Novel Markov channel predictors for interference alignment in cognitive radio network

Shi, Z; Wu, Z; Yin, Z; Yang, Z; Cheng, Q

Novel Markov channel predictors for interference alignment in cognitive radio network

Shi, Z Wu, Z Yin, Z Yang, Z Cheng, Q

Permalink

Publication Type:: Journal Article
Citation:: Wireless Networks, 2018, 24 (6), pp. 1915 - 1925
Issue Date:: 2018-08-01

Closed Access

	Filename	Description	Size
	Novel Markov channel predictors.pdf	Published Version	1.04 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Shi, Z	en_US
dc.contributor.author	Wu, Z	en_US
dc.contributor.author	Yin, Z	en_US
dc.contributor.author	Yang, Z	en_US
dc.contributor.author	Cheng, Q	en_US
dc.date.issued	2018-08-01	en_US
dc.identifier.citation	Wireless Networks, 2018, 24 (6), pp. 1915 - 1925	en_US
dc.identifier.issn	1022-0038	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125295
dc.description.abstract	© 2017, Springer Science+Business Media New York. In cognitive radio (CR) network, how to mitigate interference between different users is a key task. Interference alignment (IA) is a promising technique to tackle the multi-user interference in communication system. Compared with other interference management methods (such as zero-forcing), IA can not only effectively eliminate the interference, but also greatly increase the system capacity. However, the perfect channel state information (CSI) is required for both transmitters and receivers to apply the IA algorithm, which is hard to achieve in practical applications. In this paper, the effect of imperfect CSI on IA in CR system is analyzed in terms of signal to interference plus noise ratio and achievable sum rate. A linear finite state Markov chain (LFSMC) predictor, which incorporates the finite state Markov chain into the AR predictor, is proposed to reduce the impact of imperfect CSI on system performance of CR network. Moreover, for the sake of simplifying the initialization of LFSMC predictor, a simplified LFSMC (S-LFSMC) predictor is provided. Simulation results indicate that both of the LFSMC and S-LFSMC predictor can greatly improve the performance of IA system with the inaccurate CSI. Specifically, the LSFMC predictor can achieve satisfied performance compared with other predictors mentioned in this paper. And the LSFMC predictor which is simpler and its performance is still much better than traditional predictors. Therefore, we can choose a suitable predictor (LAFMC or S-LSFMC) based on the different requirements.	en_US
dc.relation.ispartof	Wireless Networks	en_US
dc.relation.isbasedon	10.1007/s11276-017-1445-x	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Novel Markov channel predictors for interference alignment in cognitive radio network	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	24	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	24	en_US

Abstract:

© 2017, Springer Science+Business Media New York. In cognitive radio (CR) network, how to mitigate interference between different users is a key task. Interference alignment (IA) is a promising technique to tackle the multi-user interference in communication system. Compared with other interference management methods (such as zero-forcing), IA can not only effectively eliminate the interference, but also greatly increase the system capacity. However, the perfect channel state information (CSI) is required for both transmitters and receivers to apply the IA algorithm, which is hard to achieve in practical applications. In this paper, the effect of imperfect CSI on IA in CR system is analyzed in terms of signal to interference plus noise ratio and achievable sum rate. A linear finite state Markov chain (LFSMC) predictor, which incorporates the finite state Markov chain into the AR predictor, is proposed to reduce the impact of imperfect CSI on system performance of CR network. Moreover, for the sake of simplifying the initialization of LFSMC predictor, a simplified LFSMC (S-LFSMC) predictor is provided. Simulation results indicate that both of the LFSMC and S-LFSMC predictor can greatly improve the performance of IA system with the inaccurate CSI. Specifically, the LSFMC predictor can achieve satisfied performance compared with other predictors mentioned in this paper. And the LSFMC predictor which is simpler and its performance is still much better than traditional predictors. Therefore, we can choose a suitable predictor (LAFMC or S-LSFMC) based on the different requirements.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/125295