{"id":111,"date":"2020-02-19T20:40:29","date_gmt":"2020-02-19T20:40:29","guid":{"rendered":"https:\/\/zhangnsf1618358.cs.fsu.edu\/?page_id=111"},"modified":"2021-02-16T23:31:27","modified_gmt":"2021-02-16T23:31:27","slug":"parest","status":"publish","type":"page","link":"https:\/\/zhangnsf1618358.cs.fsu.edu\/?page_id=111","title":{"rendered":"PAREST"},"content":{"rendered":"\n
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PAREST: MIMO LINK CSI ESTIMATION <\/strong><\/h6>\n\n\n\n
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About ParEst <\/h5>\n\n\n\n

We propose ParEst (which stands for “Parallel Estimation”), which estimates the Channel State Information (CSI) of multiple transmitters based on our discovery that the CSI can be approximated as a linear combination of base sinusoids on constant frequencies.\u00a0\u00a0<\/p>\n\n\n\n

ParEst can immediately replace the existing CSI estimation methods in Wi-Fi and LTE\/5G NR. For example, in LTE, to estimate the channel states, the node may transmit a peak on each antenna in the De-Modulation Reference Signal (DMRS). A cutoff method can be used, i.e., the signal around each peak can be carved out for each transmitter. However, the tail of each peak will be cutoff, leading to errors. In contrast, given the same received signal, ParEst estimates the CSI from all antennas jointly by solving an optimization problem. The complexity is very low, because the base sinusoids are on fixed frequencies and many steps are precomputed. <\/p>\n\n\n\n

ParEst is much more accurate and efficient than the existing methods for estimating the CSI from multiple antennas. As MIMO and MU-MIMO have become increasingly more important in Wi-Fi and cellular networks such as LTE and 5G NR, ParEst is expected to have a significant impact in wireless commutation and networking.<\/p>\n<\/div>\n\n\n\n

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Publication <\/h5>\n\n\n\n