Monte Carlo based channel estimation and evaluation for 5G millimeter-wave systems
type: CreativeWork
description: Current wireless communications systems utilizing frequencies below 6 GHz are quickly becoming unable to meet consumer needs due to limited bandwidth. The need for ever increasing data throughput for wireless systems is pushing systems to begin using higher millimeter-wave frequencies to provide frequency bandwidths that are orders of magnitude larger than those operating at the lower frequencies. Operating at millimeter-wave frequencies brings along new challenges that do not exist at lower frequencies such as mass produced hardware that is physically smaller to overcome the larger free space loss which can bring increase uncertainty in channels and hardware components. The uncertainty increases in these systems must be corrected by channel estimators to correctly receive data. Channel estimators are e↵ected by the increased hardware error and their efficacy is therefore reduced. This in turn increases the error in received data and reduces the throughput of a communications system. A multitude of channel estimation techniques have been developed in an attempt to reduce bit errors in the received data and maximize the system throughput. Methods can be as basic as performing division and averaging of known transmitted values to estimate the channel. Other channel estimators have been designed using techniques ranging from machine learning to optimization and minimization. None of these estimators use responses and uncertainties of hardware components that are typically provided by manufacturers. By directly using known hardware responses and any corresponding uncertainties, a channel estimator can improve its estimation. The research outlined here first provides a framework for the consistent evaluation and comparison of estimators in millimeter-wave systems and second develops an improved channel estimator that leverages known hardware responses and their uncertainties. The comparison between estimators is important to select the best estimator for a scenario befor…
abstract: Current wireless communications systems utilizing frequencies below 6 GHz are quickly becoming unable to meet consumer needs due to limited bandwidth. The need for ever increasing data throughput for wireless systems is pushing systems to begin using higher millimeter-wave frequencies to provide frequency bandwidths that are orders of magnitude larger than those operating at the lower frequencies. Operating at millimeter-wave frequencies brings along new challenges that do not exist at lower frequencies such as mass produced hardware that is physically smaller to overcome the larger free space loss which can bring increase uncertainty in channels and hardware components. The uncertainty increases in these systems must be corrected by channel estimators to correctly receive data. Channel estimators are e↵ected by the increased hardware error and their efficacy is therefore reduced. This in turn increases the error in received data and reduces the throughput of a communications system. A multitude of channel estimation techniques have been developed in an attempt to reduce bit errors in the received data and maximize the system throughput. Methods can be as basic as performing division and averaging of known transmitted values to estimate the channel. Other channel estimators have been designed using techniques ranging from machine learning to optimization and minimization. None of these estimators use responses and uncertainties of hardware components that are typically provided by manufacturers. By directly using known hardware responses and any corresponding uncertainties, a channel estimator can improve its estimation. The research outlined here first provides a framework for the consistent evaluation and comparison of estimators in millimeter-wave systems and second develops an improved channel estimator that leverages known hardware responses and their uncertainties. The comparison between estimators is important to select the best estimator for a scenario befor…
author: Weiss, Alec
language: eng
language: English
keywords: channel estimation, millimeter-wave, Monte Carlo, orthogonal frequency division multiplexing, phased arrays
identifier: T 9372
date published: 2022
date modified: 2026-07-24T01:43:42Z
provider: Colorado School of Mines
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Colorado School of Mines. Arthur Lakes Library

PropertyValue
property ID: Handle
value: https://hdl.handle.net/11124/15464