Using adaline neural network for performance improvement of smart antennas in TDD wireless communications

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 corresponding to mobile users especially in fast fading scenarios. To mitigate this, downlink beams...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2005-11, Vol.16 (6), p.1616-1625
Main Authors: Kavak, A., Yigit, H., Ertunc, H.M.
Format: Article
Language:English
Subjects:
Adaline network
Algorithms
Antennas and propagation
Applied sciences
Array signal processing
Artificial intelligence
autoregressive model
Base stations
beamforming
Cell Phone
Computer science
control theory
systems
Computer Simulation
Computer-Aided Design
Connectionism. Neural networks
Degradation
Downlink
Exact sciences and technology
linear prediction
Microwaves
Mobile antennas
Models, Theoretical
Neural networks
Neural Networks (Computer)
Pattern recognition. Digital image processing. Computational geometry
Predictive models
Quality Control
smart antennas
Studies
vector channel
Vectors
Wireless communication
Wireless communications
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Summary: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 corresponding to mobile users 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 to be used as beamforming weight vectors for the new mobile position in the downlink transmission period. We show that ADAptive LInear NEuron (ADALINE) network modeling based prediction of spatial signatures provides certain level of performance improvement compared to conventional beamforming method that employs spatial signature obtained in previous uplink interval. We compare the performance of ADALINE with autoregressive (AR) modeling based predictions under varying channel propagation (mobile speed, multipath angle spread, and number of multipaths), and filter order/delay conditions. ADALINE modeling outperforms AR modeling in terms of downlink SNR improvement and relative error improvement especially under high mobile speeds, i.e., V=100 km/h.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/TNN.2005.857947