Browsing by Author "Zhou, Zhou"
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- Data analysis for characterization of IG110 and A3 by X-Ray diffraction and Raman spectroscopyWu, Huali; Gakhar, Ruchi; Chen, Allen; Zhou, Zhou; Scarlat, Raluca O. (2020-10)This article contains data related to the research journal paper titled 'Comparative Analysis of Microstructure and Reactive Sites for Nuclear Graphite IG-110 and Graphite Matrix A3", Journal of Nuclear Materials 528 (2020) 151802. This article includes details of the calculation process of the crystallite edge area, additional tables and figures of XRD and Raman data, and additional summary of data reduction methods used in prior literature for the characterization of IG-110 nuclear graphite. Reduced data associated with this article is provided in the supplementary information. Raw data associated with this article is in the supplementary material of the companion article. (C) 2020 Published by Elsevier Inc.
- Machine Learning-Based Receiver in Multiple Input Multiple Output Communications SystemsZhou, Zhou (Virginia Tech, 2021-08-10)Bridging machine learning technologies to multiple-input-multiple-output (MIMO) communications systems is a primary driving force for next-generation wireless systems. This dissertation introduces a variety of neural network structures for symbol detection/equalization tasks in MIMO systems configured with two different waveforms, orthogonal frequency-division multiplexing (OFDM) and orthogonal time frequency and space (OTFS). The former one is the major air interface in current cellular systems. The latter one is developed to handle high mobility. For the sake of real-time processing, the introduced neural network structures are incorporated with inductive biases of wireless communications signals and operate in an online training manner. The utilized inductive priors include the shifting invariant property of quadrature amplitude modulation, the time-frequency relation inherent in OFDM signals, the multi-mode feature of massive antennas, and the delay-Doppler representation of doubly selective channel. In addition, the neural network structures are rooted in reservoir computing - an efficient neural network computational framework with decent generalization performance for limited training datasets. Therefore, the resulting neural network structures can learn beyond observation and offer decent transmission reliability in the low signal-to-noise ratio (SNR) regime. This dissertation includes comprehensive simulation results to justify the effectiveness of the introduced NN architectures compared with conventional model-based approaches and alternative neural network structures.
- MIMO-OFDM Symbol Detection via Echo State NetworksZhou, Zhou (Virginia Tech, 2019-10-30)Echo state network (ESN) is a specific neural network structure composed of high dimensional nonlinear dynamics and learned readout weights. This thesis considers applying ESN for symbol detection in multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. A new ESN structure, namely, windowed echo state networks (WESN) is introduced to further improve the symbol detection performance. Theoretical analysis justifies WESN has an enhanced short-term memory (STM) compared with the standard ESN such that WESN can offer better computing ability. Additionally, the bandwidth spent as the training set is the same as the demodulation reference signals defined in 3GPP LTE/LTE-Advanced systems for the ESN/WESN based symbol detection. Meanwhile, a unified training framework is developed for both comb and scattered pilot patterns. Complexity analysis demonstrates the advantages of ESN/WESN based symbol detector compared to conventional symbol detectors such as linear minimum mean square error (LMMSE) and sphere decoder when the system is employed with a large number of OFDM sub-carriers. Numerical evaluations show that ESN/WESN has an improvement of symbol detection performance as opposed to conventional methods in both low SNR regime and power amplifier (PA) nonlinear regime. Finally, it demonstrates that WESN can generate a better symbol detection result over ESN.