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Channel Estimation in TDD and FDD-Based Massive MIMO Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Channel Estimation in TDD and FDD-Based Massive MIMO Systems./
作者:	Mirzaei, Javad.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	160 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: A.
Contained By:	Dissertations Abstracts International83-02A.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28416856
ISBN:	9798522944100

Channel Estimation in TDD and FDD-Based Massive MIMO Systems.
Mirzaei, Javad.

Channel Estimation in TDD and FDD-Based Massive MIMO Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 160 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: A.

Thesis (Ph.D.)--University of Toronto (Canada), 2021.

This item must not be sold to any third party vendors.

There are three parts to this thesis. In the first part, we study the channel estimation problem in frequency-selective multi-user (MU) multi-cell massive multiple-input multiple-output (MIMO) systems, where, a time-domain semi-blind channel estimation technique is proposed. Compared to frequency-domain, the time-domain channel estimation requires fewer parameters be estimated. Importantly, the time-domain estimation has enough samples for an accurate channel estimate. Given this many samples in the time-domain, the proposed channel estimation technique obtains a better estimate of the channel. Here, there is no assumption on orthogonality of users' channels, knowledge of large-scale fading coefficients, and the orthogonality between the training symbols of the users in all cells.The second part of the thesis studies the channel estimation problem in correlated massive MIMO systems with a reduced number of radio-frequency (RF) chains. Leveraging the knowledge of channel correlation matrices, we propose to estimate the channel entries in its eigen-domain. Due to the limited number of RF chains, channel estimation is typically performed in multiple time slots. Using the minimum mean squared error (MMSE) criterion, the optimal precoder and combiner in each time slot are aligned to transmitter and receiver eigen-directions, respectively. Meanwhile, the optimal power allocation for each training time slots is obtained via a waterfilling-type expression.In the final part, we study the downlink channel estimation for frequency-division-duplex (FDD) massive MIMO systems. Acquiring downlink channel state information in these systems is challenging due to the large training and feedback overhead. Motivated by the partial reciprocity of uplink and downlink channels, we first estimate the frequency-independent channel parameters, i.e., the path gains, delays, angles-of-arrivals (AoAs) and angles-of-departures (AoDs), via uplink training, since these parameters are common in both uplink and downlink. Then, the frequency-specific channel parameters are estimated via downlink training using a very short training signal. To efficiently estimate the channel parameters in the uplink, the underlying distribution of the channel parameters is incorporated as a prior into our estimation algorithm. This distribution is captured using deep generative models (DGMs). The proposed channel estimation technique significantly outperforms the conventional channel estimation techniques in practical ranges of signal-to-noise ratio (SNR).

ISBN: 9798522944100Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Deep generative model

Channel Estimation in TDD and FDD-Based Massive MIMO Systems.
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520 $a There are three parts to this thesis. In the first part, we study the channel estimation problem in frequency-selective multi-user (MU) multi-cell massive multiple-input multiple-output (MIMO) systems, where, a time-domain semi-blind channel estimation technique is proposed. Compared to frequency-domain, the time-domain channel estimation requires fewer parameters be estimated. Importantly, the time-domain estimation has enough samples for an accurate channel estimate. Given this many samples in the time-domain, the proposed channel estimation technique obtains a better estimate of the channel. Here, there is no assumption on orthogonality of users' channels, knowledge of large-scale fading coefficients, and the orthogonality between the training symbols of the users in all cells.The second part of the thesis studies the channel estimation problem in correlated massive MIMO systems with a reduced number of radio-frequency (RF) chains. Leveraging the knowledge of channel correlation matrices, we propose to estimate the channel entries in its eigen-domain. Due to the limited number of RF chains, channel estimation is typically performed in multiple time slots. Using the minimum mean squared error (MMSE) criterion, the optimal precoder and combiner in each time slot are aligned to transmitter and receiver eigen-directions, respectively. Meanwhile, the optimal power allocation for each training time slots is obtained via a waterfilling-type expression.In the final part, we study the downlink channel estimation for frequency-division-duplex (FDD) massive MIMO systems. Acquiring downlink channel state information in these systems is challenging due to the large training and feedback overhead. Motivated by the partial reciprocity of uplink and downlink channels, we first estimate the frequency-independent channel parameters, i.e., the path gains, delays, angles-of-arrivals (AoAs) and angles-of-departures (AoDs), via uplink training, since these parameters are common in both uplink and downlink. Then, the frequency-specific channel parameters are estimated via downlink training using a very short training signal. To efficiently estimate the channel parameters in the uplink, the underlying distribution of the channel parameters is incorporated as a prior into our estimation algorithm. This distribution is captured using deep generative models (DGMs). The proposed channel estimation technique significantly outperforms the conventional channel estimation techniques in practical ranges of signal-to-noise ratio (SNR).
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650 4 $a Artificial intelligence. $3 516317
650 4 $a Computer science. $3 523869
650 4 $a Wireless networks. $3 1531264
650 4 $a Simulation. $3 644748
650 4 $a Signal to noise ratio. $3 3681178
650 4 $a Communication. $3 524709
650 4 $a Neural networks. $3 677449
650 4 $a Signal processing. $3 533904
650 4 $a Antennas. $3 3681648
650 4 $a Pilots. $3 3681649
650 4 $a Efficiency. $3 753744
653 $a Deep generative model
653 $a Frequency division duplex
653 $a Hybrid beamforming
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653 $a Time-division duplex
653 $a Frequency-division duplex
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