Real Time Channel Estimation for MIMO Systems

dc.contributor.authorTaneja, Ashu
dc.contributor.supervisorSharma, Surbhi
dc.date.accessioned2011-07-22T10:38:31Z
dc.date.available2011-07-22T10:38:31Z
dc.date.issued2011-07-22T10:38:31Z
dc.descriptionM.E. (ECED)en
dc.description.abstractHigh transmission data rate, spectral efficiency, and reliability are necessary for future wireless communications systems. In a multipath-rich wireless channel, deploying multiple antennas at both the transmitter and receiver achieves high data rate, without increasing the total transmission power or bandwidth. When perfect knowledge of the wireless channel conditions is available at the receiver, the capacity has been shown to grow linearly with the number of antennas. However, the channel conditions must be estimated since perfect channel knowledge is never known a priori. In practice, the channel estimation procedure can be aided by transmitting pilot symbols that are known at the receiver. Different channel estimation techniques are used - Least Square (LS), Minimum Mean Square (MMSE), Iterative channel estimation, MAP channel estimation, Channel estimation based on adaptive filtering and so on. WARP is Wireless Open Access Research Platform. The WARP board is equipped with the radio nodes which are responsible for the processing of signals to be transmitted and received. The input samples are transmitted through the antenna associated with the transmitting radio node of the WARP FPGA board. These samples are then received by the antenna associated with receiving radio node. The generation of information bits and their modulation along with activation of modules of WARP FPGA such as radio boards, clocks and downloading of signals to be transmitted through the antenna are done through the program running offsite of the WARP FPGA board. The samples received on the antenna associated with the receiving radio node of the WARP FPGA board along with the transmitted pilots are used to estimate the channel. The SNR vs BER are evaluated using different channel estimation techniques for different modulation techniques. The performance of these channel estimation techniques in real time validates the analysis when compared to that of simulated channel estimation techniques.en
dc.description.sponsorshipUGCen
dc.format.extent1644045 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10266/1425
dc.language.isoenen
dc.subjectREal time MIMOen
dc.titleReal Time Channel Estimation for MIMO Systemsen
dc.typeThesisen

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