22,177 research outputs found
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
Spectral and Energy Efficiency in Cognitive Radio Systems with Unslotted Primary Users and Sensing Uncertainty
This paper studies energy efficiency (EE) and average throughput maximization
for cognitive radio systems in the presence of unslotted primary users. It is
assumed that primary user activity follows an ON-OFF alternating renewal
process. Secondary users first sense the channel possibly with errors in the
form of miss detections and false alarms, and then start the data transmission
only if no primary user activity is detected. The secondary user transmission
is subject to constraints on collision duration ratio, which is defined as the
ratio of average collision duration to transmission duration. In this setting,
the optimal power control policy which maximizes the EE of the secondary users
or maximizes the average throughput while satisfying a minimum required EE
under average/peak transmit power and average interference power constraints
are derived. Subsequently, low-complexity algorithms for jointly determining
the optimal power level and frame duration are proposed. The impact of
probabilities of detection and false alarm, transmit and interference power
constraints on the EE, average throughput of the secondary users, optimal
transmission power, and the collisions with primary user transmissions are
evaluated. In addition, some important properties of the collision duration
ratio are investigated. The tradeoff between the EE and average throughput
under imperfect sensing decisions and different primary user traffic are
further analyzed.Comment: This paper is accepted for publication in IEEE Transactions on
Communication
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
200
A Novel Algorithm for Cooperative Distributed Sequential Spectrum Sensing in Cognitive Radio
This paper considers cooperative spectrum sensing in Cognitive Radios. In our
previous work we have developed DualSPRT, a distributed algorithm for
cooperative spectrum sensing using Sequential Probability Ratio Test (SPRT) at
the Cognitive Radios as well as at the fusion center. This algorithm works
well, but is not optimal. In this paper we propose an improved algorithm-
SPRT-CSPRT, which is motivated from Cumulative Sum Procedures (CUSUM). We
analyse it theoretically. We also modify this algorithm to handle uncertainties
in SNR's and fading.Comment: This paper has been withdrawn by the author due to the submission of
detailed journal version of the same paper, to arXi
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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