6,216 research outputs found
Sensing Throughput Tradeoff for Cognitive Radio Networks with Noise Variance Uncertainty
This paper proposes novel spectrum sensing algorithm, and examines the
sensing throughput tradeoff for cognitive radio (CR) networks under noise
variance uncertainty. It is assumed that there are one white sub-band, and one
target sub-band which is either white or non-white. Under this assumption,
first we propose a novel generalized energy detector (GED) for examining the
target sub-band by exploiting the noise information of the white sub-band,
then, we study the tradeoff between the sensing time and achievable throughput
of the CR network. To study this tradeoff, we consider the sensing time
optimization for maximizing the throughput of the CR network while
appropriately protecting the primary network. The sensing time is optimized by
utilizing the derived detection and false alarm probabilities of the GED. The
proposed GED does not suffer from signal to noise ratio (SNR) wall (i.e.,
robust against noise variance uncertainty) and outperforms the existing signal
detectors. Moreover, the relationship between the proposed GED and conventional
energy detector (CED) is quantified analytically. We show that the optimal
sensing times with perfect and imperfect noise variances are not the same. In
particular, when the frame duration is 2s, and SNR is -20dB, and each of the
bandwidths of the white and target sub-bands is 6MHz, the optimal sensing times
are 28.5ms and 50.6ms with perfect and imperfect noise variances, respectively.Comment: Accepted in CROWNCOM, June 2014, Oulu, Finlan
Cooperative Spectrum Sensing Using Random Matrix Theory
In this paper, using tools from asymptotic random matrix theory, a new
cooperative scheme for frequency band sensing is introduced for both AWGN and
fading channels. Unlike previous works in the field, the new scheme does not
require the knowledge of the noise statistics or its variance and is related to
the behavior of the largest and smallest eigenvalue of random matrices.
Remarkably, simulations show that the asymptotic claims hold even for a small
number of observations (which makes it convenient for time-varying topologies),
outperforming classical energy detection techniques.Comment: Submitted to International Symposium on Wireless Pervasive Computing
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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
DVB-T signal detection for indoor environments in low-SNR regime
The problem of coexistence between the primary
(licensed) and secondary (non-licensed) users can be solved
in various ways. One of them assumes the application of the
detailed Radio Environment Maps being a kind of database,
where some crucial information about the licensed
transmission can be stored. In this paper we propose the
new methods for signal detection in low signal-to-noise
regime and compare it through hardware experiment with
other known techniques used for spectrum sensing.Peer ReviewedPostprint (author’s final draft
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