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.

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.

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.

Image reconstruction remains a challenging task in image processing. In this letter, a compressive sensing total-variation primal-dual (CTPD) algorithm is developed for image reconstruction. The proposed CTPD algorithm involves minimization of a multi-term cost function with sparse and total-variation composite regularization. Instead of finding its optimal solution directly, this minimization problem is first cast into a convex-concave saddle point optimization problem, which can be solved by using a proximal primal-dual iteration algorithm with several simple sub-steps, each producing low complexity solvers. Moreover, in order to speed-up convergence, an accelerated version of the CTPD (A-CTPD) algorithm is also presented. In the A-CTPD algorithm, the primal-dual iteration is reformulated as a fixed-point iteration, which allows the use of an Anderson acceleration technique for faster convergence without sacrificing image reconstruction accuracy. Several numerical experiments demonstrate the efficiency of our proposed algorithms in comparison with other algorithms such as least squares (LS), the least absolute shrinkage and selection operator (LASSO), total variation (TV) image reconstruction, the alternating direction method of multipliers (ADMM), or fast iterative shrinkage-thresholding algorithm (FISTA).

By introducing amplify‐and‐forward relaying into a cognitive radio system, typical cognitive relay networks are studied for the optimisation problems of the spectrum and power allocation. By applying the mixed spectrum access of overlay and underlay approaches, an interference evaluation strategy is proposed to use different spectrum and power allocation methods while the secondary users (SUs) are located in different service regions of the primary users (PUs). In the interference evaluation strategy, the service area of the PUs is divided according to the possible interference strength of the PUs from the SUs compared with the location‐aware strategy in which the service area is divided only by the location information. An optimal power allocation algorithm is developed to maximise the throughput of the SUs under the condition of anti‐interference performance of the PUs and the total power constraints of the SUs. A type of computing algorithm that joins the subgradient and the Newton's method is used in order to resolve the complex optimisation problem. Numerical results show that the performance using the interference evaluation strategy, such as the throughput, the power consumption, and the energy efficiency, outperforms that using the location‐aware strategy.

Cognitive orthogonal frequency division multiplexing (C-OFDM) is an extremely promising technique for achieving high transmission capacity in the next generation cellular and wireless local area network (WLAN) systems with limited spectrum resources. Since the greedy bit allocation algorithm is the optimal method for single user, however, in the case of plenty of sub-carriers and bits to be distributed, the computational complexity will be extraordinarily unacceptable. This paper presents a new bit and power allocation scheme for C-OFDM systems based on the margin adaptive (MA) principle in which the overall transmission power is minimized by constraining the fixed data rate and bit error rate. The presented bit allocation algorithm is derived from geometric progression of the additional transmission power required by the sub-carriers and the arithmetic-geometric means inequality. Consequently, compared with the algorithm existing now, this algorithm has a simple procedure and low computational complexity. Simulation results show that the proposed algorithm is superior to the traditional algorithm, while reducing the computational complexity from exponential to linear in the number of sub-carriers.