Advances and New Applications of Spectral Analysis

Thumbnail Image

Files

isbn9789526414218.pdf (498.14 KB)

URL

Journal Title

Journal ISSN

Volume Title

School of Electrical Engineering | Doctoral thesis (article-based) | Defence date: 2023-10-16
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.

Authors

Date

2023

Department

Informaatio- ja tietoliikennetekniikan laitos
Department of Information and Communications Engineering

Major/Subject

Mcode

Degree programme

Language

en

Pages

62 + app. 98

Series

Aalto University publication series DOCTORAL THESES, 142/2023

Abstract

Spectral analysis is a mathematical tool for modeling signals and extracting information from signals. Among the areas where it finds applications are radar, sonar, speech processing, and communication systems. One of applications of spectral analysis is to model random signals with the so-called rational models like autoregressive (AR) model. Spectral analysis is used for estimating direction-of-arrival (DOA) in array processing as well. In addition, spectral analysis is used for channel estimation in communication systems such as massive/mmWave MIMO systems. It is because channels in these systems can be modeled with multipaths' gains and angles. This thesis proposes methods for the problems of noisy AR parameter estimation, DOA estimation in unknown noise fields, and massive/mmWave MIMO channel estimation and data detection with one-bit ADCs. The AR model offers a flexible yet simple tool for modeling complex signals. In practical scenarios, the observation noise may contaminate the AR signal. In this thesis, five methods for estimating noisy AR parameter estimation are proposed. In developing the methods, the concepts such as eigendecomposition (ED) and constrained minimization are used. The most common assumption about the structure of the observation noise in DOA estimation problem is the uniform noise assumption. According to it, the noise covariance matrix is a scaled identity matrix. However, other noise covariance matrix structures such as nonuniform or block-diagonal are more accurate in some practical situations. A generalized least-squares (GLS)-based DOA estimator that takes into consideration the signal subspace perturbation and also enjoys a properly designed DOA selection strategy is proposed for the case of uniform sensor noise. For the case of nonuniform noise, a non-iterative subspace-based (NISB) method is developed which is computationally efficient compared to state-of-the-art competitors. Moreover, a unified approach to DOA estimation in uniform, nonuniform, and block-diagonal sensor noise is presented. The use of one-bit ADCs instead of high-resolution ADCs is considered as an elegant solution for reducing power consumption of large-scale systems such as massive/mmWave MIMO and radar systems. In this thesis, we use the analogy between binary classification problem and one-bit parameter estimation to develop algorithms for massive MIMO and mmWave systems. In this regard, a method called SE-TMR is developed for one-bit mmWave UL channel estimation. Another method called L1-RLR-TMR is also offered for one-bit mmWave UL channel estimation. At last, the concept of AdaBoost is combined with Gaussian discriminant analysis (GDA) for developing computationally very efficient channel estimators and data detectors. It is shown that the proposed methods which use approximate versions of GDA as weak classifiers in iterations of the AdaBoost-based algorithms are exceptionally efficient, specifically in large-scale systems.

Description

Supervising professor

Vorobyov, Sergiy A. Prof., Aalto University, Department of Signal Processing and Acoustics, Finland

Keywords

autoregressive signals, noisy observation, DOA estimation, nonuniform and block-diagonal noise, one-bit ADC, channel estimation, data detection

Other note

Parts

  • [Publication 1]: M. Esfandiari, S. A. Vorobyov, and M. Karimi. New estimation methods for autoregressive process in the presence of white observation noise. Signal Processing, vol. 171, pp. 1-11, Art. no. 107480, 2020.
    DOI: 10.1016/j.sigpro.2020.107480 View at publisher
  • [Publication 2]: M. Esfandiari, S. A. Vorobyov, and M. Karimi. Non-iterative Subspace-based Method for Estimating AR Model Parameters in the Presence of White Noise with Unknown Variance. In Proceedings of the 56th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, November 2019.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202306053543
    DOI: 10.1109/IEEECONF44664.2019.9048977 View at publisher
  • [Publication 3]: M. Esfandiari, S. A. Vorobyov, S. Alibani, and M. Karimi. Non-Iterative Subspace-Based DOA Estimation in the Presence of Nonuniform Noise. IEEE Signal Processing Letters, vol. 26(6), pp. 848-852, June 2019.
    DOI: 10.1109/LSP.2019.2909587 View at publisher
  • [Publication 4]: M. Esfandiari and S. A. Vorobyov. Enhanced Standard ESPRIT for overcoming Imperfections in DOA Estimation. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada, pp. 4375-4379, June 2021.
    DOI: 10.1109/ICASSP39728.2021.9413726 View at publisher
  • [Publication 5]: M. Esfandiari and S. A. Vorobyov. A Novel Angular Estimation Method in the Presence of Nonuniform Noise. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, pp. 5023-5027, May 2022.
    DOI: 10.1109/ICASSP43922.2022.9747777 View at publisher
  • [Publication 6]: M. Esfandiari, S. A. Vorobyov, and R. W. Heath Jr.. Sparsity Enforcing with Toeplitz Matrix Reconstruction Method for mmWave UL Channel Estimation with One-bit ADCs. In Proceedings of the IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM) , Trondheim, Norway, pp. 141-145, June 2022.
    DOI: 10.1109/SAM53842.2022.9827806 View at publisher
  • [Publication 7]: M. Esfandiari, S. A. Vorobyov, and R. W. Heath Jr.. ADMM-Based Solution for mmWave UL Channel Estimation with One-Bit ADCs via Sparsity Enforcing and Toeplitz Matrix Reconstruction. In Proc. 57th IEEE Int. Conf. Communications, IEEE ICC’23, Rome, Italy, May 2023
  • [Publication 8]: M. Esfandiari and S. A. Vorobyov. A Unified Approach to DOA Estimation in Unknown Noise Fields Using ULA. , 18 pages, submitted to a journal, April 2023
  • [Publication 9]: M. Esfandiari, S. A. Vorobyov, and R. W. Heath Jr.. AdaBoost-Based Efficient Channel Estimation and Data Detection in One-Bit Massive MIMO., 10 pages, submitted to a journal, June 2023

Citation

Permanent link to this item

https://urn.fi/URN:ISBN:978-952-64-1421-8

Collections

[diss] Sähkötekniikan korkeakoulu / ELEC