Cooperative Spectrum Sensing based on 1-bit Quantization in Cognitive Radio Networks

The wireless frequency spectrum is a very valuable resource in the field of communications. Over the years, different bands of the spectrum were licensed to various communications systems and standards. As a result, most of the easily accessible parts of it ended up being theoretically occupied. This made it somewhat difficult to accommodate new wireless technologies, especially with the rise of communications concepts such as the Machine to Machine (M2M) communications and the Internet of Things (IoT). It was necessary to find ways to make better use of wireless spectrum. Cognitive Radio is one concept that came into the light to tackle the problem of spectrum utilization. Various technical reports stated that the spectrum is in fact under-utilized. Many frequency bands are not heavily used over time, and some bands have low activity. Cognitive Radio (CR) Networks aim to exploit and opportunistically share the already licensed spectrum. The objective is to enable various kinds of communications while preserving the licensed parties' right to access the spectrum without interference. Cognitive radio networks have more than one approach to spectrum sharing. In interweave spectrum sharing scheme, cognitive radio devices look for opportunities in the spectrum, in frequency and over time. Therefore, and to find these opportunities, they employ what is known as spectrum sensing. In a spectrum sensing phase, the CR device scans certain parts of the spectrum to find the voids or white spaces in it. After that it exploits these voids to perform its data transmission, thus avoiding any interference with the licensed users. Spectrum sensing has various classifications and approaches. In this thesis, we will present a general review of the main spectrum sensing categories. Furthermore, we will discuss some of the techniques employed in each category including their respective advantages and disadvantages, in addition to some of the research work associated with them. Our focus will be on cooperative spectrum sensing, which is a popular research topic. In cooperative spectrum sensing, multiple CR devices collaborate in the spectrum sensing operation to enhance the performance in terms of detection accuracy. We will investigate the soft-information decision fusion approach in cooperative sensing. In this approach, the CR devices forward their spectrum sensing data to a central node, commonly known as a Fusion Center. At the fusion center, this data is combined to achieve a higher level of accuracy in determining the occupied parts and the empty parts of the spectrum while considering Rayleigh fading channels. Furthermore, we will address the issue of high power consumption due to the sampling process of a wide-band of frequencies at the Nyquist rate. We will apply the 1-bit Quantization technique in our work to tackle this issue. The simulation results show that the detection accuracy of a 1-bit quantized system is equivalent to a non-quantized system with only 2 dB less in Signal-to-Noise Ratio (SNR). Finally, we will shed some light on multiple antenna spectrum sensing, and compare its performance to the cooperative sensing

Learning-Based Approaches for Intelligent Cognitive Radio

Today with the growing demand for more data transmissions and increased network capacities, cognitive radio technology is ever more relevant. Traditional static spectrum allocation is no longer a feasible option. Through dynamic spectrum access, cognitive devices are able to tap into unused licensed spectrum bands. Thus, improving the spectrum utilization efficiency and fueling spectrum scarce applications. Cognitive Radio (CR) networks consist of smart radio devices that have the ability to sense and adapt to the rapidly changing radio environment. A cognitive device goes through a process of intelligent decision-making, which intrinsically shapes them into smart devices. Motivated by the superior performance of machine learning in various research paradigms, a cooperative Secondary Network (SN) is proposed that operates under a hybrid underlay-interweave access model. By taking advantage of both access models, the SN maximizes its throughput. A detection problem is formulated for each access model and Machine Learning (ML) techniques are applied to the SN, namely Gaussian Mixture Model (GMM), Support Vector Machine (SVM), and Naive Bayes' (NB) to classify the state of the channel. The multi-class SVM (MSVM) algorithm is reformulated and used to further classify the state of each primary user in the network. The performance of the hybrid network is evaluated based on the Receiver Operating Characteristics (ROC) and classification accuracy. In addition, we show that the accuracy of the MSVM is improved through the cooperation of the secondary users. Our results show that the proposed ML-based hybrid model is robust to low Signal-to-Noise Ratio (SNR) environments, and yields an improved performance compared with traditional cooperative sensing techniques. Moreover, we show that the Gaussian SVM surpasses other proposed learning algorithms achieving as high as an 80% detection rate with as low as 10% false alarm. Energy detection-based spectrum sensing, relies on measuring the energy level in the spectrum, and accordingly deciding the current occupancy state of the channel. Therefore, CR devices are required to determine the corresponding channel state given a measured energy level. CR networks that use supervised learning techniques to perform the sensing task require data examples of energy levels and the corresponding channel state for training purposes , i.e., labeled data. Having readily available labeled data is a complex task for CR networks, since it requires cooperation from both primary and secondary users. Such cooperation violates the ground rules for the interweave and underlay CR access models. Tackling the problem of labeled data scarcity in practical CR applications, we propose a two-stage learning framework for cooperative spectrum sensing. The algorithm combines the superior performance of the SVM algorithm and low cost training data of the GMM. Thus, rendering the two-stage learning framework suitable for practical CR applications. Finally, a system model is proposed and the performance of the system is evaluated based on the ROC for its upper and lower performance bounds. Additionally, our results show that the two-stage learning attains a higher detection performance compared with using the GMM algorithm