# Evaluating Debiasing Techniques for Intersectional Biases

Shivashankar Subramanian Xudong Han
Timothy Baldwin Trevor Cohn Lea Frermann
School of Computing and Information Systems
University of Melbourne, Australia
<EMAIL_ADDRESS><EMAIL_ADDRESS>
<EMAIL_ADDRESS>
###### Abstract

Bias is pervasive in NLP models, motivating the development of automatic
debiasing techniques. Evaluation of NLP debiasing methods has largely been
limited to binary attributes in isolation, e.g., debiasing with respect to
binary gender or race, however many corpora involve multiple such attributes,
possibly with higher cardinality. In this paper we argue that a truly fair
model must consider ‘gerrymandering’ groups which comprise not only single
attributes, but also intersectional groups. We evaluate a form of bias-
constrained model which is new to NLP, as well an extension of the iterative
nullspace projection technique which can handle multiple protected attributes.

##  1 Introduction

Text data reflects the social and cultural biases in the world, and NLP models
and applications trained on such data have been shown to reproduce and amplify
those biases. Discrimination has been identified across diverse sensitive
attributes including gender, disability, race, and religion Caliskan et al.
(2017); May et al. (2019); Garimella et al. (2019); Nangia et al. (2020); Li
et al. (2020). While early work focused on debiasing typically binarized
protected attributes in isolation (e.g., age, gender, or race; Caliskan et al.
(2017)), more recent work has adopted a more realistic scenario with multiple
sensitive attributes Li et al. (2018) or attributes covering several classes
Manzini et al. (2019).

In the context of multiple protected attributes, gerrymandering refers to the
phenomenon where an attempt to make a model fairer towards some group results
in increased unfairness towards another group Buolamwini and Gebru (2018);
Kearns et al. (2018); Yang et al. (2020). Notably, algorithms can be fair
towards independent groups, but not towards all intersectional groups. Despite
this, debiasing approaches within NLP have so far been evaluated only using
independent group fairness when modelling datasets with multiple attributes,
disregarding intersectional subgroups defined by combinations of sensitive
attributes (see Figure 1).

The primary goal of this work is to evaluate independent and intersectional
identity debiasing approaches in relation to fairness gerrymandering for text
classification tasks. To this end, we evaluate bias-constrained models Cotter
et al. (2019b) and iterative nullspace projection (INLP; Ravfogel et al.
(2020)), a post-hoc debiasing method which we extend to handle intersectional
groups. The constrained model jointly optimizes model performance and model
fairness, while INLP seeks to learn a hidden representation which is
independent of the protected attributes. INLP does not consider the trade-off
between accuracy and fairness, but rather it iteratively maximizes fairness in
an unconstrained fashion.

In this work, we address the following questions:

  * •

Are debiasing approaches based on independent groups more prone to fairness
gerrymandering than methods using intersectional groups?

  * •

How do INLP and bias-constrained approaches, and their extensions to handle
intersectional groups, fare compare in terms of both predictive accuracy and
fairness?

##  2 Background

$\blockarray{*{2}{c}l}\block{*{2}{>{}c<{}}l}whiteperson(\mathcal{W})&personofcolour(\neg\mathcal{W})\\\
\block{[*{2}{c}]>{}l<{}}\mathcal{A}&\mathcal{B}male(\mathcal{M})\\\
\mathcal{C}\mathcal{D}female(\mathcal{F})\\\ $ Figure 1: Group intersection
and gerrymandering: $\mathcal{A}$=white male, $\mathcal{B}$=male person of
colour, $\mathcal{C}$=white female, $\mathcal{D}$=female person of colour.

Debiasing with respect to more than a single protected attribute ($|Z|>1$)
requires grouping data points according to their associated protected values.
In this work, debiasing approaches are trained _wrt_ the three settings
detailed below Kearns et al. (2018); Yang et al. (2020), evaluated based on
aggregated violations over GGERRY, which is a commonly used evaluation
strategy for intersectional identities Yang et al. (2020).

#### Independent groups (GINDEP)

Each private attribute is treated as an independent group. In the case of all
binary attributes, this gives 2$|Z|$ groups. As illustrated in Figure 1, for
binary gender and race,111Higher cardinality attributes are also supported.
$\mathcal{F}$, $\mathcal{M}$, $\mathcal{W}$, and $\neg\mathcal{W}$ are four
independent groups, which are overlapping, e.g., with $\mathcal{F}$ referring
to both white females and female persons of colour ($\mathcal{C}$ and
$\mathcal{D}$).

#### Intersectional groups (GINTER)

Non-overlapping, exhaustive combinations of groups, which in the binary
attribute case gives 2|Z| combinations. For our example in Figure 1, the
intersectional groups are $\mathcal{A}$, $\mathcal{B}$, $\mathcal{C}$ and
$\mathcal{D}$.

#### Gerrymandering intersectional groups (GGERRY)

Overlapping subgroups are defined by group intersections of any subset of
private attributes (3|Z| for binary attributes, where the 3rd value denotes a
wildcard). For the example in Figure 1, the GGERRY groups are $\mathcal{F}$,
$\mathcal{M}$, $\mathcal{W}$, $\neg\mathcal{W}$, $\mathcal{A}$, $\mathcal{B}$,
$\mathcal{C}$, and $\mathcal{D}$.

##  3 Proposed Approaches

We aim to learn a model to predict main task labels (e.g., sentiment) from
input data $X$ (e.g., reviews) by means of a learnt hidden representation
$X_{h}$. $X_{h}$ will generally encode implicit biases, and our goal is to
predict the output without the influence of social identities encoded in
$X_{h}$. We assume access to the protected attributes for each instance.
Below, we detail the two primary models targeted in this work: INLP, and the
bias-constrained model.

###  3.1 INLP

INLP is applied to frozen hidden representations, where we first learn a
linear classifier $\mathbf{W}$ using $X_{h}$ as the independent variables to
predict a protected attribute (binary- or multi-valued). Next, $X_{h}$ is
projected onto the nullspace of $\mathbf{W}$ (denoted
$P_{N(\mathbf{W})}X_{h}$) to remove the protected information, which forms the
input for learning a classifier of the main task label. In real-world
scenarios, bias may arise due to a combination of multiple protected
attributes, hence we extend the single attribute setting of Ravfogel et al.
(2020) to this situation. Assuming $k$ protected attributes, and classifiers
trained to predict those denoted as $\\{\mathbf{W}^{i}\\}_{i=1}^{k}$, we
compute the intersection of nullspaces Strang (2014) across identities,

$\bigcap_{i=1}^{k}N(\textbf{W}^{i})=N\left(\left[\begin{matrix}\mathbf{W}^{1}\\\
\mathbf{W}^{2}\\\ \vdots\\\ \mathbf{W}^{k}\end{matrix}\right]\right)\,.$ (1)

No special properties are imposed over $\mathbf{W}^{i}$ (e.g., orthogonality),
which allows for parallel training.

Even after debiasing by projecting $X_{h}$ onto the nullspace of classifiers,
hidden representations can still retain protected information Ravfogel et al.
(2020). Hence we follow Ravfogel et al. (2020) and resort to iteratively
removing bias, rather than doing it in a single step. Nullspace projection
will reduce the rank of data rapidly with intersectional identities222The rank
reduces by 1 for each protected attribute. (see Figure 2). To circumvent this
issue, we project onto the nullspace of the _principal component_ of the bias
subspace ($N_{p}$). This is equivalent to projecting onto the nullspace of
some of the intersectional identities in each iteration; similar strategies
have been employed for multi-class supervised principal component analysis
Piironen and Vehtari (2018).

Following Ravfogel et al. (2020), while incorporating our principal component-
based nullspace projection as described above, the intersection of nullspaces
across iterations is computed as

$\bigcap_{i=0}^{m}N_{p}(\textbf{W}_{i})=N\left(\sum_{i=0}^{m}P_{R^{\prime}(\textbf{W}_{i})}\right),$
(2)

where $N_{p}$ denotes the nullspace based on the principal component of the
bias subspace, $\textbf{W}_{i}$ is the set of classifiers trained in each
iteration to predict the protected attributes, and $R^{\prime}$ is the
principal component of the rowspace computed by stacking classifier weight
vectors across multiple protected attributes. Unlike Eq 1, the formulation in
Eq 2 requires orthogonality of subspaces across each iteration.

###  3.2 Bias-constrained Model

A bias-constrained model combines the main task objective and fairness
constraints,

$\displaystyle\min_{\theta}$ $\displaystyle\ell(\mathcal{F}(x,\theta),y)$ s.t.
$\displaystyle\forall g\in[G]\,\,\gamma_{g}\lvert\phi_{g}-\phi\rvert\leq\nu,$

where $\ell(\mathcal{F}(x,\theta),y)$ denotes the primary objective (e.g.,
error rate); $G$ are the groups defined in Section 2; $\phi_{g}$ denotes the
group-wise performance; and $\phi$ denotes overall performance of the model
(e.g., TPR). $\nu$ is a slack variable, which controls the maximum deviation
allowed. $\gamma_{g}$ is the inverse proportion of positive examples in each
group, $\frac{|g|}{|g_{y=1}|}$, meaning that the more underrepresented a group
is _wrt_ some target class (say toxicity), the smaller its accepted deviation
from the overall performance.

Each group-wise constraint is denoted as $\psi_{g}$. The constraints involve a
linear combination of indicator variables, which is not differentiable _wrt_
$\theta$. A common approach to handle this constrained optimization problems
is using the Lagrangian,

$\displaystyle\mathcal{L}(\theta,\lambda)=\ell(\mathcal{F}(x,\theta),y)+\sum_{g=1}^{|G|}\lambda_{g}\psi_{g}\,,$
(3)

which is minimized over $\theta$ and maximized over $\lambda$. Similar
formulations have been used for learning fair models with structured data
Cotter et al. (2019b); Yang et al. (2020); Zafar et al. (2019). In this work,
we apply this method to NLP tasks and use the two-player zero-sum game
approach for optimization, where the first player chooses $\theta$ to minimize
$\mathcal{L}(\theta,\lambda)$, and the second player enforces fairness
constraints by maximizing $\lambda$ Kearns et al. (2018); Cotter et al.
(2019b); Yang et al. (2020). Specifically we use the implementations available
in TensorFlow constrained optimization Cotter et al. (2019a,
b).333https://github.com/google-research/tensorflow_constrained_optimization

The approach iterates over $\mathtt{T}$ iterations, where the primal player
updates $\theta$ with a fixed $\lambda$, and dual player updates $\lambda$
with a fixed $\theta$. This results in multiple models,
$\\{\theta_{1},\theta_{2},...\theta_{\mathtt{T}}\\}$ and
$\\{\lambda_{1},\lambda_{2},...\lambda_{\mathtt{T}}\\}$, which trade off
performance and fairness. We use the Adam optimizer Kingma and Ba (2014) with
learning rate $10^{-3}$.

##  4 Experimental Results

We evaluate using two binary classification datasets: (1) Twitter hate speech
detection; and (2) occupation classification using biography text. For INLP we
use logistic regression to construct $\mathbf{W}$. All the approaches have
hyperparameters controlling the performance–fairness trade-off, and as no
choice of hyperparameter will optimize both objectives simultaneously, we
adopt the concept of Pareto optimality Godfrey et al. (2007).

###  4.1 Settings

For INLP we consider different definitions of the bias subspace, which we
illustrate _wrt_ Figure 1: (a) two linear classifiers, trained to discriminate
between independent groups GINDEP: $\mathcal{F}$ and $\mathcal{M}$,
$\mathcal{W}$ and $\neg\mathcal{W}$; (b) four linear classifiers to
discriminate between intersectional groups GINTER: $\mathcal{A}$,
$\mathcal{B}$, $\mathcal{C}$, and $\mathcal{D}$ (one vs. rest); and (c) eight
linear classifiers to discriminate between GGERRY groups. We refer to the
three approaches as INLPINDEP, INLPINTER, and INLPGERRY respectively. For the
constrained model, constraints capturing the performance deviation of GINDEP,
GINTER, and GGERRY are used in Eq 3, which we refer to as CONINDEP, CONINTER,
and CONGERRY, respectively.

#### Metrics

We assess a model’s predictive performance using F-score, and fairness based
on average-violations Yang et al. (2020) of true positive rate (equality of
opportunity), across all the GGERRY groups:
$\frac{1}{|\textup{G}_{\mathtt{GERRY}}|}\sum_{g=1}^{\textup{G}_{\mathtt{GERRY}}}|\mathtt{tpr}_{g}-\mathtt{tpr}|$.

###  4.2 Hate speech detection

Figure 2: F-score vs. fairness results for INLP (hatespeech dev set).
Projecting onto the null space of the principal bias component (dotted line
and stars) results in a better trade-off than directly projecting onto the
nullspace of the entire bias sub-space (dashed line).

(a) Hatespeech detection

(b) Occupation classification

Figure 3: Pareto frontier model test results for the trade-off between $F_{1}$
score and fairness, measured as $1-$ the average TPR violation across all
GGERRY groups. The top-right represents ideal performance.

We use the English Twitter hate speech detection dataset of Huang et al.
(2020), where each tweet is labeled with a binary hate speech label, as well
as binary demographic group identity indicators for the tweet authors: gender
(f or m), age ($\leq$ or $>$ median), location (US or not US), and race (white
or other). We use the data splits from Huang et al. (2020), but further filter
each partition to the subset of tweets for which all demographic author labels
are available, resulting in 23K (train), 5K (dev), and 5K (test) tweets.

We use a biGRU as our base model, following Huang et al. (2020). In Figure 2,
we show the benefits of our proposed INLP approach for debiasing
gerrymandering intersectional identities, compared to the naive extension of
INLP for multiple protected attributes (which projects onto the entire bias
subspace). With 80 GGERRY groups, the naive extension leads to a drastic drop
in performance after 4 iterations (the dashed blue line), as in each iteration
the rank is reduced by 80. On the other hand, the proposed approach achieves a
much more nuanced trade-off between accuracy and fairness (the blue and yellow
dotted line + stars, resp.).

We present the Pareto frontier for the different models on the test-set in
Figure 3(a), by varying the hyper-parameters of the approaches444For INLP, #
iterations = 1 to dim($X_{h}$); for the constrained models, # iterations
($\mathtt{T}$) = 1 to 100, $\nu$ = 1e-4 to 1, $\gamma_{g}$ = 1 or
$\frac{|g|}{|g_{y=1}|}$. each under of the three different debiasing settings,
i.e. GINDEP, GINTER, and GGERRY. From the results, for F-score close to the
base biGRU (= biased model), the independent model provides the best trade-
off, while debiasing for intersectional identities provides a better trade-off
overall, and constrained models perform better than INLP.

###  4.3 Occupation classification

For our second experiment, we use the occupation classification dataset of De-
Arteaga et al. (2019). This comprises short web biographies, annotated by
gender and profession. We augment the dataset with economic status (wealthy
vs. rest, using World Bank data) based on the country the individual is based
in, labelled based on the first sentence of each bio, which we perform entity
linking on using AIDA-light Nguyen et al. (2014), and map locations and
institutions to countries based on their Wikipedia articles. We computed the
accuracy of the economic status mapping by manually analyzing the output of
over 200 biographies, resulting in a 93% accuracy and 90% agreement (Jaccard
coefficient) between three annotators. We use a subset of two highly
stereotyped classes of the dataset — nurse vs. surgeon — resulting in
13K/1.7K/5.4K instances in train/dev/test.55590% of surgeons are male and 87%
of nurses are female; 97.5% of nurses are from wealthy economies.

We use the BERT (base uncased) Devlin et al. (2019) [CLS]-encoding followed by
an MLP (300-d with ReLU activations) for classification. The Pareto
results666Constrained models are run for $\mathtt{T}=1$ to 500 iterations. on
the test set are shown in Figure 3(b). INLPINTER and INLPGERRY perform better
than INLPINDEP, and the constrained models once again perform best overall,
with intersectional identities providing no significant gains over CONINDEP.
Even in a highly stereotyped setting, constrained models achieve high
predictive performance, in contrast to INLP which improves fairness but
greatly reduces predictive performance, consistent with the single protected
attribute results in Ravfogel et al. (2020).

###  4.4 Constrained analysis

|  | Trade-off 5% |  | Trade-off 10%
---|---|---|---|---
Approach |  | F1 | Max violation | Avg violation |  | F1 | Max violation | Avg violation
INLPINDEP |  | 0.763 | 0.333 | 0.079 |  | 0.732 | 0.242 | 0.076
INLPINTER |  | 0.785 | 0.312 | 0.083 |  | 0.736 | 0.258 | 0.064
INLPGERRY |  | 0.778 | 0.453 | 0.086 |  | 0.737 | 0.199 | 0.054
CONINDEP |  | 0.777 | 0.230 | 0.048 |  | 0.724 | 0.274 | 0.031
CONINTER |  | 0.772 | 0.254 | 0.063 |  | 0.746 | 0.172 | 0.043
CONGERRY |  | 0.759 | 0.135 | 0.059 |  | 0.715 | 0.150 | 0.047
Biased model |  | 0.806 | 0.338 | 0.102 |  | 0.806 | 0.338 | 0.102
(a) Hatespeech classification

|  | Trade-off 5% |  | Trade-off 10%
---|---|---|---|---
Approach |  | F1 | Max violation | Avg violation |  | F1 | Max violation | Avg violation
INLPINDEP |  | 0.949 | 0.291 | 0.087 |  | 0.949 | 0.291 | 0.087
INLPINTER |  | 0.956 | 0.313 | 0.090 |  | 0.956 | 0.313 | 0.090
INLPGERRY |  | 0.917 | 0.242 | 0.090 |  | 0.904 | 0.224 | 0.088
CONINDEP |  | 0.953 | 0.134 | 0.041 |  | 0.940 | 0.102 | 0.028
CONINTER |  | 0.949 | 0.225 | 0.065 |  | 0.908 | 0.110 | 0.028
CONGERRY |  | 0.951 | 0.170 | 0.049 |  | 0.905 | 0.137 | 0.035
Biased model |  | 0.955 | 0.350 | 0.100 |  | 0.955 | 0.350 | 0.100
(b) Occupation classification

Table 1: Test performance of salient models on hatespeech and occupation
classification. Least biased models within a given performance tradeoff
thresholds are chosen from development set.

In addition to Pareto curves (Figure 3), we compare the fairest models under a
minimum performance constraint, where the most fair models (based on average
violations) which exceed a performance threshold on the development set are
chosen, and evaluated on the test set. This allows us to measure the
generalizability of models occupying optimal Pareto region for the development
set, to an unseen test set. We provide results under two constrained scenarios
— models with least bias on development set trading off 5% and 10% of
predictive performances. In addition to average violation, we also report
maximum violation as
$\underset{g\in\text{{G}${}_{\mathtt{GERRY}}$}}{\max}|\mathtt{tpr}_{g}-\mathtt{tpr}|$,
which measures the upper bound of unfairness towards any subgroup Yang et al.
(2020).

For hatespeech classification, results are provided in Table 4(a). With a
slack of up to 10% tradeoff in predictive performance, INLPGERRY shows better
predictive performance and less violations than both INLPINTER and INLPINDEP.
For the closer 5% tradeoff, INLPINTER has higher F-score with a slightly worse
avg violation and better max violation. The constrained models debiasing for
GGERRY are fairest (lowest max and avg violation). Similarly, for occupation
classification (Table 4(b)), INLPGERRY achieves lowest maximum violation among
INLP models, while overall the constrained models, specifically CONINDEP,
perform best. This confirms the conclusion based on Pareto plots that adding
intersectional identities for INLP-based debiasing is beneficial, whereas for
constrained models which directly regulate the expected performance _wrt_
different subgroups, they only provide mild gains.

##  5 Conclusion and Future Work

We examined the impact of independent vs. intersectional groups on classifier
fairness. We proposed an extended version of INLP, which we compared against
bias-constrained models. INLP is a post-hoc debiasing method, while the
constrained model requires full joint training. Debiasing in INLP happens in
an unconstrained way, where sensitive information is removed from the latent
representations independent of predictive performances, leading to fairer
models at the cost of overall accuracy Han et al. (2021). From a practical
perspective, post-hoc debiasing may be the only option if it is impossible or
undesirable to re-train a model from scratch with a regularized objective. Our
paper shows that lighter-weight post-hoc debiasing is indeed possible in the
case of overlapping groups, and quantifies the additional advantage of joint
training and debiasing in the context of two data sets. The constrained models
especially are more robust to stereotyped settings, and using intersectional
identities benefits INLP in particular.

## Acknowledgements

We thank Michael Twiton for his inputs on the INLP approach. This work was
funded in part by the Australian Government Research Training Program
Scholarship, and the Australian Research Council.

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# Design and Evaluation of Reconfigurable
Intelligent Surfaces in Real-World Environment

Georgios C. Trichopoulos, Panagiotis Theofanopoulos, Bharath Kashyap,
Aditya Shekhawat, Anuj Modi, Tawfik Osman, Sanjay Kumar, Anand Sengar,
Arkajyoti Chang, and Ahmed Alkhateeb
The authors are with the School or Electrical, Computer and Energy
Engineering, Arizona State University, (Email: gtrichop,
panagiotis.theofanopoulos, bgkashya, aditya.shekhawat, aymodi1, tmosman,
svijay25, asengar2, arkajyoti.chang, alkhateeb@asu.edu).

###### Abstract

Reconfigurable intelligent surfaces (RISs) have promising coverage and data
rate gains for wireless communication systems in 5G and beyond. Prior work has
mainly focused on analyzing the performance of these surfaces using computer
simulations or lab-level prototypes. To draw accurate insights about the
actual performance of these systems, this paper develops an RIS proof-of-
concept prototype and extensively evaluates its potential gains in the field
and under realistic wireless communication settings. In particular, a
160-element reconfigurable surface, operating at a 5.8GHz band, is first
designed, fabricated, and accurately measured in the anechoic chamber. This
surface is then integrated into a wireless communication system and the
beamforming gains, pathloss, and coverage improvements are evaluated in
realistic outdoor communication scenarios. When both the transmitter and
receiver employ directional antennas and with 5m and 10m distances between the
transmitter-RIS and RIS-receiver, the developed RIS achieves $15$-$20$dB gain
in the signal-to-noise ratio (SNR) in a range of $\pm 60^{\circ}$ beamforming
angles. In terms of coverage, and considering a far-field experiment with a
blockage between a base station and a grid of mobile users and with an average
distance of $35m$ between base station (BS) and the user (through the RIS),
the RIS provides an average SNR improvement of $6$dB (max $8$dB) within an
area $>75$m2. Thanks to the scalable RIS design, these SNR gains can be
directly increased with larger RIS areas. For example, a 1,600-element RIS
with the same design is expected to provide around $26$dB SNR gain for a
similar deployment. These results, among others, draw useful insights into the
design and performance of RIS systems and provide an important proof for their
potential gains in real-world far-field wireless communication environments.

##  I Introduction

Reconfigurable Intelligent Surfaces (RISs) have attracted significant interest
in the recent years from both academia and industry [1, 2, 3, 4]. This is
motivated by the promising gains that RISs are envisioned to offer for both
millimeter wave (mmWave)/terahertz (THz) and sub-6GHz wireless communication
systems. At mmWave/THz bands, RIS surfaces provide a potential solution for
the critical coverage challenge by intelligently reflecting the wireless
signals to the receiver direction [5, 6, 7, 8]. At sub-6GHz bands, RIS systems
could be leveraged to enhance the propagation characteristics [9, 10] and
increase the spatial multiplexing gains. RISs also find interesting
applications such as security [11, 12] and sensing [13, 14]. For example, RISs
can suppress signals propagating toward eavesdroppers by creating ”quite”
zones around suspicious UEs. Thus, RISs can modify the channel appropriately
and provide physical layer security in wireless networks [15, 16, 17]. With
all this potential, it is important to accurately evaluate the performance of
the RIS surfaces in reality. Based on this motivation, this work considers
designing a low-power and portable proof of concept prototype for RIS-
integrated wireless communications systems and leveraging it to validate the
potential RIS gains in realistic communication environments. Next, we provide
a brief background for the RIS circuits and systems before reviewing the
relevant prior work in Section I-B and summarizing the key contributions in
Section I-C.

###  I-A A Brief Background

RISs comprise reconfigurable reflective surfaces that employ tunable
subwavelength structures (e.g. antennas) to modulate the phase and/or
amplitude of reflected waves. A smooth flat surface (e.g. mirror) reflects
signals in the specular direction (incident angle = angle of reflection)
because of the predetermined constant phase delay induced as the wave
traverses the surface. On the other hand, RISs are capable of anomalous
reflection (angle of reflection $\neq$ incident angle) by artificially
modulating the phase and/or amplitude of the reflected wave [18]. Besides
redirecting the signal to desired directions in the far-field, RISs can also
focus the energy when the user (or BS) is in the radiating near-field of the
RIS. The sub-wavelength unit cell receives the incident signal and re-radiates
it back into free space with a different amplitude and/or phase. Such
modulation can be achieved by tuning the electromagnetic properties of the
unit cell. For example, a switch placed at the feed of a patch antenna can
alter the path of the electric current (open/short termination) and modulate
the amplitude and phase of the re-radiated field. More switches can allow
multi-bit wavefront modulation with improved beam control and efficiency.
Several topologies can be found in the literature using single or multiple
active devices as well as single or multi-layered substrates [19]. In all
approaches, a biasing circuit and control unit (e.g. microcontroller) is
required to be integrated within the RIS to control the state of the tunable
devices by varying the biasing voltage across the device terminals. RISs are
considered two-dimensional structures because the lateral dimensions are
multiple wavelengths and thickness only a fraction of a wavelength. Such
geometrical properties could allow for seamless installation on building
surfaces (indoors or outdoors) even on curved surfaces.

In mmWave and THz non-line-of-sight (NLoS) paths, if the user is not in the
specular reflection direction, then communication relies on diffuse scattering
[20] from the rough surfaces of the surroundings (e.g. walls, terrain). Unless
the user is near the specular direction, signal strength is impacted
drastically and enabling anomalous reflection can provide viable propagation
paths and boost the strength of received signal. Using the bistatic radar
equation, we can estimate the received power $P_{r}$ when an RIS is placed
between the user equipment (UE) and the base station (BS), as illustrated in
Fig. 1:

$P_{r}=\frac{P_{t}G_{BS}G_{UE}\lambda^{2}\sigma}{(4\pi)^{3}R_{i}^{2}R_{d}^{2}}$
(1)

Where $P_{t}$ is the signal power transmitted by the BS, $G_{BS}$, $G_{UE}$
are the gains of the BS and UE antennas respectively, $R_{i}$ and $R_{d}$ the
distances between BS-RIS and RIS-UE. The radar cross section $\sigma$ of the
RIS can be approximated as a rectangular, flat conductive surface with losses.
As such, the monostatic RCS of the an electrically large RIS with area $A$,
efficiency $\eta$, and wavelength $\lambda$ is:

$\sigma=\frac{4\pi\eta A^{2}}{{\lambda}^{2}}$ (2)

In a bistatic scenario, similar to the deployments in wireless communications,
the RIS is viewed with an angle $\theta_{i}$ from the BS and $\theta_{d}$ from
the UE (as shown in Fig. 1), assuming BS and UE are on the same plane. Then,
the bistatic RCS of the RIS can be approximated by:

$\sigma=\frac{4\pi\eta cos\theta_{i}cos\theta_{d}A^{2}}{{\lambda}^{2}}$ (3)

We notice here that the received power increases quadratically with the size
of the RIS. Namely, a ten-fold increase in the area $A$ provides 20 dB
stronger signal for the same propagation scenario. Similarly, using RISs in
higher frequencies, the received power will increase inversely proportional to
the square of the wavelength. Such signal improvement is also crucial when
considering that higher frequency signals suffer more losses (e.g. free path
loss, penetration loss). For example, a window glass or a brick wall can have
attenuation that exceeds 25 dB and 91 dB respectively at 38 GHz [21].

###  I-B Prior Work on Reconfigurable Intelligent Surfaces

The design and analysis of RIS-integrated wireless communication systems have
attracted significant interest in the last few years. From the signal
processing perspective, RIS systems bring interesting challenges to the design
of the large-dimensional passive beamforming matrices at the RIS surfaces. For
example, [22] investigates the design of low-complexity and energy-efficient
RIS beamforming algorithms, [23] designs robust RIS beamforming solutions, and
[24, 25, 26] develop techniques for the joint basestation-RIS beamforming
design problem. One main critical challenge for the operation of these systems
is the high training overhead associated with the channel acquisition,
especially if these surfaces are nearly passive. To address this challenge,
[4] developed RIS architectures where sparse active elements can be leveraged
to acquire some knowledge about the channel. This can also enable leveraging
compressive sensing and deep learning approaches to efficiently estimate the
RIS channels [4]. From the network perspective, it is important to accurately
analyze the coverage performance when RIS surfaces are adopted. Some initial
studies into this direction have been provided in [5, 6, 7, 8] for various
network architectures. All this prior work in [22, 23, 24, 25, 26, 4, 5, 6, 7,
8], however, was limited to simulation data. To accurately evaluate the
potential of the RIS surfaces, it is crucial to build proof-of-concept
prototypes and assess the expected coverage and data rate gains of RIS-
integrated systems in real world wireless communication environments.

From the circuits and prototyping perspectives, various tuning topologies have
been reported in the microwave frequency region using PIN or varactor diodes
[27, 28, 29]. These topologies are used as beamformers in wireless
communications, imaging, and sensing. In [30], an implementation of a
reconfigurable intelligent surface was presented where the authors showed an
indoor coverage analysis using a 2.4 GHz 3,200-element RIS. The user and/or
base station, however, were in the near field of surface. In [31], the authors
presented a sub-6 GHz metasurface-based RIS to enhance received signal in
wireless communication systems. Although the work shows clear improvement in
the received signal, the experiments are limited to topologies where (i) both
the receiver and transmitter are coupled to directive antennas and (ii) the
receiver is always in the near field of the RIS. Additionally, the RIS
prototype uses varactor switches, unit cells smaller than $\lambda$/2, vias,
and a multilayered PCB structure, which is a viable solution for microwave
frequencies, but a non-scalable approach for mmWave and THz frequencies. This
highlights the need to develop more efficient prototyping approaches for RIS
systems and accurately evaluate their performance in realistic wireless
communication scenarios where both the transmitter and receiver are in the
far-field of the RIS surface.

Reconfigurable surfaces (either mirror or lenses) have also been implemented
in the mmWave bands using also PIN diodes [32] and there are many research
efforts for higher frequency topologies approaching the THz bands [33].
However, new switching topologies and materials are required (e.g. $VO_{2}$,
graphene, liquid crystal) to overcome the limited performance of PIN and
varactor diodes [34] and simplified unit cell layouts to enable practically
manufacturability of mmWave and THz RISs [35, 36, 37, 38]. For the RIS to be
attractive in wireless communications, the surface needs to be scalable to
large areas with thousands of unit cells and switches, low-profile and
conformal to fit in various irregular indoor or outdoor surfaces, consume low
power, and be cost efficient in manufacturing, installation, and maintenance.
Although most of these features are inherent in reflective surfaces, switching
performance and manufacturability due to the biasing circuit topologies are
engineering challenges.

###  I-C Contribution

In this paper, we designed and fabricated a low-power reconfigurable
intelligent surface and demonstrated its beamforming and coverage gain
potential capabilities in real-world wireless communications environments. The
main contributions can be summarized as follows:

  * •

Present a single layer, single switch-per-cell RIS design operating at 5.8 GHz
that is also compatible with mmWave and THz fabrication and integration
technologies. The layout requires no vertical connections (e.g. vias) and
biasing network in integrated on the same plane. The RIS is capable of
electronic beam scanning in both azimuth and elevation planes.

  * •

Characterize the beamforming capabilities in an outdoor environment. We
carried out radiation pattern characterization in the controlled environment
of an anechoic chamber and then evaluated the beamforming gains in realistic
near-field and far-field outdoor settings in the presence of scattering from
the ground and the surrounding environment.

  * •

Carry out coverage measurements at 5.8 GHz in occluded (line-of-sight (LoS)
obstructed), outdoor areas using mobile UE with omni-directional antennas. The
LoS path between the BS and mobile UE is blocked by a building and the signal
coverage is improved when using the proposed RIS to provide an alternative
signal path.

The paper is structured as follows: In section II, we present current RIS
design and discuss the fundamental operation of reflectarray antennas, which
is a main component in RISs. In section III, we discuss the quantized
beamforming theory of RISs and its relationship to wireless communication
between a BS and UE. Then, section IV presents the design and characterization
of the RIS, including a discussion on the unit cell layout (building block of
the RIS), testing of the integrated switches and beamforming, and integration
with the necessary control circuitry. In section V, we present a wireless
communication testbed - including a BS, UE, and the RIS - to evaluate the
performance of the RIS in realsitic wireless communication field tests.
Finally, in section V, we evaluate the potential gains of the RIS beamforming
in improving the SNR at the mobile users and extending the wireless
communication coverage beyond LoS areas.

##  II RIS-Based Wireless Communication System

In this section, we describe the adopted RIS-assisted wireless communication
system and briefly formulate the beamforming design problem.

Figure 1: Overview of the proposed reconfigurable intelligent surface (RIS). A
control circuit provides the necessary excitation (codebook) to the unit cells
and redirects the incident wave to the desired direction.

###  II-A System and Signal Models

As depicted in Fig. 1, we consider a simple scenario where the communication
between a single-antenna transmitter and a single-antenna receiver is assisted
by a reconfigurable intelligent surface. The surface is assumed to have a
uniform planar array structure of $M\times N$ elements. Adopting an OFDM
(Orthogonal Frequency Division Multiplexed) system model with $K$ sub-
carriers, we define ${\mathbf{h}}_{\mathrm{TX},k}$ and
${\mathbf{h}}_{\mathrm{RX},k}$ as the $k$th subcarrier $MN\times 1$ uplink
channels from the transmitter and receiver to the RIS, $k=1,2,...,K$. Further,
we define $h_{\mathrm{TR},k}$ as the direct LoS channel between the
transmitter and receiver on the $k$th subcarrier. If $s_{k}$ denotes the
transmitted signal over the $k$th subcarrier, then the received signal can be
written as

$r_{k}={\mathbf{h}}_{\mathrm{RX},k}^{T}\boldsymbol{\Psi}{\mathbf{h}}_{\mathrm{TX},k}s_{k}+h_{\mathrm{TR},k}s_{k}+n_{k},$
(4)

where $n_{k}\sim\mathcal{N}_{\mathbb{C}}(0,\sigma_{n}^{2})$ represents the
receive noise and $s_{k}$ has an average power
${\mathbb{E}}\left[s_{k}\right]=\frac{P}{K}$ with P representing the total
transit power. The matrix $\boldsymbol{\Psi}$ denotes the $MN\times MN$ RIS
interaction matrix. Note that $\boldsymbol{\Psi}$ is a diagonal matrix. To
capture that, we define
$\boldsymbol{\psi}=\mathrm{diag}\left(\boldsymbol{\Psi}\right)$ as the
$MN\times 1$ RIS reflection vector, which includes the phase control at each
RIS element. In particular, each element $mn$ is expressed as
$e^{j\varphi_{mn}}$, with the $\varphi_{mn}$ denoting the RIS modulation phase
of this element. Now, neglecting the LoS link (for the scenarios with blocked
LoS), the RIS beamforming/excitation vector $\boldsymbol{\psi}$ can be
designed to maximize the achievable rate following

$\displaystyle\boldsymbol{\psi}^{\star}=$
$\displaystyle\arg\max_{\varphi_{mn},\forall m,n}\ \ \ \
\sum_{k=1}^{K}\log_{2}\left(1+\rho{\left|{\mathbf{h}}_{\mathrm{RX},k}^{H}\boldsymbol{\Psi}{\mathbf{h}}_{\mathrm{TX},k}\right|^{2}}\right),$
(5) $\displaystyle\hskip 20.0pt\text{s.t.}\hskip
40.0pt\varphi_{mn}\in\left[0^{\circ},360^{\circ}\right],\ \ \forall m,n,$ (6)

where $\rho=\frac{P}{K\sigma_{n}^{2}}$ denoting the per-carrier SNR. Next,
since the RIS-transmitter and RIS-receiver channels are mostly dominated by
LoS paths, we consider the following approaximation for the design of the RIS
reflection vector

$\displaystyle\boldsymbol{\psi}^{\star}=$
$\displaystyle\arg\max_{\varphi_{mn},\forall m,n}\ \ \ \
\sum_{k=1}^{K}{\left|{\mathbf{h}}_{\mathrm{RX},k}^{H}\boldsymbol{\Psi}{\mathbf{h}}_{\mathrm{TX},k}\right|^{2}},$
(7) $\displaystyle\hskip 20.0pt\text{s.t.}\hskip
40.0pt\varphi_{mn}\in\left[0^{\circ},360^{\circ}\right],\ \ \forall m,n.$ (8)

In practice, the channels between the RIS and the transmitters/receivers will
likely be LoS. Now, focusing on this case, the optimization problem in (7)-(8)
can be further reduced to

$\displaystyle\boldsymbol{\psi}^{\star}=$
$\displaystyle\arg\max_{\varphi_{mn},\forall m,n}\ \ \ \
{\left|\left({\mathbf{a}}_{\mathrm{RIS}}^{*}(\theta_{d},\phi_{d})\odot{\mathbf{a}}_{\mathrm{RIS}}(\theta_{i},\phi_{i})\right)^{T}\boldsymbol{\psi}\right|^{2}},$
(9) $\displaystyle\hskip 20.0pt\text{s.t.}\hskip
40.0pt\varphi_{mn}\in\left[0^{\circ},360^{\circ}\right],\ \ \forall m,n.$ (10)

where ${\mathbf{a}}_{\mathrm{RIS}}(\theta,\phi)$ denotes the RIS array
response vector for the angles $\theta,\phi$. The angles $\theta_{i}$ and
$\phi_{i}$ represent the elevation/azimuth angles of the incident signal and
the angles $\theta_{d}$ and $\phi_{d}$ represent the elevation/azimuth angles
of the desired reflection direction. In the next subsection, we briefly
present the design approach for the beamforming codebook adopted in this work.

###  II-B 1-bit Beamforming Codebook

Consider the RIS beamforming design problem in (9)-(10), if the incident and
desired reflection directions are $(\theta_{i},\phi_{i})$ and
$(\theta_{d},\phi_{d})$, respectively, then the optimal RIS phase shifting
configuration for each element $\varphi_{mn}$ in $\boldsymbol{\psi}$ is given
by

$\varphi_{mn}=\varphi_{i,mn}-\varphi_{d,mn},$ (11)

where $\varphi_{i,mn}$ and $\varphi_{d,mn}$ are respectively the phase of the
incident wave and phase for the desired reflection direction on the $mn^{th}$
RIS unit cell. For a two-dimensional planar RIS with unit cells arranged on
the $x-y$ plane, these phases are given by [39]

$\varphi_{i,mn}=k_{0}(x_{m}\sin\theta_{i}\cos\phi_{i}+y_{n}\sin\theta_{i}\sin\phi_{i}),$
(12)
$\varphi_{d,mn}=k_{0}(x_{m}\sin\theta_{d}\cos\phi_{d}+y_{n}\sin\theta_{d}\sin\phi_{d}),$
(13)

with $k_{0}$ representing the free space wavenumber and $(x_{m},y_{m})$ are
the coordinates of the $mn^{th}$ RIS element. However, at mmWave/THz
frequencies practical phase shifting topologies can only produce discrete
values of $\varphi_{mn}$ that are typically quantized using 1-, 2- or 3- bit
quantization schemes. A single-bit phase quantization scheme is adopted in
this work owing to its simplicity and lower cost when compared to higher bit
quantization methods [40]. As such, all the phase values in the range
$[-90^{0},+90^{0}]$ are rounded off to $0^{0}$ (designated as state ‘0’/OFF)
and the rest of the phase values are rounded off to $180^{0}$ (designated as
state ‘1’/ON). The two states are realized using PIN diodes with state ‘0’
representing the OFF state of the diode and state ‘1’ corresponding to the
diode’s ON state, as will be explained in detail in Section III. Thus, the
quantized phase shift at the $mn^{th}$ RIS unit cell is :

$\varphi^{\mathrm{quant}}_{mn}=\left|180^{0}\cdot\mathrm{round}(\frac{\varphi_{mn}}{180^{0}})\right|.$
(14)

With this RIS beamforming design, the resulting far-field radiation pattern of
the RIS at a direction $\theta,\phi$ can be approximated by the array factor

$AF_{\mathrm{RIS}}(\theta,\phi)=\sum_{m=1}^{M}\sum_{n=1}^{N}e^{-jk_{0}(x_{m}u+y_{n}v)}e^{j\varphi_{i,mn}}e^{j\varphi^{\mathrm{quant}}_{mn}},$
(15)

where $k_{0}(x_{m}u+y_{n}v)$ represents the phase modulation due to Green’s
function, with $u=\sin\theta\cos\phi$ and $v=\sin\theta\sin\phi$.

For efficient wireless communication operation with the RIS, we design a
codebook $\boldsymbol{\mathcal{P}}$ of pre-defined beams (RIS phase shifting
configuration). Each RIS beam codeword in this codebook reflects the wave that
is incident from a direction $(\theta_{i},\phi_{i})$ to a desired reflection
direction $(\theta_{d},\phi_{d})$. For example, if the desired sets of
directions for the incident and reflected waves are respectively defined by

$\displaystyle\mathcal{I}=\left\\{\left(\theta_{i,1},\phi_{i,1}\right),\left(\theta_{i,2},\phi_{i,2}\right),...,\left(\theta_{i,N_{i}},\phi_{i,N_{i}}.\right)\right\\}$
(16)
$\displaystyle\mathcal{D}=\left\\{\left(\theta_{d,1},\phi_{d,1}\right),\left(\theta_{d,2},\phi_{d,2}\right),...,\left(\theta_{d,N_{d}},\phi_{d,N_{d}}\right)\right\\}$
(17)

with cardinalities $\left|\mathcal{I}\right|=N_{i}$ and
$\left|\mathcal{D}\right|=N_{d}$, then the RIS codebook
$\boldsymbol{\mathcal{P}}$ will have $N_{i}N_{d}$ beam codewords. Each
codeword vector
$\overline{\boldsymbol{\psi}}_{i,d}\in\boldsymbol{\mathcal{P}}$ is designed
following the 1-bit (quantized) beamforming approach in (11)-(14) for a
particular incident/reflection directions from $\mathcal{I}$ and
$\mathcal{D}$.

##  III Reconfigurable Intelligent Surface Design

In this section, we present the design and characterization of the RIS,
including the unit cell layout, measurement of the radiation pattern of both a
fixed-beam (no switches) and a multi-beam RIS prototype (integrated PIN
switches), and integration of the control circuitry. The RIS is designed at a
center frequency of 5.8 GHz which is part of the unlicensed spectrum used in
wireless local area networks (WLANs). The goal of this work is to design a
single layer topology (besides the ground plane) without requiring the use of
vertical components (e.g. vias). As such, the RIS is comprised of the ground
plane, the substrate, and the top metalization layer. Such topology is
attractive for future mmWave and THz RISs where multilayer structures increase
losses and fabrication complexity.

Figure 2: Layout of the RIS unit cell. (a) The unit cells comprises a main resonant patch antenna connected to a parasitic rectangular patch through an RF PIN diode (switch). The necessary biasing lines and RF chokes are integrated for switch activation (ON/OFF). (b), (c) Activation of the RF PIN diode alters the current distribution, resulting in phase modulation of the reflected signal (180 degrees) without a significant modulation of the magnitude within the bandwidth of interest. TABLE I: Key dimensions of the RIS unit cell Unit Cell Design Parameters | Dimension (mm)
---|---
$\mathrm{W_{U}}$ | 25.85
$\mathrm{W_{1}}$ | 12.51
$\mathrm{L_{1}}$ | 12.51
$\mathrm{W_{2}}$ | 5.48
$\mathrm{W_{D}}$ | 0.7
$\mathrm{L_{D}}$ | 1.5

###  III-A 1-bit Unit Cell Design

The RIS comprises 160 (16 $\times$ 10) unit cells that contain a passive
antenna, a radio-frequency (RF) PIN diode (switch), and biasing lines, as
shown in Fig. 2 and Fig. 3. Depending on the applied voltage across the diode
terminals (reverse bias: OFF, forward bias: ON), the antenna re-radiates the
received signal with a phase difference that depends on the current
distribution. Typically, such a phase shift is enabled either by changing the
resonant frequency of the antenna [41, 42] or by providing extra path to the
current on the feed of the antenna [43, 35]. The latter approach requires a
short transmission line terminated to the ground. Such topology would require
a via, thus, to minimize fabrication complexity, we opted for the resonance
approach by adding a parasitic patch next to the antenna connected through the
PIN diode. Additionally, biasing lines are needed for both diode terminals,
therefore, two narrow lines are connected to the antenna and the parasitic
patch respectively, as depicted in Fig. 2.

To isolate the RF signal from the biasing lines, a radial stub is used in each
one of the lines. The biasing lines are routed in groups of five unit cells to
ensure minimum wiring complexity, as shown in the full array topology in Fig.
3. As opposed to current approaches in the literature, the proposed topology
1) requires only a single tuning device (switch) and 2) comprises a single
layer with no vertical connections to the ground. Although single switch
approaches result in quantization sidelobes, in this study, we use the RIS
only in limited scanning range to avoid sidelobe interference. Nevertheless,
using pre-coded, phase randomization methods [35, 44] we can eliminate the
undesired side lobes using practical topologies. This step has not been the
focus of this work and is left for future prototype implementations.

Figure 3: The layout of the 160-element (16 $\times$ 10) antenna array used in
the proposed RIS. The inset shows the routing of the biasing lines for 5 unit
cells (no vias). This constitutes a 2D topology that is compatible with low
complexity RIS implementation in the mmWave and THz bands.

To evaluate the response of the unit cell under different biasing states, we
use an industry standard commercial electromagnetics (EM) solver (ANSYS,
HFSS). As such, we design an infinite array of unit cells using periodic
boundary conditions on each primary direction. Although this process omits
radiation effects at the edge of the aperture, it is considered reliable in
estimating the EM response of an individual unit cell. The substrate used here
is Rogers RT/ Duroid 6002 RF laminate with dielectric constant, height and
loss tangent of 2.94, 2.54mm, 0.0012 respectively. The antenna/switch
(BAR50-02V) co-design is carried out using the measured $S$-parameters
provided by the manufacturer (MACOM). When the array is illuminated from
boresight, the calculated reflected signal exhibits small ($<1$ dB) magnitude
modulation and a phase difference of 180∘ at 5.8 GHz for the two states of the
switch. For an acceptable phase modulation range of 180∘ $\pm$ 20 ∘ between
the two states, the expected bandwidth is approximately 150 MHz, as shown in
Fig. 4. Next, we integrate the unit cells into a large array and analyze the
radiation effects under various switch excitations (codebook).

(a) Reflection Phase Response (b) Reflection Magnitude Response

Figure 4: Numerical analysis of the unit cell response under boresight
illumination ($\vartheta_{i}$=0) for two switch states (ON/OFF).

###  III-B Array Design

The radiation characteristics of the RIS have been studied using both the
analytical expression of Eq. (14) as well as using a full-wave numerical
method. To account for the electrically large model, we used ANSYS HFSS finite
element boundary integral (FE-BI) which reduces the computational complexity.
In the first scenario, we model the RIS with a feed horn antenna
($\theta_{i}=-27.5^{\circ}$) and calculate the radiation patterns for the
excitation of three different reflection angles (0∘, 17∘, and 60 ∘). The three
excitations result in distinct main lobes at the desired directions, as shown
in the analytical and full-wave simulation results of Fig. 5. The discrepancy
between the patterns with respect to the sidelobe levels is attributed to the
diffraction and surface wave phenomena that are not accounted for in the array
factor analysis. Additionally, to simulate the beamforming characteristics
when the feed is in the far-field, as expected in several wireless
communications scenarios, we replace the feed horn antenna by a plane wave
excitation and plot the normalized radar cross section (RCS) pattern for three
reflection angles (22.5∘, 40∘, and 60∘). Due to the phase quantization error,
the reflected beam present a second grading lobe at the opposite angle around
the specular direction, as shown in Fig. 6. The quantization lobes can be
mitigated by adding random phase delays at each unit cell [35, 44].

(a) At a Reflection Angle of $0^{\circ}$ (b) At a Reflection Angle of
$17^{\circ}$ (c) At a Reflection Angle of $60^{\circ}$

Figure 5: Computed radiation patterns when the RIS is illuminated by a feed
horn antenna (near-field, $\vartheta_{i}=-27.5^{\circ}$) for various
reflection angles ($\vartheta_{d}$).

Figure 6: Computed RIS radar cross section patterns under plane wave
illumination (normal incidence) for various reflections angles
($\vartheta_{d}$): (a) $22.5^{\circ}$, (b) $40^{\circ}$, and (c) $60^{\circ}$.

###  III-C Characterization of a fixed beam RIS

To evaluate the accuracy of the array design simulations, we fabricated a
fixed beam reflectarray and measured the radiation pattern at 5.8 GHz. The RIS
comprises 160 unit cells (16 $\times$ 10) with an overall array dimension of
414 mm $\times$ 259 mm. In place of the RF PIN diodes, we place short or open
terminations and design the RIS to operate as a reflectarray antenna with a
reflection angle at $+60^{\circ}$. Then the layout was fabricated using a
chemical etching process on a 2.54 mm thick Rogers RT/duroid 6002 substrate.
First, the array layout is printed on a thermal sheet using an inkjet printer
and then heat pressed on the substrate at 530 $C^{\circ}$. Afterward, the
substrate with the imprinted mask is immersed into sodium persulphate
($Na_{2}S_{2}O_{8}$) to etch the copper and the mask is removed using acetone.
To test the radiation pattern, a 12.5 dBi feed horn antenna is fixed above the
reflectarray at an angle of -27.5∘, as depicted in Fig. 7. The measurement
took place in an anechoic chamber (ASU’s Compact Antenna Test Range) to ensure
for minimum reflections from any surroundings. As plotted in Fig. 8, the
normalized gain is measured in the [$-90^{\circ}$,$+90^{\circ}$] range
($E$-plane) and shows good agreement with the full-wave numerical analysis.

Figure 7: This figure illustrates the adopted measurement setup for the fixed-
beam (passive) RIS prototype characterization. Figure (a) shows the anechoic
chamber measurement setup and figure (b) summarizes the dimensions of the
fixture. Figure 8: This figure shows a comparison between the measured (in the
anechoic chamber) and computed radiation patterns of the 5.8 GHz fixed beam
(passive) RIS prototype, which confirms the agreement between the designed and
actual beams. Figure 9: Testboard for the characterization of the RF PIN diode
switch. (a) Layout of the testboard and (b) phase response of the reflected
signal for two different switch states (forward/reverse biased). Figure 10:
Schematic of the RIS control circuit. A codeword is generated and parsed to
the RF PIN diodes through a microcontroller and 20 8-bit shift registers (LEDs
are only needed for troubleshooting).

###  III-D Implementation of the Reconfigurable Intelligence Surface

In this section, we detail the fabrication and assembly of the RIS prototype.
To verify the computed performance of the RF PIN diodes, we fabricated a test
board that included several BAR50 PIN diodes with the necessary biasing
contact pads and RF chokes, as shown in Fig. 9(a). Here, the diodes are
terminated to a rectangular patch that is similar to the RIS unit cell
topology, thus emulating a mismatch that generates around 180∘ phase
difference in the reflected signal between the PIN diode’s biasing states.
Using a vector network analyzer (VNA), we measured the reflection coefficient
($S_{11}$) of 4 test diodes for both biasing states. As plotted in Fig. 9(b),
the phase difference is around $188^{\circ}$ for a wide frequency range around
5.8 GHz which was in accordance to the circuit simulations. Then, we assembled
160 diodes on the array and integrated them with the necessary control
circuitry. Fig. 10 shows a schematic of the RIS control circuitry that
comprises the biasing circuit and a micro-controller (which also stores the
beam codebook). The micro-controller (Arduino MEGA2560) splits the bit
sequence of each codeword (which consists of 160 bits) into 10 segments, each
corresponding to the configuration weights of 16 unit cells. Every codeword
segment is then pushed to a pair of in-series connected 8-bit shift registers
(20 in total). As such, every output of a shift register is connected in
series with an LED and resistor to provide 0.8 V across each PIN diode for
forward biasing (0V for reverse biasing) and limit the current to 3 mA. We
note here that the LEDs are used only for debugging and testing purposes and
are not necessary for the RIS operation. With this biasing circuit design, the
bit sequence of each codeword determines the unit cell induced phased delay
(0∘ or 180 ∘) across the 160-element array, and thus steering the beam to the
corresponding direction. Fig. 11 shows the front (antenna array) and rear side
(control unit) of the RIS assembled on the fixture. The total DC power
consumption of the RIS including the biasing circuitry is less than 0.4 Watts.
Nevertheless, power consumption can be dramatically reduced with transistor-
based switches [45] to micro-Watt levels even for RISs with thousands of
elements due to the small leakage current of such devices.

###  III-E Characterization of the RIS beamforming

Similar to the fixed beam reflectarray measurement, the RIS is assembled with
a feed horn antenna (near-field) inside the anechoic chamber, as shown in Fig.
12(a). Being in the near-field, the feed horn impresses a Gaussian field
distribution on the RIS aperture, as shown in the plots of magnitude and phase
in Fig. 12(b). The recorded radiation patterns for 5 reflected directions
($\vartheta_{d}$ = $\\{$ 0∘, 15∘, 30∘, 45∘, 60∘ $\\}$) show very good
agreement with the full-wave simulations, as plotted in Fig. 13. The right
column in Fig. 13 shows the quantized phase distribution
$\Phi^{quant}=(\phi_{mn}^{quant})$ when applying the respective codeword
vectors $\overline{\boldsymbol{\psi}}_{-27.5^{\circ},d}$ on the RIS switches.
Finally, Fig. 14 compares the measured scanned angles on the same plot. The
measurements confirm the capability of the RIS to scan in the $\pm 60^{o}$
range maintaining a single main lobe. Additionally, the side lobe level (SLL)
is maintained below -7 dB for more than 100 MHz bandwidth (5.75-5.85 GHz) and
below -5 dB for more than 400 MHz at $\theta_{d}=30^{\circ}$.

(a) RIS Front-End

(b) RIS Back-End

Figure 11: The developed 160-element RIS prototype at the sub-6GHz band:
Figure (a) shows the front-end which has the reflecting elements and figure
(b) shows the back-end with the RIS control circuit.

Figure 12: RIS characterization in the anechoic chamber. (a) Photo of the RIS
setup in the chamber and (b) magnitude and phase distribution of the incident
feed horn antenna beam on the RIS aperture. Figure 13: Characterization of the
RIS beamforming for various reflection angles $\theta_{d}$ at 5.8 GHz. Left
column: Comparison between computed and measured radiation patterns. Right
column: Quantized (1-bit) phase shift distribution $\Phi^{quant}$ on the RIS
surface to generate the respective radiation patterns. Figure 14: Comparison
of RIS measured radiation patterns for five $\theta_{d}$ angles.

##  IV Integrating the RIS into a Wireless Communication Testbed

Figure 15: The RIS-assisted wireless communication system consists of an RIS,
a transmitter (BS), and a receiver (UE). The transmitter and receiver are
portable and can be placed in the far-field of the RIS surface.

To demonstrate the potential of reconfigurable intelligent surfaces, we
integrate the fabricated prototype, described in Section III, into a wireless
communication testbed. In this section, we describe the developed testbed and
beam selection process.

###  IV-A Testbed Description

As illustrated in Fig. 15, our RIS-integrated wireless communication testbed
consists of (i) a single-antenna transmitter, (ii) a single-antenna receiver,
and (iii) the RIS. Next, we summarize the key aspects of the testbed

  * •

Transmitter: We consider a single-antenna transmitter that is implemented
using an NI USRPs 2901 operating at 5.8GHz. The USRP is connected to a horn
antenna with a 18.5 dBi gain, emulating the antenna gain of a BS. As will be
explained shortly, the transmitter operation is controlled by a Raspberry Pi
that is wirelessly controlled (over a 2.4GHz channel).

  * •

Receiver: Similar to the transmitter, the communication receiver is
implemented using an NI USRP 2901 operating at the 5.8GHz band. The USRP is
connected to either a dipole antenna or a horn antenna with 12.5 dBi gain. The
receiver is controlled by a laptop that manages the overall operation of the
RIS-integrated wireless communication system.

  * •

RIS: The developed RIS, consisting of 160 elements (16 $\times$ 10) is placed
and leveraged in reflecting the transmitted signal to the receiver direction.
Section III provides a detailed description for the design and fabrication of
the adopted RIS.

The developed RIS-integrated wireless communication system operates at the
sub-6GHz band with a center frequency of $5.8$GHz and a $20$MHz bandwidth. The
system adopts an OFDM transmission/reception with $64$ subcarriers. In the
next subsection, we describe the operation framework including the selection
of the reflection beamforming codewords at the RIS surface.

###  IV-B Testbed Operation and Beam Selection

The main objective of the developed testbed is to evaluate the coverage gains
when using an RIS. Toward this objective, we adopt the following operation
framework. For given locations of the transmitter and receiver, the central
controller triggers the transmitter to send OFDM-based pilot sequences. During
this transmission, the controller wirelessly orders the Raspberry Pi that is
controlling the RIS configurations to switch between the $N_{i}N_{d}$ beams in
the codebook $\boldsymbol{\mathcal{P}}$. With this beam training, the receiver
measures the power of the received signal $r_{k}$, from equation (4), and
selects the optimal RIS configuration $\boldsymbol{\psi}^{\star}$ that solves

${\overline{\boldsymbol{\psi}}}^{\star}=\arg\max_{\overline{\boldsymbol{\psi}}\in\boldsymbol{\mathcal{P}}}\
\ \ \
\sum_{k=1}^{K}{\left|r_{k}\left(\overline{\boldsymbol{\psi}}\right)\right|^{2}},$
(18)

where $r_{k}\left(\overline{\boldsymbol{\psi}}\right)$ is the received signal
at the k-th subcarrier when the reflection beam codeword
$\overline{\boldsymbol{\psi}}$ is used by the RIS. For the 1-bit codebook,
$\boldsymbol{\mathcal{P}}$, we follow the approach described in Section II-B
to design it. The exact angular range and number of beams for the codebook
adopted in the field measurements are provided in Section V.

##  V Field Tests and Results

In this section, we present the results of our field tests that evaluate the
beamforming capabilities and coverage improvement of the developed RIS-based
wireless communication system. First, we describe the setups adopted in the
measurement campaigns in Subsection V-A before demonstrating the results in
the following subsections. It is worth mentioning that all our measurement
campaigns are conducted outdoors to account for wave propagation phenomena
that occur in real-world scenarios, including scattering from the terrain,
posts, and edge diffraction. Therefore, the results of these campaigns draw
important insights into the actual performance of RISs in realistic wireless
communication deployments.

###  V-A Measurement Setups

In this subsection, we describe the two adopted setups, namely the ASU parking
lot and the ASU Gammage Memorial Auditorium, where our measurements are
performed.

Figure 16: Measurement Setup 1 (ASU Campus Parking Lot). To test the RIS’
electronic beamscanning capabilities in the field, the RIS is illuminated from
the boresight direction ($\vartheta_{i}=0$) and the reflected signal is
recorded at various angles and distances. [Inset: RIS mounted on a the
tripod.]

Measurement Setup 1: ASU Campus Parking Lot: To characterize the beamforming
codebook in an LoS real-world scenario, we carried out the measurements in a
parking lot. The satellite image of the parking lot with the measurement setup
overlaid is shown in Fig. 16. The setup consists of the prototype-RIS mounted
on a tripod, a C-band corrugated conical horn antenna with a gain of 12.5 dBi
used as the feed, and a C-band pyramidal horn with a gain of 18.5 dBi used on
the receiver. The feed and the receiver horns were respectively connected to
transmitting and receiving USRP modules. The feed horn is positioned in front
of the RIS at 5m distance from the surface. The tripod-mounted-RIS is depicted
in the inset of Fig. 16. The receiver is positioned at a distance of 10 m in
front of the the RIS and rotated in a circular arc from 0∘-60∘ to capture the
deflected signals from the RIS. Further, to characterize the pathloss, the
measurements were repeated at 20 m and 40 m from the RIS.

Measurement Setup 2: ASU Gammage Memorial Auditorium: To emulate a user
environment with significant signal degradation, we carried out the coverage
measurements around the entrance of ASU’s Gammage Memorial Auditorium, shown
in Fig. 17. Here, the base station is modeled using a 19dBi horn antenna
placed on one side of a tall (5m) and thick (2m) concrete wall, covering the
north and eastern side of the outdoor area of the venue. On the other side of
the wall, the receiver uses an ommi-directional antenna and moves in the area
in front of the venue entrance. The concrete wall serves as an occlusion
between the transmitter and the receiver, thus we examine the level of the
received signal with and without the use of the RIS.

Figure 17: Measurement Setup 2 (ASU Gammage Memorial Auditorium). In this
field test, a strong occlusion (blockage) exists between the BS and the mobile
user. The RIS is strategically placed to leverage its beamscanning
capabilities in extending the coverage to the LoS-obstructed mobile users.

###  V-B Characterization of the Beamforming Codebook

Figure 18: Evaluating the RIS’ electronic beamscanning (azimuth plane). The
high receive power at the diagonal line confirms the agreement between the
codebook beam directions and the receiver location (which is moving along a
circular path, as shown in Fig. 16). [Inset: 2D plot of the received signal
when the receiver is oriented 40∘ from the broadside]

Here, we present the results obtained from the field measurements carried out
to characterize the beam-scanning capabilities of the proposed RIS. A 20 MHz
bandwidth OFDM signal centered around 5.8 GHz is coupled to the feed horn from
the transmitting USRP module. The feed horn is aligned in front of the RIS
such that it illuminates the RIS from broadside. The reflected signals are
recorded using a receiver horn antenna along a 10∘-60∘ arc with a radius of 10
m at every 2.5∘. After beam-scanning at every point, we form a reception
pattern for every scanning RIS scanning angle. The 3D surface plot of Fig. 18
shows the beamforming capability of the RIS proposed in this work. As the
beams from the RIS are scanned from 0∘-60∘ employing the beamforming codebook,
the peak power is received at the corresponding receiver location. The
received power at an exemplary angle of 40∘ from the broadside is shown in the
inset of Fig. 18. For receiver angles less than 20∘, coupling is noticed
between the transmitter and the receiver. This is primarily due to the
transmitter’s backlobes as well as its close proximity to the receiver for
directions near broadside. This is an artifact due to the limitations of the
measurement setup and would not be present in a deployment where the
transmitter is much farther or blocked by an occlusion. Nevertheless, the
power corresponding to the the desired direction is still sufficiently high to
offset this coupling effect and the receiver is able to detect the RIS beam.
Overall, Fig. 18 shows that the developed RIS can achieve around $18$-$20$dB
SNR gain in the considered deployment scenario. It is worth mentioning here
that these beamforming gains can potentially be improved if these codebooks
are further optimized to match the RIS hardware impairments and the
surrounding environment [46, 47].

###  V-C Pathloss Measurements

To characterize the pathloss, similar beamforming measurements were repeated
at distances of 20 m and 40 m from the RIS. Fig. 19 depicts the change in the
received power levels, at few nominal angles (10∘, 20∘ and 30∘) from
broadside, as a function of distance from the RIS. As expected, the pathloss
increases as the distance from the RIS increases as well as when the beam
deviates away from the broadside direction.

Figure 19: Path loss as a function of distance: The receiver is fixed at
various directions ( $\theta_{d}={10^{\circ},20^{\circ},30^{\circ}}$) from
broadside and the distance between the LIS and the receiver is increased from
10 m to 40 m.

###  V-D Signal Coverage Measurements

(a) Without RIS

(b) With RIS

Figure 20: This figure illustrates the field test setup with strong occlusion
between the base station and the mobile user. Figure (a) shows the coverage
map without the RIS while figure (b) shows the coverage map with the RIS. The
field measurements indicate an average SNR gain of $\sim 6$dB (max 8 dB) when
the RIS is deployed.

In this section, we present the results of the signal coverage measurements.
The goal is to evaluate the improvement of the received signal power for a
mobile user that employs an omnidirectional antenna. This experiment was done
using the measurement setup 2 (ASU Gammage Memorial Auditorium), described in
Section V-A. In the absence of RIS, the received signal on a 28-point grid is
low due to the occlusion (signal blockage) from the large wall, as shown in
the coverage map in Fig. 20(a). To evaluate the potential of the RIS in
expanding the signal coverage, we place the RIS at a strategic place such that
the RIS is in LoS of both the base station and the occluded area (in front of
the venue entrance). As such, the RIS intercepts the signal from the base
station and redirects it to the mobile user. In this scenario, the path from
the base station to the RIS to the UE ranges between 30-40 meters depending on
the grid position. As the user moves to each grid position, the RIS scans the
codebook beams horizontally to find the optimum signal reception. The second
coverage map in Fig. 20(b) shows the improved received signal level using the
RIS beam scanning. The SNR improvement along the occluded/blocked region is up
to 8 dB with an average of 6dB. We note here that the SNR improvement is a
function of the RIS size, thus a reasonably 10 times larger aperture (1,600
elements) would result in 28 dB SNR improvement for the same coverage
scenario. The 20 dB improvement is due the fact that the received signal
depends on the square of the RIS area, as in (1) and (3).

##  VI Conclusion

In this work, we developed a proof-of-concept prototype for reconfigurable
intelligent surfaces and evaluated their potential gains in real-world
environments and practical settings. In particular, we designed and fabricated
a sub-6GHz 160-element RIS prototype, which relies on a planar single layer
reconfigurable reflectarray (no vias) capable of scanning in both azimuth and
elevation planes. Thanks to the simplicity of the geometry and the capability
to operate with single-bit switches, the proposed design can be directly
scaled to higher frequencies such as mmWave and THz systems using either RF
PIN switches, transistor based switches (e.g. CMOS-based), or tunable
materials (e.g. graphene). For this RIS prototype, we characterized the
beamforming capabilities for both the passive (no RF diodes) and active
implementations which achieved a half-power-beamwidth (HPBW) of approximately
9 degrees and 16 degrees on the azimuth and elevation planes, respectively.
Then, we integrated the RIS into a wireless communication system to accurately
evaluate its beamforming and coverage gains in realistic communication
scenarios. Our results indicated that the developed RIS system can provide
around $20$dB SNR gain when both the transmitter/receiver use directional
antennas and when they are at distances of $5$m and $10$m from the RIS.
Further, when the average BS $\rightarrow$ RIS $\rightarrow$ UE distance is
$35$m, with blocked LoS link, and when only one side uses a directional
antenna while the other side adopts an omni-directional antenna, an SNR gain
of 8 dB is achievable. This gain can be further increased by increasing the
size of the RIS. For example, a ten-fold increase in the area of the RIS to a
moderate 1,600-element array will further increase the SNR by 20.

This work has shown that RISs is a promising technology in extending wireless
coverage in scenarios where occlusions are strong, even if the RIS is in the
far field of both base station and mobile user. Additionally, RISs have the
potential to operate in extremely low power levels which is important for i)
sustainable wireless communications and ii) to enable deployments where access
to power supply is limited or even non-existent. The current non-optimized
prototype consumes less than 0.4 Watt (including the biasing circuitry). As an
alternative to PIN diodes, transistor-based switches [45] are promising
devices for extremely low power consumption RISs. The low leakage current of
field-effect-transistor (FET) switches may consume less than 4 $u$Watt of DC
power for an RIS with 10,000 switches. Finally, beamforming performance can be
improved in future designs by eliminating the grating lobes that appear in the
opposite direction to the main beam. This improvement can be achieved by
adding a fixed, random phase delay on each unit cells and mitigate the
quantization errors stemming from low-bit sampling [35, 44].

##  VII Acknowledgements

We would like to thank Rogers Inc. Chandler, AZ for providing the substrates
for the implementation of all prototypes presented in this work. Many thanks
to Craig Birtcher for his help in the radiation pattern measurements in the
Compact Antenna Test Range.

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