This paper investigates the issue of wireless network topology inference via passive sensing of radio frequency (RF) signals without accessing their content. In non-cooperative scenarios, topology inference faces numerous challenges including the indirect, limited, and unreliable nature of available information, with no practical application instances demonstrated to date. To address these issues, we design a blind wireless network topology inference framework consisting of three key functional modules: signal detection, specific emitter identification (SEI), and topology inference. Guided by this framework, we present a systematic approach. Specifically, to confirm the identity of the detected signals, we propose a cross-domain robust SEI method based on transfer learning. These capabilities enable the mapping of raw RF signals to network interaction behaviors. Furthermore, to adapt to network dynamics and interaction behaviors across different time scales, we propose an adaptive topology inference method based on multivariate Hawkes processes (MHP) with dual timestamps. Finally, we develop an experimental validation system to evaluate the proposed framework. Experimental results demonstrate that our framework can accurately reconstruct the network topology from over-the-air RF signals, representing a significant step from theory to practice. Additional performance analysis confirms the superiority of the proposed topology inference method in both accuracy and adaptability.

Introducing friendly jammer to unmanned aerial vehicle (UAV) covert communication could further enhance the covert performance. However, due to the additional communication overhead required for information synchronization (IS) among cooperating parties, the security risk is further elevated. It is noteworthy that cognitive jamming (CJ) could sense the cooperating node signals without IS links. Therefore, in this paper, we propose the cooperative UAVs covert communication scheme assisted by the CJ that maximize the minimum average covert rate (MACR). Specifically, the UAV (Alice) tries to transmit its private message to legitimate users under the surveillance of the Wardens. Since the Warden is in passive surveillance, the practical location uncertainty of the Warden is considered, and we further analyze the worst-case situation that derives the minimal detection error probability of the Wardens. Moreover, we maximize the MACR by alternatively optimizing the sense time ratio and trajectory of CJ, the transmit power and trajectory of the Alice. To expedite algorithmic convergence, we further introduce a principled initialization scheme. The simulation results reveal that the covert performance of the proposed algorithm outperforms the other schemes, especially, in the multi-warden scenario. Furthermore, compared with the full-time jammer and probabilistic jammer, the CJ can not only achieve a higher covert rate with less average jamming power, but also require no information synchronization link with Alice based on spectrum sensing, which promises considerable application prospects in real-world systems.

This paper proposes a joint secure sensing and communication framework for full-link covert transmissions, which integrates an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) system and compliant distributed cooperative jammers to enhance the communication quality of legitimate users while promoting the efficient utilization of limited resources. In the proposed scheme, upon sensing potential eavesdropper embodied by unmanned aerial vehicle (UAV), the dual-functional base station (BS) covertly transmits the acquired UAV state information to friendly jammers within relevant coverage area and issues activation commands promptly. Simultaneously, with IRS assistance, reconfigurable parameters such as signal phase in NOMA transmissions are adjusted to satisfy public user’s service requirements while facilitating covert communications for legitimate user. To ensure dynamic adaptability and link sustainability, the BS leverages historical sensing data to predict the UAV’s flight trajectory in real time and infer its movement intent. If the UAV exhibits a tendency to deviate from the currently effective jamming zone, the BS proactively activates friendly jammers in adjacent regions to maintain covert transmission rates and ensure robust system operation. To address the non-convex optimization challenge arising from jointly optimizing sensing beamforming, communication beamforming, and the IRS reflection matrix with highly coupled variables, we disassemble the problem into three subproblems. Correspondingly, an alternating optimization framework is designed by employing the semidefinite relaxation (SDR), Gaussian randomization, penalty-based methods, and Dinkelbach transformation to jointly maximize covert transmission rates while guaranteeing both sensing accuracy and communication quality of service (QoS). Simulation results demonstrate that the proposed scheme achieves superior covert transmission rates compared with benchmark schemes. Moreover, the dual-covertness mechanisms for sensing and communication further enhance the system security, validating the framework’s robustness in dynamic resource-constrained environments.

Security and reliability have always evolved alongside advancements in communication technologies. Facing imminent the sixth generation of mobile communication (6G) era, this work explores a jamming exploitation-enabled covert transmission framework for satellite-aerial-terrestrial integrated networks (SATINs), aiming to meet user privacy requirements in future complex adversarial scenarios characterized by stereoscopic coverage and multi-domain collaboration. The research scenario involves a three-dimensional space comprising four core elements: a satellite, an unmanned aerial vehicle (UAV) equipped with an active simultaneously transmitting and reflecting reconfigurable intelligent surface (active STAR-RIS), an eavesdropper possessing dual functionalities of jamming and detection, and a ground terminal. Upon sensing malicious jamming from the eavesdropper, the UAV aerial platform serving as a relay node utilizes its on-board active STAR-RIS to achieve the targeted reflection and manipulation of the malicious jamming signals while concurrently facilitating the effective forwarding of the legitimate signals. Namely, breaking through the conventional mindset of “jamming suppression”, it equivalently constructs a “self-interference loop” centered on the eavesdropper, thereby degrading the adversary’s detection sensitivity. For this process, we construct a covert analysis framework featuring the joint design of the static/dynamic scenarios and the active STAR-RIS reflection-transmission matrices dominated by the UAV’s limited power, derive the analytical expression of the Kullback-Leibler (KL) divergence, and establish rigorous covertness constraints for the system. To address the highly coupled non-convex problem in the joint optimization,we propose a solution combining semidefinite relaxation (SDR), Dinkelbach transformation, Gaussian randomization, and the proximal policy optimization (PPO) framework, maximizing the system covert transmission rate while satisfying various constraints. Numerical results demonstrate that, compared with benchmark schemes, the proposed scheme exhibits superior flexibility and covert transmission advantages in adversarial environments.

Specific emitter identification (SEI) separates the radio frequency fingerprint (RFF) from signals, which is of great significance in solving Internet of Things (IoT) security problems. However, the scarcity of high-quality, diverse, and labeled data in real-world scenarios limits the application of SEI. Under such conditions, the SEI is referred to as few-shot SEI (FS-SEI). To surmount this challenge, we propose a diffusion model-based data augmentation method capable of generating a substantial volume of diverse, high-quality data. Specifically, we develop a multi-scale convolutional block attention module denoising diffusion probabilistic model (MSCBAM-DDPM), which enhances feature capture capabilities, laying the foundation for the generation of diverse data. Furthermore, we propose an adaptive two-stage multi-domain loss function that guides the model to learn the characteristics of the original data and further derive other similar features, thereby achieving the goal of generating diverse and high-quality data. Finally, we theoretically derive the feasibility of the proposed loss function and further demonstrate the excellent diversity and quality of the data generated by our method, as well as its considerable gain for FS-SEI, through extensive experiments on real-world signal datasets.

Privacy security constitutes a critical challenge in low-altitude wireless communications. Motivated by the application requirements for stereoscopic coverage and multi-domain collaboration, this paper investigates a friendly jamming-assisted air-ground cooperative covert transmission scheme. In the considered system, an unmanned aerial vehicle (UAV) equipped with a reconfigurable intelligent surface (RIS) serves as a network hub. It relays confidential signals from an aerial hovering platform to ground users while cooperating with terrestrial jammer to realize environment-independent directional jamming. Benefiting from the UAV's relaying functionality, this architecture can significantly enhance the flexibility of the jamming mechanism and the security of the jamming node. With the objective of maximizing the UAV's energy efficiency associated with effective throughput, we formulate a joint optimization problem under strict covertness constraints. To solve this problem, we propose an algorithm that integrates semidefinite relaxation (SDR), the Dinkelbach method, and Gaussian randomization within a double deep Q-network (DDQN) framework. The UAV trajectory, onboard resource, user scheduling and RIS parameters are jointly optimized to simultaneously ensure the communication covertness and transmission performance. Numerical simulation results validate the superiority of the proposed scheme compared to benchmark solutions.

The integrated communication and jamming system can integrate communication and jamming functionalities to reinforce each other in one system, arising from the increasing demand for equipment miniaturization and resource multiplexing. This paper proposes the integrated communication and jamming network (ICAJN) where multi-functional nodes serve asmobileaccess points, providing flexible communication and jamming coverage in the temporary, infrastructure-less scenarios. In addition to coverage, connectivity is also essential to ensure distributed coordination and signalling interaction among nodes. However, achieving simultaneous coverage and connectivity is challenging due to two main factors: on one hand, there exists an inherent tradeoff between coverage and connectivity in terms of node deployment; on the other hand, the dynamic electromagnetic environment complicates the real-time link gain estimation. The goal of this paper is to incorporate environment-awareness capabilities into the ICAJN, dynamically adjusting the working state to achieve simultaneous coverage and connectivity. To this end, the workflow of the ICAJN is structured into an environment-awareness phase and an execution phase, and the event-triggered sensing protocol (ETSP) is introduced to activate sensing function on demand. In the environment-awareness phase, the spatial interpolation is developed to estimate the gains of wireless links in the yet-to-reach locations, and further infer communication/jamming coverage boundaries. In the execution phase, the working state optimization algorithm is proposed to determine optimal node deployment locations, transmit power, and grouping policies in the current environment. Extended simulation results demonstrate that incorporating environment-awareness capabilities into ICAJN can significantly improve the network performance. And the designed protocol shows adaptability to the dynamic electromagnetic environments.

Autonomous aerial vehicle (AAV) covert communication could further enhance the quality and coverage of covert channels. However, in complex low-altitude environments, the air-to-ground (A2G) links are susceptible to the fading effects, which may degrade the performance of covert communication. In this paper, we investigate the fluid antenna (FA) enabled AAV covert communication, where a AAV equipped with FA serves multiple ground users in the presence of an active Warden. We aim to maximize the minimum average covert rate by jointly optimizing the beamforming vectors, AAV trajectory and fluid antenna positions. First, we derive closed-form expressions for the minimum detection error probability (MDEP) and the optimal detection threshold, accounting for uncertainties in both the noise variance and the self-interference channel coefficient. Secondly, we propose an alternating optimization algorithm subject to the covertness constraint, power constraint, and the Warden’s position uncertainty. Specifically, the original nonconvex problem is decomposed into tractable subproblems via the block coordinate descent, which could be solved successively by successive convex approximation, semidefinite relaxation, and Dinkelbach transformation. What’s more, a low-complexity algorithm is developed for the single-user scenario to improve the practical applicability of the proposed framework. Finally, simulation results validate the effectiveness of the proposed FA-AAV covert communication scheme. Moreover, compared to the fixed position antenna scheme, the FA-AAV could improve the covert performance, especially in strong channel fading environments, which is beneficial for practical application.

Natural disasters often destroy critical infrastructure, such as terrestrial communication networks and transportation routes, thereby severely disrupting post-disaster rescue operations. To rapidly re-establish communication links and provide flexible computational support in disaster rescue scenarios, the integration of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) has emerged as a promising solution. Nevertheless, the highly complex and resource-constrained characteristics of disaster environments pose significant challenges for UAV-assisted computation task offloading. In this paper, we investigate the joint task offloading and resource allocation (JTORA) problem to minimize the energy consumption associated with communication and computation during task offloading. Specifically, we develop a twin-delayed deep deterministic policy gradient (TD3)-based JTORA (JTORA-TD3) algorithm, which enables the UAV to optimize decisions of task offloading and resource allocation intelligently. To further enhance the training efficiency of the JTORA-TD3 algorithm in a complex disaster rescue environment, we integrate a large AI model (LAM) into the TD3 framework. Based on the textual interaction, we propose an LAM-enabled TD3-based JTORA (JTORA-LAM4TD3) algorithm. Simulation results demonstrate that the proposed JTORA-LAM4TD3 algorithm significantly outperforms baselines. These findings confirm the effectiveness of integrating LAMs with deep reinforcement learning (DRL) for solving the decision optimization problem.

In this paper, we aim at reconfigurable intelligent surfaces (RIS) aided multi-user communication systems including multiple user pairs, where an important target user pair needs to guarantee covertness in the presence of an adversarial warden with imperfect channel state information (CSI). By adopting the grouping technology, the user pairs in the same group share the same channel while different groups use orthogonal channels. Then, the mutual interference from the perspective of cooperation is considered to provide a covert cover for the target user pair. As a result, a grouping enhanced cooperative covert communications scheme with the aid of RIS is proposed. First, the analytical expressions of detection error probability, equivalent covert constraint based on Kullback-Leibler divergence and general user covert rate are derived. Then, to simultaneously guarantee the covertness and the robustness, the maximization of the worst-case sum rate with covert constraint is modeled as a non-convex optimization problem by jointly designing user group selection, transmission power allocation and RIS phase shift. To deal with the intractable problem consisting of mixed-integer programming, an alternative optimization algorithm that includes three subproblems is proposed. In each subproblem, fractional programming, relaxation variables and successive convex approximation methods are applied. Besides, S-procedure and general sign definition lemmas are applied to transform the CSI uncertainty into linear matrix inequalities. Finally, the simulation results demonstrate that the proposed cooperative covert communications scheme always outperforms the benchmark schemes with a better robustness.

Multiple-input multiple-output (MIMO) technology can effectively improve transmission throughput and reliability by utilizing spatial wireless resources, which has aroused widespread research attentions. Comparing with the stationary communication environment considered in most studies, the nonstationary channel may cause severe performance degradation of MIMO technology. This promotes the research of adaptive MIMO transmission strategy, which can intelligently adapt to dynamic environment and provide reliable and efficient communication. In this paper, our purpose is to design an intelligent MIMO system that can adjust the MIMO transmission mode and modulation order according to nonstationary environment. The dynamic decision problem is formulated as a markov decision process (MDP) and the state, action and reward function are designed. Then, an adaptive MIMO transmission strategy via leveraging proximal policy optimization (PPO) learning framework is proposed. The trained PPO agent can learn the proper joint transmission strategy so as to maximize the spectral efficiency subject to the constraint of target bit error rate (BER) performance. Simulation results demonstrate that the proposed scheme can achieve significant performance improvement over benchmark schemes.

Specific emitter identification (SEI) is a physical-layer authentication technique that identifies devices by extracting radio frequency fingerprints (RFFs) from received signals. Open-set SEI (OS-SEI) refers to classifying known classes while rejecting unknown classes, which typically requires a sufficient amount of labeled training samples. However, in open-world scenarios, labeled samples are often limited, and unlabeled samples may contain unknown classes. Moreover, open-world recognition not only requires detecting unknown class samples but also identifying specific novel classes within these unknown samples and integrating them into the recognition model. Current OS-SEI methods can only categorize all unknown samples as a single class, lacking the ability to further differentiate these unknown classes. To address these challenges, we formulate a novel semi-supervised open-world SEI (SSOW-SEI) problem, which aims to overcome the shortcomings of OS-SEI in utilizing unlabeled data, distinguishing unknown classes, and addressing class distribution mismatches between labeled and unlabeled data. Furthermore, we develop an end-to-end similarity-adaptive (SAA) framework for SSOW-SEI. Specifically, after automatically extracting sample features, SAA first identifies novel classes by measuring pairwise similarities between the features, and then recognizes known classes using adaptive cross-entropy, which balances the learning rate between known and novel classes to prevent model bias toward known classes. Additionally, entropy regularization is applied to mitigate model overfitting. Extensive experimental results demonstrate that the proposed SAA framework effectively leverages limited labeled data, handles large volumes of unlabeled data, and accurately identifies both known and novel classes. The results also highlight its strong generalization, stability, and enhanced adaptability to novel classes.

In this paper, we propose a deep learning (DL)-based task-driven spectrum prediction framework, named DeepSPred. The DeepSPred comprises a feature encoder and a task predictor, where the encoder extracts spectrum usage pattern features, and the predictor configures different networks according to the task requirements to predict future spectrum. Based on the DeepSPred, we first propose a novel 3D spectrum prediction method combining a flow processing strategy with 3D vision Transformer (ViT, i.e., Swin) and a pyramid to serve possible applications such as spectrum monitoring task, named 3D-SwinSTB. 3D-SwinSTB unique3D Patch Merging ViT-to-3D ViT Patch Expandingand pyramid designs help the model accurately learn the potential correlation of the evolution of the spectrogram over time. Then, we propose a novel spectrum occupancy rate (SOR) method by redesigning a predictor consisting exclusively of 3D convolutional and linear layers to serve possible applications such as dynamic spectrum access (DSA) task, named 3D-SwinLinear. Unlike the 3D-SwinSTB output spectrogram, 3D-SwinLinear projects the spectrogram directly as the SOR. Finally, we employ transfer learning (TL) to ensure the applicability of our two methods to diverse spectrum services. The results show that our 3D-SwinSTB outperforms recent benchmarks by more than 5%, while our 3D-SwinLinear achieves a 90% accuracy, with a performance improvement exceeding 10%.

This work investigates the problem of finite blocklength covert communications with relay assistance in Internet of Things (IoT) to extend the communications range. We reconstruct the framework for analyzing covert communications under decoded and forwarded protocols based on Willie’s optimal detection method. The analytic expression of Kullback-Leibler (KL) divergence is derived, the upper bound of KL divergence is solved by using the convexity of KL divergence, and the strict covertness constraint of the system is obtained. Meanwhile, to maximize the effective throughput, a covert communication parameter configuration scheme is proposed. Theoretical analysis and simulation results indicate that the compromised relationship of transmit power between Alice and relay nodes, and a reasonable power allocation scheme can enhance the effective throughput of the system.

This letter presents a novel multi-channel improved contrastive predictive coding (CPC) method to extract signals features. Specifically, to extract low pulse width radar signals features and meet the real-time requirements of signals identification, we design a lightweight encoder to realize the CPC features encoding function. Then, we construct a multi-channel CPC features decoder to mine and extract subtle individual signals features from the perspective of multi-domain and multi-channel information input. Simulation results verify the effectiveness of our proposed method, which can achieve state-of-the-art results in both accuracy and running time compared to the existing optimal methods. All our models and code are available at https://github.com/jn-z/MC-CPC.

Specific emitter identification (SEI) is a device identification technology that extracts radio frequency (RF) fingerprint from received signals. However, channel effects on RF fingerprint can vary between the training and testing stage, and SEI based on deep learning (DL) will be unable to withstand channel changes. To address this problem, we propose a channel-robust SEI scheme driven by causal learning. We analyze received signals from the causal perspective and construct a structural causal model (SCM) of SEI. In the SCM, received signals are considered as mixtures of the causal element and interference element, and only the former affects identification. Additionally, we design a new RF fingerprint feature representation called the centralized logarithmic power spectrum (CLPS) to reduce the impact of channel effects. Furthermore, we propose a causal purification network (CPNet) driven by causality to further alleviate channel effects. CPNet weakens the spurious associations between the channel and emitter labels through feature decorrelation and feature purification, strengthens the correlation between RF fingerprint and labels, and improves the generalization of SEI. Finally, our approach is evaluated extensively using 20 ZigBee devices under different channel environments. Experimental results demonstrate that our scheme can effectively alleviate channel effects, improve SEI performance under various channel environments, and exhibit good generalization and stability.

With the rapid development of emitter individual identification technology in cognitive radio networks, electromagnetic emitter individual target identification based on deep learning has received much attention. However, the confusion of unintentional features (i.e., individual fingerprint features) and modulation features resulting from the received signal might lead to low identification accuracy. In order to address this narrow, we propose an emitter individual identification network based on the competitive collaboration framework, called Specific Emitter Identification with Adaptive Decomposition and Extraction of individual fingerprint features (SEI-ADE), which can adaptively decompose and extract individual fingerprint features. Firstly, a signal adaptive decomposition network is proposed to distinguish the emitter signal and the interference signal by adopting the gradient inversion layer and the non-sequential characteristics of the signal. Then, in order to distinguish and extract corresponding features, the feature extractor and the training loss constraints are constructed for individual fingerprint feature signals, modulation signals, and external emitter interference signals, respectively. The proposed framework can continuously adjust the gradient loss, classification loss, and timing coding contrast loss, thus minimizing the entire training loss. For the separation of the modulation signal and individual fingerprint feature signal, the signal is transformed into the feature domain, and a mask prediction network is proposed to locate the domain of the individual fingerprint feature. The obtained experimental results show the outstanding performance of our proposal, compared with the current benchmarks. All our models and code are available athttps://github.com/jn-z/SEI-ADE.

In this paper, unmanned aerial vehicle (UAV) and reconfigurable intelligent surfaces (RIS) are combined together to further enhance the covert communication system. In particular, a multi-antenna transmitter Alice transmits information to Bob, through UAV-RIS in the presence of an adversarial warden, whose channel state information (CSI) is not perfectly known at Alice. To simultaneously guarantee the covertness and the robustness, the minimum worst-case average covert transmission rate is maximized by jointly optimizing the beamforming vector, the phase shift vector and UAV trajectory. Moreover, the robust design of beamforming, phase shift and three-dimensional (3D) trajectory is formulated as a non-convex problem. To deal with the intractable optimization problem, an alternative optimization algorithm consisting of three subproblems is designed to deal with the joint optimization problem. The general sign-definiteness is utilized to transform the CSI uncertainty and successive convex approximation (SCA) methods are proposed to transform non-convex constraints. Besides, considering the high complexity of the proposed SCA based algorithm, we further develop a low-complexity algorithm for a special case of perfect CSI scenario, where analytical expressions of the beamforming and phase shift are derived. Finally, simulation results demonstrate that the noise uncertainty for hiding legitimate communication is not necessarily as high as possible for a large equivalent receiving power threshold β and the proposed scheme outperforms the state-of-the-art schemes.

The radio environment map (REM) visually displays the spectrum information over the geographical map and plays a significant role in monitoring, management, and security of spectrum resources. In this paper, we present an efficient 3D REM construction scheme based on the sparse Bayesian learning (SBL), which aims to recover the accurate REM with limited and optimized sampling data. In order to reduce the number of sampling sensors, an efficient sparse sampling method for unknown scenarios is proposed. For the given construction accuracy and the priority of each location, the quantity and sampling locations can be jointly optimized. With the sparse sampled data, by mining the sparsity of the spectrum situation and channel propagation characteristics, a SBL-based spectrum data hierarchical recovery algorithm is developed to estimate the missing data of unsampled locations. Finally, the simulated three-dimensional (3D) REM data in the campus scenario are used to verify the proposed methods as well as to compare with the state-of-the-art. We also analyze the recovery performance and the impact of different parameters on the constructed REMs. Numerical results demonstrate that the proposed scheme can ensure the construction accuracy and improve the computational efficiency under the low sampling rate.

Unmanned aerial vehicles (UAVs) will be widely deployed due to their flexibility, mobility, and miniaturization, providing the necessary support for spectrum sharing between different communication systems in the space–air–ground-integrated IoT network (SAGIN). However, there are potential security threats to spectrum sharing among different communication systems due to the openness of a wireless network, the unreliability of node behavior, and the trust barriers of the networks. In this article, a secure spectrum sharing scheme based on lightweight UAV-blockchain (LUBC) is proposed to address the above security issues. First, a spectrum sharing model based on the overlay mode is developed to improve the spectrum efficiency of SAGIN, where UAVs relay signals from the satellite to the ground users in exchange for spectrum access opportunities and serve their own users simultaneously in the nonorthogonal multiple access (NOMA) mode. Second, a secure spectrum sharing framework based on LUBC is proposed to solve the security and privacy issues of spectrum trading in SAGIN. Then, aiming at maximizing the primary user’s throughput under the premise of meeting the minimum power allocation factor of UAV network, the spectrum auction based on NOMA is formulated as a multirelay selection optimization problem, which is solved by the blockchain-based sequential Vickrey auction mechanism. Finally, the security evaluation and numerical results are conducted to verify the security and effectiveness of the proposed spectrum sharing scheme for SAGIN.

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.

This article proposes a framework of cooperative multistation secure transmission in high-frequency skywave communications for wide-area Internet of Things applications by simultaneously exploiting the benefits of massive multiple-input multiple-output and coordinated multiple points communications. In this framework, the original message sent to the user is divided into several submessages by the core network, each of which will be transmitted to the user by the base stations (BSs). A joint optimization problem is established to maximize the user rate by optimally utilizing multiple BSs with multiple antennas for cooperative precoding. Semidefinite programming is introduced for the precoding vector design for the partial channel state information scenario. To solve the mixed-integer optimization problem of selecting BSs, a low complexity algorithm is proposed. Simulation results show that the performance of the proposed algorithm approaches that of exhaustive search, which demonstrates the effectiveness of the proposed algorithm.

Unmanned aerial vehicle (UAV)-enabled air-ground integrated communication systems are vulnerable to jamming attack, mainly due to the high probability with line-of-sight wireless channel conditions. Although a few UAV anti-jamming transmission schemes have been recently proposed, the fundamental performance limits by simultaneously considering the UAV and jammer’s mobility have not yet been reported. These observations motivate us to formulate an interactive reward region (IRR) characterization problem for the scenario that the UAV and the jammer compete with each other to maximize their respective rewards via power and mobility control. Then, we propose a transmit and jamming power optimization algorithm to characterize the IRR with power control, by leveraging successive convex approximation technique, where two transmit power design schemes are given in closed-form expressions from a game-theoretic perspective. Furthermore, the IRR with joint power and mobility control is characterized based on alternating optimization, in which both the UAV and jammer can simultaneously adjust their power and trajectories. Finally, extensive simulation results reveal that joint transmit power and trajectory optimization can enlarge the IRR, and increasing the UAV mobility and flight time length improves the UAV reward.

The vehicular crowdsensing, which benefits from edge computing devices (ECDs) distributedly selecting autonomous vehicles (AVs) to complete the sensing tasks and collecting the sensing results, represents a practical and promising solution to facilitate the autonomous vehicular networks (AVNs). With frequent data transaction and rewards distribution in the crowdsensing process, how to design an integrated scheme which guarantees the privacy of AVs and enables the ECDs to earn rewards securely while minimizing the task execution cost (TEC) therefore becomes a challenge. To this end, in this article, we develop a blockchain-based collaborative crowdsensing (BCC) scheme to support secure and efficient vehicular crowdsensing in AVNs. In the BCC, by considering the potential attacks in the crowdsensing process, we first develop a secure crowdsensing environment by designing a blockchain-based transaction architecture to deal with privacy and security issues. With the designed architecture, we then propose a coalition game with a transferable reward to motivate AVs to cooperatively execute the crowdsensing tasks by jointly considering the requirements of the tasks and the available sensing resources of AVs. After that, based on the merge and split rules, a coalition formation algorithm is designed to help each ECD select a group of AVs to form the optimal crowdsensing coalition (OCC) with the target of minimizing the TEC. Finally, we evaluate the TEC of the task and the rewards of the ECDs by comparing the proposed scheme with other schemes. The results show that our scheme can lead to a lower TEC for completing crowdsensing tasks and bring higher rewards to ECDs than the conventional schemes.

Spectrum mapping has emerged as an important problem in wireless communications, which generates a spectrum map for the spectrum resource analysis and management. Given the constrained transceiver volume and the limited energy consumption, how to effectively reconstruct the spectrum situation by the limited sampling data is a pressing challenge for spectrum mapping. In this paper, by exploiting the sparse nature of spectrum situation, we firstly attempt to solve the three-dimensional (3D) compressed spectrum mapping problem in the way of compressed sensing. Then, we develop a quadrature and right-triangular (QR) pivoting based measurement matrix optimization algorithm. By iteratively selecting new dominant sampling locations, it promotes the recovery accuracy compared to random measurement. After that, we propose a 3D spatial subspace based orthogonal matching pursuit (OMP) algorithm to recover spectrum situation for 3D compressed spectrum mapping. Finally, simulations are presented to show the comparisons in terms of localization, source signal strength recovery, recovery success rate and situation recovery. Results show our proposed 3D spectrum mapping scheme not only effectively reduces the sampling number, but also achieves a high level of spectrum mapping accuracy.

In this paper, we investigate massive multi-input multi-output (MIMO) high frequency (HF) skywave communications. We first introduce a model for HF skywave massive MIMO channels within the orthogonal frequency division multiplexing transmission framework by using the matrix of sampled steering vectors. Considering the large antenna array aperture and increased signal bandwidth, the effect of the propagation delay across the large-scale antenna array cannot be ignored, and thus the steering vectors vary across different subcarriers. Specifically, we derive a wideband beam based channel model and show that the beam domain statistical channel state information (CSI) is frequency-independent. Then, we consider minimum mean-squared error (MMSE) based uplink receiver and downlink precoder with perfect CSI at the base station (BS). With a large number of antennas at the BS, the sum-rate can be asymptotically increased proportionally to the number of user terminals (UTs) while the transmit power per UT is scaled down inverse-proportionally to the number of antennas. In order to reduce the design complexities of the MMSE receiver and precoder, we derive a polynomial expansion based design using a deterministic equivalent. Simulation results demonstrate very significant performance advantages of the proposed HF skywave massive MIMO system.

In this paper, we investigate massive multi-input multi-output (MIMO) high frequency (HF) skywave communications. We first introduce a model for HF skywave massive MIMO channels within the orthogonal frequency division multiplexing transmission framework by using the matrix of sampled steering vectors. The steering vectors vary across different subcarriers due to the effect of the propagation delay across the largescale antenna array. Specifically, we derive a wideband beam based channel model and show that the beam domain statistical channel state information (CSI) is frequency-independent. Then, we consider minimum mean-squared error based uplink receiver and downlink precoder with perfect CSI at the base station (BS). With a large number of antennas at the BS, the sum-rate can be asymptotically increased proportionally to the number of user terminals (UTs) while the transmit power per UT is scaled down inverse-proportionally to the number of antennas. Simulation results demonstrate very significant performance advantages of the proposed HF skywave massive MIMO system.

Satellite-based Internet of Things (IoT) is recognized as a cost-effective approach for global access. In this article, we aim at improving the spectrum efficiency of satellite systems to serve a huge number of IoT devices. To this end, we present a cognitive satellite-terrestrial framework, where a multibeam satellite system with full frequency reuse shares the spectrum with terrestrial networks based on the underlay paradigm. Considering DVB-S2X recommendations, geometric configurations, and channel characteristics, we investigate a robust multigroup multicast beamforming design for the satellite-based IoT coexisting with terrestrial networks in the presence of a phase error on channel state information, and characterize the achievable rate region under the outage probability constraint for the terminal and the power consumption constraint for the satellite. Based on the concept of rate profile, an associated optimization problem is formulated to design robust beamformers and determine the Pareto boundary of the region. To solve the intractable problem, we propose a two-level iterative algorithm on the basis of joint bisection search and penalty function enabled nonsmooth optimization. In particular, we develop a Bernstein-type inequality aided method and a large deviation inequality aided method to obtain a tractable and conservative approximation for the probabilistic constraint, respectively. Numerical results are provided to confirm the validity and superiority of our proposed scheme over the existing approaches and reveal the impact of key parameters on the achievable system performance.

Unmanned-aerial-vehicles (UAVs)-enabled wireless communication for Internet-of-Things (IoT) applications has attracted increasing attention. This article studies a UAV-assisted data dissemination system, where a rotary-wing UAV is dispatched to disseminate data to terrestrial IoT devices. We target to minimize the completion time via a joint optimization of the UAV trajectory and transmit power, while considering the indispensable constraints which cover the maximum energy budget, speed, transmit power of the UAV, and data requirement for each IoT device. First, we formulate the UAV data dissemination as a completion time minimization problem. To tackle the nonconvex optimization problem, the original problem is transformed into two subproblems: 1) the trajectory optimization and 2) the transmit power optimization, respectively, by introducing auxiliary variables and leveraging the concave-convex procedure. Then, we develop a joint trajectory and transmit power algorithm via tailoring the successive convex approximation and alternating descent method. We further improve the algorithm by maximizing the throughput instead of minimizing the completion time in the transmit power optimization process. The improved algorithm not only reduces the computational complexity but also enhances the achieved performance. In addition, simulation results demonstrate the superior performance of the proposed algorithms under various parameter configurations.

Unmanned aerial vehicles (UAVs) are envisioned to be widely deployed as an integral component in the next generation cellular networks, where spectrum sharing between the aerial and terrestrial communication systems will play an important role. However, there exist significant security and privacy challenges due to the untrusted broadcast features and wireless transmission of the UAV networks. This article endeavors to resolve the security issues through proposing a novel privacy-preserving secure spectrum trading and sharing scheme based on blockchain technology. Specifically, from the operator's perspective, a pricing-based incentive mechanism is first introduced, in which a primary mobile network operator (MNO) leases its owned spectrum to a secondary UAV network in exchange for some revenue from the UAV operators. To address the potential security issues, a spectrum blockchain framework is then proposed to illustrate detailed operations of how the blockchain helps to improve the spectrum trading environment. Under this framework, a Stackelberg game is formulated to jointly maximize the profits of the MNO and the UAV operators considering uniform and nonuniform pricing schemes. Security assessment and numerical results confirm the security and efficiency of our schemes for spectrum sharing in UAV-assisted cellular networks.

Internet of Spectrum Devices (IoSD) has been proposed as a bridging network among various spectrum-monitoring devices and massive spectrum-utilizing devices to enable a highly efficient spectrum sharing and management paradigm for future wireless networks. Spectrum data analytics is one of the key enabling techniques in IoSD. Correlations between spectrum state evolutions of different frequency points measured by an IoSD have been exploited to realize joint time-frequency spectrum prediction for improving the prediction accuracy. However, this kind of interrelationship has not been utilized efficiently to enhance the positive influences or avoid the negative influences when inferring the spectrum state. To fill the above gap, characteristics of spectrum state evolutions in the frequency domain are first modeled as multidimensional feature vectors in this article. Then, extensive clustering analyzes based on bisecting the K-means clustering and the agglomerative hierarchical clustering are conducted on spectrum state evolutions with multidimensional feature vectors. Real-world experiments demonstrate that the proposed multidimensional features can represent the characteristics of spectrum state evolutions in a more comprehensive way. Furthermore, clustering with the proposed vectors is integrated to the joint time-frequency spectrum inference problem to form the clustering-based joint spectral-temporal-spectrum-prediction (C-JSTSP) scheme. Experiments verify that the proposed C-JSTSP scheme can improve the inference performance on both the inference accuracy and the runtime overhead.

With the extensive research of multiantenna technology, beamforming (BF) will play an important role in the future communication systems due to its high transmission gain and satisfying directivity. If we can detect the non-cooperative beams, it is of great significance in counter reconnaissance, beam tracking, and spectrum sensing of multiantenna transmitters. This paper investigates the wireless sensor networks (WSNs), which is used to detect the unknown non-cooperative beam signal. In order to perceive the presence of beam signals without the prior information, we first derive the detection probability based on the sensors’ received signal strength (RSS). Then, based on the strong directivity of the beam signal, we propose an improved “k rank” fusion algorithm by jointly exploiting the energy detection (ED) information and location information of the sensors. Finally, the beam detection performance of different fusion algorithms is compared in simulation, and we find that our proposed algorithm showed better detection probability and lower error probability. The simulation results verify the correctness and effectiveness of the proposed algorithm.

The multiple signal classification (MUSIC) algorithmis computationally expensive in the application to joint two-dimensional (2-D) direction-of-arrival (DOA) and time-of-arrival (TOA) estimation based on uniform circular array (UCA) using orthogonal frequency-division multiplexing (OFDM) signal. This letter proposed an efficient way to compute the 3-D spatial-temporal spectrum. We extended the manifold separation technique, by which we obtained the 3-D discrete Fourier transform (DFT) form of the spectrum. On this basis, we proposed two 2-D DOA and TOA estimators called FFT-MUSIC and Two-step FFT-MUSIC. The former computed the spectrum by large size fast Fourier transform (FFT) to reduce the grid error in searching DOAs and TOAs. The latter roughly located the DOAs and TOAs by relatively small size FFT, followed by the subspace-based technique for a fine-grained searching within a local grid. Simulation results showed that both estimators can reduce the computation cost by one to two orders of magnitude, as compared to their conventional counterparts, while maintaining a similar accuracy.

The problem of Byzantine attack in collaborative spectrum sensing (CSS) is considered in this paper. To defend against Byzantine attack, a robust defense framework is proposed to efficiently identify the Byzantine attackers. Specifically, we first propose a robust defense framework, where a reference is built based on the extended sensing, and the transmit results and sensors are continuously evaluated via the reference and identified at intervals. In the framework, except of data falsification, multiple practical factors are considered, including the variation characteristic of sensors' attributes, reporting channel imperfection, and inference errors based on the transmit results. Further, we derive the closed-form expressions of the reference and the identification performance and make optimization of the identification threshold in two cases: with and without the prior knowledge of attack behaviors, where the probability of correctly detecting Byzantine attackers is maximized under the constraint of the probability of falsely identifying honest sensors as attackers. In particular, when the prior knowledge is unavailable, maximized likelihood estimation is made based on the reference to achieve the optimization. Furthermore, we present in-depth simulations to demonstrate the high robustness of the proposed defence framework to multiple practical factors under a homogeneous scenario and a heterogeneous scenario.

Unmanned aerial vehicles (UAVs) have attracted increasing attention in wireless communications due to the high mobility. This paper investigates a fixed-wing UAV-to-UAV (U2U) communications system, with the aim of minimizing the information transmission time via proactively designing the UAV paths. First, we propose a general optimization framework for U2U communications, which covers the communication throughput requirement, interference from terrestrial transmitters, UAV maximum/minimum speeds and accelerations, and minimum U2U distance. To tackle the formulated optimization, the communication throughput constraint that contains uncertain locations of terrestrial transmitters is transformed into a deterministic expression with the aid of S-procedure, and the nonlinear equality constraints on the UAV paths are replaced by linear equality constraints with additional positive semidefinite matrix constraints. Then, we develop a path planning algorithm based on the exact penalty method and successive convex approximation. Furthermore, we design a heuristic path planning algorithm that solves the completion time minimization problem by iteratively addressing a series of throughput maximization problems. The proposed heuristic algorithm strikes a good tradeoff between the computational complexity and the achievable performance. Finally, the simulation results are presented to verify the proposed path planning algorithms under various parameter configurations.

In this paper, we consider the spectrum sharing planning problem for a full-duplex unmanned aerial vehicle (UAV) relaying systems with underlaid device-to-device (D2D) communications, where a mobile UAV employed as a full-duplex relay assists the communication link between separated nodes without direct link. Our design aims to maximize the sum throughput under the transmit power budget, while guaranteeing the coexistence with terrestrial D2D pairs, satisfying the information causality and UAV's trajectory constraints. First, the transmit power planning with a given trajectory is investigated, where a successive convex algorithm is developed by leveraging the D.C. (difference of two convex) programming. Then, we propose a two-step trajectory design method for the given transmit power since the constraints of D2D pairs result in a non-convex feasible set. Furthermore, an efficient spectrum sharing method for an aerial UAV and terrestrial D2D communications is designed by alternately optimizing the transmit power and UAV's trajectory. Finally, simulation results under various parameter configurations are provided to show the effectiveness of the proposed algorithms.

Spectrum misuse behaviors, brought either by illegitimate access or by rogue power emission, endanger the legitimate communication and deteriorate the spectrum usage environment. In this paper, our aim is to detect whether the spectrum band is occupied, and if it is occupied, recognize whether the misuse behavior exists. One vital challenge is that the legitimate spectrum exploitation and misuse behaviors probabilistically coexist and the illegitimate user may act in an intermittent and fast-changing manner, which brings about much uncertainty for spectrum sensing. To tackle it, we first formulate the spectrum sensing problems under illegitimate access and rogue power emission as a uniform ternary hypothesis test. Then, we develop a novel test criterion, named the generalized multi-hypothesis Neyman-Pearson (GMNP) criterion. Following the criterion, we derive two test rules based on the generalized likelihood ratio test and the Rao test, respectively, whose asymptotic performances are analyzed and an upper bound is also given. Furthermore, a cooperative spectrum sensing scheme is designed based on the global GMNP criterion to further improve the detection performances. In addition, extensive simulations are provided to verify the proposed schemes' performance under various parameter configurations.

Device-to-device communication and energy harvesting are both key technologies to improve spectrum and energy efficiency. In this paper, we investigate the resource allocation problem for the energy harvesting-powered D2D communication underlaying cellular networks, where D2D pairs firstly harvest energy and then transmit information signals. The goal is to maximize the sum throughput via joint time scheduling and power control while satisfying the SINR requirement of cellular user and taking into account the energy constraint. The formulated non-convex problem is transformed into a nonlinear fractional programming problem with a tactful reformulation. Coupled with D.C. (difference of two convex functions) programming, a near optimal solution of the non-convex problem can be obtained by iteratively solving a sequence of convex problems. Then, a first-order algorithm is employed to solve these convex problems. Numerical simulations are conducted to validate the effectiveness of the proposed algorithm and evaluate the system throughput performance.

Welcome to the third one in the sequel of three IEEE JSAC special issues on Spectrum Sharing and Aggregation for Future Wireless Networks. In recognition of the fact that a substantial number of submissions have been received in response to the call for papers, the decision has been made to publish three issues on the cutting-edge advances in spectrum sharing and aggregation. The first two issues were published in October 2016 with 20 papers and November 2016 with 19 papers, respectively. This is the third issue with 17 papers, covering a feast of hot research topics as follows.

Cooperative spectrum sensing (CSS) in cognitive radio networks plays an important role in saluting the problem of spectrum scarcity. Unfortunately, the existence of spectrum sensing data falsification (SSDF) attack, also known as Byzantine attack, brings fatal threat to the reliability of CSS. In SSDF attack, malicious user(s) will send false sensing results to the fusion center to mislead the global decision about spectrum occupancy. In order to have a sufficient acknowledgement of spectrum state, we introduce an stable defense reference on the basis of existing defense references in this paper. Not only has the drawback that the existing defense references have strong assumptions such as the attackers are in minority and/or a trusted node exists for data fusion been overcome, but also situation such as sensors' sensing and reporting process has been taken into account, which is much closer to the actual situation. Based on the proposed reference, we also adopt a novel and easy to implement technique to counter Byzantine attacks in CRNs. Our analysis indicates that the proposed reference is robust against Byzantine attacks and can successfully remove the Byzantines in a short time span.

In this paper, we study robust spectrum sharing under channel uncertainty in a cognitive radio network (CRN), where a great number of secondary users (SUs) and a primary user (PU) coexist with each other sharing the same frequency spectrum. Considering the practical challenge that the channel gain between SU transmitters (SU-Txs) and PU receiver (PU-Rx) is typically uncertain, a probabilistic interference constraint is introduced in this paper. Although the interference constraint can be transformed into a linear outage probability formation, the optimization problem is a non-convex and non-linear programming (NCNLP). To circumvent the difficulty of directly solving the problem, we convert the original objective function and the outage probability constraint into suitable forms by the mathematical transformation and apply the convex optimization theory to solving this intractable problem. Moreover, we employ the interior point method to design an efficient algorithm and acquire a near-optimal solution. Finally, numerical simulations are carried out under various parameter configurations, which demonstrate that the proposed robust spectrum sharing algorithm can achieve higher performance than the state-of-the-art schemes.

This paper presents a systematic approach to exploiting TV white space (TVWS) for device-to-device (D2D) communications with the aid of the existing cellular infrastructure. The goal is to build a location-specific TVWS database, which provides a lookup table service for any D2D link to determine its maximum permitted emission power (MPEP) in an unlicensed digital TV (DTV) band. To achieve this goal, the idea of mobile crowd sensing is first introduced to collect active spectrum measurements from massive personal mobile devices. Considering the incompleteness of crowd measurements, we formulate the problem of unknown measurements recovery as a matrix completion problem and apply a powerful fixed point continuation algorithm to reconstruct the unknown elements from the known elements. By joint exploitation of the big spectrum data in its vicinity, each cellular base station further implements a nonlinear support vector machine algorithm to perform irregular coverage boundary detection of a licensed DTV transmitter. With the knowledge of the detected coverage boundary, an opportunistic spatial reuse algorithm is developed for each D2D link to determine its MPEP. Simulation results show that the proposed approach can successfully enable D2D communications in TVWS while satisfying the interference constraint from the licensed DTV services. In addition, to our best knowledge, this is the first try to explore and exploit TVWS inside the DTV protection region resulted from the shadowing effect. Potential application scenarios include communications between internet of vehicles in the underground parking and D2D communications in hotspots such as subway, game stadiums, and airports.

Welcome to the IEEE JSAC special issue on Spectrum Sharing and Aggregation for Future Wireless Networks. The conception of this special issue is motivated by the following observations: the ever-increasing penetration of both the mobile Internet and of the Internet-of-things is gradually clogging up the most valuable spectral bands available in the sub-2 GHz frequency range for future wireless networks. Hence there is an urgent need for improved spectrum exploitation to satisfy this demand. It is expected that the wireless tele-traffic will continue to grow quite dramatically in the ensuing years, hence further widening the spectrum-supply versus demand gap. In order to mitigate this gap, spectrum sharing and aggregation have been well recognized as promising approaches, which led to rapid advances by harnessing a large cross-section of the research community. Nonetheless, there are numerous unsolved technical challenges. This special issue aims for reporting on some of these cutting-edge advances in spectrum sharing and aggregation, whilst opening new avenues of research in this area.

The papers in this special issue represent the second one in the sequel of three special issues on spectrum sharing and aggregation for future wirelessn networks.

This paper presents an efficient method to construct white space database for devices to communicate in TV white space (TVWS). The goal is to build a TVWS database which senses the spectrum signal strength from white space devices (WSDs). Considering the incompleteness of measurement data, we formulate the problem of spatial inference as a matrix completion problem and propose a data recovery method by combining a fixed point continuation algorithm (FPCA) with a popular k-nearest neighbor (KNN) algorithm. Simulation results show that the proposed approach has a better performance in the TVWS database recovery than the traditional FPCA.

This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where one critical challenge is the uncertainty of the quality of sensing data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute abnormal data, which makes the existing defense schemes ineffective. To tackle these unique challenges, we propose a robust spectrum sensing scheme by developing a data cleansing framework, where the underutilization of licensed spectrum bands and the sparsity of nonzero abnormal data are jointly exploited to robustly cleanse out the potential nonzero abnormal data component from the original corrupted sensing data. Simulation results demonstrate that the proposed robust sensing scheme outperforms the state-of-art schemes under various abnormal data parameter configurations.

Starting from Neyman-Pearson criterion, this paper derives an optimal spectrum sensing scheme which exploits spatial diversity among multiple cognitive sensors and temporal diversity among consecutive time slots jointly. In the proposed scheme, the impact of the imperfect reporting channel on the design of the spectrum sensing scheme is effectively integrated. Simulation results show that compared with singular (either spatial or temporal) diversity-based sensing schemes, the proposed scheme brings not only improvement of sensing performance, but also significant reduction of sensing overhead.

Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the observations. In this paper, we argue that only connected is not enough, beyond that, general objects should have the capability to learn, think, and understand both physical and social worlds by themselves. This practical need impels us to develop a new paradigm, named cognitive Internet of Things (CIoT), to empower the current IoT with a “brain” for high-level intelligence. Specifically, we first present a comprehensive definition for CIoT, primarily inspired by the effectiveness of human cognition. Then, we propose an operational framework of CIoT, which mainly characterizes the interactions among five fundamental cognitive tasks: perception-action cycle, massive data analytics, semantic derivation and knowledge discovery, intelligent decision-making, and on-demand service provisioning. Furthermore, we provide a systematic tutorial on key enabling techniques involved in the cognitive tasks. In addition, we also discuss the design of proper performance metrics on evaluating the enabling techniques. Last but not the least, we present the research challenges and open issues ahead. Building on the present work and potentially fruitful future studies, CIoT has the capability to bridge the physical world (with objects, resources, etc.) and the social world (with human demand, social behavior, etc.), and enhance smart resource allocation, automatic network operation, and intelligent service provisioning.

This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where the sensing data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute with abnormal data, which makes the existing cooperative sensing schemes ineffective. To tackle these challenges, we first propose a generalized modeling approach for sensing data with an arbitrary abnormal component. Under this model, we then analyze the impact of general abnormal data on the performance of the cooperative sensing, by deriving closed-form expressions of the probabilities of global false alarm and global detection. To improve sensing data quality and enhance cooperative sensing performance, we further formulate an optimization problem as stable principal component pursuit, and develop a data cleansing-based robust spectrum sensing algorithm to solve it, where the under-utilization of licensed spectrum bands and the sparsity of nonzero abnormal data are jointly exploited to robustly cleanse out the potential nonzero abnormal data component from the original corrupted sensing data. Extensive simulation results demonstrate that the proposed robust sensing scheme performs well under various abnormal data parameter configurations.

This paper investigates the issue of spatial-temporal opportunity detection for spectrum-heterogeneous cognitive radio networks, where at a given time secondary users (SUs) at different locations may experience different spectrum access opportunities. Most prior studies address either spatial or temporal sensing in isolation and explicitly or implicitly assume that all SUs share the same spectrum opportunity. However, this assumption is not realistic and the traditional non-cooperative sensing (NCS) and cooperative sensing (CS) schemes are not very effective in a more realistic setting considering the heterogeneous spectrum availability among SUs. We define new performance metrics to guide the spatial-temporal opportunity detection and propose a two-dimensional sensing (TDS) framework to improve the opportunity detection performance, which exploits correlations in time and space simultaneously by effectively fusing sensing results in a spatial-temporal sensing window. Furthermore, in terms of maximum interference constrained transmission power (MICTP), we classify the spatial opportunities for SUs into three groups: black, grey, and white, and propose a TDS-based distributed power control scheme to further improve the spectrum utilization by exploiting both grey and white spectrum opportunities. The effectiveness of the proposed scheme is demonstrated through in-depth numerical simulations under a variety of scenarios.

In this paper, decentralized cooperative spectrum sensing in cognitive radio networks is studied based on the recent advances in unsupervised learning. To balance a tradeoff between the sensing reliability and the cooperation overhead (e.g., energy, delay, and signaling, etc.), a distributed clustering algorithm, without any central coordinator, is introduced for inducing the sensors with the best detection performance to join together and take charge of cooperative spectrum sensing. Numerical results show that the proposed scheme can obtain detection performance comparable to that of optimal soft combination scheme with reduced cooperation overhead. Moreover, the proposed scheme does not require any priori knowledge of spectrum sensors' received signal-to-noise-ratios (SNRs) or locations.

Opportunistic spectrum access is an important issue in cognitive radio systems. In this paper, we propose a distributed opportunistic spectrum access scheme based on sequential channel-sensing in decentralized CRN (cognitive radio network). Different from the traditional spectrum sensing scheme, the scheme proposed allows a secondary users to sense many channels one by one sequentially in a slot. Simulation results show that the proposed scheme can attain better performance of throughput. At the same time, we analyze three sequential channel-sensing orders of the proposed scheme. The simulation results show that the learning based channel-sensing order is better in the performance of throughput.