Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
