By Topic

Using autoregressive and ADALINE neural network modeling to improve downlink performance of smart antennas

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
H. Yigit ; Dept. of Electron. & Comput. Ed., Kocaeli Univ., Izmit, Turkey ; A. Kavak ; H. M. Ertunc

In time-division-duplex (TDD) mode wireless communications, downlink beamforming performance of a smart antenna system at the base station can be degraded due to variation of spatial signature vectors of an antenna array especially in fast fading scenarios. To mitigate this, downlink beams must be controlled by properly adjusting their weight vectors in response to changing propagation dynamics. This can be achieved by modeling the spatial signature vectors in the uplink period and then predicting them for the new mobile position in the downlink transmission period. In this paper, we show that linear prediction of spatial signature vectors using autoregressive (AR) and ADALINE (adaptive linear neuron) network modeling provide certain level of performance improvement compared to conventional beamforming method under varying channel (mobile speed) and filter (delay) order conditions. ADALINE structure outperforms AR modeling in terms of downlink SNR improvement and relative error improvement.

Published in:

Mechatronics, 2004. ICM '04. Proceedings of the IEEE International Conference on

Date of Conference:

3-5 June 2004