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<!DOCTYPE html>
<html>
<div class="topnav">
<a href="/"><i class="fas fa-home"></i> Home</a>
<a href="/undergrad"><i class="fas fa-user-graduate"></i> Undergrad</a>
<a class="active" href="/graduate"><i class="fas fa-university"></i> Graduate School</a>
<a href="/research"><i class="fab fa-leanpub"></i> Research</a>
<!-- <a href="/activities"><i class="fas fa-football-ball"></i> Activities</a> -->
<a href="/professional"><i class="fas fa-award"></i> Resume/CV</a>
<a href="/contact"><i class="far fa-address-book"></i> Contact</a>
<a href="/tutorials"><i class="fas fa-chalkboard-teacher"></i></i> Tutorials</a>
</div>
<head>
<script src="https://kit.fontawesome.com/9d2d5bf903.js" crossorigin="anonymous"></script>
<link rel="stylesheet" href="styles.css">
</head>
<body>
<div id="page-container">
<h1> Graduate School</h1>
<div class="row" style="padding-top:20px;">
<div class="column left-middle">
<img class="logo" src="figs/UT_Logo.png" style="width:40%" alt="UT">
</div>
<div class="column right-middle">
<img class="logo" src="figs/NCSU.png" style="padding-top:20px" alt="NCSU">
</div>
</div>
<div class="row">
<div class="column left">
<h2 align="center"> Fast Facts </h2>
<ul>
<li>
<li>Supervised by Prof. Robert W Heath Jr. </li>
<li>Researching ML-assisted wireless communications</li>
<li>SPAWC 2020 Special Session Accepted Paper </li>
<li>Asilomar 2020 Special Session Accepted Paper </li>
<li>ICASSP 2021 Special Session Accepted Paper </li>
<li>Paper accepted in IEEE Trans. Signal Proc. </li>
<li>VTC 2022 paper accepted</li>
<li>Paper accepted in IEEE Communications Magazine </li>
<li>SPAWC 2023 Accepted Paper </li>
<li>IEEE TWC paper in submission </li>
</ul>
</div>
<div class="column middle">
<h2 align="center"> Graduate Degrees </h2>
<p style="font-size:19px">
I currently attend <strike>the University of Texas at Austin</strike> North Carolina State University, where I am pursuing a PhD
supervised by <a href="http://ncsu.mimowireless.com/" target="_blank"> Prof. Robert W. Heath Jr</a>.
My primary research is integrating signal processing and machine learning for wireless
communications.
<!-- http://www.profheath.org/ -->
</p>
<p>
I finished my first year, and I can safely say it was a huge year for me.
I took foundational courses in probability, estimation, and machine learning.
In spring, I took a course on statistical machine learning, which was both
incredibly interesting and quite tough. My research kicked off well, with
"Deep Learning-based Carrier Frequency Offset Estimation with 1-Bit ADCs"
accepted to <a href="https://spawc2020.netlify.app/">SPAWC 2020</a>.
I was able to give a recorded presentation of that paper as well, which are two
big milestones in my research career. I was also intended to intern at
<a href="https://www.ll.mit.edu/">MIT Lincoln Labs</a>,
however, that was canceled as a result of COVID-19. Luckily, the wireless team
at <a href="https://connectivity.fb.com/">Facebook</a> was looking for
assistance with simulations and AI for developing Open Radio Access
Network (ORAN) functionality.
My work with Facebook was fruitful and led to continued
collaboration and support. We published the first of our work on coverage
and capacity optimization (CCO) in ICASSP 2021, which I presented in a recorded format.
This paper was especially interesting for considering the Pareto Frontier of
CCO as well as comparing Bayesian Optimization and deep reinforcement learning.
Later that year, I completed my master's degree at UT and have submitted my first
journal paper on multi-sinusoidal parameter estimation from low resolution sampling.
We proposed a novel neural architecture that uses successive estimation and cancellation
to produce an effecient and powerful estimator that outperforms traditional methods.
We also used this work to present a simple learning heuristic: the "learning threshold".
This statistcal measure determines what level of generalization or feature learning
that a neural network achieves compared to simple distributional learning.
In my third (current) year, I have been working on codebook design and become
very familiar with 5G NR beam management and feedback. This work is still waiting
to be published at VTC 2022 and a future journal, so stay tuned!
</p>
<h4> Deep Learning-based Carrier Frequency Offset Estimation with 1-Bit ADCs </h4>
<i> Ryan M. Dreifuerst, Robert W. Heath Jr, Mandar Kulknari, Jianzhong (Charlie) Zhang </i>
<p style="font-size:15px">
Low resolution architectures are a power efficient solution for high bandwidth communication at millimeter wave
and TeraHertz frequencies. In such systems, carrier synchronization is important yet has not received much attention in prior
work. In this paper, we develop and analyze deep learning architectures for estimating the carrier frequency of a complex sinusoid
in noise from the 1-bit samples of the in-phase and quadrature components. Carrier frequency offset estimation from a sinusoid
is used in GSM and is a first step towards developing a more comprehensive solution with other kinds of signals.
We train four different deep learning architectures each on five datasets which represent possible training considerations.
Specifically, we consider how training with various signal to noise ratios (SNR), quantization, and sequence lengths affects
estimation error. Further, we compare each architecture in terms of scalability for MIMO receivers. In simulations, we compare
computational complexity, scalability, and mean squared error versus classic signal processing techniques. We conclude that
training with quantized data, drawn from signals with SNR between 0-10dB tends to improve deep learning estimator performance
across the range of interest. We conclude that convolutional models have the best performance, while also scaling for
massive MIMO situations more efficiently than FFT models. Our approach is able to accurately estimate carrier frequencies
from 1-bit quantized data with fewer pilots and lower signal to noise ratios (SNRs) than traditional signal processing methods.
</p>
<h4> Frequency Synchronization for Low Resolution Millimeter-Wave </h4>
<i> Ryan M. Dreifuerst, Robert W. Heath Jr, Mandar Kulknari, Jianzhong (Charlie) Zhang </i>
<p style="font-size:15px">
Low resolution data converters can enable power efficient high bandwidth
communication at millimeter-wave and terahertz frequencies.
Synchronization of such systems is a critical step in accurate decoding,
yet current approaches require long block lengths or fail to reach the
Cram{\'e}r Rao Bound (CRB). Prior solutions have traditionally been
divided into two distinct focuses: algorithms and designed sequences
for synchronization. In this paper, we develop a jointly optimized
neural architecture for frequency synchronization from configurable
sequences and estimators. Our proposed technique uses two neural
networks to generate sequences and determine the carrier frequency
offset of the sequence after propagating through a channel and applying
one-bit quantization. Our simulations show that we can improve estimation
performance at low signal to noise ratio (SNR) by up to 8dB at little
cost compared to the same estimator without the sequence generator.
Our proposed system is fast, efficient, and easily updated, allowing it
to handle time-varying systems. In conclusion, we believe further
investigation in jointly optimized pilot sequences and estimators will
be fundamental to handling signal processing techniques with low
resolution data converters. </p>
<h4> Optimizing Coverage and Capacity in Cellular Networks using Machine Learning </h4>
<i> Ryan M. Dreifuerst et. al</i>
<p style="font-size:15px">
Wireless cellular networks have many parameters that are normally tuned upon
deployment and re-tuned as the network changes. Many operational parameters
affect reference signal received power (RSRP), reference signal received
quality (RSRQ), signal-to-interference-plus-noise-ratio (SINR), and,
ultimately, throughput. In this paper, we develop and compare two approaches
for maximizing coverage and minimizing interference by jointly optimizing
the transmit power and downtilt (elevation tilt) settings across sectors.
To evaluate different parameter configurations offline, we construct a
realistic simulation model that captures geographic correlations. Using this
model, we evaluate two optimization methods: deep deterministic policy
gradient (DDPG), a reinforcement learning (RL) algorithm, and multi-objective
Bayesian optimization (BO). Our simulations show that both approaches
significantly outperform random search and converge to comparable Pareto
frontiers, but that BO converges with two orders of magnitude fewer
evaluations than DDPG. Our results suggest that data-driven techniques can
effectively self-optimize coverage and capacity in cellular networks. </p>
<h4> SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network </h4>
<i> Ryan M. Dreifuerst and Robert W. Heath Jr</i>
<p style="font-size:15px">
The detection and estimation of sinusoids is a fundamental signal
processing task for many applications related to sensing and
communications. While algorithms have been proposed for this setting,
quantization is a critical, but often ignored modeling effect. In
wireless communications, estimation with low resolution data converters
is relevant for reduced power consumption in wideband receivers. Similarly,
low resolution sampling in imaging and spectrum sensing allows for
efficient data collection. In this work, we propose SignalNet, a neural
network architecture that detects the number of sinusoids and estimates
their parameters from quantized in-phase and quadrature samples. We
incorporate signal reconstruction internally as domain knowledge within
the network to enhance learning and surpass traditional algorithms in mean
squared error and Chamfer error. We introduce a worst-case learning
threshold for comparing the results of our network relative to the
underlying data distributions. This threshold provides insight into why
neural networks tend to outperform traditional methods and into the
learned relationships between the input and output distributions. In
simulation, we find that our algorithm is always able to surpass the
threshold for three-bit data but often cannot exceed the threshold for
one-bit data. We use the learning threshold to explain, in the one-bit
case, how our estimators learn to minimize the distributional loss,
rather than learn features from the data.
</p>
</div>
<div class="column right">
<h2 align="center"> Courses of Note </h2>
<p>
<table align="center">
<b> <tr>
<th> Course Name </th>
<th> Course Code </th>
<th> Credit Hours </th>
<th> Grade </th>
</tr> </b>
<!-- Lists the courses here -->
<tr>
<td> Probability and Stochastic Processes </td>
<td> EE 381J </td>
<td> 3.0 </td>
<td> A </td>
</tr>
<tr>
<td> Statistical Estimation Theory </td>
<td> ASE 381P-6 </td>
<td> 3.0 </td>
<td> A </td>
</tr>
<tr>
<td> Statistical Machine Learning </td>
<td> EE 381V </td>
<td> 3.0 </td>
<td> A- </td>
</tr>
<tr>
<td> Digital Communications </td>
<td> EE 381K-2 </td>
<td> 3.0 </td>
<td> A+ </td>
</tr>
<tr>
<td> Data Mining </td>
<td> EE 380L-10 </td>
<td> 3.0 </td>
<td> A </td>
</tr>
<tr>
<td> Autonomous Robots </td>
<td> CS 393R </td>
<td> 3.0 </td>
<td> A </td>
</tr>
<tr>
<td> Convex Optimization </td>
<td> EE 381K-18 </td>
<td> 3.0 </td>
<td> B+ </td>
</tr>
<tr>
<td> Wireless Communications </td>
<td> EE 381K-11 </td>
<td> 3.0 </td>
<td> A+ </td>
</tr>
<tr>
<td> Space-Time Communication Theory </td>
<td> ECE 792 </td>
<td> 3.0 </td>
<td> A+ </td>
</tr>
<tr>
<td> Machine Learning for Adv. MIMO Sys.</td>
<td> ECE 592 </td>
<td> 3.0 </td>
<td> A+ </td>
</table>
</p>
</div>
</div>
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