Please use this identifier to cite or link to this item: http://drsr.daiict.ac.in//handle/123456789/1051
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dc.contributor.advisorDas, Rajib Lochan
dc.contributor.advisorVasavada, Yash
dc.contributor.authorM V, S Chandra Kishan
dc.date.accessioned2022-05-06T06:04:43Z
dc.date.available2023-02-25T06:04:43Z
dc.date.issued2021
dc.identifier.citationM V S Chandra Kishan (2021). Compressive Sensing based Channel Estimation for Orthogonal Time Frequency Space (OTFS). Dhirubhai Ambani Institute of Information and Communication Technology. vii, 49 p. (Acc.No: T00992)
dc.identifier.urihttp://drsr.daiict.ac.in//handle/123456789/1051
dc.description.abstractHigh speed mobility is a challenging case in wireless communication where orthogonal frequency division multiplexing (OFDM) performance degrades due tohigh Doppler effect. A new modulation scheme orthogonal time frequency space (OTFS) that operates in the delay-Doppler (DD) domain is proposed in the literature. Considering the sparse nature of the delay-Doppler (DD) channel, we model the estimation of the channel as a sparse signal recovery problem. To solve this problem, we use compressed sensing (CS) based estimation techniques. We apply orthogonal matching pursuit (OMP), generalized OMP (gOMP), orthogonal least square (OLS) and generalized OLS (gOLS) based algorithms for DD channel estimation. We compare the performance of the proposed CS-based estimation schemes.We analyse the performance of the CS techniques for a grid pattern which has pilot symbols embedded in the data frame. We further extended the OTFS system for multi user case and analyse the performance of the CS-based channel estimation schemes.
dc.subjectOTFS modulation
dc.subjectdelay-Doppler channel
dc.subjectcompressed sensing (CS)
dc.subjectpilot arrangement
dc.subjectmulti-user OTFS
dc.classification.ddc621.382 M V
dc.titleCompressive Sensing based Channel Estimation for Orthogonal Time Frequency Space (OTFS)
dc.typeDissertation
dc.degreeM. Tech (EC)
dc.student.id201915003
dc.accession.numberT00992
Appears in Collections:M Tech (EC) Dissertations

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