An Innovative Signal Detection Algorithm in Facilitating the Cognitive Radio Functionality for Wireless Regional Area Network Using Singular Value Decomposition

This thesis introduces an innovative signal detector algorithm in facilitating the cognitive radio functionality for the new IEEE 802.22 Wireless Regional Area Networks (WRAN) standard. It is a signal detector based on a Singular Value Decomposition (SVD) technique that utilizes the eigenvalue of a received signal. The research started with a review of the current spectrum sensing methods which the research classifies as the specific, semiblind or blind signal detector. A blind signal detector, which is known as eigenvalue based detection, was found to be the most desired detector for its detection capabilities, time of execution, and zero a-priori knowledge. The detection algorithm was developed analytically by applying the Signal Detection Theory (SDT) and the Random Matrix Theory (RMT). It was then simulated using Matlab® to test its performance and compared with similar eigenvalue based signal detector. There are several techniques in finding eigenvalues. However, this research considered two techniques known as eigenvalue decomposition (EVD) and SVD. The research tested the algorithm with a randomly generated signal, simulated Digital Video Broadcasting-Terrestrial (DVB-T) standard and real captured digital television signals based on the Advanced Television Systems Committee (ATSC) standard. The SVD based signal detector was found to be more efficient in detecting signals without knowing the properties of the transmitted signal. The algorithm is suitable for the blind spectrum sensing where the properties of the signal to be detected are unknown. This is also the advantage of the algorithm since any signal would interfere and subsequently affect the quality of service (QoS) of the IEEE 802.22 connection. Furthermore, the algorithm performed better in the low signal-to-noise ratio (SNR) environment. In order to use the algorithm effectively, users need to balance between detection accuracy and execution time. It was found that a higher number of samples would lead to more accurate detection, but will take longer time. In contrary, fewer numbers of samples used would result in less accuracy, but faster execution time. The contributions of this thesis are expected to assist the IEEE 802.22 Standard Working Group, regulatory bodies, network operators and end-users in bringing broadband access to the rural areas

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.Peer reviewe

Spectrum Adaptation in Cognitive Radio Systems with Operating Constraints

The explosion of high-data-rate-demanding wireless applications such as smart-phones and wireless Internet access devices, together with growth of existing wireless services, are creating a shortage of the scarce Radio Frequency (RF) spectrum. However, several spectrum measurement campaigns revealed that current spectrum usage across time and frequency is inefficient, creating the artificial shortage of the spectrum because of the traditional exclusive command-and-control model of using the spectrum. Therefore, a new concept of Cognitive Radio (CR) has been emerging recently in which unlicensed users temporarily borrow spectrum from the licensed Primary Users (PU) based on the Dynamic Spectrum Access (DSA) technique that is also known as the spectrum sharing concept. A CR is an intelligent radio system based on the Software Defined Radio platform with artificial intelligence capability which can learn, adapt, and reconfigure through interaction with the operating environment. A CR system will revolutionize the way people share the RF spectrum, lowering harmful interference to the licensed PU of the spectrum, fostering innovative DSA technology and giving people more choices when it comes to using the wireless-communication-dependent applications without having any spectrum congestion problems. A key technical challenge for enabling secondary access to the licensed spectrum adaptation is to ensure that the CR does not interfere with the licensed incumbent users. However, incumbent user behavior is dynamic and requires CR systems to adapt this behavior in order to maintain smooth information transmission. In this context, the objective of this dissertation is to explore design issues for CR systems focusing on adaptation of physical layer parameters related to spectrum sensing, spectrum shaping, and rate/power control. Specifically, this dissertation discusses dynamic threshold adaptation for energy detector spectrum sensing, spectrum allocation and power control in Orthogonal Frequency Division Multiplexing-(OFDM-)based CR with operating constraints, and adjacent band interference suppression techniques in turbo-coded OFDM-based CR systems

State of the Art, Taxonomy, and Open Issues on Cognitive Radio Networks with NOMA

The explosive growth of mobile devices and the rapid increase of wideband wireless services call for advanced communication techniques that can achieve high spectral efficiency and meet the massive connectivity requirement. Cognitive radio (CR) and non-orthogonal multiple access (NOMA) are envisioned to be important solutions for the fifth generation wireless networks. Integrating NOMA techniques into CR networks (CRNs) has the tremendous potential to improve spectral efficiency and increase the system capacity. However, there are many technical challenges due to the severe interference caused by using NOMA. Many efforts have been made to facilitate the application of NOMA into CRNs and to investigate the performance of CRNs with NOMA. This article aims to survey the latest research results along this direction. A taxonomy is devised to categorize the literature based on operation paradigms, enabling techniques, design objectives and optimization characteristics. Moreover, the key challenges are outlined to provide guidelines for the domain researchers and designers to realize CRNs with NOMA. Finally, the open issues are discussed.Comment: This paper has been accepted by IEEE Wireless Communications Magazine. Pages 16, Figures