5,574 research outputs found
Deep Learning Meets Cognitive Radio: Predicting Future Steps
Learning the channel occupancy patterns to reuse
the underutilised spectrum frequencies without interfering with
the incumbent is a promising approach to overcome the spectrum
limitations. In this work we proposed a Deep Learning (DL)
approach to learn the channel occupancy model and predict its
availability in the next time slots. Our results show that the
proposed DL approach outperforms existing works by 5%. We
also show that our proposed DL approach predicts the availability
of channels accurately for more than one time slot
On the Construction of Radio Environment Maps for Cognitive Radio Networks
The Radio Environment Map (REM) provides an effective approach to Dynamic
Spectrum Access (DSA) in Cognitive Radio Networks (CRNs). Previous results on
REM construction show that there exists a tradeoff between the number of
measurements (sensors) and REM accuracy. In this paper, we analyze this
tradeoff and determine that the REM error is a decreasing and convex function
of the number of measurements (sensors). The concept of geographic entropy is
introduced to quantify this relationship. And the influence of sensor
deployment on REM accuracy is examined using information theory techniques. The
results obtained in this paper are applicable not only for the REM, but also
for wireless sensor network deployment.Comment: 6 pages, 7 figures, IEEE WCNC conferenc
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
- …