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Recurrent Neural Network Based Narrowband Channel Prediction

Recurrent Neural Network Based Narrowband Channel Prediction
Recurrent Neural Network Based Narrowband Channel Prediction
In this contribution, the application of fully connected recurrent neural networks (FCRNNs) is investigated in the context of narrowband channel prediction. Three different algorithms, namely the real time recurrent learning (RTRL), the global extended Kalman filter (GEKF) and the decoupled extended Kalman filter (DEKF) are used for training the recurrent neural network (RNN) based channel predictor. Our simulation results show that the GEKF and DEKF training schemes have the potential of converging faster than the RTRL training scheme as well as attaining a better MSE performance.
2173-2177
Liu, W.
af656440-51e8-45c8-b694-a802e71b671e
Yang, L-L.
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Liu, W.
af656440-51e8-45c8-b694-a802e71b671e
Yang, L-L.
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1

Liu, W., Yang, L-L. and Hanzo, L. (2006) Recurrent Neural Network Based Narrowband Channel Prediction. IEEE VTC'06 (Spring), Australia. 07 - 10 May 2006. pp. 2173-2177 .

Record type: Conference or Workshop Item (Paper)

Abstract

In this contribution, the application of fully connected recurrent neural networks (FCRNNs) is investigated in the context of narrowband channel prediction. Three different algorithms, namely the real time recurrent learning (RTRL), the global extended Kalman filter (GEKF) and the decoupled extended Kalman filter (DEKF) are used for training the recurrent neural network (RNN) based channel predictor. Our simulation results show that the GEKF and DEKF training schemes have the potential of converging faster than the RTRL training scheme as well as attaining a better MSE performance.

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More information

Published date: 2006
Additional Information: Event Dates: 7-10 May 2006
Venue - Dates: IEEE VTC'06 (Spring), Australia, 2006-05-07 - 2006-05-10
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 262635
URI: http://eprints.soton.ac.uk/id/eprint/262635
PURE UUID: 46e733c8-de48-4aae-bd0b-551f16d0e62f
ORCID for L-L. Yang: ORCID iD orcid.org/0000-0002-2032-9327
ORCID for L. Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 25 May 2006
Last modified: 10 Dec 2019 01:58

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