal solutions for the schemes with random phase shifts. Through numerical results, we show that our proposed schemes can achieve significant system sum-rate gain compared to the baseline scheme without IRS.

Nowadays, the booming demand of big data analytics and the constraints of computational ability and network bandwidth have made it difficult for a stand-alone agent/service provider to provide suitable information for every user from the large volume online data within the limited time. To handle this challenge, a recommender system (RS) can call in a group of agents to collaborate to learn users' preference and taste, which is known as a distributed recommender system (DRS). DRSs can improve the accuracy of a traditional RS by requesting agents to share information with each other. However, it is challenging for DRSs to make personalized recommendations for each user due to the large amount of candidates. In addition, information sharing among agents raises a privacy concern. Thus, we propose a privacy-preserving DRS in this paper, and then model each service provider as a distributed online learner with context-awareness. Service providers collaborate to make personalized recommendations by learning users' preferences according to the user context and users' history behaviors. We adopt the federated learning framework to help train a high quality privacy- preserving centralized model over a large number of distributed agents which is probably unreliable with relatively slow network connections. To handle big data scenario, we build an item-cluster tree to deal with online and increasing datasets from top to the bottom. We further consider the structure of social network and present an efficient algorithm to avoid more performance loss adaptively. Theoretical proofs show that our proposed algorithm can achieve sublinear regret and differential privacy protection simultaneously for service providers and users. Numerical results confirm that our novel framework can handle increasing big datasets and strike a trade-off between privacy-preserving level and the prediction accuracy.

Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. In this paper, we minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. Due to uncertain channel conditions, we formulate a robust power minimization problem subject to the receiver's signal-to-noise ratio (SNR) requirement and the IRS's power budget constraint. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences. To improve the learning performance, we derive a convex approximation as a lower bound on the robust problem, which is integrated with the DRL framework and thus promoting a novel optimization-driven deep deterministic policy gradient (DDPG) approach. In particular, when the DDPG algorithm generates a part of the action (e.g., passive beamforming), we can use the model-based convex approximation to optimize the other part of the action (e.g., active beamforming) efficiently. Our simulation results demonstrate that the optimization-driven DDPG algorithm can improve both the learning rate and reward significantly compared to the conventional DDPG algorithm.

In this paper, we employ multiple wireless-powered user devices as wireless relays to assist information transmission from a multi-antenna access point to a single-antenna receiver. To improve energy efficiency, we design a hybrid relaying communication strategy in which wireless relays are allowed to operate in either the passive mode via backscatter communications or the active mode via RF communications, depending on their channel conditions and energy states. We aim to maximize the overall SNR by jointly optimizing the access point's beamforming strategy as well as individual relays' radio modes and operating parameters. Due to the non-convex and combinatorial structure of the SNR maximization problem, we develop a deep reinforcement learning approach that adapts the beamforming and relaying strategies dynamically. In particular, we propose a novel optimization-driven hierarchical deep deterministic policy gradient (H-DDPG) approach that integrates the model-based optimization into the framework of conventional DDPG approach. It decomposes the discrete relay mode selection into the outer-loop by using deep Q-network (DQN) algorithm and then optimizes the continuous beamforming and relays' operating parameters by using the inner-loop DDPG algorithm. Simulation results reveal that the H-DDPG is robust to the hyper parameters and can speed up the learning process compared to the conventional DDPG approach.

In this article, we employ multiple energy harvesting relays to assist information transmission from a multiantenna hybrid access point (HAP) to a receiver. All the relays are wirelessly powered by the HAP in the power-splitting (PS) protocol. We introduce the novel concept of hybrid relay communications, which allows each relay to switch between two radio modes, i.e., the active RF communications and the passive backscatter communications, according to its channel and energy conditions. We aim to jointly optimize the HAP's beamforming, individual relays' radio modes, PS ratios, and the relays' collaborative beamforming strategies to enhance the throughput performance at the receiver. The resulting formulation becomes a combinatorial and nonconvex problem. We first propose a convex approximation to the original problem, which serves as a lower bound of the relay performance. Then, we design an iterative algorithm that decomposes the binary relay mode optimization from the other operating parameters. In the inner loop of the algorithm, we exploit the structural properties to optimize the relay performance with the fixed relay mode by using alternating optimization. In the outer loop, different performance metrics are derived to guide the search for a set of passive relays to further improve the relay performance. The simulation results verify that the hybrid relaying communications can achieve 20% performance improvement compared to the conventional relay communications with all active relays.

Mobile edge computing (MEC) is an emerging technology to support resource-intensive yet delay-sensitive applications using small cloud-computing platforms deployed at the mobile network edges. However, the existing MEC techniques are not applicable to the situation where the number of mobile users increases explosively or the network facilities are sparely distributed. In view of this insufficiency, unmanned aerial vehicles (UAVs) have been employed to improve the connectivity of ground Internet of Things (IoT) devices due to their high altitude. This article proposes an innovative UAV-enabled MEC system involving the interactions among IoT devices, UAV, and edge clouds (ECs). The system deploys and operates a UAV properly to facilitate the MEC service provisioning to a set of IoT devices in regions where the existing ECs cannot be accessible to IoT devices due to terrestrial signal blockage or shadowing. The UAV and ECs in the system collaboratively provide MEC services to the IoT devices. For optimal service provisioning in this system, we formulate an optimization problem aiming at minimizing the weighted sum of the service delay of all IoT devices and UAV energy consumption by jointly optimizing UAV position, communication and computing resource allocation, and task splitting decisions. However, the resulting optimization problem is highly nonconvex and thus, difficult to solve optimally. To tackle this problem, we develop an efficient algorithm based on the successive convex approximation to obtain suboptimal solutions. Numerical experiments demonstrate that our proposed collaborative UAV-EC offloading scheme largely outperforms baseline schemes that solely rely on UAV or ECs for MEC in IoT.

In this paper, we consider a smart jammer that only attacks the channel if it detects activities of legitimate devices on that channel. To cope with smart jamming attacks, we propose an intelligent deception strategy in which the legitimate device will send fake transmissions to lure the jammer. Then, if the jammer launches attacks to the channel, the legitimate device can either backscatter the jamming signals to transmit data or harvest energy from the jamming signals for future active transmission. In this way, we can not only undermine the attack ability of the jammer, but also leverage jamming attacks as means to enhance system performance. In addition, to find an optimal defense strategy for the legitimate device under uncertainty of wireless environment as well as incomplete information from the jammer, we develop Q-learning and deep Q-learning algorithms based on the Markov decision process. Through simulation results, we demonstrate that our proposed solution is able to not only deal with smart jamming attacks, but also successfully leverage jamming attacks to improve the system performance.

Vehicular Ad-Hoc Networks (VANETs) have received a great attention recently due to their potential and various applications. However, the initial phase of the VANET has many research challenges that need to be addressed, such as the issues of security and privacy protection caused by the openness of wireless communication networks among the city-wide applied regions. Specially, anomaly detection for a VANET has become a challenging problem, due to the changes in the scenario of VANETs comparing with traditional wireless networks. Motivated by this issue, we focus on the problem of anomaly detection in VANETs, and propose an effective anomaly detection approach based on the convolutional neural network in this paper. The proposed approach takes into account the spatio-temporal and sparse features of VANET traffic, and it uses a convolutional neural network architecture and a loss function based on Mahalanobis distance for anomaly detection. Furthermore, a comprehensive assessment is provided to validate the proposed approach, which illustrates the effectiveness of this approach.

In this work, we consider multiple energy harvesting relays to assist information transmission from a hybrid access point (HAP) to a distant receiver. The multi-antenna HAP also beamforms RF power to the relays by using a power-splitting protocol. We aim to maximize the throughput by jointly optimizing the HAP's beamforming strategy as well as individual relays' energy harvesting and collaborative beamforming strategies. With dense user devices, the throughput maximization takes account of the direct links from the HAP to the receiver as they are short and contribute considerably to the overall throughput. Moreover, we introduce the concept of hybrid relaying communications which allows the energy harvesting relays to switch between two radio modes. In particular, the relays can operate either in RF communications or backscatter communications, depending on their channel conditions and energy status. This results in a non-convex and combinatorial throughput maximization problem. With the fixed relay mode, we can find a feasible lower performance bound via convex approximation, which further motivates our algorithm design to update the relay mode in an iterative manner. Simulation results verify that the proposed hybrid relaying strategy can achieve significant performance improvement compared to the conventional relaying strategy with all relays operating in the RF communications mode.

By considering the Simultaneous Wireless Information and Power Transfer (SWIPT) schemes, this paper focuses on secure transmission model design in multiple-input-single-output (MISO) channels. In these channels, the channel state information is assumed to be perfect. Our objective is to maximize the worst-case secrecy rate with respect to both potential eavesdroppers and obvious eavesdroppers under the constraints of energy-harvesting and total transmission power. We present an optimization model to indirectly obtain maximum security rate in a single receiver system. Due to the high computational complexity of the solution process caused by the formulated non-convex optimization problem, we propose a novel indirect method to handle this issue. Then, a Semi-Definite Programming (SDP) relaxation method is used to approach the optimal solution. Moreover, we reveal the conditions for ensuring that the above semi-definite relaxation is compact. Simulation results demonstrate that the gained performance in our system is much better than those of the existing competing schemes.

In this paper, we consider a backscatter-aided hybrid data offloading scheme for a battery-less wireless sensor network. All sensor devices on the edge are coordinated by a hybrid access point (HAP), while also provides power for them via wireless power transfer. Co-located with the HAP, an edge computing server is set up to provide the computation and caching capabilities for the edge devices with insufficient power and computation resources. Each node is allocated a fixed time- slot for data offloading via either the conventional active communications or the passive backscatter communications. Such a hybrid data offloading scheme can flexibly control the trade- off between power consumption and data rate in offloading. We aim to minimize the total energy consumption by optimizing the offloading strategy of each edge device and the HAP's wireless power allocation over different edge devices. We show that the energy minimization problem exhibits a convex reformulation. For practical consideration, we devise a distributed algorithm to solve the problem. The numerical results demonstrate that the distributed algorithm can achieve a near- optimal performance. With a fixed transmit power at the HAP, our proposed hybrid offloading scheme provides a higher offloading throughput compared to the state-of-the-art data offloading schemes.

Mobile crowdsourced edge caching is emerging as a promising caching paradigm by crowdsourcing the storage resources of massive edge devices (EDs) for content caching. The successful technology adoption and commercial deployment rely on a comprehensive understanding of the economic interactions among different network entities involved in such a system. In this paper, we focus on the economic interactions between one content provider (CP) and a large number of EDs, where the CP shares a certain revenue with EDs as the incentive of caching contents, and EDs decide whether to cache contents and share the cached contents with others. We formulate their interactions as a two-stage Stackelberg game. In Stage I, the CP decides the ratio of revenue shared with EDs, aiming at maximizing its own profit. In Stage II, each ED chooses to be an agent who caches contents and shares the cached contents with other EDs, or a requester who does not cache but requests contents from agents. e first analyze the existence and uniqueness of the Stage II subgame equilibrium by using the evolutionary game theory. Then, we identify the piece-wise structure of the CP's profit function, and derive the optimal revenue sharing ratio for the CP in Stage I. Simulation results show that a higher revenue sharing ratio for EDs or a larger serving capacity of EDs can drive more EDs to choose to be agents and meanwhile achieve a higher total welfare for EDs at the equilibrium. Moreover, a larger content price of the CP will lead to a larger welfare loss for EDs.

With the development of e-commerce and smart cities, utilizing unmanned vehicles to deliver packages has emerged as one of the most important methods to make customers receive packages efficiently and effectively. Hence, how to reasonably utilize multiple unmanned vehicles at the same time is a problem. Another main challenging issue is how to satisfy customers' personalized need. In this paper, we propose a novel context-aware multi-armed bandit-based online learning algorithm with active partition method for context space. To solve the massive injecting data flow problem, we utilize a tree-based structure expanding from top to bottom to choose different vehicles, which supports ever-increasing big metering datasets with historical and contextual information. We prove that our proposed context-aware online learning algorithm achieves sublinear regret performance. Experiment results show our proposal can enhance customers' satisfaction and reduce space cost tremendously.

In this paper, we exploit the radio diversity gain in a multi-user hybrid radio network wirelessly powered by a power beacon station (PBS). Each user has a dual-mode radio that can switch between the passive and active modes, according to the channel and energy conditions. This provides extra degree of freedom to improve the overall network performance. As such, we propose a throughput maximization problem by jointly optimizing the PBS' energy beamforming and the radios' transmission scheduling strategies in two modes. We show that the throughput maximization is easily tractable by solving a semi-definite program. However, it becomes non-convex and intractable when we allow radios' cooperation in data transmissions. To this end, we propose a set of heuristic algorithms with different complexities for cooperative relay transmissions, which are shown to significantly improve the sum throughput compared to the non-cooperative case. The simulation results show that a simple adaptive scheme can achieve the maximum throughput according to the PBS' power supply.

In this work, we exploit the signal and energy cooperation in wireless powered multi-user networks. In particular, multiple relays are employed to assist data transmissions from a multi-antenna hybrid access point (HAP) to a distant receiver. The HAP also transfers wireless power to the relays in either a power-splitting (PS) or time-switching (TS) protocol. With dense user deployment, the direct links from the HAP to the receivers are short and can contribute considerably to the overall throughput. To account for the direct links, we propose a throughput maximization problem by jointly optimizing the HAP's beamforming strategy to control the information and power transfer to the relays as well as individual relays' energy harvesting and collaborative beamforming strategies. The main challenge lies in that the direct links require the beamforming design to balance the performances of relay and direct transmissions. Though the throughput maximization problem is non-convex and the globally solution may not be available, we obtain two feasible lower performance bounds corresponding to the PS and TS protocols. Our simulation results also verify that the new design with direct links achieves significant performance improvement compared with the conventional scheme that ignores the direct links.

In this paper, we consider wireless powered Internet of Things (IoT) by a power beacon station (PBS). Each IoT device can be a sensor node that has continuous data transmission using a dual-mode radio, which operates in either active radio frequency (RF) communications or passive backscatter communications. The flexibility in the radio mode switching provides an additional degree of freedom to improve the overall network performance. To exploit the radio's diversity gain, we formulate the sum throughput maximization by jointly optimizing the transmission strategy of each node, the time allocation, and beamforming strategies of the PBS. Besides, capitalizing the fact that two nodes in different modes can complement each other, we propose the passive relaying scheme to exploit the user's cooperation gain that leverages the passive radios to relay for active RF communications. Though the backscatter-aided throughput maximization is nonconvex due to the coupling among different nodes, we design the passive relaying game to balance energy harvesting and relay performance. The simulation results verify that it can significantly enhance the sum throughput of a hybrid radio network, along with the optimal time allocation and beamforming strategies at the PBS.

In this paper, we consider a wireless powered multi-relay network in which a multi-antenna hybrid access point underlaying a cellular system transmits information to distant receivers. Multiple relays capable of energy harvesting are deployed in the network to assist the information transmission. The hybrid access point can wirelessly supply energy to the relays, achieving multi-user gains from signal and energy cooperation. We propose a joint optimization for signal beamforming of the hybrid access point as well as wireless energy harvesting and collaborative beamforming strategies of the relays. The objective is to maximize the network throughput subject to probabilistic interference constraints at the cellular user equipment. We formulate the throughput maximization with both the time-switching and power-splitting schemes, which impose very different couplings between the operating parameters for wireless power and information transfer. Although the optimization problems are inherently non-convex, they share similar structural properties that can be leveraged for an efficient algorithm design. In particular, by exploiting monotonicity in the throughput, we maximize it iteratively via customized polyblock approximation with reduced complexity. The numerical results show that the proposed algorithms can achieve close to optimal performance in terms of the energy efficiency and throughput.

In this paper, we consider a multi-user device-to-device network capable of harvesting energy from radio frequency (RF) signals. We assume that each user has a dual-mode radio architecture and can operate in either conventional active RF communications or passive backscatter communications. In the latter, the data transmission relies on the reflection of ambient RF signals. We capitalize the fact that the two communications technologies can complement each other in a hybrid radio network, by proposing the passive relaying scheme for active RF communications. Specifically, each active RF radio is allocated a fixed time slot for its data transmission. It is also assisted by a set of passive radios via backscattering the RF signals of the active radio. Though passive relaying is energy efficient as it consumes minuscule amount of energy in backscatter communications, it indeed competes the channel time that can be used by the passive radios to harvest RF energy. We propose a game-theoretic approach to balance energy harvesting and relaying performance, by allowing each passive radio to iteratively optimize its reflection coefficients. The simulation results verify that the passive relaying scheme significantly enhances the sum throughput of a hybrid radio network.

Ride-sharing systems enable individual car owners with idle time to provide commercial taxi-like services via an online platform. By crowdsourcing a large population of individual car owners, it can provide more flexible services with a lower serving cost, comparing with the traditional taxi system. Due to the autonomous nature of car owners (drivers), a decentralized driver dispatching algorithm that can achieve a stable (self-motivated) and optimal passenger-driver matching is highly desired for a ride-sharing system. In this paper, we will study such a driver dispatching algorithm systematically. We first show that the optimal passenger-driver matching achieved by the centralized driver dispatching algorithm is often not stable, in the sense that some drivers and passengers may break with their matched partners and form new matching pairs. To this end, we introduce a virtual order fee on each passenger (which the platform will charge the drives who want to serve the passenger) to motivate the behaviors of drivers. Specifically, we propose a novel auction-based decentralized driver dispatching algorithm, where each driver proposes the most profitable passenger that he wants to serve, considering the potential profit that he can achieve and the order fee that he needs to pay from/to serving each passenger. The virtual order fee on a passenger will be gradually increased when multiple drivers want to serve the passenger, until there exists only one driver who is willing to serve. We analytically show that such a decentralized driver dispatching algorithm will converge to an equilibrium (stable) outcome, which achieves the optimal passenger-driver matching (i.e., that maximizes the social income of the whole system). Simulation results further show how the converging speed and the achieved social income change with the system parameters such as the step size of order fee increasement. Moreover, it is easy to implement the proposed distributed algorithm in a practical system.

The integration of wireless power transfer (WPT) with the backscatter communications provides a promising way to sustain batteryless wireless networks. In this paper, we consider a backscatter communication network, in which the passive radio uses the harvested energy from a power beacon station (PBS) to supply its data transmissions, while some other radios can help as the wireless relays. To improve the throughput performance of a distant transceiver pair, we propose a two-hop backscatter relay model and formulate a throughput maximization problem to jointly optimize WPT and the relay strategies. Noting that the proposed problem is non-convex, an iterative algorithm with reduced complexity is proposed to decompose the original problem into a power allocation subproblem in the outer loop and an optimization of the relay strategy in the inner loop. Numerical results reveal that the power allocation converges to the optimum and the relay strategy significantly improves the throughput when the radios' power demand is low.

Integrating ambient backscatter communications into RF-powered cognitive radio networks has been shown to be a promising method for achieving energy and spectrum efficient communications, which is very attractive for low-power or no-power communications. In such scenarios, a secondary user (SU) can operate in either transmission mode or backscatter mode. Specifically, an SU can directly transmit data if sufficient energy has been harvested (i.e., transmission mode). Or an SU can backscatter ambient signals to transmit data (i.e., backscatter mode). In this paper, for investigating the performance of such systems, we apply stochastic geometry to analyze coverage probability and achievable rates for both primary and secondary users considering both communication modes. Analytical tractable expressions are obtained. Extensive simulations are performed and the numerical results show the validity of our analysis. Furthermore, the results indicate that the performance of secondary systems can be improved with the integration of both communication modes with only limited impact on the performance of primary systems.

The integration of wireless power transfer (WPT) with the low-power backscatter communications provides a promising way to sustain battery-less wireless networks. In this paper, we consider a backscatter communication network wirelessly powered by a power beacon station (PBS). Each backscatter radio uses the harvested energy to power its data transmissions, in which some other radios can help as the wireless relays with an aim to improve throughput performance by cooperative transmission. Under this setting, we formulate a throughput maximization problem to jointly optimize WPT and the relay strategy of the backscatter radios. An iterative algorithm with reduced complexity and communication overhead is proposed to decompose the original problem into two sub-problems distributed at the PBS and the backscatter receiver. Moreover, we take uncertain channel information into consideration and formulate robust counter-parts of the throughput maximization problem when either the backscatter or relay channel is subject to estimation errors. The difficulty of the robust counter-part lies in the coupling of the PBS' power allocation and relay strategy in matrix inequalities, which is addressed by alternating optimization with guaranteed convergence. Numerical results reveal that the cooperative relay strategy of the backscatter radios significantly improves the throughput performance.

Integrating ambient backscatter communications into RF‐powered cognitive radio networks has been shown to be a promising method for achieving energy and spectrum efficient communications, which is very attractive for low‐power or no‐power communications. In such scenarios, a secondary user (SU) can operate in either transmission mode or backscatter mode. Specifically, an SU can directly transmit data if sufficient energy has been harvested (i.e., transmission mode). Or an SU can backscatter ambient signals to transmit data (i.e., backscatter mode). In this paper, we investigate the performance of such systems. Specifically, channel inversion power control and an energy store‐and‐reuse mechanism for secondary users are adopted for efficient use of harvested energy. We apply stochastic geometry to analyze coverage probability and achievable rates for both primary and secondary users considering both communication modes. Analytical tractable expressions are obtained. Extensive simulations are performed and the numerical results show the validity of our analysis. Furthermore, the results indicate that the performance of secondary systems can be improved with the integration of both communication modes with only limited impact on the performance of primary systems.

Due to the extremely low power consumption, ambient backscatter communications has attracted great interest from both industry and research communities. However, the short communication range and unpleasant reliability are the two major challenges which prevent ambient backscatter from wide deployment in WSNs. In this work, we propose the design of a robust cooperative routing protocol (RCRP) for ambient backscatter based wireless sensor network (AmB-WSN), to account for the volatility of ambient RF environment. In RCRP, the backscatter sensor nodes (BSNs) work cooperatively to reduce the probability of routing path failure. To ensure robustness, we propose novel routing metrics to construct a robust counter-part for each nominal routing path, which are related to the strength of ambient RF signals and the BSNs' residual energy. Extensive simulations reveal that RCRP can achieve enhanced routing stability, improved throughput performance, and reduced end-to-end delay, which make it preferable and scalable for multi-hop AmB-WSN.

Backscatter communications allows the wireless radio to work in passive mode that transmits information by reflecting incident radio frequency signals. It consumes significantly less power compared to the conventional active radio that modulates information on self-generated carrier signals. However, the active radio is deemed more reliable as it can adapt to the varying channel conditions via transmit power control. In this paper, we aim to maximize the throughput of a multi-user network wirelessly powered by a power beacon station (PBS), assuming that each transceiver can switch between the passive and active radio modes. The joint optimization of the radios' mode selection, the PBS' energy beamforming and time allocation is formulated into a mixed integer nonlinear program (MINLP). Relying on an approximate upper bound of the MINLP, we employ a heuristic mode selection algorithm to determine each user's radio mode under uncertain channel state information. Simulation reveals that passive mode is preferred by the radios with better channel conditions and the active mode will be preferred if we ensure higher system reliability when the channels are subject to uncertainties.

This paper investigates the resource allocation problem in orthogonal frequency division multiple access multi-user networks, where subcarriers are grouped into chunks due to simplicity of implementation. The aim is to achieve max-min fairness among users by adjusting the transmission power allocation and chunk allocation while taking into account several important constrains. The problem is formulated as a mixed integer nonlinear programming problem, whose optimal solution is extremely hard to find. Then a low complexity suboptimal algorithm is proposed, which solves the chunk allocation and power allocation in two steps separately. A fast optimal power allocation algorithm is designed by exploiting the special structure of the problem. Simulations verify the performance of the proposed algorithm in terms of the users' minimal transmission rate and running time comparing with benchmark algorithms.

In this paper, we consider a device-to-device (D2D) network underlying a cellular system wherein the densely deployed D2D user devices can act as wireless relays for a distant transceiver pair. We aim to devise a beamforming strategy for the relays that maximizes the data rate of the distant transceiver while satisfying interference constraints at the cellular receivers. Towards that end, we first formulate a beamforming problem whose solution is robust against the channel uncertainties in the relay-destination hop. Motivated by practical observations, we assume that the random channels in this hop follow unimodal distributions and propose a novel unimodal distributionally robust model to capture the channel uncertainties. Then, we extend the formulation so that it can also guard against the channel uncertainty in the source-relay hop under the worst case robust model. The resulting robust beamforming problem is generally non-convex and intractable. Therefore, we design an iterative algorithm, which is based on solving semidefinite programs, to find an approximate solution to it. Simulation results show that under mild conditions, our robust model significantly improves the throughput of D2D relay transmissions when compared with the conventional robust models that merely rely on the channels' moment information. It also outperforms the Bernstein-type inequality-based convex approximation, which assumes that the channel follows a Gaussian distribution.

Motivated by the observation that energy harvesting (EH) from radio-frequency (RF) signal is subject to fluctuations, multiple EH-enabled relays are employed to collaboratively enhance data communications in a device-to-device (D2D) network underlying a cellular system. Each relay is equipped with a single antenna and unable to harvest energy and transmit data simultaneously. Thus, the D2D user equipment (DUE) needs to optimally schedule the channel time for the relays' EH and data transmissions, which depends on their EH capabilities and channel conditions. Considering that the relays' channel estimations are usually unreliable, we formulate a robust throughput maximization problem to optimize the relays' EH time and transmit power, subject to a probabilistic interference constraint at the cellular user equipment (CUE). We show that the proposed problem, though non-convex, can be tackled by exploiting its monotonicity structure. Specifically, we design a successive approximation algorithm that involves solving a sequence of semi-definite programs (SDPs) and show numerically that it always achieves the global optimum. This validates our analysis and demonstrates the efficacy of the proposed algorithm.

We consider a wireless network with densely deployed user devices (e.g., a device-to-device or wireless sensor network) underlaying a cellular system, in which some user devices act as relays to facilitate data transmissions between a distant transceiver pair under imperfect channel information. Motivated by the observation that most of the channel distributions are unimodal, we formulate a novel distributionally robust beamforming problem, in which the random channel coefficient follows a class of unimodal distribution with known first- and second-order moments. Our design objective is to maximize the worst-case signal-to-noise ratio (SNR) at the dedicated user device while satisfying a probabilistic interference constraint at the cellular user equipment (CUE). Though such a unimodal distributionally robust (UDR) beamforming problem is non-convex, we show that an approximate solution can be computed efficiently using semidefinite programming. Our simulation results show that under mild conditions, the UDR model yields significant beamforming performance improvement over conventional robust models that merely rely on first- and second-order moments of the channel distribution.

In this paper, multiple relays capable of harvesting energy from radio-frequency (RF) signals are employed to collaboratively forward data from a source transmitter to its destined receiver. Due to the relays' inability to harvest energy and transmit data simultaneously, the source needs to optimally schedule the relays' energy harvesting (EH) and data transmission. Considering different channel conditions and energy constraints, the relays need to optimally design a beamforming vector that specifies each relay a power amplifier coefficient to forward the source signal and suppress the noise. By joint EH scheduling and beamforming, we maximize the overall throughput formulated in a nonconvex problem. We first propose a centralized scheme that achieves the optimal throughput by exploiting the monotonicity in the problem structure. We further propose a distributed suboptimal scheme in a game theoretic approach, which requires the source and the relays to iteratively update EH scheduling and beamforming vector, respectively. We show that the suboptimal scheme has a threshold-based structure for the relays' power control depending on the source-relay channel conditions. Numerical results show near-optimal performance of the distributed scheme compared with the centralized optimal scheme.

We consider a cognitive radio network, where primary users (PUs) share their spectrum with energy harvesting (EH) enabled secondary users (SUs), conditioned on a limited SUs' interference at PU receivers. Due to the lack of information exchange between SUs and PUs, the SU-PU interference channels are subject to uncertainty in channel estimation. Besides channel uncertainty, SUs' EH profile is also subject to spatial and temporal variations, which enforce an energy causality constraint on SUs' transmit power control and affect SUs' interference at PU receivers. Considering both the channel and EH uncertainties, we propose a robust design for SUs' power control to maximize SUs' throughput performance. Our robust design targets at the worst-case interference constraint to provide a robust protection for PUs, while guarantees a transmission probability to reflect SUs' minimum QoS requirements. To make the non-convex throughput maximization problem tractable, we develop a convex approximation for each robust constraint and successfully design a successive approximation approach that converges to the global optimum of the throughput objective. Simulations show that SUs will change transmission strategies according to PUs' sensitivity to interference, and we also exploit the impact of SUs' EH profile (e.g., mean, variance, and correlation) on SUs' power control.

In cognitive radio networks, it is challenging for secondary users (SUs) to estimate and control their interference at the receivers of primary users (PUs), due to incomplete or erroneous channel information between SUs and PUs. Thus, SUs need to estimate the worst-case aggregate interference at PU receivers to ensure guaranteed protection for PUs from excessive interference. As it is rare that all SU-PU channels experience the worst-case conditions simultaneously, we propose a practical model (namely, the worst-case selective robust model) for SUs to estimate their aggregate interference power. This model employs an adjustable parameter to control the number of SU-PU channels that are in the worst-case conditions. For an individual SU-PU channel, the estimation of worst-case channel gain is subject to a distribution uncertainty. Given this robust model, we study SUs' power control problem in a non-cooperative game where each SU selfishly maximizes its own throughput performance subject to coupled interference constraints at PU receivers. We study the existence and uniqueness of Nash equilibrium and propose an iterative algorithm for SUs to achieve the equilibrium in a distributed manner. Numerical results show that our algorithm provides guaranteed protection for PUs and fair throughput performance for SUs, provided with uncertain SU-PU channel information.

In cognitive radio networks, it is challenging for secondary users (SUs) to keep track of their interference at the receivers of primary users (PUs), due to the error in channel estimation and irregular information exchange between SUs and PUs. In this paper, we practically consider that SUs have only partial knowledge about the channel gains from SUs to PUs, based on which SUs estimate the worst-case channel gains and decide transmit power to robustly protect PUs. As it is rare that all SU-PU channels experience the worst-case conditions simultaneously, we proposed the worst-case selective robust model for SUs to estimate the aggregate interference power at PU receivers by predicting that only a part of SU-PU channels are in the worst-case conditions. We study SUs' robust power control problem in a non-cooperative game, where each SU maximizes its own throughput subject to interference constraints at PU receivers. We propose an iterative algorithm for SUs to achieve unique Nash equilibrium in a distributed manner. Extensive numerical results show that our algorithm provides guaranteed protection for PUs provided with uncertain SU-PU channel information.

The harmonic coexistence of secondary users (SUs) and primary users (PUs) in cognitive radio networks requires SUs to identify the idle spectrum bands. One common approach to achieve spectrum awareness is through spectrum sensing, which usually assumes known distributions of the received signals. However, due to the nature of wireless channels, such an assumption is often too strong to be realistic, and leads to unreliable detection performance in practical networks. In this paper, we study the sensing performance under distribution uncertainty, i.e., the actual distribution functions of the received signals are subject to ambiguity and not fully known. Firstly, we define a series of uncertainty models based on signals' moment statistics in different spectrum conditions. Then we present mathematical formulations to study the detection performance corresponding to these uncertainty models. Moreover, in order to make use of the distribution information embedded in historical data, we extract a reference distribution from past channel observations, and define a new uncertainty model in terms of it. With this uncertainty model, we propose two iterative procedures to study the false alarm probability and detection probability, respectively. Numerical results show that the detection performance with a reference distribution is less conservative compared with that of the uncertainty models merely based on signal statistics.

In cognitive radio networks, channel information is desired by unlicensed secondary users (SUs) to perform effective power control so as to avoid undue interference to licensed primary users (PUs). However, in general, there is no regular information exchange between PUs and SUs, which implies that SUs are unable to obtain up-to-date channel information at the PU side. Besides, the small-scale fading, in addition to shadowing, brings great uncertainty in SUs' channel estimation. In this paper, we consider limited information exchange between SUs and PUs, and study the impact of channel uncertainty on SUs' throughput performance with power control. We model the uncertain channel gain to be a random variable following a state-dependent probability distribution function, and design a power control method that is robust against the channel uncertainty. We formulate the robust power control problem as a chance constrained robust optimization and solve it by an iterative algorithm. Numerical results show that the proposed power control can provide better protection for PUs than existing methods that overlook the uncertainty in channel measurement.

In cognitive radio networks, it is often impossible to have regular information exchange between PUs and SUs. This implies that SUs are unable to obtain up-to-date channel information at the PU side, and will face technical challenges in accurately controlling their interference to PUs through power control. In this paper, we assume that SUs can estimate the channel information in the reciprocal channel, and study the channel uncertainty due to estimation errors and its impact on SUs' performance and PUs' protection. Specifically, we model the uncertain channel gain to be a random variable following a state-dependent distribution function, and propose a power control mechanism that is robust against the channel uncertainty. We study the robust power control in two cases. In the first case, all SU transmitters (e.g., secondary base stations) transmit with the same power, while in the second case each SU transmitter may choose distinct transmit power based on its own preference. In either case, we formulate the power control problem as a chance constrained robust optimization problem and design an iterative algorithm, respectively. Numerical results show that our robust power control mechanism can provide better protection for PUs than existing methods that overlook the uncertainty in channel measurement, and the second-case power control generally provides better Quality of Service (QoS) for SUs than that in the first case.

The successful coexistence of secondary users (SUs) and primary users (PUs) in cognitive radio networks requires SUs to be spectrum aware and know which spectrum bands are occupied by PUs. Such awareness can be achieved in several ways, one of which is spectrum sensing. While existing spectrum sensing methods usually assume known distributions of the received primary signals, such an assumption is often too strong and unrealistic, and leads to unreliable detection performance in practical networks. In this paper, we design robust spectrum sensing algorithms under the distribution uncertainty of primary signals. After formulating the optimal sensing design as a robust optimization problem, we decompose it into a series of analytically tractable semi-definite programs, and propose an iterative algorithm to search the optimal decision threshold while maintaining the desirable false alarm probability during the iterations. Numerical results verify that our robust sensing algorithm improves the worst-case detection probability and reduces the system sensitivity on decision variables.

In this paper, we present a novel framework for spectrum sharing in cognitive radio networks. The secondary users (SUs) can share the spectrum resource with primary users (PUs) in a cooperative manner, where PUs trade their information and surplus resource, and SUs access the primary spectrum intelligently based on SUs' heterogeneous demands and PUs' resource prices. After paying PUs a subscription fee for the spectrum information, SUs become spectrum-aware and avoid the overhead on spectrum sensing. During SUs' channel access, PUs further charge SUs based on the amount of resource taken by SUs. We model this sharing problem in a flexible time-slotted structure, where SUs' decisions include the selection of proper transmission channel and slot length to meet their demands. This joint decision problem is studied as a spectral temporal allocation game. We prove the existence of a Nash equilibrium and design a strategy update process which can converge to an equilibrium.

The successful coexistence of cognitive radio systems with licensed system requires the secondary users the capability of interference-awareness, i.e., knowing which spectrum bands are occupied by primary users, i.e., the legacy users. Spectrum sensing thus is a key enabling module, which usually models the sensing process as a binary hypothesis testing assuming known signal distribution. However, an unrealistic assumption regarding the signal distribution easily leads to unreliable detection probability. In this paper, we study the sensing performance considering the distribution uncertainty in hypothesis testing, i.e., the actual distribution function of the received signal strength is not known. According to different signal characteristics, we define appropriate uncertainty sets respectively for different hypotheses. Then we present an approximate approach to determine the robust decision threshold, and investigate the performance bounds for the detection probability under distribution uncertainty. Moreover, we provide an analytical expression for the lower bound of detection probability. Numerical results are given to validate our conclusions.

The successful coexistence of cognitive radio systems and licensed systems requires the secondary users to have the capability of sensing and keeping track of primary transmissions. While existing spectrum sensing methods usually assume known distributions of the primary signals, such an assumption is often not true in practice. As a result, applying existing sensing methods directly will often lead to unreliable detection performance in practical networks. In this paper, we try to improve the sensing performance under the distribution uncertainty of primary signals. We formulate the optimal sensing design as a robust optimization problem, and propose an iterative algorithm to determine the optimal decision threshold for each user. Extensive simulations demonstrate the effectiveness of our proposed algorithm.

In a cognitive radio network, the full-spectrum is usually divided into multiple channels. However, due to the hardware and energy constraints, a cognitive user (also called secondary user) may not be able to sense two or more channels simultaneously. As different channels may have different primary user activities and time-varying channel qualities, an important task is to select which channels to sense and access for a given time period so that the available spectrum left by the primary users can be fully utilized by the secondary user. In this paper, we propose an optimal sensing channel selection policy based on partially observable Markov decision process (POMDP). The proposed policy takes the time-varying channel state into consideration and intends to optimally exploit spectrum resources for the secondary user. In addition to selecting optimal channel to sense, we also derive the optimal sensing time which leads to maximized throughput of the secondary user.

Positioning of real world objects (e.g., people) in indoor environment will facilitate location dependent or context-aware applications. Due to severe multi-path fading effect in indoor wireless environment, received signal strength indicator (RSSI) based indoor positioning systems usually require a great amount of human intervention for data measurement during the system initiation. This paper proposes a novel two-phase positioning technique that has been implemented and tested in real environment. Experiment results show that our method can significantly cut down the requirements on data acquisition and achieve satisfactory performance in terms of error distance.

Spectrum sensing is important for cognitive radios to utilize the idle spectrum opportunities, and recently cooperation schemes have been introduced to enhance spectrum sensing in specific areas. However, when a mobile cognitive node roams among heterogenous wireless network, it will be difficult to catch the changes of primary user's behavior, or to setup the cooperation relationship with local network nodes in a short time. In this paper, an self-learning spectrum sensing framework is proposed, which can enable the single mobile cognitive node to work in unknown wireless environment. When the wireless environment changes, the main sensing parameters (such as decision threshold, sampling frequency) could be adapted to optimum in the self- earning process. One adaptive algorithm is proposed to find the optimal decision threshold in energy detection sensing method. Simulation results show that, the proposed scheme could converge to optimal sensing parameters in spatial and temporal varying environment.

Cognitive radio technology is used to improve spectrum efficiency by having the cognitive radios act as secondary users to access primary frequency bands when they are not currently being used. In general conditions, cognitive secondary users are mobile nodes powered by battery and consuming power is one of the most important problem that facing cognitive networks; therefore, the power consumption is considered as a main constraint. In this paper, we study the performance of cognitive radio networks considering the sensing parameters as well as power constraint. The power constraint is integrated into the objective function named power efficiency which is a combination of the main system parameters of the cognitive network. We prove the existence of optimal combination of parameters such that the power efficiency is maximized. Then we reformulate the objective function to incorporate the throughput. According to different constraints or degree of significance, we may put proper weight to each term so that we could obtain more preferable combination of parameters. Computer simulations have given the optimal solution curve for different weights. We can draw the conclusion that if we put more emphasis on power efficiency, the transmit power is a more critical parameter, however if throughput is more important, the effect of sensing time is significant.