Spectrum scarcity and low utilization constrain the development of wireless communication. Cognitive radio enables sensor nodes (SNs) to monitor primary user (PU) channel usage, thereby enabling access to idle channels and improving spectrum efficiency. To address sensing impairments in wireless environments, cooperative spectrum sensing (CSS) has been implemented in cognitive wireless sensor networks (CWSNs) to ensure reliable spectrum detection. However, malicious SNs (MSNs) launching Byzantine attack can cause severe security threats to CSS. To ensure the effective operation of CSS against MSNs, this paper proposes a support vector machine (SVM) based on improved sand cat swarm optimization (SCSO), denoted as SCSO-SVM, for accurate MSN identification. The algorithm leverages the small-sample superiority of SVM and employs an enhanced SCSO to adaptively optimize its kernel and penalty parameters. In contrast to existing isolation forest (IF)-based algorithms and SVM methods based on other meta-heuristic algorithms, the proposed algorithm significantly reduces time consumption. Furthermore, it achieves high classification accuracy and F1-score with limited training samples. At last, simulation results demonstrate that across various Byzantine attack scenarios, the proposed SCSO-SVM algorithm reduces the time consumption by an order of magnitude compared to IF and spectral clustering -based fusion algorithm (IFSC), improved IF algorithm, particle swarm optimization (PSO)-based SVM (PSO-SVM), and grey wolf optimizer (GWO)-based SVM (GWO-SVM), while maintaining high accuracy.

To address the decline in positioning accuracy caused by long-term GNSS observation outages under tightly coupled (TC) in urban canyons, this paper proposes a pseudorange compensation mechanism based on a bidirectional long short-term memory network with multi-scale convolutional attention (BiLSTM-MSCA). Under frequent occlusion of satellite signals, the proposed network is used to learn the pseudorange incremental relationship between INS information and GNSS signals, and then compensate for the GNSS pseudorange observations. The proposed BiLSTM-MSCA utilizes the bidirectional information of input INS and GNSS signals in the time domain and enhances the extraction of key information to improve the prediction accuracy of the network. Experiments based on the measured data of urban canyons show that the horizontal positioning accuracy of the proposed method is improved by 20 % compared with the existing neural network assisted method under the condition of 100s GNSS observation loss.

Cooperative Spectrum Sensing (CSS) is vulnerable to hybrid Byzantine attack (HBA) from malicious users (MUs). Existing defense mechanisms, such as hard/soft fusion and reputation-based models, often struggle to adapt to dynamic attack patterns and fluctuating noise environments. For this aim, this paper proposes a self-parameterized and adaptive defense framework, termed dynamic Byzantine detection (DBD) to achieve robust Byzantine identification in an unsupervised manner. Following a state-change detection paradigm, we analyze the relationship between energy measurements from consecutive sensing periods and transform the problem into detecting distribution shifts to eliminate the reliance on prior “clean” data or ground-truth labels. Then, we further employ an improved hierarchical density-based clustering algorithm, with parameters self-determined via ak-distance analysis, to identify and effectively remove MUs across multiple levels. In a complex time-varying attack sequence involving independent and collusive MUs, DBD consistently achieves high detection accuracy and strong stability, while most benchmarks exhibit significant performance degradation. Under severe noise power fluctuation, DBD demonstrates superior robustness compared to an online support vector machine (SVM) that relies on ground-truth training data. Furthermore, a series of numerical simulation results demonstrate that DBD achieves notably superior performance over traditional methods while operating more efficiently than an online SVM, presenting a practical and effective solution for securing CSS.

The explosive growth of Internet of Things (IoT) networks necessitates innovative solutions for robust physical layer security (PLS) and ubiquitous connectivity. As a transformative technology, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) effectively overcome the half-space coverage limitations of conventional surfaces. This paper investigates secure downlink transmission in STARRIS-assisted massive MIMO IoT systems employing the energysplitting protocol, specifically addressing practical challenges like spatially correlated fading and multi-antenna eavesdroppers. We establish a robust transmission framework where the base station designs data and artificial noise precoders using imperfect instantaneous channel state information (CSI), while STARRIS phase shifts and power allocation are optimized based on statistical CSI to minimize signaling overhead. We construct a linear minimum mean-square error (LMMSE) estimator for aggregated channels and derive tractable closed-form expressions for the ergodic secrecy rate. Furthermore, asymptotic analysis quantifies performance scaling laws as the antenna array dimensions increase. To tackle the secrecy maximization problem, we propose low-complexity iterative algorithms that jointly optimize STAR-RIS coefficients and power allocation. Numerical results corroborate our theoretical analysis, demonstrating that the proposed scheme yields substantial secrecy gains over passive RIS baselines, offering a sustainable security solution for future green IoT networks.

A selective soft-message-forward (SSMF) cooperation with new threshold decision (TD)-based detection is presented to improve the bit error ratio (BER) performance and power efficiency in two-way physical-layer network-coded multiple-input-multiple-output (MIMO) Internet of Things (IoT) systems. First, the SSMF-based physical-layer network coding (PNC) is proposed by selecting appropriate received signals for soft decoding at relay nodes for better BER performance and low complexity. The signals least affected by noises after zero forcing detection are mapped into log-likelihood ratio metrics for most reservation of the soft messages. Second, a new TD-based detection is obtained by classifying the amplified received signals into different processing regions to reduce false decoding for better performance. Finally, an adaptive power allocation (APA) for the SSMF-based PNC is given to further improve power efficiency. By adaptively allocating the transmitting power in source nodes, the amplitudes of two received signals are similar at the relay and thus efficiently eliminate the near-far effect. Simulation results indicate that the SSMF-based PNC surpasses the existing PNC with a binary phase-shift keying (BPSK) modulation. At BER of$10^{-3}$, the SSMF-based PNC outperforms the selective decode-and-forward (DF), SMF, and DF schemes by approximately 0.6, 1.0, and 1.7 dB, respectively. At BER of$10^{-3}$, the APA scheme outperforms the equal power allocation one by about 2 dB. Therefore, it can be efficiently used in two-way integrated satellite and ground wireless MIMO IoT systems.

In the realm of cognitive radio (CR), the soft and hard combinations are commonly applied for the fusion rule of cooperative spectrum sensing (CSS) to detect the primary user (PU) signal for available vacant spectrum resources and allow cooperative secondary users (SUs) to opportunistically access the channel without harmful interference to the PU’s normal communication. However, in the process of submitting the sensing information to the fusion center (FC), the soft combination requires great communication overhead from the SU to the FC, and the hard combination reports only one bit of the local decision resulting in unsatisfactory detection performance. In view of this, in this paper we make an in-depth investigation on the quantization of raw measurement data and propose a quantization-based multi-bit combination method that utilizes the information of each sampling point. On the basis of the proposed multi-bit combination approach, each SU quantifies the information obtained from the local sensing information and transmits the quantified sensing data in a few bits to the FC. Furthermore, we formulate an optimization problem for the error probability of the multi-bit combination to achieve the optimal CSS performance. Finally, numerical simulation results confirm the effectiveness and robustness of the proposed multi-bit combination method, establishing its superiority in various environments, in terms of the overall error probability.

This letter investigates the design and optimization of secure beamforming in an intelligent reflecting surface (IRS)-assisted multiuser cognitive radio simultaneous wireless information and power transfer (CR-SWIPT) system. The proposed method leverages IRS to address cognitive energy harvesting nodes as potential eavesdroppers (Eve). The objective is to maximize the achievable secrecy rate while satisfying multiple constraints, such as transmit power control, energy harvesting, phase shifts, and maximum tolerable interference power. To solve this highly non-convex optimization problem, we propose a hybrid algorithm that combines successive convex approximation (SCA) and inexact block coordinate descent (IBCD). By decomposing the problem into three sub-problems, local optimal beamforming matrices and phase-shift matrices are obtained using the SCA method and complex circle manifold (CCM) method, respectively. Simulation results show that the secrecy rate at the SWIPT information decoding node in the IRS-assisted multiuser CR-SWIPT system improves significantly, with an approximate 40% enhancement compared to the random phase shift scheme with maximum transmit power. The effectiveness of the proposed algorithm is further validated through secrecy rate performance evaluations under different system configurations.

To solve low-resource utilization, complex storage, and busy traffic in space–air–ground integrated networks (SAGINs), a new multilayered decentralized coded caching (ML-DCC) scheme is proposed in hierarchical networks with nonuniform file popularity and multilevel cache capacity. First, a file popularity prediction is performed by a random forest model with grid search. Second, a multipopularity coded caching (MPCC) strategy by grouping files is executed in a two-hop network with fixed cache size. Finally, a new ML-DCC scheme is proposed to adopt nonuniform file popularity and multilevel cache capacity with both block-coded caching and zero-bit padding to obtain high-resource utilization and efficient file exchange in file transmissions. The innovations are random forest classifier with bagging integration and grid search to improve network traffic and link load, decentralized coded caching to reduce average transferred files, and coded caching and grouping files by popularity to improve network load. Simulation results show that the data payload of the proposed scheme is significantly improved by about 1.88, 1.18, 1.38, 3.53, and 4.39 times, when compared with those of the shared cache, multilevel popularity, hierarchical coded caching, MPCC, and uncoded caching schemes, respectively. Under the expected load of$R=211.57F$bits of the shared link, the cache size used by the proposed ML-DCC is only 1/42.85 and 1/24.39 of those of the highest popularity first (HPF) and nonuniform cache schemes, respectively.

To both eliminate interference from unrequested subfiles and improve sum degree-of-freedom (DoF) in multirelay Internet of vehicles (IoV) systems, a new decentralized multi-subsystem weighted interference cancellation (DMS-WIC) scheme with coded caching is proposed. It divides the transmission into two stages with optimized parameters to minimize link load. It also improves the sum DoF by jointly combining the weighted interference cancellation, orthogonal unicast, and decentralized coded multicast together. Given the number of interference-free subfiles larger than that of users, traditional interference cancellation wastes cache capacities of both users and relays by ignoring partial cache capacities and thus it fails to achieve a balance between the number of both users and interference cancellation subfiles. The DMS-WIC achieves additional DoF by adopting orthogonal unicast and subsequently dividing cache of both users and relays compared with those of traditional interference cancellation. In other cases, the weighted requested subfiles are transmitted by combining physical layer file division and network layer coding delivery together to achieve interference alignment and cancellation gains. The DMS-WIC adaptively adjusts files and subsystem partition parameters by perceiving systematical parameters. Besides, a multirelay cooperative coded caching is introduced to further reduce transmission and complexity. Given the number of relays larger than that of users, the relays deliver coded subfiles with different user sets. Otherwise, a relay skips the fix of the user number units, until all user sets are taken after the completion of a transmission. Simulation results show that the proposed DMS-WIC with coded caching achieves more than 10.6% sum DoF gain compared with that of traditional interference cancellation.

Optimized polar-coded cooperation with an approximate prior probability-based threshold decision is proposed to improve the reliability and complexity in selective decode-and-forward (SDF) cooperation of multiple satellite constellation systems. First, an SDF scheme with polar coding is adopted in cooperative communications for efficient code construction and relay forwarding. Second, in the logarithmic belief propagation (BP) polar decoding, the variable iterative threshold is derived with independent and variable signal-to-noise ratio (SNR)-based approximate prior probability. This threshold reduces complexity by eliminating active nodes with high reliability in each decoding iteration. Third, the information detection factor is introduced to evaluate the accuracy of decoded bits, thereby limiting the average decoding iterations to improve decoding efficiency. Simulation results show that the proposed polar-coded SDF cooperation obtains 1.5 dB performance gain at a bit error rate (BER) of 10−3compared with that of existing LDPC-related schemes. The complexity of the optimized logarithmic BP decoding is reduced by 25%–50% compared with that of the traditional BP decoding. Therefore, the proposed scheme has good BER performance and low complexity in cooperative communications of multisatellite and wireless integrated systems.

In order to solve the shortage of unmanned aerial vehicles (UAVs) spectrum resources, cooperative spectrum sensing (CSS) is applied to identify the underutilized spectrums in cognitive UAV networks (CUAVNs). However, the UAV’s location flexibility makes the detection performance unstable, especially under the fixed sensing delay constraint, and limits or even damages the available spectrum resources, resulting in a significant degradation of spectrum efficiency (SE) and energy efficiency (EE) as well as the increase of energy consumption (EC). In view of this, we develop a CSS framework among minisensing slots in a periodic spectrum sensing in this paper. Further, we are motivated by the sequential detection to take the flexible sensing delay constraint into consideration, with aim of making in-depth analysis on various CSS scenarios for CUAVNs. Concurrently, in order to ensure the performance of CSS, the false alarm probability is used as a limiting condition to change the sensing delay constraint. Finally, simulation results corroborate the correctness and effectiveness of our theoretical analysis and show that the flexible sensing delay constraint is better than the fixed sensing delay constraint, in terms of SE, EE and EC.

Cooperative spectrum sensing (CSS) has been considered as a powerful approach to improve the utilization of scarce radio spectrum resources. However, spectrum sensing data falsification (SSDF) can hugely degrade the achievable detection accuracy of the primary user (PU) channel status in cognitive radio (CR) networks and thus requires learning from past CR network environments. Using the supervised learning (SL) method, a large number of labels about the real state of the channel availability need to be acquired. In a practical setting, however, obtaining off-the-shelf label data is a challenging undertaking because it necessitates cooperation among PUs and secondary users (SUs) and increases communication overhead in CR networks. Motivated by this, we solve the issue of label data scarcity in CR networks in this paper, and propose a semi-supervised learning-enabled two-stage framework for CSS against SSDF attack, in which combines the superior performance of semi-supervised support vector machine (S3VM) and the fast convergence of K-means. At last, simulation results demonstrate that S3VM is equipped with a greater detection performance than support vector data description (SVDD), and the same detection performance as support vector machine (SVM), especially it also the robustness and excellent performance using a small amount of labeled data.

Cooperative spectrum sensing (CSS) is the pivotal function of cognitive radio (CR) to prevent harmful interference with primary users (PUs) and identify the available spectrum for improving the spectrum's utilization. However, it opens an opportunity for Byzantine attackers to exploit the decision-making process by sending false sensing decisions, thereby causing degradation of the cognitive radio network (CRN) quality of service but currently there are no specific security protocols for it. For this aim, we formulate a dynamic Byzantine attack model to characterize attack behaviors of the malicious user (MU) in this paper. Based on this dynamic attack model, it is expected that the fusion center (FC) benefit from Byzantine attack to contribute to the CSS performance. To this end, the FC needs to have certain knowledge of attack strategies, then we propose an estimation algorithm for the FC to estimate the MU's attack parameters. Furthermore, the reputation value (RV) is introduced to evaluate the impact of Byzantine attack exploitation on the CSS performance and analyze the conditions of Byzantine attack exploitation. Finally, simulation results show the effectiveness and correctness of Byzantine attack exploitation.

Cooperative spectrum sensing (CSS) is the key technology of cognitive radio networks (CRNs) to identify the available spectrum by achieving spatial diversity gain. However, the reporting overhead and the imperfect reporting channel significantly degrade the efficiency and performance of CSS. In this paper, we consider the reporting overhead and the effect of the imperfect reporting channel in the process of CSS. By means of the sequential idea and the differential advantage, we propose a reliable sequential 0/1 differential (SZ/OD) reporting mechanism, with aim of mitigating the negative effect of the imperfect reporting channel on CSS. Simulation results show that compared to the existing reporting mechanisms, SZ/OD requires fewer reports in support of a higher detection accuracy and is not affected by imperfect reporting channels.

With the increasing demands of unmanned aerial vehicles (UAVs), cooperative spectrum sensing (CSS) is an emerging technology that identifies the unused spectrum for cognitive UAV networks (CUAVNs). However, different from traditional single-slot CSS (SCSS) among multiple independent spectrum sensing nodes, realizing CSS will become a difficult task because of the UAV's location flexibility, the sensing time is limited in particular. For this aim, we formulate a multi-slot CSS (MCSS) among multiple mini-slots of a UAV sensing node to accomplish cooperation in this paper. In order for guaranteeing the achievable throughput of CUAVNs, we make use of the sequential idea to proceed with MCSS within the sensing time constraint. Furthermore, we make an in-depth investigation on the performance, cost and benefit for MCSS. On basis of these analyses, we conduct a utility function to optimize the UAV's utility. Finally, numerical simulation results show that our proposed MSCS is superior than the traditional SCSS, in terms of the CSS performance and utility.

Abstract In an interweave cognitive radio system, cooperative spectrum sensing has been recognised as a key technology to enable secondary users to opportunistically access licensed spectrum band without harmful interference to primary users. At the same time, the unmanned aerial vehicle equipped with spectrum sensing and data transmission facilities is gaining more popularity in different applications. An unmanned aerial vehicle‐based interweave cognitive radio is investigated in which the unmanned aerial vehicle is used as a secondary user, but unlike the participation of multiple secondary users in traditional cooperative spectrum sensing, a virtual cooperative spectrum sensing model is introduced into the periodic spectrum sensing frame structure. Afterwards, the authors further propose an energy‐efficient virtual cooperative spectrum sensing with the sequential 0/1 fusion rule to reduce the average number of decisions without any loss in the detection performance. Sequentially, the authors formulate the optimisation of virtual cooperative spectrum sensing for unmanned aerial vehicle‐based interweave cognitive ratio system as the optimal sequential 0/1 fusion problem on the basis of the K ‐out‐of‐ N fusion rule and prove the formulated problem indeed has one optimal K , which yields the highest throughput. Finally, numerical simulations are presented to demonstrate the correctness of theoretical analyses and the effectiveness of the virtual cooperative spectrum sensing with the sequential 0/1 fusion rule.

Cooperative spectrum sensing (CSS) by exploiting diversity via the observations of spatially located secondary users improves the accuracy of the primary user (PU) detection, but cooperative paradigms are threatened by Byzantine attack. In this study, the authors propose a flexible Byzantine attack model, which goes beyond the existing models for its generalisation. Under this generalised Byzantine attack model, they give insights into the blind scenario where Byzantines make the fusion centre (FC) incapable of deciding the presence of the PU. To solve the blind problem, they formulate data transmission revelation (DTR) as trust reputation management to check consistency of the local decision. Moreover, they evaluate the usability of Byzantine data based on DTR and propose a sequential detection (SD) approach to reuse Byzantine data, which is a remarkable issue involved in CSS, however, ignored by most previous studies. Simulation results clearly reveal that in contrast to other approaches associated with sequential probability ratio test, the proposed SD benefits from Byzantine data to greatly improve the correct sensing ratio and the sample size, and still functions well in the blind scenario.

Cooperative spectrum sensing is a key technology of cognitive radio networks (CRNs) to the reliability of spectrum sensing but is prone to be affected by a series of user characteristics, which results in a serious decline in the throughput of CRNs and the harmful interference to the primary user network. The performance analysis and optimisation of existing cooperative spectrum‐sensing schemes do not completely cover key user characteristics. In this study, the joint effects of key user characteristics are studied with the objective to determine the parameters that affect the cooperative spectrum‐sensing functionality. To this aim, the mobile CRN model and spatial–temporal spectrum‐sensing model are formulated. Furthermore, the authors propose a dynamic double threshold energy detection (DDTED) scheme to derive the miss‐detection probability and false alarm probability involving user characteristics. Moreover, the performance optimisation is carried out by a dynamic threshold factor. Finally, simulation results show that a variety of user characteristics have different effects on the performance of cooperative spectrum sensing, and the proposed DDTED scheme can achieve better performance than the existing approach.

Recently, many technological issues concerning co-operative spectrum sensing (CSS) of cognitive radio networks (CRNs) have been studied, but most of them focus on maximizing spectral efficiency (SE) under the opportunistic spectrum access (OSA) scheme. In this paper, we investigate the mean energy efficiency (EE) maximization problem under the hybrid spectrum sharing (HSS) scheme. Due to channel fading, the effects of reporting channel errors on the EE should be considered. Specifically, the minimum transmit data rate constraint is imposed to ensure the quality of service (QoS) requirements of secondary users (SUs). Our goal is to maximize the mean EE while maintaining the sensing accuracy by jointly optimizing the sensing slot length and the number of cooperative SUs, subject to the rate constraint and the transmit and interference power constraints. To address the non-convexity of the optimization problem, we propose an energy-efficient CSS iterative power adaptation algorithm. Simulation results demonstrate that the proposed algorithm can achieve higher average EE than the conventional OSA scheme.

In cognitive radio networks (CRNs), spectrum sensing data falsification (SSDF) is one of the most typical attack which hugely degrades the detection performance of cooperative spectrum sensing (CSS). SSDF and defense strategies have been an active field of research, but countermeasures of existing researches are sensitive to the number of malicious users (MUs). In this paper, we propose a two-stage credit threshold (TSCT) scheme based CSS to counter arbitrary number of MUs who exist in CRNs. We divide the network region into cells according to channel condition, and CSS procedure is conducted into two stages, which are the secondary user (SU) stage and cell stage. Our proposed scheme can effectively remove MUs in the SU stage and weaken the bad effects of remnant MUs in the cell stage. In comparison to existing schemes, simulation results show that the proposed scheme can provide with better detection performance regardless of detection rounds, and can work well when MUs outnumber SUs while previous schemes fail.

Cooperative spectrum sensing is one of the key technologies to accurately detect the primary user (PU) activity in cognitive radio networks (CRNs). However, collaboration among multiusers provides malicious users (MUs) with an opportunity to launch spectrum sensing data falsification (SSDF) attack. Various approaches have been proposed regarding how to mitigate the negative effect of SSDF attack, while extensive references have strong assumptions such as MUs are in minority and need more decision samples. In this paper, we develop a general SSDF attack model. We further propose a robust data fusion scheme, named robust weighted sequential probability ratio test (RWSPRT), which can deal with various attack probabilities. In the proposed RWSPRT, according to the correct decision ability, the reputation value (RV) of each SU is integrated into weight coefficient of weighted sequential probability ratio test (WSPRT) to improve the performance of cooperative spectrum sensing. Simulation results show that RWSPRT performs more robust than traditional data fusion techniques whereas requires less number of samples, even when a large number of MUs exists in CRNs.