The performance of multipath-enhanced device-free localization severely depends on the information about the propagation paths within the network. While known for the line-of-sight, the propagation paths have yet to be determined for multipath components. This work provides a novel Bayesian calibration approach for determining the propagation paths by estimating reflection points. Therefore, first a statistical fading model is presented, that describes user-induced changes in the received signal of multipath components. The model is derived and validated empirically using an extensive set of wideband and ultra-wideband measurement data. Second, the Bayesian approach is presented, which, based on the derived empirical fading model, relates measured changes in the power of a multipath component to the location of the reflection point. Exploiting the geometric properties of multipath components caused by single-bounce reflections, the solution space of possible locations of reflection points is constrained to the delay ellipse. Thus, a one-dimensional elliptic estimation problem can be formulated, which is solved using a point mass filter. The applicability of the proposed approach is demonstrated and evaluated based on measurement data. Independent of the underlying measurement system, the Bayesian calibration approach is shown to robustly estimate the locations of the reflection points in different environments.
Robust and Fast Automatic Modulation Classification with CNN under Multipath Fading Channels
Informatics and Information Security Research Center (BİLGEM), TÜBİTAK（土耳其科学技术研究理事会信息学与信息安全研究中心）；Istanbul Technical University（伊斯坦布尔技术大学）；Balıkesir University（巴勒克埃西尔大学）；Yıldız Technical University（耶尔德兹技术大学）；Boğaziçi University（海峡大学）
Joint Random Access and Localization in Cell-Free User-Centric Networks with Frequency-Selective Fading Channels
Communications and Information Theory Chair (CommIT), Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin（柏林工业大学电气与计算机科学学院通信与信息论讲席）
Nonparametric Variational Bayesian Learning for Channel Estimation with OTFS Modulation
School of Information Science and Engineering, Shandong University（山东大学信息科学与工程学院）；Aerospace System Engineering Shanghai（上海航天系统工程技术研究所）；School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Department of Information Engineering, the Chinese University of Hong Kong（香港中文大学信息工程系）
