CYCLOSTATIONARY FEATURES BASED LOW COMPLEXITY MUTLIRESOLUTION SPECTRUM SENSING FOR COGNITVE RADIO APPLICATIONS

The demand for variety of services using wireless communication has grown remarkably in the past few many years, consequently causing an acute problem of spectrum scarcity. Today, it is one of the most challenging problems in modern wireless communication. To overcome this, the concept of cognitive radio has been proposed and this technology is fast maturing. The first and foremost function a cognitive radio must do is to sense the spectrum as accurately as possible and do it with least complexity. Among many techniques of spectrum sensing, the Multi-resolution Spectrum Sensing (MRSS) is a popular technique in recent literature. Various multi resolution techniques are used that include wavelet based spectrum estimation and spectral hole detection, wavelet based multi-resolution in analog domain and multi-resolution multiple antenna based detection. However, the basic idea is the same - the total bandwidth is sensed using coarse resolution energy detection, then, fine sensing is applied to the portion of interest. None of these techniques, however, use multi-resolution sensing using cyclostationary features for cognitive radio applications which are more reliable but computationally expensive. In this thesis, we suggest a cyclostationary features based low complexity multi-resolution spectrum sensing for cognitive radio applications. The proposed technique discussed in this thesis is inspired by the quickness of multi-resolution and the reliability of cyclostationary feature detection. The performance of the proposed scheme is primarily evaluated by its complexity analysis and by determining the minimum signal-to-noise ratio that gives 90% probability of correct classification. Both subjective and objective evaluation show that the proposed scheme is not only superior to the commonly used energy detection method but also to various multi-resolution sensing techniques as it relies on the robustness of cyclostationary feature detection. The results found are encouraging and the proposed algorithms are proved to be not only fast but also more robust and reliable

CYCLOSTATIONARY FEATURES BASED LOW COMPLEXITY MUTLIRESOLUTION SPECTRUM SENSING FOR COGNITVE RADIO APPLICATIONS

The demand for variety of services using wireless communication has grown remarkably in the past few many years, consequently causing an acute problem of spectrum scarcity. Today, it is one of the most challenging problems in modern wireless communication. To overcome this, the concept of cognitive radio has been proposed and this technology is fast maturing. The first and foremost function a cognitive radio must do is to sense the spectrum as accurately as possible and do it with least complexity. Among many techniques of spectrum sensing, the Multi-resolution Spectrum Sensing (MRSS) is a popular technique in recent literature. Various multi resolution techniques are used that include wavelet based spectrum estimation and spectral hole detection, wavelet based multi-resolution in analog domain and multi-resolution multiple antenna based detection. However, the basic idea is the same - the total bandwidth is sensed using coarse resolution energy detection, then, fine sensing is applied to the portion of interest. None of these techniques, however, use multi-resolution sensing using cyclostationary features for cognitive radio applications which are more reliable but computationally expensive. In this thesis, we suggest a cyclostationary features based low complexity multi-resolution spectrum sensing for cognitive radio applications. The proposed technique discussed in this thesis is inspired by the quickness of multi-resolution and the reliability of cyclostationary feature detection. The performance of the proposed scheme is primarily evaluated by its complexity analysis and by determining the minimum signal-to-noise ratio that gives 90% probability of correct classification. Both subjective and objective evaluation show that the proposed scheme is not only superior to the commonly used energy detection method but also to various multi-resolution sensing techniques as it relies on the robustness of cyclostationary feature detection. The results found are encouraging and the proposed algorithms are proved to be not only fast but also more robust and reliable

Enhanced Spectrum Sensing Techniques for Cognitive Radio Systems

Due to the rapid growth of new wireless communication services and applications, much attention has been directed to frequency spectrum resources. Considering the limited radio spectrum, supporting the demand for higher capacity and higher data rates is a challenging task that requires innovative technologies capable of providing new ways of exploiting the available radio spectrum. Cognitive radio (CR), which is among the core prominent technologies for the next generation of wireless communication systems, has received increasing attention and is considered a promising solution to the spectral crowding problem by introducing the notion of opportunistic spectrum usage. Spectrum sensing, which enables CRs to identify spectral holes, is a critical component in CR technology. Furthermore, improving the efficiency of the radio spectrum use through spectrum sensing and dynamic spectrum access (DSA) is one of the emerging trends. In this thesis, we focus on enhanced spectrum sensing techniques that provide performance gains with reduced computational complexity for realistic waveforms considering radio frequency (RF) impairments, such as noise uncertainty and power amplifier (PA) non-linearities. The first area of study is efficient energy detection (ED) methods for spectrum sensing under non-flat spectral characteristics, which deals with relatively simple methods for improving the detection performance. In realistic communication scenarios, the spectrum of the primary user (PU) is non-flat due to non-ideal frequency responses of the devices and frequency selective channel conditions. Weighting process with fast Fourier transform (FFT) and analysis filter bank (AFB) based multi-band sensing techniques are proposed for overcoming the challenge of non-flat characteristics. Furthermore, a sliding window based spectrum sensing approach is addressed to detect a re-appearing PU that is absent in one time and present in other time. Finally, the area under the receiver operating characteristics curve (AUC) is considered as a single-parameter performance metric and is derived for all the considered scenarios. The second area of study is reduced complexity energy and eigenvalue based spectrum sensing techniques utilizing frequency selectivity. More specifically, novel spectrum sensing techniques, which have relatively low computational complexity and are capable of providing accurate and robust performance in low signal-to-noise ratio (SNR) with noise uncertainty, as well as in the presence of frequency selectivity, are proposed. Closed-form expressions are derived for the corresponding probability of false alarm and probability of detection under frequency selectivity due the primary signal spectrum and/or the transmission channel. The offered results indicate that the proposed methods provide quite significant saving in complexity, e.g., 78% reduction in the studied example case, whereas their detection performance is improved both in the low SNR and under noise uncertainty. Finally, a new combined spectrum sensing and resource allocation approach for multicarrier radio systems is proposed. The main contribution of this study is the evaluation of the CR performance when using wideband spectrum sensing methods in combination with water-filling and power interference (PI) based resource allocation algorithms in realistic CR scenarios. Different waveforms, such as cyclic prefix based orthogonal frequency division multiplexing (CP-OFDM), enhanced orthogonal frequency division multiplexing (E-OFDM) and filter bank based multicarrier (FBMC), are considered with PA nonlinearity type RF impairments to see the effects of spectral leakage on the spectrum sensing and resource allocation performance. It is shown that AFB based spectrum sensing techniques and FBMC waveforms with excellent spectral containment properties have clearly better performance compared to the traditional FFT based spectrum sensing techniques with the CP-OFDM. Overall, the investigations in this thesis provide novel spectrum sensing techniques for overcoming the challenge of noise uncertainty with reduced computational complexity. The proposed methods are evaluated under realistic signal models

Experimental Evaluation of Spectrum Sensing Algorithms for Wireless Microphone Signal

Spectrum congestion has become a critical concern in wireless communication systems due to the limited availability of frequency spectrum. Hence, efficient utilization of spectrum is one of the most important challenges in the evolution of wireless communi-cation systems and radio devices. Cognitive radio (CR) has been introduced as an effec-tive solution for spectrum utilization. Spectrum sensing (SS) is one of the key elements in the implementation of effective and reliable CR systems. SS algorithms are used to obtain awareness about the spectrum usage and existence of primary users in a certain spectrum band. Energy detection (ED) based SS is the most common sensing algorithm due to its low computation and implementation complexity. On the other hand, ED based SS is highly dependent on the precise knowledge of the receiver noise variance. Hence, the performance of the ED algorithm is degraded significantly, when there is uncertainty in the estimation of the noise variance. In this thesis, the wireless microphone (WM) system using the CR concept is intro-duced and the sensing performance of WM signals using three different algorithms are studied. The considered algorithms are based on the ED, namely fast Fourier transform (FFT) based ED, analysis filter bank (AFB) based ED and maximum-minimum ED (Max-Min ED) are studied. Following the analytical models and scenarios of energy detector based SS algorithms, the sensing algorithms are implemented using National Instruments’ (NI) Universal Software Radio Peripheral (USRP) and the NI-LabVIEW software platform, together with the necessary toolboxes. This prototype implementa-tion provides reliable performance evaluation of these spectrum sensing approaches us-ing real world receiver implementation and communication signals from a signal genera-tor, as well as actual WM signals. The results of this study suggest that the performance of Max-Min ED is more robust than FFT & AFB based ED under realistic noise vari-ance uncertainty

Subband based Cooperative Spectrum Sensing

Wireless communication technology with traditional rigid spectrum allocations and low scalability is wasting lots of spectral resources. Spectral congestion is becoming critical with heavily increasing utilization of wireless communications technology. Cognitive radio (CR) technology with dynamic spectrum management capabilities is widely advocated for utilizing effectively the unused spectrum resources. The main idea behind CR technology is to trigger secondary communications to utilize the unused spectral resources. However, CR technology heavily relies on spectrum sensing techniques which are applied to estimate the presence of primary user (PU) signals. The studies of this thesis focus on energy detection (ED) based semi-blind sensing schemes. ED based sensing only requires the knowledge of noise variance, which can be obtained according to the previous noise measurements. To counteract the practical wireless channel effects, collaborative approach of PU signal estimation i.e., cooperative spectrum sensing (CSS) techniques are investigated. CSS eliminates the problems of both hidden nodes and fading multipath channels. Additionally, subband based CSS scheme will be developed. Fast Fourier transform (FFT) and analysis filter bank (AFB) based receiver side processing methods are used. Subband energies are then processed for ED based CSS methods. The studies show that filter bank based multicarrier (FBMC) waveform with better spectral containment improves the performance significantly. Additionally, cooperative maximum-minimum energy detection (Max-Min ED) method is proposed. The proposed method is immune to the noise uncertainty effects, which is a critical issue in traditional ED based spectrum sensing. Cooperative maximum-minumum energy detection (Max-Min ED) shows better spectrum sensing performance compared with traditional CSS schemes under noise uncertainty conditions. Overall, the thesis contributes to better understanding and handling of subband based CSS in CR system. The proposed novel cooperative Max-Min ED greatly reduces the complexity compared to existing techniques which are robust to the noise uncertainty effects. These contributions are expected to provide a useful tool for the design and implementation of flexible, efficient, and simple spectrum sensing mechanism for CR technology

COGNITIVE RADIO SOLUTION FOR IEEE 802.22

Current wireless systems suffer severe radio spectrum underutilization due to a number of problematic issues, including wasteful static spectrum allocations; fixed radio functionalities and architectures; and limited cooperation between network nodes. A significant number of research efforts aim to find alternative solutions to improve spectrum utilization. Cognitive radio based on software radio technology is one such novel approach, and the impending IEEE 802.22 air interface standard is the first based on such an approach. This standard aims to provide wireless services in wireless regional area network using TV spectrum white spaces. The cognitive radio devices employed feature two fundamental capabilities, namely supporting multiple modulations and data-rates based on wireless channel conditions and sensing a wireless spectrum. Spectrum sensing is a critical functionality with high computational complexity. Although the standard does not specify a spectrum sensing method, the sensing operation has inherent timing and accuracy constraints.This work proposes a framework for developing a cognitive radio system based on a small form factor software radio platform with limited memory resources and processing capabilities. The cognitive radio systems feature adaptive behavior based on wireless channel conditions and are compliant with the IEEE 802.22 sensing constraints. The resource limitations on implementation platforms post a variety of challenges to transceiver configurability and spectrum sensing. Overcoming these fundamental features on small form factors paves the way for portable cognitive radio devices and extends the range of cognitive radio applications.Several techniques are proposed to overcome resource limitation on a small form factor software radio platform based on a hybrid processing architecture comprised of a digital signal processor and a field programmable gate array. Hardware reuse and task partitioning over a number of processing devices are among the techniques used to realize a configurable radio transceiver that supports several communication modes, including modulations and data rates. In particular, these techniques are applied to build configurable modulation architecture and a configurable synchronization. A mode-switching architecture based on circular buffers is proposed to facilitate a reliable transitioning between different communication modes.The feasibility of efficient spectrum sensing based on a compressive sampling technique called "Fast Fourier Sampling" is examined. The configuration parameters are analyzed mathematically, and performance is evaluated using computer simulations for local spectrum sensing applications. The work proposed herein features a cooperative Fast Fourier sampling scheme to extend the narrowband and wideband sensing performance of this compressive sensing technique.The précis of this dissertation establishes the foundation of efficient cognitive radio implementation on small form factor software radio of hybrid processing architecture

Deteção espetral de banda larga para rádio cógnitivo

Doutoramento em TelecomunicaçõesEsta tese tem como objetivo o desenvolvimento de uma unidade autónoma de deteção espetral em tempo real. Para tal são analisadas várias implementações para a estimação do nível de ruído de fundo de forma a se poder criar um limiar adaptativo para um detetor com uma taxa constante de falso alarme. Além da identificação binária da utilização do espetro, pretende-se também obter a classificação individual de cada transmissor e a sua ocupação ao longo do tempo. Para tal são exploradas duas soluções baseadas no algoritmo, de agrupamento de dados, conhecido como maximização de expectativas que permite identificar os sinais analisados pela potência recebida e relação de fase entre dois recetores. Um algoritmo de deteção de sinal baseado também na relação de fase de dois recetores e sem necessidade de estimação do ruído de fundo é também demonstrado. Este algoritmo foi otimizado para permitir uma implementação eficiente num arranjo de portas programáveis em campo a funcionar em tempo real para uma elevada largura de banda, permitindo também estimar a direção da transmissão detetada.The purpose of this thesis is to develop an autonomous unit for real time spectrum sensing. For this purpose, several implementations for the estimation of the background noise level are analysed to create an adaptive threshold and ensure a constant false alarm rate detector. In addition to the binary identification of the spectrum usage, it is also intended to obtain an individual classification of each transmitter occupation and its spectrum usage over time. To do so, two solutions based on the expectation maximization data clustering, that allow to identify the analyzed signals by the received power and phase relation between two receivers, were explored. A signal detection algorithm, also based on the phase relationship between two receivers and with no need for noise estimation is also demonstrated. This algorithm has been optimized to allow an efficient implementation in a FPGA operating in real time for a high bandwidth and it also allows the estimation of the direction of arrival of the detected transmission

Spectrum Sensing Scheduling in Cognitive Radio Networks

In cognitive radio (CR) networks, spectrum sensing has gained great importance for opportunistic spectrum access. There are many factors that affect the efficiency of spectrum sensing. High sensing accuracy can help reduce the chance of interference to primary user and improve the spectrum utility. However, high sensing accuracy requires a large amount of sensing resources including multiple collaborative CRs and the sensing duration. We propose a cost based framework for spectrum sensing scheduling, in which all these factors are modeled by certain cost or gain of the system. A sequential energy detector is used for accumulating all energy measurements for sensing. Depending on the decision made, the CRs decide whether to wait as the channel is occupied or to start data transmission as there is a spectral hole. The optimal number of CRs, the sensing accuracy levels and the waiting/transmission time are obtained such that the average gain per unit time including both sensing and wait/data transmission stages are maximized. We provide various experimental results to show the effectiveness of the proposed design and the effects of various parameters on the performance are analyzed. The idea is then extended to a multiple channel CR network. The channel profile generated from a single channel design is utilized for CR assignment to channels that request for sensing. Two approaches, viz., greedy approach and non-greedy approach are designed for scheduling. Then the two approaches are compared on the basis of total average gain obtained from each approaches. The non-greedy approach outperforms the greedy approach with respect to the total average gain.School of Electrical & Computer Engineerin