# Review of the mechanisms of SARS-CoV-2 evolution and transmission

Jiahui Chen1, Rui Wang1111The first two authors contribute equally., and Guo-
Wei Wei1,2,3222 Corresponding author. E-mail<EMAIL_ADDRESS>
1 Department of Mathematics,
Michigan State University, MI 48824, USA.
2 Department of Electrical and Computer Engineering,
Michigan State University, MI 48824, USA.
3 Department of Biochemistry and Molecular Biology,
Michigan State University, MI 48824, USA.

###### Abstract

The mechanism of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
evolution and transmission is elusive and its understanding, a prerequisite to
forecast emerging variants, is of paramount importance. SARS-CoV-2 evolution
is driven by the mechanisms at molecular and organism scales and regulated by
the transmission pathways at the population scale. In this review, we show
that infectivity-based natural selection was discovered as the mechanism for
SARS-CoV-2 evolution and transmission in July 2020. In April 2021, we proved
beyond all doubt that such a natural selection via infectivity-based
transmission pathway remained the sole mechanism for SARS-CoV-2 evolution.
However, we reveal that antibody-disruptive co-mutations [Y449S, N501Y] on the
spike protein receptor-binding domain (RBD) debuted as a new vaccine-resistant
transmission pathway of viral evolution in highly vaccinated populations a few
months ago. Over one year ago, we foresaw that mutations on RBD residues, 452
and 501, would “both have high chances to mutate into significantly more
infectious COVID-19 strains”. Mutations on these residues underpin prevailing
SARS-CoV-2 variants Alpha, Beta, Gamma, Delta, Epsilon, Theta, Kappa, Lambda,
and Mu at present and are expected to be vital to emerging variants in the
future. We anticipate that viral evolution will combine RBD co-mutations at
these two sites, creating future variants that are about ten times more
infectious than the original SARS-CoV-2. Additionally, two complementary
transmission pathways of viral evolution, i.e., infectivity and vaccine
resistance will prolong our battle with COVID-19 for years. We predict that
RBD co-mutation sets [A411S, L452R, T478K], [L452R, T478K, N501Y], [L452R,
T478K, E484K, N501Y], [K417N, L452R, T478K], and [P384L, K417N, E484K, N501Y]
will have a high chance to grow into dominating variants due to their high
infectivity and/or strong ability to break through current vaccines, calling
for the development of new vaccines and antibody therapies.

Key words: mechanism of evolution, transmission pathway, mutation,
infectivity, vaccine-resistant, binding affinity change, deep learning,
persistent homology.

##  1 Introduction

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to over 225 million
confirmed cases and over 4.6 million fatalities. Researchers have been racing
against the devastation of COVID-19 in the past 22 months. Although there were
two earlier outbreaks of deadly pneumonia caused by $\beta$-coronaviruses:
SARS-CoV (2002) and Middle East respiratory syndrome coronavirus (MERS-CoV)
(2012) in the 21st century, SARS-CoV-2 astonished the unprepared scientific
community. Currently, Google Scholar has accumulated 188,000 items under
“COVID-19” and 139,000 items under “SARS-CoV-2” since 2020. Nonetheless, the
scientific community still does not know enough about SARS-CoV-2 and COVID-19
22 months after the outbreak of the pandemic. One of the greatest challenges
of our time is the understanding of the mechanisms of SARS-CoV-2 evolution and
transmission [4, 10, 15]. The importance of such an understanding cannot be
overemphasized. It is a prerequisite to forecast the emerging SARS-CoV-2
variants, which, in turn, is an imperative for the design of the next
generation of mutation-proof vaccines and monoclonal antibodies (mAbs).

SARS-CoV-2 encodes 29 genes and its genome has about 29,900 nucleotides. As
illustrated in our Mutation Tracker (see Figure 1), nearly 29,900 mutations
are essentially evenly distributed on the whole viral genome, showing a
confounding pattern. Additionally, the mutagenesis of SARS-CoV-2 genomes is
driven by a large number of competing processes, including molecular-scale
random shifts, replication errors, transcription errors, translation errors,
proofreading, and recombination, organism scale host editing induced by the
immune response and host-viral recombination, and population-scale natural
selection. Moreover, discrepancies among reported experimental binding free
energies can be over 100 fold for viral spike (protein) receptor-binding
domain (RBD) complexes with angiotensin-converting enzyme 2 (ACE2) or
antibodies (see Table 1 of a Ref. [6]), creating a baffling situation. These
perplexing factors make the understanding of viral transmission and evolution
one of the most challenging tasks.

The recent global surge in COVID-19 infections has been fueled by new SARS-
CoV-2 variants, namely Alpha, Beta, Gamma, Delta, Theta, Epsilon, Kappa,
Lambda, Mu, etc. A common feature for these variants is that they all involve
one of two spike (S) protein receptor-binding domain (RBD) residues 452 and
501. The importance of these sites were predicted by us more than one year
ago. We foresaw that residues, 452 and 501, out of 194 RBD residues, “have a
high chance to mutate into significantly more infectious COVID-19 strains”, by
“combining sequence alignment, probability analysis, and binding free energy
calculation” (see the last sentence in the Abstract of Ref. [9]).
Additionally, on page 5218 of the aforementioned paper [9], we also predicted
three lists of RBD mutations: 1149 most likely, 1912 likely, and 625 unlikely
for a total of 3,686 possible RBD mutations. Currently, essentially all 724
observed RBD mutations belong to the list of most likely ones. Finally, in the
same work, we discovered the mechanism of evolution that governs the emergence
of SARS-CoV-2 variants [9]. Specifically, we revealed that RBD mutations are
governed by natural selection, when there were only 89 RBD mutations and their
highest observed frequency was only about 50 globally (see Figure 3 of Ref.
[9]) in June 2020 [9]. Stated differently, mutations that can increase virus
infectivity will prevail. Based on 506,768 sequences isolated from patients,
we demonstrated in April 2021 that the 100 most observed RBD mutations out of
651 known RBD mutations all have their predicted mutation-induced binding free
energy (BFE) changes above the average BFE change of -0.28kcal/mol [34]. The
odds for all these 100 most observed mutations to accidentally have the above-
average BFE changes is smaller than one chance in 1.27 nonillions, i.e.,
1.27$\times 10^{30}$.

Figure 1: Illustration of SARS-CoV-2 mutations given by Mutation Tracker.
Interactive version is available at web site:
https://users.math.msu.edu/users/weig/SARS-CoV-2˙Mutation˙Tracker.html.

The objective of this review is to offer a general introduction of the
mechanisms of SARS-CoV-2 evolution and to forecast emerging vaccine-
breakthrough SARS-CoV-2 variants. We first discuss how mutations impact SARS-
CoV-2 infectivity. The molecular mechanism of viral infectivity and some of
our work on mutation-induced infectivity changes are reviewed. Additionally,
we discuss how the interplay of mechanisms at three scales, i.e., molecular,
organism, and population scales, impact SARS-CoV-2 evolution. We demonstrate
the co-existence of two complementary transmission pathways of viral
evolution, i.e., infectivity and vaccine-resistant. Finally, we review our
studies on mutational impacts to SARS-CoV-2 antibodies and vaccines.

##  2 Mutational impacts on SARS-CoV-2 infectivity

Figure 2: Illustration of SARS-CoV-2 and its interaction with host cell. a
SARS-Cov-2 spike protein and ACE2 interaction. Here, viral spike (S), envelop
(E), membrane (M), and nucleocapsid (N) proteins and host cell TMPRSS2 and
ACE2 are labeled. b SARS-CoV-2 S protein RBD-ACE2 complex (PDB 6M0J) and
important RBD mutations.

SARS-CoV-2 infectivity is a key factor in COVID-19 prevention and control, and
economic reopening. Viral infectivity can be referred to as the viral
infection rate of a population, which depends on disease prevention measures,
such as social distancing, use of masks, quarantine, contact tracing, etc.
However, viral intrinsic infectivity is a more fundamental concept and can be
determined by experiments, i.e., counting the number of viruses in a specific
volume over a unit of time by using either traditional or modern methods [17].
The former includes plaque assay, focus forming assay, endpoint dilution
assay, protein assay, hemagglutination assay, bicinchoninic acid assay, single
radial immunodiffusion assay, and transmission electron microscopy. The latter
has tunable resistive pulse sensing, flow cytometry, quantitative polymerase
chain reaction, and enzyme-linked immunosorbent assay (ELISA) [13, 17].
Traditional methods are generally slow and labor-intensive, while modern
methods can significantly reduce viral quantification time. Among these
methods, ELISA is based on protein-protein interactions (PPIs), such as
antibody-antigen binding events, being counted by chromogenic or fluorescence
reporters. In the studies of SARS-CoV, epidemiological and biochemical studies
show that the infectivity of different SARS-CoV strains in host cells is
proportional to the BFE between the S protein RBD and the ACE2 expressed by
the host cell (see Figure 2) [21, 26, 29, 17, 31]. ACE2 is a single-pass
transmembrane protein with its active domain exposed on the cell surface and
is expressed in the lungs and many other tissues. It is the main cell entry
point for SARS-CoV and SARS-CoV-2, and some other coronaviruses. As shown in
Figure 2, SARS-CoV-2 cell entry is primed by TMPRSS2 (transmembrane serine
protease 2)[17].

Both traditional and modern methods for viral infectivity measurement are
expensive and time-consuming. For the rapidly spreading COVID-19 caused by the
constantly mutating SARS-CoV-2, it is extremely challenging to experimentally
determine the virus infectivity associated with all SARS-CoV-2 variants found
around the world. Consequently, no such comprehensive experimental
quantification program has been established for all RBD mutations so far.

We established a computational platform to quantitatively predict RBD-ACE2 BFE
changes induced by RBD mutations [9]. Our platform consists of the genotyping
of SARS-CoV-2 genetic sequences isolated for patients [36], sequence alignment
[9], the algebraic topology representation of PPIs [2, 24], the deep learning
modeling of mutation-induced RBD-ACE2 BFE changes [39], and the training using
existing mutational datasets. Based on genotyping, we have developed a real-
time interactive Mutation Tracker (see Figure 1) to trace all SARS-CoV-2
mutations from complete genome sequences deposited at the GISIAD database
(https://www.gisaid.org/). We unveiled that numerous mutations on commonly
used SARS-CoV-2 diagnostic targets have caused false-negative test results
[37], which was confirmed experimentally [1]. We decoded the ramification of
mutations on SARS-CoV-2 vaccines, antibody therapies, and drugs [36]. We found
that the top 8 mutations in the United States (US) belong to two groups. One
group consisting of 5 concurrent mutations is prevailing, while the other
group with three concurrent mutations is gradually fading [33].

##  3 Mechanisms of SARS-CoV-2 evolution

Our approach to SARS-CoV-2 infectivity modeling is, by no means, inclusive.
Many potential factors, such as the insertion of multi-basic residues at the
S1/S2 subunit cleavage site [16], the high-frequency mutation D614G [19],
temperature and pH conditions [5], and numerous other hypotheses, have been
extensively studied in the literature since the outbreak of the pandemic.
However, none of these studies leads to the fundamental understanding of SARS-
CoV-2 global transmission trajectory, infection pattern, and evolution
mechanism.

SARS-CoV-2 evolution is an intricate process determined by the interplay among
molecular-scale mechanisms, organism scale mechanisms, and population-scale
mechanisms. Mutagenesis is a basic biological process at the molecular scale
that changes the genetic information of organisms and thus, is a primary
source for viral evolution. SARS-CoV-2 is a positive-strand RNA virus and
belongs to the beta coronavirus genus with a genome size of 29.99 kb. Figure 1
shows that SARS-CoV-2 has over 28,685 unique mutations. Therefore, on average,
each SARS-CoV-2 nucleotide has one known mutation. At the molecular scale,
mutations are the consequence of random genetic drifts during the replication,
transcription errors due to limited polymerase fidelity, ribosome fidelity
associated translation errors, and various other errors caused by adversarial
cellular environments generated by the host immune responses [27].
Additionally, at the organism scale, host gene editing is a dominating
mechanism for SARS-CoV-2 mutations [38]. However, unlike other single-stranded
RNA viruses, such as flu virus and HIV, SARS-CoV-2 has a genetic proofreading
mechanism achieved by an enzyme called non-structure protein 14 (NSP14) in
synergy with NSP12, i.e., RNA-dependent RNA polymerase [14]. Therefore, at the
same condition, SARS-CoV-2 may not mutate as fast as flu viruses. Nonetheless,
at the population scale, the evolution of SARS-CoV-2 is regulated by the
mechanism of transmission. Viral infectivity and virulence are vital factors
that determine viral transmission. On the other hand, the rapid, wide, and
global transmission of SARS-CoV-2 fuels its widespread mutations.

Figure 3: Reproduction of Figure 3 of Reference [9]. The time evolution of 89
SARS-CoV-2 S protein RBD mutations. The green lines represent the mutations
that strengthen the infectivity of SARS-CoV-2, and the red lines represent the
mutations that weaken the infectivity of SARS-CoV-2. Many mutations overlap
their trajectories. Here, the collection date of each genome sequence
deposited in GISAID was applied according to the information recorded in June
2020 [9].

In July 2020, the COVID-19 infection cases were about 12 million globally. We
carried out the single nucleotide polymorphism (SNP) calling of over 15,000
SARS-CoV-2 genomes isolated from patients to identify 725 nondegenerated
mutations on the SARS-CoV-2 S protein, including 89 mutations that occurred on
the RBD, which are important to the binding of SARS-CoV-2 S protein and ACE2
(Our arXiv version was dated May 27, 2020). We found that most of these
mutations have strengthened the RBD binding with ACE2 using our mathematical
model based on a SARS-CoV-2 S protein RBD-human ACE2 complex structure from
the Protein Data Bank (PDB ID 6M0J), leading to more infectious SARS-CoV-2
strains. We stated that “we hypothesize that natural selection favors those
mutations that enhance the viral transmission and if our predictions are
correct, the predicted infectivity strengthening mutations will outpace
predicted infectivity weakening mutations over time.” To our best knowledge,
this work was the first of its kind that associates RBD mutations with
COVID-19 population-level observations and natural selection mechanisms.
Figure 3 recaps Figure 3 in our original paper [9]. Since there were only 89
RBD mutations and their highest frequency was only about 50, statistics were
not very convincing when we discovered natural selection as the SARS-CoV-2
mechanism of evolution. However, if our hypothesis is correct, our mechanism
of SARS-CoV-2 evolution is viable, and our predictions of BFE changes induced
by RBD mutations were sufficiently accurate, one would see the dramatic
increase in frequencies of infectivity-strengthening RBD mutations globally as
the pandemic unfolded in the following months.

Figure 4: Reproduction of Figure 2 of Reference [34]. Illustration of SARS-
CoV-2 mutation-induced BFE changes for the complexes of S protein and ACE2.
Here, 100 most observed mutations out of 651 mutations on S protein RBD and
their frequencies are illustrated as recorded in April 2021 [34]. The highest
frequency was 168,801, while the lowest frequency was 28. Therefore, the
frequencies of the rest of 551 mutations were lower than 28.

By April 2021, the COVID-19 infection cases had increased to over 140 million.
Most of the infections at that time were due to variants now known as Alpha,
Beta, and Gamma. Their RBD (co-)mutations are [N501Y], [K417N, E484K, N501Y],
and [K417T, E484K, N501Y], respectively. The mutation at residue 501 has
significantly strengthened the viral infectivity as predicted by us in May
2020 [9]. In the next few months, the Delta variant, involving RBD mutation
L452R and T478K, had rapidly fueled a new wave of COVID-19 infections around
the world. The mutation at residue 452, as predicted by us in May 2020 [9],
increases the RBD-ACE2 BFE by 0.55 kcal/mol, making the virus more infectious
[34]. Additionally, our prediction indicates that T478K increases the RBD-ACE2
BFE by 1.00 kcal/mol [34]. The combined effect of L452R and T478K makes the
Delta variant the most infectious variant among all variants formally named by
the World Health Organization (WHO).

Since early 2021, there has been a dramatic increase in the effort to sequence
SARS-CoV-2 genomes. By April 2021, our Mutation Tracker
(https://users.math.msu.edu/users/weig/SARS-CoV-2˙Mutation˙Tracker.html) had
recorded 651 RBD mutations and their highest frequency was 168,801 based on
506,768 SARS-CoV-2 genome sequences isolated from patients [34]. Additionally,
some deep mutational data on RBD and ACE2 became available in the literature
around early 2021 [30, 22], which significantly improved the predictive
accuracy of our deep learning model [7]. Therefore, we were in a much better
position in April 2021 to revisit the evolution mechanism discovered in July
2020. Figure 4 recaps the Figure 2 in our paper published in May 2021 [34].
The 100 most observed RBD mutations were shown to have BFE above the average
values of $-0.28$kcal/mol. The odd for this to happen as an accident is one
chance in $2^{100}$. Therefore, our hypothesis that natural selection is a
principle mechanism for SARS-CoV-2 evolution was proven beyond all doubts in
April 2021.

##  4 Mutational impacts on SARS-CoV-2 antibodies and vaccines

One of the most important issues concerning SARS-CoV-2 variants is how
mutations will escape existing vaccines and disrupt known mAbs. A more
intriguing question is whether vaccination will alter the mechanisms of SARS-
CoV-2 transmission and induce vaccine-resistant variants. It is imperative to
understand how the emerging variants impact vaccines. The host immune response
to a common vaccine depends on race, gender, age, health condition, personal
genetic background, etc. Therefore, antibodies induced by vaccination differ
from person to person. For a given vaccine, it is impossible to estimate how
many different antibodies will be generated in a population. However, the
statistics of various antibodies can be used to study the efficacy of
vaccination in a population. We collected seven antibody-RBD complex
structures extracted from COVID-19 patients in March 2020 [6] and extended the
number of antibody-RBD complex structures to 56 in March 2021 [7]. Currently,
we have a library of collections of 130 antibody-RBD complex structures [35]
and use these complexes as a statistical ensemble to understand how RBD
mutations will impact antibodies and vaccines.

Figure 5: Reproduction of the right chart of Figure 11 of Reference [8]. The S
protein RBD is in a binding complex with Eli Lilly antibodies LY-CoV555 (PDB
7KMG) and LY-CoV016 (PDB 7C01).  Figure 6: Reproduction of Figure 12 of
Reference [8]. The BFE changes of S protein and antibody LY-CoV555 (PDB 7KMG)
induced by the 100 most observed RBD mutations are shown. Here, mutations
L452R, V483F/A, E484K/Q, F486L, F490L/S, Q493K/R, and S494P were shown to
potentially disrupt the binding of antibodies and S protein RBD.  Figure 7:
Illustration of the analysis of SARS-CoV-2 mutations given by Mutation
Analyzer. Interactive version is available at web site:
https://weilab.math.msu.edu/MutationAnalyzer/.

Most recently, we have studied how RBD mutations and various variants will
impact mAbs that have obtained the emergency use authorization (EUA) from the
FDA, i.e., those from Regeneron and Eli Lilly and mAbs in clinical trials,
including those from Celltrion and Rockefeller University [8]. Figure 5 shows
the binding complex of RBD and Eli Lilly mAbs. The impacts of the 100 most
observed RBD mutations to the binding complex of RBD and antibody LY-CoV555
are demonstrated in Figure 6. We noted that L452R, V483F/A, E484K/Q, F486L,
F490L/S, Q493K/R, and S494P could disrupt Eli Lilly mAbs. Indeed, Eli Lilly
mAbs were taken off the market in March due to Beta [K417N, E484K, N501Y] and
Gamma [K417T, E484K, N501Y] variants. However, Eli Lilly mAbs were allowed to
return to the market in September because they are quite effective on the
Delta variant [452R, T478K]. Our predictions in Figure 6 provide a good
explanation. An extensive comparison of experimental data and our predictions
of mutational impacts to mAbs was given in Ref. [8]. A comprehensive analysis
of the 75 most observed RBD mutational disruptions of 130 existing antibodies
was presented in our recent work [35]. Our analysis of all RBD mutations is
available at an interactive website, Mutation Analyzer
(https://weilab.math.msu.edu/MutationAnalyzer/) as shown in Figure 7.

##  5 Vaccination impacts on SARS-CoV-2 evolution

Table 1: RBD mutation analysis of WHO-named SARS-CoV-2 variants and emerging variants (EV). Antibody disruption (AD) count and BFE change (kcal/mol) are given. Variants | RBD (co-)mutation(s) | AD count | BFE change
---|---|---|---
Alpha | [N501Y] | 24 | 0.55
Beta | [K417N, E484K, N501Y] | 90 | 0.81
Gamma | [K417T, E484K, N501Y] | 81 | 0.66
Delta | [L452R, T478K] | 40 | 1.58
Epsilon | [L452R] | 39 | 0.58
Zeta | [E484K] | 38 | 0.10
Eta | [E484K] | 38 | 0.10
Theta | [E484K, N501Y] | 60 | 0.65
Iota | [E484K] | 38 | 0.10
Kappa | [L452R, E484Q] | 52 | 0.58
Lambda | [L452Q, F490S] | 59 | 1.42
Mu | [R346K, E484K, N501Y] | 64 | 0.77
EV | [F490S, N501Y] | 72 | 0.99
EV | [S494P, N501Y] | 73 | 0.64
EV | [K417N, L452R, T478K] | 82 | 1.74
EV | [L452R, T478K, N501Y] | 59 | 2.13
EV | [A411S, L452R, T478K] | 49 | 2.08
EV | [L452R, T478K, E484K, N501Y] | 75 | 2.22
EV | [P384L, K417N, E484K, N501Y] | 101 | 1.08
Figure 8: Analysis of RBD co-mutations. Here, the $x$-axis lists RBD co-
mutations and the $y$-axis represents the predicted total BFE change of each
set of RBD co-mutations. The color of each bar represents the natural log of
frequency for each set of RBD co-mutations. The number on the top of each bar
is the AI-predicted number of antibody and RBD complexes that may be
significantly disrupted by the set of RBD co-mutations with BFE change smaller
than $-0.3$ kcal/mol. [34].

Currently, the WHO has named variants from Alpha to Mu. All of these variants
strengthen the binding between RBD and ACE2 as shown in Table 1. Judged by RBD
(co-)mutations, the Delta variant is the most infectious one. The Lambda
variant is as infectious as the Delta variant, followed by the Beta variant.
Zeta, Eta, and Iota are the least infectious in the list. In terms of their
ability to disrupt antibodies, Beta and Gamma variants are among the top,
followed by new variant Mu. The Alpha variant has the lowest ability to escape
vaccines. Therefore, Beta and Gamma variants are more likely to break through
current vaccines. Note that the variant Lambda is not only as infectious as
the Delta, but also capable of disrupting many antibodies, and thus, is
potentially more dangerous than all other WHO-named variants. A list of
vaccine-escape (vaccine-breakthrough) mutations was tabulated in our early
work[34], including S494P, Q493L, K417N, F490S, F486L, R403K, E484K, L452R,
K417T, F490L, E484Q, and A475S.

An important trend is that many variants involve two or three RBD co-
mutations. Indeed, the Delta variant consists of two infectivity strengthening
mutations. Table 1 also lists a few RBD co-mutations that do not have
corresponding WHO-named variants at present. Many of these co-mutations are
about multiples of ten times more infectious than the original SARS-CoV-2.
Figure 8 provides a summary of the 50 most observed RBD co-mutation sets
presented in Figure 2 of our recent work [35]. We noticed that only five out
of 50 sets of RBD co-mutations do not involve any of residues 452 and 501.
Future variants that combine RBD co-mutations at 452 and 501 will be extremely
contagious. Delta variant has the highest global frequency, following by
Gamma, Beta, etc. All the most observed RBD co-mutations, except one on the
right, have positive BFE changes and strengthen the viral infectivity.
Therefore, these co-mutation sets also support our hypothesis that the
evolution of SARS-CoV-2 is governed by infectivity-based natural selection.

Worth noting that the co-mutation set [Y449S, N501Y] has a negative BFE change
of $-0.26$ kcal/mol, highlighting its infectivity weakening characteristic. We
postulated that an alternative transmission pathway had led to the occurrence
of co-mutation set [Y449S, N501Y] [35]. Indeed, this co-mutation set has the
highest antibody disruption count of 94 among the 50 most observed RBD co-
mutation sets, showing its exceptionally strong ability to break through
current vaccines. Our further analysis indicated that this co-mutation set has
kicked off around May 2021 in countries having a high level of vaccination,
including Denmark, the United Kingdom, etc. Therefore, we conclude that
vaccine-breakthrough is a new transmission pathway for SARS-CoV-2 evolution
and it will become a dominating pathway for emerging SARS-CoV-2 variants when
populations fully carry SARS-CoV-2 antibodies either through vaccination or
via infection.

Figure 8 reveals a general trend that co-mutation sets on the right side, with
relatively high antibody disruption counts have relatively high frequencies.
Many of these co-mutation sets involve vaccine-escape mutations predicted in
our early work: S494P, Q493L, K417N, F490S, F486L, R403K, E484K, L452R, K417T,
F490L, E484Q, and A475S [34]. It becomes clear that emerging SARS-CoV-2
variants will tend to combine high infectivity with strong vaccine-
breakthrough capability. Based on this observation, we anticipate that co-
mutation sets [A411S, L452R, T478K], [L452R, T478K, N501Y] and [K417N, L452R,
T478K] are on the path to becoming emerging SARS-CoV-2 variants. Additionally,
although their frequencies are relatively low at present, co-mutation sets
[L452R, T478K, E484K, N501Y] and [P384L, K417N, E484K, N501Y] have very high
BFE changes (i.e., 2.22 and 1.07 kcal/mol, respectively) and very high
antibody disruption counts (i.e., 75 and 101, respectively)[35]. We foresaw
that these co-mutation sets would also pose a severe threat.

##  6 Methods and validations

Figure 9 provides a bird’s eye view of the data collection and computational
platform introduced in our predictions and comparison with some experimental
data. First, viral genomes isolated COVID-19 patient samples are sequenced and
deposited in the GISIAD database. We download 1,770,415 complete SARS-CoV-2
genome sequences with high coverage and exact collection date from the GISAID
database [28]. For each sequence, we carried out SNP calling to obtained
mutation information for the whole genome based on a reference SARS-CoV-2
genome [40]. The Cluster Omega (https://www.ebi.ac.uk/Tools/msa/clustalo/) is
used for multiple sequence alignment. We share our genotyping results with the
research community using our interactive Mutation Tracker.

Figure 9:  Illustration of genome sequence data pre-processing and BFE change
predictions [35].

We employ the structure of the RBD-ACE2 complex (6M0J) to predict RBD
mutation-induced BFE changes [9, 7], which provides mutation-induced
infectivity change estimations. In our recent work, we have collected a
library of 130 antibody-RBD complexes, which are used to predict mutational
impacts to existing antibodies [6, 7, 34, 35, 8]. These antibodies were
extracted from COVOD-19 patients and their detailed information is available
elsewhere [35].

In our deep learning models, persistent homology [3, 12, 23] is used to
represent RBD-ACE2 and RBD-antibody interactions. Element-specific persistent
homology offers a controllable simplification of biomolecular complexity and
retains crucial physical and biological information of PPIs [2, 24].
Additional features were also generated to complement topological ones [32,
9]. In our early work, a deep learning algorithm called NetTree, which
combines convolutional neural networks (CNNs) and gradient boosting trees
(GBTs), was constructed to tackle the challenge of scarce data in our early
predictions [32, 9]. Our early model was trained with a large dataset of 8,338
PPI entries from the SKEMPI 2.0 dataset [18, 32]. In our recent work, our deep
learning models were trained with additional deep mutational datasets
associated with SARS-CoV-2 RBD [30, 22], and ACE2[25]. Additionally, we made
use of deep mutational data on both proteins for the binding complex of RBD
and protein CTC-445.2 [22]. Our results are available at our interactive
website Mutation Analyzer [8].

Figure 10:  Reproduction of Figure 2 of reference [7] for a comparison between
experimental deep mutation enrichment data and TopNetTree predictions for the
SARS-CoV-2 S protein RBD and CTC-445.2 complex (7KL9 [22]). Top left: deep
mutational scanning heatmap showing the average effect on the enrichment for
single-site mutants of the RBD when assayed by yeast display for binding to
CTC-445.2.89. Top right: the RBD colored by average enrichment at each residue
position bound to CTC-445.2. Bottom: machine learning predicted BFE changes
for the CTC-445.2 and S protein complex induced by single-site mutations on
the RBD.

The extensive validations of our deep learning predictions were presented in
our recent papers [7, 8, 35]. Figure 10 shows our prediction of RBD mutation-
induced BFE chances for a complex of protein CTC-445.2 and spike protein RBD.
Our predictions were systematically compared with experimental deep mutational
enrichment data [7, 22]. Additionally, a similar comparison between
experimental RBD deep mutation enrichment data and predicted BFE changes for
SARS-CoV-2 RBD binding to ACE2 (6M0J) was also shown a high-level correlation
in our other work [8, 22]. Moreover, extensive validations were carried out
with experimental data of the impacts of emerging variants on antibodies in
clinical trials [8]. Further, our predicted BFE changes induced by RBD
mutations L452R and N501Y for ACE2 and RBD complex have a highly similar trend
with experimental data in relative luciferase units [8, 11]. Finally, a
comparison of experimental CT-P59 IC50 fold change (reduction) [20] and
predicted BFE changes induced by mutations L452R and T478K showed a high
correlation [35].

##  7 Conclusion

Understanding the mechanisms of evolution and transmission of severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) is a challenging task and a
prerequisite to predict future variants. In this review, we demonstrate that
the SARS-CoV-2 mechanism of evolution and transmission was discovered one year
ago. In July 2020, we unveiled that mutations on the SARS-CoV-2 spike (S)
protein receptor-binding domain (RBD) strengthened viral infectivity, which
serves a unique viral transmission pathway [9]. We revealed that infectivity-
based natural selection governs SARS-CoV-2 evolution. In April 2021, we had
proven beyond all doubt that infectivity-based natural selection was the sole
mechanism for SARS-CoV-2 evolution and transmission. However, we show that the
mechanism of SARS-CoV-2 evolution also depends on an alternative transmission
pathway. We identify that an infectivity-weakening RBD co-mutation set [Y449S,
N501Y] stands out among heavily vaccinated populations because it has an
exceptionally strong ability to break through existing vaccines, signaling a
new mechanism of SARS-CoV-2 evolution and transmission. Our studies integrate
genotyping of over 1.77 million SARS-CoV-2 genomes isolated from patients, a
library collection of 130 antibodies, tens of thousands of SARS-CoV-2 related
deep mutational experimental data, algebraic topology, and deep learning.

In the same paper published one year ago [9], we also predicted mutations at
RBD residues 452 and 501 would “have high chances to mutate into significantly
more infectious COVID-19 strains.” It is now known that RBD mutations at these
sites are essential for prevailing variants Alpha, Beta, Gamma, Delta,
Epsilon, Theta, Kappa, Lambda, and Mu. We anticipate that RBD mutations at 452
and 501 will be persistently vital to the next generation of new variants. The
combination of RBD co-mutations at 452 and 501 will lead to new variants that
are about multiples of ten times more infectious than the original SARS-CoV-2
and have a much higher ability to escape current vaccines.

Currently, SARS-CoV-2 is evolving mainly through the infectivity-based
transmission pathway in unvaccinated and less infected populations. In future,
when most of world’s populations carry SARS-CoV-2 antibodies either through
vaccination or infection, the antibody-disruption-based transmission pathway
of viral evolution will become more important. These two complementary
transmission pathways of viral evolution will govern the emergency of emerging
SARS-CoV-2 variants, which will extend our battle with COVID-19 for a long
time. We forecast that a few co-mutation sets, including [A411S, L452R,
T478K], [L452R, T478K, N501Y], [K417N, L452R, T478K], [L452R, T478K, E484K,
N501Y], and [P384L, K417N, E484K, N501Y], have a great potential to grow into
unprecedentedly dangerous new SARS-CoV-2 variants. We call for the development
of the next generation of vaccines to prevent these new mutations.

## Data and codes availability

Mutational data for complete SARS-CoV-2 genomes are available for download at
our interactive web page Mutation Tracker
(https://users.math.msu.edu/users/weig/SARS-CoV-2˙Mutation˙Tracker.html),
which is updated regularly.

Observed frequencies (counts) of all spike protein receptor-binding domain
(RBD) mutations, RBD mutation-induced binding free energy (BFE) changes of the
angiotensin-converting enzyme 2 (ACE2) and RBD complex, and counts of RBD
mutation-induced disruptions of RBD and antibody complexes are given by our
interactive web page Mutation Analyzer
(https://weilab.math.msu.edu/MutationAnalyzer/), which is updated regularly.

As a review, this work does not contain new codes. However, related codes are
available in cited references.

## Acknowledgments

This work was supported in part by NIH grant GM126189, NSF grants DMS-2052983,
DMS-1761320, and IIS-1900473, NASA grant 80NSSC21M0023, Michigan Economic
Development Corporation, MSU Foundation, Bristol-Myers Squibb 65109, and
Pfizer.

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# 5G Wings: Investigating 5G-Connected Drones Performance in Non-Urban Areas
††thanks: This material is based upon work supported by T-Mobile, the Air
Force Office of Scientific Research under award number FA9550-20-1-0090, and
the National Science Foundation under Grant Numbers CNS-2120485 and
CNS-2232048.

Mohammed Gharib, Bryce Hopkins, Jackson Murrin, Andre Koka, Fatemeh Afghah
Department of Electrical and Computer Engineering, Clemson University,
Clemson, SC, USA.
E-mail:{alghari, bryceh, jmurrin, arkoka<EMAIL_ADDRESS>
###### Abstract

Unmanned aerial vehicles (UAVs) have become extremely popular for both
military and civilian applications due to their ease of deployment, cost-
effectiveness, high maneuverability, and availability. Both applications,
however, need reliable communication for command and control (C2) and/or data
transmission. Utilizing commercial cellular networks for drone communication
can enable beyond visual line of sight (BVLOS) operation, high data rate
transmission, and secure communication. However, deployment of cellular-
connected drones over commercial LTE/5G networks still presents various
challenges such as sparse coverage outside urban areas, and interference
caused to the network as the UAV is visible to many towers. Commercial 5G
networks can offer various features for aerial user equipment (UE) far beyond
what LTE could provide by taking advantage of mmWave, flexible numerology,
slicing, and the capability of applying AI-based solutions. Limited
experimental data is available to investigate the operation of aerial UEs over
current, without any modification, commercial 5G networks, particularly in
suburban and NON-URBAN areas. In this paper, we perform a comprehensive study
of drone communications over the existing low-band and mid-band 5G networks in
a suburban area for different velocities and elevations, comparing the
performance against that of LTE. It is important to acknowledge that the
network examined in this research is primarily designed and optimized to meet
the requirements of terrestrial users, and may not adequately address the
needs of aerial users. This paper not only reports the Key Performance
Indicators (KPIs) compared among all combinations of the test cases but also
provides recommendations for aerial users to enhance their communication
quality by controlling their trajectory.

###### Index Terms:

5G, UAV, LTE, Cellular-connected drones, measurement study.

##  I Introduction

Unmanned aerial vehicles, also known as drones, have been attracting
significant attention from industry, academia, and the military by being cost-
effective, available, versatile, and having high maneuverability. Various
applications such as entertainment, package delivery, border surveillance, and
remote sensing are a few examples to name [1]. Regardless of the intended
application of the aerial vehicle, reliable C2 communication is crucial in all
application domains to ensure safe operation. Additionally, high data rate
communication is of significant importance in UAVs’ applications in disaster
relief, augmented reality, and environmental and infrastructure monitoring[2].
BVLOS operation, in turn, paves the way for various new applications of
autonomous UAVs. However, it needs an ultra-reliable low latency communication
[3]. Technologies such as WiFi [4], LoRA [5], and WiMAX [6] are available for
UAV communication. However, the short communication range of WiFi, the low
bandwidth of LoRA, and the latency, as well as the lack of support for highly
mobile users in WiMAX, are the limiting factors that make the utilization of
aforementioned technologies more concerning. More importantly, these
technologies do not often meet the security expectations for UAV
communication. The wide deployment, availability, high data transmission
rates, reliability, and security of cellular networks make them ideal
candidates for UAV communication. However, cellular networks are essentially
designed and optimized to serve terrestrial users and might not be ready to
serve aerial users, without modifications. Besides 3D coverage gaps due to
cellular antennas being tilted toward the terrestrial users, frequent
handovers, interference caused by UAVs to neighbor towers, mobility among
different providers to maintain connectivity, and UAV identification are among
the challenges of serving drones by cellular networks.

5G features such as low latency, higher bandwidth, ultra-reliability, strong
security, highly directional signal delivery enabled by massive MIMO, slicing,
mmWave, flexible numerology, and advanced beamforming techniques, as well as
the capability of applying AI-based solutions such as AI-based path planning
for interference mitigation and coverage maximization put 5G in a unique
position to serve aerial users. Current T-mobile 5G deployment includes stand-
alone (SA) and non-standalone (NSA) networks and provides layer cake spectrum
strategy to supply coverage in three different bands, low-band which covers
frequencies less than 1 GHz, mid-band which covers 1 to 6 GHz, and high-band,
also referred to as mmWave, which covers frequencies greater than 24 GHz. The
latter provides much higher bandwidth compared to the mid-band 5G, and hence
drastically increased throughput. However, to face the frequency-dependent
path loss and to provide sufficient coverage for its high-frequency signal, it
requires directional antennas in both UE and basestation (BS) side [7].

Some challenges regarding 5G-connected drones are the coverage at different
elevations, the performance at higher elevations, and the interference of the
aerial UE on the primary terrestrial users. While the latter category is out
of the scope of this paper, the concern with the first two categories rises
from the fact that the tilt angle of cellular antennas is optimized such that,
considering on-the-ground obstacles, signal coverage is maximized and
interference is minimized for terrestrial users. The former studies of
cellular network communication for aerial users (discussed in Section (II),
showed evidence of cellular coverage for aerial vehicles flying in low
altitudes allowed by FCC/FAA. While there are valuable works in the literature
[8, 9, 10], the problem of cellular coverage has not yet been sufficiently
studied for commercial 5G networks. Most of the literature considered the
evaluation of 5G networks for terrestrial users or utilized a testbed with a
private 5G network for the measurement. An exhaustive performance analysis is
required to assess the feasibility of using commercial 5G networks in
different geographical areas and network conditions.

In this paper, we analyze the coverage and the performance of the cellular 5G
network for aerial users via exhaustive real measurements over a commercial
LTE/5G network. We perform a comprehensive study on the low-band 5G, mid-band
5G, and LTE networks by measuring KPIs at different elevations for an aerial
user with different velocities in a sub-urban area near Clemson University,
Clemson, SC USA. The aim is to investigate several open research questions
about cellular-connected UAV communication including but not limited to the
variation of different network KPIs at different deployment modes, the impact
of elevation and velocity on the network’s KPIs, and the 3D coverage in a
sparse suburban area compared to the 2D ground-level coverage. To investigate
the impact of deployment modes on the KPIs, we equipped a DJI M30T drone with
three cellphones, each running RFInsight, a T-mobile application to measure
cellular network performance. RFInsights provided the collected data as plain
text files, which were then cleaned to remove superfluous data collected
during takeoff, landing, and times other than the main flights. The cleaned
logfiles were then analyzed to extract beneficial, meaningful information. The
details of data gathering, cleanup, and processing are reported in Section
(III) and the results from the processed data are represented in Section (IV).
To investigate the aforementioned questions on the capability of the current,
without any modification, LTE/5G networks to serve low-altitude drones in
suburban areas, Reference Signal Received Power (RSRP), Reference Signal
Received Quality (RSRQ), Reference Signal Signal to Noise Ratio (RSSNR),
uplink throughput, and downlink throughput are measured and analyzed for all
the combinations of the tested velocities and elevations, for LTE, low-band
5G, and mid-band 5G networks.

The main contribution of this paper is to exhaustively evaluate the
performance of the current 5G cellular network in its low and mid bands for
aerial communication and compare it with that of LTE in sub-urban areas. The
additional contribution of this paper is to offer trajectory planning
recommendations for aerial users which intend to utilize commercial 5G as
their communication platform, to enhance their communication performance. The
rest of this paper is organized as follows. In Section (II), we review the
state-of-the-art with an exhaustive comparison among the related works. In
Section (III), we present the test, data collection, and data pre-processing.
We show the results in Section (IV). Finally, we conclude the paper and
present future directions in Section (V).

##  II Related Work

TABLE I: An Overview of Related Works

Related Work | UAV | LTE | 5G | RSRP | SINR | RSRQ | Speed | Elev. | Throughput | C/T11footnotemark: 1 | Area22footnotemark: 2 | Measurement Tool
---|---|---|---|---|---|---|---|---|---|---|---|---
|  |  |  |  |  |  |  |  | UL | DL |  |  |
Narayanan et al.[11] | $\times$ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | $\times$ | ✓ | ✓ | C | U | Ookla
Rochman et al.[12] | $\times$ | ✓ | ✓ | ✓ | ✓ | ✓ | $\times$ | $\times$ | ✓ | ✓ | C | U | SigCap,NSG33footnotemark: 3
Narayanan et al.[9] | $\times$ | $\times$ | ✓ | ✓ | ✓ | ✓ | $\times$ | $\times$ | ✓ | ✓ | C | U | 5GTracker
Rochman et al. [10] | $\times$ | ✓ | ✓ | ✓ | $\times$ | ✓ | $\times$ | $\times$ | $\times$ | ✓ | C | U | SigCap, FCCS44footnotemark: 4, NSG
Maeng et al.[13] | ✓ | ✓ | $\times$ | ✓ | $\times$ | ✓ | ✓ | ✓ | $\times$ | $\times$ | T | R | KNO55footnotemark: 5, QualiPoc, TEMS
Gharib et al.[14] | ✓ | ✓ | $\times$ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | C | R | TEMS
Lin et al.[15] | ✓ | ✓ | $\times$ | ✓ | ✓ | $\times$ | ✓ | ✓ | ✓ | ✓ | C | S | TEMS
Amorim et al.[16] | ✓ | ✓ | $\times$ | ✓ | ✓ | $\times$ | ✓ | ✓ | $\times$ | $\times$ | C | R | RF Antenna Scanner
Amorim et al.[17] | ✓ | ✓ | $\times$ | ✓ | $\times$ | ✓ | ✓ | ✓ | ✓ | ✓ | C | U | Dedicated App
Sekander et al.[18] | ✓ | $\times$ | ✓ | $\times$ | ✓ | $\times$ | $\times$ | $\times$ | $\checkmark$ | ✓ | T | U, S, R | Simulation
Festag et al.[8] | ✓ | ✓ | ✓ | ✓ | ✓ | $\times$ | ✓ | ✓ | ✓ | ✓ | T | S | ICPM echo, iPerf
Säe et al.[19] | ✓ | ✓ | ✓ | ✓ | $\times$ | ✓ | ✓ | ✓ | ✓ | ✓ | T | S | MediaTek, QualiPoc
Raouf et al.[20] | ✓ | ✓ | ✓ | ✓ | $\times$ | $\times$ | $\times$ | ✓ | ✓ | ✓ | C | U, R | USRP & Py Script
This Work | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | C | S | RFInsights

As commercial 5G stand-alone (SA) and non-stand-alone (NSA) networks have been
deployed by commercial phone carriers, there has been significant interest in
using these networks for UAV communication. To discover the feasibility of
this, several measurement-based studies of current commercial 5G capabilities
have been performed. Generally, we categorize the related work of this paper
into three categories, general 5G measurement studies over commercial
networks, LTE UAV measurement studies, and 5G UAV measurement studies. The
first category includes measurement-based studies that focus exclusively on
terrestrial users [7, 12, 9, 10]. While these studies do not consider UAV
communication, they are valuable for understanding the variations of different
5G KPIs.

5G Performance Analysis for Terrestrial Users: Narayanan et al. [11] analyzed
cellular throughput, handover, and energy performance of deployed 5G networks.
The authors concluded that mmWave 5G networks always have higher performance
than 4G networks when line-of-sight connection is guaranteed, but leads to
much higher energy consumption for communications with lower data transfer
requirements [11]. In [12], Rochman et al. conducts one of the first studies
of sustained 5G mmWave coverage across multiple United States cities,
performing tests in Chicago, Miami, and San Francisco. They found that
sustained mmWave throughput is limited by the temperature of the UE, which
rises as intensive mmWave use continues. Analysis of the standard KPIs for 5G
has proven to be difficult, as cellular providers are not transparent with
their current 5G deployments. To combat this, Narayanan et al. [9] present an
open-source tool that allows for crowd-sourcing of large 5G KPI datasets using
android smartphones. Other smartphone measurement tools are used by Rochman et
al. in [10], to measure 4G and 5G performance. In [10], authors studied 5G
deployments across multiple cities and found that 5G networks of all types are
highly sensitive to physical obstructions compared to 4G networks.

4G/LTE Performance for UAV Communication: In [13], an analysis of air-to-
ground link propagation characteristics is performed using a tool introduced
in the same work, referred to as AERIQ. This paper extracts RSRP information
from a variety of experiments and proves the testing capabilities of their
AERIQ platform, while also finding that an increase in 3D distances results in
a decrease in coherence bandwidth, due to the effects of ground reflection
being more pronounced at longer distances. Gharib et al. [14] performed an
analysis in rural areas, looking at communication characteristics between an
aerial UAV and a commercial LTE network in Flagstaff, AZ. They concluded that
rural areas experience better signal quality and fewer handover processes for
low-elevation flights, and the increase in UAV velocity causes a slight
degradation in the performance of the 4G network.

Xingqin et al. conducted testing in a suburban area for different elevations,
using the TEMS Pocket smartphone application to take measurements. They found
that 4G networks could support the initial deployment of drones, but
infrastructure improvements would be required to support drones that operated
at higher altitudes and speeds. This conclusion is supported by Amorim et al.
[16], where they performed flight tests to find a mathematical model for path
loss of radio communication among multiple UAVs. Using a scanner mounted on a
UAV, they recorded RSRP and distance information at several points in 3D space
to calculate a path loss sample at each point. They found that path loss
attenuation of radio signals is significantly higher at high altitudes
compared to ground levels. Amorim et al. [17] analyzed the reliability of the
UAV control link. After flying at 15-, 40-, and 100-meter elevations and
measuring latency, they concluded that the control link connection through
LTE-A cellular networks falls short of the 99.9% reliability goal defined by
3GPP. In [17], authors proposed the use of a dual-operator scheme, which
manages to reach the desired performance on LTE networks in most cases.

5G Network for UAV Communication: Bor-Yaliniz et al. [21] performed an
analysis to determine if commercial 5G networks should be used to support
drone use. They found that there is no simple way to determine how best to
utilize UAVs with 5G networks. However, by comparing a large number of 5G
design options, like relaying and cloud-RAN, they concluded that 5G slicing
and modularity are network elements that provide flexibility to be utilized to
support drones on the networks. They, however, performed no flight testing of
5G networks. An early instance of 5G UAV flight testing is provided by
Festaget al. [8]. They performed long-distance flight testing over a 7km
course at a height of 100m, analyzing end-to-end latency. They concluded that
cellular communication between UAVs and 5G base stations meets the demands for
video streaming in principle, even when the network has not been optimized for
aerial users. They also state that network slicing abilities in 5G networks
have the potential to improve network performance further.

Säe et al. [19] considered an alternative to traditional drive testing, by
conducting flight tests using an established, commercial-grade 5G test
network. They were able to calculate 4G and 5G radiation patterns within the
3D space. This led them to conclude that UAV flight testing with a smartphone
as the measurement device is a reasonable alternative to drive testing for the
purposes of evaluating the coverage capabilities of a given 5G network. Raouf
et al. [20] performs network power measurements using UAV flight testing,
allowing for comparison of aerial coverage between 4G LTE and commercial 5G
low-mid band networks. Using the NSF AERPAW measurement platform in Raleigh
NC, Raouf et al. performed flights at 140m and 180m for the urban and rural
sites, respectively. They found that generally the measured signal power
increases as UAV altitude increases, due to the better line-of-sight
characteristics at high altitudes. They also found that the spectrum of down-
link frequencies is significantly more crowded than the up-link spectrum in
both environments. Table (I) summarizes a comprehensive comparison among the
literary works and compares them with our work. Overall, there is a continued
need to investigate deployed 5G potential for aerial communications. Previous
works in this area have performed a speculative analysis of drone use with 4G
and 5G networks, while others have performed physical flight testing. This
paper provides a comprehensive comparison between UAV altitude, velocity, and
the connected cellular network band. In addition, this paper includes altitude
and velocity testing of a real 5G network, as opposed to a 5G testbed.
††footnotetext: Commercial network versus testbed measurement. ††footnotetext:
R: Rural, S: Suburbun, U: Urbun  ††footnotetext: Network Signal Guru
††footnotetext: FCC Speedtest  ††footnotetext: Keysight NEMO Outdoor

##  III Data Collection and Processing

(a) Drone Setup

(b) Phone Setup

(c) Flight Test Map

(d) Overall View

(e) Nearby Cell Coverage

Figure 1: Measurement Setting

In this section, we describe the procedure of data collection, as well as the
data log file process. Our physical testing equipment is shown in Fig. (1(a)).
It was comprised of a commercial DJI Matrice 30T drone, two Samsung S22+, and
one Samsung S22 Ultra cellphones, and a 3D printed mount to securely attach
the cellphones on top of the drone. Fig. (2(b)) shows the phones and the
printed mount together before the installation on the drone. Our data log
files were collected with T-Mobile’s in-house RFInsights application,
represented in Fig. (2), with built-in Ookla SpeedTest connected to different
servers for uplink and downlink throughput measurement. RFInsight collects the
data in a time granularity of one second. Our data was collected at an active
construction zone in Central, South Carolina as seen in Fig. (1(e)). This site
had been deforested and leveled leaving an easy area to fly a drone in with
minimal obstacles. Central, South Carolina is a suburban town next to the city
of Clemson, South Carolina. All of the tests were conducted on the T-Mobile
cellular network. Fig. (1(d)) shows an overall view of the city with the
T-Mobile base stations, and the test area colored by red. Throughout the
testing process, we detected signals from four base stations. Fig. (1(e))
shows those BSs and the corresponding on-ground coverage areas, where the test
area is highlighted in red.

(a)

(b)

Figure 2: RFInsight Application Snapshot

It is worth mentioning that the main practical concern in performing cellular
network measurements with regular cellphones is overheating [12]. As touched
on previously, the est setup utilized three different cell phones at once on
the drone, exacerbating the overheating concern. To prevent overheating, a
special 3D printed mount was designed that attaches to the top of the UAV,
firmly securing the three cell phones. The open-air design allowed for the
phones to be naturally cooled. Once the 3D printed mount was employed the
overheating problem was eliminated, even when running Ookla SpeedTest which
produced the most heat of any testing conducted. The full setup can be seen in
Fig. (1(a)).

With the ability to collect three different data sets at the same time, we
compared LTE, 5G low-band, and 5G mid-band as mmWave towers were not available
in this suburban test site. The phones were able to be locked on specific
T-Mobile bands exclusively. The first S22+ was set to all LTE bands, later
being seen that the phone was connected on band 66 the whole flight. Band 66
is the 10MHz channel bandwidth on 2.175 GHz for downlink and 1.775 GHz for
uplink. The second S22+ was set to test 5G mid-band on band n41 at 2.5 GHz
frequency with 100 MHz channel bandwidth. This left the S22 ultra, set to test
5G low-band on band n71 at 600 MHz frequency with 15 MHz channel bandwidth.
Nine different test scenarios were investigated, each with a specified
elevation and velocity. The different elevations flown at in the test
scenarios are 400 feet, 300 feet, and 200 feet. For each of these elevations,
three different velocities were tested: 30 mph, 20 mph, and 10 mph. This led
to having nine different data scenarios to analyze the state of communication
at different altitudes and velocities. For each test scenario, the drone was
flown in a 1,500-foot linear path, illustrated in Fig. (1(c)), stopped at the
far end, and then flown back 1,500 feet to the starting point. The log files
are then collected and pre-processed to remove unnecessary data such as those
belonging to the time before achieving exact elevation or speed.

##  IV Measurement Results for 5G-Connected UAV

In this section, we review the results for RSRP, RSRQ, RSSNR, downlink
throughput, and uplink throughput. RSRP is the average received power from a
single reference signal, measured with dBm, and ranges typically from -140 dBm
for a weak signal to -44 dBm for an excellent one. Fig. (3) shows the RSRP
measurement for the fixed speed of 10 mph and the combination of different
elevations versus different bands. We find that the low-band 5G outperforms
the other bands in this metric for all the scenarios. Increasing the
elevation, as the figures show, decreases the signal power in most of the
scenarios. However, we find that in LTE, the 400 ft elevation outperformed the
300 ft for RSRP. It could be an indicator that, in LTE networks, increasing
the elevation decreases the power, though also increases the chance of line-
of-sight signals that can increase the received power. While due to the space
limitation, it is not represented here, increasing the speed slightly
decreases the RSRP.

(a) LTE

(b) Low-band 5G

(c) Mid-band 5G

Figure 3: RSRP comparison for the combination of different elevations and
network bands (speed=10 mph )

Fig. (4) shows the measured RSRQ for the combination of different network
bands versus different elevations and a fixed speed of 10 mph. RSRQ is the
ratio of the signal power to the interference power, measured in dB, and
ranges typically between less than -20 dB for a bad signal to more than -10 dB
for an excellent signal. Just like the RSRP, increasing the elevation
decreases the signal quality, in general. However, in LTE network the signal
quality at 400 ft is better than that of 300 ft for the same reasoning
mentioned for RSRP. Results show that the mid-band 5G has consistently higher
RSRQ in comparison with low-band 5G. Mid-band 5G also outperforms LTE in the
200 ft and 300 ft settings, though LTE achieves higher RSRQ than the mid-band
5G at 400 ft. While the uplink and downlink throughput of mid-band 5G is much
higher than that of LTE, the reason behind the higher LTE RSRQ is the fact
that LTE has a separated reference signal frequency for adjacent cells, while
5G generally uses the same synchronization signal block (SSB) for all cells.
This may cause interference among the SSBs of adjacent cells, resulting in
less signal quality or power. Our results do not indicate a significant RSRQ
variation among different speeds. RSSNR measures the ratio of the reference
signal power to the power of the signal noise, reported in dB. The typical
range of RSSNR is starting from less than 7dB for weak signals to more than
12.5dB for excellent signals. Fig. (5) compares the measured RSSNR values
among different network bands for different flight elevations. The RSSNR
results support the general conclusions extracted from RSRP and RSRQ figures
for the same reasoning.

(a) LTE

(b) Low-band 5G

(c) Mid-band 5G

Figure 4: RSRQ comparison for the combination of different elevations and
network bands (speed=10 mph )

(a) LTE

(b) Low-band 5G

(c) Mid-band 5G

Figure 5: RSSNR comparison for the combination of different elevations and
network bands (speed=10 mph )

The next measured parameter is the throughput which represents the rate at
which data is sent and received over the channel. From the viewpoint of the
user, the throughput is the most sensible metric for the quality of
communication. Since the uplink and downlink of cellular network channels
might not be symmetric, we measured the throughput for both of them
separately. To measure throughput, Ookla SpeedTest was utilized, which
allocates multiple simultaneous TCP connections to maximize uplink throughput
or downlink throughput. Ookla operates in phases, alternating between
measuring uplink and then downlink throughput for about 15 seconds each. Fig.
(6) shows the cumulative distribution function (CDF) of downlink throughput
measured for the combination of different bands and speeds at 400 ft
elevation. The mid-band 5G throughput is multiple times higher than that of
LTE and low-band 5G, representing the high-speed download capability of this
band. For better demonstration, we changed the x-axes range for LTE and low-
band to [0 200] mbps. We see an obvious superiority of LTE over low-band 5G.
In most cases, higher velocity decreased the downlink throughput. However, we
see some exceptions as in Fig. (6(c)) the 20mph outperformed the 10mph test,
which could be because of the channel condition variation due to the time
difference between the two tests. Fig. (7) shows a CDF comparison for the
combination of different elevations versus different speeds for mid-band 5G.
The 400 ft elevation test shows a significant throughput degradation compared
to those of lower elevations. Last but not least, Fig. (8) shows the CDF of
uplink throughput to compare the different bands against different elevations.
In this metric, the mid-band 5G represented the best performance followed by
the low-band 5G. LTE, however, did not represent comparable uplink throughput
at all. The performance degradation at higher elevations is also obvious.
While not reported here, the speed increment had the exact same performance
degradation effect as the elevation increment in downlink throughput.

(a) LTE

(b) Low-band 5G

(c) Mid-band 5G

Figure 6: CDF of downlink throughput for the combination of different speeds
and bands at the elevation of 400 ft

(a) 10 mph

(b) 20 mph

(c) 30 mph

Figure 7: CDF of downlink throughput for the combination of different
elevations and speeds at mid-band 5G

(a) LTE

(b) Low-band 5G

(c) Mid-band 5G

Figure 8: CDF of uplink throughput for the combination of different elevations
and bands at the speed of 20 mph

##  V Conclusion

In this paper, we took an initial step in addressing the lack of sufficient
performance evaluation studies for 5G-connected low-altitude drone
communication. We exhaustively analyzed the low-band and mid-band 5G network
performance and compared it to that of LTE via real measurements on a
commercial cellular network in a non-urban area. We covered the impact of the
elevation and velocity on the RSRP, RSRQ, RSSNR, uplink throughput, and
downlink throughput. The combination of the covered KPIs, the utilized
commercial network deployment, and the 3D analysis of the network performance,
altogether make this work unique. We found that increasing elevation degrades
network performance in almost every tested velocity-altitude combination, with
negligible performance degradation observed when incrementing vehicle
velocity. While the performance of the low-band 5G and LTE networks were
comparable in terms of downlink throughput, we found that the mid-band 5G has
manifolded throughput compared to them. For uplink throughput, we found that
low-band and mid-band 5G has comparable performance much higher than that of
LTE. All in all, we found the already deployed mid-band 5G is a promising
candidate for aerial communication, even without any change in the network
setting. We planned to investigate the mmWave 5G performance for drone
communication as a future direction. The study of the handover process is also
another future direction.

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