1,326 research outputs found
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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Max-Min SNR Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty
This paper proposes novel spectrum sensing algorithms for cognitive radio
networks. By assuming known transmitter pulse shaping filter, synchronous and
asynchronous receiver scenarios have been considered. For each of these
scenarios, the proposed algorithm is explained as follows: First, by
introducing a combiner vector, an over-sampled signal of total duration equal
to the symbol period is combined linearly. Second, for this combined signal,
the Signal-to-Noise ratio (SNR) maximization and minimization problems are
formulated as Rayleigh quotient optimization problems. Third, by using the
solutions of these problems, the ratio of the signal energy corresponding to
the maximum and minimum SNRs are proposed as a test statistics. For this test
statistics, analytical probability of false alarm () and detection ()
expressions are derived for additive white Gaussian noise (AWGN) channel. The
proposed algorithms are robust against noise variance uncertainty. The
generalization of the proposed algorithms for unknown transmitter pulse shaping
filter has also been discussed. Simulation results demonstrate that the
proposed algorithms achieve better than that of the Eigenvalue
decomposition and energy detection algorithms in AWGN and Rayleigh fading
channels with noise variance uncertainty. The proposed algorithms also
guarantee the desired in the presence of adjacent channel
interference signals
SNR-Walls in Eigenvalue-based Spectrum Sensing
Various spectrum sensing approaches have been shown to suffer from a
so-called SNR-wall, an SNR value below which a detector cannot perform robustly
no matter how many observations are used. Up to now, the eigenvalue-based
maximum-minimum-eigenvalue (MME) detector has been a notable exception. For
instance, the model uncertainty of imperfect knowledge of the receiver noise
power, which is known to be responsible for the energy detector's fundamental
limits, does not adversely affect the MME detector's performance. While
additive white Gaussian noise (AWGN) is a standard assumption in wireless
communications, it is not a reasonable one for the MME detector. In fact, in
this work we prove that uncertainty in the amount of noise coloring does lead
to an SNR-wall for the MME detector. We derive a lower bound on this SNR-wall
and evaluate it for example scenarios. The findings are supported by numerical
simulations.Comment: 17 pages, 3 figures, submitted to EURASIP Journal on Wireless
Communications and Networkin
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
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