9 research outputs found
Spatial-Spectral Joint Detection for Wideband Spectrum Sensing in Cognitive Radio Networks
Spectrum sensing is an essential functionality that enables cognitive radios
to detect spectral holes and opportunistically use under-utilized frequency
bands without causing harmful interference to primary networks. Since
individual cognitive radios might not be able to reliably detect weak primary
signals due to channel fading/shadowing, this paper proposes a cooperative
wideband spectrum sensing scheme, referred to as spatial-spectral joint
detection, which is based on a linear combination of the local statistics from
spatially distributed multiple cognitive radios. The cooperative sensing
problem is formulated into an optimization problem, for which suboptimal but
efficient solutions can be obtained through mathematical transformation under
practical conditions.Comment: To appear in the Proceedings of the 2008 IEEE International
Conference on Acoustics, Speech and Signal Processing, Las Vegas, NV, March
30-April 4, 200
A Bayesian Framework for Collaborative Multi-Source Signal Detection
This paper introduces a Bayesian framework to detect multiple signals
embedded in noisy observations from a sensor array. For various states of
knowledge on the communication channel and the noise at the receiving sensors,
a marginalization procedure based on recent tools of finite random matrix
theory, in conjunction with the maximum entropy principle, is used to compute
the hypothesis selection criterion. Quite remarkably, explicit expressions for
the Bayesian detector are derived which enable to decide on the presence of
signal sources in a noisy wireless environment. The proposed Bayesian detector
is shown to outperform the classical power detector when the noise power is
known and provides very good performance for limited knowledge on the noise
power. Simulations corroborate the theoretical results and quantify the gain
achieved using the proposed Bayesian framework.Comment: 15 pages, 9 pictures, Submitted to IEEE Trans. on Signal Processin
Performance of Statistical Tests for Single Source Detection using Random Matrix Theory
This paper introduces a unified framework for the detection of a source with
a sensor array in the context where the noise variance and the channel between
the source and the sensors are unknown at the receiver. The Generalized Maximum
Likelihood Test is studied and yields the analysis of the ratio between the
maximum eigenvalue of the sampled covariance matrix and its normalized trace.
Using recent results of random matrix theory, a practical way to evaluate the
threshold and the -value of the test is provided in the asymptotic regime
where the number of sensors and the number of observations per sensor
are large but have the same order of magnitude. The theoretical performance of
the test is then analyzed in terms of Receiver Operating Characteristic (ROC)
curve. It is in particular proved that both Type I and Type II error
probabilities converge to zero exponentially as the dimensions increase at the
same rate, and closed-form expressions are provided for the error exponents.
These theoretical results rely on a precise description of the large deviations
of the largest eigenvalue of spiked random matrix models, and establish that
the presented test asymptotically outperforms the popular test based on the
condition number of the sampled covariance matrix.Comment: 45 p. improved presentation; more proofs provide
Mathematical Foundations of Cognitive Radios, Journal of Telecommunications and Information Technology, 2009, nr 4
Recently, much interest has been directed towards software defined radios and embedded intelligence in telecommunication devices. However, no fundamental basis for cognitive radios has ever been proposed. In this paper, we introduce a fundamental vision of cognitive radios from a physical layer viewpoint. Specifically, our motivation in this work is to embed human-like intelligence in mobile wireless devices, following the three century-old work on Bayesian probability theory, the maximum entropy principle and minimal probability update. This allows us to partially answer such questions as, what are the signal detection capabilities of a wireless device, when facing a situation in which most parameters are missing, how to react and so on. As an introductory example, we will present previous works from the same authors following the cognitive framework, and especially the multi-antenna channel modeling and signal sensing
Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks
Cognitive radio has been widely considered as one of the prominent solutions
to tackle the spectrum scarcity. While the majority of existing research has
focused on single-band cognitive radio, multiband cognitive radio represents
great promises towards implementing efficient cognitive networks compared to
single-based networks. Multiband cognitive radio networks (MB-CRNs) are
expected to significantly enhance the network's throughput and provide better
channel maintenance by reducing handoff frequency. Nevertheless, the wideband
front-end and the multiband spectrum access impose a number of challenges yet
to overcome. This paper provides an in-depth analysis on the recent
advancements in multiband spectrum sensing techniques, their limitations, and
possible future directions to improve them. We study cooperative communications
for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also
investigate several limits and tradeoffs of various design parameters for
MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that
differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE
Journal, Special Issue on Future Radio Spectrum Access, March 201
Spatial-spectral joint detection for wideband spectrum sensing in cognitive radio networks
Spectrum sensing is an essential functionality that enables cognitive radios to detect spectral holes and opportunistically use under-utilized frequency bands without causing harmful interference to primary networks. Since individual cognitive radios might not be able to reliably detect weak primary signals due to channel fading/shadowing, this paper proposes a cooperative wideband spectrum sensing scheme, referred to as spatial-spectral joint detection, which is based on a linear combination of the local statistics from spatially distributed multiple cognitive radios. The cooperative sensing problem is formulated into an optimization problem, for which suboptimal but efficient solutions can be obtained through mathematical transformation under practical conditions