Data paucity in IoT attack/anomaly traffic samples inhibits accurate threat identification, limiting defensive modeling capabilities. Existing generative models, such as WGAN-GP and CTGAN, often fail to preserve fine-grained temporal dynamics and multi-dimensional feature correlations in packet-level traffic, leading to low fidelity in synthesized data and poor generalization in downstream tasks. To address these limitations, this paper proposes VWRAM, a novel traffic generation framework combining Variational Autoencoder (VAE) with Wasserstein generative adversarial network (WGAN) and residual attention mechanism. It first utilizes Gramian Angular Summation Field (GASF) to transform one-dimensional time-series network traffic data into two-dimensional images, and then performs gamma correction using a power-law expression to enhance image quality. The framework employs a hybrid architecture combining VAE and WGAN to synthesize network traffic feature maps. To enhance the extraction of channel and spatial characteristics, a bidirectional multi-scale channel fusion residual module and a dual attention mechanism (accounting for both channel and spatial dimensions) are incorporated into the generator of GAN. This design effectively bridges the gap in modeling complex, multi-scale IoT traffic patterns with high structural and statistical fidelity.Finally, theoretical analysis and experimental results on the MedBIoT dataset demonstrate that the proposed framework exhibits significant advantages over representative generative models. Specifically, VWRAM achieves an anomaly detection accuracy of 93%, outperforming state-of-the-art models like SyNIG and CTGAN by 6% and 15%, respectively. Furthermore, it demonstrates superior distributional fidelity, achieving the lowest Jensen-Shannon Divergence of 0.0918 in key traffic features.

In scenarios with extremely harsh channel conditions and severely limited communication resources, the reliability and effectiveness of semantic communication require urgent enhancement to satisfy the increasing demands of 6G technology. To address this issue, we propose an importance prioritized framework that integrates both message importance and feature importance to identify critical semantics for reliable and efficient semantic transmission. Considering service personalization and task intelligence, we analyze the message importance by factoring the receiver’s preferences and the communication tasks requirements. Specifically, a large AI model is introduced to quantify message importance, while an importance-based metric for semantic accuracy is established to evaluate the overall reliability of semantic communication. To safeguard significant messages in harsh channel conditions, an unequal error protection strategy based on message importance is employed. Furthermore, we propose a novel approach for feature importance analysis based on loss variation to accurately identify critical features. A feature importance prediction network is designed for algorithm deployment. Additionally, a semantic compression strategy based on feature importance is utilized to prioritize the transmission of essential features in limited communication resources scenarios. Extensive experimental results demonstrate substantial performance advantages of our framework and methods, especially in low signal-to-noise ratio and communication resource shortages, providing a reliable and efficient solution for semantic communication in adverse communication environments.

The proliferation of generative AI services, from cloud-based synthesis to on-device content creation, is increasingly bottlenecked by the need for efficient communication over dynamic wireless channels. However, deploying these models at the network edge is impeded by two fundamental challenges, namely the weak coupling between the model and physical channel conditions and the prohibitive computational latency of diffusion-based inference. Existing approaches often treat diffusion as a static module trained at a fixed noise level and indexed by abstract timesteps without physical interpretation, which yields suboptimal performance under time-varying channels. To address this, we propose an signal-to-noise ratio guided latent diffusion model (SNR-LDM). The diffusion process is reparameterized with a physically interpretable mapping between denoising timesteps and channel SNR, aligning the denoising trajectory with the channel state and enabling SNR-adaptive inference. To enhance semantic fidelity, we introduce multimodal guidance that fuses textual and visual prompts to steer content generation. We further develop two inference schemes, namely a dynamic multi-step procedure for highest reconstruction quality and a single-step analytic inversion for ultra-low latency, which makes deployment on resource-constrained edge devices feasible. Experiments show that the proposed model achieves about 4 dB higher PSNR than adaptive baselines and yields up to 8.2% lower LPIPS than diffusion-based benchmarks. The framework links large-scale generative modeling with the communication-constrained network edge and enables robust and efficient deployment of generative AI services.

By focusing on the intrinsic meaning of information, semantic communication (SemCom) marks a fundamental paradigm shift from physical bit transmission to personalized semantic service. Considering the importance of personalized features related to the speaker in speech for source recovery and understanding, we propose a semantic-driven framework for personalized speech transmission, named PerSemCom, which combines speaker acoustic features with semantic information. Specifically, we first introduce an efficient semantic extraction mechanism to achieve the conversion from speech to text transcriptions, and design a semantic corrector coupled with multi-domain knowledge to mitigate the effects of wireless channel distortion. Building upon the reliable transcriptions at receiver, we further establish a speaker embedding vector knowledge base and achieve high-fidelity speech reconstruction through quantitative modeling of speaker-specific acoustic features. Extensive experimental results demonstrate that our proposed framework outperforms existing schemes in terms of subjective perception at harsh channel conditions. Complexity analysis and latency measurements also show competitive advantages in computational efficiency and real-time capabilities. Reconstructed personalized speech samples have been publicly available at https://kwtankw.github.io/PerSemCom/.

Unmanned aerial vehicles (UAVs) acting as aerial base stations are regarded as an effective solution for emergency communication, due to their high mobility and low cost. In this paper, we consider the differentiated communication requirements in disaster relief scenarios, where the uplink demands high throughput and the downlink requires low latency and high reliability. To simultaneously capture the timeliness and reliability requirements of downlink transmissions, we propose a novel QoS evaluation metric based on finite blocklength theory. Building on this, we formulate a system utility maximization problem and solve it by jointly optimizing UAV trajectory and ground node access control. To address this problem, we propose a novel algorithm named PW-QMIX, which integrates a priority-based heuristic access control policy with a QMIX-based trajectory optimization scheme, effectively tackling the challenges posed by the high-dimensional joint action space. Furthermore, to tackle the challenge of dynamic observation caused by UAV movement and sensing limitations, we design a weight generation network (WGN) and incorporate it into the input layer of QMIX. The WGN dynamically generates weight matrices based on observed nodes, enhancing UAV’s adaptability to local observation variations. Simulation results validate the significance of jointly optimizing uplink and downlink communications. Moreover, compared with state-of-the-art algorithms, the proposed PW-QMIX demonstrates significant advantages in convergence and scalability.

The rapid growth of multimodal data in 6G-enabled applications demands a paradigm shift from traditional bit-rate-oriented communication to semantic transmission. While diffusion models offer a promising solution for high-fidelity generation, their application is hindered by a fundamental disconnect from the physical channel and high computational overhead. Existing methods typically treat the denoising process as independent of the channel conditions, using abstract integer timesteps that lack physical meaning, and require a full, lengthy sampling chain for reconstruction. To address these challenges, we propose signal-to-noise ratio-guided latent diffusion model (SNR-LDM), which re-parameterizes the diffusion process to map denoising time steps directly to physical SNR. This enables adaptive inference from noise levels precisely matched to the estimated channel state, significantly reducing redundant computations while further guiding the reconstruction process through multimodal prompts to ensure semantic consistency. Experimental results demonstrate that, across a wide range of channel conditions, our proposed SNR-LDM achieves a PSNR gain of approximately 4 dB over ADJSCC while simultaneously reducing the LPIPS from 0.75 to 0.25.

Unmanned aerial vehicle (UAV) wireless image transmission faces challenges of limited bandwidth and dynamic channel conditions. To address these issues, we propose a novel semantic communication architecture employing a dual-level semantic transmission strategy that decomposes original images into coarse-grained and fine-grained semantic information. Specifically, by integrating the efficient MobileViT network into a joint source-channel coding (JSCC) framework, we design a lightweight semantic encoder-decoder dedicated to coding the image details. To ensure reliable transmission, we introduce a decoupled control-data transmission (DCDT) mechanism that transmits the dual-level semantic information over independent channels, with a fusion module at the receiver integrating them to produce the reconstructed image. Experimental results demonstrate that the proposed system significantly outperforms the traditional method and standard JSCC method in both reconstruction quality and channel robustness, while achieving performance comparable to the heavyweight model with substantially reduced model complexity, thereby validating its deployment potential on resource-constrained UAVs.

Unmanned aerial vehicles (UAVs) significantly augment mobile edge computing (MEC) networks with their flexible deployment. In this paper, we investigate a priority-driven multi-UAV cooperative MEC system, in which the task priority are jointly determined by the task queue and task type. The system aims to maximize the task priority gain, subject to the constraints on offloading decision, UAV trajectory design and task scheduling. To solve this problem, we develop a priority scheduling insert based heterogeneous Q-mixing networks (PSI-HQMIX) framework, where the PSI scheme dynamically updates the position of tasks within the queues and the HQMIX algorithm is used to obtain the optimal offloading decisions and trajectories. Simulation results demonstrate that the proposed algorithm outperforms benchmark algorithms in terms of the achieved average priority gains and convergence.

Rapid expansion of the Internet of Unmanned Agents (IUAs) has led to a dramatic increase in demand for network capacity. Multicast-broadcast transmission, as an one-to-many communication paradigm, efficiently alleviates resource consumption by delivering common content to massive users over shared time-frequency resources. To address the flexible and large-scale networking requirements under IUA scenarios, this article introduces the multicast broadcast single frequency network (MBSFN) framework deploying aerial base stations (ABSs) based on autonomous aerial vehicles (AAVs). To deal with energy consumption challenges under AAVs’ coupled operational constraints, this article proposes a multiagent deep reinforcement learning (MADRL) approach, grounded in the multiagent deep deterministic policy gradient (MADDPG) algorithm, to optimize energy efficiency (EE) while ensure user Quality of Service (QoS) in ABS MBSFN systems. By modeling ABS as cooperative agents, the MADDPG algorithm dynamically adjusts discrete power levels, integrating straight-through estimators (STEs), reparameterization techniques, and Gumbel distribution sampling to resolve challenges in discrete action space optimization. The simulation results demonstrate that the proposed scheme achieves an average EE of 88.7% of the theoretical optimum and outperforms the single-agent DDPG method by up to 30%. Further analysis in diverse UE distributions validates the robustness and practicality of the proposed approach, highlighting its potential for energy-efficient multicast-broadcast deployments in AAV-enabled networks.

In response to the critical need for rapid post-disaster assessments, this article introduces an innovative application of artificial intelligence (AI) in unmanned aerial vehicles (UAVs) for disaster relief. A lightweight distributed learning algorithm (namely, YO-FR), is designed to enable multiple UAV agents to share and process environmental data, highlighting the importance of data and knowledge-empowered distributed learning. Moreover, we create a real-world mini-data set collected by UAVs for post-disaster road defects (mini-UPRDs), followed by a data enhancement technology to facilitate feature extraction and promote knowledge-driven learning. The viability of YO-FR is underscored by its enhanced detection precision and processing speed, as evidenced by its performance on the enhanced mini-UPRD data set, surpassing that of existing algorithms. By implementing AI algorithms on UAV platforms, this research offers a theoretical and practical foundation for the practical deployment of IUA in critical application areas, such as emergency management and disaster response.

The radio map has emerged as a promising tool for optimizing spectrum resource utilization and shaping the future landscape of intelligent wireless networks. However, the deployment of radio maps across the network introduces computational and latency challenges, restricting their real-time applications from the user’s perspective. In this paper, we introduce an innovative scheme for constructing and distributing radio maps in unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks. Initially, we transform the distribution of radio maps into a collaborative process between UAV server and smart mobile devices (SMD), proposing four distribution modes tailored to different network conditions. This ensures that each SMD can access radio maps with the lowest cost. Additionally, our scheme integrates a deep reinforcement learning (DRL) framework, fostering seamless coordination between UAV server and SMD to enhance overall system performance and operational efficiency. Simulation results validate the efficiency and efficacy of our proposed scheme in optimizing radio map distribution strategies and resource allocation, further confirming the potential real-time applications of radio maps in future wireless networks.

Ground-air cooperative package distribution is a promising delivery method, especially during Corona virus Disease 2019. It can extend the coverage of vehicles by exploring the flexibility of unmanned aerial vehicles (UAVs), expand the distribution of vehicles and reduce carbon emissions. Most existing studies focus on their trajectory optimization, while often overlooking their coordination for global information, and the complexity and reliability of collaborative delivery problem. To address the above issues, we first formulate an optimization problem to minimize the service cost of both UAVs and vehicles. To ensure service reliability, the constraints of UAVs during takeoff, service, and landing phases are comprehensively considered. We then propose a lightweight reinforcement learning solution to minimize the flight distance of UAVs and the number of required vehicles. Finally, theoretical analysis and performance evaluations show that compared with other representative algorithms, the designed algorithm has advantages in terms of robustness, effectiveness and stability.

Depression is an illness that involves emotional and mental health. Currently, depression detection through interviews is the most popular way. With the advancement of natural language processing and sentiment analysis, automated interview-based depression detection is strongly supported. However, current multimodal depression detection models fail to adequately capture the fine-grained features of depressive behaviors, making it difficult for the models to accurately characterize the subtle changes in depressive symptoms. To address this problem, we propose a Multi Fine-Grained Fusion Network (MFFNet). The core idea of this model is to extract and fuse the information of different scale feature pairs through a Multi-Scale Fastformer (MSfastformer), and then use the Recurrent Pyramid Model to integrate the features of different resolutions, promoting the interaction of multi-level information. Through the interaction of multi-scale and multi-resolution features, it aims to explore richer feature representations. To validate the effectiveness of our proposed MFFNet model, we conduct experiments on two depression interview datasets. The experimental results show that the MFFNet model performs better in depression detection compared to other benchmark multimodal models.

Non-orthogonal multiple access (NOMA) with mixed numerology is a promising technology that blends flexibility and high spectral efficiency. However, since NOMA enables multiplexing of the same time-frequency resources for different users and mixed-numerology allows superimposing sub-signals with different numerologies, high peak-to-average power ratio (PAPR) as well as power fluctuation problem in NOMA detection becomes cumbersome especially when applying PAPR reduction techniques. This study considers minimizing PAPR in mixed numerology NOMA systems using selected-mapping (SLM) method. The Riemann sequence is one of the simplest phase sequence that can be used to generate a set of signal copies for PAPR reduction. Our analysis reveals that as the amplitude variation caused by the Riemann sequence grows, the upper bound of PAPR for signal copies decreases correspondingly. Leveraging this insight, we introduce a new maximum-range Riemann (MRR)-based phase sequences, in which the amplitude factor can be adjusted to control the power fluctuations. Compared to previous works, our study delves deeper into the influence of phase sequence design of SLM on PAPR reduction performance. Simulations show that the proposed method offers significant performance advantages of PAPR reduction and bit error rate (BER) improvement even with consideration of power-amplifier.

Some Internet of Things (IoT) applications represented by vehicular networks, Internet of Medical Things (IoMT), and fire alarm systems have high requirements on the freshness of receiving information. Due to limited computing capability of IoT devices, mobile edge computing (MEC) is applied to reduce packet calculation time and improve packet freshness. In this paper, we investigate a MEC system for sharing vehicle status information and use the age-of-information (AoI) to define the freshness of information in the MEC system. The whole system is modeled as a two-stage tandem queue model with multi source-destination pairs. We derive the closed-form expression for the average AoI of partial computing and analyze the impact of system parameters on the average AoI, which provides guidance on how to set parameters to maximize the information freshness of the MEC system. As a more flexible scheme, partial computing we used reduces the AoI of the MEC system compared to remote computing. Numerical analysis validates our theory.

With the development of wireless communication and Internet of Things (IoT), there are massive wireless devices that need to share the limited spectrum resources. Dynamic spectrum access (DSA) is a promising paradigm to remedy the problem of inefficient spectrum utilization brought upon by the historical command-and-control approach to spectrum allocation. In this article, we investigate the distributed DSA problem for multiusers in a typical multichannel cognitive radio network. The problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP), and we propose a centralized off-line training and distributed online execution framework based on cooperative multi-agent reinforcement learning (MARL). We employ the deep recurrent$Q$-network (DRQN) to address the partial observability of the state for each cognitive user. The ultimate goal is to learn a cooperative strategy which maximizes the sum throughput of a cognitive radio network in a distributed fashion without information exchange between cognitive users. Finally, we validate the proposed algorithm in various settings through extensive experiments. The experimental results show that the proposed CoMARL-DSA algorithm outperforms the state-of-the-art deep$Q$-learning for spectrum access (DQSA) in terms of successful access rate and collision rate by at least 14% and 12%, respectively.

With innovative applications of unmanned aerial vehicle (UAV) ad hoc networks in various areas, their demands on broad bandwidth, large capacity and low latency become prominent. The combination of millimeter wave, directional antenna and time division multiple access techniques, which enables concurrent transmissions, is promising to deal with it. In this paper, we study the resource allocation problem in UAV ad hoc networks. Specifically, the slot assignment, antenna boresight and transmit power are jointly optimized to promote the network capacity. First, we formulate the optimization problem as the maximization of the fairness-weighted network capacity, subject to the constraint on priority guarantee. Then, because the formulated problem is a mixed integer non-linear programming problem (MINLP), which is NP-hard, two algorithms called dual-based iterative search algorithm (DISA) and sequential exhausted allocation algorithm (SEAA) are respectively proposed to efficiently solve it with acceptable complexity. DISA slacks the MINLP into a continuous-variable optimization problem and solves it with the Lagrangian dual method in an iterative manner. As a heuristic method, SEAA schedules links sequentially, i.e., from high-priority to low-priority ones. Numerical results demonstrate that both DISA and SEAA can efficiently allocate resources for UAVs, while guaranteeing the fairness and priority of links.

Machine learning (ML) has been widely used for efficient resource allocation (RA) in wireless networks. Although superb performance is achieved on small and simple networks, most existing ML-based approaches are confronted with difficulties when heterogeneity occurs and network size expands. In this paper, specifically focusing on power control/beamforming (PC/BF) in heterogeneous device-to-device (D2D) networks, we propose a novel unsupervised learning-based framework named heterogeneous interference graph neural network (HIGNN) to handle these challenges. First, we characterize diversified link features and interference relations with heterogeneous graphs. Then, HIGNN is proposed to empower each link to obtain its individual transmission scheme after limited information exchange with neighboring links. It is noteworthy that HIGNN is scalable to wireless networks of growing sizes with robust performance after trained on small-sized networks. Numerical results show that compared with state-of-the-art benchmarks, HIGNN achieves much higher execution efficiency while providing strong performance.

Multiaccess edge computing (MEC) is regarded as a promising solution to overcome the limit on the computation capacity of mobile devices. This article investigates an energy-efficient unmanned aerial vehicle (UAV)-enabled MEC framework incorporating nonorthogonal multiple access (NOMA), where multiple UAVs are deployed as edge servers to provide computation assistance to terrestrial users and NOMA is adopted to reduce the energy consumption of task offloading. A utility is formed to mathematically evaluate the weighted energy cost of the system. Due to the coupling of parameters, the minimization of utility is a highly nonconvex problem and therefore, the problem is decomposed into two more tractable subproblems, i.e., the optimal allocation of radio and computation resources given UAV trajectories, and the trajectory planning based on given resource allocation schemes. These two problems are converted to convex ones via successive convex approximation (SCA) and quadratic approximation, respectively. Then, an efficient iterative algorithm is proposed where these two subproblems are alternately solved to gradually approach the optimal resource management of the proposed system. Sufficient numerical results show that our proposed strategy has a remarkable advantage over existing systems in terms of energy efficiency.

Mobile edge computing (MEC) has risen as a promising paradigm to provide high quality of experience via relocating the cloud server in close proximity to smart mobile devices (SMDs). In MEC networks, the MEC server with computation capability and storage resource can jointly execute the latency-sensitive offloading tasks and cache the contents requested by SMDs. In order to minimize the total latency consumption of the computation tasks, we jointly consider computation offloading, content caching, and resource allocation as an integrated model, which is formulated as a mixed integer nonlinear programming (MINLP) problem. We design an asymmetric search tree and improve the branch and bound method to obtain a set of accurate decisions and resource allocation strategies. Furthermore, we introduce the auxiliary variables to reformulate the proposed model and apply the modified generalized benders decomposition method to solve the MINLP problem in polynomial computation complexity time. Simulation results demonstrate the superiority of the proposed schemes.

Unmanned aerial vehicle (UAV) has been witnessed as a promising approach for offering extensive coverage and additional computation capability to smart mobile devices (SMDs), especially in the scenario without available infrastructures. In this paper, a UAV-assisted mobile edge computing system with stochastic computation tasks is investigated. The system aims to minimize the average weighted energy consumption of SMDs and the UAV, subject to the constraints on computation offloading, resource allocation, and flying trajectory scheduling of the UAV. Due to nonconvexity of the problem and the time coupling of variables, a Lyapunov-based approach is applied to analyze the task queue, and the energy consumption minimization problem is decomposed into three manageable subproblems. Furthermore, a joint optimization algorithm is proposed to iteratively solve the problem. Simulation results demonstrate that the system performance obtained by the proposed scheme can outperform the benchmark schemes, and the optimal parameter selections are concluded in the experimental discussion.

The distributed mobile robotic network consists of a group of mobile nodes, such as mobile sensors, unmanned vehicles, unmanned submarines, unmanned air vehicles, or mobile robots. The mobile robotic network keeping a regular topology can utilize efficient network protocols and is also promising in many application scenarios. We propose a distributed algorithm that controls multiple distributed robotic nodes to form regular topology, including straight line, ring, triangular lattice, and square lattice. Our algorithm generates artificial forces, including the attractive force towards a reference point to gather the distributed nodes, the repulsive force from neighboring nodes to keep the desirable distance among them, the formation force to form a specific shape, and the obstacle avoidance force to avoid possible obstacles, such that each node simply follows the resultant force to move. The algorithm works in a fully distributed manner, converges fast, and is easy to deploy, requiring only one-hop local network geometry information. And, it is effective under both 2D and 3D scenarios. A computer demo is developed to demonstrate the effectiveness of the algorithm for large numbers of robotic nodes.

Mobile edge computing (MEC) brings computation capacity to the edge of mobile networks in close proximity to smart mobile devices (SMDs) and contributes to energy saving compared with local computing, but resulting in increased network load and transmission latency. To investigate the tradeoff between energy consumption and latency, we present an energy-aware offloading scheme, which jointly optimizes communication and computation resource allocation under the limited energy and sensitive latency. In this paper, single and multicell MEC network scenarios are considered at the same time. The residual energy of smart devices' battery is introduced into the definition of the weighting factor of energy consumption and latency. In terms of the mixed integer nonlinear problem for computation offloading and resource allocation, we propose an iterative search algorithm combining interior penalty function with D.C. (the difference of two convex functions/sets) programming to find the optimal solution. Numerical results show that the proposed algorithm can obtain lower total cost (i.e., the weighted sum of energy consumption and execution latency) comparing with the baseline algorithms, and the energy-aware weighting factor is of great significance to maintain the lifetime of SMDs.

Object detection is important in car sharing services. Accuracy, efficiency, and low memory consumption are desirable for object detection in car sharing services. This paper presents a network system that satisfies all these requirements. Our approach first divides the object detection task into multiple simpler local regression tasks. Then, we propose the generalized Haar filter-based convolutional neural network to reduce the consumption of memory and computing resource. To achieve real-time performance, we introduce a sparse window generation strategy to reduce the number of input image patches without sacrificing accuracy. We perform experiments on both vehicle and pedestrian data sets. Experimental results demonstrate that our approach can accurately detect objects under challenging conditions. Note to Practitioners-Object detection is an important part of intelligent vehicle technologies, which play an important role in car sharing services. Object detection provides metadata for collision avoidance, self-driving systems, and driver-assistance systems, which can result in better safety and consumer experiences in car sharing services. Although deep learning has achieved an excellent performance in object detection, they consume a large amount of storage and computing resource, which makes them difficult to be deployed for car sharing services. This paper suggests a novel approach which is based on the generalized Haar filter and the local regression strategy. Our approach is accurate, efficient, and light. The experimental results verify the effectiveness of the proposed approach in car sharing services.

Cognitive radio (CR) has emerged as an advanced and promising technology to exploit the wireless spectrum opportunistically. In cognitive radio networks (CRNs), any pairwise communicating nodes are required to rendezvous on a commonly available channel prior to exchange information. In the earlier research, the most popular method is selecting a Common Control Channel (CCC) in CRNs to establish the rendezvous. However, employing a CCC has many problems such as the control channel saturation, vulnerability to jamming attacks, and inapplicability to dynamic network scenarios. Therefore, the blind rendezvous, which requires neither CCC nor the information of the target user's available channels, has recently attracted a lot of research interests. As a contribution to this research area, in this paper we propose a Sender-Jump Receiver-Wait (SJ-RW) blind rendezvous algorithm, which has fully satisfied the following requirements: 1) guaranteeing rendezvous; 2) realizing full rendezvous diversity, i.e., any pair of users can rendezvous on all commonly available channels; 3) requiring no time-synchronization; 4) supporting both symmetric and asymmetric models; 5) supporting multi-user/multi-hop scenarios and 6) consuming short Time-to-Rendezvous (TTR). Theoretical analysis, computer simulations and experiment with testbed have validated the proposed SJ-RW algorithm.

With the access of a myriad of smart handheld devices in cellular networks, mobile crowdsourcing becomes increasingly popular, which can leverage omnipresent mobile devices to promote the complicated crowdsourcing tasks. Device‐to‐device (D2D) communication is highly desired in mobile crowdsourcing when cellular communications are costly. The D2D cellular network is more preferable for mobile crowdsourcing than conventional cellular network. Therefore, this paper addresses the channel access and power control problem in the D2D underlaid cellular networks. We propose a novel semidistributed network‐assisted power and a channel access control scheme for D2D user equipment (DUE) pieces. It can control the interference from DUE pieces to the cellular user accurately and has low information feedback overhead. For the proposed scheme, the stochastic geometry tool is employed and analytic expressions are derived for the coverage probabilities of both the cellular link and D2D links. We analyze the impact of key system parameters on the proposed scheme. The Pareto optimal access threshold maximizing the total area spectral efficiency is obtained. Unlike the existing works, the performances of the cellular link and D2D links are both considered. Simulation results show that the proposed method can improve the total area spectral efficiency significantly compared to existing schemes.

Summary Extracting secret keys from the common randomness of wireless channels has attracted prominent attention recently. Orthogonal frequency‐division multiplexing (OFDM) systems can provide extra randomness in view of the use of multiple subchannels. So far, the secret key capacity of OFDM systems is still an open issue. In this paper, the secret key capacity of OFDM systems based on the subchannel state information is analyzed, and an expression of the secret key capacity is derived under the assumption that the subchannels are independent. To increase the secret key capacity, a power allocation scheme based on geometric program is proposed. Furthermore, an underlying propagation protocol is designed to realize the power allocation scheme. Performance simulations show that the proposed scheme achieves greater secret key capacity in comparison with equal power allocation scheme, especially at low signal‐to‐noise ratio region. Besides, the secret key bits mismatch rate during the secret key generation based on the power allocated subchannels is decreased.

We propose a channel selection strategy with hybrid architecture, which combines the centralized method and the distributed method to alleviate the overhead of access point and at the same time provide more flexibility in network deployment. By this architecture, we make use of game theory and reinforcement learning to fulfill the optimal channel selection under different communication scenarios. Particularly, when the network can satisfy the requirements of energy and computational costs, the online decision algorithm based on noncooperative game can help each individual sensor node immediately select the optimal channel. Alternatively, when the network cannot satisfy the requirements of energy and computational costs, the offline learning algorithm based on reinforcement learning can help each individual sensor node to learn from its experience and iteratively adjust its behavior toward the expected target. Extensive simulation results validate the effectiveness of our proposal and also prove that higher system throughput can be achieved by our channel selection strategy over the conventional off-policy channel selection approaches.

Cognitive radio (CR) has emerged as a promising technology to increase the utilization of spectrum resource. A pivotal challenge in CR lies on secondary users' (SU) finding each other on the frequency band, i.e., the spectrum locating. In this demo, we implement two kinds of multi-channel rendezvous technology to solve the problem of spectrum locating: (i) the common control channel (CCC) based rendezvous scheme, which is simple and effective when a control channel is always available; and (ii) the channel-hopping (CH) based blind rendezvous, which could also obtain guaranteed rendezvous on all commonly available channels of pairwise SUs in a short time without a CCC. Furthermore, the cognitive nodes in the demonstration could adjust their communication channels autonomously according to the dynamic spectrum environment for continuous data transmission.

The blind rendezvous, which requires neither Common Control Channel (CCC) nor the information of the target user's available channels, has recently attracted a lot of research interests. As a contribution to this research area, in this paper we propose a Sender-Jump Receiver-Wait blind rendezvous algorithm, which has fully satisfied the following requirements: 1) guaranteeing rendezvous; 2) realizing full rendezvous diversity, i.e., any pair of users can rendezvous on all commonly available channels; 3) requiring no time-synchronization; 4) supporting both symmetric and asymmetric models; 5) supporting multi-user/multi-hop scenarios and 6) consuming short Time-to-Rendezvous (TTR). Theoretical analysis and computer simulations have validated our algorithm.