11,724 research outputs found
Cooperative prediction for cognitive radio networks
Combining spectrum sensing (SS) and primary user (PU) traffic forecasting
provides a cognitive radio network with a platform from which informed and proactive
operational decisions can be made. The success of these decisions is largely dependent on
prediction accuracy. Allowing secondary users (SU) to perform these predictions in a
collaborative manner allows for an improvement in the accuracy of this process, since
individual SUs may suffer from SS and prediction inaccuracies due to poor channel
conditions. To overcome these problems a collaborative approach to forecasting PU traffic
behaviour, that combines SS and forecasting through SU cooperation, has been proposed in
this article. Both pre-fusion and post-fusion scenarios for cooperative prediction were
investigated and a number of binary prediction methods were considered (including the
authors’ own simple technique). Cooperative prediction performance was investigated,
under various PU traffic conditions, for a group of ten SUs experiencing different channel
conditions and a sub-optimal cooperative forecasting algorithm was proposed to minimise
cooperative prediction error. Simulation results indicated that the accuracy of the prediction
methods was influenced by the PU traffic pattern and that cooperative prediction lead
to a significant improvement in prediction accuracy under most of the traffic conditions
considered. However, this came at the cost of increased computational complexity. The
pre-fusion scenario was found to be the most accurate scenario (up to 25 % improvement),
but was also eleven times more complex than when no fusion was employed. The cooperative
forecasting algorithm was found to further improve these results.Sentech Chair in Broadband Wireless Multimedia Communication (BWMC), the National Research Foundation (NRF) and the Independent Communications Authority of South Africa (ICASA).http://link.springer.com/journal/112772017-08-30hb2016Electrical, Electronic and Computer Engineerin
Optimization for Prediction-Driven Cooperative Spectrum Sensing in Cognitive Radio Networks
Empirical studies have observed that the spectrum usage in practice follows regular patterns. Machine learning (ML)-based spectrum prediction techniques can thus be used jointly with cooperative sensing in cognitive radio networks (CRNs). In this paper, we propose a novel cluster-based sensing-after-prediction scheme and aim to reduce the total energy consumption of a CRN. An integer programming problem is formulated that minimizes the cluster size and optimizes the decision threshold, while guaranteeing the system accuracy requirement. To solve this challenging optimization problem, the relaxation technique is used which transforms the optimization problem into a tractable problem. The solution to the relaxed problem serves as a foundation for the solution to the original integer programming. Finally, a low-complexity search algorithm is proposed which achieves the global optimum, as it obtains the same performance with exhaustive search. Simulation results demonstrate that the total energy consumption of CRN is greatly reduced by applying our clustered sensing-after-prediction scheme
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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