554 research outputs found
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
Optimizing cooperative cognitive radio networks with opportunistic access
Optimal resource allocation for cooperative cognitive radio networks with opportunistic access to the licensed spectrum is studied. Resource allocation is based on minimizing the symbol error rate at the receiver. Both the cases of all-participate relaying and selective relaying are considered. The objective function is derived and the constraints are detailed for both scenarios. It is then shown that the objective functions and the constraints are nonlinear and nonconvex functions of the parameters of interest, that is, source and relay powers, symbol time, and sensing time. Therefore, it is difficult to obtain closed-form solutions for the optimal resource allocation. The optimization problem is then solved using numerical techniques. Numerical results show that the all-participate system provides better performance than its selection counterpart, at the cost of greater resources
Machine learning techniques applied to multiband spectrum sensing in cognitive radios
This research received funding of the Mexican National Council of Science and Technology (CONACYT), Grant (no. 490180). Also, this work was supported by the Program for Professional Development Teacher (PRODEP).In this work, three specific machine learning techniques (neural networks, expectation maximization and k-means) are applied to a multiband spectrum sensing technique for cognitive radios. All of them have been used as a classifier using the approximation coefficients from a Multiresolution Analysis in order to detect presence of one or multiple primary users in a wideband spectrum. Methods were tested on simulated and real signals showing a good performance. The results presented of these three methods are effective options for detecting primary user transmission on the multiband spectrum. These methodologies work for 99% of cases under simulated signals of SNR higher than 0 dB and are feasible in the case of real signalsPeer ReviewedPostprint (published version
Analytical Studies of Fragmented-Spectrum Multi-Level OFDM-CDMA Technique in Cognitive Radio Networks
In this paper, we present a multi-user resource allocation framework using
fragmented-spectrum synchronous OFDM-CDMA modulation over a frequency-selective
fading channel. In particular, given pre-existing communications in the
spectrum where the system is operating, a channel sensing and estimation method
is used to obtain information of subcarrier availability. Given this
information, some real-valued multi-level orthogonal codes, which are
orthogonal codes with values of , are provided
for emerging new users, i.e., cognitive radio users. Additionally, we have
obtained a closed form expression for bit error rate of cognitive radio
receivers in terms of detection probability of primary users, CR users' sensing
time and CR users' signal to noise ratio. Moreover, simulation results obtained
in this paper indicate the precision with which the analytical results have
been obtained in modeling the aforementioned system.Comment: 6 pages and 3 figure
Error Rate Analysis of Cognitive Radio Transmissions with Imperfect Channel Sensing
This paper studies the symbol error rate performance of cognitive radio
transmissions in the presence of imperfect sensing decisions. Two different
transmission schemes, namely sensing-based spectrum sharing (SSS) and
opportunistic spectrum access (OSA), are considered. In both schemes, secondary
users first perform channel sensing, albeit with possible errors. In SSS,
depending on the sensing decisions, they adapt the transmission power level and
coexist with primary users in the channel. On the other hand, in OSA, secondary
users are allowed to transmit only when the primary user activity is not
detected. Initially, for both transmission schemes, general formulations for
the optimal decision rule and error probabilities are provided for arbitrary
modulation schemes under the assumptions that the receiver is equipped with the
sensing decision and perfect knowledge of the channel fading, and the primary
user's received faded signals at the secondary receiver has a Gaussian mixture
distribution. Subsequently, the general approach is specialized to rectangular
quadrature amplitude modulation (QAM). More specifically, optimal decision rule
is characterized for rectangular QAM, and closed-form expressions for the
average symbol error probability attained with the optimal detector are derived
under both transmit power and interference constraints. The effects of
imperfect channel sensing decisions, interference from the primary user and its
Gaussian mixture model, and the transmit power and interference constraints on
the error rate performance of cognitive transmissions are analyzed
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