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Joint Channel and Doppler Offset Estimation in Dynamic Cooperative Relay Networks

Nevat, I; Peters, GW; Doucet, A; Yuan, J; (2014) Joint Channel and Doppler Offset Estimation in Dynamic Cooperative Relay Networks. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS , 13 (12) pp. 6570-6579. 10.1109/TWC.2014.2361760. Green open access

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Abstract

We develop a new and efficient algorithm to solve the problem of joint channel and Doppler offset estimation in time-varying cooperative wireless relay networks. We first formulate the problem as a Bayesian dynamic nonlinear state space model, then develop an algorithm, which is based on particle adaptive marginal Markov chain Monte Carlo, method to jointly estimate the time-varying channels and static Doppler offsets. We perform detailed complexity analysis of the proposed algorithm and show that it is very efficient and requires moderate computational complexity. In addition, we develop a new version of the recursive marginal Cramér-Rao lower bound and derive expressions for the achievable mean-square error. Simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms and performs close to the Cramér-Rao lower bound.

Type: Article
Title: Joint Channel and Doppler Offset Estimation in Dynamic Cooperative Relay Networks
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TWC.2014.2361760
Publisher version: http://dx.doi.org/10.1109/TWC.2014.2361760
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0
Keywords: Particle filter, MCMC, channel estimation, Doppler offset estimation, cooperative relay networks
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/1470706
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