Channel Estimation for Sparse Channel OFDM Systems using Least Square and Minimum Mean Square Error Techniques

Ali Farzamnia, Ngu War Hlaing, Manas Kumar Haldar, Javad Rahebi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Citations (Scopus)

Abstract

Orthogonal Frequency Division Multiplexing (OFDM), is a kind of signal modulation that divides a high data rate modulating stream and locates them onto several modulated narrowband channels called sub-carriers. Therefore, signal will be less sensitive to frequency selective fading. Channel estimation is used for increasing the capacity of orthogonal frequency division multiple access (OFDMA) systems by improving the system performance in terms of bit error rate. OFDM pilot based channel estimation is applied in this paper. Channel tracking and channel estimation techniques must be employed at the receiver side of the OFDM system. Channel estimations require channel state information in order to decode the data. In this paper, a proposed Least square (LS) method is considered for sparse channels. Channel coefficients for OFDM are estimated by applying this method and Minimum Mean Square Error (MMSE) method. Results show that the performance of MMSE technique is better than LS method. It is found that the LS method for sparse channel gives lower bit error rate than the general LS method.

Original languageEnglish
Title of host publicationProceedings of 2017 International Conference on Engineering and Technology, ICET 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538619490, 9781538619483
ISBN (Print)9781538619506
DOIs
Publication statusPublished - 8 Mar 2018
Externally publishedYes
Event2017 International Conference on Engineering and Technology - Antalya, Turkey
Duration: 21 Aug 201723 Aug 2017

Conference

Conference2017 International Conference on Engineering and Technology
Abbreviated titleICET 2017
Country/TerritoryTurkey
CityAntalya
Period21/08/1723/08/17

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