Millimeter-wave radar offers a promising sensing modality for autonomous systems thanks to its robustness in adverse conditions and low cost. However, its utility is significantly limited by the sparsity and low resolution of radar point clouds, which poses challenges for tasks requiring dense and accurate 3D perception. Despite that recent efforts have shown great potential by exploring generative approaches to address this issue, they often rely on dense voxel representations that are inefficient and struggle to preserve structural detail. To fill this gap, we make the key observation that latent diffusion models (LDMs), though successful in other modalities, have not been effectively leveraged for radar-based 3D generation due to a lack of compatible representations and conditioning strategies. We introduce RaLD, a framework that bridges this gap by integrating scene-level frustum-based LiDAR autoencoding, order-invariant latent representations, and direct radar spectrum conditioning. These insights lead to a more compact and expressive generation process. Experiments show that RaLD produces dense and accurate 3D point clouds from raw radar spectrums, offering a promising solution for robust perception in challenging environments.

With the rapid development of low-altitude economy, the low-altitude intelligent network (LAIN) serves as a critical infrastructure for supporting diversified aerial activities. However, current LAIN lacks a physical-network synchronization mechanism and fails to handle heterogeneity at the architecture level, which leads to difficulties in real-time adaptive adjustments and efficient unified coordination. To address these issues, this paper proposes the networked embodied intelligence (NEI) paradigm, with a network-level sensing-decision-action-feedback (SDAF) closed-loop mechanism to synchronize physical and network states. Building on this paradigm, we functionally reconfigure LAIN into four collaborative subnetworks: sensing, computing, communication and navigation. As a further step, we propose the NEI-LAIN architecture, where these subnetworks collaborate via the SDAF loop to achieve global collaboration and continuous evolution. Simulation results demonstrate that the proposed NEI-LAIN can significantly enhance the performance of communication robustness, resource utilization and task responsiveness in highly dynamic scenarios. Finally, we discuss its implementation challenges and future research directions.

Due to the scalability and portability, low-altitude intelligent networks (LAINs) are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles (UAVs) are characterized by the distributed topology and high mobility, thus vulnerable to security threats, which may degrade routing performances for data transmissions. Hence, how to ensure the routing stability and security of LAINs is challenging. In this paper, we focus on the routing with multiple UAV clusters in LAINs. To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques to manage the identify and mobility of UAVs. Besides, we formulate the routing problem to optimize the end-to-end (E2E) delay and transmission success ratio (TSR) simultaneously, which is an integer nonlinear programming problem and intractable to solve. Therefore, we reformulate the problem into a decentralized partially observable Markov decision process. We design the multi-agent double deep Q-network-based routing algorithms to solve the problem, empowered by the soft-hierarchical experience replay buffer and prioritized experience replay mechanisms. Finally, extensive simulations are conducted and the numerical results demonstrate that the proposed framework reduces the average E2E delay by 59% and improves the TSR by 29% on average compared to benchmarks, while simultaneously enabling faster and more robust identification of low-trust UAVs.

Precise positioning and identification of unauthorized unmanned aerial vehicles (UAVs) are of crucial importance for spectrum security and privacy protection in future intelligent networks. Although various single-modality approaches have been investigated, their performance degrades under the sensor-specific noise, resulting in suboptimal performance and robustness. To address these security challenges, we propose a multi-layer radio frequency (RF)-vision fusion framework that synergistically exploits temporal-spectral features of UAV RF signals and spatial-visual information to achieve precise and robust UAV positioning and identification. Moreover, a corresponding unified RF-Vision fusion Network (RFViNet) is designed to exploit the RF-vision cross-modal complementary and semantic synergy. Specifically, by leveraging the novel RFinformed proposal generation, RF-enhanced feature modulation, and RF-guided semantic query modules, the RFViNet effectively exploits the complementary strengths of RF and visual modalities. Furthermore, a practical RF–vision platform is developed to evaluate the performance of our method under various challenging conditions. Experimental results on the real-world dataset demonstrate that the proposed method achieves a competitive 85.8% average precision AP50, highlighting its potential for enhancing the spectrum security in future intelligent wireless networks.

Intelligent spectrum management is crucial for improving spectrum efficiency and achieving secure utilization of spectrum resources. However, existing intelligent spectrum management methods, typically based on small-scale models, suffer from notable limitations in recognition accuracy, convergence speed, and generalization, particularly in the complex and dynamic spectrum environments. To address these challenges, this paper proposes a novel spectrum foundation model, termed SpectrumFM, establishing a new paradigm for spectrum management. SpectrumFM features an innovative encoder architecture that synergistically exploits the convolutional neural networks and the multi-head self-attention mechanisms to enhance feature extraction and enable robust representation learning. The model is pre-trained via two novel self-supervised learning tasks, namely masked reconstruction and next-slot signal prediction, which leverage large-scale in-phase and quadrature (IQ) data to achieve comprehensive and transferable spectrum representations. Furthermore, a parameter-efficient fine-tuning strategy is proposed to enable SpectrumFM to adapt to various downstream spectrum management tasks, including automatic modulation classification (AMC), wireless technology classification (WTC), spectrum sensing (SS), and anomaly detection (AD). Extensive experiments demonstrate that SpectrumFM achieves superior performance in terms of accuracy, robustness, adaptability, few-shot learning efficiency, and convergence speed, consistently outperforming conventional methods across multiple benchmarks. Specifically, SpectrumFM improves AMC accuracy by up to 12.1% and WTC accuracy by 9.3%, achieves an area under the curve (AUC) of 0.97 in SS at -4 dB signal-to-noise ratio (SNR), and enhances AD performance by over 10%.

Semantic communication has shown exceptional performance in various tasks, such as image classification, owing to the advancements in deep learning technologies. However, due to the openness of wireless channels and the vulnerability of neural networks, semantic communication faces significant challenges from semantic impairment in the physical channel. In this paper, semantic impairment refers to the minor perturbations that cause discrepancies between the received features and the expected ones, which can lead to errors in image classification. We design four constraints from the perspectives of semantic level, concealment level and efficiency level to simulate potential malicious semantic impairment. These constraints are employed to generate adversarial perturbations specifically targeting semantic communication systems, ensuring that the perturbations can more effectively disrupt the normal function of the systems. Moreover, we innovatively propose knowledge graph enhanced anti-impairment cognitive semantic communication, which combines knowledge graph and adversarial training to boost robustness against semantic impairment. Specifically, we leverage the shared knowledge graph to transmit triplet information from the transmitter to the receiver in the form of indices and introduce the triplet information as additional information into the decoder to facilitate the decoding process. Simulation results show that our proposed knowledge graph enhanced cognitive semantic communication system achieves higher classification accuracy and robustness in environments with low signal-to-noise ratio and semantic impairment, compared to existing Better Portable Graphics (BPG) and Joint Source-Channel Coding(JSCC) schemes.

Cellular-connected unmanned aerial vehicles (UAVs) play essential roles across various smart-city applications in low-altitude domains, such as logistics delivery, environmental monitoring, etc. To ensure their reliable deployment, it significantly depends on efficient and intelligent UAV coverage-aware navigation, where deep reinforcement learning (DRL) has emerged as a promising method. However, current DRL-based methods typically simplify UAV flight dynamics by action discretization, which neglects realistic continuous flight dynamic and limits their practical implementation. Moreover, they suffer from the sample inefficiency, since learning the navigation policy requires extensive and costly real-time interactions with the environment. To overcome these two challenges, we propose a novel offline-online radio map-enhanced soft actor-critic (OORM-SAC) framework for the energy-efficient UAV coverage-aware navigation under continuous dynamics constraints. Specifically, our proposed OORM-SAC leverages the classic SAC algorithm to handle large continuous action spaces. It aims to learn continuous steering control to navigate toward the destination while minimizing energy consumption and communication outage. Moreover, to enhance learning efficiency, OORM-SAC adopts a hybrid offline-online learning approach, where the energy-efficient flight pattern is first pre-trained offline and the policy is then refined through online environmental interactions. Furthermore, it incorporates the radio map construction during the online phase to generate diverse virtual training samples, which further accelerates the policy learning. Experimental results demonstrate the effectiveness of OORM-SAC in navigation tasks under continuous dynamic constraints. The OORM-SAC method exhibits superior learning efficiency and generates intelligent trajectories that effectively balance energy consumption and communication requirements.

Unmanned aerial vehicle (UAV) communication is of crucial importance for heterogeneous practical wireless communications. However, it is susceptible to the severe spectrum scarcity with the rapidly expanding market of wireless broadband, multimedia users, and high data-rate applications. Exploring the underutilized unlicensed spectrum through spectrum sharing is promising to tackle this issue, but the openness of the unlicensed spectrum makes UAVs susceptible to security threats from potential jammers. Therefore, a licensed and unlicensed UAV spectrum sharing network against uncertain jamming attack is studied. Moreover, to overcome the high complexity of the pure model-based optimization resource allocation schemes, the low learning efficiency and strong data dependency of data-driven deep reinforcement learning (DRL) methods, a novel optimization-driven DRL framework is proposed for the resource allocation. In particular, a model-based optimization module is exploited to derive the worst-case lower bound and a better informed target value of the formulated complex non-convex optimization problem. Furthermore, the model-based informed target value is integrated into the DRL to guide the agents for better strategies. Simulation results demonstrate that our proposed scheme can significantly improve the convergence speed and achieve a better reward performance than the pure DRL based scheme. It is also shown that the exploitation of the unlicensed spectrum can achieve approximately twice the sum transmission rate compared to using only the licensed spectrum.

Pinching antennas are a promising technology for enhancing the performance of future indoor communication systems by leveraging spatial degrees of freedom. This paper pioneers the application of pinching antenna arrays in integrated sensing and communication (ISAC) systems and investigates dynamic array partitioning strategies. To maximize the secrecy sum rate (SSR), a partitioned array optimization problem under binary constraints is formulated, while satisfying sensing performance requirements and transmit power limitations. Specifically, the antenna partitioning constraints are modeled as minimum and maximum numbers of transmit antennas, along with binary constraints determining whether each antenna element functions in transmit or receive mode. To solve the non-convex optimization problem, a beamforming algorithm integrating semidefinite relaxation, generalized Rayleigh quotient, and minimum mean square error is proposed. Then, an element-wise iterative optimization method and a deep Q-network (DQN)-based partitioning approach are respectively developed to optimize the array configuration, thereby enhancing security performance under guaranteed sensing constraints. Simulation results demonstrate that the DQN-based approach outperforms the conventional iterative optimization method. In terms of security performance, the pinching antenna array can achieve a 69.70% reduction in the number of antennas and a 30.16% saving in transmit power compared to conventional fixed-position antenna (FPA) systems. Moreover, the pinching antenna system attains a 35.72% improvement in SSR performance, surpassing traditional FPA configurations.

Active object tracking (AOT) in complex and dynamic environments remains a significant challenge for autonomous unmanned aerial vehicle (UAV) tracking systems, especially in anomalous situation such as prolonged occlusion and intense interference. In this paper, we propose a novel embodied learning framework, called the learning to ask for help (LA4H) framework, which integrates cross-modal anomaly cognition and adaptive expert assistance mechanisms to enhance the robustness and generalization of UAV active target tracking. The LA4H framework enables the agent to autonomously recognize and classify anomalous states through a cross-modal anomaly cognition module, and to adaptively request expert intervention when necessary via an assistance decision network. A teacher-student policy learning paradigm is further employed to distill the temporal-semantic knowledge, improving tracking efficiency and real-time performance. Extensive experiments in both simulated and real-world scenarios demonstrate that the LA4H significantly outperforms the state-of-the-art baselines in terms of tracking success rate, path efficiency, and generalization to unseen scenarios, while substantially reducing reliance on expert intervention. The results demonstrate the effectiveness of integrating expert knowledge and anomaly cognition for robust embodied AI in practical UAV applications.

To address the challenges of information leakage, low energy efficiency, and the Doppler effect in mobile Internet of Vehicles (IoV), this paper proposes an enhanced IoV cooperation framework, where privacy information is forwarded by the untrusted relay assisted by unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS), which can improve security and energy efficiency. To meet the requirements of green communication, we formulate a secrecy energy efficiency maximization problem by jointly optimizing the transmit power allocation, the relay’s amplification factor, the two-hop RIS phase shift matrices, and the UAV trajectory. Given the non-convex nature of this problem, we introduce an iterative algorithm based on the convex-concave procedure and Dinkelbach’s method to optimize the transmit power and amplification factor. Additionally, we conceive the majorization-minimization (MM) algorithm to optimize the two-hop RIS phase shift matrices, and a designed firefly algorithm-deep deterministic policy gradient (FA-DDPG) algorithm is proposed to obtain the UAV trajectory. Simulation results demonstrate the effectiveness of the proposed scheme in enhancing secrecy energy efficiency. Specifically, compared to the DDPG-only and FA-based schemes, the proposed scheme achieves an improvement of 33.3% and 64.2%, respectively, in secrecy energy efficiency.

With the rapid popularization of Internet of Things (IoT) devices, the freshness of data has become a key factor affecting decision quality and system efficiency. The application of unmanned aerial vehicle (UAV) technology provides a new solution for IoT data collection. This article mainly studies how multiple UAVs can improve the freshness of IoT data collection through joint optimization of trajectory planning and channel selection in a three-dimensional (3D) interference environment. We conducted markov decision process (MDP) modeling on the combinatorial optimization problem of the model and proposed an intelligent joint trajectory planning and channel selection for data collection (ITPCS-DC) algorithm based on multi-agent deep reinforcement learning (MADRL). This algorithm can not only avoid the agent falling into local optimum caused by 3D interference, but also effectively reduce the age of information (AoI) of IoT data collection. Simulation results show that the proposed ITPCS-DC algorithm can achieve higher rewards, lower average AoI, reduced channel switching costs, and shorter trajectory lengths compared to other benchmark algorithms. Moreover, it has better adaptability to more complex collaborative environments.

Resource allocation is paramount to improve spectral efficiency in spectrum-sharing networks. However, numerous existing resource allocation schemes, especially those based on deep reinforcement learning techniques, overlook the impact of time-variant channel quality caused by high dynamics of wireless environment and heterogeneous action space due to discrete actions and continuous parameters, which may significantly degrade the desired system performance. To tackle these issues, in this paper, two intelligent resource allocation schemes that can jointly optimize channel allocation and transmit power in a dynamic spectrum-sharing network are proposed. In particular, an intelligent framework, enhanced by channel prediction, is first proposed to capitalize fully on the latent evolutionary characteristics of time-varying channels, facilitating efficient resource allocation design. Subsequently, a hybrid action representation-based intelligent framework is proposed to learn the latent dependence between channel allocation and transmit power for each secondary user. Simulation results demonstrate that our proposed schemes achieve superior performance compared with several benchmark schemes, highlighting that the sum rate can be improved by exploiting latent channel characteristics and latent hybrid actions dependence.

The enhancement of spectrum efficiency and the realization of secure spectrum utilization are critically dependent on spectrum cognition. However, existing spectrum cognition methods often exhibit limited generalization and suboptimal accuracy when deployed across diverse spectrum environments and tasks. To overcome these challenges, we propose a spectrum foundation model, termed SpectrumFM, which provides a new paradigm for spectrum cognition. An innovative spectrum encoder that exploits the convolutional neural networks and the multi-head self attention mechanisms is proposed to effectively capture both fine-grained local signal structures and high-level global dependencies in the spectrum data. To enhance its adaptability, two novel self-supervised learning tasks, namely masked reconstruction and next-slot signal prediction, are developed for pre-training SpectrumFM, enabling the model to learn rich and transferable representations. Furthermore, low-rank adaptation (LoRA) parameter-efficient fine-tuning is exploited to enable SpectrumFM to seamlessly adapt to various downstream spectrum cognition tasks, including spectrum sensing (SS), anomaly detection (AD), and wireless technology classification (WTC). Extensive experiments demonstrate the superiority of SpectrumFM over state-of-the-art methods. Specifically, it improves detection probability in the SS task by 30% at -4 dB signal-to-noise ratio (SNR), boosts the area under the curve (AUC) in the AD task by over 10%, and enhances WTC accuracy by 9.6%.1

Spectrum map construction is a key technology for enhancing dynamic spectrum management and spectrum efficiency in the sixth-generation wireless communication networks. However, traditional spectrum map construction methods face a dual bottleneck in model generalizability and data dependence. Specifically, the model-driven methods struggle to adapt to dynamic and complex electromagnetic environments, whereas data-driven methods depend on the quality of training data, including sampling density and spatial correlation complexity. To address these challenges, a vision transformer-based large model for spectrum map construction is proposed, namely GenSpectraLM. Inspired by bidirectional encoder representations from transformers masked semantic inference and masked autoencoders local-global construction mechanism, GenSpectraLM employs self-supervised masked pretraining to implicitly learn electromagnetic propagation patterns from diverse datasets. Then, fine-tune is performed to achieve cross-scenario generalization. Simulation results demonstrate that GenSpectraLM achieves accurate spectrum map construction with the root mean squared error of 1.3286 at a sampling rate of 25%. It consistently outperforms benchmark methods by approximately 40%, effectively addressing data efficiency challenges in complex environments.

Spectrum sensing is of crucial importance for improving spectrum efficiency and realizing immersive communication. Deep learning (DL) has been introduced for spectrum sensing, with test statistics generated directly from signal samples in an automatic manner. However, most of the existing data-driven spectrum sensing methods are based on supervised learning and they usually require a massive amount of labeled training data to achieve high detection performance. It is difficult to obtain sufficient labeled training data in practice. To address this issue, a pseudo labeling multi-scale network (PLMSNet) for semi-supervised spectrum sensing is proposed to make the best use of a majority of unlabeled samples and achieves well detection performance with only a few of labeled training samples. Moreover, the proposed scheme is implemented in a real-world software defined radio (SDR) communication system. Both simulation and real-world experiments demonstrate that our proposed method achieves superior detection performance compared with the benchmark methods.

Unmanned aerial vehicle (UAV) communication is of crucial importance in realizing heterogeneous practical wireless application scenarios. However, the densely populated users and diverse services with high data rate demands has triggered an increasing scarcity of UAV spectrum utilization. To tackle this problem, it is promising to incorporate the underutilized unlicensed spectrum with the licensed spectrum to boost network capacity. However, the openness of unlicensed spectrum makes UAVs susceptible to security threats from potential jammers. Therefore, a spectrum sharing UAV network coexisting with licensed cellular network and unlicensed Wi-Fi network is considered with the anti-jamming technique in this paper. The sum rate maximization of the secondary network is studied by jointly optimizing the transmit power, subchannel allocation, and UAV trajectory. We first decompose the challenging non-convex problem into two subproblems, 1) the joint power and subchannel allocation and 2) UAV trajectory design subproblems. A low-complexity iterative algorithm is proposed in a alternating optimization manner over these two subproblems to solve the formulated problem. Specifically, the Lagrange dual decomposition is exploited to jointly optimize the transmit power and subchannel allocation iteratively. Then, an efficient iterative algorithm capitalizing on successive convex approximation is designed to get a suboptimal solution for UAV trajectory. Simulation results demonstrate that our proposed algorithm can significantly improve the sum transmission rate compared with the benchmark schemes.

The rapid proliferation of UAV technology in civilian and military sectors has brought significant benefits but also raised concerns about unauthorized unmanned aerial vehicle (UAV), which pose potential risks to public safety. Current anti-UAV systems, which rely on radar, acoustic, antenna or visual sensors, encounter specific challenges, such as limited positioning ranges, susceptibility to noise, background interference and adverse environmental conditions. To address these limitations, we propose an RF-visual fusion-based memory augmentation network (RVUAV-Net) that integrates RF and visual images, enhancing the UAV positioning accuracy and robustness by utilizing the motion patterns derived from the historical UAV trajectory. Our proposed method capitalizes on the spatial-temporal characteristics of the historical data, enabling precise UAV positioning during partial occlusion and effective responses to high-speed movements. Moreover, our proposed RVUAV-Net minimizes false alarms in the complex environments by distinguishing UAVs from similar flying objects. Experimental results demonstrate superior positioning performance of our proposed method, highlighting its potential for anti-UAV in real-world scenarios where accuracy and continuity are critical.

In this paper, we propose a novel physical-layer key generation (PKG) scheme for orthogonal frequency division multiplexing-orbital angular momentum (OFDM-OAM) systems to significantly enhance the confidentiality capacity (CC). In the proposed scheme, we first establish the OAM channel model under uniform circular array (UCA) misalignment in the line-of-sight (LoS) channel. Facing the risk of information leakage during key negotiation, we couple key generation with the OFDM communication process. Then, we analyze the CC of the OFDM-OAM system and derive its closed-form expression. Simulation results illustrate that the proposed OFDM-OAM PKG scheme has achieved high CC compared with existing works. In addition, as the offset angle of the eavesdropper’s UCA increases, the CC increases, and the bit error rate (BER) of the eavesdropper tends to be 0.5.

deep learning (DL) has been widely applied to enhance automatic modulation classification (AMC). However, the elaborate AMC neural networks are susceptible to various adversarial attacks, which are challenging to handle due to the generalization capability and computational cost. In this article, an explainable DL based defense scheme, called Shapley additive explanation enhanced adversarial fine-tuning (SHAP-AFT), is developed in the perspective of disclosing the attacking impact on the AMC network. By introducing the concept of cognitive negative information, the motivation of using SHAP for defense is theoretically analyzed first. The proposed scheme includes three stages, i.e., the attack detection, the information importance evaluation, and the AFT. The first stage indicates the existence of the attack. The second stage evaluates contributions of the received data and removes those data positions with negative Shapley values corresponding to the dominating negative information caused by the attack. Then the AMC network is fine-tuned based on adversarial adaptation samples using the refined data pattern. Simulation results show the effectiveness of the Shapley value as the key indicator as well as the superior defense performance of the proposed SHAP-enhanced adversarial fine-tuning (SHAP-AFT) scheme in face of different attack types and intensities.

Federated learning (FL), as an innovative paradigm of distributed learning, provides reliable support for the growing edge intelligence (EI). The limitations of traditional FL’s reliance on ground base stations (BSs) make the development of aerial server unmanned aerial vehicles (UAVs) inevitable, thereby developing the air-ground integrated FL (AGIFL). However, current efforts predominately focus on static offline training based on existing datasets and some efforts consider online training in dynamic sample environments, where new samples need to be fully pre-trained to determine sample quality. To this end, we study how to realize high-performance of FL in dynamic environment without training all samples. Specifically, we formulate a joint optimization problem for sample selection, UAV deployment, and resource distribution aiming to minimize the trade-off between the user energy consumption and FL performance. To address the optimization problem without explicit expression, we employ meta-learning to derive an upper bound on the gradient norm of the loss function to evaluate learning performance, and describe how time-varying small-batch ratios affect this bound. Then, we propose an optimization algorithm that ensures convergence, capitalizing on the block coordinate descent techniques. To demonstrate the efficacy of our algorithm, we conduct both extensive simulations and proof-of-concept field experiments. The findings indicate an average improvement of approximately 39% in reducing the objective value when compared to the benchmarks.

With the rapid development of wireless communication services, spectrum map-based localization has become an important technology in the sixth-generation (6G) wireless communication networks due to their low cost and ease of implementation. However, signal source localization based on spectrum map construction is heavily dependent on the construction accuracy of the spectrum map. This challenge is further exacerbated in urban environments due to high-density connections and complex terrain. To address the aforementioned challenges, a data-and-semantic dual-driven method is proposed, which incorporates semantic knowledge of both binary city maps and binary sampling location maps. This approach first extracts spatial dimension information that reflects signal propagation, improving the accuracy of the constructed spectrum map and signal source localization in the complex urban environments. Then, to reduce the reliance of signal source localization on the accuracy of spectrum map construction, a data-and-semantic dual-driven intelligent inference framework for simultaneously spectrum map construction and signal source localization (DSD-SCL) is proposed. Moreover, a joint training framework is employed to collaboratively optimize both spectrum map construction and signal source localization. Simulation results demonstrate that DSD-SCL exhibits superior performance in terms of stability and convergence speed. Meanwhile, it significantly enhances the construction accuracy of spectrum maps and the localization accuracy of signal sources, particularly in low sampling density and multisignal source scenarios.

In wireless communications, unauthorized broadcasting within licensed spectrum bands disrupts legitimate signals and interferes with adjacent frequencies, risking critical systems. Existing methods for detecting unauthorized broadcasting often underutilize known signal data, reducing their effectiveness in dynamic, open-set scenarios. To address this, we propose a novel framework combining a temporal convolutional autoencoder (TCAE) with boundary-guided support vector data description (BGSVDD) for accurate detection of unauthorized signals in the radio frequency spectrum. The TCAE captures temporal signal features effectively with an adaptive temporal convolutional network (ATCN), while the BGSVDD uses a small set of known unauthorized samples to create robust decision boundaries with our proposed adaptive misclassification penalty (AMP) loss. Moreover, a global-local support vector (GLSV) strategy enables efficient online model updates, maintaining detection performance in evolving wireless environments with minimal resource overhead. Experiments with real-world broadcast signals show our method outperforms state-of-the-art techniques, especially under challenging interference conditions. Tests on public datasets further confirm its strong generalization across diverse spectrum protection applications.

Automatic modulation classification (AMC) plays a vital role in advancing future wireless communication networks. Although deep learning (DL)-based AMC frameworks have demonstrated remarkable classification capabilities, they typically require large-scale training datasets and assume consistent class distributions between training and testing data-prerequisites that prove challenging in few-shot and open-set scenarios. To address these limitations, we propose a novel few-shot open-set automatic modulation classification (FSOS-AMC) framework that integrates a multi-sequence multi-scale attention network (MS-MSANet), meta-prototype training, and a modular open-set classifier. The MS-MSANet extracts features from multi-sequence input signals, while meta-prototype training optimizes both the feature extractor and the modular open-set classifier, which can effectively categorize testing data into known modulation types or identify potential unknown modulations. Extensive simulation results demonstrate that our FSOS-AMC framework achieves superior performance in few-shot open-set scenarios compared to state-of-the-art methods. Specifically, the framework exhibits higher classification accuracy for both known and unknown modulations, as validated by improved accuracy and area under the receiver operating characteristic curve (AUROC) metrics. Moreover, the proposed framework demonstrates remarkable robustness under challenging low signal-to-noise ratio (SNR) conditions, significantly outperforming existing approaches.

Cellular-connected autonomous aerial vehicles (AAVs) act as aerial users to accomplish navigation missions while maintaining a cellular connection, which are of great importance for developing low-altitude Internet of Things (IoT) systems. However, the full aerial cellular coverage is an extreme challenge. To tackle this issue, existing coverage-aware navigation studies focus on designing AAV flight trajectories to avoid weak coverage areas while completing the mission, where considerable efforts of deep-reinforcement-learning-(DRL)-based methods have been conducted. However, they lack adaptability to various targets, which limits their practical applicability in real-world scenarios. In this article, we propose a novel DRL-based multitarget coverage-aware navigation (MTCN) framework, where the AAV learns a navigation policy to minimize the weighted sum of navigation time and the expected communication outage duration. MTCN allows the AAV to efficiently navigate to arbitrary targets without the need of the separate policy training for each target. Furthermore, to improve the sample efficiency of the MTCN, we introduce the radio map-enhanced MTCN (RM-MTCN) framework, which leverages a virtual radio map to generate supplementary navigation training data. RM-MTCN can significantly reduce the need for extensive real-world interactions. Extensive experimental results demonstrate that both MTCN and RM-MTCN achieve generalized navigation behaviors across different targets while avoiding weak communication coverage areas. It is also shown that RM-MTCN exhibits faster learning speed and better performance compared to MTCN, which indicates the effectiveness of leveraging the virtual radio map during the policy training.

The rapid development of the autonomous aerial vehicle (AAV) technology has significantly facilitated its application in the burgeoning Internet of Things (IoT) ecosystem. AAV visual navigation has emerged as a particularly vibrant and crucial research area, holding the potential to greatly enhance automated IoT services. deep reinforcement learning (DRL), recognized as an effective method for visual navigation, encounters two significant challenges in the complex outdoor environments, i.e., the partial observability and the local optima trapping. In this article, to address these two challenges, we propose a novel semantic map-based cognitive escape reinforcement learning (SM-CERL) navigation method, which consists of two innovatively designed modules, namely the semantic mapping module (SMM) and the cognitive escape module (CEM). By deeply exploring the similarity structure of raw images and mapping them to the advanced semantic representations, the SMM constructs a semantic map with rich implications, which provides the AAV with the memory to enhance its understanding of the environment. Meanwhile, the CEM can proactively identify local optima and leverage the rule knowledge to establish efficient escape strategies. The holistic fusion of semantic mapping and cognitive escape mechanisms efficiently enhances the environmental comprehension and prevents AAVs from being trapped in local optima. Extensive experimental results demonstrate that our SM-CERL outperforms the existing classical and state-of-the-art methods in terms of the navigation accuracy and efficiency.

In this work, we investigate an active reconfigurable intelligent surface (RIS)-aided non-orthogonal multiple access (NOMA)-enabled space-air-ground integrated network (SAGIN) with cognitive radio, leveraging the flexible deployment of an unmanned aerial vehicle (UAV) and the ubiquitous coverage of satellite networks. The UAV serves uplink and downlink users in the secondary network via NOMA and time division multiple access mechanisms, respectively, while satellites provide wireless backhaul for the UAV and primary users. We aim to maximize the weighted sum mean rate and energy efficiency for the secondary network by jointly the optimizing power allocation, the RIS reflection coefficients (RC), the user matching factors, and the UAV trajectory. We propose an alternating optimization framework based on the block coordinate ascent (BCA) technique, which decouples the problem into multiple variable blocks for alternating optimization until convergence. Moreover, we investigate the performance of energy-efficient active RIS with a sub-connected architecture, decoupling the RIS RC optimization into amplification factor and phase shift subproblems to be solved separately. Finally, simulation results validate the effectiveness of the proposed schemes, and demonstrate weakness of passive RIS and rationality and economics of sub-connected active RIS architecture.

Spectrum sharing unmanned aerial vehicle (UAV) network is a promising technology for future communication systems to mitigate the spectrum scarcity problem. However, the future sixth-generation large-scale wireless communication networks are expected not only to provide a high data rate for massive numbers of users but also to meet their stringent service requirements. Particularly in dynamic spectrum sharing UAV networks, the coupling of multi-dimensional resources and diverse user demands make the efficient and real-time resource allocation exceptionally challenging. A partially observable deep multi-agent active inference (PODMAI) framework is proposed to tackle these issues. The variational free energy is minimized to update the policy exploiting the belief based learning method. A decentralized training and execution multi-agent strategy is designed to navigate the challenges posed by partially observable information. To further satisfy the dynamic user demand and supplement partial observations, a joint spatial-temporal-attention prediction network is designed to construct the demand prediction enhanced PODMAI framework for resource allocation. Exploiting the established framework, an intelligent spectrum allocation and trajectory optimization scheme is elaborated for a spectrum sharing UAV network with multi-modal dynamic transmission rate demands. Simulation results demonstrate that our proposed scheme outperforms benchmark schemes in terms of the network sum transmission rate. Additionally, our proposed scheme exhibits faster convergence compared to the conventional reinforcement learning. Overall, our proposed framework can enrich intelligent resource allocation frameworks and pave the way for realizing real-time resource allocation.

Semantic communication is envisioned as a potential communication paradigm enabled by artificial intelligence and is promising to break the Shannon limit for future 6G networks. This paradigm benefits uninhabited aerial vehicles (UAVs) to conserve communication resources and minimize latency by only transmitting task-relevant semantic information. However, resource allocation in the multiple collaborative UAV scenarios remains unexplored, particularly regarding multi-modal semantic communication. To tackle this challenge, this paper investigates a semantic-aware intelligent resource allocation method for multi-UAV-assisted semantic communication networks in the UAV image-sensing task-oriented scenario. A multi-modal semantic communication framework with multi-UAV relay collaboration is developed. At the semantic level, a novel quality of experience (QoE) and the transmission cost model are introduced, based on which a semantic-aware resource allocation problem is formulated, aiming to maximize QoE while minimizing the transmission cost by jointly optimizing the UAV trajectory, the spectrum bandwidth, the transmit power and the number of the transmitted semantic symbols. To deal with optimization challenges involving hybrid variables and coordination among UAVs, a multi-UAV hybrid decision-controlled deep reinforcement learning (DRL) scheme is proposed. Simulation results demonstrate the effectiveness of the proposed scheme compared with the benchmark schemes in achieving a good balance between the QoE and the transmission cost.

It is challenging to ensure both high accuracy and low complexity when localizing radiation sources. To address this challenge, we propose two novel methods leveraging time difference of arrival (TDOA) measurements. Specifically, we introduce a TDOA estimation method and a direct position determination (DPD) method based on parallel factor (PARAFAC) analysis in the cross-correlation domain. Initially, multiple sensors synchronously capture the source signal, and the cross-correlation function between signals received from a reference sensor and other sensors is calculated. Then, the primary cross-spectrum data undergoes an expansion and integration process to establish the PARAFAC model. Through cross-spectrum expansion, virtual nodes are formed, which further improves the estimation performance. The TDOA estimates for each sensor are obtained by normalizing and extracting the phase from this matrix. Additionally, we introduce a novel DPD method tailored for multipath propagation scenarios. Simulations and real-world measurements demonstrate the superiority and effectiveness of our proposed methods compared with cutting-edge methods.

Unmanned aerial vehicles (UAVs) are widely used for object detection. However, the existing UAV-based object detection systems are subject to severe challenges, namely, their limited computation, energy and communication resources, which limits the achievable detection performance. To overcome these challenges, a UAV cognitive semantic communication system is proposed by exploiting a knowledge graph. Moreover, we design a multi-scale codec for semantic compression to reduce data transmission volume while guaranteeing detection performance. Considering the complexity and dynamicity of UAV communication scenarios, a signal-to-noise ratio (SNR) adaptive module with robust channel adaptation capability is introduced. Furthermore, an object detection scheme is proposed by exploiting the knowledge graph to overcome channel noise interference and compression distortion. Simulation results conducted on the practical aerial image dataset demonstrate that our proposed semantic communication system outperforms benchmark systems in terms of detection accuracy, communication robustness, and computation efficiency, especially in dealing with low bandwidth compression ratios and low SNR regimes.

The fifth-generation new radio (NR) cellular communication is featured with numerous advancements over Long Term Evolution (LTE) and earlier technologies. It enables more flexible physical-layer resource scheduling across multiple dimensions, and two representative techniques are beamspace transmissions and time-frequency numerology selection. Nevertheless, the lightweight physical-layer secure transmission in NR remains under investigation, especially taking NR beamspace and mobility into consideration. In this work, we propose a physical-layer wireless key generation (KG) efficient beamspace adaptation scheme for NR, where the KG capacity is theoretically characterized by critical NR components including beam direction and beamwidth. In addition, we consider the impacts of user mobility on KG performance. Since NR beamspace plays a key role in deciding the channel probing window in the spatial dimension, the NR beamspace directly affects channel probing results and hence the KG efficiency. To this end, NR beam parameters are obtained to improve the KG performance. Especially, we propose to optimize the NR beamwidth for maximizing the secrecy-delay efficiency, because a tradeoff exists in adapting the beamwidth where smaller beamwidth can improve the channel estimation accuracy but increase the beam sweeping delay. Theoretical analysis and simulation results show that the beam direction adaptation provides spatial degrees of freedom for NR to enhance KG, by enabling beam selection pointing at target areas with richer multipath scatterings. Experimental results demonstrate that the narrow beam is beneficial to enhancing the channel estimation accuracy and the resultant key agreements.

Precise unmanned aerial vehicle (UAV) detection over long distances is of crucial importance for guaranteeing the airspace security. Although deep learning-based vision detectors have been developed, they still rely on a large amount of hand-crafted fixed feature priors. The existing static dense-based detectors suffer from the severe mismatch and imbalance between the small size and the high mobility of UAVs. To solve the problem, a novel multimodal fusion-based dynamic sparse UAV detection framework is proposed. The framework reformulates the feature priors in a completely dynamic sparse paradigm by using the radar data. Based on the framework, a vision-radar fusion-based dynamic sparse network (Vira-DSNet) is proposed for more balanced and robust UAV detection. The Vira-DSNet exploits our designed dynamic sparse candidate generator and radar-guided semantic feature transform to generate a small set of customized high-quality object candidates and semantic features based on the radar data. Moreover, based on Hungarian bisection matching, our Vira-DSNet eliminates the post-processing and is completely end-to-end differentiable. Furthermore, the Vira-DSNet is deployed in our developed actual vision-radar fusionbased UAV detection system to evaluate the performance in the practical applications. Experimental results demonstrate that our Vira-DSNet achieves an average precision AP50of 88.2%. It is also shown that the average recall AR1of Vira-DSNet is higher than the state-of-the-art scheme by 10.1%, while maintaining the real-time performance.

Unmanned aerial vehicles (UAVs) are increasingly being integrated into next-generation networks to enhance communication coverage and network capacity. However, the dynamic and mobile nature of UAVs poses significant security challenges, including jamming, eavesdropping, and cyber-attacks. To address these security challenges, this paper proposes a federated learning-based lightweight network with zero trust for enhancing the security of UAV networks. A novel lightweight spectrogram network is proposed for UAV authentication and rejection, which can effectively authenticate and reject UAVs based on spectrograms. Experiments highlight LSNet’s superior performance in identifying both known and unknown UAV classes, demonstrating significant improvements over existing benchmarks in terms of accuracy, model compactness, and storage requirements. Notably, LSNet achieves an accuracy of over 80% for known UAV types and an Area Under the Receiver Operating Characteristic (AUROC) of 0.7 for unknown types when trained with all five clients. Further analyses explore the impact of varying the number of clients and the presence of unknown UAVs, reinforcing the practical applicability and effectiveness of our proposed framework in real-world FL scenarios.

In this paper, we propose a novel federated framework for constructing the digital twin (DT) model, referring to a living and self-evolving visualization model empowered by artificial intelligence, enabled by distributed sensing under edge-cloud collaboration. In this framework, the DT model to be built at the cloud is regarded as a global one being split into and integrating from multiple functional components, i.e., partial-DTs, created at various edge servers (ESs) using feature data collected by associated sensors. Considering time-varying DT evolutions and heterogeneities among partial-DTs, we formulate an online problem that jointly and dynamically optimizes partial-DT assignments from the cloud to ESs, ES-sensor associations for partial-DT creation, and as well as computation and communication resource allocations for global-DT integration. The problem aims to maximize the constructed DT's model quality while minimizing all induced costs, including energy consumption and configuration costs, in long runs. To this end, we first transform the original problem into an equivalent hierarchical game with an upper-layer two-sided matching game and a lower-layer overlapping coalition formation game. After analyzing these games in detail, we apply the Gale-Shapley algorithm and particularly develop a switch rules-based overlapping coalition formation algorithm to obtain short-term equilibria of upper-layer and lower-layer subgames, respectively. Then, we design a deep reinforcement learning-based solution, called DMO, to extend the result into a long-term equilibrium of the hierarchical game, thereby producing the solution to the original problem. Simulations show the effectiveness of the introduced framework, and demonstrate the superiority of the proposed solution over counterparts.

Uncrewed aerial vehicles (UAVs) positioning is of crucial importance in diverse applications. However, it is extremely challenging to realize the precise UAVs positioning over long distances due to the small size and dramatic scale variations associated with the high mobility in the wide area. To tackle this issue, a multimodal scale normalization framework is proposed for the scale-robust precise pixel-level UAV positioning. The framework exploits our proposed distance-aware image slicing and distance-aware scale normalization module. Moreover, a modal fusion-based scale normalization network is proposed that can accept arbitrary low-resolution UAV patches and produce the consistent high-resolution images at a uniform UAV instance scale with a single learnable model. The proposed framework is generic and can be directly used in the existing pixel-level positioning pipelines to improve the positioning performance and scale robustness. To verify the proposed framework in the real application, a practical vision-radar UAV positioning system is developed. Experimental results on the real-world dataset demonstrate the generality and effectiveness of our framework. Moreover, the ablation experiments also confirm the contribution of each module in the framework.

Unmanned Aerial Vehicle (UAV) clusters are widely used in spectrum reconnaissance, where spectrum samples are transmitted back to the sink through flying ad hoc network (FANET). High sampling frequencies are required, but result in a large amount of data not being transmitted over the wireless network, affecting throughput and reliability of the network. The problem boils down to what a good sampling frequency is and how to find it. There is an unknown dynamic sampling frequency threshold, beyond which high packet loss occurs. Prior work focused on achieving higher frequencies through FANET optimization but neglected methods to find this threshold and achieve a maximum value. We propose an adaptive protocol based on binary search. With limited bandwidth and consistent sampling frequency, the protocol dynamically adjusts sampling frequencies in a phased manner at the application layer to maximize the sampling frequency that the network can handle. In addition to spectrum reconnaissance, this protocol can be extended to other applications with consistent sampling frequencies. We have constructed an application based on the EXata simulation platform to analyze the performance of the protocol in delay, stability, reliability, and search times. Simulation results show the protocol improves the efficiency and survivability of spectrum reconnaissance.

Automatic modulation classification (AMC) is a promising technology to realize intelligent wireless communications in the sixth-generation (6G) wireless communication networks. Recently, many data-and-knowledge dual-driven schemes have achieved high accuracy in AMC. However, most of these schemes focus on generating additional prior knowledge of unknown signals, which needs more computation cost in the inference phase. To solve these problems, we propose for the first time a modulation knowledge graph (MKG), and a novel knowledge graph (KG) driven AMC (KGAMC) framework by training the networks under the guidance of MKG domain knowledge. To achieve the best performance by exploiting KGAMC, a KG-driven multi-time-scale network (KG-MTSNet) is proposed to extract the MKG knowledge and the scale and frequency features of the sampled signals. Moreover, to utilize the knowledge, a designed feature aggregation loss is implemented to improve the signal feature presentation obtained by the data-driven model. Simulation results demonstrate that KGAMC significantly boosts the performances of data-driven models, and the KG-MTSNet achieves a superior classification performance compared to other benchmarks. Furthermore, the effectiveness of KGAMC is demonstrated in terms of the interpretability of the feature extraction and the sample shortage situation.

Learning-task oriented semantic communication is pivotal in optimizing transmission efficiency by extracting and conveying essential semantics tailored to the specific tasks, such as image reconstruction and classification. Nevertheless, the challenge of eavesdropping poses a formidable threat to semantic privacy due to open nature of wireless communications. In this paper, intelligent reflective surface (IRS)-enhanced secure semantic communication (IRS-SSC) is proposed to guarantee the physical layer security from a task-oriented semantic perspective. Specifically, a multi-layer codebook is exploited to discretize continuous semantic features and describe semantics with different numbers of bits, thereby meeting the need for hierarchical semantic representation and further enhancing the transmission efficiency. Novel semantic security metrics, i.e., secure semantic rate (S-SR) and secure semantic spectrum efficiency (S-SSE), are defined to map the task-oriented security requirements at the application layer into the physical layer. To achieve artificial intelligence (AI)-native secure communication, we propose a noise disturbance enhanced hybrid deep reinforcement learning (NdeHDRL)-based resource allocation scheme. This scheme dynamically maximizes the S-SSE by jointly optimizing the bits for semantic representations, reflective coefficients of the IRS, and the subchannel assignment. Moreover, we propose a novel semantic context awared state space (SCA-SS) to fusion the high-dimensional semantic space and the observable system state space, which enables the agent to perceive semantic context and solves the dimensional catastrophe problem. Simulation results demonstrate the efficiency of our proposed schemes in both enhancing the security performance and the S-SSE compared to several benchmark schemes.

Incorporating an active reconfigurable intelligent surface on an autonomous aerial vehicles (AAVs), denoted as an aerial reconfigurable intelligent surface (ARIS), introduces a novel dimension for secure transmissions. Given the constraint of limited battery capacity in AAVs, energy management emerges as a key challenge within AAV networks. In response, we propose a secure energy efficiency (SEE) transmission scheme for ARIS networks, where active ARIS is strategically deployed to enhance information security. In addition, a SEE optimal problem is formulated by considering the imperfect wiretap channel state information to optimize the active beamforming vector and the ARIS position. For this non-convex problem, we first reformulate the fractional SEE objective into an equivalent form and subsequently decompose it into two distinct subproblems: optimizing the AAV’s position and designing the active beamforming. For the AAV’s position optimization, we propose a sophisticated deep deterministic policy gradient algorithm that enables the AAV to autonomously determine the optimal ARIS position through a self-learning strategy. Regarding beamforming design, we transform this aspect into a quadratic constrained quadratic programming problem and design an alternating direction multiplier method to optimize the reflection coefficient. Subsequently, an alternating optimization algorithm is proposed to synergistically solve these subproblems. Empirical simulations validate our proposed scheme, indicating an improvement in SEE of up to 47.2%. This significant improvement underscores the efficacy of the proposed ARIS-assisted secure transmission scheme in enhancing both security and energy efficiency in AAV networks.

Spectrum maps reflect the utilization and distribution of spectrum resources in the electromagnetic environment, serving as an effective approach to support spectrum management. However, the construction of spectrum maps in urban environments is challenging because of high-density connection and complex terrain. Moreover, the existing spectrum map construction methods are typically applied to a fixed frequency, which cannot cover the entire frequency band. To address the aforementioned challenges, a UNet-based data-and-semantic dual-driven method is proposed by introducing the semantic knowledge of binary city maps and binary sampling location maps to enhance the accuracy of spectrum map construction in complex urban environments with dense communications. Moreover, a joint frequency-space reasoning model is exploited to capture the correlation of spectrum data in terms of space and frequency, enabling the realization of complete spectrum map construction without sampling all frequencies of spectrum data. The simulation results demonstrate that the proposed method can infer the spectrum utilization status of missing frequencies and improve the completeness of the spectrum map construction. Furthermore, the accuracy of spectrum map construction achieved by the proposed data-and-semantic dual-driven method outperforms the benchmark schemes, especially in scenarios with low sampling density.

Automatic modulation classification (AMC) is a promising technology to realize intelligent wireless communications in the sixth generation (6G) wireless communication networks. Recently, many data-and-knowledge dual-driven AMC schemes have achieved high accuracy. However, most of these schemes focus on generating additional prior knowledge or features of blind signals, which consumes longer computation time and ignores the interpretability of the model learning process. To solve these problems, we propose a novel knowledge graph (KG) driven AMC (KGAMC) scheme by training the networks under the guidance of domain knowledge. A modulation knowledge graph (MKG) with the knowledge of modulation technical characteristics and application scenarios is constructed and a relation-graph convolution network (RGCN) is designed to extract knowledge of the MKG. This knowledge is utilized to facilitate the signal features separation of the data-oriented model by implementing a specialized feature aggregation method. Simulation results demonstrate that KGAMC achieves supe-rior classification performance compared to other benchmark schemes, especially in the low signal-to-noise ratio (SNR) range. Furthermore, the signal features of the high-order modulation are more discriminative, thus reducing the confusion between similar signals.

Unmanned aerial vehicles (UAVs) are widely used for object detection. However, the existing UAV-based object detection systems are subject to the serious challenge, namely, the finite computation, energy and communication resources, which limits the achievable detection performance. In order to overcome this challenge, a UAV cognitive semantic communication system is proposed by exploiting knowledge graph. Moreover, a multi-scale compression network is designed for semantic compression to reduce data transmission volume while guaranteeing the detection performance. Furthermore, an object detection scheme is proposed by using the knowledge graph to overcome channel noise interference and compression distortion. Simulation results conducted on the practical aerial image dataset demonstrate that compared to the benchmark systems, our proposed system has superior detection accuracy, communication robustness and computation efficiency even under high compression rates and low signal-to-noise ratio (SNR) conditions.

Semantic communication is a promising approach to address the challenge of limited spectrum resources in the sixth-generation (6G) communication networks. However, prior works on semantic communication focus primarily on semantic coding, and they do not investigate how to efficiently construct a semantic knowledge base. In this paper, a codebook-based unified hierarchical semantic knowledge base (UH-SKB) framework is studied for multi-task semantic communications. To maximize semantic representation spaces and effectively explore the semantic relevance among multiple tasks, the semantic knowledge base is constructed jointly in both the horizontal and vertical directions. A deep K-subspace cluster method is proposed to facilitate semantic relevance extraction and semantic subspace construction for high-dimensional semantic information. Simulation results demonstrate that the proposed UH-SKB can support multi-task semantic communications efficiently, achieving up to 13.4%, 14% and 6.3% performance improvement respectively for reconstruction, segmentation and classification tasks compared to standalone semantic knowledge bases at the novel dataset when SNR is 0 dB. Moreover, the proposed UH-SKB exhibits 95.3% knowledge search efficiency improvement on the reconstruction task compared to standalone semantic knowledge bases.

Resource allocation is paramount to improve spectral efficiency in spectrum-sharing networks. However, numerous existing resource allocation schemes, especially those based on deep reinforcement learning, overlook the impact of time-variant channel quality caused by high dynamics of wireless environment, resulting in limited performance. To tackle this issue, an intelligent resource allocation scheme, enhanced by channel prediction, is proposed to jointly optimize channel allocation and transmission power. A multiple-channel prediction network utilizing the gated recurrent unit is designed to learn the evolutionary characteristics of time-varying channels. Meanwhile, an intelligent framework is proposed to capitalize fully on channel quality variations for resource allocation. Simulation results demonstrate that our proposed scheme achieves superior performance compared with other benchmark schemes, highlighting that the sum transmission rate can be improved by exploiting channel characteristics.

A novel image decomposition-guided network (IDGN) for image interpolation is proposed in this paper by incorporating the fundamentals of subband image decomposition into the design of our deep-learning network. In our work, a filter bank consisting of a Gaussian filter and a differenceof-Gaussian filter is designed for decomposing the low-resolution input image into multiple subbands of the same resolution without downsampling. These subbands are inherited with different low-frequency and high-frequency information and are ready to be interpolated individually in our developed network. For training our IDGN, the decomposed low-resolution subbands need to be paired up with their corresponding ground-truth high-resolution subbands. Since our human visual system is sensitive to high-frequency signals, a perception-regulated (PR) loss function is proposed to guide our IDGN by putting more emphasis on the high-frequency subbands during the training process. Extensive experimental results have shown that our IDGN can achieve superior performance when compared with a number of state-of-the-art image interpolation methods.

Recent advancements in unmanned aerial vehicle (UAV) technology have propelled the popularity of edge intelligence (EI) applications with deep learning in UAV swarm. Nevertheless, the high computational demands of deep neural networks (DNNs) conflict with the limited computing power and battery capacity of UAV. Furthermore, many UAV applications require real-time performance such as object detection and recognition. In this paper, we study how to achieve fast DNN inference in UAV swarm by the collaboration of multiple UAVs, and formulate the problem of minimizing the completion time of a series of arriving DNN inference tasks, under memory and energy constraints. To solve the aforementioned challenging problem with combinatorial explosion, we propose an efficient solution exploiting deep reinforcement learning (DRL) with action space simplification to find the allocation strategy of each DNN inference task within a resource-constrained UAV swarm. Simulation results validate the effectiveness of the proposed solution compared to five benchmark algorithms.

In hierarchical cognitive radio networks, the edge or cloud servers utilize the data collected by the edge devices for modulation classification, which, however, is faced with problems of the computation load, transmission overhead, and data privacy. In this article, an edge learning (EL)-based framework jointly mobilizing the edge device and the edge server for intelligent co-inference is proposed to realize the collaborative automatic modulation classification (C-AMC) between them. A spectrum semantic compression neural network is designed for the edge device to compress the collected raw data into a compact semantic embedding that is then sent to the edge server via the wireless channel. On the edge server side, a modulation classification neural network combining the bidirectional long-short term memory and attention structures is elaborated to determine the modulation type from the noisy semantic embedding. The C-AMC framework decently balances the computation resources of both the sides while avoiding the high transmission overhead and data privacy leakage. Both the offline and online training procedures of the C-AMC framework are elaborated. The compression strategy of the C-AMC framework is also developed to further facilitate the deployment, especially for the resource-constrained edge device. Simulation results show the superiority of the EL-based C-AMC framework in terms of the classification accuracy, computational complexity, and the data compression rate as well as reveal useful insights paving the practical implementation.

Satellite and high-altitude platform (HAP) are considered as the key parts of the next generation networks, and specifically for the Internet-of-Things networks, which are utilized to provide unobstructed connections and massive user access for terrestrial networks. In this article, we investigate the power allocation (PA) and system performance of nonorthogonal multiple access (NOMA)-enabled integrated satellite-HAP-terrestrial systems under practical limitations. Particularly, a practical system model is established by considering the channel estimation errors and imperfect successive interference cancelation at the receiver. To achieve the different quality of service requirements among multiple served users, we propose a novel NOMA-based PA scheme. In addition, the analytical and asymptotic expressions for the outage probability of NOMA users are obtained to verify the proposed scheme as well as the ergodic capacity. Finally, numerical results are corroborated with Monte Carlo simulations, which show the correctness of our analytical results, and the benefits of our proposed scheme. The proposed scheme indicates that the HAP relay link plays a significant role in the system performance.

Unmanned aerial vehicle (UAV) swarms have found widespread applications in executing high-complexity and remote-risk missions. However, the limited onboard resources and energy of UAV swarms may hinder their support for computation-intensive yet delay-sensitive applications, especially when faced with exponentially growing big data. This article focuses on investigating a UAV swarm-assisted aerial–ground collaborative computing system, where one UAV swarm is divided into different groups and collaborates with a remote ground base station (BS) to provide computation services for ground smart mobile devices (SMDs). A comprehensive optimization framework is presented to maximize the system’s computation efficiency by jointly designing group formation, UAV trajectories, and resource allocation. The formulated problem involves a fractional structure with nonlinear coupling of different variables, rendering it highly nonconvex. To address this challenge, we propose a Dinkelbach-based looped iterative optimization (DLIO) algorithm. Specifically, Dinkelbach’s method is initially adopted to reformulate the original problem into a parametric structure, which is then decomposed into subproblems for group formation, resource allocation, and UAVs’ trajectory scheduling. Subsequently, these subproblems are addressed by a looped iterative optimization (LIO) algorithm. The outer loop determines group forming and resource allocation, while the inner loop utilizes the method of successive convex approximation (SCA) to solve trajectory scheduling for UAVs. Simulation results validate the effectiveness of our proposed DLIO algorithm, ensuring rapid convergence and significant improvements in the system’s computation efficiency compared to other benchmarks.

Mobile edge computing (MEC) is expected to provide low-latency computation service for wireless devices (WDs). However, when WDs are located at cell edge or communication links between base stations (BSs) and WDs are blocked, the offloading latency will be large. To address this issue, we propose an intelligent reflecting surface (IRS)-assisted cell-free MEC system consisting of multiple BSs and IRSs for improving the transmission environment. Consequently, we formulate a min–max latency optimization problem by jointly designing multiuser detection (MUD) matrices, IRSs’ reflecting beamforming vectors, WDs’ offloading data size and edge computing resource, subject to constraints on edge computing capability and IRSs phase shifts. To solve it, an alternating optimization algorithm based on the block coordinate descent (BCD) technique is proposed, in which the original nonconvex problem is decoupled into two subproblems for alternately optimizing computing and communication parameters. In particular, we optimize the MUD matrix based on the second-order cone programming (SOCP) technique, and then develop two efficient algorithms to optimize IRSs’ reflecting vectors based on the semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques, respectively. Numerical results show that employing IRSs in cell-free MEC systems outperforms conventional MEC systems, resulting in up to about 60% latency reduction can be attained. Moreover, numerical results confirm that our proposed algorithms enjoy a fast convergence, which is beneficial for practical implementation.

Wireless powered mobile edge computing (WPMEC) has been recognized as a promising solution to enhance the computational capability and sustainable energy supply for lowpower wireless devices (WDs). However, when the communication links between the hybrid access point (HAP) and WDs are hostile, the energy transfer efficiency and task offloading rate are compromised. To tackle this problem, we propose to employ multiple intelligent reflecting surfaces (IRSs) to WP-MEC networks. Based on the practical IRS phase shift model, we formulate a total computation rate maximization problem by jointly optimizing downlink/uplink IRSs passive beamforming, downlink energy beamforming, and uplink multiuser detection (MUD) vector at HAPs, task offloading power and local computing frequency of WDs, and the time slot allocation. Specifically, we first derive the optimal time allocation for downlink wireless energy transmission (WET) to IRSs and the corresponding energy beamforming. Next, with fixed time allocation for the downlink WET to WDs, the original optimization problem can be divided into two independent subproblems. For the WD charging subproblem, the optimal IRSs passive beamforming is derived by utilizing the successive convex approximation (SCA) method and the penaltybased optimization technique, and for the offloading computing subproblem, we propose a joint optimization framework based on the fractional programming (FP) method. Finally, simulation results validate that our proposed optimization method based on the practical phase shift model can achieve a higher total computation rate compared to the baseline schemes.

Anti-unmanned aerial vehicle (UAV) systems are crucial for preventing unauthorized individuals from exploiting UAVs for illegal activities, including surveillance and attacks. Precise real-time positioning of small UAVs is the premise of the effective operation of anti-UAV systems. However, its performance is confined due to the small size of the target and its high susceptibility to disturbance caused by birds or other aircraft. To tackle this problem, a radio-frequency (RF)-visual directional fusion framework is proposed for precise UAV positioning. In the framework, radio signals are aligned with images by jointly calibrating the array antenna and camera. The spatial spectrum is extracted by an array antenna to concentrate on target areas within the image modal. Moreover, in order to improve the precision of joint calibration, a segmentation-based denoising method is proposed to remove the spectrum noise. Furthermore, a practical anti-UAV positioning platform is established, and two synchronized data sets, which include visual images and UAV RF signals, are collected on the platform. Experimental results demonstrate that our proposed framework improves positioning accuracy and robustness compared to the benchmark methods.

Precise real-time unmanned aerial vehicle (UAV) positioning is crucial for preventing unauthorized UAVs from damaging cooperative intelligent transportation systems (C-ITSs). However, UAV positioning remains extremely challenging due to the small target size and high flexibility. Therefore, we develop a modal-oriented self-tuning fusion framework for precise UAV millimeter-wave(MMW)-vision positioning. The framework selects and extracts cross-modal features based on modality characters, and migrates the Doppler features of MMW radar data to the image features for precise pixel-level positioning. Based on the framework, a modal-oriented self-tuning fusion network is proposed to adaptively enhance UAV feature without direct supervision by exploiting the cross-modal correlations. A novel characteristic-based 3DMMW feature extraction method is presented to extract UAV Doppler motion characteristics while a self-tuning cross-modal affine transfer is proposed for UAV visual feature enhancement. Due to lack of dataset for our task, we establish a practical positioning platform and two novel datasets containing synchronized visual images and MMW radio frequency (RF) sequences in various scenarios. Experimental results confirm that our framework outperforms the benchmark methods in terms of positioning accuracy while maintaining real-time performance. Moreover, ablation studies also confirm the effectiveness of each module in the framework.

The clustering unmanned aerial vehicles (UAVs) positioning is significant for preventing unauthorized clustering UAVs from causing physical and informational damages. However, current positioning systems suffer from limited sensing view and positioning range, which result in poor positioning performance. In order to tackle those issues, a novel vehicle-mounted radar-vision clustering UAVs positioning system is developed, which achieves precise, wide-area, and dynamic-view sensing and positioning of the clustering UAVs. Moreover, a matching-based spatiotemporal fusion framework is established to mitigate cross-modal and cross-view spatiotemporal misalignment by adaptively exploiting the cross-modal and cross-view feature correlations. Furthermore, we propose an attention-based spatiotemporal fusion method that achieves a trinity projective attention with the unique structure and task-oriented format for effective feature matching and precise clustering UAVs positioning. Our method also exploited the modality-oriented cross-modal feature and the UAV-motion-oriented cross-view UAV spatiotemporal motion feature.We demonstrate the advantages of our proposed framework and positioning method in our developed clustering UAVs positioning system in practice. Experimental results confirm that our proposed method outperforms the benchmark methods in terms of the positioning precision, especially under the occlusion scenarios. Moreover, ablation studies confirm the effectiveness of each unit of our method.

In this paper, we propose an adaptive semantic resource allocation paradigm with semantic-bit quantization (SBQ) compatible with existing wireless communications, where the inaccurate environment perception introduced by the additional mapping relationship between semantic metrics and transmission metrics is solved. Specifically, SBQ is a hybrid uniform-non-uniform quantization method, which aims to facilitate the coding between semantics and bits. In order to investigate the performance of semantic communication networks, the quality of service for semantic communication (SC-QoS), including the semantic quantization efficiency (SQE) and transmission latency, is proposed for the first time. A problem of maximizing the overall effective SC-QoS is formulated by jointly optimizing the transmit beamforming of the base station, the bits for semantic representation, the subchannel assignment, and the bandwidth resource allocation. To address the non-convex formulated problem, an intelligent resource allocation scheme is proposed based on a hybrid deep reinforcement learning (DRL) algorithm, where the intelligent agent can perceive both semantic tasks and dynamic wireless environments. Simulation results demonstrate that our design can effectively combat semantic noise and achieve superior performance in wireless communications compared to several benchmark schemes. Furthermore, compared to mapping-guided paradigm based resource allocation schemes, our proposed adaptive scheme can achieve up to 13% performance improvement in terms of SC-QoS.

Wireless signal recognition (WSR) is crucial in modern and future wireless communication networks since it aims to identify properties of the received signal. Although many deep learning-based WSR models have been developed, they still rely on a large amount of labeled training data. Thus, they cannot tackle the few-sample problem in the practically and dynamically changing wireless communication environment. To overcome this challenge, a novel SSwsrNet framework is proposed by using the deep residual shrinkage network (DRSN) and semi-supervised learning. The DRSN can learn discriminative features from noisy signals. Moreover, a modular semi-supervised learning method that combines labeled and unlabeled data using MixMatch is exploited to further improve the classification performance under few-sample conditions. Extensive simulation results on automatic modulation classification (AMC) and wireless technology classification (WTC) demonstrate that our proposed WSR scheme can achieve better performance than the benchmark schemes in terms of classification accuracy. This novel method enables more robust and adaptive signal recognition for next-generation wireless networks.

Semantic communication (SemCom) is envisioned as a promising technique to break through the Shannon limit. However, semantic inference and semantic error correction have not been well studied. Moreover, error correction methods of existing SemCom frameworks are inexplicable and inflexible, which limits the achievable performance. In this paper, to tackle this issue, a knowledge graph (KG) is exploited to develop SemCom systems. Two cognitive semantic communication frameworks are proposed for the single-user and multiple-user communication scenarios. Moreover, a simple, general, and interpretable semantic alignment algorithm for semantic information detection is proposed. Furthermore, an effective semantic correction algorithm is proposed by mining the inference rule from the KG. Additionally, the pre-trained model is fine-tuned to recover semantic information. For the multi-user cognitive SemCom system, a message recovery algorithm is proposed to distinguish the messages of different users by matching the knowledge level and the context at the destination. Extensive simulation results conducted on a public dataset demonstrate that our proposed single-user and multi-user cognitive SemCom systems are superior to benchmark communication systems in terms of the data compression rate and communication reliability. Finally, we present realistic single-user and multi-user cognitive SemCom systems results by building a software-defined radio prototype system.

Fake news detection is of crucial importance and has received great attention. However, the existing fake news detection methods rarely consider the news release time, which limits the achievable detection performance, especially for detecting the instant fake news clusters that have sudden and aggregated characteristics. To tackle this issue, a temporal enhanced multimodal graph neural networks (TEMGNNs) method is proposed. The multimodal graph with semantic complementary enhancement is developed by feature aggregation of textual information, image information, and external knowledge. Moreover, the associations among different modalities are obtained by using the graph attention networks and the weights of each modality are adaptively learned. Furthermore, the aggregation of news with adjacent time and the same topic to form a temporal news cluster and learning temporal features for fake new detection by using our proposed graph neural networks. Extensive experiments results obtained on two public datasets demonstrate that our proposed method has the best performance compared with the benchmark methods. It is also shown that the exploitation of the temporal information and multimodal information benefits for fake news detection.

Recommendation systems are of crucial importance due to their wide applications. Knowledge graph (KG) enabled recommendation schemes have attracted great attention due to their superior performance and interpretability. However, the rich social information is not exploited for those systems, which limits the recommendation performance . In this paper, a novel explainable recommendation scheme is proposed by exploiting our designed social enhanced knowledge graph attention network (SKGAN). The hidden relations among users and items are learned and used for recommendation with the collaborative KG (CKG) and the user social graph (USG). Moreover, the high-order semantic information in both CKG and USG are obtained by using the graph convolution networks (GCNs) and the node level attention algorithm. Furthermore, a graph level user-specific attention algorithm is proposed to capture the user personalized preference between CKG and USG. Extensive experiment results demonstrate that normalized discounted cumulative gain (NDCG), precision, recall and hits ratio (HR) achieved with our proposed recommendation system are the best among those obtained with the state-of-the-art benchmark recommendation systems.

Optimizing deep neural network (DNN) models to meet quality of service (QoS) requirements in terms of accuracy and computation is of crucial importance for realizing efficient on-device inference in resource-constrained artificial intelligence of things (AIoT). However, most existing works can hardly satisfy the aforementioned QoS requirements since the intrinsic multi-scale characteristic of DNN structures has been seldom considered. In this paper, we formulate a QoS-ensured DNN model structure optimization problem as a novel multi-scale Markov decision process (MSMDP), which can collaboratively decide the DNN structures from different scales. To efficiently solve the above problem, we propose a multi-scale reinforcement learning (MSRL) algorithm, which jointly optimizes block and channel number by interactive multi-scale decision, while ensuring QoS by QoS-based decision evaluation and policy update. Extensive experiments are conducted in both the actual AIoT scenarios and public datasets for different tasks by using different AIoT devices. The results confirm that our proposed MSRL outperforms the baseline schemes in terms of QoS satisfaction, convergence performance, and complexity. Specifically, our algorithm respectively reduces 98.6% computation and 95.7% model size at most while ensuring the QoS compared with the state-of-the-art methods.

Automatic modulation classification (AMC) is of crucial importance in the sixth generation wireless communication networks. Deep learning (DL)-based AMC schemes have attracted extensive attention due to their superior accuracy compared with the conventional methods. However, a pure data-driven DL method relies on a large amount of labeled training samples and the classification accuracy is poor, especially in the low signal-to-noise ratio (SNR). In order to tackle this problem, two data-and-knowledge dual-driven AMC schemes are designed. A novel data and semantic knowledge driven AMC scheme is proposed by exploiting the semantic attribute information of different modulations. Moreover, a prior knowledge driven multi-task learning visual model is established to improve the classification performance in low SNR. Furthermore, another novel data and multi-domain knowledge joint driven AMC scheme is proposed by using the semantic attribute knowledge and the prior knowledge based multi-task learning visual model. Extensive simulation results demonstrate that our proposed data-and-knowledge dual-driven AMC schemes achieve the best performance compared with the benchmark schemes in terms of classification accuracy. Moreover, it is shown that the expert knowledge spawns for AMC accuracy improvement and a decrease in the required number of training samples.

Unmanned aerial vehicle (UAV) communication is of crucial importance in realizing heterogeneous practical wireless application scenarios. However, it is susceptible to the severe spectrum scarcity and interference issues since it operates in the unlicensed frequency band. To tackle those issues, a dynamic spectrum sharing UAV network adopting an anti-jamming technique is considered. Two intelligent spectrum allocation and trajectory optimization schemes are designed, capitalizing on the proposed external interaction and internal inference based frameworks. For the first scheme, a novel external interaction based hybrid online-offline multi-agent actor-critic and deep deterministic policy gradient (MA2C-DDPG) framework is proposed taking into account the hybrid characteristics of discrete spectrum allocation and continuous UAV trajectory. As for the second scheme, another novel framework, the deep active inference (DAI) based on internal inference is proposed, which minimizes the internal variational free energy. Moreover, a belief learning based method is exploited to enhance the agents’ perception and improve the action selection in the dynamic spectrum sharing environment. Extensive simulation results demonstrate the high efficiency of our proposed schemes. It is shown that our proposed schemes significantly improve the secondary network sum transmission rate compared to various benchmark schemes. Moreover, the proposed MA2C-DDPG and DAI frameworks demonstrate the advantages in improving the training stability and convergence speed.

Existing path planning schemes designed for wireless sensor networks generally account for abstract payload sensors, rendering them inapplicable to concrete sensor-array-based localization systems due to differences in measurement models. In this paper, we establish a general framework for path planning of a practical sensor array and factor in an oft-neglected degree of freedom regarding optimality, i.e., the array’s orientation/attitude. The optimization problem is formulated based on the A-optimality criterion under constraints arising from the maximum distance between consecutive waypoints, maximal heading change, and forbidden regions. To facilitate semidefinite relaxation (SDR), we recast the optimization function into a fractional nonhomogeneous quadratic structure and transform the constraints into a bilinear form. By applying SDR and replacing the bilinear terms with a matrix variable, the problem is relaxed into a single-ratio fractional program. By leveraging the Charnes-Cooper variable transformation, we transform the single-ratio fractional program into a mixed semidefinite/second-order cone program (SD/SOCP) that can be solved in polynomial time. Finally, we apply the results to angle-of-arrival (AOA) and direct localization. Simulation results demonstrate that the proposed path planning scheme attains near-optimal performance.

Secure communications are of paramount importance in spectrum sharing networks due to the allocation and sharing characteristics of spectrum resources. To further explore the potential of intelligent reflective surfaces (IRSs) in enhancing spectrum sharing and secure transmission performance, a multiple intelligent reflection surface (multi-IRS)-assisted sensing-enhanced wideband spectrum sharing network is investigated by considering physical layer security techniques. An intelligent resource allocation scheme based on double deep Q networks (D3QN) algorithm and soft Actor-Critic (SAC) algorithm is proposed to maximize the secure transmission rate of the secondary network by jointly optimizing IRS pairings, subchannel assignment, transmit beamforming of the secondary base station, reflection coefficients of IRSs and the sensing time. To tackle the sparse reward problem caused by a significant amount of reflection elements of multiple IRSs, the method of hierarchical reinforcement learning is exploited. An alternative optimization (AO)-based conventional mathematical scheme is introduced to verify the computational complexity advantage of our proposed intelligent scheme. Simulation results demonstrate the efficiency of our proposed intelligent scheme as well as the superiority of multi-IRS design in enhancing secrecy rate and spectrum utilization. It is shown that inappropriate deployment of IRSs can reduce the security performance with the presence of multiple eavesdroppers (Eves), and the arrangement of IRSs deserves further consideration.

Spectrum sensing and communication security are of crucial importance in cognitive radio networks (CRNs). In this paper, we utilize intelligent reflecting surfaces (IRS) to simultaneously enhance spectrum sensing accuracy and the secrecy performance of secondary users (SUs) through physical layer security (PLS) techniques. Additionally, we employ IRS as a novel approach to achieve the target probability of detection. We formulate a joint sensing and transmission security optimization problem to maximize the sum secrecy rate of SUs under both perfect and imperfect channel state information (CSI). To transform the probability of detection into a tractable expression, we adopt a safe approximation for theQ-function. We use a computationally-efficient block coordinate descent (BCD)-based algorithm to optimize the beamforming design and IRS phase shifts alternately. Specifically, we employ theS-procedure to handle the semi-infinite constraints under the imperfect CSI case. Simulation results demonstrate that by leveraging IRS for spectrum sensing, we can significantly reduce the sensing time while achieving the required probability of detection and the probability of false alarm. Furthermore, our proposed scheme improves both sensing accuracy and secrecy rate in both cases compared to the benchmark schemes.

Spectrum sharing is promising as a solution to address the spectrum crunch by enabling the coexistence of different networks in the same frequency band. However, interference from concurrent transmissions remains an obstacle to further enhance spectral efficiency. Therefore, to overcome the bottleneck caused by multi-user interference, both rate-splitting multiple access (RSMA)-enabled underlay and overlay spectrum-sharing strategies are proposed in this paper. To facilitate a robust resource allocation design, the common and the private beamforming vectors as well as the common rate allocation are jointly optimized under the norm-bounded channel state information (CSI) error model to maximize the worst-case weighted sum rate (WSR) of the secondary networks. To address the formulated challenging non-convex quadratically-constrained resource allocation optimization problems, a computationally efficient successive convex approximation (SCA)-based algorithm capitalizing on semidefinite relaxation (SDR) is proposed. Simulation results demonstrate that the proposed algorithms outperform non-orthogonal multiple access (NOMA)-based benchmark schemes in worst-case WSR and robustness. Moreover, the results indicate that the proposed novel RSMA-enabled overlay spectrum-sharing strategy can offer a higher flexibility in resource allocation compared to their underlay counterparts. Furthermore, the tradeoff between interference management and spectral performance enhancement for the proposed RSMA-enabled overlay spectrum-sharing strategy is unveiled.

Semantic communication is envisioned as a potential communication paradigm based on artificial intelligence that holds the promise of breaking the Shannon limit for future 6G networks. This paradigm offers a promising opportunity for Unmanned Aerial Vehicles (UAVs) to conserve communication resources and minimize latency by only transmitting relevant semantic information. Despite the promising potential of UAV semantic communication networks, resource allocation in this context remains largely unexplored, particularly regarding multi-modal communication that adjusts the types of transmitted information (image, text, video, etc.) according to the task objectives and the available resources. This paper addresses the semantic-oriented resource allocation for multi-modal semantic communication with a focus on the UAV image-sensing task-oriented scenario. Firstly, a multi-modal semantic communication for the original image-sensing tasks of UAVs is designed. Subsequently, a semantic-level resource allocation problem based on the approximate semantic entropy and the semantic rate is formulated in terms of the transmit power allocation, channel assignment, and the number of transmitted semantic symbols. To solve the problem formulated, which involves a hybrid discrete-continuous action space, a novel algorithm called Hybrid-Decision-Controlled Deep Reinforcement Learning-based Semantic Communication Allocation (HDCD-SC) is introduced. The simulation results demonstrate that the proposed HDCD-SC algorithm can dynamically adjust the transmission modal according to the available resources, and achieve better performance in terms of latency, amount of semantic information, and notable reductions in energy and bandwidth costs when compared to other benchmarks.

In physical-layer security schemes, radio frequency fingerprint (RFF) identification is vulnerable to the channel variations, and the identification performance of mobile devices using long term evolution (LTE) signals remains to be validated. In this paper, we propose an RFF extraction method based on LTE demodulation reference signal (DMRS) signal processing for LTE mobile devices. First, we analyze the impacts of the RFF and channel fading on DMRS in the LTE uplink channel. Then, we propose an RFF extraction method based on the wavelet decomposition and reconstruction of DMRS. By removing the low-frequency components of DMRS, which are mainly affected by the channel effects, our proposed method is robust to the channel impairments. Finally, our simulation and experimental results show that our method can effectively reduce the channel impacts and retain the RFF of devices. The effectiveness of this method is verified via different classification tasks. The classification accuracy can reach 98.5% and 93.9% in the stationary and mobile scenarios, respectively.

Resource allocation is of crucial importance in wireless communications. However, it is extremely challenging to design efficient resource allocation schemes for future wireless communication networks since the formulated resource allocation problems are generally non-convex and consist of various coupled variables. Moreover, the dynamic changes of practical wireless communication environment and user service requirements thirst for efficient real-time resource allocation. To tackle these issues, a novel partially observable deep multi-agent active inference (PODMAI) framework is proposed for realizing intelligent resource allocation. A belief based learning method is exploited for updating the policy by minimizing the variational free energy. A decentralized training with a decentralized execution multi-agent strategy is designed to overcome the limitations of the partially observable state information. Exploited the proposed framework, an intelligent spectrum allocation and trajectory optimization scheme is developed for a spectrum sharing unmanned aerial vehicle (UAV) network with dynamic transmission rate requirements as an example. Simulation results demonstrate that our proposed framework can significantly improve the sum transmission rate of the secondary network compared to various benchmark schemes. Moreover, the convergence speed of the proposed PODMAI is significantly improved compared with the conventional reinforcement learning framework. Overall, our proposed framework can enrich the intelligent resource allocation frameworks and pave the way for realizing real-time resource allocation.

Spectrum sensing and the communication security are of crucial importance in cognitive radio networks (CRNs). In this paper, intelligent reflecting surface (IRS) is exploited in CRNs to simultaneously enhance the spectrum sensing accuracy and the secure performance achieved by using physical layer security (PLS) techniques. The sum secrecy rate of the secondary users (SUs) is maximized by jointly optimizing the sensing time, the beamforming design and the IRS phase shifts. A safe approximation is adopted to transform the probability of detection into a tractable expression. A computationally efficient block coordinate descent (BCD)-based algorithm with the techniques of successive convex approximation (SCA) and semidefinite relaxation (SDR) is exploited to optimize the beamforming and the phase shifts alternately. Simulation results demonstrate that our proposed algorithm can significantly improve both the sensing performance and the secrecy rate compared with the benchmark schemes.

Unmanned aerial vehicles (UAVs) have been widely used to perform search and tracking tasks in military and civil fields. To perform these tasks autonomously, a swarm of multiple UAVs need to be endowed with intelligence through machine learning (ML). However, the traditional centralized ML cannot be directly applied in UAV networks, since it is challenging to transmit raw data with limited bandwidth and energy budget. As a distributed manner, federated learning (FL) is more suitable for UAV networks than traditional ML schemes in order to boost edge intelligence for UAVs. Considering the limited energy supply of UAVs, we study how to minimize UAVs’ overall training energy consumption by jointly optimizing the local convergence threshold, local iterations, computation resource allocation, and bandwidth allocation, subject to the FL global accuracy guarantee and maximum training latency constraint. The formulated nonconvex mixed-integer programming problem is solved by a joint training and resource allocation optimization algorithm. In addition, we also study how to solve the problem considering fairness among different UAVs by changing the objective to minimizing the maximum energy consumption of UAVs, and extend the aforementioned approach to this problem. Our simulation results show that while satisfying both the training accuracy and latency constraints, the proposed algorithm can reduce more UAVs’ overall training energy consumption and the maximum energy consumption in the UAV swarm than four baseline schemes.

Wireless network security can be improved by radio frequency fingerprinting due to its stability and uniqueness. Although many radio frequency fingerprint identification (RFFI) methods based on deep learning have been proposed, they have low identification accuracy, especially at low signal-to-noise ratio. In order to overcome this drawback, a data-and-knowledge dual-driven RFFI scheme is proposed by utilizing a knowledge-driven multiscale attention convolutional network (AttMsCN). The protocol knowledge is exploited to provide more advanced semantics. Moreover, the AttMsCN is utilized to capture higher level features. Simulation results demonstrate that our designed scheme has the best performance than the representative schemes in the matter of convergence speed and identification accuracy.

This paper exploits an intelligent reflecting surface (IRS) assisted wireless powered mobile edge computing and caching (WP-MECC) network. In particular, an IRS is utilized to reflect energy signals from a power station (PS) to various IoT devices for energy harvesting during uplink wireless energy transfer (WET). These devices collect energy to support their own partially local computing for computational tasks and their offloading capabilities to an access point (AP), with the help of IRS via time or frequency division multiple access (TDMA or FDMA). The AP is equipped with a local cache connected with a MEC server via a backhaul link, which prefetches the data to facilitate edge computing capabilities. The maximization of a utility function is formulated to evaluate the overall network performance, which is defined as the difference between the sum of computational bits (offloading bits and local computing bits) and total backhaul cost. Due to multiple coupled variables, we first design the optimal caching strategy. Then, an auxiliary vector is introduced to coordinate the energy consumption of local computing and offloading, where its optimal solution can be achieved by an exhaustive search. Moreover, we utilize the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling for the TDMA scheme or the optimal bandwidth allocation for the FDMA counterpart in closed form. The IRS phase shifts are iteratively designed by employing the quadratic transformation (QT) and the Riemannian Manifold Optimization (RMO). Finally, simulation results are demonstrated to validate the network utility performance and confirm the advantage of the employment of IRS, the optimal IRS phase shift design and caching strategy, in comparison to the benchmark schemes.

Spectrum maps are of crucial importance for realizing efficient spectrum management in the sixth-generation (6G) wireless communication networks. However, existing spectrum map construction schemes mainly depend on spatial interpolation or just simply exploit the frequency correlation and cannot accurately construct the spectrum map when measurement data at the target frequency are not available. To overcome this challenge, we propose two accurate spectrum map construction schemes using different intelligent frequency-spatial reasoning methods. The frequency correlation among different spectrum maps at different frequencies is fully exploited to construct highly accurate spectrum maps of the frequencies without spectrum data by combining the joint frequency-spatial spectrum representation method with deep learning data-driven techniques. Specifically, a joint three-dimensional spectrum representation model is established and both a novel autoencoder network and a novel conditional generative adversarial network suitable for processing the three-dimensional spectrum data are proposed to realize the intelligent frequency-spatial reasoning. Simulation results demonstrate that our proposed schemes are superior to the benchmark schemes in terms of the spectrum map construction accuracy. Moreover, simulation results demonstrate that our proposed neural networks have a fast convergence speed, which achieves a better tradeoff between the computation efficiency and the construction accuracy.

A heterogeneous unmanned aerial vehicle (UAV) network where UAVs carrying different resources form coalition and cooperatively carry out tasks is of crucial importance for fulfilling diverse tasks. However, the existing coalition formation (CF) game model only optimizes the composition of UAVs in a single coalition, which results in disjoined coalitions. In order to tackle this issue, a sequential overlapping coalition formation (OCF) game is proposed by considering the overlapping and complementary relations of resource properties and the task execution order. Moreover, different from the traditional Pareto and Selfish orders, a bilateral mutual benefit transfer (BMBT) order is proposed to optimize the cooperative task resource allocation through partial cooperation among overlapping coalition members. Furthermore, using the preference relation between UAVs carrying resources and tasks requiring the same type of resource, a preference gravity-guided Tabu Search (PGG-TS) algorithm is developed to obtain a stable coalitional structure. Numerical results verify that the utility of the proposed OCF game scheme based on the PGG-TS algorithm increases by 18% against that of the non-overlapping CF game scheme, and the utility of the proposed BMBT order increases by 25%, compared with other orders.

Spectrum data compression with a high-rate compression and accurate reconstruction is of crucial importance for reducing the ultra-large data transmission from the edge sensors to the cloud for establishing high-quality spectrum maps. However, the current methods ignore the imbalanced edge-cloud computation resources and cannot tackle the outlier signals, resulting in significant challenges for achieving effective compression. Therefore, we develop an efficient heterogeneous edge-cloud learning framework. In the framework, paralleled methods compress normal data and outlier data distinctively based on their different structure information. Meanwhile, those methods are asymmetric for achieving low-cost compression at the edge and accurate reconstruction on the cloud. Based on the framework, we propose an outlier-processable attention-based asymmetric compression algorithm. A novel attention-based asymmetric convolutional neural network performs the normal data compression while a non-linear outlier compression algorithm realizes the outlier data compression. Compared with the state-of-the-art schemes in real-world settings, our proposed framework’s convergence speed increases by 120% . Meanwhile, our framework’s reconstruction accuracy increases by 68.42% under the interfered environments while maintaining superior compression speed and comprehensive performance. We also confirm our framework’s generalization ability to transfer among different tasks by deploying it under various spectrum environments.

As a way of providing users flexible computing services, networks exist that can make full use of air and ground computing resources. Such networks are called air-ground collaborative mobile edge computing (AGC-MEC) networks. AGC-MEC supports numerous emerging real-time applications for which timely computed results are critical. Researchers have developed a novel metric “age of information (AoI)” that can capture the freshness of computed results. This is the first paper to study the problem of AoI-aware scheduling forAir-groundCollaborative mobileEdge computing (i.e., IACE). So as to minimize the weighted AoI of all the terrestrial user equipments (UEs), we have jointly optimized task scheduling, computing resource allocation, and unmanned aerial vehicle (UAV) trajectory taking into account the constraints on the computing resources and the available energy of the UAV. The formulated problem, which is a challenge to solve, is a mixed-integer nonlinear programming (MINLP) problem. To obtain an effective solution, we propose an iterative algorithm based on the alternating optimization approach, which entails dividing the considered problem into three subproblems. Extensive simulations show that the proposed algorithm can achieve lower weighted AoI than five benchmark algorithms, while satisfying the resource constraints. Furthermore, simulation results demonstrate two interesting insights. First, the introduction of an aerial MEC server facilitates a flexible offloading design of the UEs which is critical to guaranteeing the freshness of computed results. Second, by optimizing the scheduling, the proposed design can unlock performance gains, especially in the resource-limited regime.

Cognitive radio (CR) is one of the most disruptive techniques for enabling the next generation wireless communication networks due to its potential in improving the spectral efficiency. In this paper, intelligent reflecting surface (IRS) is exploited to enhance both the accuracy of spectrum sensing and the secondary transmission in a CR network (CRN) employing the opportunistic spectrum access. A novel detection threshold based on the probability of false alarm is derived for improving the spectrum sensing performance. The average achievable rate of the secondary network is maximized under both the two-stage and one-stage IRS phase shifts case. To tackle the challenging non-convex optimization problem under the two-stage case, a computationally efficient block coordinate descent (BCD)-based algorithm is proposed coputilizing the techniques of successive convex approximation (SCA) and semidefinite relaxation (SDR). Moreover, a BCD method and a tractable approximation of the probability of detection are exploited to tackle the problem under one-stage IRS phase shifts case. Simulation results demonstrate that our proposed designs are superior to the benchmark schemes in terms of the achievable rate and the sensing performance, and IRS can greatly improve the spectral efficiency of the CRN.

Orthogonal frequency division multiplexing (OFDM) systems have been widely applied in practice since OFDM has diverse outstanding advantages. However, their performance improvement is confronted with bottlenecks. In this paper, in order to tackle this issue, intelligent resource allocation driven by reinforcement learning is studied in intelligent reflecting surface (IRS) enhanced OFDM systems. The system sum rate is maximized by jointly optimizing the subcarrier allocation, the transmit beamforming of the base station and the phase shift of the IRS. An intelligent resource allocation scheme based on combining deep Q networks (DQN) and deep deterministic policy-gradient (DDPG) is proposed to tackle the formulated challenging non-convex problems. In order to further improve the spectrum efficiency, spectrum sharing is considered in the IRS-enhanced OFDM system. The secondary users sum rate maximization framework is formulated by jointly optimizing the channel allocation, the transmit beamforming of the secondary base station (SBS) and the phase shift of the IRS. Dueling double deep Q networks (D3QN) and twin delayed deep deterministic policy gradient (TD3) are exploited to tackle the hybrid action space issue under interference. Simulation results demonstrate that our proposed schemes can significantly improve the transmission rate compared to the benchmark schemes.

Cognitive radio (CR) is one of the most disruptive techniques for enabling the next generation wireless communication networks due to its potential in improving the spectral efficiency. In this paper, an intelligent reflecting surface (IRS) is exploited to assist both spectrum sensing and secondary transmission in the CR network (CRN) employing opportunistic spectrum access. A novel IRS-enhanced spectrum sensing scheme and a redesigned detection threshold are proposed to improve the sensing performance. We formulate the throughput maximization problem by jointly optimizing the sensing time, the beamforming, and the IRS phase shifts. A computationally efficient block coordinate descent (BCD)-based algorithm is proposed to tackle the non-convex problem. Simulation results show that our proposed scheme is superior to other benchmark schemes in terms of both the throughput and the sensing performance.

Spectrum map is of crucial importance for realizing efficient spectrum management in the sixth-generation (6G) wireless communication networks. However, the existing spectrum map construction schemes mainly depend on spatial interpolation and cannot construct the spectrum map when the measurement data of the target frequency are not obtained. In order to overcome this challenge, an accurate spectrum map construction scheme is proposed by using an intelligent frequency-spatial reasoning approach. The frequency correlation among different spectrum maps at different frequencies is fully exploited to construct the highly accurate spectrum maps of the frequencies without spectrum data. A novel autoencoder adapting to the three-dimensional (3D) spectrum data is proposed. Simulation results demonstrate that our proposed scheme is superior to the benchmark schemes in terms of the construction accuracy. Moreover, it is shown that our proposed autoencoder network has a fast convergence speed.

In this paper, we study an uplink reconfigurable intelligent surfaces-non-orthogonal multiple access (RIS-NOMA) assisted mobile edge computing (MEC) network with simultaneous wireless information and power transfer (SWIPT), where the users want to offload their computing tasks to the BS via a RIS and a relay based on the SWIPT technique. The goal of the paper is to minimize the delay concerning the computing tasks of the users by jointly optimizing the power allocation ratio, the phase shift matrix of the RIS, the offloading task ratio, and the offloading transmit power. For solving the challenging optimization problem, we conceive a low-complexity algorithm by optimizing two subproblems separately, based on the penalty method as well as the successive convex approximation. Simulation results demonstrate that the proposed RIS-NOMA assisted MEC network with SWIPT outperforms both the conventional RIS-NOMA assisted MEC network without SWIPT and the RIS-orthogonal multiple access assisted MEC network.

In this paper, we study the resource allocation problem for an intelligent reflecting surface (IRS)-assisted OFDM system. The system sum rate maximization framework is formulated by jointly optimizing subcarrier allocation, base station transmit beamforming and IRS phase shift. Considering the continuous and discrete hybrid action space characteristics of the optimization variables, we propose an efficient resource allocation algorithm combining multiple deep Q networks (MDQN) and deep deterministic policy-gradient (DDPG) to deal with this issue. In our algorithm, MDQN are employed to solve the problem of large discrete action space, while DDPG is introduced to tackle the continuous action allocation. Compared with the traditional approaches, our proposed MDQN-DDPG based algorithm has the advantage of continuous behavior improvement through learning from the environment. Simulation results demonstrate superior performance of our design in terms of system sum rate compared with the benchmark schemes.

Automatic modulation classification is of crucial importance in wireless communication networks. Deep learning based automatic modulation classification schemes have attracted extensive attention due to the superior accuracy. However, the data-driven method relies on a large amount of training samples and the classification accuracy is poor in the low signal-to-noise radio (SNR). In order to tackle these problems, a novel data-and-knowledge dual-driven automatic modulation classification scheme based on radio frequency machine learning is proposed by exploiting the attribute features of different modulations. The visual model is utilized to extract visual features. The attribute learning model is used to learn the attribute semantic representations. The transformation model is proposed to convert the attribute representation into the visual space. Extensive simulation results demonstrate that our proposed automatic modulation classification scheme can achieve better performance than the benchmark schemes in terms of the classification accuracy, especially in the low SNR. Moreover, the confusion among high-order modulations is reduced by using our proposed scheme compared with other traditional schemes.

Non-orthogonal multiple access (NOMA) is a promising candidate for the sixth generation wireless communication networks due to its high spectrum efficiency (SE), energy efficiency (EE), and better connectivity. It can be applied in cognitive radio networks (CRNs) to further improve SE and user connectivity. However, the interference caused by spectrum sharing and the utilization of non-orthogonal resources can downgrade the achievable performance. In order to tackle this issue, intelligent reflecting surface (IRS) is exploited in a downlink multiple-input-single-output (MISO) CRN with NO-MA. To realize a desirable tradeoff between SE and EE, a multi-objective optimization (MOO) framework is formulated. An iterative block coordinate descent (BCD)-based algorithm is exploited to optimize the beamforming design and IRS reflection coefficients iteratively. Simulation results demonstrate that the proposed scheme can achieve a better balance between SE and EE than baseline schemes.

Semantic communication is envisioned as a promising technique to break through the Shannon limit. However, the existing semantic communication frameworks do not involve inference and error correction, which limits the achievable performance. In this paper, in order to tackle this issue, a cognitive semantic communication framework is proposed by exploiting knowledge graph. Moreover, a simple, general and interpretable solution for semantic information detection is developed by exploiting triples as semantic symbols. It also allows the receiver to correct errors occurring at the symbolic level. Furthermore, the pre-trained model is fine-tuned to recover semantic information, which overcomes the drawback that a fixed bit length coding is used to encode sentences of different lengths. Simulation results on the public WebNLG corpus show that our proposed system is superior to other benchmark systems in terms of the data compression rate and the reliability of communication.

This article investigates intelligent reflecting surface (IRS)-assisted wireless-powered Internet of Things (IoT) networks. Specifically, multiple IoT devices first collect energy radiated from a power station (PS), then each device uses its harvested energy to support data transmission to an access point (AP) via frequency-division multiple access (FDMA). In addition, an IRS aims to improve wireless energy transfer (WET) and wireless information transfer (WIT) capabilities using passive reflection beamformers. The system sum throughput, as a performance metric, is maximized evaluate the overall performance of the considered model, which is subject to the constraints of IRS phase shifts, transmission time scheduling, and bandwidth allocation. This problem is not convex with respect to multiple coupled variables, and cannot be directly solved. To circumvent this nonconvexity, the transmission time scheduling and the bandwidth allocation are optimally designed in the closed form by the Lagrange dual method and the Karush–Kuhn–Tucker (KKT) conditions. Moreover, an alternating optimization (AO) algorithm is used to optimally design the IRS’s phase shifts during the WET and WIT phases in an alternating fashion. Specifically, we propose elementwise block coordinate decent (EBCD) and complex circle manifold (CCM) algorithms to iteratively derive the optimal phase shifts in the closed form. We also characterize the convergence behavior of the proposed algorithms. Finally, numerical results are presented to validate the performance of the proposed scheme, where the benefits of the IRS are highlighted in terms of sum throughput, transmission time scheduling, and energy harvesting, compared with the benchmark schemes.

Spectrum prediction is of crucial importance for realizing the cognitive Internet of Things to tackle the spectrum scarcity problem. Deep-learning-based spectrum prediction methods have attracted extensive attention due to their superior accuracy. However, the training speed of deep networks is low and the architecture of traditional networks is uninterpretable. In order to tackle these problems, a radio frequency machine-learning-driven spectrum prediction scheme is proposed by exploiting a novel model-enabled autoregressive (AR) network. A cell with only two parameters is exploited in each layer of the AR, which accelerates the network training. Moreover, the domain knowledge of the AR structure enables our proposed scheme to be explainable. Simulation results show that our proposed scheme has the best prediction accuracy than the long short-term memory (LSTM)-based scheme and the AR scheme. It is also shown that its convergence speed is higher than that of the LSTM-based scheme.

Mobile edge computing (MEC) provides wireless devices (WDs) more computing capability and lower latency by allowing them to offload their computation tasks to a nearby more powerful server. Furthermore, adopting the relaying technique can effectively improve the offloading performance, especially when the wireless channel conditions between WD and MEC server are poor. In this article, we consider a multiuser relay-aided MEC system targeting at minimizing the energy consumption. The relay can either execute the computation task by itself or offload the task to the MEC server. Under the partial computation offloading mode, an energy minimization problem is investigated by jointly optimizing transmit power, offloading time duration, and central processing unit (CPU) frequencies. To solve the nonconvex optimization problem, an iterative algorithm based on successive convex approximation (SCA) is developed. Furthermore, the closed-form expressions for the optimal transmission powers and the CPU frequencies are derived. The simulation results show that the proposed scheme can achieve a lower energy consumption than other benchmark schemes.

Wireless technique classification (WTC) is of crucial importance in Internet of Things for realizing efficient spectrum sharing and interference management. However, the existing deep-learning-based methods have low classification accuracy, especially at low signal-to-noise ratio levels. In this article, a multiscale convolutional neural network framework is proposed for WTC. A multiscale module is exploited to capture the higher abstraction features. Simulation results demonstrate that our proposed scheme can achieve a better classification performance and a higher convergence speed compared to the state-of-the-art schemes.

Cognitive radio networks (CRNs) are expected to be promising techniques for improving the spectrum efficiency of wireless network utility in the squeezed sub-6-GHz frequency bands. Nevertheless, frequency allocation and transmission scheduling for secondary users (SUs) in CRNs suffer from no prior knowledge of other SUs’ network behaviors or the distribution of the amount of data generated at each SU. As a countermeasure, this article develops a protocol for the joint channel selection and transmission scheduling such that SUs with heterogeneous data transmission demands could be served with limited spectrum resources. Then, we formulate the dynamic optimization of the protocol as mutually embedded Markov decision processes (MDPs). To address the intractable MDPs,$Q$-learning-based channel selection and transmission scheduling based on reinforcement learning with basis function approximation are, respectively, proposed. It is shown that compared with various baselines, the proposed channel selection algorithm enables each SU to select the best frequency-domain channel that does not interfere with other SUs. In particular, the proposed transmission scheduling algorithm outperforms algorithms based on off-the-shelf approaches, such as$Q$-learning and Lyapunov optimization, in terms of both energy efficiency and long-term accumulative amount of bits at each SU.

Millimeter-wave (mmWave) fifth-generation (5G) and beyond 5G localization enables the provisioning of extremely accurate positioning information, a feature that has attracted substantial research efforts. In this paper, we contribute to this effort by exploring optimal array geometric structures of direct position determination (DPD) systems, inspired by sensor placement problems that predominantly focus on two-step localization and have not yet been extended to DPD. Specifically, we research an optimal array placement and orientation strategy for two-dimensional (2-D) DPD systems that use sensors equipped with uniform linear arrays (ULA) to localize an agent. The A-optimality criterion in Bayesian optimal (experimental) design theory is invoked to formulate this problem. We use an optimization subproblem that optimizes array orientations when array locations are arbitrary but fixed to tackle this high-dimensional optimization problem. Then the optimization problem is converted into a typical optimal angular separation problem in two-step localization. Experimental results show that judiciously designed array geometric structures can lead to significant performance improvements.

The promising physical-layer authentication (PLA) scheme enjoys low computational complexity and provides a lightweight solution for the wireless transmission security problem. However, the ideal but impractical assumptions are made on the priori knowledge in the traditional PLA schemes, which fail because the priori knowledge can be interfered by the impersonation attacks. Hence, a blind PLA scheme featured by the composite radio sample characteristics, active monitoring slots, and blind hypotheses tests is proposed in this paper. In particular, the intrinsic location-specific channel response integrated with the transmitter-specific signal knowledge is sampled during the active monitoring slots and is used to conduct minimum error (ME) and Neyman-Pearson (NP) hypothesis tests. The authentication reliability is enhanced significantly by jointly using the ME and NP criteria. For both the stationary and the non-stationary signal cases, our theoretical analysis shows that the temporal channel variations, the spatial channel correlations and the spectral bandwidth contribute to improve the authentication reliability. Our simulation results not only demonstrate the effectiveness of the proposed PLA scheme, but also indicate the possible scenarios in which ME outperforms NP, and NP outperforms ME.

This paper investigates the robust beamforming design in a secrecy multiple-input single-output (MISO) network aided by the intelligent reflecting surface (IRS) with simultaneous wireless information and power transfer (SWIPT). Specifically, by considering that the energy receivers (ERs) are potential eavesdroppers (Eves) and imperfect channel state information (CSI) of the direct and cascaded channels can be obtained, we investigate the max-min fairness robust secrecy design. The objective is to maximize the minimum robust information rate among the legitimate information receivers (IRs). To solve the formulated non-convex design problem in bounded and probabilistic CSI error models, we utilize the alternating optimization (AO) and successive convex approximation (SCA) methods to obtain an approximate problem. Then, an iteration-based algorithm framework was proposed, where the unit modulus constraint (UMC) of the IRS is handled by the penalty dual decomposition (PDD) method. Moreover, a stochastic SCA method is proposed to handle the outage constrained design with statistical CSI. Finally, simulation results validate the promising performance of the proposed design.

Non-orthogonal multiple access (NOMA) is a promising candidate for the sixth generation wireless communication networks due to its high spectrum efficiency (SE), energy efficiency (EE), and better connectivity. It can be applied in cognitive radio networks (CRNs) to further improve SE and user connectivity. However, the interference caused by spectrum sharing and the utilization of non-orthogonal resources can downgrade the achievable performance. In order to tackle this issue, intelligent reflecting surface (IRS) is exploited in a downlink multiple-input-single-output (MISO) CRN with NOMA. To realize a desirable tradeoff between SE and EE, a multi-objective optimization (MOO) framework is formulated under both the perfect and imperfect channel state information (CSI). An iterative block coordinate descent (BCD)-based algorithm is exploited to optimize the beamforming design and IRS reflection coefficients iteratively under the perfect CSI case. A safe approximation and the$ \mathcal {S}$-procedure are used to address the non-convex infinite inequality constraints of the problem under the imperfect CSI case. Simulation results demonstrate that the proposed scheme can achieve a better balance between SE and EE than baseline schemes. Moreover, it is shown that both SE and EE of the proposed algorithm under the imperfect CSI can be significantly improved by exploiting IRS.

Mobile edge computing (MEC) has recently emerged as an enabling technology to support computation-intensive and delay-critical applications for energy-constrained and computation-limited Internet of Things (IoT). Due to the time-varying channels and dynamic task patterns, there exist many challenges to make efficient and effective computation offloading decisions, especially in the multi-server multi-user IoT networks, where the decisions involve both continuous and discrete actions. In this paper, we investigate computation task offloading in a dynamic environment and formulate a task offloading problem to minimize the average long-term service cost in terms of power consumption and buffering delay. To enhance the estimation of the long-term cost, we propose a deep reinforcement learning based algorithm, where deep deterministic policy gradient (DDPG) and dueling double deep Q networks (D3QN) are invoked to tackle continuous and discrete action domains, respectively. Simulation results validate that the proposed DDPG-D3QN algorithm exhibits better stability and faster convergence than the existing methods, and the average system service cost is decreased obviously.

This paper studies an intelligent reflecting surface (IRS) aided wireless powered sensor network (WPSN). Specifically, a power station (PS) provides wireless energy to multiple internet of thing (IoT) devices which supports them to deliver their own messages to an access point (AP). Moreover, we deploy an IRS to enhance the performance of the WPSN by intelligently adjusting the phase shift of each reflecting element. To evaluate the performance of the IRS assisted WPSN, we maximize its sum throughput to jointly optimize the phase shift matrices and the transmission time allocations. Due to the non-convexity of the formulated optimization problem, we first derive the optimal phase shifts of the wireless information transfer (WIT) in closed-form. Consequently, a semi-definite programming (SDP) relaxed approach is considered to jointly design the phase shift matrix of the wireless energy transfer (WET) and the transmission time allocations. In addition, we propose a low complexity scheme to gain insights and reduce the computational complexity incurred by the SDP relaxed scheme. Specifically, the optimal solutions of the phase shifts and the transmission time allocation are derived in closed-form by the Majorization-Minimization (MM) algorithm, the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. Finally, numerical results are presented to validate the proposed schemes and confirm the beneficial role of the IRS in comparison to the benchmark schemes, where the proposed IRS assisted scheme achieves almost 100% higher sum throughput, in comparison to the counterpart without IRS.

This paper aims to investigate the benefit of using intelligent reflecting surface (IRS) in multi-user multiple-input single-output (MU-MISO) systems, in the presence of eavesdroppers. We maximize the weighted sum secrecy rate by jointly designing the secure beamforming (BF), the artificial noise (AN), as well as the phase shift of the IRS. An alternating optimization (AO) method is proposed to deal with the formulated non convex problem. In particular, the secure beamforming and AN jamming matrix are optimally designed via the successive convex approximation (SCA) approach for given phase shift, which can be derived by considering the alternating direction method of multiplier (ADMM) and element-wise block coordinate decent (EBCD) methods. Finally, simulation results are presented to show the benefit of the IRS in terms of improving the secrecy performance, when compared to other methods.

In adversarial scenarios, it is crucial to timely monitor what tactical messages that opponent transmitters are sending to intended receiver(s), and disrupt the transmissions immediately if in need. The issue becomes more challenging in face of an intelligent transmitter. To address the above-stated issue, a full-duplex (FD) technique is utilized to enable simultaneous jamming and eavesdropping (SJE) at a friendly jammer node. In particular, the “Two Birds with One Stone” strategy is utilized at the jammer node to realize effective rate degradation and information eavesdropping. A confrontation game between an intelligence-empowered FD jammer and its opponent is investigated. Specifically, to capture their adversarial relationship in an environment with incomplete information, a power-domain Bayesian-Stackelberg game is proposed. The existence of a Stackelberg equilibrium (SE) power solution is proved. The semi-closed-form solutions of SE are derived, which are proved to be asymptotically optimal (have a gap of less than 1% with the exact utility), and improves the jammer node 10% utility compared with the Nash equilibrium. Additionally, the SJE strategy outperforms the half-duplex (HD) and other benchmark schemes.

Energy-efficient design and secure communications are of crucial importance in wireless communication networks. However, the energy efficiency achieved by using physical layer security can be limited by the channel conditions. In order to tackle this problem, an intelligent reflecting surface (IRS) assisted multiple input single output (MISO) network with independent cooperative jamming is studied. The energy efficiency is maximized by jointly designing the transmit and jamming beamforming and IRS phase-shift matrix under both the perfect channel state information (CSI) and the imperfect CSI. In order to tackle the challenging non-convex fractional problems, an algorithm based on semidefinite programming (SDP) relaxation is proposed for solving energy efficiency maximization problem under the perfect CSI case while an alternate optimization algorithm based onS-procedure is used for solving the problem under the imperfect CSI case. Simulation results demonstrate that the proposed design outperforms the benchmark schemes in term of energy efficiency. Moreover, the tradeoff between energy efficiency and the secrecy rate is found in the IRS-assisted MISO network. Furthermore, it is shown that IRS can help improve energy efficiency even with the uncertainty of the CSI.

Visible light communication (VLC) is a promising technique in the fifth and beyond wireless communication networks. In this paper, a secure multiple-input single-output VLC network is studied, where simultaneous lightwave information and power transfer (SLIPT) is exploited to support energy-limited devices taking into account a practical non-linear energy harvesting model. Specifically, the optimal beamforming design problems for minimizing transmit power and maximizing the minimum secrecy rate are studied under the imperfect channel state information (CSI). S-Procedure and a bisection search is applied to tackle challenging non-convex problems and to obtain efficient resource allocation algorithm. It is proved that optimal beamforming schemes can be obtained. It is found that there is a non-trivial trade-off between the average harvested power and the minimum secrecy rate. Moreover, we show that the quality of CSI has a significant impact on achievable performance.

A new queue-length aware online scheduling scheme is proposed for a buffer-aided wireless-powered communication network (WPCN) with non-orthogonal multiple access (NOMA). The throughput of the considered network is maximized by designing the optimal resource allocation scheme, while preserving the stability of both energy and data queues. The formulated optimization problem is particularly challenging, since it is a long-term mixed-integer optimization problem. In order to solve it efficiently, we first transform the long-term optimization problem into a series of short-term ones at each time slot by taking advantage of the Lyapunov optimization framework, which can be efficiently solved. The analytical expression of the rate allocation reveals that in contrast to the case of WPCN without buffering, the optimal decoding order depends on the length of data buffer. Simulation results show that the proposed scheme outperforms the non-buffering scheme in terms of the long-term time-average sum rate.

Energy-efficient design and secure communications are of crucial importance in the future wireless communication networks. However, the energy efficiency when using physical layer security can be limited by the channel conditions. In order to tackle this problem, an intelligent reflecting surface (IRS) assisted multiple input single output (MISO) network with independent cooperative jamming is studied in this paper. The energy efficiency is maximized by jointly designing the transmit and jamming beamforming and IRS phase-shift matrix. An alternative optimization algorithm is proposed based on semidefinite programing (SDP) relaxation for solving the challenging non-convex fractional optimization problem. Simulation results demonstrate that our proposed design outperforms the benchmark schemes in term of energy efficiency. The study sheds light on the tradeoff between energy efficiency and the secrecy rate in the IRS-assisted MISO network.

Cognitive radio is a promising technology to improve spectral efficiency. However, communication security of a secondary network is limited by its transmit power and channel fading. In order to tackle this issue, by exploiting the high flexibility and the possibility of establishing line-of-sight links, a cognitive unmanned aerial vehicle (UAV) communication network is studied. The average secrecy rate of the secondary network is maximized by robustly optimizing the UAVs trajectory and transmit power. Our formulated problem takes into account practical imperfect location estimation. To solve the non-convex problem, an iterative suboptimal algorithm based on the Bernstein-type inequalities is presented. Our simulation results demonstrate that the proposed scheme can improve the secure communication performance significantly compared to a benchmark scheme based on fixed trajectory.

Mobile edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. IoT with mobility has found tremendous new services in the 5G era and the forthcoming 6G eras such as autonomous driving and vehicular communications. However, mobility of IoT devices has not been studied in the sufficient level in the existing works. In this paper, the offloading decision and resource allocation problem is studied with mobility consideration. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by jointly optimizing the CPU-cycle frequencies, the transmit power, and the user association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on Lyapunov optimization and Semi-Definite Programming (SDP). Simulation results demonstrate that our proposed scheme can balance the system service cost and the delay performance, and outperforms other offloading benchmark methods in terms of the system service cost.

Cooperative non-orthogonal multiple access (C-NOMA) and buffering are promising techniques to improve spectrum efficiency in the next generation of wireless networks. In this article, a buffer-aided cooperative NOMA network with direct links is studied. The throughput maximization problem is firstly formulated under the assumption of fixed power allocation and optimally solved by designing a mode selection policy. In order to further improve system throughput, the problem is extended into the case that power allocation and mode selection are jointly optimized. An optimal solution is obtained, while a sub-optimal one is also provided in order to decrease the implementation complexity. Furthermore, considering the case where the buffer has finite size and the users are delay-sensitive, a throughput-delay aware strategy is also presented. Moreover, it is shown that the proposed schemes outperform the baseline one in terms of throughput. Finally, simulations demonstrate that the sub-optimal solution achieves similar performance to the optimal one, while significantly reduces the implementation complexity.

Visible light communication (VLC) is a promising technology for the next generation wireless communication systems due to its high spectral efficiency and energy efficiency. In this article, a secure multiple-input single-output (MISO) VLC network is studied, where simultaneous lightwave information and power transfer (SLIPT) is exploited to support multiple energy-limited devices tacking into account a practical non-linear energy harvesting model. The transmit power minimization and the minimum secrecy rate maximization problems are formulated under both perfect and imperfect channel state information, respectively. To further improve the user connectivity, those problems are also investigated in MISO-VLC networks with non-orthogonal multiple access (NOMA). To tackle these challenging non-convex problems, semidefinite program relaxation and $-Procedure are exploited. It is proved that optimal beamforming schemes can be obtained for the considered two types problems in MISO-VLC SLIPT networks, while a sub-optimal solution can be obtained for the transmit power minimization problem in those networks with NOMA. It is found that there is a non-trivial trade-off between the average harvested power and maximum-minimum secrecy rate. Moreover, it is shown that the performance achieved with NOMA outperforms that of conventional orthogonal multiple access in MISO-VLC SLIPT networks, despite the existence of imperfect channel state information.

Mobile edge computing (MEC) has been envisaged as a promising technique in the next-generation wireless networks. In order to improve the security of computation tasks offloading and enhance user connectivity, physical layer security and non-orthogonal multiple access (NOMA) are studied in MEC-aware networks. The secrecy outage probability is adopted to measure the secrecy performance of computation offloading by considering a practically passive eavesdropping scenario. The weighted sum-energy consumption minimization problem is firstly investigated subject to the secrecy offloading rate constraints, the computation latency constraints and the secrecy outage probability constraints. The semi-closed form expression for the optimal solution is derived. We then investigate the secrecy outage probability minimization problem by taking the priority of two users into account, and characterize the optimal secrecy offloading rates and power allocations with closed-form expressions. Numerical results demonstrate that the performance of our proposed design are better than those of the alternative benchmark schemes.

Cognitive radio is a promising technology to improve spectral efficiency. However, the secure performance of a secondary network achieved by using physical layer security techniques is limited by its transmit power and channel fading. In order to tackle this issue, a cognitive unmanned aerial vehicle (UAV) communication network is studied by exploiting the high flexibility of a UAV and the possibility of establishing line-of-sight links. The average secrecy rate of the secondary network is maximized by robustly optimizing the UAV's trajectory and transmit power. Our problem formulation takes into account two practical inaccurate location estimation cases, namely, the worst case and the outage-constrained case. In order to solve those challenging non-convex problems, an iterative algorithm based on S-Procedure is proposed for the worst case while an iterative algorithm based on Bernstein-type inequalities is proposed for the outage-constrained case. The proposed algorithms can obtain effective suboptimal solutions of the corresponding problems. Our simulation results demonstrate that the algorithm under the outage-constrained case can achieve a higher average secrecy rate with a low computational complexity compared to that of the algorithm under the worst case. Moreover, the proposed schemes can improve the secure communication performance significantly compared to other benchmark schemes.

The aggregated visible light communication (VLC) and radio frequency (RF) system, which can be viewed as a heterogeneous multi-input-multi-output system, can improve data rate compared to the conventional RF communication systems. In this paper, we first develop optimal power allocation schemes for the aggregated VLC-RF systems for the single and the multi-light-emitting diode scenarios under different dimming control setups. Moreover, we study the energy efficiency maximization problem of the considered system with the minimum rate requirement, transmitted power constraint, and the dimming control consideration which is non-convex. By using the Dinkelbach-type algorithm, we tackle this problem by solving a sequence of convex problems which converges to the global solution. Finally, the effect of critical parameters, such as total power threshold, dimming level, and bandwidths, are revealed by some selected numerical results.

Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. Resource allocation strategies for maximizing the computation efficiency are critically important. In this paper, computation efficiency maximization problems are formulated in wireless-powered MEC networks under both partial and binary computation offloading modes. A practical non-linear energy harvesting model is considered. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) are considered and evaluated for offloading. The energy harvesting time, the local computing frequency, and the offloading time and power are jointly optimized to maximize the computation efficiency under the max-min fairness criterion. Two iterative algorithms and two alternative optimization algorithms are respectively proposed to address the non-convex problems formulated in this paper. Simulation results show that the proposed resource allocation schemes outperform the benchmark schemes in terms of user fairness. Moreover, a tradeoff is elucidated between the achievable computation efficiency and the total number of computed bits. Furthermore, simulation results demonstrate that the partial computation offloading mode outperforms the binary computation offloading mode and NOMA outperforms TDMA in terms of computation efficiency.

This paper considers a non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system in the presence of a malicious eavesdropper. We employ the partial offloading mode such that each user can divide the individual computation task into two parts for local executing and offloading, respectively. The secrecy outage probability is adopted to measure the secrecy performance of computation ofloading by considering the practically passive eavesdropping scenario. Under this setup, we investigate the problem of minimizing the weighted sum-energy consumption for all users, subject to the secrecy ofloading rates constraints, the computation latency constraints and the secrecy outage probability constraints, and then derive the semi-closed form solution for this problem. Numerical results are provided and demonstrate that the merits of our proposed design are better than those of the alternative benchmark schemes.

Low latency and energy efficiency are two important performance requirements in various fifth-generation (5G) wireless networks. In order to jointly design the two performance requirements, in this paper a new performance metric called effective energy efficiency (EEE) is defined as the ratio of the effective capacity (EC) to the total power consumption in a cellular network with underlaid device to device (D2D) communications. We aim to maximize the EEE of the D2D network subject to the D2D device power constraints and the minimum rate constraint of the cellular network. Due to the non-convexity of the problem, we propose a two-stage difference-of-two-concave (DC) function approach to solve this problem. Towards that end, we first introduce an auxiliary variable to transfer the fractional objective function into a subtractive form. We then propose a successive convex approximation (SCA) algorithm to iteratively solve the resulting non-convex problem. The convergence and the global optimality of the proposed SCA algorithm are both analyzed. The numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

In this paper, we investigate the hybrid precoding design for joint multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. The subarray-based sparse radio frequency chain structure is considered at base station (BS). Then, we formulate a joint hybrid analog/digital precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at BS and minimum harvested energy at receivers are considered. Due to the difficulty in solving the formulated problem, we first design the codebook-based analog precoding approach and then, we only need to jointly optimize the digital precoding and power splitting ratio. Next, we equivalently transform the fractional objective function of the optimization problem into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied. For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques, which is solved by a proposed iterative algorithm. Finally, simulation results are provided to show the performance of the proposed algorithm.

Millimeter wave (mmWave) communication is a promising technology in future wireless networks because of its wide bandwidths that can achieve high data rates. However, high beam directionality at the transceiver is needed due to the large path loss at mmWave. Therefore, in this paper, we investigate the beam alignment and power allocation problem in a nonorthogonal multiple access (NOMA) mmWave system. Different from the traditional beam alignment problem, we consider the NOMA scheme during the beam alignment phase when two users are at the same or close angle direction from the base station. Next, we formulate an optimization problem of joint beamwidth selection and power allocation to maximize the sum rate, where the quality of service (QoS) of the users and total power constraints are imposed. Since it is difficult to directly solve the formulated problem, we start by fixing the beamwidth. Next, we transform the power allocation optimization problem into a convex one, and a closed-form solution is derived. In addition, a one-dimensional search algorithm is used to find the optimal beamwidth. Finally, simulation results are conducted to compare the performance of the proposed NOMA-based beam alignment and power allocation scheme with that of the conventional OMA scheme.

Energy-efficient computation is of crucial importance in mobile edge computing (MEC) networks. However, few investigations have studied resource allocation strategies for maximizing the computation efficiency. A computation efficiency maximization framework is established in wireless-powered MEC networks relying on non-orthogonal multiple access (NOMA) under both partial and binary computation offloading modes. A practical non-linear energy harvesting model is considered. The energy harvesting time, the local computing frequency, the operation mode selection, the offloading time and power are all jointly optimized to maximize the computation efficiency under the max-min fairness criterion. An iterative algorithm and an alternative optimization algorithm are proposed to solve the formulated challenging non-convex problems. Simulation results show that our proposed resource allocation schemes outperform the benchmark schemes in terms of computation efficiency. Moreover, a tradeoff is elucidated between the computation efficiency and the computation throughput.

The number of wireless devices is growing rapidly on a daily basis echoing the increasing number of applications of the Internet of Thing. Facing massive connections and unavoidable interference, how to provide a green communication is a concerning matter. In this regard, nonorthogonal multiple-access (NOMA) is a natural communications technology that can scale with the massive number of simultaneous connections for a limited bandwidth. In this paper, we aim to maximize the energy efficiency (EE) for an NOMA-based cloud radio access network, where sub-6 GHz and millimeter wave bands are used in fronthaul and access links, respectively. In particular, we formulate the power optimization problem to maximize the EE of the system subject to the fronthaul capacity and transmit power constraints. To address this nonconvex problem, we first convert the fractional objective function into a subtractive form. A two-layer algorithm is then proposed. In the outer loop, the ℓ1-norm technique is adopted to transform the nonconvex fronthaul capacity constraint into a convex one, whereas in the inner loop, the weighted minimum mean square error approach is applied. Simulation results indicate that the proposed NOMA scheme can obtain higher EE as well as throughput when compared with orthogonal multiple-access methods.

This paper studies a multiple-input single-output non-orthogonal multiple access cognitive radio network relying on simultaneous wireless information and power transfer. A realistic non-linear energy harvesting model is applied and a power splitting architecture is adopted at each secondary user (SU). Since it is difficult to obtain perfect channel state information (CSI) in practice, instead either a bounded or Gaussian CSI error model is considered. Our robust beamforming and power splitting ratio are jointly designed for two problems with different objectives, namely, that of minimizing the transmission power of the cognitive base station and that of maximizing the total harvested energy of the SUs, respectively. The optimization problems are challenging to solve, mainly because of the non-linear structure of the energy harvesting and CSI errors models. We converted them into convex forms by using semi-definite relaxation. For the minimum transmission power problem, we obtain the rank-2 solution under the bounded CSI error model, while for the maximum energy harvesting problem, a two-loop procedure using a 1-D search is proposed. Our simulation results show that the proposed scheme significantly outperforms its traditional orthogonal multiple access counterpart. Furthermore, the performance using the Gaussian CSI error model is generally better than that using the bounded CSI error model.

This paper develops a tractable analysis framework to evaluate the reliability and security performance of cooperative non-orthogonal multiple access (co-NOMA) in cognitive networks, where both a primary base station (PBS) and a NOMA-strong primary user (PU) send confidential messages to multiple uniformly distributed PUs in the presence of randomly located external eavesdroppers. For constricting the interference to the PUs imposed by cognitive femto base stations (CFBSs), a mobile association scheme is introduced. Moreover, an eavesdropper-exclusion zone is introduced around the PBS for improving the secrecy performance of the primary networks. To characterize the security-reliability tradeoff of the considered network, we first derive the activation probability of CFBSs and the conditional probability density function associated with the distance between the relay user and other PUs. Then, the connection outage probability (COP) and the secrecy outage probability (SOP) of each PU with NOMA (co-NOMA) or non-cooperative NOMA (nco-NOMA) are separately derived to obtain the overall COP and SOP in the primary networks. Finally, the tradeoff between COP and SOP with co-NOMA (identified as transmission SOP) is investigated for simultaneously reflecting the security and reliability. Numerical results demonstrate the performance improvements of the proposed co-NOMA scheme in comparison to that of the nco-NOMA scheme in terms of different parameters. Furthermore, the security-reliability tradeoff performance of co-NOMA is shown.

We propose a novel non-orthogonal multiple access (NOMA)-inspired jamming and relaying scheme to enhance the physical layer security of untrusted relay networks. Particularly, during the first phase, with the application of the downlink NOMA principle, the source sends a superimposed version of a desired signal and a jamming signal, where the jamming signal is designed to deliberately confuse the untrusted relay by exploiting the beamforming design and adapting the transmission rate at the source. During the second phase, relying on the uplink NOMA principle, the untrusted relay forwards its received signals and, simultaneously, the source transmits a new desired signal that cannot be wiretapped by the untrusted relay to maximize the secrecy sum rate. Scenarios of single-antenna and multiple-antenna relaying are considered, and the impact of different antenna configurations at the source and the untrusted relay on the secrecy performance is investigated. Analytical expressions of an ergodic secrecy sum rate (ESSR) lower bound and an asymptotic ESSR scaling law are derived to evaluate the secrecy performance of the proposed NOMA-inspired jamming and relaying scheme. Computer simulations are presented to validate the accuracy of the derived analytical results, and demonstrate the significant ESSR improvement of the proposed scheme over the conventional orthogonal multiple access-based relaying schemes.

This paper investigates a secure wireless-powered integrated service system with full-duplex self-energy recycling. Specifically, an energy-constrained information transmitter (IT), powered by a power station (PS) in a wireless fashion, broadcasts two types of services to all users: a multicast service intended for all users and a confidential unicast service subscribed to by only one user while protecting it from any other unsubscribed users and an eavesdropper. Our goal is to jointly design the optimal input covariance matrices for the energy beamforming, the multicast service, the confidential unicast service, and the artificial noises from the PS and the IT, such that the secrecy-multicast rate region (SMRR) is maximized subject to the transmit power constraints. Due to the non-convexity of the SMRR maximization (SMRRM) problem, we employ a semidefinite programming-based two-level approach to solve this problem and find all of its Pareto optimal points. In addition, we extend the SMRRM problem to the imperfect channel-state information case, where a worst-case SMRRM formulation is investigated. Moreover, we exploit the optimized transmission strategies for the confidential service and energy transfer by analyzing their own rank-one profile. Finally, numerical results are provided to validate our proposed schemes.

In this paper, we investigate a novel simultaneous lightwave information and power transfer (SLIPT) in visible light communication (VLC) systems, where a photo diode (PD) and a solar panel are utilized as the information receiver and the energy harvester, respectively. By systematically analyzing both the information receiver and the energy harvester, we obtain the explicit expressions to characterize the illumination-rate-energy region. Based on the derived expressions, we investigate the downlink unicast transmission of multi-LED multi-user SLIPT VLC networks, and study the total transmit power minimization problem under the rate requirements, the minimum energy harvesting requirements, and dimming control constraints. To solve such non-convex problem, we exploit the semidefinite relaxation (SDR) technique and relax the problem into a convex problem, which can be efficiently solved via interior-point methods. Moreover, for the sake of users' fairness, we further investigate the beamformer design to maximize the minimal rate under both minimum energy harvesting and dimming control constraints. Finally, the numerical results are provided to evaluate the proposed SLIPT system.

This paper investigates a two-tier heterogeneous networks (HetNets), where millimeter wave (mmWave) frequency is employed at the macro base station (MBS), and the small cell BSs (SBSs) consider orthogonal frequency division multiple access (OFDMA). Subarray structure based hybrid analog/digital precoding scheme is studied to reduce the hardware cost and energy consumption. Our goal is to maximize the energy efficiency (EE) of the HetNets with limited wireless backhaul capacity and all users' quality of service (QoS) constraints. Due to nonconvexity of the mixed integer nonlinear fraction programming (MINLFP), the formulated problem cannot be solved directly. In order to circumvent this issue, we propose a two-loop iterative resource allocation algorithm. Specifically, we reformulate the outer-loop problem into a difference of convex programming (DCP) by employing integer relaxation and Dinkelback method. In addition, the first-order approximation is adopted to linearize this inner-loop DCP problem into a convex optimization framework. Lagrange dual method is adapted to achieve the optimal power allocation. Furthermore, the convergence of the proposed iterative algorithm is analyzed. Numerical results are presented to demonstrate our proposed algorithms.

Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. However, resource allocation strategies for maximizing the computation efficiency have not been fully investigated. In this paper a computation efficiency maximization problem is formulated in the wireless-powered MEC network under a practical non-linear energy harvesting model. The energy harvesting time, the local computing frequency, the offtoading time, and power are all jointly optimized to maximize the computation efficiency under the max-min fairness criterion. The problem is non-convex and challenging to solve. An iterative algorithm is proposed to solve this problem. Simulation results show that our proposed resource allocation scheme outperforms the benchmark schemes in terms of the computation efficiency and verify the efficiency of our proposed algorithm. A tradeoff is elucidated between the achievable computation efficiency and the computation bits.

This paper studies a multiple-input-single-output non-orthogonal multiple access cognitive radio network relying on simultaneous wireless information and power transfer. A realistic non- linear energy harvesting model is applied and a power splitting architecture is adopted at each secondary user. Since it is difficult to obtain the perfect channel state information (CSI) in practice, a bounded CSI error model is considered. Our robust beamforming and power splitting ratio are jointly designed for minimizing the transmission power of the cognitive base station. The original non-convex optimization problem is then converted into convex forms by using semi- definite relaxation. For the minimum transmission power problem, we prove that the optimal solution has a limited rank of less than or equal to 2. Our simulation results show that the proposed scheme significantly outperforms its traditional orthogonal multiple access counterpart.

Cognitive radio (CR) and non-orthogonal multiple access (NOMA) are two promising technologies in the next generation wireless communication systems. The security of a NOMA CR network (CRN) is important but lacks of study. In this paper, a multiple-input single-output NOMA CRN relying on simultaneous wireless information and power transfer is studied. In order to improve the security of both the primary and secondary network, an artificial noise-aided cooperative jamming scheme is proposed. Different from the most existing works, a power minimization problem is formulated under a practical non-linear energy harvesting model. A suboptimal scheme is proposed to solve this problem based on semidefinite relaxation and successive convex approximation. Simulation results show that the proposed cooperative jamming scheme is efficient to achieve secure communication and NOMA outperforms the conventional orthogonal multiple access in terms of the power consumption.

With the emergence of diverse mobile applications (such as augmented reality), the quality of experience of mobile users is greatly limited by their computation capacity and finite battery lifetime. Mobile edge computing (MEC) and wireless power transfer are promising to address this issue. However, these two techniques are susceptible to propagation delay and loss. Motivated by the chance of short-distance line-of-sight achieved by leveraging unmanned aerial vehicle (UAV) communications, an UAV-enabled wireless powered MEC system is studied. A power minimization problem is formulated subject to the constraints on the number of the computation bits and energy harvesting causality. The problem is non-convex and challenging to tackle. An alternative optimization algorithm is proposed based on sequential convex optimization. Simulation results show that our proposed design is superior to other benchmark schemes and the proposed algorithm is efficient in terms of the convergence.

This paper investigates a wireless powered sensor network, where multiple sensor nodes are deployed to monitor a certain external environment. A multiantenna power station (PS) provides the power to these sensor nodes during wireless energy transfer phase, and consequently the sensor nodes employ the harvested energy to transmit their own monitoring information to a fusion center during wireless information transfer (WIT) phase. The goal is to maximize the system sum throughput of the sensor network, where two different scenarios are considered, i.e., PS and the sensor nodes belong to the same or different service operator(s). For the first scenario, we propose a global optimal solution to jointly design the energy beamforming and time allocation. We further develop a closed-form solution for the proposed sum throughput maximization. For the second scenario in which the PS and the sensor nodes belong to different service operators, energy incentives are required for the PS to assist the sensor network. Specifically, the sensor network needs to pay in order to purchase the energy services released from the PS to support WIT. In this case, this paper exploits this hierarchical energy interaction, which is known as energy trading. We propose a quadratic energy trading-based Stackelberg game, linear energy trading-based Stackelberg game, and social welfare scheme, in which we derive the Stackelberg equilibrium for the formulated games, and the optimal solution for the social welfare scheme. Finally, numerical results are provided to validate the performance of our proposed schemes.

This paper studies a cognitive small cell downlink network, where one cognitive base station (CBS) transmits information to a cognitive user and transfers energy to energy harvesting receivers (EHRs). The spectrum sensing interval, the spectrum sensing time, and the beamforming matrices of the CBS are jointly optimized to maximize the energy efficiency (EE) of the CBS and to minimize the energy cost of the CBS under both the bounded channel state information (CSI) model and the probabilistic CSI model. The interference constraints of the macrocell users, the secrecy rate constraint, the transmit power constraint of the CBS and the energy harvesting constraints of the EHRs are all considered in this paper. All the three formulated optimization problems are nonconvex, for which semidefinite relaxation, a 2-D line search method, S-Procedure, and Bernstein-type inequalities are exploited. This paper also derives the conditions under which the EE maximization problem and the energy cost of the CBS minimization problem using the bounded CSI model have rank-one solutions. Simulation results demonstrate that the proposed algorithms have significant gain on the CBS EE under the perfect CSI and also gain on the CBS energy cost under imperfect CSI, all compared to the benchmark scheme.

Cognitive radio (CR) and non-orthogonal multiple access (NOMA) have been deemed two promising technologies due to their potential to achieve high spectral efficiency and massive connectivity. This paper studies a multiple-input single-output NOMA CR network relying on simultaneous wireless information and power transfer conceived for supporting a massive population of power limited battery-driven devices. In contrast to most of the existing works, which use an ideally linear energy harvesting model, this study applies a more practical non-linear energy harvesting model. In order to improve the security of the primary network, an artificial-noise-aided cooperative jamming scheme is proposed. The artificial-noise-aided beamforming design problems are investigated subject to the practical secrecy rate and energy harvesting constraints. Specifically, the transmission power minimization problems are formulated under both perfect channel state information (CSI) and the bounded CSI error model. The problems formulated are non-convex, hence they are challenging to solve. A pair of algorithms either using semidefinite relaxation (SDR) or a cost function are proposed for solving these problems. Our simulation results show that the proposed cooperative jamming scheme succeeds in establishing secure communications and NOMA is capable of outperforming the conventional orthogonal multiple access in terms of its power efficiency. Finally, we demonstrate that the cost function algorithm outperforms the SDR-based algorithm.

Mobile-edge computing (MEC) and wireless power transfer are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are significantly impacted by the severe propagation loss. In order to address this issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless-powered system is studied in this paper. The computation rate maximization problems in a UAV-enabled MEC wireless powered system are investigated under both partial and binary computation offloading modes, subject to the energy-harvesting causal constraint and the UAV's speed constraint. These problems are non-convex and challenging to solve. A two-stage algorithm and a three-stage alternative algorithm are, respectively, proposed for solving the formulated problems. The closed-form expressions for the optimal central processing unit frequencies, user offloading time, and user transmit power are derived. The optimal selection scheme on whether users choose to locally compute or offload computation tasks is proposed for the binary computation offloading mode. Simulation results show that our proposed resource allocation schemes outperform other benchmark schemes. The results also demonstrate that the proposed schemes converge fast and have low computational complexity.

Wideband sensing-based cognitive radio with simultaneous wireless information and power transfer can be designed for efficient spectrum and energy usage. We investigate maxmin fairness energy-harvesting optimization problem in such a system by taking into account of the individual link fairness. In particular, we maximize the energy harvested by the worstcase link when the problem is subject to the rate requirements, transmit power constraint, interference power constraint and subchannels assignment constraint. Due to the nonconvexity of the formulated problem, we relax the integer variable and introduce an auxillery variable. The Lagrangian and subgradient methods are adopted to obtain a suboptimal solution. Simulation results are presented to verify the fairness performance of the proposed algorithm, and to reveal a new tradeoff between the network harvested energy and link fairness.

Spectrum sensing based on absolute value cumulating (AVC sensing) has attracted much attention due to its effectiveness in the presence of Laplaican noise and simpleness of implementation. In this paper, we investigate its detection performance and optimal detection threshold. Specifically, based on the derived mean and variance of the test statistic, an accurate expression of the detection probability is given. Using the accurate expression, an optimization problem is formulated to minimize the total error rate of AVC sensing by optimizing the detection threshold with a constraint on false alarm probability, and the optimal detection threshold is derived. Numerical results are provided to support our work.

Due to the broadcast nature of the radio propagation, requirements of high data rates and low probability of detection (LPD) form a well‐known trade‐off problem in covert wireless communication. Frequency hopping (FH) is a communication technology that is able to efficiently solve this problem by randomly switching its transmission channels. However, it is extremely complex to design an FH sequence (FHS) in a coloured noise scenario where different channels have different noise levels. To address this issue, in this paper, we propose a novel probabilistic FHS based on spectrum sensing. The FHS achieves the LPD among typical FHS under the constraint on a prescribed bit error rate. The authors present two algorithms that are used to generate the FHS and we analyse their computational complexity. To evaluate the performance of the sequence, we derive an expression for the probability of detection (PD). Simulation results show that the system bit error rate and the PD of the proposed FHS are low, and can be flexibly adjusted according to various practical requirements.

Physical‐layer security from an energy‐efficient perspective is of crucial importance in cognitive radio (CR). A CR network is considered where a secondary user (SU) coexists with a primary user in the presence of an eavesdropper and channel fading. Secure energy efficiency (EE) maximisation problems are formulated in secure green CR based on the condition that a minimum secrecy capacity (SC) of a SU is guaranteed. A peak interference power constraint and an average (ATP)/peak transmit power (PTP) constraint are imposed in the SU's Tx. Using fractional programming and the Lagrange dual method, energy‐efficient optimal power allocation strategies are proposed to efficiently solve the secure EE maximisation problems. It is shown that the secure EE of the SU achieved under the ATP constraint is higher than that obtained under the PTP constraint. The tradeoff is elucidated between the secure EE and the SC of the SU.

A multiple-input single-output cognitive radio downlink network is studied with simultaneous wireless information and power transfer. In this network, a secondary user coexists with multiple primary users and multiple energy harvesting receivers. In order to guarantee secure communication and energy harvesting, the problem of robust secure artificial noise-aided beamforming and power splitting design is investigated under imperfect channel state information (CSI). Specifically, the transmit power minimization problem and the max-min fairness energy harvesting problem are formulated for both the bounded CSI error model and the probabilistic CSI error model. These problems are non-convex and challenging to solve. A 1-D search algorithm is proposed to solve these problems based on S-Procedure under the bounded CSI error model and based on Bernstein-type inequalities under the probabilistic CSI error model. It is shown that the optimal robust secure beamforming can be achieved under the bounded CSI error model, whereas a suboptimal beamforming solution can be obtained under the probabilistic CSI error model. A tradeoff is elucidated between the secrecy rate of the secondary user receiver and the energy harvested by the energy harvesting receivers under a max-min fairness criterion.

A max-min fairness resource allocation is studied for wideband cognitive radio under sensing-based spectrum sharing with simultaneous wireless information and power transfer. Specifically, the throughput of the worse-case secondary user is maximized by jointly optimizing the sensing time, transmit power and subchannel allocation, subject to constraints on energy harvesting, interference power and transmit power. The formulated max-min fairness resource allocation problem is a mixed integer non-convex programming. An efficient one-dimensional search algorithm based on the proposed transmit power and subchannel allocation scheme is designed to solve the formulated problem. Several tradeoffs are found, such as a tradeoff between the sensing performance and the throughput of the secondary user under a max-min fairness criterion.

Cooperative spectrum sensing, proposed to improve the performance of spectrum sensing in cognitive radio systems where there are multiple secondary users who can cooperatively detect the presence of one primary user, is receiving significant attention. However, few cooperative sensing algorithms take the correlation among the received primary user signals into account. A feasibly efficient cooperative spectrum sensing scheme based on Cholesky decomposition of the correlation matrix of the received signals is proposed. The ratio of the maximum eigenvalue to the minimum eigenvalue of the matrix obtained by Cholesky decomposition is used to construct the test statistic. Analytical approximations for the false alarm probability and decision threshold are derived using a moment matching method. The new scheme is in the category of blind cooperative spectrum sensing schemes requiring neither information about the primary user signal nor the channel nor the noise power. The new scheme can work better than the existing eigenvalue‐based cooperative spectrum sensing methods in some conditions, and it has lower complexity.

Spectrum sensing is performed at the beginning of each time slot in traditional cognitive radio networks, which is unreasonable and needless since the presence or the absence of a primary user (PU) always lasts several time slots. A hidden Markov model is used to describe the imperfect spectrum sensing process over Rayleigh fading channels. On the basis of the sensing results, a hybrid interweave/underlay mode is exploited by the secondary user (SU) to achieve a higher throughput. To solve the tradeoff problem among the average energy consumption for spectrum sensing, the average throughput of SU and the average interference to the PU, an optimisation problem is proposed. The optimal sensing interval to determine when the next spectrum sensing should be performed is obtained by solving the optimisation problem. Numerical results are given to verify the authors’ analysis.

A definition of least cyclostationary order (LCO) is proposed based on the inner cyclic period of single carrier and orthogonal frequency division multiplexing (OFDM) signals. The LCOs of single carrier and OFDM signals are derived. Since the LCO has a relationship with the carrier frequency offset (CFO), a blind CFO estimation scheme for single carrier and OFDM signals can be proposed. The novel scheme is entirely blind without any priori information and is proved to be the same as cumulants estimation when the LCO is known. However, the new scheme is superior to the estimation based on cumulant when the LCO is unknown. The mean‐square error of M ‐phase shift keying signals approaches the Cramér–Rao bound in additive white Gaussian noise (AWGN) channel. Simulation results are given to verify the effectiveness of the proposed blind estimation scheme in Rician fading channel and in AWGN channel.

An efficient cooperative spectrum sensing (SS) scheme based on Cholesky decomposition of the covariance matrix is proposed. The maximum eigenvalue (ME) of the matrix obtained by Cholesky decomposition is used as a test statistic. Analytical expressions for the false alarm probability and the decision threshold are derived. The effects of noise uncertainty on the ME SS algorithm and on the Cholesky ME cooperative SS scheme are assessed. It is proved that the proposed SS scheme is more robust than the conventional ME SS scheme in terms of noise uncertainty. Simulation results show that the performance of the proposed scheme is better than that of existing ME SS scheme in some conditions.

Green communications is an inevitable trend for future communication network design, especially for a cognitive radio network. Power allocation strategies are of crucial importance for green cognitive radio networks. However, energy-efficient power allocation strategies in green cognitive radio networks have not been fully studied. Energy efficiency maximization problems are analyzed in delay-insensitive cognitive radio, delay-sensitive cognitive radio, and simultaneously delay-insensitive and delay-sensitive cognitive radio, where a secondary user coexists with a primary user and the channels are fading. Using fractional programming and convex optimization techniques, energy-efficient optimal power allocation strategies are proposed subject to constraints on the average interference power, along with the peak/average transmit power. It is shown that the secondary user can achieve energy efficiency gains under the average transmit power constraint, in contrast to the peak transmit power constraint. Simulation results show that the fading of the channel between the primary user transmitter and the secondary user receiver and the fading of the channel between the secondary user transmitter and the primary user receiver are favorable to the secondary user with respect to the energy efficiency maximization of the secondary user, whereas the fading of the channel between the secondary user transmitter and the secondary user receiver is unfavorable to the secondary user.

Spectrum sensing has been well studied because of its significance in cognitive radio. Different from the existing works which a primary user (PU) is assumed to have only one constant transmit power, a more practical scenario that the PU transmitting with multiple power levels is considered. A continuous hidden Markov model (CHMM)‐based blind algorithm for not only detecting the presence of PU but also recognising the transmit power level of the PU is proposed. The training problem of CHMM is solved by combining the wavelet singularity detection with k ‐means clustering algorithm. An effective method for estimation of the number of power levels is proposed. Two different strategies are designed to perform spectrum sensing. Simulation results show the efficiency of the proposed algorithm.

The opportunistic spectrum access technology is one of the most promising methods for alleviating the spectrum scarcity problem, which enables a secondary user (SU) to utilise a primary user spectrum band that is detected idle. However, the throughput achieved by cognitive radios is limited by the interference constraint imposing on the SU. Multiple input single output antenna techniques and antenna selection (AS) techniques are exploited to combat the interference constraint and improve the achievable average throughput of the SU. The optimal power allocation strategy is proposed to maximise the achievable average throughput. Performance analyses for the achievable average throughputs are performed under the maximum channel gain AS strategy, the minimum interference channel gain AS strategy and the ratio AS strategy. It is proved that the optimal transmitted AS strategy is the ratio AS strategy when the optimal power allocation strategy is used. The optimal sensing parameters are designed to further improve the maximum average throughput. Extensive simulation results are conducted to verify this analysis.

Spectrum sensing is the precondition of implementation of cognitive radio. Motivated by the fact that eigenvalue detection algorithms are based on eigenvalue decomposition over the covariance matrix, we propose an efficient spectrum sensing algorithm based on Cholesky decomposition over that matrix. Using eigenvalues of the matrix which is obtained by Cholesky decomposition over finite covariance matrix, the efficient spectrum sensing algorithm is proposed. Attractive advantages of our proposed technique are: a) no assumptions on the sampling size and the dimension of the random matrix are required; b) exact and simple closed-form analytical expressions for the false alarm probability and decision threshold are derived under practical scenarios of finite size of the covariance matrix and samples; c) numerical simulations show that the presented algorithm achieves performance improvement compared with previous algorithms based on eigenvalue.

Traditional symbol rate estimators based on wavelet transform (WT) suffer from difficulties in choosing wavelet scale. Additionally, the most estimators are only adapted to limited signal types. Furthermore, those estimators are susceptible to noise, which is impractical to implement. In this paper, to overcome shortages of those traditional estimators, a self- adapting symbol rate estimator is proposed. The proposed estimator has advantages in reliably working under low Eb-to-N0. More importantly, it can be widely used to estimate various types of signal. The numerical simulations show that the proposed estimator has superiority in performance compared with the improved estimators based on wavelet transform.