[acronym]long-short

# Physical Layer Security in a Private 5G Network for Industrial and Mobility
Application

Shivraj Hanumant Gonde
Martin Kubisch  Secure Land Communications
Airbus
Ulm, Germany
<EMAIL_ADDRESS>Central Research and Technology
Airbus
Munich, Germany
<EMAIL_ADDRESS>Christoph Frisch
Thomas Meyerhoff  Chair for Security in Information Technology
Technical University of Munich
Munich, Germany
<EMAIL_ADDRESS>Central Research and Technology
Airbus
Hamburg, Germany
<EMAIL_ADDRESS>Svetoslav Duhovnikov
Dominic Schupke  Central Research and Technology
Airbus
Munich, Germany
<EMAIL_ADDRESS>Central Research and Technology
Airbus
Munich, Germany
<EMAIL_ADDRESS>
###### Abstract

Cellular communication technologies such as 5G are deployed on a large scale
around the world. Compared to other communication technologies such as WiFi,
Bluetooth, or Ultra Wideband (UWB), the 5G communication standard describes
support for a large variety of use cases, e.g., Internet of Things (IoT),
vehicular, industrial, and campus-wide communications. An organization can
operate a Private 5G network to provide connectivity to devices in their
manufacturing environment. Physical Layer Key Generation (PLKG) is a method to
generate a symmetric secret on two nodes despite the presence of a potential
passive eavesdropper. To the best of our knowledge, this work is one of the
first to implement PLKG in a real Private 5G network. Therefore, it highlights
the possibility of integrating PLKG in the communication technology highly
relevant for industrial applications. This paper exemplifies the establishment
of a long-term symmetric key between an aerial vehicle and IT infrastructure
both located in a manufacturing environment and communicating via the radio
interface of the Private 5G network.

###### Index Terms:

Physical Layer Security, Wireless Communication, 5G

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##  I Introduction

The promise of solving mathematically complex problems more efficiently has
led to major developments in the field of quantum computing. A quantum
computer reduces the time needed to solve some complex problems, which
threatens existing systems relying on traditional symmetric and asymmetric
cryptography algorithms such as Advanced Encryption Standard (AES), Diffie-
Hellman (DH), and Rivest-Shamir-Adleman (RSA).

Asymmetric cryptography algorithms, which are based on mathematical problems
such as discrete logarithm and prime factorization, are not solvable on a
classical computer in polynomial time but can be solved on a quantum computer
using Shor’s algorithm [1]. Increasing the key length used for symmetric
cryptography algorithms such as AES from 128 bits to 256 bits is one solution
to safeguard against attacks from a quantum computer [2]. Quantum Key
Distribution (QKD) and Post Quantum Cryptography (PQC) are popular solutions
to overcome threats posed by quantum computers.

Ranging from sensors to aerial vehicles, devices intended for mobile use cases
rely on wireless communication for connectivity with varying levels of
criticality and Quality of Service (QoS) requirements. Out of the wide variety
of options available for establishing wireless connectivity, cellular
technology in comparison is one of the most widely adopted with large-scale
deployments around the world. This makes it a suitable candidate for many
applications such as IoT, vehicular networks, or industrial networks.

Due to the broadcast nature of wireless channels, securing communication
between wireless nodes is important. Physical Layer Security (PLS), as
explored in this study, is a solution which could be integrated into many use
cases. PLKG, a subset of PLS, generates a secret bit stream on two wireless
nodes even in the presence of an eavesdropper. This method of generating bits
can be implemented with low overhead as part of the channel estimation process
typically carried out using the pilot signals. Recent developments in the area
of cellular technology, electric vertical take-off and landing (eVTOL)
aircraft, and quantum computing motivate this work; it explores a different
approach to establish a secure link between communicating nodes against
quantum threats.

_Contribution:_ This work aims to establish a secure communication link
between an eVTOL outside the manufacturing environment (e.g. when handed over
to the customer) and the IT infrastructure present in the manufacturing
environment using a symmetric key pair, which is generated while the eVTOL was
being manufactured. During the final stages of manufacturing, while the eVTOL
is still in the trusted manufacturing environment, PLKG is used to generate a
symmetric key pair between the eVTOL and the IT infrastructure. The generated
key is used to secure communications between the eVTOL and IT infrastructure
at a later stage, i.e., after the eVTOL moves out of the manufacturing
environment, where key generation is more cumbersome. Beyond this use case,
PLKG finds application in many more cases in the mobility domain and other
industrial domains, where secure wireless connectivity is required. Hereby,
the contribution of the work is an implementation of PLKG in a real Private 5G
network and an evaluation of the implementation to assess the feasibility of
using such a method for generating long-term keys of sufficient length and
entropy.

_Outline:_ Section ii@ discusses the features of wireless channels which
enable PLKG, the steps involved in PLKG, and the contributions of this work
and previous works. Section iii@ explains the use case and measures taken to
implement PLKG in a Private 5G network. Details of the implementation and
results are presented in Section<EMAIL_ADDRESS>Section v@ concludes this paper.

##  II Background

PLKG in a wireless channel is possible due to its frequency selective fading
nature in environments where there is motion around the wireless nodes or in
situations where one or both the nodes are moving. The bits generated in such
channels are similar on both nodes as frequency-selective fading is reciprocal
in nature within a period of time, referred to as coherence time, if no
interferers are present. Hence, the frequency-selective nature of the wireless
channel, if estimated by two communicating nodes within the coherence time,
will be highly correlated in a non-interfered situation. Important features of
a wireless channel that enable PLKG are as follows:

  1. 1.

Frequency-selective fading: For generating dissimilar bits using PLKG, the
wireless channel should affect each frequency component of the signal
differently, i.e., it has to be frequency-selective. In a flat fading channel,
repeating bits would occur, and the entropy of the generated bit stream would
be low.

  2. 2.

Time-varying nature: The frequency-selective nature of a channel should vary
in time so that every channel estimate results in a different set of bits. A
frequency-selective wireless channel static in time would produce highly
correlated bit streams from consecutive channel probes.

An important factor to consider is the presence of an eavesdropper. When two
nodes, Alice and Bob, generate secret bits using PLKG, a third node Eve, who
is aware of the algorithm used by Alice and Bob, cannot generate the same bit
stream if it is located at least half a wavelength away from both Alice and
Bob. This is due to spatial de-correlation in wireless channels as discussed
in [3].

###  II-A Physical Layer Key Generation

Figure 1: Sequence of steps involved in Physical Layer Key Generation.

To generate a symmetric secret bit stream on two communicating wireless nodes,
steps illustrated in Fig. 1 have to be performed by both nodes (Alice and Bob)
described as follows:

  1. 1.

Channel Probing: Alice and Bob exchange a signal with each other within the
coherence time and capture the fading it undergoes due to the wireless
channel, i.e., channel state information (CSI) estimation. Two ways to
implement this step in practice are to measure (i) the Channel Frequency
Response [8] and (ii) the Received Signal Strength [9]. The former is better
regarding the amount of information extracted from the channel, and the latter
is relatively easier to retrieve, e.g., from commercial WiFi Network Interface
controllers (NICs) [10].

  2. 2.

Quantization: To convert the CSI estimates into bits, a quantization algorithm
is used. It can be a single-level crossing-based or a multi-level quantization
where the entire range of possible values of the CSI is divided into multiple
regions, each corresponding to a predefined bit or sequence of bits.

  3. 3.

Information Reconciliation: A mismatch in CSI estimate at Alice and Bob is
expected due to asymmetric hardware defects and non-simultaneous measurements.
This leads to mismatches in the bit stream generated by Alice and Bob. In PLKG
and QKD where the goal is to establish a symmetric key pair, an information
reconciliation step is performed to correct mismatches or errors in the bit
stream. [10] summarizes information reconciliation for PLKG in the existing
literature.

  4. 4.

Privacy Amplification: Due to the information reconciliation step, which often
includes a public discussion, knowledge of the generated bit stream is leaked.
A final step strengthens the generated bit stream to compensate for this and
generate a cryptographic key.

In this study, the channel frequency response is measured and used as CSI. The
measured CSI is quantized to generate the bit stream. Information
Reconciliation and Privacy Amplification are out-of-scope for this work. We
focus on the Channel Probing and Quantization steps depicted in Fig. 1, as
they depend on the nature of the wireless channel and features of the Private
5G network. Information reconciliation is circumvented by choosing suitable
parameters for the quantization algorithm, i.e., larger sampling intervals in
quantization are used to reduce bit mismatch at 5G Base Station (gNB) and User
Equipment (UE). For Privacy Amplification, we use a hash function to compress
the quantized bits.

It should be noted that Private 5G enables a trusted environment for key
generation (here the manufacturing site), since the spectrum is licensed
locally for exclusive use. The access to this local spectrum as well as
interferers can be managed by the spectrum owner. Therefore, reciprocity can
be ensured.

Practical implementations of PLKG have been performed for WiFi, Bluetooth and
UWB [4, 5, 6, 7, 14]. Compared with these communication technologies, 5G by
design supports a wider range of applications due to its advantages including
larger coverage area and increased bandwidth and capacity. Given the flexible
nature of a 5G New Radio (5G-NR) physical layer, which includes multiple
options for subcarrier spacing, density of reference signals in time and
frequency, and flexible time slot configuration for uplink and downlink, a
PLKG implementation in a Private 5G can be refined according to the
environment. A simulation study for PLS implementation in Long-Term Evolution
(LTE) wireless standard has been presented in [12] and a practical
implementation which measures entropy of generated bit stream by recording CSI
on a single LTE node is presented in [13]. Lack of practical 5G based PLKG
testbench is discussed in [15]. This work demonstrates PLKG in a real Private
5G network by recording CSI on both nodes (gNB and UE) and evaluates its
feasibility in a manufacturing environment.

Figure 2: An illustration of the use case where an eVTOL (Alice) and IT
infrastructure (Bob) both present in the manufacturing environment communicate
via 5G-NR interface to generate a long-term symmetric key pair using PLKG in
the presence of a passive eavesdropper.

##  III Concept

PLKG can be used in mobile ad-hoc networks, wireless sensor networks, and
wireless local area networks, where the devices can use generated keys
immediately. This section explains the usage of the generated keys via PLKG as
long-term keys used to establish a secure link between an eVTOL and IT
infrastructure in the manufacturing environment. This work aims to explore the
applicability of PLKG in the presented use case. Parameters considered in this
work within a Private 5G network to enable PLKG are explained in the second
part of this section.

###  III-A Use Case

PLKG is used to generate a symmetric key pair on an eVTOL and IT
infrastructure of the manufacturing environment. The manufacturing environment
is a final assembly line where hardware and software components are put
together to assemble the eVTOL, after which it is delivered to the customer.
During this assembly process, PLKG is carried out to generate a key pair for
the eVTOL and the IT infrastructure of the manufacturing environment. The
environment is considered to be dynamic, with robots and personnel moving
around the eVTOL during PLKG. The manufacturing environment has a Private 5G
network operating on the 5G-NR n78 band in Time Division Duplex (TDD) mode. A
benefit of using TDD mode is that it enables both gNB and UE to probe the same
set of frequency components separated in time. The generated keys are used by
the vehicle to establish a secure communication link with the manufacturing
environment for operations such as software updates and data offloading during
its regular maintenance, which is not necessarily in an environment controlled
by the manufacturer. The advantages of using PLKG in this scenario are as
follows:

  1. 1.

Information Theoretically Secure: secret bits generated via PLKG are
information-theoretically secure, i.e., they are secure against threats posed
by quantum computers.

  2. 2.

Low Overhead Algorithm: Sufficiently long bits can be generated via PLKG with
low overhead. CSI estimates can be extracted from data and reference signals
which are transmitted for normal communication between nodes, i.e., there is
no need for an exclusive session for PLKG to take place.

  3. 3.

Automation: The entire process of PLKG can be fully automated. Hence, no
involvement of personnel is needed and the time during which a given eVTOL
carries out PLKG can be hidden or randomized as well.

Eve in the manufacturing environment is assumed to be situated at a location
$\gg 4cm$ (half a wavelength of carrier frequency in 5G n78 band is around
4cm) from both the eVTOL and wireless terminal of the IT infrastructure as
shown in Fig. 2. To obtain similar CSI, Alice and Bob exchange pilot signals
within the coherence period of the wireless channel. A passive Eve located
$>>4cm$ from Alice and Bob records a different CSI as depicted in Fig. 2. This
results in an uncorrelated bit stream generated by Eve [3, 11].

###  III-B PLKG in 5G

To implement PLKG in a Private 5G network, a setup with an Amarisoft Classic
gNB, a Raspberry Pi with SIM8200EA-M2 5G HAT as UE, and two USRP B210
Software-defined radio (SDR) as recording devices were built. The SDR recorded
raw IQ samples at the antenna port of both devices from which relevant symbols
were extracted and demodulated. The gNB and UE communicated via a 5G link on
the n78 band in TDD mode. A bandwidth of 20MHz was used with 30kHz subcarrier
spacing, resulting in 612 subcarriers for communication. Demodulation
Reference Signals (DMRS) were configured to have two occurrences within a slot
with mapping Type A and length 2. Typically, for channel estimation, DMRS
symbols are used. In this study, DMRS and Quadrature Phase Shift Keying (QPSK)
modulated data symbols were used to get the channel estimate. Multiple QPSK
modulated data symbols within a single frame were averaged to get a less noisy
estimate of the channel as shown in Fig. 3. Time-sharing of the channel was
configured such that the uplink and downlink took place for a duration of 2ms
and 2.5ms contiguous blocks respectively in every 5ms time period. The
remaining 0.5ms was not used by uplink or downlink and occurred between an
uplink and downlink block. Measurements were carried out in an indoor lab as
well as in an open space outdoors.

Ideally, to implement PLKG on 5G-NR enabled devices (UE or gNB), the DMRS
symbols after demodulation can be used as an input to the quantization
algorithm to generate the bit stream.

##  IV Results

Using the Private 5G network setup built for PLKG, experiments were performed
indoors and outdoors to record CSI estimates in environments that in part
mimic a manufacturing environment. The recorded CSI was quantized, and a bit
stream was generated. Using a hash function, the bit stream was compressed and
the respective estimate of bit level security was calculated based on
Lempel–Ziv–Welch lossless compression algorithm.

###  IV-A Quantization Algorithm

A quantization algorithm generates bits from the CSI estimates. A two-level
(L=2) quantization can be used where a threshold is defined, and bits are
derived based on the CSI being above or below the threshold. This approach
results in blocks of 1’s and 0’s when the channel does not change rapidly over
consecutive subcarriers, i.e., consecutive frequency components. To overcome
this challenge, the quantization region was divided into multiple levels on
the y-axis denoted by L. In addition to multiple levels, the width of each
interval was computed in two ways. One where all levels have the same width
and another where all the levels are equiprobable as first proposed for PLKG
in [5]. The number of levels L was varied from 2 to 16. The number of bits
sampled from each CSI estimate is denoted by S which was varied from 2 to 16.
Elimination criteria were also implemented to eliminate CSI estimates
representing a static channel and in situations where received signals had a
very low signal-to-noise ratio.

###  IV-B Results

A larger variance was observed in a dynamic channel, i.e., in an environment
where people were moving around the gNB and UE, as compared to a static
channel where people and objects around the communicating nodes were
stationary. Compared to a static channel, the variance in CSI estimates in a
dynamic channel indoors and outdoors was 1.35 times and 3.0 times higher,
respectively. In a manufacturing environment, it is assumed that moving
people, objects, and robots create a dynamic channel.

Based on this, PLKG was carried out in a dynamic channel. After channel
probing, CSI estimates were quantized and tested for bias, i.e., to check if
an equal number of 1’s and 0’s were generated in the entire bit stream.
Ideally, the bias should be close to 0.5. Bias in this study was observed to
be closer to 0.5 when L increased beyond 2. This bias test is only used as an
indicator to find an anomaly in the generated bit stream before the next test
is applied.

To compute the upper bound of entropy for the generated bit stream, the
Lempel–Ziv–Welch lossless compression algorithm was used. After compression,
the size of the bit stream reduced to 0.2 to 0.1 times of the input bit
stream, i.e., the bit stream generated after quantization. Table I summarizes
the results of PLKG showing the number of bits generated before and after
compression for equal width of quantization levels, L = 4, 7 and S = 3, 5, 7,
9.

L | S |  | Number of bits
---
generated after
quantization
| Number of bits
---
generated after
compression
4 | 3 | 1020 | 142
4 | 5 | 1765 | 202
4 | 7 | 2576 | 282
4 | 9 | 3294 | 343
7 | 3 | 1020 | 186
7 | 5 | 1765 | 295
7 | 7 | 2576 | 400
7 | 9 | 3294 | 499

TABLE I: Length of generated bit streams after quantization stage and after
compression in a period of 5 seconds is shown. L denotes the number of levels
used for quantization and S denotes the number of bits generated from each CSI
estimate.

For a symmetric key to be established between the nodes, the generated bit
stream on both sides should be similar. Fig. 3 shows the CSI estimates at gNB
and UE recorded within a duration of 10ms. For L = 4 and S = 3, similar bit
streams were derived at gNB and UE. For the considered quantization parameters
at a channel probing rate of 10ms for a duration of 5 seconds, a maximum of
142 bits of entropy can be derived as seen in Table I.

Figure 3: CSI recorded using QPSK modulated data symbols at UE (Alice) and gNB
(Bob). The x-axis represents the subcarrier index. Subcarrier index 512
corresponds to the center frequency of transmission, i.e., 3.75GHz. The y-axis
depicts the magnitude of the subcarrier.

###  IV-C Discussion

Limitations due to reciprocity: The limit on L and S was due to the
limitations with respect to reciprocity. For L = 7, the resulting bit stream
at gNB and UE had mismatches. To reduce the number of mismatches to 0, L = 4
and S = 3 were chosen. Other than increasing the duration of PLKG, for
achieving a higher key generation rate, reciprocity between measurements at
gNB and UE must be improved by compensating for hardware defects. The poor
reciprocal behavior can be in part attributed to the measurement setup which
consisted of a SDR tapping out signal from the antenna port, and to the use of
a demodulator at a very early stage of development to extract CSI estimates.
Impairments specific to this such as the DC offset in CSI estimate at UE as
seen in Fig. 3 affect reciprocity. To overcome this, the DC offset was
suppressed by interpolating to the nearest neighbours as seen for CSI estimate
at gNB in Fig. 3. In this study, the correlation coefficient between CSI
measurements at gNB and UE varied from 0.1 to 0.85, the cause of the low
correlation must be further investigated along with better methods to extract
less noisy CSI estimates from the received signal.

Eve: Including an eavesdropper in future studies will help narrow down
suitable quantization parameters. For example, in scenarios where CSI
estimates of gNB and UE have low correlation coefficients, reducing L and S
will result in a similar bit stream. The extent up to which L and S can be
lowered in the presence of an eavesdropper must be studied. Very low values of
L and S will result in a similar bit stream at Alice, Bob, and Eve.

PLKG in Private 5G: In a Private 5G network, as compared to a Public 5G
network, physical layer parameters of 5G signals can be tuned for better PLKG
performance. For a specific wireless channel, physical layer parameters such
as subcarrier spacing, bandwidth, and TDD slot configuration can be fine-tuned
such that it can capture the frequency selective fading profile of the channel
more effectively while maintaining reciprocity.

##  V Conclusion

This work is one of the first to demonstrate a practical implementation of
PLKG in a real Private 5G network. The feasibility of deriving a symmetric bit
stream with an entropy of 142 bits on gNB and UE in a duration of 5 seconds
was shown. The chosen indoor and outdoor environments were found to have
sufficient entropy to generate a 256-bit long key within a few seconds. A
distinction between static and dynamic channel was made and it was found that
dynamic channel was suitable for PLKG in both outdoor and indoor environment.
The result of this study motivates further development of 5G based PLKG
testbench and investigation into PLKG for next generation cellular networks
due to its wide range of applications.

## Acknowledgment

This work was partly funded by the Bavarian Ministry of Economic Affairs,
Regional Development and Energy as part of the project 6G Future Lab Bavaria.

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