A cooperative spectrum sensing algorithm based on Bayesian Compressed sensing

In cognitive radios (CR), compressive spectrum sensing (CSS) is a promising technique to detect wideband spectrum holes with reduced sampling rate requirement of analog-to-digital converter (ADC). It is implied in compressive sensing (CS) that the minimum sampling rate is determined by the sparsity order of the observed wideband signal. Unfortunately, the sparsity order is previously unknown and dynamically varies. Meanwhile, CS technology fails to work efficiently for the nonsparse spectrum case. For example, the CSS performance degenerates significantly for low signal-to-noise ratio (SNR) scenarios since the observed signal becomes nonsparse in frequency domain. To overcome the above-mentioned challenges, we propose a novel CSS algorithm with temporally-correlated prior knowledge mining (TPKM-CSS) to detect spectrum holes without a prior sparsity order information. The proposed scheme can handle both the sparse and nonsparse spectrum cases. The proposed solution has three contributions: firstly, CR implements multiple parallel sampling simultaneously to acquire wireless spectrum signals, which have a low-rank spectrum matrix. Secondly, the temporal correlation between continuous CS data is further exploited to improve the estimation accuracy of the current spectrum. Finally, the theoretical performance of the proposed TPKM-CSS is evaluated, wherein the cumulative distribution function (CDF) of the spectrum reconstruction error is derived in the closed form. The simulation results show that the proposed scheme works well for both sparse and nonsparse spectrum scenarios. Compared with the CSS schemes that are based on low-rank matrix recovery (LMR-CSS), maximizing correlation (MC), iterative compressive filtering (ICF), or Bayesian CS (BCS), the proposed scheme yields the superior performance in terms of the probability of detection and the probability of false alarm, especially for nonsparse signals. Furthermore, the simulated statistical performance of the spectrum reconstruction error matches well the theoretical CDF curve.

Robust Bayesian Compressive Sensing for Signals in Structural Health Monitoring

This paper investigates the design issues of spectrum sensing in the cognitive radio (CR) networks of opportunistic unlicensed spectrum access. The cognitive radios can perform a communication using the incumbent user spectrum band without the interference caused by the cognitive radio users. In this case, the cognitive radios must know the real-time radio environments of the incumbent user spectrum band using the spectrum sensing, beacon signal, and geo-location database access. Then in this paper, we are going to provide spectrum sensing issues which include the sensing techniques, the regulatory requirements, the analysis of DTV detection threshold, and main considerations associated with the spectrum sensing design in cognitive radio systems. Also, this paper introduces design trade-offs in order to optimize the sensing parameters such as sensing time and sensing complexity.

The compressed spectrum sensing problem for cognitive radio (CR) spectrum sharing in device-to-device (D2D) communications is investigated, with an emphasis on collaborative sensing strategies for multi-user CR-based D2D networks. Because the D2D users are assumed to be geographically close to each other, the same spectral occupancy is expected, which can be exploited in the sensing algorithm design. We first investigate a single user compressed spectrum sensing algorithm, where the successive fast iterative shrinkage-thresholding algorithm (FISTA) is employed. The successive FISTA-based single-user sensing algorithm is then used to develop two collaborative compressed spectrum sensing schemes, namely Equal-Gain Combining (EGC) and Census-Weighted Detection Results Combining (CWDRC). It is demonstrated that both algorithms are effective in low signal-to-noise ratio (SNR) conditions. We further show that the census-based CWDRC algorithm significantly reduces system overhead compared to other collaborative sensing strategies with only a small degradation in the probability of detection. Thus, CWDRC is attractive for collaborative spectrum sensing in CR-based D2D systems.

Antenna 3D pattern measurement can be a tedious and time consuming task even for antennas with manageable sizes inside anechoic chambers. Performing onsite measurements by scanning the whole 4π [sr] solid angle around the antenna under test (AUT) is more complicated. In this paper, with the aim of minimum duration of flight, a test scenario using unmanned aerial vehicles (UAV) is proposed. A practically optimal scanning strategy is presented. The suggested techniques are beneficial particularly for the case of large directive antennas. The UAV follows a predefined trajectory in the scanning windows around the AUT and reads the field strength. Then, using compressed sensing (CS) method, the antenna pattern is reconstructed. It is shown that applying Bayesian CS algorithm to the samples of field intensity gathered by UAV can efficiently reconstruct the pattern. Discrete cosine Transform (DCT) is utilized for sparsifying the antenna patterns. Performance is evaluated by obtaining the reconstructed patterns for different antenna types. The effects of the antenna type and area of scanning are analyzed. It is shown that satisfying performance can be achieved with measuring about 50 percent of the total pattern samples. The reconstruction error of different CS implementations is computed and superiority of Bayesian CS is illustrated.

Implementation of compressive sensing in ECG and EEG signal processing

There are two major challenges in wide-band spectrum sensing in a heterogenous spectrum environment. One is the spectrum acquisition in the wide-band scenario due to limited sampling capability; the other is how to collaborate in a heterogenous spectrum environment. Compressed spectrum sensing is a promising technology for wide-band signal acquisition but it requires effective collaboration to combat noise. However, most collaboration methods assume that all the secondary users share the same occupancy of primary users, which is invalid in a heterogenous spectrum environment where secondary users at different locations may be affected by different primary users. In this paper, we propose an automatic clustering collaborative compressed spectrum sensing (ACCSS) algorithm. A hierarchy probabilistic model is proposed to represent the compressed reconstruction procedure, and Dirichlet process mixed model is introduced to cluster the compressed measurements. Cluster membership estimation and compressed spectrum reconstruction are jointly implemented in the fusion center. Based on the probabilistic model, the compressed measurements from the same cluster can be effectively fused and used to jointly reconstruct the corresponding primary user's spectrum signal. Consequently, the spectrum occupancy status of each primary user can be attained. Numerical simulation results demonstrate that the proposed ACCSS algorithm can effectively estimate the cluster membership of each secondary user and improve compressed spectrum sensing performance under low signal-to-noise ratio.

We consider a cognitive radio (CR) network that makes opportunistic access to a spectrum licensed to the primary users. During its operation, the CR network carries out spectrum sensing on a frame-by-frame basis to detect active primary users, thereby avoiding interfering with them. We formulate a collision-throughput tradeoff problem which, based on the sensing time requirement and the traffic pattern of primary users, finds optimal value for the frame duration of CR operation so that the throughput of the CR network is maximized, yet the collision probability of the primary users is not greater than a threshold. We derive the theoretical formula for achievable throughput of CR network and find the optimal solution for frame duration. Computer simulations are presented to evaluate the performance of our approach.

We focus on a specific class of denial-of-service (DoS) attacks that is executed through Channel Eviction Triggering (CET), whereby adversary nodes unduly invoke mechanisms inherent in a cognitive radio (CR) network (CRN) operation to protect the licensed users and thus disrupt secondary access to the otherwise idle licensed bands. Skewing the spectrum sensing decision of CRN through sensing misreports is a manifestation of CET attacks. Whereas most studies in the literature focus on making the cooperative sensing more robust against such sensing misreports, we tackle the problem from the novel perspective of incentive alleviation. We distinguish two classes of such DoS attacks, which we refer to as CET and CET-jamming attacks. In the former case, the incentive of adversary CRs is to remove the competition of truthful CRs in accessing the licensed spectral ranges. The latter class of DoS attack deals with scenarios in which the adversary nodes are mainly interested in denying the chances of communication of CRN over primary bands and as such their incentive cannot be modelled by the same utility maximisation model as truthful CRs. We propose a solution for each class of attacks, and our numerical results verify the effectiveness of the proposed CET defence scheme in both cases. Copyright © 2012 John Wiley & Sons, Ltd.

Spectrum sensing is a proactive way in cognitive radio systems to achieve dynamic spectrum access; and compressive spectrum sensing (CSS) techniques alleviate the demand for high-speed sampling in wideband spectrum sensing. Most existing literature discusses Neyman-Pearson channel energy detection and threshold adaption schemes to achieve an optimal performance of detection in a conventional non-compressive spectrum sensing scenario. However, in the CSS, it is found that the channel energy statistics and optimal threshold depend not only on noise energy but also on compression ratio, sparsity of spectrum, and nature of recovery algorithms. To investigate the channel energy statistics of recovered spectrum, we postulate a statistical model of channel energy for CSS and propose a learning algorithm based on a mixture model and expectation-maximization techniques. In addition, having verified the validity of the postulated model, we propose a practical threshold adaption scheme for CSS aiming to maintain constant false alarm rates in channel energy detection. In simulations, it is shown that the postulated channel energy statistic models with parameters learned by the proposed learning algorithm fit well with empirical distributions under circumstances of various channel models and recovery algorithms. Moreover, it is presented that the proposed threshold adaption scheme maintains the false alarm rate near the predefined constant, which in turn validates the postulated model.

The estimation of the localization of targets in wireless sensor network is addressed within the Bayesian compressive sensing (BCS) framework. BCS can estimate not only target locations but also noise variance of the environment. Furthermore, we provide adaptive iteration BCS localization (AIBCSL) algorithm, which is based on BCS and will choose measurement sensors according to the environment adaptively with only an initial value, while other frameworks require prior knowledge such as target numbers to choose measurements. AIBCSL suppose that environment noise variance is identical in interested area in a short period of time and change measurement numbers until terminal condition is reached. To suppress noise, we optimize estimation result by energy threshold strategy (ETS), which takes that transmit energy of noise focused on single grid is much lower than signal into consideration. And multisnapshot BCS (MT-BCS) will be explained and lead to a good result in low SNR level situation.

In recent years, channel-hopping-based medium access control protocols have been proposed to improve the capacity in a decentralized multichannel cognitive radio (CR) network without using extra control channels. Each CR user has to stochastically follow a default channel-hopping sequence in order to locate a channel and conduct its frame transmission. In this paper, theoretical analysis is conducted on the probability of channel availability and the average frame delay for primary users (PUs) by considering the impact caused by imperfect sensing of CR users and imperfect synchronization between the primary and CR networks. According to the proposed analytical model with realistic considerations, an optimal channel-hopping sequence (OCS) approach is designed for the CR users based on a dynamic programming technique. It is designed by exploiting the optimal load balance between channel availability and channel utilization within the delay constraints of PUs. By adopting the OCS approach, maximum aggregate throughput of CR users can be achieved while considering PU's quality-of-service (QoS) requirements. Moreover, in addition to the paired CR networks, the logical partition problem that occurs in generalized CR networks will also be addressed. This problem can severely degrade the aggregate throughput due to the decreased probability of connectivity between CR users, especially in a CR network with heavy traffic. Therefore, both wake-up successive contention (WSC) and wake-up counter-reset successive contention (WCSC) algorithms are proposed to increase the number of negotiations by both exploring the blind spot of imperfect sensing and amending the contention mechanisms between CR users. Compared to conventional channel-hopping sequences, numerical results illustrate that the proposed approaches can effectively maximize aggregate throughput for CR users under the QoS requirements of PUs.

SummaryThe discovery of malevolent users in cognitive radio networks (CRNs) is done by using blockchain‐based technique. For diagnosing the availability of frequency resources, the spectrum sensing (SS) plays an essential role in CRN. It is critical to enable cognitive radio (CR) technology in wireless communication systems for the next generation. Due to the increasing demand of the users, some bands are overloaded and others become underutilized. The CR applied models for vigorously assigning unlicensed users. The missed detection problem occurs in the conventional spectrum sensing schemes, which hampers the proper utilization of the spectrum. These motivated the development of a newly devised passive reputation method with an optimized deep model for spectrum sensing in CRN‐based blockchain. The sensing spectrum in CRN is designed using the cooperative spectrum sensing algorithm. The spectrum sensing is done with the decision made using AlexNet where the weights of AlexNet are trained with a hybrid optimization algorithm, named Honey Badger Remora Optimization (HBRO) algorithm. Thus, the output attained from the Alexnet_HBRO algorithm is considered. Meanwhile, the data are recorded in the blockchain, and then, the passive reputation is performed, and the determined output is considered. At last, the spectrum sensing is carried out by fusing AlexNet and passive reputation output where weights are optimally generated using the proposed Snake Honey Badger Remora Optimization (SHBRO) algorithm. The proposed SHBRO offered a high detection probability of 1, false alarm probability of 0.003, sensing time of 551.567s, and computation time of 210.102s.

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.

Compressive Spectrum Sensing in Cognitive Radio Networks: Recovery and Detection