Low Complexity MIMO Channel Prediction for Fast Time-Variant Vehicular Communications Channels Based on Discrete Prolate Spheroidal Sequences

Simple item page
dc.contributor.authorTalaei, Farnoosh
dc.contributor.authorZhan, Jinlong
dc.contributor.authorDong, Xiaodai
dc.date.accessioned2021-03-09T20:38:14Z
dc.date.available2021-03-09T20:38:14Z
dc.date.copyright2021en_US
dc.date.issued2021
dc.description.abstractChannel state information (CSI) is required at the transmitter for achieving the maximum potentials of multiple-input multiple-output (MIMO) systems. In fast time-variant vehicular communications channels high data rate feedback lines are required in a frequency division duplex (FDD) transceiver for updating the transmitter with the rapidly changing CSI. Even with high data rate feedback lines, the delay caused by channel estimation and feedback may lead to outdated CSI at the transmitter. To reduce both the feedback load and CSI delay, this paper presents a reduced rank autoregressive (AR) channel predictor based on low dimensional discrete prolate spheroidal (DPS) sequences. The new subframe-wise DPS basis expansion model (DPS-BEM) channel predictor properly exploits the channel's restriction to low dimensional subspaces for reducing the prediction error and the computational complexity. The proposed channel predictor can be applied for updating the precoding matrix in time-variant MIMO systems. Simulation results demonstrate that the proposed channel predictor outperforms the DPS based minimum energy (ME) predictor at different Doppler frequencies and has better performance than the conventional Wiener predictor for slower time-variant channels and almost similar performance for very fast time-variant channels with reduced amount of computational complexity.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipThis work was supported by the Natural Sciences and Engineering Research Council of Canada under Grant 520198.en_US
dc.identifier.citationTalaei, F., Zhan, J., & Dong, X. (2021). Low complexity MIMO channel prediction for fast time-variant vehicular communications channels based on discrete prolate spheroidal sequences. IEEE Access, 9, 23398-23408. DOI: 10.1109/ACCESS.2021.3056297en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2021.3056297
dc.identifier.urihttp://hdl.handle.net/1828/12763
dc.language.isoenen_US
dc.publisherIEEE Accessen_US
dc.subjectMIMO communication
dc.subjectPredictive models
dc.subjectChannel estimation
dc.subjectDelays
dc.subjectDoppler effect
dc.subjectTransmitting antennas
dc.subjectTracking
dc.subjectMIMO channel prediction
dc.subjecttime varying vehicle-to-everything (V2X) channel
dc.subjectfeedback delay
dc.subjectdiscrete prolate spheroidal sequences
dc.subjectprecoder
dc.subject.departmentDepartment of Electrical and Computer Engineering
dc.titleLow Complexity MIMO Channel Prediction for Fast Time-Variant Vehicular Communications Channels Based on Discrete Prolate Spheroidal Sequencesen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Talaei_Farnoosh_IEEEAccess_2021.pdf
Size:
1.2 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Faculty and Staff Publications
 
University of Victoria Libraries

We acknowledge and respect the Lək̓ʷəŋən (Songhees and Esquimalt) Peoples on whose territory the university stands, and the Lək̓ʷəŋən and W̱SÁNEĆ Peoples whose historical relationships with the land continue to this day.