Missing Spectrum-Data Recovery in Cognitive Radio Networks Using Piecewise Constant Nonnegative Matrix Factorization

In this paper, we propose a missing spectrum data recovery technique for cognitive radio (CR) networks using Nonnegative Matrix Factorization (NMF). It is shown that the spectrum measurements collected from secondary users (SUs) can be factorized as product of a channel gain matrix times an activation matrix. Then, an NMF method with piecewise constant activation coefficients is introduced to analyze the measurements and estimate the missing spectrum data. The proposed optimization problem is solved by a Majorization-Minimization technique. The numerical simulation verifies that the proposed technique is able to accurately estimate the missing spectrum data in the presence of noise and fading.Comment: 6 pages, 6 figures, Accepted for presentation in MILCOM'15 Conferenc

Advanced sequential Monte Carlo methods and their applications to sparse sensor network for detection and estimation

The general state space models present a flexible framework for modeling dynamic systems and therefore have vast applications in many disciplines such as engineering, economics, biology, etc. However, optimal estimation problems of non-linear non-Gaussian state space models are analytically intractable in general. Sequential Monte Carlo (SMC) methods become a very popular class of simulation-based methods for the solution of optimal estimation problems. The advantages of SMC methods in comparison with classical filtering methods such as Kalman Filter and Extended Kalman Filter are that they are able to handle non-linear non-Gaussian scenarios without relying on any local linearization techniques. In this thesis, we present an advanced SMC method and the study of its asymptotic behavior. We apply the proposed SMC method in a target tracking problem using different observation models. Specifically, a distributed SMC algorithm is developed for a wireless sensor network (WSN) that incorporates with an informative-sensor detection technique. The novel SMC algorithm is designed to surmount the degeneracy problem by employing a multilevel Markov chain Monte Carlo (MCMC) procedure constructed by engaging drift homotopy and likelihood bridging techniques. The observations are gathered only from the informative sensors, which are sensing useful observations of the nearby moving targets. The detection of those informative sensors, which are typically a small portion of the WSN, is taking place by using a sparsity-aware matrix decomposition technique. Simulation results showcase that our algorithm outperforms current popular tracking algorithms such as bootstrap filter and auxiliary particle filter in many scenarios

A Channel Ranking And Selection Scheme Based On Channel Occupancy And SNR For Cognitive Radio Systems

Wireless networks and information traffic have grown exponentially over the last decade. Consequently, an increase in demand for radio spectrum frequency bandwidth has resulted. Recent studies have shown that with the current fixed spectrum allocation (FSA), radio frequency band utilization ranges from 15% to 85%. Therefore, there are spectrum holes that are not utilized all the time by the licensed users, and, thus the radio spectrum is inefficiently exploited. To solve the problem of scarcity and inefficient utilization of the spectrum resources, dynamic spectrum access has been proposed as a solution to enable sharing and using available frequency channels. With dynamic spectrum allocation (DSA), unlicensed users can access and use licensed, available channels when primary users are not transmitting. Cognitive Radio technology is one of the next generation technologies that will allow efficient utilization of spectrum resources by enabling DSA. However, dynamic spectrum allocation by a cognitive radio system comes with the challenges of accurately detecting and selecting the best channel based on the channelâs availability and quality of service. Therefore, the spectrum sensing and analysis processes of a cognitive radio system are essential to make accurate decisions. Different spectrum sensing techniques and channel selection schemes have been proposed. However, these techniques only consider the spectrum occupancy rate for selecting the best channel, which can lead to erroneous decisions. Other communication parameters, such as the Signal-to-Noise Ratio (SNR) should also be taken into account. Therefore, the spectrum decision-making process of a cognitive radio system must use techniques that consider spectrum occupancy and channel quality metrics to rank channels and select the best option. This thesis aims to develop a utility function based on spectrum occupancy and SNR measurements to model and rank the sensed channels. An evolutionary algorithm-based SNR estimation technique was developed, which enables adaptively varying key parameters of the existing Eigenvalue-based blind SNR estimation technique. The performance of the improved technique is compared to the existing technique. Results show the evolutionary algorithm-based estimation performing better than the existing technique. The utility-based channel ranking technique was developed by first defining channel utility function that takes into account SNR and spectrum occupancy. Different mathematical functions were investigated to appropriately model the utility of SNR and spectrum occupancy rate. A ranking table is provided with the utility values of the sensed channels and compared with the usual occupancy rate based channel ranking. According to the results, utility-based channel ranking provides a better scope of making an informed decision by considering both channel occupancy rate and SNR. In addition, the efficiency of several noise cancellation techniques was investigated. These techniques can be employed to get rid of the impact of noise on the received or sensed signals during spectrum sensing process of a cognitive radio system. Performance evaluation of these techniques was done using simulations and the results show that the evolutionary algorithm-based noise cancellation techniques, particle swarm optimization and genetic algorithm perform better than the regular gradient descent based technique, which is the least-mean-square algorithm

Distributed Detection and Estimation in Wireless Sensor Networks

In this article we consider the problems of distributed detection and estimation in wireless sensor networks. In the first part, we provide a general framework aimed to show how an efficient design of a sensor network requires a joint organization of in-network processing and communication. Then, we recall the basic features of consensus algorithm, which is a basic tool to reach globally optimal decisions through a distributed approach. The main part of the paper starts addressing the distributed estimation problem. We show first an entirely decentralized approach, where observations and estimations are performed without the intervention of a fusion center. Then, we consider the case where the estimation is performed at a fusion center, showing how to allocate quantization bits and transmit powers in the links between the nodes and the fusion center, in order to accommodate the requirement on the maximum estimation variance, under a constraint on the global transmit power. We extend the approach to the detection problem. Also in this case, we consider the distributed approach, where every node can achieve a globally optimal decision, and the case where the decision is taken at a central node. In the latter case, we show how to allocate coding bits and transmit power in order to maximize the detection probability, under constraints on the false alarm rate and the global transmit power. Then, we generalize consensus algorithms illustrating a distributed procedure that converges to the projection of the observation vector onto a signal subspace. We then address the issue of energy consumption in sensor networks, thus showing how to optimize the network topology in order to minimize the energy necessary to achieve a global consensus. Finally, we address the problem of matching the topology of the network to the graph describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R. Chellapa and S. Theodoridis, Eds., Elsevier, 201

Removing non-stationary noise in spectrum sensing using matrix factorization

Spectrum sensing is key to many applications like dynamicspectrum access (DSA) systems or telecom regulators who need to measure utilization of frequency bands. The International Telecommunication Union (ITU) recommends a 10 dB threshold above the noise to decide whether a channel is occupied or not. However, radio frequency (RF) receiver front-ends are non-ideal. This means that the obtained data is distorted with noise and imperfections from the analog front-end. As part of the front-end the automatic gain control (AGC) circuitry mainly affects the sensing performance as strong adjacent signals lift the noise level. To enhance the performance of spectrum sensing significantly we focus in this article on techniques to remove the noise caused by the AGC from the sensing data. In order to do this we have applied matrix factorization techniques, i.e., SVD (singular value decomposition) and NMF (non-negative matrix factorization), which enables signal space analysis. In addition, we use live measurement results to verify the performance and to remove the effects of the AGC from the sensing data using above mentioned techniques, i.e., applied on block-wise available spectrum data. In this article it is shown that the occupancy in the industrial, scientific and medical (ISM) band, obtained by using energy detection (ITU recommended threshold), can be an overestimation of spectrum usage by 60%