Cooperative spectrum prediction for improved efficiency of cognitive radio networks
In this thesis, the spectrum and energy efficiency of cooperative spectrum prediction (CSP) in cognitive radio networks are investigated. In addition, the performance of CSP is evaluated using a hidden Markov model (HMM) and a multilayer perceptron (MLP) neural network. The cooperation between secon...
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| Format: | Others |
| Language: | English en |
| Published: |
2018
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| Online Access: | https://dspace.library.uvic.ca//handle/1828/8986 |
| Summary: | In this thesis, the spectrum and energy efficiency of cooperative spectrum prediction (CSP) in cognitive radio networks are investigated. In addition, the performance of CSP is evaluated using a hidden Markov model (HMM) and a multilayer perceptron (MLP) neural network. The cooperation between secondary users in predicting the next channel status employs AND, OR and majority rule fusion schemes. These schemes are compared for HMM and MLP predictors as a function of channel occupancy in terms of prediction error, spectrum efficiency and energy efficiency. The impact of busy and idle state prediction errors on the spectrum efficiency is determined. Further, the spectrum efficiency is compared for different numbers of primary users (PUs).
Simulation results are presented which show a significant improvement in the spectrum efficiency using CSP with the majority rule at the cost of a small degradation in energy efficiency compared to single spectrum prediction (SSP) and traditional spectrum sensing (TSS). The HMM predictor provides better performance than the MLP predictor. Moreover, the total probability of prediction error with the majority rule provides the best performance compared to SSP and the other fusion rules. On the other hand, the AND and OR rules have the worst performance in the high and low traffic cases, respectively. The majority rule provides a good tradeoff between busy and idle state prediction errors compared with the AND and OR rules and SSP. Further, a reduction in the busy state prediction error increases the SE more compared to a reduction in the idle state prediction error. === Graduate |
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