Please use this identifier to cite or link to this item: https://doi.org/10.1109/TVT.2010.2042089
Title: Design of learning-based MIMO cognitive radio systems
Authors: Gao, F.
Zhang, R. 
Liang, Y.-C.
Wang, X.
Keywords: Channel training
Cognitive radio (CR)
Environment learning
Multiantenna systems
Spectrum sharing (SS)
Issue Date: May-2010
Source: Gao, F., Zhang, R., Liang, Y.-C., Wang, X. (2010-05). Design of learning-based MIMO cognitive radio systems. IEEE Transactions on Vehicular Technology 59 (4) : 1707-1720. ScholarBank@NUS Repository. https://doi.org/10.1109/TVT.2010.2042089
Abstract: This paper addresses the design issues of the multiantenna-based cognitive radio (CR) system that is able to concurrently operate with the licensed primary-radio (PR) system. We propose a practical CR transmission strategy consisting of three major stages, namely, environment learning, channel training, and data transmission. In the environment-learning stage, the CR transceivers both listen to the PR transmission and apply blind algorithms to estimate the spaces that are orthogonal to the channels from the PR. Assuming time-division duplex (TDD)-based transmission for the PR, cognitive beamforming is then designed and applied at CR transceivers to restrict the interference to/from the PR during the subsequent channel-training and data-transmission stages. In the channel-training stage, the CR transmitter sends training signals to the CR receiver, which applies the linear-minimum-mean-square-error (LMMSE)-based estimator to estimate the effective channel. Considering imperfect estimations in both learning and training stages, we derive a lower bound on the ergodic capacity that is achievable for the CR in the data-transmission stage. From this capacity lower bound, we observe a general learning/training/ throughput tradeoff associated with the proposed scheme, pertinent to transmit power allocation between the training and transmission stages, as well as time allocation among the learning, training, and transmission stages. We characterize the aforementioned tradeoff by optimizing the associated power and time allocation to maximize the CR ergodic capacity. © 2006 IEEE.
Source Title: IEEE Transactions on Vehicular Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/55580
ISSN: 00189545
DOI: 10.1109/TVT.2010.2042089
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

64
checked on Dec 14, 2017

WEB OF SCIENCETM
Citations

49
checked on Nov 17, 2017

Page view(s)

21
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.