| Date | 2021.3.31 (16：00 - 17：30) |
|---|---|
| Venue | |
| Title | ※On-line Seminar via Zoom and then Google Classroom later. https://zoom.us/j/99606209170?pwd=TVJoN1drVHlaZ0NKRDZOSGo0Y0xjZz09 Meeting ID: 996 0620 9170 Pass Code: 7KGNq? ----------------------------------------------------------------------------------------------- ＜Program＞ ◆16:00-16:45 Speaker：Dr. Janne Lehtomaki (Adjunct Professor, Centre for Wireless Communications, University of Oulu, Finland) Title："Channel Estimation and Data Decoding Analysis of Massive MIMO with 1-Bit ADCs" 〈Abstract〉 Energy detection approach is popular for spectrum occupancy measurements due to its simplicity. One problem with energy detection is how to find the detection threshold as it depends on the noise variance which is typically unknown. The forward consecutive mean excision algorithm (FCME) is one method that can be used for threshold setting for energy detection. It can be applied in time and/or frequency domains. In this talk, analysis of the FCME is performed and its applications to spectrum occupancy measurements are discussed. For example, it is presented how to use large probabilities of false alarm with the FCME method in order to improve its signal detection performance. ◆16:45-17:30 Speaker : Dr. Antti Tölli (Associate Professor, Centre for Wireless Communications, University of Oulu, Finland) Title： "Channel Estimation and Data Decoding Analysis of Massive MIMO with 1-Bit ADCs" 〈Abstract〉 We present an analytical framework for the channel estimation and the data decoding in massive multiple-input multiple-output uplink systems with 1-bit analog-to-digital converters (ADCs). First, we provide a closed-form expression of the mean squared error of the channel estimation for a general class of linear estimators. In addition, we propose a novel linear estimator with significantly enhanced performance compared with existing estimators with the same structure. For the data decoding, we provide closed-form expressions of the expected value and the variance of the estimated symbols when maximum ratio combining is adopted, which can be exploited to efficiently implement maximum likelihood decoding and, potentially, to design the set of transmit symbols. Comprehensive numerical results are presented to study the performance of the channel estimation and the data decoding with 1-bit ADCs with respect to the signal-to-noise ratio (SNR), the number of user equipments, and the pilot length. The proposed analysis highlights a fundamental SNR trade-off, according to which operating at the right noise level significantly enhances the system performance. |
| Language | English |
| Intended for | Everyone is welcome to join. |
| Co-Organized by | Institute of Global Innovation “Food” Umebayashi Team Excellent Leader Development for Super Smart Society by New Industry Creation and Diversity |
| Contact | Institute of Global Innovation Research, Institute of Engineering Assoc. Prof. Kenta Umebayashi e-mail: ume_k (at) cc.tuat.ac.jp |
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