# Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic
Language

Abdelkader El Mahdaouy1 Abdellah El Mekki1 Nabil El Mamoun2
Kabil Essefar1 Ismail Berrada1 Ahmed Khoumsi3
1School of Computer Sciences, Mohammed VI Polytechnic University, Morocco
2Faculty of Sciences Dhar EL Mahraz, Sidi Mohamed Ben Abdellah University,
Morocco
3Dept. Electrical & Computer Engineering, University of Sherbrooke, Canada
<EMAIL_ADDRESS>
<EMAIL_ADDRESS>

###### Abstract

The prominence of figurative language devices, such as sarcasm and irony,
poses serious challenges for Arabic Sentiment Analysis (SA). While previous
research works tackle SA and sarcasm detection separately, this paper
introduces an end-to-end deep Multi-Task Learning (MTL) model, allowing
knowledge interaction between the two tasks. Our MTL model’s architecture
consists of a Bidirectional Encoder Representation from Transformers (BERT)
model, a multi-task attention interaction module, and two task classifiers.
The overall obtained results show that our proposed model outperforms its
single-task counterparts on both SA and sarcasm detection sub-tasks.

##  1 Introduction

The popularity of the Internet and the unprecedented reach of social media
platforms allow users to express their opinions on a wide range of topics.
Thereby, Sentiment Analysis (SA) has become a cornerstone for many
applications such as digital marketing, product review analysis, customer
feedback, social media monitoring, etc. SA consists of determining the
expressed sentiment (positive, negative, or neutral) conveyed by a text or a
piece of text.

Over the past decade, significant research advances have been achieved for
Arabic SA Badaro et al. (2019); Al-Ayyoub et al. (2019); Oueslati et al.
(2020); Abu Farha and Magdy (2021). However, the mutual interaction and impact
of figurative language devices, like sarcasm and irony, and Arabic SA remain
under-explored Abu Farha and Magdy (2020, 2021); Abbes et al. (2020). These
latter devices allow us to express ourselves intelligently beyond the literal
meaning of words. Although the literature uses the terms irony and sarcasm
interchangeably, they have different meanings and there is no consensus on
their definition Farías et al. (2016); Hernández Farías and Rosso (2017);
Zhang et al. (2019). Both sarcasm and irony devices pose a real challenge for
SA as they can reverse the expressed sentiment polarity from positive to
negative Hernández Farías and Rosso (2017); Abu Farha and Magdy (2020, 2021).
Therefore, there is an urgent need to develop sarcasm-aware SA tools.

Previous research works on Arabic SA and sarcasm detection have dealt with
both tasks separately Ghanem et al. (2019, 2020); Abbes et al. (2020); Abu
Farha and Magdy (2020). Abbes et al. (2020) have built a corpus for irony and
sarcasm detection in Arabic language from twitter using a set of ironic
hashtags. Unlike the previous work of Karoui et al. (2017) that have relied on
ironic hashtags to label the tweets, the annotation is performed manually by
two Arabic language specialists. Abu Farha and Magdy (2021) have presented an
overview of existing Arabic SA methods and approaches, and a benchmarking
using three existing datasets. Their results have shown that most of the
evaluated models perform poorly on the SemEval and ASTD datasets. Due to the
label inconsistencies discovered, they have re-annotated the previously
mentioned datasets for SA and sarcasm detection. In addition to the highly
subjective nature of SA task, they have reported a large performance drop in
the case of sarcastic tweets Abu Farha and Magdy (2020, 2021).

(a) Distribution of sarcastic tweets per region

(b) Distribution of sarcastic tweets per sentiment polarity

(c) Distribution of sentiment polarities

Figure 1: ArSarcasm-v2 dataset: distribution of sarcastic tweets and their
sentiment polarities. True and False denote sarcastic and non-sarcastic
tweets, respectively.

Following the recent breakthroughs in Arabic Natural Language Processing
(NLP), achieved using AraBERT model Antoun et al. (2020), Abdul-Mageed et al.
(2020) have introduced two Arabic transformer-based language models, namely
ARBERT and MARBERT. ARBERT is trained on large textual corpora of Modern
Standar Arabic (MSA), while MARBERT is trained on 1 billion DA and MSA tweets
corpus. They have shown new cutting edge performances on wide range of DA and
MSA NLP tasks (AraBench datasets), including, among others, SA and sarcasm
detection.

In this paper, we present our end-to-end deep MTL model, submitted to SA and
sarcasm detection in Arabic language shared task Abu Farha et al. (2021). Our
approach is based on MARBERT Abdul-Mageed et al. (2020), and a multi-task
attention interaction module. The latter consists of two task-specific
attention layers for extracting task-discriminative features, and of a Sigmoid
interaction layer Lan et al. (2017) for allowing interaction and knowledge
sharing between sarcasm detection and SA. The task-interaction is performed
using the task-specific attention outputs, a learnable shared matrix, and the
Sigmoid activation. The obtained results show that our MTL model surpasses the
other evaluated single-task and MTL models. Besides, the incorporation of an
attention mechanism and the task-interaction boosts the performance of both
sarcasm detection and SA.

The rest of the paper is organized as follows. Section 2 presents the shared
task’s dataset. Section 3 introduces the proposed method. In Section 4, we
present the obtained results for both sarcasm detection and SA subtasks.
Section 5 discusses the obtained results. Finally, Section 6 concludes the
paper.

##  2 Data

The ArSarcasm Shared Task consists of two subtasks for sarcasm detection and
SA in Arabic language Abu Farha et al. (2021). The shared task’s dataset,
ArSarcasm-v2, is built from the previously introduced datasets for sarcasm and
irony detection Abbes et al. (2020); Abu Farha and Magdy (2020). The provided
dataset consists of 12,548 and 3,000 tweets for the training set and test set,
respectively. The task’s dataset is annotated for SA and sarcasm detection as
well as the regional dialect of the tweets.

Figure 1 presents the distribution of sarcastic tweets and their sentiment
polarities (Figures 1(a) and 1(c)). The distribution of all sentiment
polarities in the dataset is illustrated in 1(c). The dataset is unbalanced
for both subtasks. Most sarcastic tweets are written in MSA and Egyptian
dialect (Figure 1(a)), and are labeled with a negative sentiment (Figure
1(b)). Furthermore, approximately half of the tweets convey a neutral
sentiment (Figure 1(c)).

##  3 Method

Our multi-task model consists of three main components: BERT encoder, a multi-
task attention interaction module, and two task classifiers.

###  3.1 BERT Encoder

Fine-tuning Bidirectional Encoder Representation from Transformers (BERT)
model on downstream tasks has shown a new wave of state-of-the-art
performances in many NLP applications Devlin et al. (2019). BERT model’s
architecture consists of multiple transformer encoders for learning
contextualized word embedding of a given input text. It is trained on large
textual corpora using two self-supervised objectives, namely the Masked
Language Model (MLM) and the Next Sentence Prediction (NSP).

The encoder of our MTL model is the pre-trained MARBERT Abdul-Mageed et al.
(2020). MARBERT is fed with a sequence of wordpeices $[t_{1},t_{2},...,t_{n}]$
of the input tweet, where $n$ is the sequence length. It outputs the tweet
embedding $h_{[CLS]}$ ([CLS] token embedding) and the contextualized word
embedding of the input tokens $H=[h_{1},h_{2},...,h_{n}]\in\mathbb{R}^{n\times
d}$. Both $h_{[CLS]}$ and $h_{i}$ have the same hidden dimension $d$.

###  3.2 Multi-task attention interaction module

This module consists of two task-specific attention layers (task-specific
context-rich representation) and a Sigmoid task-interaction layer.

The task-specific sentence representation $v_{*}\in\mathbb{R}^{1\times d}$
(e.g. $v_{sarc}$ and $v_{sent}$) is obtained using the attention mechanism
over the contextualized word embedding matrix $H$ :

$C=tanh(HW^{a})$ $\alpha=softmax(C^{T}W^{\alpha})$ $v_{*}=\alpha\cdot H^{T}$

where $W^{a}\in\mathbb{R}^{d\times 1}$ and $W^{\alpha}\in\mathbb{R}^{n\times
n}$ are the learnable parameters of the attention mechanism.
$C\in\mathbb{R}^{n\times 1}$ and $\alpha\in[0,1]^{n}$ weights words hidden
representations according to their relevance to the task.

The task interaction mechanism Lan et al. (2017) is performed using a
learnable shared matrix $W^{i}\in\mathbb{R}^{d\times d}$ and a bias vector
$b^{i}\in\mathbb{R}^{d}$. The interaction of both task are given by:

$v^{\prime}_{sarc}=v_{sarc}\odot\sigma(W^{i}v_{sent}+b^{i})$ (1)
$v^{\prime}_{sent}=v_{sent}\odot\sigma(W^{i}v_{sarc}+b^{i})$ (2)

where $v_{sarc}$ and $v_{sent}$ are the output of the sarcasm task-specific
attention layer and the sentiment task-specific attention layer, respectively.
$\odot$ is the element-wise product.

###  3.3 Task classifier

We employ two task classifiers $F_{sarc}$ and $F_{sent}$ for sarcasm detection
and SA, respectively. Each classifier consists of one hidden layer and one
output layer. They are fed with the concatenation of the pooled output
embedding and the task output of the multi-task attention interaction module
$v^{\prime}_{*}$ (e.g. $v^{\prime}_{sarc}$ and $v^{\prime}_{sent}$). The
outputs of the task classifiers are given by:

$\hat{y}_{sarc}=F_{sarc}([h_{[CLS]},v^{\prime}_{sarc}])$ (3)
$\hat{y}_{sent}=F_{sarc}([h_{[CLS]},v^{\prime}_{sent}])$ (4)

###  3.4 Multi-task learning objective

We train our MTL model to jointly minimize the binary cross-entropy loss
$\mathcal{L}_{BCE}$, for sarcasm detection, and the cross-entropy loss
$\mathcal{L}_{CE}$, for SA. The total loss is given by:

$\mathcal{L}=\mathcal{L}_{BCE}(y_{sarc},\hat{y}_{sarc})+\mathcal{L}_{CE}(y_{sent},\hat{y}_{sent})$
(5)

where $\hat{y}_{*}$ is the predicted output and $y_{*}$ is the ground truth
label.

##  4 Results

In this section, we present the experiment settings and the obtained results.

###  4.1 Experiment settings

We have compared our model (MTL_ATTINTER) with two single-task models (ST and
ST_ATT) and two MTL models (MTL and MTL_ATT).

  * •

ST consists of MARBERT with one classification layer.

  * •

ST_ATT employs the attention mechanism on top of the contextualized word
embedding of MARBERT. The classification is performed using the attention
layer output and the [CLS] token embedding.

  * •

MTL is similar to ST model and uses classification layer for each task.

  * •

MTL_ATT is the MTL counterpart of ST_ATT model.

|  | Sarcasm | Sentiment
---|---|---|---
|  | Precision | Recall | Accuracy | F1 | F$1^{Sarc}$ | Precision | Recall | Accuracy | F1 | F1PN
ST | Dev | 0.7649 | _0.7683_ | 0.8673 | 0.7666 | 0.6132 | 0.7422 | 0.7519 | 0.7641 | 0.7465 | 0.7284
Test | 0.706 | 0.708 | 0.768 | 0.707 | 0.573 | 0.672 | 0.667 | 0.713 | 0.665 | 0.749
ST_ATT | Dev | 0.7736 | 0.7588 | 0.8622 | 0.7658 | 0.6156 | _0.7541_ | 0.7429 | 0.7629 | _0.7479_ | 0.7253
Test | 0.724 | 0.722 | 0.778 | 0.723 | 0.598 | 0.664 | 0.665 | 0.709 | 0.661 | 0.742
MTL | Dev | 0.7935 | 0.7611 | 0.8633 | 0.7753 | 0.6347 | 0.7424 | 0.748 | _0.7649_ | 0.7448 | 0.7288
Test | 0.725 | 0.714 | 0.771 | 0.719 | 0.599 | 0.676 | 0.656 | 0.703 | 0.662 | 0.736
MTL_ATT | Dev | 0.8064 | 0.7581 | 0.8606 | 0.7778 | 0.6421 | 0.7478 | _0.7524_ | _0.7649_ | 0.7465 | 0.7326
Test | 0.741 | 0.72 | 0.773 | 0.728 | 0.617 | 0.663 | 0.676 | 0.717 | 0.66 | 0.752
MTL_ATTINTER | Dev | _0.8106_ | 0.766 | _0.8661_ | _0.7846_ | _0.6522_ | 0.7511 | 0.7414 | 0.7582 | 0.7436 | _0.7358_
Test | 0.7268 | 0.7122 | 0.7680 | 0.7183 | 0.6000 | 0.6713 | 0.7183 | 0.7107 | 0.6625 | 0.7480

Table 1: Models evaluation on both SA and sarcasm detection subtasks

|  | Sarcasm | Sentiment
---|---|---|---
|  | Precision | Recall | Accuracy | F1 | F$1^{Sarc}$ | Precision | Recall | Accuracy | F1 | F1PN
MTL_ATTINTER |  | 0.7268 | 0.7122 | 0.7680 | 0.7183 | 0.6000 | 0.6713 | 0.7183 | 0.7107 | 0.6625 | 0.7480

Table 2: The obtained results of our Official submission

We have implemented the MARBERT’ tweets preprocessing pipeline Abdul-Mageed et
al. (2020). The evaluated models have been trained using Adam optimizer with a
learning rate of $5\times 10^{-6}$. Based on several experiments, the batch
size and the number of epochs have been fixed to $64$ and $5$, respectively.
Besides, we have used $80$% and $20$% of the provided training data for
training set and development set, respectively. For comparison purposes, we
have used the macro-average Precision, Recall, F1, and F1 score of positive
and negative (F1PN) evaluation measures. We have also employed the Accuracy
and the F1 score of the sarcastic tweets (F1Sarc).

###  4.2 Experiment results

Table 1 shows the obtained models’ performances for both SA and sarcasm
detection. The best results, for each evaluation measure, are highlighted with
italic font and bold fond for the dev set and the test set, respectively. The
overall obtained results show that MTL models outperform their single-task
counterparts for most evaluation measures. In fact, incorporating attention
mechanism into both ST_ATT and MTL_ATT improves the F1, F1Sarc and F1PN. The
former compute the F1 score for sarcastic tweets only, while the latter
consider only positive and negative sentiment.

MTL_ATTINTER and MTL_ATT achieve the best performances for most evaluation
measures on both the dev and the test sets of sarcasm detection sub-task.
Specifically, they show far better F1 performance for the sarcastic class
prediction. For SA, the other evaluated models achieve slightly better
performance. However, MTL_ATTINETER and MTL_ATT yield the best F1PN
performances on the dev set and the test set. Therefore, our proposed model
excels in detecting sarcastic tweets as well as predicting positive and
negative sentiments.

#### Official results

Since one submission was allowed, we have submitted the results of our
MTL_ATTINETER model. Table 2 shows the official submission results. Our system
is top ranked on SA Sub-task and has secured the fourth position among
submitted systems for sarcasm detection.

(a) Confusion matrix of the sarcasm detection task

(b) Confusion matrix of SA task

(c) Confusion matrix of SA among non-sarcastic tweets

(d) Confusion matrix of SA among sarcastic tweets

Figure 2: The confusion matrices of our MTL model’s prediction on both SA and
sarcasm detection tasks

##  5 Discussion

To investigate the strengths and weaknesses of our model, we have analyzed the
confusion matrix of each subtask (Figures 2(a) and 2(b)) as well as the
confusion matrices of sentiment analysis among sarcastic and non-sarcastic
tweets respectively (Figures 2(d) and 2(c)). The analysis of these matrices
shows that our MTL model leverages signals from both tasks and boosts the
performances. This can be explained by the fact that most sarcastic tweets
convey a negative sentiment. Besides, negative tweets tend to have a large
probability of being sarcastic than the positive ones. This could be also
deduced from Table 1, where MTL models achieve the best F1Sarc and F1PN scores
compared to single-task models.

##  6 Conclusion

In this paper, we have proposed an end-to-end deep Multi-Task Learning model
for SA and sarcasm detection. Our model leverages the MARBERT’s contextualized
word embedding with a multi-task attention interaction module. The aim is to
allow task-interaction and knowledge sharing for both SA and sarcasm
detection. Our model shows very promising results on both subtasks. Therefore,
it proves the effectiveness of using task-specific attention layers as well as
the task-interaction mechanism in multi-task learning.

Future research work will focus on developing task-interaction and class-
interaction modules and mechanisms for SA and sarcasm detection.

## References

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# Indoor Positioning Trends in 5G-Advanced: Challenges and Solution towards
Centimeter-level Accuracy

Jakub Nikonowicz, Aamir Mahmood, Muhammad Ikram Ashraf, Emil Björnson, Mikael
Gidlund

###### Abstract

After robust connectivity, precise positioning is evolving into an innovative
component of 5G service offerings for industrial use-cases and verticals with
challenging indoor radio environments. In this direction, the 3GPP Rel-16
standard has been a tipping point in specifying critical innovations, followed
by enhancements in Rel-17 and Rel-18. In this article, we elaborate on the 5G
positioning framework, measurements, and procedures before shifting the focus
mainly to recently identified carrier-phase (CP) measurements in Rel-18 as a
complementary measure for time- and angular-based positioning methods. We
discuss the associated challenges and potential solutions for exploiting CP,
including integer ambiguity, multipath sensitivity, and signaling aspects.
Furthermore, we study how phase-continuous reference signaling can counter
noisy phase measurements using realistic simulations to achieve centimeter-
level accuracy in indoor factory (InF) scenarios.

##  I Introduction

Continuous work on the fifth-generation (5G) network aims to extend its
applications beyond traditional mobile broadband, where one of the fundamental
features is precise positioning. The need for high-precision positioning is
anticipated in logistics, autonomous harbors and vehicles, localized sensing,
digital twins, augmented and virtual reality, and more. It is desirable to
deliver positioning services using cellular technology, instead of requiring a
dedicated infrastructure. The 5G network design is evolving mainly in response
to the needs of industrial segments, i.e., Industry 4.0. We can foresee the
requirement for centimeter accuracy positioning in fully automated factories
for acquiring precise knowledge of resources placement, track moving assets
and machinery, up to product storage. As a result, smart factories remain the
primary sector for precise positioning, and an indoor factory (InF)
environment defines current requirements and challenges [1].

In this respect, the 5G new radio (NR) provides numerous innovations, from new
positioning architecture and reference signals to measurement/parameter
enhancements, for achieving positioning accuracy down to the centimeter. The
3GPP Rel-16 targets precise positioning and low-latency requirements of
diverse application scenarios, including Industrial Internet-of-things (IIoT),
transportation, and logistics. For instance, the indoor positioning accuracy
(horizontal and vertical) and latency requirements for commercial use-cases
are less than 3m, with less than 1s of latency and 80% service availability.
Meanwhile, the Rel-17 aims to improve the positioning accuracy up to tens of
centimeters and lower latency up to tens of millisecond for InF-IIoT.

The 5G positioning methods, in general, are derived from timing, angular,
power-based techniques and their combinations wherein Rel-17 proposes improved
signaling and procedures over Rel-16 considering that i) wider bandwidth
increases timing measurements resolution, ii) larger antenna array apertures
in massive MIMO (multiple-input multiple-output) for mid-band and mmWave
allows narrower radio beams and increased angular resolution, and iii) joint
processing of time-and angular-based methods helps mitigate dense multipath in
InF settings.

Further, the ongoing Rel-18 study item [2] is investigating solutions to
improve accuracy up to a few cm with a latency of a few ms, especially by i)
evaluating bandwidth aggregation for intra-band carriers, ii), integrating
sidelink information, and iii) identifying reference signals, physical layer
measurements/procedures to enable carrier phase (CP)-based positioning. Fig. 1
shows the 5G positioning methods, as well as the evolving requirements and
emerging use-cases in 3GPP releases [3].

Figure 1: The 5G positioning architecture and possible measurements/methods
together with performance requirements across 3GPP releases for evolving needs
of IIoT use-cases. Rel-17 and onward efforts are focused on accuracy
enhancements by improving NR parameters (e.g., bandwidth, power, antennas),
combination of measurements (e.g., CP with time/angle measurements), and other
countermeasures (e.g., multipath resolution).

The earlier studies (e.g., [1, 3]) are focused on general description of the
5G positioning architecture and methods proposed in Rel-17&18\. Meanwhile, the
study [4] analyzed the CP-based positioning, which is finding the traction to
complement time/angle-based methods and eliminate their sensitivity to
sampling resolution and system parameters.

However, the various entailing critical issues of realizing CP-based
positioning enhancements are not considered in these works. In this respect,
our main contributions are:

  * •

To establish the relationship between NR system parameters and delay/angle
error variance, we describe the 5G positioning architecture, including
reference signals and basic positioning measurements/methods.

  * •

We introduce CP-based enhancements in time/angle-based positioning schemes
along with related problems and challenges.

  * •

We consider CP measurement enhancements under continuous reference signals as
a potential solution to counter noisy phase measurements.

  * •

We provide simulation results for CP measurements, with/without continuous
reference signals, in estimating 3D distance error in the InF channel models.

  * •

Finally, further improvements/benefits in continuous CP measurements are
envisioned.

##  II Basic positioning approaches

The 3GPP specifications entail the basic architecture, main positioning
technologies, and possible combinations (see Fig. 1), which are briefly
resumed here.

TABLE I: Relationship between 5G NR parameters to delay/angle error variance
[5, 1, 6].

Estimate | Parameter | Relationship | Comments
---|---|---|---
Delay error variance | Bandwidth (BW) | $\propto{1}/{\text{BW}^{2}}$ |  | NR typically provides 100 MHz bandwidth at FR1 and
---
400 MHz at FR2
Average received power (P) | $\propto{1}/{\text{P}}$ | Received power is also increased by the array gain
Subcarrier spacing (SCS) | $\propto{1}/{\text{SCS}}$ |  | Higher SCS increases resolution in resolving LOS from
---
the multipath
Antenna array geometry
| Antenna elements (N)
---
| $\propto{1}/{\text{N}}$
---
Only the array gain matters, so the array geometry can be arbitrary
Angle error variance | Bandwidth (BW) | – | Independent
Average received power (P) | $\propto{1}/{\text{P}}$ | Less pronounced compared to delay error variance
Subcarrier spacing (SCS) | $\propto{\text{SCS}}$ |  | For fixed BW, increasing SCS leads to fewer details in
---
the channel frequency response
Antenna array geometry
| Horizontal antenna elements ($\text{N}_{\text{H}}$)
---
Vertical antenna elements ($\text{N}_{\text{V}}$)
Antenna spacing (S)
| $\propto{1}/{\text{N}_{\text{H}}^{3}}$
---
$\propto{1}/{\text{N}_{\text{V}}^{3}}$
$\propto{1}/{\text{S}^{2}}$
| The array geometry matters, the number of horizontal and vertical
---
elements affect differently depending on the location of the device

In the 5G positioning framework, the location management function (LMF) is a
central entity to estimate UE position based on assistance/measurements from
next-generation (NG)-RAN and UE through the access and mobility management
function (AMF). Moreover, a new NR positioning protocol A (NRPPa) carries the
positioning information between NG-RAN and LMF. Besides, NR introduced two
reference signals specifically for enabling accurate positioning measurements
in downlink (DL) and uplink (UL) directions, respectively, as positioning
reference signal (PRS) and sounding reference signal (SRS). These reference
signals are configurable to enhance the precision of UL/DL time/angular
measurements (see Sec. IV-C). LMF provides DL-PRS configuration to UEs using
LTE positioning protocol (LPP) while RAN configures UL-SRS to UEs using radio
resource control (RRC) protocol. To understand the relations of these
measurements with the system parameters, Table I provides a summary of the
various factors affecting errors in time/angle-based positioning.

###  II-A Angle-based Positioning

Massive MIMO and smart antenna techniques in 5G allow precise control over the
beamforming and angle estimation. Angle-of-departure (AOD) and angle-of-
arrival (AOA) measurements are part of the beam management procedure specified
by 3GPP. Based on UL or DL transmission direction, two positioning techniques
are:

  * •

DL-AOD: using PRS, a UE measures the beam reference signal received power
(RSRP) of gNB and reports it to LMF via LPP [1].

  * •

UL-AOA: using SRS, gNB measures AOA, i.e., the azimuth and elevation angles,
and reports them to the LMF by the NRPPa [1].

The achievable accuracy of triangulation based on angle measurements is
limited due to the range/resolution of reportable absolute values for power
measurements, which is $[-156,-31]$dBm, with 1dB resolution [1]. Therefore,
there are many suggestions to improve AOA/AOD performance; for instance, by
introducing CP measurement in addition to beam RSRP measurement [7] (see Sec.
III-B).

###  II-B Time-based Positioning

As RSRP-based approaches require establishing an accurate propagation model to
reliably estimate signal energy, it is challenging to achieve high measurement
accuracy in dynamic radio environments, limiting the scope of AOA/AOD-based
techniques [8].

An alternative solution is trilateration methods. Time-of-arrival (TOA)-based
positioning transforms signals propagation delay into the distance between gNB
and UE. However, this approach requires precise UE-gNB synchronization. TDOA
is an improved ranging method based on differential TOA, requiring time
synchronization between gNBs only. As with the angular methods, the PRS and
SRS are used to estimate the DL or UL TDOA between different gNBs.

  * •

DL time-difference-of-arrival (DL-TDOA): UE receives the PRS from several gNBs
and calculates the TOA of each PRS signal. The TOA of one gNB is taken as a
reference to compute the reference-signal-time-difference (RSTD) to TOAs from
the remaining gNBs. UE sends the RSTD measurements to LMF to compute the UE
position using known geographical coordinates of gNBs.

  * •

UL time-difference-of-arrival (UL-TDOA): UE transmitted SRS is received by
neighboring gNBs; a transmission measurement function calculates the relative-
time-of-arrival (RTOA) and sends it to the LMF to compute the UE position.

  * •

Multi-cell round-trip-time (Multi-RTT): gNB and UE perform Rx-Tx time
difference measurement, using PRS and SRS signaling, for the signal of each
cell. LMF initiates the procedure whereby multiple gNBs and a UE perform the
gNB Rx-Tx and UE Rx-Tx measurements, respectively. Multi-RTT has higher
accuracy than TDOA-based methods, and it relaxes requirements on time
synchronization [1].

Clock synchronization between gNB and UE or between gNBs is required to
accurately estimate TOA or TDOA, respectively. However, the precision of time
measurements is limited to intervals of $T_{c}$, with flexible resolution of
$2^{k}T_{c}$, where $T_{c}=0.51$ns and $k$ is an integer in the interval [2,
5] for FR1 and [0, 5] for FR2 [1]. Additionally, a dense multipath environment
can cause NLOS propagation, making the measurements unreliable. Thus, the
measurement quality critically depends on system timing and multipath
propagation conditions [9, 10].

##  III Carrier Phase (CP)-based Enhancements

This section identifies positioning solutions in NR to meet the industrial
positioning accuracy requirements both for FR1 and FR2 according to [3]. Note
that NR positioning naturally shares some of the challenges/problems of Global
Navigation Satellite System (GNSS). Although NR brings them to the InF
environment, GNSS-based solutions can still inspire indoor settings. The basic
principle of distance measurement in GNSS is pseudo-range. As the pseudorandom
code mode is similar to Gold sequence correlation in TOA measurements,
solutions to improve GNSS positioning (e.g., CP measurements) are also
considered in NR. Compared to GNSS, however, wireless networks can operate in
more complex scenarios, but with flexible carrier frequency configurations and
fewer error sources.

Generally, the CP measurement captures the difference between the phase of the
incoming carrier signal and the receiver-generated reference signal. Under LOS
conditions, the phase is in the $[0,2\pi)$ range, and the measurement error is
only a small fraction of the wavelength; reaching the centimeter level. As
illustrated in Fig. 2, CP-measurements can improve the accuracy of
trilateration-based (i.e., using solely timing measurements) and
triangulation-based (i.e., using AOD/AOA measurements in multi-antenna
systems) positioning [7].

###  III-A CP in Time-based Solutions

As mentioned earlier, PRS are pseudo-random sequences with good
autocorrelation. The UE correlates the time domain samples with the known PRS
pattern when measuring the signal propagation delay. After detecting the code
phase, the receiver replicates the transmitted pseudo-random code and moves
the replica until the maximum correlation is achieved. The offset corresponds
to the transmitter-to-receiver signal propagation time. Hence, it depends on
estimating the earliest peak delay in the magnitude of the normalized cross-
correlation function. Consequently, the accuracy of the range-based methods
depends mainly on the UE sampling resolution and the signal bandwidth [8].

Contrary to delay in code-phase detection, carrier-phase detection translates
the phase difference and wavelength into a distance. When the receiver
intercepts the transmitted signal, it is locked by a phase-locked loop (PLL).
From the moment the first path is received, the phase shift between the
locally generated reference signal and the replica of the received carrier
wave is constant. Therefore, the measuring point is not as critical to the
measurement quality [10]. However, when initiating positioning, the received
phase is in the range $[0,2\pi)$, so only a fraction of a single wavelength
can be measured. This causes integer ambiguity (IA) problem—how many full
wavelengths ($N$) precede the measured fraction at the propagated distance [8]
(see Section IV-A).

Nevertheless, finding the CP difference requires UE–gNB synchronization,
analogous to TOA measurement. Herein, the differentiation of measurements
known from TDOA is useful. Differential CP measurements allow to cancel out UE
clock offset, and eliminate typical measurement errors caused by the
propagation environment. The differential measurement, therefore, assumes that
gNBs are synchronized. However, the gNB subsets arrive at the UE with a slight
time difference, and the phase measurement may be distorted/incorrect due to
the gNBs’ clock mismatch.

###  III-B CP in Angle-based Solutions

Carrier-phase (CP) measurement can also improve positioning accuracy by
triangulation. Accurate measurement of an angle in a plane requires a
configuration with (at least) two receiving antennas. The transmitter emits a
sinusoidal signal with wavelength $\lambda$ that propagates spherically so
that the wave phase changes continuously in $[0,2\pi)$. The instantaneous
signal phases $\phi_{1}(t),\phi_{2}(t)$ measured at the two antennas will
generally be different, but the difference $\Delta=\phi_{1}(t)-\phi_{2}(t)$ is
constant and enables AOA estimation. Suppose the separation between the
receive antennas is $d$ and that the impinging wave is planar. The AOA
$\theta$ perpendicular to the line between the antennas satisfies
$\cos(\theta)=\Delta\lambda/(2\pi d)$, from which the angle can be extracted
(but there are multiple solutions). Similar techniques can be used to measure
the AOD from a multi-antenna transmitter to a single-antenna receiver; for
example, the transmitter antennas can take turn in sending the sinusoid so
that receiver can measure the respective phase-shifts.

Moreover, the CP method is simpler and more efficient than the beam-sweep
method. It provides an accurate angle estimate, and simultaneously, multiple
terminals can acquire AOA/AOD information from a single PRS transmission.
Precise angle measurement using a dual-antenna configuration is also a
solution proposed in Bluetooth-SIG [7].

Figure 2: Illustration of carrier phase (CP) in angle/time-based solutions.

##  IV Open Challenges and Potential Solutions

While already used in outdoor systems, phase measurements challenge migrating
to InF environments. Cluttered propagation conditions, shorter distances,
higher accuracy/latency targets, and a different signal structure require
reconsidering  problems/solutions known in GNSS.

###  IV-A Integer Ambiguity

Introducing CP measurements in NR can reduce the measurement error, typically
10% of the carrier wavelength under right conditions [11], and consequently,
increase the positioning accuracy. However, using CP is coupled with a
difficulty, commonly referred to as integer ambiguity (IA); that is, the phase
measurement is in the $2\pi$ range, but the total distance contains unknown
integer numbers of the carrier wavelength. Consequently, a fast/reliable IA
solution is essential to meet the low-latency positioning demands. The unknown
wavelength multiplicity between transmitter and receiver is already
successfully solved in GNSS CP measurements, laying the groundwork for
adaptation in NR.

The main issue in IA is the large search range of possible integers,
especially for the short wavelengths in the FR2 band. Using TOA can
significantly reduce the IA search range or, alternately time needed to solve
it. TOA depends on the geometric distance $d$ between the transmitter and
receiver, their clock offsets $\delta t$ converted into the distance by the
speed of light $c$, and arrival timing measurement errors $\omega_{t}$.
Whereas, CP at the receiver is the result of the same distance, the same clock
errors, and phase measurement errors $\omega_{p}$, reduced by the integer
multiple of the wavelength, $N\lambda$. Having both measurements at disposal
allows comparative equation regarding duplicated $d$ and $\delta t$. As such,
the IA search space mainly depends on the measurement errors
$(\omega_{t}-\omega_{p})/\lambda$, where $\omega_{p}$ is only a small fraction
of the carrier wavelength, and $\omega_{t}$ is dominant. In NR, $\omega_{t}$
may be reduced to a few meters; however, bearing in mind the target
applications of centimeter/millimeter carrier wavelengths, the search space
for IA may remain relatively large.

Another approach to reduce the IA search range is the idea of virtual
wavelength. Specifically, instead of transmitting a single frequency, the
transmitter transmits reference signals in two or more frequencies to get
phase measurements at multiple frequencies. As the two-phase equations follow
the same propagation pattern, and only differ with wavelengths, represented by
$\lambda_{1}$ and $\lambda_{2}$, both equations can be unified by using two-
sided multiplication by $\lambda_{2}/(\lambda_{2}-\lambda_{1})$ and
$\lambda_{1}/(\lambda_{2}-\lambda_{1})$, respectively. Then, subtracting the
phase equations creates the virtual phase measurement for wavelength,
$\lambda_{2}-\lambda_{1}$. This provides the opportunity to make differential
wavelength much longer than the initial $\lambda_{1}$ and $\lambda_{2}$ [4].
Adapting this solution in NR gives numerous possibilities as the network can
configure the transmission frequencies to optimally reduce the IA search space
and computational overhead.

###  IV-B Multipath and NLOS

The IIoT use-cases exhibit harsh channel conditions caused by multiple
reflections from objects (called “clutter”) in a dense industrial environment.
Thereby, the measurements suffer from, a) disturbed phase continuity, b)
excessive delays with respect to the LOS transmission time, and c) the angular
deviation from the actual LOS direction. NLOS causes fundamental error in
phase after reflection; simultaneously, a significant error is introduced into
the location equations, and the IA search space vastly extends. Consequently,
the centimeter-level accuracy in CP-based positioning becomes unattainable in
desired time unless NLOS measurements are excluded or mitigated.

In LOS scenarios with multipaths, the Rician-K factor measures the ratio of
the received power from the LOS path to the received power from all other
paths [9]. It is mainly the angle of the LOS path that is useful for
positioning and antenna arrays can be used to distinguish it from the clutter.
This is a classical signal processing problem that has resulted in the
multiple signal classification algorithm (MUSIC), signal parameters via
rotational invariant techniques (ESPRIT), and space-alternating generalized
expectation maximization (SAGE) algorithms [12]. The associated computational
complexity and robustness are challenging for real-time positioning.

It can be challenging to distinguish a LOS scenario from a NLOS environment
with one or multiple strong reflected paths. An estimated Rician-K factor can
be clearly above 0dB but the dominant path might be a reflection from a
misleading direction. Channel features such as maximum received power, delay
spread, and departure/arrival angle spread can be used to distinguish between
LOS/NLOS situations. An RSRP-based NLOS link identification algorithm is
developed in [13], which performs binary hypothesis testing between LOS/NLOS
link types using average NLOS channel power of the subcarrier.

Cooperative positioning is another approach, where erroneous distance
measurements due to NLOS conditions are detected/corrected using information
from neighboring anchors. However, a standalone device must be capable of
autonomously detecting the NLOS state and correcting erroneous measurements
only with local information. To this end, the concept of simple classifiers
based on the received signal strength and first path information can be
replaced by more advanced machine learning (ML) techniques [9]. ML-based
solutions aggregate a variety of features. For instance, under NLOS
conditions, multiple reflections cause the signals to be more attenuated and
have lower energy and amplitude. While for LOS, the strongest signal path
corresponds to the first arrival, for NLOS, the strongest path is preceded by
weak components.

###  IV-C Continuous-PRS

The implementation of the CP-based positioning enhancement is based on the PRS
signal transmitted by gNB. PRS is designed to support DL-based positioning
schemes while delivering accuracy, coverage, and interference management. PRS
can cover the full NR bandwidth, and its transmission over multiple symbols
(using comb-size of 2, 4, 6, and 12, i.e., the density of subcarriers in a PRS
symbol) allows accumulating power. It can start at any physical resource block
(PRB), configured in steps of 4-PRBs from 24 to 276 PRBs, giving a maximum
bandwidth of 100 MHz for 30 kHz subcarrier spacing (SCS) in FR1 and 400 MHz
for 120 kHz SCS in FR2 [8, 1].

The current PRS structure enables the efficient determination of TOA and
phase-of-arrival (POA). While a single measurement indicating a code-based
shift is essential in TOA, in POA, a phase shift remains constant and
measurable for the signal duration and thus can be analyzed/tracked
continuously for phase noise reduction. Therefore, a continuous PRS (C-PRS)
structure is advantageous compared to the current shifted PRS configuration
pattern. Regardless of where the FFT sampling window starts during the signal
duration, the C-PRS signals maintain orthogonality, reducing noise in phase
comparison. Moreover, when PRS signal sets from neighboring gNBs arrive with
different delays, signals from distant gNBs may interfere, and only a part of
the signal may be included in the FFT window. By providing C-PRS, the
receiving UE may discard the boundary symbols, including the error-prone part,
and collect only the intermediate symbols containing the full wavelength of
the subcarriers. It is also essential that multiple oversampling by shifting
the FFT window over the entire signal interval gives an identical result. This
carries various implications:

  * •

The relative phase difference between the subcarriers is constant; thus, the
phase difference between the PRS signals of different gNBs remain constant
regardless of the position of the sampling window.

  * •

Constant difference between the gNBs signals allows optimizing the
differential measurement that solves the UE phase mismatch problem.

  * •

Iterative sampling results can be averaged to reduce noise, improve gain, and
increase angular resolution.

With today’s rapid network development, it is becoming increasingly important
to reuse existing hardware and possibly signals for both detection and
communication functions. However, the current PRS structure, with cyclic
prefix and scattering of symbols in PRBs, makes it impossible to benefit from
the advantage of the signal continuity. Due to the cyclic prefix, the end part
of the symbol is copied and used as a prefix. As a result, most subcarriers
are discontinuous at the symbol boundaries, hindering building a continuous
waveform over multiple symbols. To overcome this, 3GPP initiates discussion
over C-PRS, either as a pure carrier wave of periodic wide-band sinusoidal
signals or a continuous narrow-band signal transmitted at a pre-defined
carrier frequency [4]. To this end, work item [14] considers a block-type PRS
with a modified prefix. It proposes a low complex in-band method where the
desired tone signal can be easily generated in a sequence that rotates the
subsequent symbol phases by the length of the cyclic prefix interval. This
process enables a seamless connection of the subcarrier waveform at the symbol
boundaries (see Fig. 3).

Note that the phase tracking reference symbol (PTRS), designed to correct the
clock’s phase noise, also adopts a similar block-type symbol configuration
pattern.

##  V Performance of Continuous-PRS in InF

Figure 3: Carrier phase (CP) measurement in regular PRS vs. continuous-PRS.

We designed an example simulation scenario to demonstrate the NR positioning
enhancement achievable by CP measurements, including continuous carrier phase
(CCP) measures. The simulation parameters follow TR 38.857 as the typical InF
scenario applicable in the performance analysis of current positioning
solutions:

  * •

FR1-specific values: carrier frequency 3.8 GHz, bandwidth 100 MHz, SCS 30kHz.

  * •

FR2-specific values: carrier frequency 28 GHz, bandwidth 400 MHz, SCS 120 kHz.

The simulation scenarios FR1 and FR2 shared common values, i.e., PRS sequence
generated for 3276 subcarriers with a comb-6 pattern, complex Gaussian noise
with power adjusted to 10dB SNR at the UE-Rx. The gNB and UE positions are
fixed at (x, y, height) in meters as (100, 100, 15) and (120, 100, 1.5),
respectively. Simulations demonstrate the prospect of standalone methods by
specifying the distance error in measurement to a single anchor in three-
dimensional space.

To evaluate the performance of communication systems during standardization,
it is crucial to use a geometry-based stochastic channel model suitable for
the propagation environment, i.e., specific close-in free space reference
distance models with frequency-dependent path loss exponents. 3GPP TR 38.901
Indoor Factory model considers the influence of industrial production on
channel impulse response and corresponding power delay profile. Simulated
channel characteristics follow different versions of the InF conditions
defined in 3GPP 38.901, i.e., LOS and NLOS with sparse (S) or dense (D)
clutter, and low (L) or high (H) relative gNB position.

TOA-based distance measurements are made directly from the time lag at the
correlation peak. They are mainly limited by the sampling frequency, period
corresponding to the chirp signal,  and the presence of LOS. The comparison of
LOS conditions for FR1 and 2 indicates a reduction of the variance in the TOA
by broadening the band, as pointed out in Table I. The simulated NLOS
scenarios clearly show how seriously the distance error increases in the
absence of a direct path. Moreover, apart from a fundamental phase error after
wave reflection, NLOS vastly extends the search space of IA, for which TOA is
the entry point. Therefore, CP-based simulations were limited to LOS as the
only proper scenario.

Figure 4: Cumulative distribution function (CDF) of the 3D distance error for
the InF FR1 scenario with 100 MHz bandwidth. Figure 5: Cumulative distribution
function (CDF) of the 3D distance error for the InF FR2 scenario with 400 MHz
bandwidth.

CP-based distance measurements follow the procedure of probing the phase of
the middle subcarrier [8], as proven to reflect reliable UE–gNB distance
information. The sampling process also limits single-phase measurement, i.e.,
the actual information is rounded to the nearest sampling point. However, the
phase noise inherent in propagation significantly influences distance accuracy
within the range of a single subcarrier period. Multiple phase measurements
can provide phase noise reduction. CCP measurement assumes a constant phase
difference over the received signal and the reference produced at the
receiver. Up to this point, simulation provides double replication and
concatenation of the current OFDM symbol. Then, a 1000-fold FFT window sweep
is used in the phase comparisons, with a 1-sample shift between measurements.
In Figs. 4 & 5, the performance of the continuous-PRS is reflected using the
CDFs obtained for CCP, which shows the reduction of phase noise variance.

An aspect that needs to be considered in phase folding is the possible phase
shift resulting from the receiver mobility. In an indoor positioning system, a
mobile receiver is moving towards a fixed transmitter with a typical speed of
3km/h (0.83m/s), resulting in the frequency shift due to the Doppler effect
will be 0.003ppm. Therefore, the resulting phase shift can easily be neglected
for short, time-coherent periods.

##  VI Possible Further Improvements

The ongoing work on positioning in 5G-and-beyond networks is dealing with a
small set of problems that are entirely new. A significant part requires
effectively adapting solutions from some related fields; for instance, the CP-
based angular measurement developed for Bluetooth, the IA problem already
explored in GNSS, and NLOS mitigation in wireless communication. In this
respect, C-PRS provides similar prospects for further research through
adaptation, including:

####  VI-1 Digital femtosecond time difference

Improving the phase measurement resolution already has promising solutions in
closely-related areas, such as the synchronization of complex laboratory
infrastructure. The digital dual mixer time difference (D-DMTD) is a digital
femtosecond time difference circuit developed in CERN [15]. D-DMTD measures
the phase difference between two digital signals with very accurate resolution
using a relatively low-frequency counter. By sampling a signal of a particular
frequency with a slightly slower clock, D-DMTD stretches the signal in the
time domain, allowing intensive aliasing of the two input clocks fed to the
phase detector with femtosecond time resolution.

####  VI-2 gNB/TRPs master-slave synchronization

Like the code phase-detection for TOA, the CP-detection translates the
corresponding POA into a distance. Once the receiver intercepts the
transmitted signal, it is locked in the PLL and allows the phase difference to
be captured. However, the UE’s local oscillator phase offset can significantly
influence the measurement result. This forces, e.g., double differentiation in
CP. Yet, for differential measurements, gNBs must be precisely synchronized
(less than one carrier wavelength), which requires a sub-nanosecond variance.

It is difficult to synchronize gNBs to an ideal distant clock (GNSS) in sub-
nanoseconds accuracy due to atmospheric fluctuations, temperature
fluctuations, the inherent error of the device, etc., and positioning accuracy
of less than 1m cannot be guaranteed [14]. However, it is relatively simple
for gNBs to follow a certain near external master clock in sub-nanoseconds
variance. For this purpose, the discussed C-PRS can be used to trace the
master clock phase continuously.

####  VI-3 Near-field positioning

When an antenna array becomes sufficiently large, compared to the wavelength,
the spherical curvature of the impinging wave becomes noticeable. Since the
curvature depends on the propagation distance, this feature enables ranging in
addition to conventional AOA estimation, so a single array can localize the
transmitter. This operational regime is called the radiative near-field and
exists for ranges up to the Fraunhofer distance. The combination of physically
large arrays and the use of mmWave bands can jointly extend the near-field to
ranges larger than a kilometer. A key implementation challenge is to keep a
sufficient phase-synchronization across the array to enable accurate
estimation of the curvature.

##  VII Conclusions

This article provides an overview of existing/emerging positioning techniques,
especially illustrating how CP measurements can lead to centimeter positioning
accuracy in indoor factory channels. Giving up the comb structure and cyclic
prefix in the positioning reference signal and ensuring its temporal
continuity enables noise reduction. Moreover, these directions open new
research possibilities for providing phase measurements at the nanosecond-
level and introducing high-precision synchronization mechanisms. Yet, using CP
in InF scenarios has to overcome challenges by adopting existing solutions
from GPS domain related to integer ambiguity and multipath mitigation. The
latter is a critical issue where various channels features and fingerprinting
techniques can be intelligently incorporated in on-device classification or
network-level optimization of positioning schemes.

## References

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  * [3] 3GPP TR 38.857, “Study on NR positioning enhancements,” Mar. 2021.
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Jakub Nikonowicz<EMAIL_ADDRESS>received the Ph.D. degrees
in telecommunication systems from Poznań University of Technology (PUT),
Poznań, Poland, 2019. Since 2019, where he is currently an assistant
professor. His research interests include statistical signal processing and
precise synchronization in distributed systems.
---
Aamir Mahmood<EMAIL_ADDRESS>is an assistant professor of
communication engineering at Mid Sweden University, Sweden. He received the
D.Sc. degrees in communications engineering from Aalto University School of
Electrical Engineering, Finland, in 2014. His research interests include time
synchronization, LPWANs, and RAN optimization/management.
---
Muhammad Ikram Ashraf<EMAIL_ADDRESS>received the M.Sc.
and Ph.D. degrees in telecommunication systems and communication engineering,
respectively, from the University of Oulu, Finland. He is currently working as
a senior research specialist, 5G Advanced, at Nokia Bell Labs, Finland. His
research interests include URLLC, TSN, Positioning, and AI/ML.
---
Emil Björnson<EMAIL_ADDRESS>received his Ph.D. degree from the KTH Royal
Institute of Technology, Stockholm, Sweden, in 2011. He is now a professor of
wireless communication at KTH. His research interests include multi-antenna
and reconfigurable intelligent surface-aided communications, radio resource
allocation, and energy efficiency. He is a Fellow of IEEE.
---
Mikael Gidlund<EMAIL_ADDRESS>is a professor of computer engineering
at Mid Sweden University, Sweden. He has worked as Senior Principal Scientist
and Global Research Area Coordinator of Wireless Technologies, ABB Corporate
Research, Sweden. His research interests include wireless communication and
networks, access protocols, and security.
---