Enforcement in Dynamic Spectrum Access Systems

The spectrum access rights granted by the Federal government to spectrum users come with the expectation of protection from harmful interference. As a consequence of the growth of wireless demand and services of all types, technical progress enabling smart agile radio networks, and on-going spectrum management reform, there is both a need and opportunity to use and share spectrum more intensively and dynamically. A key element of any framework for managing harmful interference is the mechanism for enforcement of those rights. Since the rights to use spectrum and to protection from harmful interference vary by band (licensed/unlicensed, legacy/newly reformed) and type of use/users (primary/secondary, overlay/underlay), it is reasonable to expect that the enforcement mechanisms may need to vary as well.\ud \ud In this paper, we present a taxonomy for evaluating alternative mechanisms for enforcing interference protection for spectrum usage rights, with special attention to the potential changes that may be expected from wider deployment of Dynamic Spectrum Access (DSA) systems. Our exploration of how the design of the enforcement regime interacts with and influences the incentives of radio operators under different rights regimes and market scenarios is intended to assist in refining thinking about appropriate access rights regimes and how best to incentivize investment and growth in more efficient and valuable uses of the radio frequency spectrum

Applications of Machine Learning in Spectrum Sensing for Cognitive Radios

Spectrum sensing is an essential component in cognitive radios. The machine learning (ML) approach is part of artificial intelligence which develops systems capable of learning and improving from experience. ML algorithms are promising techniques for spectrum sensing as a favored solution to tackle the limitations of conventional spectrum sensing techniques while improving detection performance. The supervised ML algorithms, support vector machine (SVM), k-nearest neighbor (kNN), decision tree (DT), and ensemble are applied to detect the existence of primary users (PUs) in the TV spectrum band. This is accomplished by building classifiers using the collected data for the TV spectrum over different locations in the city of Windsor, Ontario. Then, the dimensionality reduction technique named Principal Component Analysis (PCA) is incorporated to reduce the duration of training and testing of the model, as well as reduce the risk of overfitting. This is achieved by transforming the input data into a lower-dimensional representation, which is known as the principal components. The Ensemble classification-based approach is employed to enhance the classifier predictivity and performance. Furthermore, the performance of the Ensemble classification method is compared with SVM, kNN, and DT classifiers. Simulation results have shown that the highest performance is achieved by combining multiple classifiers, i.e., the Ensemble, therefore, the detection performance has significantly improved. Simulation results have shown the impact of employing PCA on lowering the duration of training while maintaining the performance

Convolutional Radio Modulation Recognition Networks

We study the adaptation of convolutional neural networks to the complex temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert features which are widely used in the field today and we show significant performance improvements. We show that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio

An Optimal Eigenvalue Based Spectrum Sensing Algorithm for Cognitive Radio

Spectrum is a scarce resource, and licensed spectrum is intended to be used only by the spectrum owners. Various measurements of spectrum utilization have shown unused resources in frequency, time and space. Cognitive radio is a new concept of reusing licensed spectrum in an unlicensed manner. The unused resources are often referred to as spectrum holes or white spaces. These spectrum holes could be reused by cognitive radios, sometimes called secondary users. All man-made signals have some structure that can be potentially exploited to improve their detection performance. This structure is intentionally introduced for example by the channel coding, the modulation and by the use of space-time codes. This structure, or correlation, is inherent in the sample covariance matrix of the received signal. In particular the eigenvalues of the sample covariance matrix have some spread, or in some cases some known features that can be exploited for detection. This work aims to implement, evaluate, and eventually improve on algorithms for efficient computation of eigenvalue-based spectrum sensing methods. The computations will be based on power methods for computation of the dominant eigenvalue of the covariance matrix of signals received at the secondary users. The proposed method endeavors to overcome the noise uncertainty problem, and perform better than the ideal energy detection method. The method should be used for various signal detection applications without requiring the knowledge of the signal, channel and noise power

A NOISE ESTIMATION SCHEME FOR BLIND SPECTRUM SENSING USING EMD

The scarcity of spectral resources in wireless communications, due to a fixed frequency allocation policy, is a strong limitation to the increasing demand for higher data rates. One solution is to use underutilized spectrum. Cognitive Radio (CR) technologies identify transmission opportunities in unused channels and avoid interfering with primary users. The key enabling technology is the Spectrum Sensing (SS). Different SS techniques exist, but techniques that do not require knowledge of the signals (non-coherent) are preferred. Noise estimation plays an essential role in enhancing the performance of non-coherent spectrum sensors such as energy detectors. In this thesis, we present an energy detector based on the behavior of Empirical Mode Decomposition (EMD) towards vacant channels (noise-dominant). The energy trend from the EMD processed signal is used to determine the occupancy of a given band of interest. The performance of the proposed EMD-based detector is evaluated for different noise levels and sample sizes. Further, a comparison is carried out with conventional spectrum sensing techniques to validate the efficacy of the proposed detector and the results revealed that it outperforms the other sensing methods

In-network Sparsity-regularized Rank Minimization: Algorithms and Applications

Given a limited number of entries from the superposition of a low-rank matrix plus the product of a known fat compression matrix times a sparse matrix, recovery of the low-rank and sparse components is a fundamental task subsuming compressed sensing, matrix completion, and principal components pursuit. This paper develops algorithms for distributed sparsity-regularized rank minimization over networks, when the nuclear- and $\ell_1$-norm are used as surrogates to the rank and nonzero entry counts of the sought matrices, respectively. While nuclear-norm minimization has well-documented merits when centralized processing is viable, non-separability of the singular-value sum challenges its distributed minimization. To overcome this limitation, an alternative characterization of the nuclear norm is adopted which leads to a separable, yet non-convex cost minimized via the alternating-direction method of multipliers. The novel distributed iterations entail reduced-complexity per-node tasks, and affordable message passing among single-hop neighbors. Interestingly, upon convergence the distributed (non-convex) estimator provably attains the global optimum of its centralized counterpart, regardless of initialization. Several application domains are outlined to highlight the generality and impact of the proposed framework. These include unveiling traffic anomalies in backbone networks, predicting networkwide path latencies, and mapping the RF ambiance using wireless cognitive radios. Simulations with synthetic and real network data corroborate the convergence of the novel distributed algorithm, and its centralized performance guarantees.Comment: 30 pages, submitted for publication on the IEEE Trans. Signal Proces

New Approaches in Cognitive Radios using Evolutionary Algorithms

Cognitive radio has claimed a promising technology to exploit the spectrum in an ad hoc network. Due many techniques have become a topic of discussion on cognitive radios, the aim of this paper was developed a contemporary survey of evolutionary algorithms in Cognitive Radio. According to the art state, this work had been collected the essential contributions of cognitive radios with the particularity of base they research in evolutionary algorithms. The main idea was classified the evolutionary algorithms and showed their fundamental approaches. Moreover, this research will be exposed some of the current issues in cognitive radios and how the evolutionary algorithms will have been contributed. Therefore, current technologies have matters presented in optimization, learning, and classification over cognitive radios where evolutionary algorithms can be presented big approaches. With a more comprehensive and systematic understanding of evolutionary algorithms in cognitive radios, more research in this direction may be motivated and refined