Joint Robust Relay Beamforming and Adaptive Channel Estimation using Cubature Kalman Filtering

In this paper, an adaptive algorithm is proposed for the estimation and tracking of the channel coefficients in peer-to-peer communication through a network of relays. Using the observed signals at the relay and destination nodes, the channel state information (CSI) is estimated centrally by taking...

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Bibliographic Details
Published inIEEE Wireless Communications and Networking Conference : [proceedings] : WCNC pp. 968 - 973
Main Authors Sadr, Mohammad Amin Maleki, Champagne, Benoit
Format Conference Proceeding
LanguageEnglish
Published IEEE 10.04.2022
Subjects
Adaptation models
Adaptive systems
Array signal processing
Channel estimation
Channel State Information
Cramer-Rao-Lower-Bound
Cubature Kalman Filter
Estimation
Interference
Peer-to-peer computing
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Summary:In this paper, an adaptive algorithm is proposed for the estimation and tracking of the channel coefficients in peer-to-peer communication through a network of relays. Using the observed signals at the relay and destination nodes, the channel state information (CSI) is estimated centrally by taking advantage of a Markov model for the source-relay and relay-destination channels, and employing the Cubature Kalman Filter (CKF). The estimated CSI is used for solving a robust relay beamforming problem, aiming to minimize the total transmitted power by the relays subject to signal-to-interference-plus-noise ratio (SINR) constraint at each one of the destination nodes. Through simulations, the proposed CSI estimation is shown to be unbiased and converge to the Cramer-Rao-Lower-Bound (CRLB) for low and moderate error levels. Furthermore, the ensuing beamformer design exhibits better performance compared to existing robust beamforming methods.
ISSN:1558-2612
DOI:10.1109/WCNC51071.2022.9771640