# Advanced Spectrum Sensing Techniques for Robust Train-to-Train Wireless Communication Systems: A Comprehensive Evaluation

Chadli Hadji, Yamen Alsaba, Marion Berbineau, Fouzia Elbahhar, Rodrigue Fargeon

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## ► To cite this version:

Chadli Hadji, Yamen Alsaba, Marion Berbineau, Fouzia Elbahhar, Rodrigue Fargeon. Advanced Spectrum Sensing Techniques for Robust Train-to-Train Wireless Communication Systems: A Comprehensive Evaluation. IEEE Access, 2025, 13, pp.152519 - 152529. 10.1109/access.2025.3603626. hal-05502127

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Submitted on 10 Feb 2026

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Received 16 July 2025, accepted 18 August 2025, date of publication 28 August 2025, date of current version 5 September 2025.

Digital Object Identifier 10.1109/ACCESS.2025.3603626

RESEARCH ARTICLE

# Advanced Spectrum Sensing Techniques for Robust Train-to-Train Wireless Communication Systems: A Comprehensive Evaluation

CHADLI HADJI<sup>1</sup>, YAMEN ALSABA<sup>1,2</sup>, MARION BERBINEAU<sup>1,2</sup>, (Member, IEEE),
FOUZIA ELBAHHAR<sup>1,2</sup>, AND RODRIGUE FARGEON<sup>3</sup>

Corresponding author: Yamen Alsaba (yamen.alsaba@railenium.eu)

This work was supported in part by European Project under Grant FP3-IAM4RAIL, and in part by Europe's Rail Joint Undertaking (JU) with Grant Agreement (GA) under Grant 101101966.

**ABSTRACT** In the context of full automation of train operation, flexible train management, and improved traffic capacity, the Next Generation Train Control and Monitoring System (NG-TCMS) represents a key technology. The system will be based on wireless technologies at both the backbone and consist levels, thereby creating Wireless Train Backbone (WLTB) and Wireless Consist Network (WLCN) solutions. These solutions are being developed within the framework of several European projects. The chosen technology will have to guarantee robust train-to-train (T2T) communications between consists in the case of virtual coupling of two trains. In order to be able to detect any type of unwanted signal that may disturb wireless transmission, a specific mechanism based on spectrum sensing should be developed. To this end, this paper presents a comprehensive evaluation of advanced spectrum sensing techniques tailored for robust T2T wireless communication systems. Performance evaluation was carried out using real LTE and Wi-Fi 802.11ax signals in a doubly selective channel. By implementing spectrum sensing techniques on Software-Defined Radio (SDR) platforms, this study investigates the efficacy of eigenvalue-based methods under dynamic vehicular propagation conditions, including variable train speeds and distances between trains. The research identifies the most effective spectrum sensing technique among Maximum-Minimum Eigenvalue (MME), Energy to Maximum Eigenvalue (EME), Simplified Predicted Eigenvalue Threshold (SPET), Mean-to-Square Extreme Eigenvalue (MSEE), Maximum Eigenvalue to Geometric Mean (MEGM), and Covariance Absolute Value (CAV). The findings indicate that SPET demonstrates superior performance in various scenarios, making it a promising candidate for real-world railway communication systems.

**INDEX TERMS** Train-to-train communication, cognitive radio, spectrum sensing, eigenvalue-based detection, software-defined radio, LTE, Wi-Fi 802.11ax, vehicular channel models, interference mitigation, high-mobility wireless networks.

# I. INTRODUCTION

## A. OVERVIEW

A novel generation of train communication network (NG-TCN) has recently emerged, comprising a replacement of the existing train communication system based on cables

The associate editor coordinating the review of this manuscript and approving it for publication was Hongli Dong.

with wireless components [1], [2]. This concept has attracted considerable attention in both academia and industry due to the considerable weight of the telecommunication cables on board and the high cost of maintenance. Moreover, NG-TCN is an enabler of the virtual coupling concept, in which trains are no longer mechanically connected to enhance the efficiency of the railway system. Wireless information exchanges, such as speed and breaking curve between the

<footnote n="1">¹COSYS-LEOST, Gustave Eiffel University (formerly IFSTTAR), 59650 Villeneuve-d'Ascq, France</footnote>

<footnote n="2">²IRT Railenium, 59540 Valenciennes, France</footnote>

<footnote n="3">³SNCF, 93210 Saint-Denis, France</footnote>
two trains facilitated by NG-TCN enables the reduction of the maximum breaking distance, thereby increasing the number of trains that can be accommodated on the track [3].

Many European projects and scientific works have addressed the potential application of NG-TCN in future railway systems, among which we can cite Connecta-1-2-3, Safe4Rail-2 and Roll2Rail-2 [4], [5], [6] and the R2DATO project [7]. The aforementioned works cite the New Radio V2X (NR-V2X) technology as an optimal solution to meet the requirements of NG-TCN. The NR-V2X sidelink enables vehicles to communicate without any direct role of the base station. The NR-V2X sidelink has two modes of resource allocation. In Mode 1, resources are scheduled by the base station, while in Mode 2, users autonomously select sidelink resources from a (pre-) configured sidelink resource pool(s) based on a channel sensing mechanism (Mode 2) [8]. Mode 2 is the optimal mode for train-to-train communications to be independent of the presence of any base station.

Traditional carrier sensing and frequency allocation methods can face significant challenges in dynamic and often harsh railway environments. Moreover, train-to-train wireless communications will be susceptible to interference signals coming from external signals [9] and particularly from adjacent wireless communication systems, including 4G cellular systems and Wi-Fi access points at stations. This paper addresses these challenges by conducting a performance evaluation of spectrum sensing techniques in the railway environment. This allows trains to identify available resources while simultaneously avoiding interference with other wireless signals.

The paper is structured as follows. Section II reviews recent advances in spectrum sensing algorithms. Section III introduces the system model and outlines the eigenvalue-based techniques used. Section IV details the experimental setup for testing LTE and Wi-Fi signals. Section V describes the testing protocols and scenarios. Section VI presents the results and the performance analysis. Section VII discusses the implementation of a real-time detection system. Finally, Section VIII concludes with a summary of key findings and future research directions.

## B. NOVELTY

The innovative aspect of this paper lies in its comprehensive evaluation of spectrum sensing techniques in a train-to-train environment, utilizing real LTE and Wi-Fi 802.11ax signals as sources of interference. This contrasts with previous studies that have typically considered simulated signals. To the best of our knowledge, no previous work has analyzed the performance of spectrum sensing techniques in doubly-selective channels that takes into consideration both the effects of the multipath and Doppler. Moreover, the paper introduces stress tests that vary train speed and distance between trains, thereby offering new insights into the performance of spectrum sensing algorithms under real-world conditions, particularly in high-mobility environments.

<table><caption>TABLE 1. List of acronyms.</caption><thead><tr><td>Acronym</td><td>Definition</td></tr></thead><tbody><tr><td>CAV</td><td>Covariance Absolute Value</td></tr><tr><td>CR</td><td>Cognitive Radio</td></tr><tr><td>EME</td><td>Energy to Maximum Eigenvalue</td></tr><tr><td>EVA</td><td>Extended Vehicular A</td></tr><tr><td>GSM-R</td><td>Global System for Mobile Communications–Railway</td></tr><tr><td>ITS-G5</td><td>Intelligent Transport Systems – 5 GHz</td></tr><tr><td>LTE</td><td>Long-Term Evolution</td></tr><tr><td>MEGM</td><td>Maximum Eigenvalue to Geometric Mean</td></tr><tr><td>MME</td><td>Maximum-Minimum Eigenvalue</td></tr><tr><td>MIMO</td><td>Multiple Input Multiple Output</td></tr><tr><td>MSEE</td><td>Mean-to-Square Extreme Eigenvalue</td></tr><tr><td>NG-TCN</td><td>Next Generation Train Communication Network</td></tr><tr><td>NG-TCMS</td><td>Next Generation Train Control Monitoring System</td></tr><tr><td>NR-V2X</td><td>New Radio Vehicle-to-Everything</td></tr><tr><td>PDP</td><td>Power Delay Profile</td></tr><tr><td>PU</td><td>Primary User</td></tr><tr><td>SDR</td><td>Software-Defined Radio</td></tr><tr><td>SNR</td><td>Signal-to-Noise Ratio</td></tr><tr><td>SPET</td><td>Simplified Predicted Eigenvalue Threshold</td></tr><tr><td>SS</td><td>Spectrum Sensing</td></tr><tr><td>SU</td><td>Secondary User</td></tr><tr><td>T2T</td><td>Train-to-Train</td></tr><tr><td>USRP</td><td>Universal Software Radio Peripheral</td></tr><tr><td>VehA</td><td>Vehicular A Channel Model</td></tr><tr><td>Wi-Fi</td><td>Wireless Fidelity (IEEE 802.11)</td></tr><tr><td>WLCN</td><td>Wireless Consist Network</td></tr><tr><td>WLTB</td><td>Wireless Train Backbone</td></tr></tbody></table>

This study not only assesses the efficacy of existing techniques, but also proposes a real-time application of the most effective method, making a significant contribution to the field of railway communications.

## C. CONTRIBUTION

This paper presents an in-depth examination of the fundamental principles underlying key spectrum sensing technologies, along with an investigation of their implementation on Software-Defined Radio (SDR) platforms utilizing Matlab. Several eigenvalue-based spectrum sensing (ESS) algorithms were implemented and evaluated under various conditions. The main contribution of the paper is to identify and validate the most effective spectrum sensing technique for dynamic railway communication environments. Additionally, this work also includes the development of a real-time detection system that can adapt to varying the Signal-to-Noise Ratio (SNR) and challenging channel conditions, such as those encountered in high-speed railway scenarios.
## II. RECENT RELATED WORKS ON SPECTRUM SENSING ALGORITHMS PERFORMANCE ANALYSIS

Spectrum sensing techniques have been thoroughly investigated in Cognitive Radio (CR) technology, which has emerged as a crucial tool to improve spectrum efficiency in modern wireless communication systems. Spectrum Sensing (SS) is a critical function within CR, enabling the detection of available spectral bands for opportunistic use. In CR, Primary Users (PUs) refers to as licensed users are authorized to operate in a specific frequency band. The goal of spectrum sensing is to detect the presence or the absence of the PU signal, allowing secondary users (SUs) to opportunistically use the spectrum without causing interference to the PU [10]. However, SS techniques can also be exploited to detect the presence of a wireless signal in a particular wireless resource, thereby preventing collisions and interference.

SS techniques can be classified into two main categories: energy-based and second-order statistics-based [11]. In the energy-based approach, the detector compares the energy of the received signal during a finite time interval and compares it to a predetermined threshold. However, this approach requires prior knowledge of the noise variance, which is a challenging condition to fulfill in practical scenarios. Conversely, second-order statistics-based or ESS techniques leverage the correlation of transmitted information signals to ascertain their presence by calculating the eigenvalues of the covariance matrix. Among the diverse SS techniques, ESS has demonstrated robust performance due to its resilience and precision.

Based on various criteria and threshold calculation, a number of ESS techniques have been proposed in the literature. These include the Mean-to-Square Extreme Eigenvalue (MSEE) [12], the Maximum Eigenvalue to Geometric Mean (MEGM) [13], the Maximum-Minimum Eigenvalue (MME) [14], the Energy to Maximum Eigenvalue (EME) [14], the Simplified Predicted Eigenvalue Threshold (SPET) [15], in addition to the Covariance Absolute Value (CAV) [16].

Previous research has examined a range of facets related to ESS techniques and their real-time implementation on SDR platforms. In [17], the SPET technique was implemented on an SDR card to evaluate its real-time performance. A comprehensive performance evaluation of multiple SS techniques using SDR cards was conducted in [18]. The work presented in [19] extended this analysis by examining the performance of SS techniques in a MIMO configuration. Moreover, in [20], an assessment of pivotal SS algorithms was performed in a time-selective channel environment, simulated using Matlab with randomly generated data. However, it is important to note that most of these studies have relied on simulated signals rather than real-world data.

To the best of our knowledge, no comprehensive evaluation of ESS algorithms using real LTE and Wi-Fi signals in train-to-train communication scenarios has been conducted previously. Specifically in doubly-selective channels, where both multipath and Doppler effects have been taken into account. This gap in the literature highlights the novelty and

importance of our study, which uses real LTE and Wi-Fi signals and introduces a stress test that accounts for variations in train speed and train distance, thereby providing a more realistic evaluation of these techniques.

## III. SYSTEM MODEL AND SENSING TECHNIQUES

### A. SYSTEM MODEL

We consider a train-to-train (T2T) communication system equipped with $M \ge 1$ antennas. The spectrum sensing detection problem is defined as follows considering two hypotheses:

$$H_0: y_m(n) = \eta_m(n) \quad (1)$$

$$H_1: y_m(n) = s_m(n) + \eta_m(n) \quad (2)$$

where:

* $H_0$ represents the hypothesis that the signal is absent.

* $H_1$ represents the hypothesis that the signal is present.

* $y_m(n)$ is the received signal at the $m$-th antenna.

* $s_m(n)$ represents the signal.

* $\eta_m(n)$ is the additive white Gaussian noise (AWGN) at the $m$-th antenna.

* $m$ refers to the $m$-th antenna where $m = 1, 2, \dots, M$.

We consider $L$ consecutive samples of the received signal and define the following vectors:

$$\hat{y}(n) = [y(n), y(n-1), \dots, y(n-L+1)]^T \quad (3)$$

$$y(n) = [y_1(n), y_2(n), \dots, y_M(n)]^T \quad (4)$$

where $L$ denotes the smoothing factor, and $M$ is the number of receive antennas.

The corresponding statistical covariance matrix of the received signal is given by:

$$R_y = \mathbb{E}[\hat{y}(n)\hat{y}^H(n)] \quad (5)$$

In order to reduce the computational complexity of the spectrum sensing algorithm, we use the sample autocorrelations matrices to compute the covariance matrix. The lag-$l$ autocorrelation matrices are expressed as:

$$\lambda(l) = \frac{1}{N} \sum_{i=1}^{N-1} y(i)y^H(i-l), \quad l = 0, 1, \dots, L-1 \quad (6)$$

where $H$ denotes the Hermitian (conjugate transpose) operator, and $N$ is the total number of observed signal samples.

The covariance matrix $\hat{R}_y$ can be then approximated by a block Toeplitz structure [16]:

$$\hat{R}_y \approx \begin{bmatrix} \lambda(0) & \lambda(1) & \cdots & \lambda(L-1) \\ \lambda^H(1) & \lambda(0) & \cdots & \lambda(L-2) \\ \vdots & \vdots & \ddots & \vdots \\ \lambda^H(L-1) & \lambda^H(L-2) & \cdots & \lambda(0) \end{bmatrix} \quad (7)$$

In the context of spectrum sensing, the covariance matrix characterizes the statistical relationships between antenna elements and/or time samples. By analyzing its structure, the presence of a signal such as a PU, can be inferred. Specifically, a structured or rank-deficient covariance matrix
often indicates the presence of a modulated signal, as opposed to pure noise.

To evaluate the effectiveness of various eigenvalue-based
detection techniques under realistic conditions, we compute
detection thresholds and test statistics for each method. These
are assessed across a range of realistic railway scenarios using
SDR platforms and simulated channel models (VehA, EVA,
Two-Ray) implemented in MATLAB.

B. SPECTRUM SENSING TECHNIQUES

The SS techniques implemented in the test setup are the
following: MME, EME, SPET, MSEE, MEGM, CAV.

To compare the different techniques in the considered
scenarios, we have chosen to focus on vehicular channel
models that are used normally in the literature to simulate
realistic railway communication environments such as Two-
Ray [21], [22], VehA and EVA [23], [24], [25] with a Doppler
shift to address high-speed train movement.

It is important to understand the fundamental distinctions
between the various ESS techniques. These methods leverage
the properties of the sample covariance matrix $\hat{R}_y$ to make
decisions on the presence or absence of the PU signal. The
following is a brief explanation of each technique.

*   **MME:** This technique relies on the value of the ratio between the highest eigenvalue ($\xi_1$) and the lowest eigenvalue ($\xi_q$) of the covariance matrix. The idea is that the presence of a signal will cause the highest eigenvalue to increase significantly compared to the lowest, making the value of this ratio a strong indicator of signal presence [14].

* **EME:** The EME technique compares the total energy of the received signal, represented by the average of all eigenvalues, to the smallest eigenvalue. Since the energy of the received signal increases with the presence of a signal, this ratio can effectively indicate the presence of a signal [12].

* **SPET:** SPET compares the largest eigenvalue to the average of all eigenvalues. This technique is designed to provide a simplified, yet robust, test statistic that is less sensitive to noise variations, making it highly effective in challenging environments [15].

* **MSEE:** MSEE uses a combination of the largest and smallest eigenvalues, comparing their arithmetic mean to their geometric mean. This technique captures both the spread and the distribution of eigenvalues, making it sensitive to the presence of structured signals in the noise [13].

* **MEGM:** MEGM involves the ratio of the largest eigenvalue to the geometric mean of all eigenvalues. This method is based on the premise that the largest eigenvalue will dominate in the presence of a signal, while the geometric mean provides a stable reference point [16].

* **CAV:** Unlike the other methods that directly use eigenvalues, CAV is based on the sum of absolute values

of the off-diagonal elements of the covariance matrix
relative to the diagonal elements. This approach is rooted
in the idea that the presence of a signal will increase
the correlation between different signal components,
which is reflected in the off-diagonal elements of the
covariance matrix [10].

Each spectrum sensing technique offers a distinct approach, leveraging different properties of the covariance matrix to detect signal presence. The selection of an appropriate method depends on the characteristics of the communication environment and the trade-offs between the detection accuracy and the computational complexity.

In this study, we opted for ESS over energy-based
methods due to the latter’s reliance on prior noise variance
knowledge, which is challenging in practical scenarios.
As ESS techniques are independent of noise variance, they
are more robust for real-world applications. They calculate
test statistics $T_{ESS}$ from the eigenvalues of the estimated
covariance matrix $\hat{R}_y$, with decision thresholds $\gamma_{ESS}$ designed
to meet the desired false alarm probability $P_{fa}$.

Table 2 summarizes the test statistics and thresholds for each ESS technique. It also provides an overview of the relevant equations, including detection performance metrics and threshold expressions derived from the cumulative distribution function (CDF) under the $H_0$ hypothesis, where no PU signal is present. In addition to the corresponding reference that provides the detailed threshold calculation for each technique.

In these formulas:

* $\mu$ and $\delta$ are parameters related to the Tracy-Widom law [26].

* $F_{\beta}$ is the CDF of the Tracy-Widom distribution.

* $G$ and $Q$ are functions used to derive the thresholds.

* $\gamma_1$ is calculated based on the requirements for the probability of false alarm.

* $T_1 = \frac{1}{L} \sum_{n=1}^{L} \sum_{m=1}^{L} |r_{nm}|$

* $T_2 = \frac{1}{L} \sum_{m=1}^{L} |r_{nn}|$

* $r_{nm}$ are the elements of the sample covariance matrix $\hat{R}_x$.

C. CONSIDERED PERFORMANCE METRICS

The two primary metrics used for performance evaluation are
defined as follows:

* **Probability of Miss Detection ($P_m$):** This metric measures the likelihood that the sensing technique fails to detect the presence of the signal when it is actually present. Lower values of $P_m$ indicate better detection performance.

* **Probability of False Alarm ($P_{fa}$):** This metric measures the likelihood that the sensing technique incorrectly detects the presence of a signal when it is actually absent. Lower values of $P_{fa}$ indicate a better reliability in distinguishing noise from the signal.

Algorithm 1 summarizes the steps in order to calculate the $P_m$ and $P_{fa}$ to define the best sensing technique. It is worth mentioning that in this work we focus on the probability
<table><caption>TABLE 2. Test statistics and thresholds for eigenvalue-based spectrum sensing techniques.</caption><thead><tr><td>Technique</td><td>T<sub>ESS</sub></td><td>γ<sub>ESS</sub></td><td>References</td></tr></thead><tbody><tr><td>MME</td><td>ξ<sub>1</sub>/ξ<sub>q</sub></td><td>δF<sub>β</sub><sup>-1</sup>(1-P<sub>fa</sub>)+μ/N</td><td>[14]</td></tr><tr><td>EME</td><td>(1/q) Σ<sub>n=1</sub><sup>N</sup> ŷ<sup>H</sup>(n)ŷ(n)/ξ<sub>q</sub></td><td>√(2NQ<sup>-1</sup>(P<sub>fa</sub>)+√MN)/√M(√N-√q)<sup>2</sup></td><td>[12]</td></tr><tr><td>SPET</td><td>(1/q) Σ<sub>i=1</sub><sup>q</sup> ξ<sub>i</sub>/ξ<sub>1</sub></td><td>δF<sub>β</sub><sup>-1</sup>(1-P<sub>fa</sub>)+√Nμ/N</td><td>[15]</td></tr><tr><td>MSEE</td><td>ξ<sub>1</sub>+ξ<sub>q</sub>/2√ξ<sub>1</sub>ξ<sub>q</sub></td><td>Γ<sup>-1</sup>(δF<sub>β</sub><sup>-1</sup>(1-P<sub>fa</sub>)+μ)/(√N-√q)<sup>2</sup>)</td><td>[13]</td></tr><tr><td>MEGM</td><td>ξ<sub>1</sub>/(Π<sub>i=1</sub><sup>q</sup> ξ<sub>i</sub>)<sup>1/√q</sup></td><td>δF<sub>β</sub><sup>-1</sup>(1-P<sub>fa</sub>)+μ/N</td><td>[16]</td></tr><tr><td>CAV</td><td>T<sub>1</sub>/T<sub>2</sub></td><td>γ<sub>1</sub></td><td>[10]</td></tr></tbody></table>

of miss detection as not detecting the presence of the interference signal is more costly for our application than detecting nonexistent signals (probability of false alarm).

### Algorithm 1 Spectrum Sensing Based on ESS Techniques

1. **Input:** Received signal ŷ(n), threshold γ<sub>ESS</sub>

2. **for** each signal ŷ(n) **do**

3. Compute the covariance matrix Ŷ<sub>y</sub>

4. Extract eigenvalues ξ₁, ξ₂, ..., ξₜ

5. Compute the test statistic T<sub>ESS</sub> for each technique (MME, SPET, EME, MSEE, MEGM, CAV)

6. Compare T<sub>ESS</sub> with the threshold γ<sub>ESS</sub>

7. **if** T<sub>ESS</sub> > γ<sub>ESS</sub> **then**

8. Signal detected (PU present)

9. **else**

10. No signal (PU absent)

11. **end if**

12. **end for**

13. **Evaluation:** Calculate P<sub>m</sub> and P<sub>fa</sub> for each technique

14. **Output:** Select the best technique based on P<sub>m</sub> and P<sub>fa</sub>

## D. CONSIDERED CHANNEL MODELS

Testing the ESS techniques in different vehicular channel models is essential to ensure the robustness of the spectrum sensing techniques under various conditions. The channel models selected are described below:

* **No Channel:** This baseline model assumes no specific channel effects, providing a reference point to compare the performance of different techniques without additional channel complexities.

* **Vehicular A (VehA):** This model is designed for high-speed vehicular environments, where the Doppler effect significantly influences signal propagation.

It helps in evaluating the performance of spectrum sensing techniques in high-mobility scenarios [27].

* **Extended Vehicular A (EVA):** This model simulates urban environments with multiple reflections and a complex propagation path, making it suitable for testing the resilience of spectrum sensing techniques in densely populated areas [28].

* **TwoRay:** The Two-Ray model describes a scenario involving a direct path and a single reflected path, typically from the ground. It is particularly useful for analyzing the effects of simple multipath environments on signal detection and is commonly applied in train-to-train communication systems. This model considers two primary paths: the direct path and the ground-reflected path, which is generally attenuated and delayed compared to the direct one. The delay is proportional to the distance between the trains [29].

All those channel models power delay profile (PDP) were generated in Matlab along with the Doppler effect corresponding to the speed values to cover the doubly selective nature of the train communication channel in terms of time and frequency.

## IV. TEST SETUP

This section outlines the test setup for the assessment of the detection of both LTE and Wi-Fi 802.11ax signals. The setups for both technologies utilize SDRs for signal transmission and reception, with the equipment and software tools carefully chosen to replicate real-world communication scenarios and enable detailed analysis.

Figure 1 illustrates the overall system setup, which includes both hardware and software components involved in the transmission and reception of LTE and Wi-Fi signals.
<figure><img src="image_3.png" /><figcaption>FIGURE 1. System setup.</figcaption></figure>

The hardware and software configurations for LTE and Wi-Fi share several commonalities, particularly the use of USRP B210 for signal transmission and USRP N310 for signal reception. These devices were selected for their flexibility, wide frequency range, and ability to support both LTE and Wi-Fi frequencies, making them ideal for a variety of transmission and reception scenarios.

For LTE signal transmission, we use SRSRAN 4G open-source software, whereas GNU Radio is employed for generating Wi-Fi signals. The USRP B210 SDR transmits these signals in both cases. The B210 is chosen for its flexibility and compatibility with a wide range of frequencies, making it suitable for both LTE and Wi-Fi transmission.

On the reception side, Matlab is utilized for its robust signal processing capabilities. The USRP N310 SDR receives both LTE and Wi-Fi signals. The N310 was selected for its higher sampling rate, greater multichannel capability, and ability to handle multiple input and output streams. These features make the N310 particularly well-suited for accurately capturing signals in complex environments, thereby supporting both LTE and Wi-Fi spectrum detection tests.

<table><caption>TABLE 3. Key parameters of the experimental setup.</caption><thead><tr><td>Parameter</td><td>Value / Description</td></tr></thead><tbody><tr><td>Signal types</td><td>LTE (srsRAN), Wi-Fi 802.11ax</td></tr><tr><td>Center frequency</td><td>LTE: 2.6 GHz, Wi-Fi: 5.2 GHz</td></tr><tr><td>Bandwidth</td><td>LTE: 10 MHz, Wi-Fi: 20 MHz</td></tr><tr><td>Tx/Rx SDR</td><td>USRP B210 / N310</td></tr><tr><td>Sampling rate</td><td>30.72 MSps</td></tr><tr><td>Gain (Tx / Rx)</td><td>50 dB / 60 dB</td></tr><tr><td>Signal path</td><td>Direct coaxial cable (no OTA)</td></tr><tr><td>Channel models</td><td>NoCh., VehA, EVA, TwoRay</td></tr><tr><td>Train speeds</td><td>0–300 km/h (Doppler included)</td></tr><tr><td>Evaluation metrics</td><td>10,000 Monte Carlo runs</td></tr></tbody></table>

## V. TESTING PROTOCOL AND SCENARIOS

The testing strategy was divided into two distinct scenarios for each technology:

The first scenario involves testing the six different spectrum sensing techniques (SPET, CAV, MME, MEGM, MSEE, and EME) simultaneously. In this test, we varied the SNR while keeping the speed constant. This approach allowed us to assess the performance of each technique under different signal strength levels, thereby providing insights into their sensitivity and reliability.

The second scenario is a stress test designed to evaluate the robustness of these detection techniques under dynamic conditions by considering the TwoRay channel model. In this test, we kept the SNR constant and varied both the speed and the reflected signal delay that reflects the distance between trains in the TwoRay channel model. This approach simulates real-world scenarios where the conditions change rapidly, such as high-speed mobility and varying signal propagation delays. The stress test is crucial to understand how each technique performs in challenging environments, which are common in railway communications.

By conducting these two types of tests, we were able to thoroughly examine the effectiveness of each spectrum sensing technique under varying conditions, providing a clear understanding of their strengths and weaknesses in LTE and Wi-Fi signal detection.

## VI. RESULTS AND ANALYSIS FOR LTE AND WI-FI 802.11AX

### A. ANALYSIS VERSUS SIGNAL-TO-NOISE RATIO

The evaluation of various eigenvalue-based spectrum sensing techniques was conducted under diverse channel conditions, focusing on two key performance metrics: $P_m$ and $P_{fa}$. These metrics were analyzed over a range of SNR values to assess the robustness and efficiency of each technique.

The tested algorithms include CAV, SPET, MEGM, MME, MSEE, and EME. The simulations were performed using different channel models: No Channel (ideal), VehA, Two-Ray, and EVA. All results correspond to a single antenna configuration ($M = 1$) and are averaged over 10,000 Monte Carlo runs ($N_{mc} = 10000$).

The key findings for the LTE simulation results are illustrated in Figures 2 to 5:

<figure><img src="image_4.png" /><figcaption>FIGURE 2. $P_m$ and $P_{fa}$ for TwoRay channel model for LTE.</figcaption></figure>

Across all LTE scenarios, the SPET technique consistently achieved the lowest $P_m$, confirming its strong detection capability in both ideal and fading environments. However, it is important to note that SPET also exhibits a relatively higher $P_{fa}$, particularly in low-SNR conditions. This trade-off stems from its aggressive thresholding mechanism, which favors detection sensitivity at the expense of increased false alarms.
<figure><img src="image_5.png" /><figcaption>FIGURE 3. $P_m$ and $P_{fa}$ for EVA channel model for LTE.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>FIGURE 4. $P_m$ and $P_{fa}$ without channel for LTE.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>FIGURE 5. $P_m$ and $P_{fa}$ for VehA channel model for LTE.</figcaption></figure>

Despite this drawback, the higher $P_{fa}$ is considered acceptable in railway applications, where missed detections pose a greater risk than false alarms. Failing to detect an interfering signal can lead to harmful interference, whereas a false alarm results only in unnecessary avoidance of a frequency band.

Additionally, the relative independence of $P_{fa}$ with respect to SNR is a known property of eigenvalue-based detectors under the noise-only hypothesis and has been observed consistently in the literature.

As with the LTE case, SPET demonstrates superior performance in minimizing $P_m$ under all tested Wi-Fi scenarios. Its reliability across diverse fading conditions confirms its suitability for real-time spectrum sensing in fast-varying environments. The observed high $P_{fa}$ levels, while a limitation in terms of spectrum efficiency, are tolerable

<figure><img src="image_8.png" /><figcaption>FIGURE 6. $P_m$ and $P_{fa}$ for TwoRay channel model for WIFI.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>FIGURE 7. $P_m$ and $P_{fa}$ for EVA channel model for WIFI.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>FIGURE 8. $P_m$ and $P_{fa}$ without channel for WIFI.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>FIGURE 9. $P_m$ and $P_{fa}$ for VehA channel model for WIFI.</figcaption></figure>

in safety-critical contexts where avoiding interference takes precedence over optimal channel utilization.

Overall, these results highlight the robustness of SPET and support its adoption in scenarios where the cost of missed detection is significantly higher than that of a false alarm.
<figure><img src="image_12.png" /><figcaption>FIGURE 10. $P_m$ for CAV technique in LTE detection.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>FIGURE 11. $P_m$ for EME technique in LTE detection.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>FIGURE 12. $P_m$ for MME technique in LTE detection.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>FIGURE 13. $P_m$ for MEGM technique in LTE detection.</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>FIGURE 14. $P_m$ for MSEE technique in LTE detection.</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>FIGURE 15. $P_m$ for SPET technique in LTE detection.</figcaption></figure>

## B. STRESS TEST ANALYSIS FOR LTE AND WI-FI SIGNALS

The stress test is designed to evaluate the robustness and reliability of the selected spectrum sensing techniques in high-mobility scenarios. By varying train speeds and tap delay values in the TwoRay channel model for a given SNR value, this test simulates real-world railway communication environments characterized by rapid movement and different train-to-train distances. The focus here is on assessing how well these techniques perform under dynamic conditions. The performance of the various spectrum sensing techniques was evaluated under different train speeds and signal delays, focusing on the $P_m$. The results are presented as 3D surface plots, illustrating how $P_m$ varies under different conditions for each technique. The SPET consistently achieves the lowest $P_m$, underscoring its robustness in detecting LTE and Wi-Fi signals even at higher train speeds and signal delays.

The results of the stress test are presented in Figures 10 through 15, showcasing $P_m$ for different spectrum sensing techniques under varying train speeds and signal delays.

These 3D surface plots demonstrate the comparative performance of each technique, with SPET demonstrating superior robustness. It can be observed that the CAV technique maintains a relatively stable miss detection probability across varying train speeds and signal delays. Nevertheless, there is a slight increase in $P_m$ with elevated delays, indicating a vulnerability to multipath effects.

The EME technique exhibits a higher $P_m$ compared to CAV, particularly under conditions of significant signal delay. This suggests that EME is less effective in environments with pronounced multipath propagation. MEGM shows an elevated $P_m$, especially at higher train speeds and delays. This indicates reduced effectiveness in dynamic environments with considerable Doppler shifts and multipath effects.

MME's performance parallels that of MEGM, with a higher miss detection probability in scenarios involving high mobility. It also struggles with significant Doppler shifts and multipath propagation, further reducing its effectiveness.
<figure><img src="image_18.png" /><figcaption>FIGURE 16. $P_m$ for CAV technique in Wi-Fi detection.</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>FIGURE 17. $P_m$ for EME technique in Wi-Fi detection.</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>FIGURE 18. $P_m$ for MME technique in Wi-Fi detection.</figcaption></figure>

SPET consistently attains the lowest $P_m$, underscoring its robustness in detecting LTE signals even at higher train speeds and signal delays. This positions SPET as the most reliable technique among those evaluated.

The performance of spectrum sensing techniques for Wi-Fi 802.11ax was evaluated in environments characterized by significant Doppler shifts and multipath propagation, conditions similar to those used for testing LTE.

As with LTE, the SPET algorithm demonstrated superior performance, consistently achieving the lowest $P_m$ values. It exhibited exceptional reliability in detecting Wi-Fi signals, even under challenging conditions such as higher train speeds and increased signal delays, making it the most robust technique in the study, as shown in the stress test results in Figures 16 through 21.

Whereas the performance of all techniques tends to degrade with high train speeds and signal delays, the

<figure><img src="image_21.png" /><figcaption>FIGURE 19. $P_m$ for MEGM technique in Wi-Fi detection.</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>FIGURE 20. $P_m$ for MSEE technique in Wi-Fi detection.</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>FIGURE 21. $P_m$ for SPET technique in Wi-Fi detection.</figcaption></figure>

robustness of SPET allows it to maintain reliable signal detection under these adverse conditions.

This makes SPET a critical tool to ensure stable communication in the complex and dynamic environments typical of modern railway systems.

## VII. STRESS TEST RESULTS: QUANTITATIVE COMPARISON AND ANALYSIS

To enhance the clarity of Figures 10–21, we provide in Table 4 and Table 5 the approximate values of the probability of miss detection ($P_m$) for the evaluated spectrum sensing techniques under varying train speeds and multipath delays.

These values were inferred from the 3D surface plots and represent the general trends described in the analysis.

### Observations:

* SPET shows the lowest $P_m$ in LTE, maintaining values close to 0.13–0.14 across all test conditions.

* CAV offers moderate performance with values rising gradually with speed and delay.
<table><caption>TABLE 4. Approximate $P_m$ values for LTE detection under stress test conditions.</caption><thead><tr><th>Speed (km/h)</th><th>Delay (µs)</th><th>SPET</th><th>CAV</th><th>EME</th><th>MME</th><th>MEGM</th></tr></thead><tbody><tr><td>100</td><td>0.5</td><td>0.130</td><td>0.145</td><td>0.515</td><td>0.163</td><td>0.195</td></tr><tr><td>100</td><td>1.0</td><td>0.134</td><td>0.153</td><td>0.518</td><td>0.164</td><td>0.197</td></tr><tr><td>200</td><td>0.5</td><td>0.135</td><td>0.157</td><td>0.519</td><td>0.165</td><td>0.198</td></tr><tr><td>200</td><td>1.0</td><td>0.137</td><td>0.160</td><td>0.521</td><td>0.166</td><td>0.199</td></tr><tr><td>300</td><td>0.5</td><td>0.138</td><td>0.163</td><td>0.523</td><td>0.167</td><td>0.199</td></tr><tr><td>300</td><td>1.0</td><td>0.139</td><td>0.167</td><td>0.524</td><td>0.168</td><td>0.200</td></tr></tbody></table>

<table><caption>TABLE 5. Approximate $P_m$ values for Wi-Fi detection under stress test conditions.</caption><thead><tr><th>Speed (km/h)</th><th>Delay (µs)</th><th>SPET</th><th>CAV</th><th>EME</th><th>MME</th><th>MEGM</th><th>MSEE</th></tr></thead><tbody><tr><td>100</td><td>0.5</td><td>0.312</td><td>0.385</td><td>0.840</td><td>0.487</td><td>0.502</td><td>0.511</td></tr><tr><td>100</td><td>1.0</td><td>0.336</td><td>0.400</td><td>0.855</td><td>0.494</td><td>0.505</td><td>0.514</td></tr><tr><td>200</td><td>0.5</td><td>0.348</td><td>0.410</td><td>0.860</td><td>0.490</td><td>0.506</td><td>0.512</td></tr><tr><td>200</td><td>1.0</td><td>0.360</td><td>0.420</td><td>0.865</td><td>0.492</td><td>0.508</td><td>0.513</td></tr><tr><td>300</td><td>0.5</td><td>0.370</td><td>0.425</td><td>0.870</td><td>0.493</td><td>0.509</td><td>0.514</td></tr><tr><td>300</td><td>1.0</td><td>0.385</td><td>0.430</td><td>0.875</td><td>0.495</td><td>0.510</td><td>0.515</td></tr></tbody></table>

* EME is severely affected, with $P_m$ above 0.51 in all scenarios, showing poor resilience.

* MME and MEGM remain around 0.16–0.20, suggesting stable but suboptimal performance.

Observations:

* SPET maintains the best performance for Wi-Fi as well, though with higher $P_m$ than LTE.

* CAV remains in the mid-range and scales predictably with speed and delay.

* EME suffers the most, with $P_m$ values close to or above 0.85.

* MME, MEGM, and MSEE yield similar performances in the 0.49–0.515 range.

A. REAL-TIME DETECTION SYSTEM FOR LTE SIGNAL USING SPET ALGORITHM

A real-time LTE signal detection system was developed using the SPET algorithm, which has consistently proven to be the most effective in previous evaluations. This system continuously monitors the received signal, dynamically updating the detection status based on the calculated SPET value and a predefined threshold, making it highly suitable for real-time applications.

The system interfaces with a USRP N310 device to receive LTE signals. During initialization, critical parameters such as center frequency, gain, decimation factor, and sample rate are configured to ensure accurate signal reception. Moreover, a graphical user interface (GUI) is included to display real-time feedback, showing the SPET value, threshold, and signal detection status.

During operation, the system continuously acquires signal samples from the USRP device. The SPET algorithm processes these samples, quickly and accurately determining the presence of an LTE signal. The GUI is updated in real-time, reflecting the current SPET value, detection threshold, and signal detection status, ensuring immediate feedback on the presence of LTE signal.

This real-time application of the SPET algorithm demonstrates its effectiveness in reliably detecting LTE signals,

even in complex and high-mobility environments. The system successfully identifies the LTE signal. The SPET value exceeds the threshold, triggering the “Signal Detected” message.

VIII. CONCLUSION

The objective of this research was to enhance the resistance of a wireless train-to-train communication against interference signals coming from LTE or Wi-Fi signals in the environment. We evaluated the performance of advanced spectrum sensing techniques using LTE and Wi-Fi 802.11ax signals emulated on SDR platforms. This was achieved through the utilization of tools such as GNU Radio and Matlab. The performance was evaluated in a doubly-selective channel, which was designed to replicate the harsh nature of a railway communication scenario.

Among the evaluated techniques: SPET, CAV, MEGM, MME, MSEE, and EME, SPET consistently demonstrated the highest performance, achieving the lowest probability of miss detection across varying SNRs and channel conditions. Furthermore, real-world stress tests simulating high-speed mobility and multipath propagation provided additional validation of SPET’s robustness.

In conclusion, this research makes a significant contribution to railway telecommunications by identifying and validating the optimal spectrum sensing technique for dynamic, high-mobility environments. SPET shows strong potential for practical use in real-world train communication systems. Future efforts should focus on implementing these findings in operational networks, optimizing algorithms for real-time performance, and extending tests to more varied conditions and interference scenarios.

REFERENCES

[1] J. Moreno, J. M. Riera, L. D. Haro, and C. Rodriguez, “A survey on future railway radio communications services: Challenges and opportunities,” *IEEE Commun. Mag.*, vol. 53, no. 10, pp. 62–68, Oct. 2015.

[2] J. M. Garcia-Loygorri, J. Goikoetxea, E. Echeverría, A. Arriola, I. Val, S. Sand, P. Unterhuber, and F. del Rio, “The wireless train communication network: Roll2Rail vision,” *IEEE Veh. Technol. Mag.*, vol. 13, no. 3, pp. 135–143, Sep. 2018.

[3] F. Flammini, S. Marrone, R. Nardone, A. Petrillo, S. Santini, and V. Vittorini, “Towards railway virtual coupling,” in *Proc. IEEE Int. Conf. Electr. Syst. Aircr., Railway, Ship Propuls. Road Vehicles Int. Transp. Electrific. Conf. (ESARS-ITEC)*, Nov. 2018, pp. 1–6.

[4] *Connecta-2*. Accessed: Feb. 5, 2025. [Online]. Available: https://www.s2r-connecta.eu

[5] *Safe Architecture for Robust Distributed Application Integration in Rolling Stock 2*. Accessed: Feb. 5, 2025. [Online]. Available: https://www.safe4rail.eu

[6] R. Project. *Roll2Rail Project*. Accessed: Sep. 12, 2017. [Online]. Available: http://roll2rail.eu

[7] *R2Dato Project*. Accessed: Feb. 6, 2025. [Online]. Available: https://projects.rail-research.europa.eu/eurail-fp2/

[8] S.-Y. Lien, D.-J. Deng, C.-C. Lin, H.-L. Tsai, T. Chen, C. Guo, and S.-M. Cheng, “3GPP NR sidelink transmissions toward 5G V2X,” *IEEE Access*, vol. 8, pp. 35368–35382, 2020.

[9] J. Moreno García-Loygorri, I. Val, A. Arriola, and C. Briso, “Channel model and interference evaluation for a wireless train backbone,” *IEEE Access*, vol. 7, pp. 115518–115527, 2019.

[10] J. Mitola, “Software radios: Survey, critical evaluation and future directions,” *IEEE Aerosp. Electron. Syst. Mag.*, vol. 8, no. 4, pp. 25–36, Apr. 1993.
[11] S. Haykin, D. J. Thomson, and J. H. Reed, “Spectrum sensing for cognitive radio,” *Proc. IEEE*, vol. 97, no. 5, pp. 849–877, May 2009.

[12] K. Bouallegue, I. Dayoub, M. Gharbi, and K. Hassan, “Blind spectrum sensing using extreme eigenvalues for cognitive radio networks,” *IEEE Commun. Lett.*, vol. 22, no. 7, pp. 1386–1389, Jul. 2018.

[13] N. Pillay and H. J. Xu, “Blind eigenvalue-based spectrum sensing for cognitive radio networks,” *IET Commun.*, vol. 6, no. 11, pp. 1388–1396, Jul. 2012.

[14] Y. Zeng and Y.-C. Liang, “Eigenvalue-based spectrum sensing algorithms for cognitive radio,” *IEEE Trans. Commun.*, vol. 57, no. 6, pp. 1784–1793, Jun. 2009.

[15] K. Hassan, R. Gautier, I. Dayoub, E. Radoi, and M. Berbineau, “Non-parametric multiple-antenna blind spectrum sensing by predicted eigenvalue threshold,” in *Proc. IEEE Int. Conf. Commun. (ICC)*, Jun. 2012, pp. 1629–1634.

[16] Y. Zeng and Y.-C. Liang, “Spectrum-sensing algorithms for cognitive radio based on statistical covariances,” *IEEE Trans. Veh. Technol.*, vol. 58, no. 4, pp. 1804–1815, May 2009.

[17] H. B. Thameur and I. Dayoub, “Real-time in-lab test of eigenvalue-based spectrum sensing using USRP RIO SDR boards,” *IEEE Commun. Lett.*, vol. 25, no. 3, pp. 1029–1032, Mar. 2021.

[18] H. B. Thameur and I. Dayoub, “On experimental evaluation of eigenvalue-based spectrum sensing using a real-time SDR testbed,” in *Proc. Int. Wireless Commun. Mobile Comput. (IWCMC)*, Jun. 2021, pp. 1982–1987.

[19] A. Sabra and M. Berbineau, “Experimental assessment of eigenvalue-based spectrum sensing using USRP-based MIMO testbed for cognitive radio applications,” *IEEE Sensors Lett.*, vol. 6, no. 10, pp. 1–4, Oct. 2022.

[20] H. B. Thameur and I. Dayoub, “SDR implementation of a real-time testbed for spectrum sensing under MIMO time-selective channels for cognitive radio applications,” *IEEE Sensors Lett.*, vol. 5, no. 8, pp. 1–4, Aug. 2021.

[21] P. Unterhuber, I. Rashdan, M. Walter, and T. Kürner, “Path loss models and large scale fading statistics for C-band train-to-train communication,” in *Proc. 14th Eur. Conf. Antennas Propag. (EuCAP)*, Mar. 2020, pp. 1–5.

[22] C. Rico Garcia, A. Lehner, T. Strang, and K. Frank, “Channel model for train to train communication using the 400 MHz band,” in *Proc. IEEE Veh. Technol. Conf. (VTC)*, May 2008, pp. 3082–3086.

[23] J. Calle-Sánchez, M. Molina-García, J. I. Alonso, and A. Fernández-Durán, “Long term evolution in high speed railway environments: Feasibility and challenges,” *Bell Labs Tech. J.*, vol. 18, no. 2, pp. 237–253, Sep. 2013.

[24] M. Berbineau, R. Behaegel, J. M. Garcia-Loygorri, R. Torrego, R. D'Errico, A. Sabra, Y. Yan, and J. Soler, “Channel models for performance evaluation of wireless systems in railway environments,” *IEEE Access*, vol. 9, pp. 45903–45918, 2021.

[25] C. M. K. Swain, “Coverage analysis of coded orthogonal frequency division multiplexing (C-OFDM) based high speed railways (HSR) networks under different propagation scenarios,” *E-Prime-Adv. Electr. Eng.*, *Electron. Energy*, vol. 9, Sep. 2024, Art. no. 100726.

[26] Z. Bao, G. Pan, and W. Zhou, “Tracy-widom law for the extreme eigenvalues of sample correlation matrices,” 2012, arXiv:1203.1405.

[27] D. W. Matolak, “Channel modeling for vehicle-to-vehicle communications,” *IEEE Commun. Mag.*, vol. 46, no. 5, pp. 76–83, May 2008.

[28] V8.9.0: Base Station (BS) Conformance Testing, document TS 36.141, 3GPP, Dec. 2009. [Online]. Available: https://www.3gpp.org/DynaReport/36141.htm

[29] J. M. Romero-Jerez, F. J. Lopez-Martinez, J. F. Paris, and A. J. Goldsmith, “The fluctuating two-ray fading model: Statistical characterization and performance analysis,” *IEEE Trans. Wireless Commun.*, vol. 16, no. 7, pp. 4420–4432, Jul. 2017.

<figure><img src="image_24.png" /></figure>

**CHADLI HADJI** received the Engineering degree in networks and security engineering from ESIEE Paris. He has been a Research and Development Engineer with Université Gustave Eiffel, since 2021. His research interests include wireless connectivity technologies, mobile and wireless networks, software-defined radio (SDR), and network security.

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**YAMEN ALSABA** received the B.Eng. degree in electrical and telecommunication engineering from the Higher Institute of Applied Science and Technology, Damascus, Syria, in 2005, the M.S. degree from CentraleSupélec, Paris, France, in 2010, and the Ph.D. degree from University Technology Malaysia, Johor, Malaysia, in 2019. He is currently leading the Telecommunication Team, IRT Railenium, Valenciennes, France. His research interests include wireless 5G communication systems, NR-V2X, LTE, non-orthogonal multiple access, MIMO, beamforming, physical layer security, train-train, and train-ground communication systems.

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**MARION BERBINEAU** (Member, IEEE) received the Engineering degree in informatics, electronics, and automatics from Polytech Lille (formerly EUDIL), in 1986, and the Ph.D. degree in electronics from the University of Lille, France, in 1989. She has been the Research Director of Université Gustave Eiffel (previously IFSTTAR), since 2000. She was the Director of the Least Laboratory, from 2000 to 2013, and then the Deputy Director of the COSYS Department, from 2013 to 2017. In addition to research activities and supervision of Ph.D. students, she coordinates railway research with Université Gustave Eiffel. She is also the Pole Leader of the Intelligent Mobility Pole of European Railway Research Network of Excellence (Eurnex). She is the author or co-author of several journal articles and patents. She has already participated in a lot of European and national research projects. Her current research interests include wireless communications for connected and automatic vehicles (trains and cars) (radio propagation, channel characterization and modeling, SDR, MIMO, ITS-G5, GSM-R, LTE, and 5G NR and beyond).

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**FOUZIA ELBAHHAR** received the Ph.D. degree from the Université Polytechnique Hauts-de-France, in 2000 and 2004, respectively. She is currently the Research Director of Gustave Eiffel University. She has participated in many national and European projects dedicated to transport applications. She is involved in signal processing, radar for vital signs, 5G wireless communication, and cognitive radio. She has authored or co-authored more than 100 research papers, including journal articles, book chapters, and conference proceedings. Her research interests include indoor positioning, radar, V2X Communication, and wireless radio systems. She serves as a Reviewer for several journals and international conferences.

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**RODRIGUE FARGEON** has been the Telecom Project Manager of SNCF, since 2021. With over 18 years of experience in the telecom industry, he has built a distinguished career characterized by significant accomplishments in leading wireless technology across a wide range of disciplines. His expertise spans various areas, including customer delivery, research and development (R&D), engineering, and the implementation of new technologies for diverse applications in multicultural and international environments.

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