Compressive multispectral model for spectrum sensing in cognitive radio networks

Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying priors such as Bernoulli–Gaussian-inverse Gamma (BGiG) and Gaussian-inverse Gamma (GiG) priors on the components of the signal. With the introduction of variational Bayesian inference, the sparse Bayesian learning (SBL) methods for solving the inverse problem of compressive sensing have received significant interest as the SBL methods become more efficient in terms of execution time. In this paper, we consider the sparse signal recovery problem using compressive sensing and the variational Bayesian (VB) inference framework. More specifically, we consider two widely used Bayesian models of BGiG and GiG for modeling the underlying sparse signal for this problem. Although these two models have been widely used for sparse recovery problems under various signal structures, the question of which model can outperform the other for sparse signal recovery under no specific structure has yet to be fully addressed under the VB inference setting. Here, we study these two models specifically under VB inference in detail, provide some motivating examples regarding the issues in signal reconstruction that may occur under each model, perform comparisons and provide suggestions on how to improve the performance of each model.

Compressed sensing (CS) has been applicably used in cognitive radio (CR) to sense wide-band spectrum, solving the wide-band sensing rate restriction problem. For a CR network which includes multiple nodes, it's considered that the received signals is high correlated. So in this paper, we focus on efficient compressed spectrum sensing reconstructions in CR network, proposing a probabilistic model called Cooperative Bayesian Compressed Spectrum Sensing (C-BCSS) algorithm. In the algorithm, node samples locally and a common sparse signal model is developed to the received signals where correlation exists. And then C-BCSS reconstruction is performed cooperatively at fusion center utilizing the information from all nodes. We compared C-BCSS with Multitask BCS and simulation results show that C-BCSS outperforms in condition of severe under-sampling and heavy noise.

Compressed Sensing (CS) is a promising method that recovers the sparse and compressible signals from severely under-sampled measurements. CS can be applied to wireless communication to enhance its capabilities. As this technology is proliferating, it is possible to explore its need and benefits for emerging applicationsCompressive Sensing for Wireless Communication provides: • A clear insight into the basics of compressed sensing • A thorough exploration of applying CS to audio, image and computer vision • Different dimensions of applying CS in Cognitive radio networks • CS in wireless sensor network for spatial compression and projection • Real world problems/projects that can be implemented and tested • Efficient methods to sample and reconstruct the images in resource constrained WMSN environmentThis book provides the details of CS and its associated applications in a thorough manner. It lays a direction for students and new engineers and prepares them for developing new tasks within the field of CS. It is an indispensable companion for practicing engineers who wish to learn about the emerging areas of interest.

Compressive Sensing (CS) is a digital signal processing developed theory that encloses the signal sampling and compression, based on the sparsity characteristics of signal. This can decrease sampling rate, so reduce computational complexity of the system without degrading the performance of the system. This paper describes the theoretical frame and a few key technical, then illustrates the application of compressed sensing theory to wide-band cognitive radio signals. Spectrum sensing is a critical issue in wide-band Cognitive Radio (CR) networks as it faces hard challenges such as high hardware cost, complexity, sampling rate and processing speed. Thus, this paper shows that Compressive Sensing could be exploited in wide-band Cognitive Radio networks to solve the spectrum sensing problems mentioned above.

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.

Fast acquisition in magnetic resonance imaging (MRI) is considered in this paper. Often, fast acquisition is achieved using parallel imaging (pMRI) techniques. It has been shown recently that the combination of pMRI and compressed sensing (CS), which enables exact reconstruction of sparse or compressible signals from a small number of random measurements, can accelerate the speed of MRI acquisition because the number of measurements are much smaller than that by pMRI per se. Also recently in CS, chaos filters were designed to obtain chaotic measurements. This chaotic CS approach potentially offers simpler hardware implementation. In this paper, we combine chaotic CS and pMRI. However, instead of using chaos filters, the measurements are obtained by chaotically undersampling the k-space. MRI image reconstruction is then performed by using nonlinear conjugate gradient optimization. For pMRI, we use the well-known approach SENSE - sensitivity encoding -, which requires an estimation of the sensitivity maps. The performance of the proposed method is analyzed using the point spread function, the transform point spread function, and the reconstruction error measure.

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.

In the recent years Cognitive Radio (CR) technology imposed itself like a very good solution to the increasing spectrum utilization. This technology proposes the development of a new radio type - a cognitive radio - endowed with intelligence that shall sense, share, and use the spectrum opportunities (SOP) of the preexisting wireless networks, that is the channels that are not used by the licensed users. A new routing algorithm for CR networks (CRN): IPSAG - IP Spectrum Aware Geographic based Routing Protocol is presented in the paper. As suggested by the name, the IP hop-by-hop, the geographic, and the spectrum aware routing concepts were used together in the protocol's design. The dynamism and flexibility, characteristic to both CR and IP networks, made us compare the CR concept with the IP concept (a wireless versus wired flexible environment comparison); thus we assumed the IP routing approach for CRN. The proposed strategy is based on hop-by-hop routing, local decisions being taken by nodes according to both local information (CR node's neighborhood) and global information (the entire topology of CRN), channels availability, and destination location.

As an essential task for enabling dynamic spectrum sharing, spectrum sensing in wideband cognitive radio (CR) networks becomes quite challenging due to high sampling pressure, huge data transmission overheads and serious channel fading. To overcome these challenges, this paper proposes a collaborative spectrum sensing scheme based on 1-bit compressed sensing (CS). Each SU performs local 1-bit CS and obtains its own estimated support set via signal reconstruction. To make use of joint sparsity, all the support sets are fused in the fusion center (FC) and the fused result is used as priori information to guide the next local signal reconstruction. The weighted binary iterative hard thresholding (BIHT) algorithm with support estimate vector is proposed to implement the second reconstruction. Simulation results show that our proposed scheme can achieve effective spectrum detection at low communication and computation load.

Noncoherent ultra-wideband (UWB) receivers are suboptimal but have advantage in low-complexity and low-power consumption. The theory of compressed sensing (CS) enables the reconstruction or approximation of sparse or compressible signals from a small set of incoherent projections. This paper presents a noncoherent detection approach based on CS for pulse ultra-wideband systems. The Matching Pursuit (MP) algorithm with implicit denoising operation is used as the signal reconstruction method of CS for pulse UWB. The dictionary of the MP algorithm is weighted with the prior knowledge of the average power delay profile (APDP) of the channel to achieve higher performance. Simulations show that the proposed noncoherent detection approach is resilient to additive noise and outperforms the conventional energy detection method.

Recent works in modified compressed sensing (CS) show that reconstruction of sparse or compressible signals with partially known support yields better results than traditional CS. In this paper, we extend the ideas of these works to modify three iterative algorithms to incorporate the known support in the recovery process. The performance and effect of the prior information are studied through simulations. Results show that the modification of iterative algorithms improves their performance, needing fewer samples to yield an approximate reconstruction.

Compressive sensing (CS) idea enables the reconstruction of a sparse signal from a small set of measurements. CS approach has applications in many practical areas. One of the areas is radar systems. In this article, the radar ambiguity function is denoised within the CS framework. A new denoising method on the projection onto the epigraph set of the convex function is also developed for this purpose. This approach is compared to the other CS reconstruction algorithms. Experimental results are presented1.

With growing concern about environmental issues and an emerging green communications paradigm, cognitive radio (CR) networks (CRNs) have to be considered from the energy efficiency perspective. In this paper, we focus on scheduling in CRNs, in which a cognitive base station (CBS) makes frequency allocations to the CRs at the beginning of each frame. A cognitive scheduler must consider the diversity among CRs' queues and channel capacities in terms of number of bits and the channel switching cost from one frequency to another. Taking all these into account, we formulate the scheduling problem as an energy efficiency maximization problem, which is a nonlinear integer programming (NLP) problem and is thereby hard to solve. We seek alternate computationally easier solutions. To this aim, we propose a polynomial-time heuristic algorithm, i.e., the energy-efficient heuristic scheduler (EEHS), which allocates each idle frequency to the CR that attains the highest energy efficiency at this frequency. Next, we reformulate the original problem first as a throughput maximization problem subject to energy consumption restrictions and then as an energy consumption minimization problem subject to minimum throughput guarantees. These two schedulers also have the power to provide fairness in resource allocation. We analyze the energy efficiency and successful transmission probability of the proposed schedulers under both contiguous and fragmented spectrum scenarios. Performance studies show that, compared with a pure opportunistic scheduler with a throughput maximization objective, proposed schedulers can attain almost the same throughput performance with better energy efficiency.

As a cognitive radio (CR) modulation technique, transform domain communication system (TDCS) has been proposed by utilizing white space spectrum for the network access and achieving low probability of interception (LPI). In the previously reported TDCS- based cognitive radio networks (CRN), however, the non-zero periodic correlation function between any pair of users results in multiuser interference (MUI). In this paper, a novel framework under the CR constraints is presented by employing a two- dimension spreading scheme (i.e., time and frequency domains). We first obtain a class of spreading sequences with almost perfect periodic correlation function for arbitrary spectrum utilization pattern. Then, an universal criteria is presented for synchronous MUI-free CRNs when employing TDCS. Simulation results demonstrate that the proposed TDCS-based CRN architecture is a preferable candidate for distribution wireless networks, such as CR Ad-hoc wireless sensor networks.

As the proliferation of wireless handheld devices such as smart phones and tablets grow exponentially, new technologies have to be adopted to efficiently use the radio frequency spectrum. Cognitive Radio(CR) network offers a promising solution to improve spectrum utilization and efficiency by harnessing the available spectrum resources in real-time. However, participants in the CR network can comprise of malicious adversaries who adopt dishonest and non-cooperative strategies to attack the network. In our research, we focus on a critical attack on the CR network: Primary User Emulation (PUE). In a PUE attack, the malicious adversary violates spectrum sharing etiquette by impersonating the primary user at the expense of other unselfish secondary users. To thwart such an attack, we present a strategy to authenticate the primary user, which involves collaborative learning among CR users.