This paper proposes a novel learning algorithm benefitting from square-root cubature Kalman filters for training recurrent interval type-2 fuzzy neural networks. The recurrence property in this network is feeding the ...
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This paper proposes a novel learning algorithm benefitting from square-root cubature Kalman filters for training recurrent interval type-2 fuzzy neural networks. The recurrence property in this network is feeding the output of each input to itself. Simulations results show the effectiveness of the proposed network and the proposed learning algorithm.
This paper deals with the design of low complexity dynamic spectrum access (DSA) scheme for Cognitive Radios (CRs) to search the vacant band of tunable bandwidth, B_h. Such DSA scheme with tunable Bh is very critical ...
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ISBN:
(纸本)9781479984992
This paper deals with the design of low complexity dynamic spectrum access (DSA) scheme for Cognitive Radios (CRs) to search the vacant band of tunable bandwidth, B_h. Such DSA scheme with tunable Bh is very critical for CRs considering the coexistence of multiple communication standards in wideband input signal with channel bandwidths ranging from 25 kHz to 20 MHz and users expectations of seamless integration of multiple services on single mobile terminal. The proposed scheme is designed by integration of our allpass transformation and coefficient decimation method based variable digital filter and suitably modified Upper Confidence Bound based decision making algorithm. The simulation results and complexity comparisons show that the proposed DSA scheme offers superior performance in terms of vacant band selection rate (and hence high spectrum throughput) for wide range of Bh as well as different spectrum occupancies and total gate count savings of 20-90% over existing schemes.
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