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Journal ArticleDOI

Multiantenna-Assisted Spectrum Sensing for Cognitive Radio

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TLDR
The results show that the proposed GLRT exhibits better performance than other existing techniques, particularly when the number of samples is small, which is particularly critical in vehicular applications.
Abstract
In this paper, we consider the problem of detecting a primary user in a cognitive radio network by employing multiple antennas at the cognitive receiver. In vehicular applications, cognitive radios typically transit regions with differing densities of primary users. Therefore, speed of detection is key, and so, detection based on a small number of samples is particularly advantageous for vehicular applications. Assuming no prior knowledge of the primary user's signaling scheme, the channels between the primary user and the cognitive user, and the variance of the noise seen at the cognitive user, a generalized likelihood ratio test (GLRT) is developed to detect the presence/absence of the primary user. Asymptotic performance analysis for the proposed GLRT is also presented. A performance comparison between the proposed GLRT and other existing methods, such as the energy detector (ED) and several eigenvalue-based methods under the condition of unknown or inaccurately known noise variance, is provided. Our results show that the proposed GLRT exhibits better performance than other existing techniques, particularly when the number of samples is small, which is particularly critical in vehicular applications.

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Citations
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Journal ArticleDOI

Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances

TL;DR: Cognitive radio is introduced to exploit underutilized spectral resources by reusing unused spectrum in an opportunistic manner and the idea of using learning and sensing machines to probe the radio spectrum was envisioned several decades earlier.
Journal ArticleDOI

Cognitive Radio Techniques Under Practical Imperfections: A Survey

TL;DR: This survey paper provides an overview of the enabling techniques for CR communications and discusses the main imperfections that may occur in the most widely used CR paradigms and then reviews the existing approaches toward addressing these imperfections.
Journal ArticleDOI

Intelligent Power Control for Spectrum Sharing in Cognitive Radios: A Deep Reinforcement Learning Approach

TL;DR: A deep reinforcement learning-based method is developed, which the secondary user can use to intelligently adjust its transmit power such that after a few rounds of interaction with the primary user, both users can transmit their own data successfully with required qualities of service.
Journal ArticleDOI

Detection of Rank- $P$ Signals in Cognitive Radio Networks With Uncalibrated Multiple Antennas

TL;DR: This work studies the problem of detecting a Gaussian signal with rank-P unknown spatial covariance matrix in spatially uncorrelated Gaussian noise with unknown covariance using multiple antennas and derived the generalized likelihood ratio test (GLRT).
Journal ArticleDOI

Power Control Based on Deep Reinforcement Learning for Spectrum Sharing

TL;DR: An asynchronous advantage actor critic (A3C)-based power control of SU that is a parallel actor-learners framework with root mean square prop (RMSProp) optimization is proposed that enables the network to converge quickly.
References
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Book

Matrix Analysis

TL;DR: In this article, the authors present results of both classic and recent matrix analyses using canonical forms as a unifying theme, and demonstrate their importance in a variety of applications, such as linear algebra and matrix theory.
Journal ArticleDOI

Fundamentals of statistical signal processing: estimation theory

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Journal ArticleDOI

Cognitive radio: brain-empowered wireless communications

TL;DR: Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks: radio-scene analysis, channel-state estimation and predictive modeling, and the emergent behavior of cognitive radio.
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