5,776 research outputs found
Generalized detector as a spectrum sensor in cognitive radio networks
The implementation of the generalized detector (GD) in cognitive radio (CR) systems allows us to improve the spectrum sensing performance in comparison with employment of the conventional detectors. We analyze the spectrum sensing performance for the uncorrelated and spatially correlated receive antenna array elements. Addi¬tionally, we consider a practical case when the noise power at the output of GD linear systems (the preliminary and additional filters) is differed by value. The choice of the optimal GD threshold based on the minimum total error rate criterion is also discussed. Simulation results demonstrate superiority of GD implementation in CR sys¬tem as spectrum sensor in comparison with the energy detector (ED), weighted ED (WED), maximum-minimum eigenvalue (MME) detector, and generalized likelihood ratio test (GLRT) detecto
Machine Learning Approaches for Spectrum Management in Cognitive Radio Networks
Cognitive radio (CR) provides a better way for utilization of spectrum resource by introducing an opportunistic usage of the frequency bands that are not heavily occupied by a licensed spectrum user or a primary user (PU). In cognitive radio, the detection and estimation of PU channel availability (unoccupied spectrum) are the key challenges that need to be overcome in order to prevent the interference with licensed spectrum user and improve spectrum resource utilization efficiency. This chapter focuses on developing new ways for detecting and estimating primary user channel availability based on machine-learning (ML) techniques
Adaptive quantization for spectrum exchange information in mobile cognitive radio networks
To reduce the detection failure of the exchanging signal power onto the OFDM subcarrier signal at uniform quantization, dynamic subcarrier mapping is applied. Moreover, to addressing low SNR’s wall-less than pre-determine threshold, non-uniform quantization or adaptive quantization for the signal quantization size parameter is proposed. μ-law is adopted for adaptive quantization subcarrier mapping which is deployed in mobility environment, such as Doppler Effect and Rayleigh Fading propagation. In this works, sensing node received signal power then sampled into a different polarity positive and negative in μ-law quantization and divided into several segmentation levels. Each segmentation levels are divided into several sub-segment has representing one tone signal subcarrier number OFDM which has the number of quantization level and the width power. The results show that by using both methods, a significant difference is obtained around 8 dB compared to those not using the adaptive method
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
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