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Journal ArticleDOI

Addressing feasibility of cognitive radios

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TLDR
A feasibility study of spectrum-sensing techniques using a research testbed platform for exploration and demonstration of cognitive radio systems and demonstrates practically achievable gains by exploiting multipath channel diversity through multiple antenna processing, and spatial diversity using user cooperation in a typical indoor environment.
Abstract
In this article we present a feasibility study of spectrum-sensing techniques using a research testbed platform for exploration and demonstration of cognitive radio systems. Our cognitive radio testbed is particularly suited for the development of physical and network layer functionalities and their experimental characterization in realistic wireless scenarios. Advanced testbed capabilities include real-time high-speed signal processing and protocol implementation, and support for multiple networks interaction and multiple antennas operation. This testbed is used for an experimental study of a set of prominent candidate techniques proposed in the literature for implementation of spectrum sensing functionality. We first consider three physical layer signal processing approaches based on energy, pilot, and feature detection. Our experimental results show that theoretical performance of these approaches for spectrum sensing cannot be achieved for the detection of very weak signals using a practical sensing receiver implementation. Also, we explain why these limitations exist by identifying sources of errors and provide basis for the design of robust spectrum sensing techniques. Next, we consider spectrum sensing techniques that use multiple measurements to improve sensing reliability. Our experimental study demonstrates practically achievable gains by exploiting multipath channel diversity through multiple antenna processing, and spatial diversity using user cooperation in a typical indoor environment.

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Citations
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Journal ArticleDOI

A review on spectrum sensing for cognitive radio: challenges and solutions

TL;DR: Spectrum sensing techniques from the optimal likelihood ratio test to energy detection, matched filtering detection, cyclostationary detection, eigenvalue-based sensing, joint space-time sensing, and robust sensing methods are reviewed.
Journal ArticleDOI

A Survey on Machine-Learning Techniques in Cognitive Radios

TL;DR: The learning problem in cognitive radios (CRs) is characterized and the importance of artificial intelligence in achieving real cognitive communications systems is stated and the conditions under which each of the techniques may be applied are identified.
Book

Principles Of Cognitive Radio

TL;DR: 1. The concept of cognitive radio, capacity of cognitiveRadio networks, and Propagation issues for cognitive radio: a review.
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

GLRT-Based Spectrum Sensing for Cognitive Radio with Prior Information

TL;DR: The finite-sample optimality of the generalized likelihood ratio test (GLRT) is discussed and the corresponding GLRT spectrum sensing algorithms are derived by exploiting the statistics of the received signal and the prior information on the channel, noise, as well as the data signal.
References
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Proceedings ArticleDOI

Implementation issues in spectrum sensing for cognitive radios

TL;DR: To improve radio sensitivity of the sensing function through processing gain, three digital signal processing techniques are investigated: matched filtering, energy detection and cyclostationary feature detection.
Proceedings ArticleDOI

Collaborative spectrum sensing for opportunistic access in fading environments

TL;DR: This paper studies spectrum-sharing between a primary licensee and a group of secondary users and suggests that collaboration may improve sensing performance significantly.
Proceedings ArticleDOI

Cooperative Sensing among Cognitive Radios

TL;DR: This work proposes light-weight cooperation in sensing based on hard decisions to mitigate the sensitivity requirements on individual radios and shows that the "link budget" that system designers have to reserve for fading is a significant function of the required probability of detection.
Journal ArticleDOI

Cooperative Spectrum Sensing in Cognitive Radio, Part II: Multiuser Networks

TL;DR: A practical algorithm is proposed which allows cooperation between cognitive users in random networks and develops sufficient conditions for agility gain when the cognitive population is arbitrarily large.
Journal ArticleDOI

Signal interception: a unifying theoretical framework for feature detection

TL;DR: The spectral-correlation-plane approach to the interception problem is put forth as especially promising for detection, classification, and estimation in particularly difficult environments involving unknown and changing noise levels and interference activity.