# Self-assess Momentary Mood in Mobile Devices: a Case Study with Mature
Female Participants

1st Caterina Senette  IIT
National Research Council
Pisa, Italy
<EMAIL_ADDRESS>2nd Maria Claudia Buzzi  IIT
National Research Council
Pisa, Italy
<EMAIL_ADDRESS>3rd Maria Teresa Paratore  ISTI
National Research Council
Pisa, Italy
<EMAIL_ADDRESS>
###### Abstract

Starting from the assumption that mood has a central role in domain-specific
persuasion systems for well-being, the main goal of this study was to
investigate the feasibility and acceptability of single-input methods to
assess momentary mood as a medium for further interventions in health-related
mobile Apps destined for mature women. To this aim, we designed a very simple
android App providing four user interfaces, each one showing one interactive
widget to self-assess mood. Two widgets report a hint about the momentary mood
they represent; the last two do not have the hints but were previously refined
through questionnaires administered to 63 women (age 45-65) in order to reduce
their expressive ambiguity. Next, fifteen women (age 45-65 years) were
recruited to use the app for 15 days. Participants were polled about their
mood four times a day and data were saved in a remote database. Moreover,
users were asked to fill out a preliminary questionnaire, at the first access
to the app, and a feedback questionnaire at the end of the testing period.
Results appear to prove the feasibility and acceptability of this approach to
self-assess momentary mood in the target population and provides some
potential input methods to be used in this context.

###### Index Terms:

momentary mood, mobile devices, well-being Apps, design, input methods

††publicationid: pubid: 978-1-6654-5357-8/23/$31.00 ©2023 IEEE

##  I Introduction

Recent advances in technology have resulted in a growing interest in emotion
and computers since a wide range of emotions undeniably plays a critical role
in every computer-related and goal-directed activity. Focusing on ICT
(Information and Communication Technology) systems based on goal-directed
activities, capturing emotional factors is crucial to guiding the user toward
his/her goals. Among these ICT systems, we are interested in tools for health-
related behavior interventions that promote behavioral changes to improve a
person’s health while reducing potential health risk factors. Specifically, we
are interested in self-care approaches released through information systems,
to manage the menopause transition and thus reduce the associated cardio-
metabolic risk [22, 10]. In 2014, a study by Spruijt-Metz et al. [24] advanced
the framework just-in-time adaptive interventions (JITAI) which aims to
“provide information, nudges, and interventions when needed or appropriate,
tailored to the individual’s needs and context, via mobile technologies” [24].
This type of framework is a promising reference methodology for developing
smartphone health and fitness Apps, as well as solutions for other ubiquitous
and pervasive computing technologies exploiting persuasion strategies.
Starting from the assumption that mood has a central role in domain-specific
persuasion systems for well-being, since it helps in recognizing when and how
to deliver behavioral interventions, the main goal of the current work is to
investigate the feasibility and acceptability of single-input methods to self-
assess the user’s momentary mood in a specific target population. Literature
recognizes that mature people, often have ambivalent attitudes towards
technology and tend to be reluctant to accept technological innovations.
Moreover, they often have longer learning times and lower performance measures
than younger people [4, 8]. Thus, focused studies, like the current one, are
necessary since we expect that the interface design would affect the
acceptability and feasibility of the usage of momentary mood trackers in this
target population.

Momentary mood is defined as “The psychological state identified by perceived
internal psychological and physical sensations, such as dullness on waking or
exhilaration after walking” [20]. Investigating the feasibility and
acceptability of tools assessing momentary mood could be helpful not only in
checking user availability for mobile just-in-time interventions [24] but also
in other contexts such as the Ecological Momentary Assessment (EMA) and
Experience Sampling Method (ESM) [23, 25], antecedent’s analysis of addictive
behaviors and mood regulation per se. To this aim, we designed a very simple
android App providing four user interfaces, each one showing one interactive
widget to self-assess mood. Two widgets report a hint about the momentary mood
they represent; the last two do not have the hints but were previously refined
through a questionnaire administered to 63 women (age 45-65) in order to
reduce their expressive ambiguity. Next, fifteen women (age 45-65) were
recruited to use the App for 15 days. Participants were polled about their
mood four times a day and data were saved in a remote database. Moreover,
users were asked to fill out a preliminary questionnaire, at the first access
to the App, and a feedback questionnaire at the end of the testing period.
Thus, main research questions were: (RQ1) Is the proposed approach for mood
assessment feasible and accepted in this target population? (RQ2) Are there
differences among the proposed input methods in terms of input speed,
intuitiveness, expressiveness, appropriateness to the scope, and willingness
to reuse? If so, do these differences have statistical significance?

##  II Related work

Several studies in the literature investigate the use of methodologies that
require participants to self-report on their activities, emotions, or other
elements of their daily life several times a day (EMA/ESM). Among them, we are
interested in research studies reporting mood assessments through mobile
devices. A valuable literature review covering contributions from the computer
science domain can be found in the work of Van Berkel et al. [26]. Here the
authors focused especially on mobile devices, providing important
considerations that were helpful for our work, especially those related to
data quality in data collection. The data collection was also the focus of
another interesting study by Chan et al. [3] that investigated participants’
experiences in EMA studies, providing helpful insights on how these
experiences may influence the collected data. Both studies guided especially
our design choices. It is worth noting that the majority of studies were
conducted in a medical context and participants were people with specific
health conditions. In the work of Richmond et al. [16] participants were
invited to respond to SMS text messages sent out weekly over a period of 15
consecutive weeks, asking participants to rate their experience of depression
on a simple 9-point scale. Other studies have shown the feasibility of using
EMA to gather data on bipolar disorder symptoms via smartphone [1, 21],
sometimes asking patients to respond to auto-generated surveys twice a day
[21]. All these studies provide some evidence for the utility of daily mood
reporting via modern software tools although additional evidence is needed to
better evaluate the usability and acceptability of these tools especially
among different user groups not necessarily medicalized. Focusing on methods
for assessing momentary mood, Desmet et al. [5] concentrated their attention
on tools requiring minimal effort from users who have little time or poor
motivation to report their moods. To this aim, they proposed and validated
Pick-A-Mood, a character-based pictorial scale for reporting and expressing
moods. The research closely related to our investigation is a study by
Wallbaum et al. [27], in which the authors explored the use of four different
methods to express mood on mobile devices through a comparative study
evaluating intuitiveness, inconvenience, speed of input, everyday use,
expressiveness and overall suitability of these methods. Nevertheless, our
study is different because: (i) mood assessment does not serve as a part of
interpersonal communication; (ii) the target population includes only females
aged 45-65 years; (iii) tools tested are different (apart from the PAM, see
Section IV-A1) since the study dates back to 2016 when mobile user interface
design and interaction were different from the current ones.

##  III Theoretical Background

###  III-A How to define Mood

Psychological theories of affect highlight the importance of discriminating
between emotion and mood. These terms are often confused, not only in everyday
thinking but sometimes by experts as well. This is why some of the existing
tools to assess affect (questionnaires, emotion-tracking Apps, and
physiological sensors) are all used to track emotion as well as mood.
Nevertheless, instruments for measuring emotions are not always suitable for
measuring mood [5]. Summarizing, main differences are: (i) Object-directness,
emotion is generally intentional (external focus), mood is non-intentional
(internal focus) [7]; (ii) Time-duration, emotions tend to be relatively
short-lived (seconds, minutes), are episodic and generally express strong
intensity. Moods, in contrast, have a long duration (hours or days), they are
continuous and result in a week [28]; (iii) Effects, emotions bias action,
stimulating an immediate response, on the contrary, mood influences people
without interrupting their ongoing behavior and thoughts, modifying general
readiness to engage [2].

###  III-B Reference Models

Among the main psychological theories of affect, we are interested in
dimensional theories [18] that focused on a mood state – in particular, a
momentary psychological condition resulting from psychological and physical
sensations (such as exhilaration after walking). The dimensional model
describes emotions in two (sometimes three) independent dimensions (arousal,
pleasure) in a Cartesian Space. Based on these two dimensions, Russell created
a Circumplex model of emotions [17] most commonly used to test stimuli of
emotion words, emotional facial expressions, and affective states. Every
language has its mood-related words, and since our study took place in Italy,
final terms, chosen from the Immediate Mood scale [11] and ITAMS, Italian Mood
Scale [15] are: (i) Red-quarter - nervosa, tesa, preoccupata, arrabbiata; (ii)
Yellow-quarter - attenta, energica, felice, euforica; (iii) Green-quarter -
soddisfatta, serena, rilassata, calma; (iv) Blu-quarter - apatica, triste,
depressa, stanca.

##  IV Design study

To conduct our study, we recruited 15 participants (female, age range 45-65
years, M = 55.67 years, SD = 5.40 years) from women working on our research
campus (about 1500 workers), for a 15-day study. Participants were independent
of the study and the research team, and their involvement was voluntary. Since
we are interested in health prevention paths, the age group should be
temporally consistent with ’preventive’ interventions. All of them were asked
to use their personal device and to download our Android app available via
Google Play. The app provided four user interfaces, each showing one
interactive widget to self-assess mood. Participants were polled about their
mood four times a day. The time of assessment could be modified with respect
to the default plan (10:00/14:00/18:00/21:00) keeping a period of at least 3 h
from each time. At each assessment time, the app proposed a randomly selected
widget, without repetition in order to propose all four tools to each
participant every day. When the participant first accessed the app, a
preliminary questionnaire (pre-questionnaire) was proposed, to gather the
user’s information in terms of time available for oneself, presence/absence of
pathological conditions, presence/absence of mood disorders and familiarity
with the use of mobile Apps in general and specifically mood tracking Apps.
All these data could be useful for explaining user behavior (ex post). At the
end of the 15 days, each participant was asked to answer a post-questionnaire
looking for subjective impressions of the app’s use (see Section V-C and
Section VI).

###  IV-A Tools

Two of the four widgets derive from the literature and were appropriately
adapted to our specific context, while the other two are relatively new
proposals. All of them are described in the following and shown in Fig. 1 (a),
(b), (c), (d).

####  IV-A1 PAM widget - Photographic Affect Meter

Pollak et al. presented the Photographic Affect Meter (PAM) in 2011 [14] based
on the Affect Grid by Russell et al. [19] and validated through PANAS [28].
The PAM shows 16 photographs arranged in a 4x4 grid used to represent
individual current mood. Since the final data set of images has not been
shared, researchers who want to use the PAM tool must build their own set of
images. To this aim, we collected images from the Italian Flickr repository,
tagged by millions of users. For each word selected to express Italian terms
for moods (see paragraph 3.2), we extracted a group of 5-6 pictures. Further
refinement was obtained by sharing an online survey (through the mailing list
and Facebook) asking people to select the most appropriate picture for each
mood term. We collected answers from 125 people, 29 males and 96 females, aged
between 18 and 73 years. Of these, we selected data from women aged 45-65
years (n = 63) to better represent our specific target group. Among these
data, we found sufficient polarized responses (low ambiguity) for nine mood
terms, for which one of the proposed options was chosen in at least 60% of the
cases, the remaining 40% (or less) being dispersed among the other images
(options) proposed for each term. For the remaining mood terms (7) we selected
the most voted image, having a minimum of %30 of choices (higher residual
ambiguity). The final image set will be available to other researchers if
requested.

####  IV-A2 Emoji Widget

According to the same dimensional model, we created another self-report tool
based on commonly known emoji, structurally and stylistically richer than
emoticons to better express mood nuances. To build our 16-item emoji tool
while reducing its ambiguity as much as possible, we shared an online survey
asking people to select the most appropriate emoji for each mood term,
choosing from a set of 3-6 items (redundant). We collected answers from 125
people aged between 18 and 73 years (31 males and 94 females) and from them,
we selected data from women aged 45-65 years (n = 63). In this case, polarized
responses were higher than in the image selection (PAM widget), 12 mood terms
matching with the same emoji in 60% of the sample. For the remaining four mood
terms (as occurred for the PAM widget), we selected the most voted emoji
having a minimum of 30% of choices (less residual ambiguity).

####  IV-A3 Color Picker Widget

The third tool is a variant of the Mood Meter developed by the Yale Center for
Emotional Intelligence. As the inventor stated, it helps people develop skills
such as recognizing, understanding, labeling, expressing, and regulating
emotions. The Mood Meter is a square evenly divided into four color quadrants.
Each color quadrant represents a mood category according to Russell’s model
[17]. The color strategy in itself does not contain information about mood;
the color is only a way to identify and label it. In the original tool, each
quadrant has 25 emotion words with a total of 100; in our version, we selected
four words for each quadrant in order to guarantee the same expressiveness and
adequate usability on small-screen devices for all the widgets.

####  IV-A4 Color Wheel Widget

The fourth component is a sort of mix between the Circumplex model [17] and
Plutchik’s Wheel of Emotions [13]. As with the Color Picker widget, Plutchik’s
wheel of emotions helps us to enhance emotional literacy by providing words
for emotions, as well as to understand how different emotions are related to
one another and how they tend to change over time. To be consistent with the
other widgets and to guarantee better usability on smartphones, we created a
simplified version of the tool, where only two levels of emotions (16 mood
terms) have been exploited in a single circle. Moreover, labels associated
with each element of the wheel are placed on the component center. Compared to
the original tool, we preserved concepts such as the intensity level of
emotions, the fusion of primary emotions into new emotions, and the concept of
emotional opposites as mutually exclusive couples.

|  |  |
---|---|---|---
$(a)$ |  $(b)$ |  $(c)$ |  $(d)$
Figure 1: Designed tools: (a) PAM widget; (b) Emoji widget; (c) Color picker
widget; (d) Color wheel widget.

##  V Results

###  V-A User sample characterization

Results from the preliminary questionnaire show that 46% of women had
difficulty finding time for themselves, 76% were in good health; 30% had some
mood disorder but do not usually track mood. Moreover, most of the sample
(70%) do not use health Apps or tracking mood Apps. Most of them (67%)
regularly use other generic Apps, especially social networks.

###  V-B Mood assessment data

The analysis of mood assessment data showed good engagement by most of the
users, and nearly all the participants entered the value of their mood four
times a day as requested. As shown in the literature, notifications on mobile
devices can cause user annoyance due to the interruption of other activities,
especially ongoing tasks on the mobile device itself [9, 26]. Determining the
time of assessments is crucial to gaining user attention, but it is very
difficult to achieve. For these reasons, we let the user customize the
notification time according to their habits. Twelve women did not customize
time-setting notifications, and the remaining three did so partially or
completely. Moreover, for those who did not modify the default time setting, a
time stamp associated with each recorded mood showed that 34% of total
assessments were made within 5 min from the notification event. User
reactivity to notifications was generally high: the average delay (min) of
responses to notifications was in the range of 5-25 min for 9 participants out
of 15, and 5-40 min for the remaining 6. As suggested in the work of Pejovic
and Musolesi [12], the willingness to reply increases when a user has not
received notifications for a while. Probably our target group is less engaged
in continuous activities on the smartphone, and they suffer less from
interruption load than other types of users. Considering the users’ behavior
during the day, we observed high variability among users and there was not a
moment of the day more/less likely to cause assessment delays. Analyzing
recorded values, we did not observe a recurrent mood term in a certain widget
nor recurrences in mood values at a specific time during the day.
Nevertheless, we observed that 50% of users always selected the mood within
two Russell’s quarters regardless of the widget (for widgets with quarters) in
each assessment time. Red-quarter’s moods (energized/unpleasant feelings) and
Yellow-quarter’s moods (energized/pleasant feelings) are more frequent earlier
in the day. Quiet feelings (pleasant or unpleasant) are more frequent in the
evening. In Fig. 2 (left), we show the dominant Russell’s quarters for each
time assessment and for each user. We call dominant a quarter that received a
number of selections greater than the random threshold. As shown in In Fig. 2,
users U8, U9, and U11 customized the time assessment thus, for them we cannot
evaluate dominant Russell’s quarters at the time specified. Moreover, other
than preferred Russell’s quarters, data also revealed a prevalence in the
terms chosen: Calm had the most choices (18% of total assessments), the least
frequently chosen terms were Euphoric (o.65%) and Angry (1.63%). The terms in
the middle were Tired with 12% of choices, and Peaceful with 13%. Fig.2
(right) shows a tag cloud highlighting frequencies in mood term choices as
weights.

###  V-C User perception

Other data were collected via a post-questionnaire including three sections.
In the first section we asked participants to evaluate each widget in terms of
input speed, intuitiveness, expressiveness, appropriateness to the scope, and
willingness to reuse (via 5-item Likert scale: 1 - strongly disagree, 5 -
strongly agree). Statements were formulated in positive form, e.g.: “Assessing
mood is rapid with this widget”. Results obtained by summing the scores for
each widget and for each of the five variables under study are described in
Table I in terms of Best Widget (BW) and Worst Widget (WW). The Color Picker
reached the best score for three of the five variables. The Emoji was
perceived as the worst widget for all the variables except Willingness to
reuse.

The second section asked participants to rate each widget from 1 (poor) to 5
(excellent) regardless of specific criteria. Summing the scores collected for
each widget we obtained the following order from best to worst: 1) Wheel; 2)
Color Picker; 3) Emoji; 4) PAM. The third section allowed participants to
leave comments and suggestions regarding each widget. Only 4 out of 15 women
left comments mainly claiming the perceived ambiguity of PAM and Emoji widgets
as well as the attractiveness of PAM.

TABLE I: Results from user perception in terms of Best widget (BW) and Worst
Widget (WW) for the five variables. Statistical report in dark grey: Friedman
Test results via Chi-Square index and p-values for the same 5 variables; k is
the number of interfaces (input methods), N is the user’s sample dimension and
DoF represents the experiment degrees of freedom.

| Variable | BW | Score | WW | Score
---|---|---|---|---
Input speed | Color Picker | 64 | Emoji | 36
Intuitiveness | Color Picker | 60 | Emoji | 39
Expressiveness | Wheel | 56 | Emoji | 41
Appropriateness | PAM | 59 | Emoji | 46
Willingness to reuse | Color Picker/PAM | 51 | Wheel | 46
k | N | Chi-Square | DoF | p-value
---|---|---|---|---
4 | 15 | 12.14 | 3 | 0.007
4 | 15 | 13.96 | 3 | 0.003
4 | 15 | 14.42 | 3 | 0.002
4 | 15 | 5.44 | 3 | 0.142
4 | 15 | 9.75 | 3 | 0.021

###  V-D Statistical Analysis

The next step was to understand whether the differences identified among the
input methods (via post-questionnaire) were statistically significant or they
were due to chance (RQ2). The statistical perspective must consider two limits
in our study, the smallness of the sample and the data provenance from Likert
scales. Moreover, since each one of the four interfaces has been tested by the
same group of 15 users, we are in the case of within-groups experiments. All
these things considered, the candidate tests are non-parametric tests that do
not require making assumptions about the sampled population. Among those, we
applied the Friedman test [6] with the Chi-Square index, adequately modified
to take into account the peculiarity of the Likert scale in the rank matrices.
Results, shown in Table I, suggest that the differences found in the four
interfaces, according to each of the five dependent variables can be
considered statistically significant with p-value $<$ 0.05 for all the
variables except the Appropriateness for which is not possible to refuse the
null hypothesis. The Table I also indicates how the strength of significance
is lower for the variable Willingness to reuse with a Friedman test value
slightly higher than the critical value, while it is significantly higher for
the other variables considered. It is worth noting that, due to the test
adopted, the rejection of the null hypothesis (for each variable) guarantees
that at least one pair of interfaces among the four considered, express
statistically significant differences. Additional Wilcoxon Sign-Rank tests
[29] helped us identify which interface pairs have statistically significant
differences for each variable: (i) Input speed, all pairs have statistically
significant differences except Color Picker/Color Wheel and Emoji/PAM; (ii)
Intuitiveness, all pairs have statistically significant differences except
Color Picker/Emoji and Emoji/PAM; (iii) Expressiveness, all pairs have
statistically significant differences except Emoji/PAM; (iv) Willingness to
reuse, all pairs have statistically significant differences except Color
Picker/PAM and Emoji/PAM.

|
---|---
Figure 2: Dominant Russell’s quarters during the day for each user (left); Tag
cloud of selected mood terms (right).

##  VI Discussion

Collected data produced ambivalent results. About 50% of participants were
willing to reuse all the widgets and 25% of the sample took a neutral
position, moreover, considering appropriateness for the scope, all the widgets
were judged appropriate; both results partially indicate the success of the
approach per se, regardless of the widget. Moreover, to capture ”the moment”,
the variable to be privileged is the input speed and this is higher for Color
Picker and Wheel which are textually enriched. Nevertheless, we need to
consider the trade-off between input speed and attractiveness which could
affect user motivation. All the widgets force the users to search for their
own mood and this search could be less attractive for textual labels. PAM
widget has been judged the most appropriate and, based on the comments from
participants, photos, despite being ambiguous, seem to have a potential for
attractiveness. However, in the case of PAM, it is unclear to what extent the
photo itself affects the mood rather than faithfully reporting it. The woman
could select the mood she would like to be in rather than the one she is
actually in. Arbitrary responses dues to widget ambiguity as well as poor user
motivation are responsible for low data accuracy which is the main limitation
of single-input methods. On the other hand, a ground truth would require
standardized tools (mainly questionnaires including several items) which are
impossible to map on a single-input method. The differences that emerged among
the four input methods are statistically significant although the analysis has
its major limitations in the smallness of the sample and in the subjectivity
of the data collected. Further tests involving a larger number of users in the
same population are needed to assess the actual acceptability of this
approach, as the general literature reports a high dropout rate in this type
of study [3]. Furthermore, to avoid poor data quality due to arbitrary
responses, the overall design could be enriched through incentives or rewards,
focusing on time as a key factor. The look-screen chosen requires that the
answers be provided in a short time because our goal is to capture the moment;
in other contexts, micro-interaction would not be an advantage and interface
prompts should encourage weighted responses depending on the nature of the
assessment.

##  VII Conclusion

The goal of this study was to evaluate the feasibility and acceptability of
using single-input methods for self-assessing momentary mood in mobile
devices, in a very specific target population (female, age range 45-65).
Considering that almost 50% of the women participants declared they were
usually busy during the day and not inclined to use Apps for mood tracking,
test participation was positive. The app is quite minimal; it does not disturb
but also does not offer anything in return, so this makes user retention
riskier. Nevertheless, no participants left the test before 15 days. Regarding
the input methods proposed, each one showed positive and negative aspects, and
improving their design is certainly possible, for instance by better selecting
color nuances for Color Wheel and Color Picker. While for the PAM widget,
better visibility of the photos was ensured (in our PAM widget all photos
could be enlarged for better visibility but some users did not use this
feature) since our target population (age 45-65) is more likely to have vision
problems on a mobile screen. Furthermore, to increase the attractiveness, more
photos (user’s choice) could be proposed (at each assessment) in case those
proposed were not considered exhaustive; this could reduce intrinsic ambiguity
without forcing all the widgets to be associated with textual labels. In
summary, the experience described seems to prove the feasibility and
acceptability of this approach to self-assess momentary mood in the target
population. These results could be of help for designing richer and more
attractive Apps, destined to mature women, that monitor the user’s well-being
and embed a module for momentary mood assessment.

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# Propagation Measurements and Coverage Analysis for mmWave and Sub-THz
Frequency Bands with Transparent Reflectors

Ashwini P. Ganesh∗, Wahab Khawaja∗, Ozgur Ozdemir∗, İsmail Güvenç∗, Hiroyuki
Nomoto†, and Yasuaki Ide†
∗Department of Electrical and Computer Engineering, NC State University,
Raleigh, NC 27606
†Research & Development Institute, High-Performance Plastics Company, Sekisui
Chemical CO., LTD.
e-mail: {apondey, wkhawaja, oozdemi<EMAIL_ADDRESS>{hiroyuki.nomoto,
<EMAIL_ADDRESS>This work has been supported in part by the National
Science Foundation (NSF) through the award CNS-1916766 as well as the industry
membership fees from the Broadband Wireless Access Center (BWAC) I/UCRC
Center.

###### Abstract

The emerging 5G and future 6G technologies are envisioned to provide higher
bandwidths and coverage using millimeter wave (mmWave) and sub-Terahertz (THz)
frequency bands. The growing demand for higher data rates using these bands
can be addressed by overcoming high path loss, especially for non-line-of-
sight (NLOS) scenarios. In this work, we investigate the use of passive
transparent reflectors to improve signal coverage in an NLOS indoor scenario.
Measurements are conducted to characterize the maximum reflectivity property
of the transparent reflector using channel sounder equipment from NI. Flat and
curved reflectors, each with a size of 16 inches by 16 inches, are used to
study coverage improvements with different reflector shapes and orientations.
The measurement results using passive metallic reflectors are also compared
with the ray-tracing-based simulations, to further corroborate our inferences.
The analysis reveals that the transparent reflector outperforms the metal
reflector and increases the radio propagation coverage in all three
frequencies of interest: 28 GHz, 39 GHz, and 120 GHz. Using transparent
reflectors, there is an increase in peak received power that is greater than 5
dB for certain scenarios compared to metallic reflectors when used in flat
mode, and greater than 3 dB when used in curved (convex) mode.

###### Index Terms:

5G, 6G, channel sounding, curved reflectors, flat reflectors, mmWave, ray
tracing, sub-THz, transparent reflectors.

##  I Introduction

One of the foremost challenges in millimeter wave (mmWave) and sub-THz
networks is their ability to continuously maintain widespread and resilient
wireless coverage [1, 2, 3]. This requires sustaining a line-of-sight (LOS) or
at least a strong first-order reflected non-LOS (NLOS) path to form a stable
link for radio communications. The availability of an NLOS link depends on the
reflection profile of a scatterer, which is characterized by the scatterer’s
material and the frequency of operation. A conventional approach to providing
a stable NLOS link is by the deployment of multiple access points or active
repeaters [4] and relays [5]. Strategical placements of reflectors in indoor
and outdoor environments were studied in [6, 7] using analytical derivations,
which show the strong dependency of wireless coverage on the location and the
size of the reflectors.

Recent studies on intelligent reflecting surfaces (IRS) show that the
environment can be controlled to steer electromagnetic waves in desired
directions, which makes the network less prone to disruptions such as blockage
[8, 9, 10, 11]. However, the use of IRSs requires control circuitry and
requires a high price and computation cost. Other solutions proposed to
improve the wireless coverage at mmWave and sub-THz bands include increasing
the transmit power or deploying highly-sensitive receivers. Due to rules on
peak transmit power by regulatory bodies and the high cost of highly sensitive
receivers, one of the promising alternatives to improve wireless coverage is
by using passive reflectors. The deployment of passive reflectors with good
reflection characteristics can be used to our advantage to replace the
multiple access points and repeaters. Earlier simulation and measurement
studies, such as [12] have shown that objects such as metal act as a perfect
reflector, enabling strong reflections for directional NLOS communication.
Thus, strategic deployment of passive metallic reflectors can create a
favorable propagation environment by introducing new multi-path components
(MPCs) in the channel and increasing the overall spatial diversity of the
MPCs. Metals, however, may be undesirable to be placed unconditionally and
abundantly as reflectors due to cosmetic and other reasons, especially in
indoor environments.

In places where the deployment of metallic reflectors is not reasonable,
transparent reflectors can offer a sustainable, easy-to-deploy alternative to
increase the network coverage without altering the appearance of the surface.
Penetration properties of these transparent reflectors in an indoor and an
open-door environment were studied in our recent work [13]. The experiment
result showed better reflection performance, and improvement in coverage by
preserving the radio waves within the environment in presence of passive
transparent reflectors. Amongst the different kinds of reconfigurable
intelligent surfaces (RISs) being researched currently [11], our film under
test is electromagnetic-based (EM) based RIS. The film withholds the capacity
to preserve the impinging radio waves and passively reflect the first-order
derivative for enhancing the network coverage. These reflectors from Sekisui,
as tested initially in [13], provide a strong NLOS link in a wireless network
and have a penetration loss comparable to metal. Hence, this film might be an
economical and sustainable alternative to other conventional methods of
increasing the received signal strength and indoor coverage.

Another distinctive advantage of this film is its non-planar structure and
free-form conformality that leads to easy deployment, high reusability, and
durability. They reflect all the incident rays enabling them to reach
shielded, dense areas eliminating any blind spots. These transparent
reflectors can be deployed on any structure without altering the appearance of
the surface that it is mounted on.

Figure 1: Measurement setup for evaluating reflector characteristics using a
channel sounder that operates at multiple mmWave and sub-THz frequencies.

In this work, we characterize the propagation characteristics of passive
transparent reflectors in a controlled indoor environment. Our measurement
setup that uses a channel sounder system from NI is shown in Fig. 1. A panel
made of broadband AEP-04 pyramidal absorbers from MVG [14] is used to
eliminate other second-order reflections. Our main contributions can be listed
as follows:

  * •

We conducted measurements with different shapes of metallic and transparent
reflector and characterized their coverage across an NLOS area using a linear
positioner (controlled by LabVIEW) that carries the receiver;

  * •

We compared the coverage performance of metallic and transparent reflectors at
28 GHz, 39 GHz, and 120 GHz, at various different receiver locations with
respect to the reflector’s location;

  * •

We developed ray tracing simulations to compare the coverage of flat and
curved metallic reflectors at different locations, and compared the ray-
tracing result with measurements which indicate a close match.

The rest of this paper is organized as follows. In Section II, we describe
details of our measurement scenario, while Section III describes our model for
ray tracing simulations with flat and curved metallic reflectors. Section IV
presents our measurement and simulation results with reflectors and the last
section concludes the paper.

(a)

(b)

(c)

(d)

Figure 2: Flat and curved passive reflectors of size 16$"$x16$"$.

##  II Reflector Measurements Scenario and Setup

Frequency (GHz) | 28 | 39 | 120
---|---|---|---
IF (GHz) | 10.56 | 10.56 | 12.6
Bandwidth (GHz) | 1.5 | 1.5 | 1.5
Transmitted power (dBm) | -10 | -10 | 10
TX antenna gain (dBi) | 17 | 20 | 21
RX antenna gain (dBi) | 17 | 20 | 21
TABLE I: Measurement parameters for the channel sounder.

The measurements were conducted in the time domain using an impulse channel
sounder that is built using NI’s PXI platform [15] that is controlled by
LabVIEW. The sounder operates at an intermediate frequency of 10.56 GHz for
the central frequencies of 28 GHz and 39 GHz, and an intermediate frequency of
12.6 GHz for the central frequency of 120 GHz. The system up and down converts
the intermediate frequency using the PXIe-3620 module along with a PXIe-7902
FPGA. The measurement parameters of each frequency of operation are provided
in Table I. More information on the design parameters of the channel-sounding
chassis can be found in our past work [13].

In this work, we evaluate the performance of our propagation channel by
measuring the maximum reflected power using metal and transparent reflectors
placed in an indoor environment. The evaluation is conducted using the power
delay profile (PDP) of the channel. The transmitter (TX) and the receiver (RX)
antenna radio heads were both placed on a steerable gimbal and a linear
positioner, respectively, as highlighted in Fig. 1. The antennas used are
directional horn antennas that have a 17 dBi, 20 dBi, 21 dBi TX and RX antenna
gain at 28 GHz, 39 GHz, and 120 GHz, respectively. At 28 GHz, the antennas
have an aperture of 26∘ at 3 dB beam-width (HPBW) in the E-plane and 24∘ in
H-plane. The antenna system has a maximum linear dimension of 4.072 cm with a
far field of 30.97 cm. At 39 GHz, the aperture is 15∘ in the E-plane and 16∘
in the H-plane, respectively. At 120 GHz, the horn antennas have an aperture
of 13∘ in both E-plane and H-plane, respectively. More details on the sounder
system can be found at [16].

The RX is placed on a tGlide modular linear positioner that travels a distance
of 1.8 meters for our measurements. The linear positioner uses a timing belt
drive for a custom number of measurements [17]. The maximum reflected power
received by the RX antenna is measured when moving along the positioner with a
resolution of 0.1 cm and 1800 measurements at each point. The reflector is
placed at a distance of 2.5 m angled at 30∘ perpendicular to the TX and RX. A
$16"\times 16"$ reflector is placed on an absorber panel [14] to eliminate the
second-order reflections from other sources in the background. The reflector
is placed in between the absorber panel along the elevation axes of the
transmitter and receiver radio heads. The reflector helps to make at least one
strong first-order non-LOS (NLOS) reflection path to complete the radio link.
The flat and curved shape reflectors used in the measurement setup are shown
in Fig. 2. The reflector, TX, and RX antenna placement is depicted in Fig. 1.
A time alignment was performed before each set of measurements to make sure
the TX and RX are calibrated. To limit the influence of frequency and channel
response, the measurements were carried out back-to-back.

##  III Ray-Based Simulations for Metal Reflectors

In addition to the channel measurements, we also carried out ray-based
simulations for the metallic reflectors, using both flat and convex reflector
scenarios. The ray-based simulation setup for the flat reflector is shown in
Fig. 3. We divided the metal reflector into a number of facets. We assume that
a single ray is incident at the center of every facet and subsequently
reflected towards the RX. Each ray is assigned an antenna gain based on the
azimuth and elevation angles at the TX and RX. Each ray also covers a given
distance from the TX toward the RX. The received power due to rays from the
reflector center and $N^{(\rm f)}$ facets at the center RX position, RX0 is
given as

$\displaystyle\small P_{\rm R}^{(\rm RX_{0})}$ $\displaystyle=\sqrt{N^{(\rm
f)}}\sum_{i=1}^{N^{(\rm f)}+1}\frac{P_{\rm T}}{(4\pi d_{i})^{2}}$
$\displaystyle\times\sqrt{G^{(\rm Az)}_{\rm T}\Big{(}\theta_{i}^{(\rm
cent,TX)}\Big{)}G^{(\rm El)}_{\rm T}\Big{(}\phi_{i}^{(\rm cent,TX)}\Big{)}}$
$\displaystyle\times\sqrt{G^{(\rm Az)}_{\rm R}\Big{(}\theta_{i}^{(\rm
cent,RX)}\Big{)}G^{(\rm El)}_{\rm R}\Big{(}\phi_{i}^{(\rm cent,RX)}\Big{)}}$
$\displaystyle\times\lambda^{2}\exp{\bigg{(}-j2\pi\frac{(d_{i}-d_{\rm
ref})}{\lambda}}\bigg{)}\alpha^{(\rm flt)}~{},\normalsize$ (1)

where $P_{\rm T}$ is the transmit power, $G^{(\rm Az)}_{\rm
T}\big{(}\theta_{i}^{(\rm cent,TX)}\big{)}$ and $G^{(\rm El)}_{\rm
T}\big{(}\phi_{i}^{(\rm cent,TX)}\big{)}$ are the TX antenna gains in the
azimuth and elevation planes, respectively, of the $i^{\rm th}$ ray, and
$\theta_{i}^{(\rm cent,TX)}$ and $\phi_{i}^{(\rm cent,TX)}$, represent the
azimuth and elevation angles of the $i^{\rm th}$ ray, respectively, at the TX.

Figure 3: A four-facet flat reflector scenario for the ray-based simulations.
The RX positions on the LHS and RHS are represented as RX${}_{k}^{(\rm L)}$
and RX${}_{k}^{(\rm R)}$, respectively, and $k=1,2,\ldots,n$.

The RX antenna gain of $i^{\rm th}$ ray in the azimuth and elevation plane is
represented as $G^{(\rm Az)}_{\rm R}\big{(}\theta_{i}^{(\rm cent,RX)}\big{)}$
and $G^{(\rm El)}_{\rm R}\big{(}\phi_{i}^{(\rm cent,RX)}\big{)}$,
respectively, and $\theta_{i}^{(\rm cent,RX)}$ and $\phi_{i}^{(\rm cent,RX)}$
represent the azimuth and elevation angles at the RX, respectively. The
distance traveled by the $i^{\rm th}$ ray from the TX to the RX through the
reflector is given as $d_{i}$ and the phase of each ray is represented by
$\exp{\Big{(}-j2\pi\frac{(d_{i}-d_{\rm ref})}{\lambda}}\Big{)}$, where $d_{\rm
ref}$ is the reference distance between the boresight of the TX and RX
antennas through the center of the reflector and $\alpha^{(\rm flt)}$
represents the attenuation due to the difference in the area of the
electromagnetic energy incident at the reflector compared to the physical area
of the reflector.

The ray-based simulation scenario for the convex-shaped reflector is shown in
Fig. 4. The rays reflected from the convex reflector diverge in different
directions in the azimuth plane, with azimuth divergence angles given by
$\theta^{(\rm cur)}$. The diverging rays converge virtually at a focal point
at $F=R/2$ virtually beyond the convex reflector and $R$ is the radius of
curvature. Moreover, the convex reflector is divided into different sections
of height. The difference in height of any two sections is represented as
$\delta h$ as shown in Fig. 4. For a given RX position on the linear
positioner, only a finite number of rays can be captured by the antenna of
length $l^{(\rm ant)}$ in the azimuth plane at a far field distance $d^{(\rm
a)}$. The length $l^{(\rm ant)}$ depends on the half power beamwidth of the
antenna in the azimuth plane. The received power for the convex reflector is
given as:

$\displaystyle\small P_{\rm R}^{(\rm RX_{0})}$ $\displaystyle=\sqrt{N^{(\rm
El)}N^{(\rm Az)}}\sum_{i=1}^{N^{(\rm El)}}\sum_{j=1}^{N_{i}^{(\rm
Az)}}\frac{P_{\rm T}}{(4\pi d_{i,j})^{2}}$ $\displaystyle\times\sqrt{G^{(\rm
Az)}_{\rm T}\Big{(}\theta_{i,j}^{(\rm cent,TX)}\Big{)}G^{(\rm El)}_{\rm
T}\Big{(}\phi_{i,j}^{(\rm cent,TX)}\Big{)}}$ $\displaystyle\times\sqrt{G^{(\rm
Az)}_{\rm R}\Big{(}\theta_{i,j}^{(\rm cent,RX)}\Big{)}G^{(\rm El)}_{\rm
R}\Big{(}\phi_{i,j}^{(\rm cent,RX)}\Big{)}}$
$\displaystyle\times\lambda^{2}\exp{\bigg{(}-j2\pi\frac{(d_{i,j}-d_{\rm
ref})}{\lambda}}\bigg{)}\alpha^{(\rm cur)},\normalsize$ (2)

where $N^{(\rm El)}$ is the number of height sections considered given as
$N^{(\rm El)}=\frac{h}{\delta h}$, where $h$ is the height of the convex
reflector, $N_{i}^{(\rm Az)}$ is the number of rays in the azimuth plane at
the $i^{\rm th}$ height section and $N^{(\rm Az)}=\frac{l^{(\rm
ant)}}{\gamma}$, and $\alpha^{(\rm cur)}$ is the attenuation factor for the
convex reflector. The attenuation for the convex reflector is larger compared
to the flat reflector, $\alpha^{(\rm cur)}<\alpha^{(\rm flt)}$.

Figure 4: Ray-based simulation scenario for the convex reflector.

##  IV Measurement and Simulation Results

In this section, measurement results for the metallic and Sekisui transparent
reflectors are provided. The ray-based simulation results for metallic
reflectors are also compared with the measurement results. After conducting
the measurements using channel sounder equipment, we processed the data points
obtained in Matlab and averaged them across all multipath reflections in the
NLOS environment.

###  IV-A Measurement Results Analysis

Table II provides a summary of the highest reflected powers across 28 GHz, 39
GHz, and 120 GHz, using flat and convex-shaped reflectors. Fig. 6 presents the
reflected power versus linear RX position using the metallic and Sekisui
transparent reflector (TR). We compared the flat and curved reflector results
for maximum power received in three different scenarios, using metallic,
Sekisui TR, and no reflector. Fig. 6a represents the received power using
flat-shaped reflectors at 28 GHz. This plot shows that the peak received power
using no reflector, metallic reflector, and Sekisui TR are -87.01 dB, -54 dB,
-49.61 dB, respectively. These values indicate that flat Sekisui TR
outperforms the metallic reflector by 1 dB in its reflection capacity at 28
GHz. The power distribution curve of Sekisui TR is smoother compared to
metallic reflectors. In the case of the metallic reflector, we see, large
varying fringing dips. Fig. 6b displays the profile of the convex reflector at
28 GHz, where reflected power distribution is now more uniformly flat and
reduced by nearly 20 dB. In this case, the peak reflected power using convex
Sekisui TR is -71.40 dB and the metallic reflector is -74.69 dB. The fringing
properties also seem diminished in the case of convex reflectors compared to
flat reflectors. While the peak power for the convex reflector is lower than
the flat reflector, coverage is more uniform and can be better at certain
receiver locations.

Reflector Shape | Type | 28 GHz | 39 GHz | 120 GHz
---|---|---|---|---
Flat | Sekisui | -49.61 dB | -47.36 dB | -43.13 dB
Metal | -54.00 dB | -55.36 dB | -39.95 dB
Convex | Sekisui | -71.40 dB | -78.35 dB | -54.33 dB
Metal | -74.69 dB | -76.72 dB | -58.36 dB
TABLE II: Maximum reflected power measurements using the channel sounder.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 6: Flat and curved reflector propagation measurements at three
different frequency bands.

Fig. 6c and Fig. 6d illustrate the reflectivity properties at 39 GHz, which
indicate that we receive 8 dB higher peak power using flat Sekisui TR when
compared with the metallic reflector. At 39 GHz we notice around 10 dB
enhancement in received power using convex-shaped reflectors compared to the
no-reflector scenario. Convex Sekisui TR and convex metallic reflectors have
similar performance with the metallic reflector providing around 2 dB higher
received peak power.

Fig. 6e and Fig. 6f denote the reflected power at 120 GHz, using flat and
convex reflectors, respectively. In the case of convex Sekisui TR, it follows
the same trend as 28 GHz, and there is 4 dB increase in peak received power
compared to convex metallic reflectors. However, in the case of flat
reflectors, Sekisui TR provides 3 dB lower peak received power than metallic
reflectors. This can be due to relatively rough surfaces at higher frequencies
(above 100 GHz), which causes MPCs to undergo higher absorption and reflection
loss compared to 28 GHz. Another possible explanation can be due to slight
variations in the placement of the reflector, leading to higher path-loss and
a narrower reflected profile at sub-THz frequency.

###  IV-B Comparison of Ray Tracing and Measurement Results

The ray-based simulations are carried out in Matlab. The geometry, size of the
reflector, transmit power, and antenna radiation pattern from the measurement
scenario in Fig. 1 are used for the simulation setup. Fig. 8 provides the
simulation results for the flat metallic reflector of size $16"\times 16"$ at
$28$ GHz, $39$ GHz, and $120$ GHz. The fluctuations in the received power
observed in these plots are due to constructive and destructive (CD)
interference of rays from different facets (see (1)). The number of facets
used in simulations for $28$ GHz and $39$ GHz is $36$, whereas for the $120$
GHz the number of facets is $256$. A large number of facets helps to capture
small power fluctuations at $120$ GHz as observed during the measurements.

From Fig. 8, it can be observed that the width of the fluctuation cycles due
to CD interference decreases significantly at $120$ GHz. Furthermore, as
expected, the received power is highest near the RX position where the
incident and reflected ray angles are the same. It is also observed that the
received power decreases as we move toward the right hand side (RHS) of the
linear RX positioner. The decrease is due to an increase in the azimuth angle
of the rays from the main reflection position which is $30^{\circ}$ on the
left hand side (LHS) from the center. The large angle of the rays in the
azimuth plane results in small antenna gain and hence reduced received power
(see (1)). Overall, the simulation results for the flat metal reflector match
closely with the measurement results. It may be possible to improve the match
between measurement and ray tracing results using a larger number of facets
for the reflector, and using finer resolution beam patterns at the TX and the
RX.

(a)

(b)

(c)

Figure 8: Comparison of ray-tracing simulation and measurement results
(reflector type: flat - metal reflector).

(a)

(b)

(c)

Figure 10: Comparison of ray-tracing simulation and measurement results
(reflector type: convex - metal reflector).

Fig. 10 displays the simulation results for the convex metal reflector of size
$16"\times 16"$ at $28$ GHz, $39$ GHz, and $120$ GHz. Compared to the flat
reflector, the received power does not change significantly across the RHS of
the linear positioner. The small reduction of the received power on the RHS of
the linear positioner compared to the flat reflector is due to uniform
divergence of rays in the azimuth plane (ref Fig. 4). Furthermore, the overall
received power is smaller for the convex reflector compared to the flat
reflector. This is due to the higher attenuation of rays for the convex
reflector (2). Similar to the flat reflector, we observe fluctuations in the
received power for the convex reflector due to CD interference of the rays
(2). Noticeably, small-scale fluctuations are observed at $120$ GHz flat
reflector case are also observed here.

##  V Concluding Remarks

In this work, we analyze the coverage enhancements using passive reflectors at
mmWave and sub-THz frequencies in indoor NLOS environments. Channel
measurements are performed using passive metallic and transparent reflectors
of different shapes to understand their power profile in our propagation
channel. The results reveal that flat transparent reflectors give better
coverage in the NLOS region than metal reflectors in 28 GHz and 39 GHz and
closely match metallic reflector performance at 120 GHz (by 3 dB). Using
convex-shaped reflectors, Sekisui TR performed better than the metallic
reflector at 28 GHz and 120 GHz, and slightly lower (by 2 dB) than the
metallic reflector at 39 GHz in terms of the peak received power. The
measurement findings for metallic reflectors were compared with ray tracing
simulations in the same environment, which correlate closely to our measured
results. This suggests Sekisui TR as a viable option for passively increasing
coverage in an indoor NLOS scenario. The findings also suggest that
enhancements due to using passive reflectors in sub-THz bands are comparable
to those in mmWave frequencies.

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