# Revealing Aspects of Hawai’i Tourism Using Situated Augmented Reality

Karen Abe  Independent, Honolulu, HIHonoluluHawai’iUSA<EMAIL_ADDRESS>,  Jules Park  York University, Toronto, ONTorontoOntarioCanada
<EMAIL_ADDRESS>and  Samir Ghosh  University of California, Santa Cruz,
CASanta CruzCaliforniaUSA<EMAIL_ADDRESS>
(2024; 8 March 2024)

###### Abstract.

In this position paper, we present a process artifact that aims to bring
awareness to historical context, contemporary issues, and identity harm
inflicted by tourism in Hawai‘i. First, we introduce the historical background
and how the work is informed by the positionality of the authors. We discuss
how related augmented reality work can inform strategy for building augmented
reality experiences that address cultural issues. Then, we present a mockup of
the artifact, aimed to bring awareness to 20th-century colonialism, recent
Kānaka Maoli art exclusion, and cultural prostitution. We describe how we will
share the app at the workshop and list topics for discussion.

Augmented Reality, Social Justice

††copyright: acmlicensed††journalyear: 2024††doi: XXXXXXX.XXXXXXX††conference:
Site-specific Augmented Reality for Social Justice; May 12, 2024; Honolulu,
HI††booktitle: With or Without Permission at CHI ’24: Site-Specific Augmented
Reality for Social Justice, May 12, 2024, Honolulu, HI††isbn:
978-1-4503-XXXX-X/18/06††ccs: Human-centered computing Mixed / augmented
reality

Figure 1. Mockup depicting a site-specific historical artifact situated in
mobile augmented reality

Mockup depicting a site-specific historical artifact situated in mobile
augmented reality

##  1\. Introduction

Hawai‘i is often perceived as a “paradise” by tourists, offering beautiful
beaches, water activities, and “exotic” culture. However, this image of
“paradise” masks the struggles of Hawai’i locals and the Kānaka Maoli
community who face high living costs and social injustices. (Note: Kānaka
Maoli, the indigenous people of Hawai’i. Often referred to as ”Kānaka” for
short.) Upon the overthrow of the Hawaiian Kingdom in 1893, white politicians
and businessmen spearheaded the plan for building Hawai‘i as a tourist
destination. In a working paper for The Economic Research Organization at the
University of Hawai‘i, James Mak describes the history of colonial tourism,
referring to a 1902 publication by the Merchants’ Association of Honolulu
where the committee noted their hopes of expanding tourism to foster a
“permanent population of the most desirable character” and shape Hawai‘i into
a “white republic” (Mak, 2015). To this day, the drivers and beneficiaries of
the tourism industry are controlled by non-Kānaka politicians and business
owners, thus the historical context of tourism and its colonialist intentions
are often hidden from tourists and visitors. (Abe, 2023) These perceptions
often influence contemporary social justice issues such as the recent
exclusion and reconciliation of Kānaka Maoli art in the Hawaii Convention
Center (Lincoln, 2013). Furthermore, this colonial tourism diminishes the
cultural values and identities of the Kānaka Maoli people. Haunani-Kay Trask
refers to the practice of the commercialization of altered native values and
practices as ’cultural prostitution’ which leads to cultural exploitation and
erasure (Trask, 1999).

Situated augmented reality offers an opportunity to bring digital assets that
depict and represent these issues: (1) Historical Context, (2) Contemporary
Issues, and (3) Cultural Prostitution, in front of tourists who are actively
inhabiting Hawaiian space.

We reflect on our positionality and its potential impact on how we create
artifacts that relate to culture and identity. The first author is a
Hawai‘i-grown media artist with experience in serving the Hawai‘i State
legislature and grass-roots advocacy groups. Their research-based projects
explore issues around settler colonialism, militarism, and tourism as a way to
challenge the dominant historical narratives of Hawai‘i. The first author,
although born and raised in Hawai‘i is not of Kānaka Maoli or Native Hawaiian
descent. Thus, is navigating their work as a settler colonist on the lands of
the Kānaka Maoli people. The second author and third authors are contributors
who identify as Asian-American and consider how their non-native identity
should relate to situated augmented reality work.

##  2\. Related Work

Hawaiian Airlines recent implementation of the “Travel Pono” in-flight
announcement video (Hawaiian Airlines, 2021) is an example of (non-XR) media
strategies employed by businesses to educate the audience on the impact of
tourism and present best practices for visitors. The video was crafted in a
way where Hawaiian airlines staff — flight attendants, pilots to engineering
staff — individually introduce aspects of ways to travel “Pono” or
responsibly. The video successfully presents topics of sustainability and
cultural preservation through the voices of the “local community.”

For the depiction of archival materials, Booksnake is an app developed by
digital humanities scholars that brings archival material from institutions
such as the Huntington Library and Library of Congress into augmented reality
environments as manipulable assets.(Fraga, 2023). In this way, Booksnake
demonstrates how augmented reality empowers and permits users greater access
to materials from archival institutions. In a situated augmented reality app,
these historical assets could be collocated with the present day, to encourage
tourists to engage with the historical layer of their inhabited space.

For aspects of reclaiming historical narrative, Kinfolk shows myriad
approaches in using AR to inform the user about marginalized Black and Brown
histories (Brewster and Cantave, 2017). By drawing upon these techniques,
social justice applications for situated augmented reality could describe the
marginalization of Hawaiian experiences due to colonial tourism.

##  3\. Process Artifact

In our process artifact, we intend to display assets that address the
following aspects of the effect of colonial tourism:

  * •

Historical Context \- Provide information about the history, and intention of
settlers in Hawai‘i through the display of historical artifacts that depict
scenes from the late 19th and early 20th century.

  * •

Contemporary Issues \- Bring awareness to recent issues regarding tourism. For
example, we can show news articles or secondary sources on the recent
exclusion of art in the convention center (Lincoln, 2013).

  * •

Identity Harm \- Display examples of commercialized or harmful caricatured
native values or practices. In Fig.2, we place a 1943 advertisement of a
sexualized ”Hawaiian” hula girl singing alongside a white man, as an example
of cultural prostitution of the native practice of hula. (Warner, 1943)

Figure 2. In this mockup, a user views a historical artifact and context using
situated augmented reality

In this mockup, a user is scanning the environment with their mobile phone and
sees a augmented reality panel representing a historical artifact

Figure 3. A user interface conceptually depicting a wayfinding system for
navigation in situated augmented reality in a real world setting

In this image, a wayfinding design system is represented showcasing how the
augmented reality experience will take place across site-specific locations

The prototype will showcase an augmented reality (AR) storytelling experience
that leverages cutting-edge technology enhanced with geolocation in order to
design site-specific AR interactions. Users are invited to engage with a
variety of public visual position system (VPS) locations featuring digital AR
objects placed across the convention center. By physically visiting these
sites and utilizing their smartphones to scan the designated markers, these
markers will contextually transform these sites with augmented historical
artifacts embedded within the fabric of the physical environment. The core
technological backbone powering this experience will be designed using the 8th
Wall Lightship SDK combined with the VPS World Explorer system. These tools
will allow a robust AR experience, accessible to the public through mobile
devices. By directly interacting with these site-specific AR artifacts, users
will experience a variety of stories depicting critical topics in both the
historical and contemporary Hawaiian cultures communicating information about
settler history. The stories and critical events represented in this
experience will be collected and curated from real community members from the
Hawai’ian community. AR artifacts leveraged with VPS geolocation technology
will build a seamless, site-specific experience connecting the user with both
the digital and real-world environments. Users will be encouraged to explore
and interact with these digital artifacts to learn more information, navigate
to other nearby AR artifacts available on a map interface, and immerse
themselves through AR portals into a 3D environment depicting the story.

The artifacts presented through AR will be collected through engagement of the
Kānaka Maoli and local Hawai’i community. From conversations on key
narratives, to showcasing artwork produced by contemporary Kānaka Maoli
artists, this artifact will push for greater representation of Kānaka Maoli
and local Hawai’i voices. We intend for the AR medium to serve as a tool to
amplify local and native concerns to the greater audience.

##  4\. Topics for Discussion

  * •

How do we best present and contextualize archival materials for social
justice?

  * •

How do ensure that situated augmented reality empowers locals?

  * •

What are best practice for facilitating local institutions’ augmented reality
capabilities?

## References

  * (1)
  * Abe (2023) Karen Abe. 2023.  _Home Not Paradise_.  Bachelor’s thesis. University of Southern California.  https://www.karenabe.com/home-not-paradise
  * Brewster and Cantave (2017) Idris Brewster and Glenn Cantave. 2017.  Kinfolk.  https://kinfolkhistory.com/
  * Fraga (2023) Sean Fraga. 2023.  Booksnake.  https://youtu.be/5azsHdJJQ4k
  * Hawaiian Airlines (2021) Hawaiian Airlines. 2021.  Travel Pono.  https://www.youtube.com/watch?v=KPcoUjCmhFg
  * Lincoln (2013) Mileka Lincoln. 2013.  Controversial Convention Center artwork covered up, will be removed.  https://www.hawaiinewsnow.com/story/23355775/controversial-convention-center-artwork-covered-up-will-be-removed
  * Mak (2015) James Mak. 2015.  _Creating ”Paradise of the Pacific”: How Tourism Began in Hawaii_.  Technical Report. The Economic Research Organization at the University of Hawai’i.  https://uhero.hawaii.edu/wp-content/uploads/2019/08/WP_2015-1.pdf
  * Trask (1999) Haunani-Kay Trask. 1999.  _Lovely Hula Hands: Corporate Tourism and the Prostitution of Hawaiian Culture._ University of Hawai’i Press.
  * Warner (1943) Henry Warner. 1943.  Page 51 Inside Rear Cover: Old man playing ukulele with a hula girl.  https://digital.library.manoa.hawaii.edu/items/show/29931#?c=0&m=0&s=0&cv=0&xywh=-777%2C-1%2C3308%2C2391


acmcopyright

rightsretained 978-1-4503-4249-0/16/10
http://dx.doi.org/10.1145/2980055.2987353

# Millimeter Wave Wireless Communications: New Results for Rural Connectivity

George R. MacCartney  Jr  Shu Sun  Theodore S. Rappaport  Yunchou Xing
Hangsong Yan  Jeton Koka  Ruichen Wang  and Dian Yu

<EMAIL_ADDRESS>NYU WIRELESS

New York University, Tandon School of Engineering

(2016)

###### Abstract

This paper shows the remarkable distances that can be achieved using
millimeter wave communications, and presents a new rural macrocell (RMa) path
loss model for millimeter wave frequencies, based on measurements at 73 GHz in
rural Virginia. Path loss models are needed to estimate signal coverage and
interference for wireless network design, yet little is known about rural
propagation at millimeter waves. This work identifies problems with the RMa
model used by the 3rd Generation Partnership Project (3GPP) TR 38.900 Release
14, and offers a close-in (CI) reference distance model that has improved
accuracy, fewer parameters, and better stability as compared with the existing
3GPP RMa path loss model. The measurements and models presented here are the
first to validate rural millimeter wave path loss models.

###### keywords:

Millimeter-wave; mmWave; channel model; path loss; rural macrocell; 73 GHz;
RMa; standards, 3GPP

††conference: AllThingsCellular’16 October 03-07 2016, New York City, NY, USA

G. R. MacCartney Jr., S. Sun, T. S. Rappaport, Y. Xing, H. Yan, J. Koka, R.
Wang, and D. Yu, “Millimeter Wave Wireless Communications: New Results for
Rural Connectivity," All Things Cellular’16, in conjunction with ACM MobiCom,
Oct. 7, 2016.

##  1 Introduction

In the very early days of the wireless industry, [18] predicted that wireless
would be as pervasive as utility lines and house wiring by 2020. Now, that
vision may be reached, as the fifth-generation (5G) of wireless standards are
being developed for millimeter-wave (mmWave) frequency bands to provide tens
of gigabits per second data rates, since today’s frequencies below 6 GHz are
too crowded to meet global traffic demand [20]. The 3rd Generation Partnership
Project (3GPP), the cellular industry’s global standards body, initiated a
working group in September 2015 to develop channel models for spectrum above 6
GHz, as have other groups such as METIS [14], MiWEBA [2], mmMagic [15], ETSI
[5], and IEEE 802.11ad. In just 10 months, 3GPP released TR 38.900 - Release
14 for channel models above 6 GHz in July 2016 [4].

The development of 3GPP’s channel models above 6 GHz was supported by numerous
academic and industrial measurement campaigns and ray-tracing simulations for
urban macrocell (UMa), urban microcell (UMi), and indoor hotspot (InH)
scenarios [21, 16, 1, 7, 8, 24, 13, 25, 23]. The channel models will be useful
for the development of 5G waveforms, MAC, and PHY approaches, especially in
light of the Federal Communications Commission’s (FCC) goal for the USA to
lead global 5G rollout via its recent Spectrum Frontiers ruling [6]. The
breakneck speed of 5G channel model development, however, increases the
likelihood of adoption of models that are theoretically flawed, or
unsubstantiated by empirical evidence.

While UMi, UMa, and InH scenarios were extensively studied [1, 21, 7, 8, 13,
24, 25, 16, 23], the rural macrocell (RMa) scenario was neglected and is not
fully understood. The mmWave RMa model [4] was hastily adopted from a
cumbersome and two-decade-old propagation model meant for frequencies below 6
GHz, with very light validation from a very limited measurement campaign at 24
GHz [3]. As shown here, the dual slope RMa model in [4] is not valid,
mathematically, above 9.1 GHz, meaning that a flawed and untested model
currently exists in 3GPP. Also, we show that virtually no field measurements
have been used to test the existing model. This paper offers a solution to the
mathematical problem, and validates a much simpler path loss model for the RMa
propagation scenario with field measurements at 73 GHz. This paper is
organized as follows: Section 2 describes the existing RMa path loss models in
3GPP [4] and illuminates the mathematical problem and lack of evidence for the
models, Section 3 describes the 73 GHz RMa measurement campaign conducted in
August 2016 in Riner, Virginia, Section 4 provides and discusses the empirical
results and RMa path loss models for frequencies above 6 GHz, and conclusions
are drawn in Section 5.

##  2 RMa Path Loss Model in 3GPP

RMa path loss (PL) models enable engineers to predict signal strength as a
function of propagation distance in rural environments from a tall tower
(macrocell). The RMa path loss model equations provided in 3GPP [4] are long
and cumbersome with numerous input parameters specifying the base station
height ($h_{BS}$), user terminal height ($h_{UT}$), average street width ($W$,
a questionable variable for RMa scenarios), average building height ($h$, also
questionable for rural settings), three-dimensional (3D) transmitter-receiver
(T-R) separation distance ($d_{3D}$), and carrier frequency ($f_{c}$). The RMa
line-of-sight (LOS) path loss model (for when a transmitter (TX) antenna can
see the receiver (RX) antenna) is a dual slope model with a breakpoint given
in (1),(2) [4, 11]:

$\displaystyle\begin{split}PL_{1}&=20\log(40\pi\cdot d_{3D}\cdot
f_{c}/3)+\min(0.03h^{1.72},10)\log_{10}(d_{3D})\\\
&-\min(0.044h^{1.72},14.77)+0.002\log_{10}(h)d_{3D}\\\
PL_{2}&=PL_{1}(d_{BP})+40\log_{10}(d_{3D}/d_{BP})\end{split}$ (1)

where all heights and distances are in meters (m) and the shadow fading
standard deviation is $\sigma_{SF}$ = 4 dB for $PL_{1}$ (before the
breakpoint) and $\sigma_{SF}$ = 6 dB for $PL_{2}$ (after the breakpoint). (See
[19] for treatment of large-scale path loss modeling). Eq. (1) is adopted from
ITU-R M.2135 [11] as the LOS RMa path loss model. The breakpoint $d_{BP}$ in
(1) is the particular distance where the slope of the path loss changes, and
is defined as:

$d_{BP}=2\pi\cdot h_{BS}\cdot h_{UT}\cdot f_{c}/c$ (2)

where $f_{c}$ is the carrier frequency in Hz and $c$ = $3.0\times 10^{8}$ m/s
(speed of light in air or free space).

The RMa non-LOS (NLOS) path loss model (for when buildings or foliage block
the radio path) is given in (3) and has an odd physical imperfection such that
it models close-in signals (say within 500 m) as being much stronger than
physics would dictate, and thus requires a mathematical patch by requiring a
lower bound equal to the RMa LOS path loss model [4, 24]:

$\displaystyle\begin{split}PL&=\max(PL_{RMa-LOS},PL_{RMa-NLOS})\\\ PL&{}_{RMa-
NLOS}=161.04-7.1\log_{10}(W)+7.5\log_{10}(h)\\\
&-(24.37-3.7(h/h_{BS})^{2})\log_{10}(h_{BS})\\\
&(43.42-3.1\log_{10}(h_{BS}))(\log_{10}(d_{3D})-3)\\\
&+20\log_{10}(f_{c})-(3.2(\log_{10}(11.75h_{UT}))^{2}-4.97)\end{split}$ (3)

where all heights and distances are in meters and the shadow fading standard
deviation $\sigma_{SF}$ = 8 dB. Table 1 provides the applicability range and
default parameter values for the LOS and NLOS RMa path loss models.

Table 1: 3GPP TR 38.900 RMa path loss model default values and applicability
ranges [4].

RMa LOS Default Values Applicability Range
---
10 m $<d_{2D}<d_{BP}$,
$d_{BP}<d_{2D}<10\>000$ m,
$h_{BS}=35$ m, $h_{UT}=1.5$ m, $W=20$ m, $h=5$ m
Applicability ranges: 5 m $<h<50$ m; 5 m $<W<50$ m;
10 m $<h_{BS}<150$ m; 1 m $<h_{UT}<10$ m
RMa NLOS Default Values Applicability Range
10 m $<d_{2D}<5\>000$ m,
$h_{BS}=35$ m, $h_{UT}=1.5$ m, $W=20$ m, $h=5$ m
Applicability ranges: 5 m $<h<50$ m; 5 m $<W<50$ m;
10 m $<h_{BS}<150$ m; 1 m $<h_{UT}<10$ m

Similar to LOS, the RMa NLOS path loss model was adopted from [11].

A footnote for the RMa path loss models in [4] specifies that the applicable
frequency range is $0.8$ GHz $<f_{c}<f_{H}$, where $f_{H}$ is 30 GHz for RMa,
but we found only one small measurement campaign (at 24 GHz) that tried to
validate the RMa model [3]. Surprisingly, the path loss models in [11] are for
below 6 GHz, calling into question the validity of these models for mmWave.

###  2.1 3GPP RMa LOS Path Loss Origin

The LOS RMa path loss model in [4], adopted from [11], originates from ITU
5D/88-E [10], which only shows a portion of the model in [4]. The [10]
document cites work by the NTT Wireless Systems Laboratories as the original
source of the RMa LOS path loss model, but measurements were only conducted at
2.6 GHz [9]. We can find no other publication or open-source document other
than [3] to support the 3GPP RMa path loss model above 6 GHz, yet major US
carriers such as Verizon and AT&T are eyeing rural mmWave service in their
first trials of new mmWave spectrum. This lack of empirical support, and
apparent misappropriation of the 3GPP RMa model have motivated the mmWave
channel measurements and models herein.

Even more surprising is that the 3GPP LOS RMa dual slope path loss model (1)
is mathematically invalid for frequencies above 9.1 GHz, since the breakpoint
distance (2) at 9.1 GHz or greater is farther than 10 km, the upper range
specified for the model (See Table 1). Figure 1 displays a plot of the
breakpoint distance vs. frequency for (2), and shows that the RMa path loss
model [4] reverts to a single slope model for centimeter-waves above 9.1 GHz,
and for all mmWave frequencies. An improved close-in reference distance
single-slope model that avoids this problem is shown in Section 4.

Figure 1: LOS breakpoint distance vs. frequency in (2).

###  2.2 3GPP RMa NLOS Path Loss Origin

The NLOS RMa path loss model in [4] and adopted from [11] can be traced back
to a paper by Sakagami and Kuboi from 1991 that is based on empirical data
from Tokyo at 813 MHz and 1443 MHz in a dense urban environment [22],
otherwise known as the extended Sakagami model [17]. This explains the odd
variables in (1)–(3) which are unneeded in rural settings. One difference
between the legacy Sakagami model and models in [11] and [4] is the first term
in (3) which is 161.04 dB rather than 100 or 101 dB, since the Sakagami models
had units of frequencies in MHz rather than GHz (the difference in free space
path loss at 1 m between 1 MHz and 1 GHz is $\sim$ 60 dB).

The only effort [3] to validate the 3GPP RMa model [4] above 6 GHz described a
limited measurement study at 24 GHz that combined LOS and NLOS scenarios, and
which was never peer reviewed. The study was conducted over a very limited
two-dimensional (2D) T-R separation distance range of 200 to 500 m, yet the
published RMa model in (1)–(3) [4] is specified over a 2D T-R distance from 10
m to 5 km or 10 km. Additionally, [3] did not provide a best-fit indicator
(e.g., RMSE) between the measured data and model. With such little evidence
and questionable origins of the 3GPP RMa model, we set out to conduct a rural
macrocell measurement and modeling study in LOS and NLOS beyond the 10 km
distances stated in [4].

##  3 73 GHz RMa Measurements

A measurement campaign was conducted in Riner, Virginia, a rural town in the
southwestern portion of the state using the 73 GHz mmWave frequency band. The
TX was located at Prof. Rappaport’s mountain home. A narrowband CW tone was
transmitted at a center frequency of 73.5 GHz with a maximum transmit power of
14.7 dBm (28 mW) with a 7∘ azimuth and elevation half-power beamwidth (HPBW)
antenna having 27 dBi of gain, which resulted in 41.7 dBm effective isotropic
radiated power (EIRP) (14.8 W EIRP), much lower power than traditional RMa
cellular base stations. Figure 2 displays the TX schematic where a 5.625 GHz
CW tone is mixed with a 67.875 GHz signal (22.625 GHz x3 frequency multiplied
inside the upconverter) to reach an RF center frequency of 73.5 GHz.

Figure 2: 73 GHz TX measurement equipment.

At the RX, an identical narrowbeam horn antenna with 7∘ azimuth and elevation
HPBW and 27 dBi of gain was used to capture the RF signal which was
downconverted (with 29.9 dB of gain) to bring the RF signal to an intermediate
frequency (IF) of 5.625 GHz that was subsequently amplified with a low-noise
amplifier (LNA) with 35 dB of gain (note the step attenuator in Figure 3 to
ensure linear operation). A Keysight E4407B spectrum analyzer in zero-span
mode recorded received power levels, as depicted in Figure 3 using a 15 kHz
bandwidth setting. Occasional frequency tuning was required to account for
system oscillator drift. Similar to the 73 GHz upconverter, the local
oscillator (LO) of 22.625 GHz that enters the 73 GHz downconverter is x3
frequency multiplied to 67.875 GHz to demodulate the 73.5 GHz RF signal to the
5.625 GHz IF.

Figure 3: 73 GHz RX measurement equipment.

The maximum measurable path loss of the system was 190 dB, with local time
averaging used to obtain received power at various RX locations.

###  3.1 Measurement Locations and Approach

For the RMa measurements, 14 LOS locations and 17 NLOS locations were measured
with detectable signal, and 5 additional locations resulted in outages where
signal was not detectable. The 2D T-R separation distance ranged from 33 m
(calibration distance) to 10.8 kilometers (km) for LOS scenarios and 3.4 km to
10.6 km for NLOS scenarios. The TX was located on a house porch on top of a
mountain ridge, $\sim$ 110 m above surrounding terrain as shown in Figure 5.

Figure 4: Map of TX and RX locations. The yellow star represents the TX, red
pins indicate NLOS locations, and blue pins indicate LOS locations. Figure 5:
Sketch of TX location and surroundings.

Figure 6 shows the northerly view from the TX to the surroundings below, where
there is a 31 m distance from the house to the mountain drop edge.

Figure 6: Outward view from TX.

During the measurements the TX antenna was set to a fixed downtilt of 2∘ and
the azimuth was manually set to point in a direction that maximized received
power at the various RX locations.

The measurement system was periodically calibrated at a distance of 33 meters
to ensure theoretical free space path loss and accurate performance, with
repeatability of 0.2 dB over the two-day campaign. The RX system was placed in
a van and driven to measurement locations along highways and neighborhoods. At
each RX location, the RX antenna was raised to an average height between 1.6
and 2 meters above ground while the best TX and RX antenna azimuth angles were
manually determined based on the strongest received power.

Figure 4 shows the Google Earth imagery of the TX and RX locations and the TX
azimuth scanning window of $\pm 10^{\circ}$ of true North so as to avoid a
mountain that was west of the TX, and to avoid diffraction from the east side
of the antenna due to a rising slope in the front yard of the house. RX
locations that only had one or two small trees blocking the LOS path were
considered LOS. Furthermore, two LOS locations (RX 31 and RX 32; top right
corner of Figure 4) are shown but not used in the path loss model derivation
due to the diffraction loss from the sloping yard (see Figure 6).

##  4 RMA Path Loss Model Results

###  4.1 CI Path Loss Model

As an alternative to the complicated RMa path loss model in (1)–(3) [4], the
close-in free space reference distance (CI) path loss model has a solid
physical basis, a succinct form with only one modeling parameter, and is
simultaneously applicable for frequencies both below and above 6 GHz [21, 13,
24, 12, 7, 8]. Also, the CI path loss model exhibits excellent parameter
stability and prediction accuracy for unanticipated use cases over distances
outside of the original measurement range compared to other path loss models
[24, 25]. The CI model has already been adopted as an optional path loss model
for the UMi, UMa, and InH scenarios in [4] based on works by the authors [1,
7, 8, 24, 25, 13] due to its use of a frequency dependent free space path loss
term in the first meter [21, 13, 24, 25]. The CI model, with its many virtues,
is now shown to be well suited for the RMa scenario, as data confirm this
model would be a sensible replacement for the current 3GPP RMa model. The
general form of the CI path loss model is expressed as follows:

$\begin{split}\textrm{PL}^{\textrm{CI}}(f_{c},d)[\textrm{dB}]=&\textrm{FSPL}(f_{c},d_{0})[\textrm{dB}]+10n\log_{10}\left(\frac{d}{d_{0}}\right)\\\
&+\chi_{\sigma},~{}\text{where}~{}d\geq d_{0}\end{split}$ (4)

where PL is the path loss measured in dB that is a function of T-R separation
distance $d$ in m between the TX and RX, $f_{c}$ is the carrier frequency in
GHz, and $d_{0}$ is the close-in free space reference distance in m. For
distance $d$ between the TX and RX, 2D or 3D distances may be used, as the
difference is de minimus for large separations (several km). In (4), $n$
represents the path loss exponent (PLE) [21, 13, 24, 19, 7, 8], and
$\chi_{\sigma}$ denotes the shadow fading which is a zero-mean Gaussian random
variable with standard deviation $\sigma$ in dB [19, 21]. Note that in (4),
$10\times n$, or $10\times\textrm{PLE}$, is the coefficient in front of the
log-distance term. The free space path loss in dB at a distance $d_{0}$ is
given by Friis’ free space path loss (FSPL) [19]:

$\textrm{FSPL}(f_{c},d_{0})[\textrm{dB}]=20\log_{10}\left(\frac{4\pi
f_{c}d_{0}\times 10^{9}}{c}\right)$ (5)

where $c$ is the speed of light, $3\times 10^{8}$ m/s.

In the optional CI path loss model for UMi, UMa, and InH scenarios in 3GPP
(July 2016) [4], the free space reference distance $d_{0}$ is set to 1 m,
since there is clearly no obstruction in the first meter of transmission, and
it simplifies the mathematical equation, provides a standardized modeling
approach that may be used universally, and has been shown to yield superb
model accuracy and parameter stability across a wide range of frequencies and
distances [24, 21, 25]. Based on the efficacy and stability of a 1 m reference
distance [21, 13, 24, 25], we use this in the RMa CI model. With $d_{0}$ = 1
m, the FSPL in Eq. (5) can be reformulated as:

$\begin{split}\textrm{FSPL}(f_{c},\text{1
m})[\textrm{dB}]&=20\log_{10}\left(\frac{4\pi f_{c}\times 10^{9}}{c}\right)\\\
&=20\log_{10}\left(\frac{4\pi\times 10^{9}}{c}\right)+20\log_{10}(f_{c})\\\
&=32.4+20\log_{10}(f_{c})\;[\textrm{dB}]\\\ \end{split}$ (6)

Consequently, Eq. (4) can be recast as:

$\begin{split}\textrm{PL}^{\textrm{CI}}(f_{c},d)[\textrm{dB}]=&\textrm{FSPL}(f_{c},\text{1
m})[\textrm{dB}]+10n\log_{10}(d)+\chi_{\sigma}\\\
=&32.4+10n\log_{10}(d)+20\log_{10}(f_{c})\\\
&+\chi_{\sigma},~{}\text{where}~{}d\geq 1~{}\textrm{m}\\\ \end{split}$ (7)

###  4.2 3GPP RMa Monte Carlo Simulation

To test the efficacy of the CI model, we simulated the 3GPP RMa LOS and NLOS
path loss models from (1)–(3) using default values in Table 1. A Monte Carlo
simulation was conducted for each environment at the following frequencies: 1,
2, 6, 15, 28, 38, 60, 73, and 100 GHz, with 50,000 instances each, for 2D T-R
separation distances ranging between 10 m and 10 km in LOS and between 10 m
and 5 km in NLOS. The CI model with a 1 m close-in reference distance (7) was
fit to the simulated 3GPP model sample points generated from random distances
and normal (in dB) shadow fading sample values using (1)–(3) (frequencies
above 9.1 GHz reverted to a single slope model and ignored the second slope
portion of (1)). From the simulated sample points, the equivalent CI path loss
models were developed:

$\begin{split}\textrm{PL}^{\textrm{CI}\text{-3GPP}}_{\textrm{LOS}}(f_{c},d)[\textrm{dB}]=&32.4+23.1\log_{10}(d)+20\log_{10}(f_{c})\\\
&+\chi_{\sigma_{\textrm{LOS}}},~{}\text{where}~{}d\geq 1~{}\textrm{m}\\\
\end{split}$ (8)
$\begin{split}\textrm{PL}^{\textrm{CI}\text{-3GPP}}_{\textrm{NLOS}}(f_{c},d)[\textrm{dB}]=&32.4+30.4\log_{10}(d)+20\log_{10}(f_{c})\\\
&+\chi_{\sigma_{\textrm{NLOS}}},~{}\text{where}~{}d\geq 1~{}\textrm{m}\\\
\end{split}$ (9)

where the large-scale shadow fading standard deviations
${\sigma_{\textrm{LOS}}}$ and ${\sigma_{\textrm{NLOS}}}$ were 5.9 dB and 8.3
dB, respectively. These simulated models were not based on measured data, but
were formed by using the RMa model in [4] to reproduce simulated data that
could then be fit to the CI model. The resulting $\sigma$ values in the
resulting CI models were quite reasonable but the PLE of 2.31 in LOS and 3.04
in the NLOS simulations show that the 3GPP RMa model predicts greater loss at
larger distances compared to measured results (see Section 4.3), a phenomenon
also found in other 3GPP models [24]. This exercise showed that the existing
3GPP RMa models could be recast in a much simpler and mathematically accurate
form using the CI model with a single slope for all frequencies, but as
measurements revealed, the 3GPP model is inaccurate. An elegant feature of the
CI model is that 23.1 in (8) corresponds to a PLE $n$ of 2.31 and is
equivalent to $10n=23.1$, or 23.1 dB loss for each decade increase of
distance.

###  4.3 Proposed RMa Path Loss Model

Using the measured data from the RMa measurement campaign at 73 GHz described
in Section 3, we determined the minimum mean square error (MMSE) fit (the
optimal PLE to minimize $\sigma$) using the CI model (7) for LOS and NLOS RMa
environments. Figure 7 illustrates the scatter plot of path loss versus T-R
separation distance, where blue circles represent measured LOS path loss data,
red crosses denote measured NLOS data, and the two green diamonds indicate LOS
data with partial diffraction from the edge of the yard near the TX. As shown
by Figure 7, wireless communication links can be established using a very
small TX power (and small bandwidth) to beyond 10 km in the RMa scenario at 73
GHz, implying a large coverage area for a very tall RMa base station.
Moreover, the LOS PLE is 2.16, very close to the free space PLE of 2.0 [19,
12, 21]; where the slightly higher PLE above free space may be caused by light
foliage obstructions and misalignment between TX and RX antennas. The green
diamonds in Figure 7 indicate that diffraction by mountain edges may lead to a
large amount of additional path loss in the LOS environment. The measured NLOS
data render a NLOS PLE of 2.75, lower than the UMi and UMa cases published in
[24, 1, 4] (where the PLE is between 2.9 and 3.2), which reveals a favorable
propagation condition at mmWave frequencies in an RMa scenario when using such
a tall TX antenna (110 m above the ground – also called a “boomer cell").
Fading variations over a few seconds were observed at some RX locations, with
small fluctuations in received power that ranged from approximately a fraction
of a dB about the average in LOS, and larger variations of
$\sim\pm\>3-5\;\textrm{dB}$ about the mean in NLOS due to foliage movement
caused by wind.

Figure 7: 73 GHz RMa path loss vs. T-R separation distance using a CI model
with 1 m.

From measured results depicted in Figure 7, the RMa CI path loss models for
LOS and NLOS environments (7) can be written as:

$\begin{split}\textrm{PL}^{\textrm{CI}}_{\textrm{LOS}}(f_{c},d)[\textrm{dB}]=32.4+21.6\log_{10}(d)+20\log_{10}(f_{c})\\\
+\chi_{\sigma_{\textrm{LOS}}},~{}\text{where}~{}d\geq 1~{}\textrm{m}\text{,
and }\sigma_{\textrm{LOS}}=1.7\;\textrm{dB}\\\ \end{split}$ (10)
$\begin{split}\textrm{PL}^{\textrm{CI}}_{\textrm{NLOS}}(f_{c},d)[\textrm{dB}]=32.4+27.5\log_{10}(d)+20\log_{10}(f_{c})\\\
+\chi_{\sigma_{\textrm{NLOS}}},~{}\text{where}~{}d\geq 1~{}\textrm{m}\text{,
and }\sigma_{\textrm{NLOS}}=6.7\;\textrm{dB}\\\ \end{split}$ (11)

where the shadow fading standard deviations ${\sigma_{\textrm{LOS}}}$ and
${\sigma_{\textrm{NLOS}}}$ are 1.7 dB and 6.7 dB, respectively, according to
the measured data. We note the UMa PLE in the CI model is not a function of
carrier frequency when using a 1 m FSPL reference distance based on previous
investigations [24, 7, 8], thus the RMa PLE is also independent of frequency
beyond 1 m. Furthermore, (10) and (11) are based on measurements out to and
beyond 10 km for both LOS and NLOS, whereas (1) and (3) are limited to 10 km
and 5 km, respectively. Measurements here show that RMa path loss (10), (11)
are valid for distances from 1 m to 11 km and frequencies from 500 MHz to 100
GHz. To match the existing 3GPP RMa model [4], one can increase the standard
deviations in (10) and (11) (e.g., to 4 dB and 8 dB for LOS and NLOS
environments, respectively) [4]. As this is the first in-depth empirical study
to explore RMa path loss, more experiments would be valuable to verify the
best RMa PLE and $\sigma$ model parameters. The CI models in (10) and (11)
have been validated by this 73 GHz measurement campaign, and prove that the CI
model can accurately describe RMa path loss at mmWave bands, just as it has
for UMi, UMa, and InH scenarios that are optional models in [4]. These models
are implemented in the NYUSIM open source 5G channel model simulator software
[23] as an optional model [4].

##  5 Conclusions

This paper discussed the fundamentals of propagation path loss and
demonstrated problems with the current RMa path loss model in 3GPP’s standard
above 6 GHz. We showed the rural macrocell (RMa) model used in [4] is derived
from urban models for below 6 GHz, and we presented an alternative model, the
CI model, which has a solid physical basis, is simple, accurate over all
frequencies, and is verified here. A first-of-its-kind RMa propagation
measurement campaign at 73 GHz was conducted in a rural area to confirm the
accuracy and validity of the proposed CI RMa model, while demonstrating the
remarkable distances and coverage that may be obtained using mmWave
communication beyond 10 km in an RMa scenario (the NLOS model in [4] is
limited to 5 km, and there was no effort to validate the 3GPP model, except
for a few measurements to only 500 m). Using the measured results from the RMa
campaign, the PLEs for LOS and NLOS environments were found to be 2.16 and
2.75, respectively, which were significantly different than the PLE values of
2.31 (LOS) and 3.04 (NLOS) found using simulated results based on the existing
3GPP RMa path loss model [4]. This shows that the 3GPP model predicts greater
path loss in LOS and NLOS compared to measured observations, as was also shown
in [24, 25] for other 3GPP scenarios. The CI models in (10) and (11) may be
used for RMa from 500 MHz to 100 GHz, and users may wish to increase the
standard deviations to 4 dB and 8 dB for LOS and NLOS, respectively, in order
to match [4]. It seems prudent for 3GPP to replace the current RMa models,
which are shown to have originated from urban measurements, are unverified by
rural measurements, fit poorly to measured data herein, and remain undefined
above 9.1 GHz, with the RMa CI models given in (10) and (11). At the very
least, (10) and (11) should be optional in the 3GPP standard.

##  6 Acknowledgments

This work was supported by the NYU WIRELESS Industrial Affiliates Program,
three National Science Foundation (NSF) Research Grants: 1320472, 1302336, and
1555332, and the GAANN Fellowship Program.

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