PSO AND SVD BASED ENHANCED SIGNAL DETECTION FOR COGNITIVE RADIO SYSTEM

Spectrum sensing is an essential problem in cognitive radio communication system. This paper presents covariance based spectrum sensing on the test bed if cognitive radio system. A series of tests show that the detection performance of Covariance Based spectrum sensing technique is not liable to be affected by the noise uncertainty in practical application and meets the need of the system primly. Furthermore, the performances of detection are also verified with different kinds of source signals. Simulations are carried out on MATLAB2010a and system performance is measured based on probability of detection vs. SNR, Probability of false alarm, sensing time and modulation techniques respectively

Resource management in location aware cognitive radio networks

Dynamic spectrum access (DSA) aims at utilizing spectral opportunities both in time and frequency domains at any given location, which arise due to variations in spectrum usage. Recently, Cognitive radios (CRs) have been proposed as a means of implementing DSA. In this work we focus on the aspect of resource management in overlaid CRNs. We formulate resource allocation strategies for cognitive radio networks (CRNs) as mathematical optimization problems. Specifically, we focus on two key problems in resource management: Sum Rate Maximization and Maximization of Number of Admitted Users. Since both the above mentioned problems are NP hard due to presence of binary assignment variables, we propose novel graph based algorithms to optimally solve these problems. Further, we analyze the impact of location awareness on network performance of CRNs by considering three cases: Full location Aware, Partial location Aware and Non location Aware. Our results clearly show that location awareness has significant impact on performance of overlaid CRNs and leads to increase in spectrum utilization effciency

Characterizing Cyclostationary Features of Digital Modulated Signals with Empirical Measurements Using Spectral Correlation Function

Signal detection is widely used in many applications. Some examples include Cognitive Radio (CR) and military intelligence. CRs use signal detection to sense spectral occupancy. Without guaranteed signal detection, a CR cannot reliably perform its role. Similarly, signal detection is the first step for garnering an opponent\u27s information. Wireless signal detection can be performed using many different techniques. Some of the most popular include matched filters, energy detectors (which use measurements such as the Power Spectral Density (PSD) of the signal), and Cyclostationary Feature Detectors (CFD). Among these techniques, CFD can be viewed as a compromise technique, in that it theoretically has better low Signal-to-Noise Ratio (SNR) detection performance than energy detectors and less strict requirements than matched filters. CFD uses the cyclostationarity of a signal to detect its presence. Signals that have cyclostationarity exhibit correlations between widely separated spectral components. Functions that describe this cyclostationarity include the Spectral Correlation Function (SCF). One advantage of cyclostationary approaches such as these is that Additive White Gaussian Noise (AWGN) is cancelled in these functions. This characteristic makes SCF outperform PSD under low SNR environments. However, whereas PSD has been well investigated through empirical experiments throughout many researches, SCF features under real world noise have not been investigated with empirical experiments. In this effort, firstly, the SCF features of modulated signals under real world channel noise are identified and characterized using the concept of path loss. Secondly, outperformance of SCF under low SNR environment with real world signals is verified with real world signals and noise

Spectrum Sensing Techniqes in Cognitive Radio: Cyclostationary Method

Cognitive Radios promise to be a major shift in wireless communications based on developing a novel approach which attempt to reduce spectrum scarcity that growing up in the past and waited to increase in the future. Since formulating stages for increasing interest in wireless application proves to be extremely challenging, it is growing rapidly. Initially this growth leads to huge demand for the radio spectrum. The novelty of this approach needs to optimize the spectrum utilization and find the efficient way for sharing the radio frequencies through spectrum sensing process. Spectrum sensing is one of the most significant tasks that allow cognitive radio functionality to implement and one of the most challenging tasks. A main challenge in sensing process arises from the fact that, detecting signals with a very low SNR in back ground of noise or severely masked by interference in specific time based on high reliability. This thesis describes the fundamental cognitive radio system aspect based on design and implementation by connecting between the theoretical and practical issue. Efficient method for sensing and detecting are studied and discussed through two fast methods of computing the spectral correlation density function, the FFT Accumulation Method and the Strip Spectral Correlation Algorithm. Several simulations have been performed to show the ability and performance of studied algorithms.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

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