World-First Demonstration: Estimating Outdoor Pedestrian Flow from Fluctuations in Mobile Communication System's Radio Signals Using Commercial Base Stations

Advancing toward practical sensing in the 6G era — anywhere covered by base station signals can become a sensing area, using only communication devices

News Highlights:

TOKYO - May 26, 2025 - NTT Corporation (Headquarters: Chiyoda, Tokyo; President and CEO: Akira Shimada; hereinafter "NTT") and Sophia University (Headquarters: Chiyoda, Tokyo; President: Miki Sugimura) have achieved a world-first demonstration of human detection by analyzing variations in radio propagation information extracted from synchronization signals transmitted by commercially operated base stations.

This research was conducted to evaluate the effectiveness of ISAC (Integrated Sensing and Communications), which is expected to be adopted in sixth-generation mobile communication systems (6G). By applying AI analysis to propagation data obtained from base station signals, the team successfully detected human presence without requiring any additional sensors.
　These results suggest that integrating sensing capabilities into existing mobile communication systems—originally designed solely for communication—can significantly expand their range of applications.

This technology will be exhibited at Wireless Japan × Wireless Technology Park¹, to be held from May 28 to 30, 2025.

Background

Sixth-generation mobile communication systems (6G²) are expected to significantly enhance mobile communication systems by enabling ultra-high-speed data transmission, low latency, and massive device connectivity. In line with this, the Radiocommunication Sector of the International Telecommunication Union (ITU-R³) is discussing the standardization of 6G under the framework of IMT-2030 (6G)⁴, which includes considerations for ISAC (Integrated Sensing and Communications)—a technology that integrates radio communication and sensing (Figure 1).
　ISAC enables sensing capabilities using the same radio wave propagation information used for communication, eliminating the need for installing additional dedicated sensing devices. Moreover, since ISAC relies on radio signals rather than optical imaging, it allows for privacy-conscious sensing without capturing images of the target. ISAC is also expected to perform well in environments where conventional cameras struggle—such as in darkness or where line-of-sight is obstructed.

Recognizing these advantages, the 3rd Generation Partnership Project (3GPP⁵) has defined a wide range of ISAC use cases (Table 2), envisioning the deployment of radio-based sensing across diverse indoor and outdoor environments.

However, most existing experimental validations have been limited to indoor use cases using wireless LAN systems. While theoretical studies on ISAC-based wireless sensing in mobile communication systems are increasing, experimental demonstrations remain scarce. Even when such reports exist, they often rely on experimental base stations or controlled test environments, leaving the practical feasibility of 3GPP-defined ISAC use cases unverified.

Against this backdrop, NTT and Sophia University have been conducting R&D on wireless sensing technologies using commercial base stations, based on the envisioned use cases of ISAC (Figure 3). The wireless propagation information acquisition system developed for this demonstration is capable of collecting data from commercially deployed 4G and 5G networks. Accordingly, this demonstration specifically validated ISAC technology using the most widely deployed 4G commercial base stations in real-life environments.

Figure 1 Usage Scenarios of IMT-2020 (5G) and IMT-2030 (6G) Published by ITU-R

Figure 3 Conceptual Illustration of ISAC Applications

Experimental Environment and Results

NTT and Sophia University have developed a system that utilizes synchronization signals periodically transmitted from commercial base stations to extract radio propagation information. Using this system, a field demonstration was conducted to sense the number of pedestrians on an outdoor pathway at the Yotsuya Campus of Sophia University.

(1) Experimental Environment

In this demonstration, pedestrian detection in an outdoor setting—one of the ISAC use cases—was conducted on Sophia University's Yotsuya Campus.
　Radio signals transmitted from commercial base stations installed and operating on campus were received using two antennas positioned along a campus walkway. These signals were then analyzed using the developed wireless sensing system (Figure 4).
　The radio signals used for analysis came from the 2 GHz Band 1 of a 4G system. From the signals, Received Signal Strength Indicator (RSSI) and Channel State Information (CSI) were extracted and used for sensing.

The measurement period spanned 3.5 hours, from 10:30 AM to 2:00 PM, including both low-traffic lecture hours and high-traffic lunch breaks.
　In addition to the wireless sensing system, a camera installed on campus was used to obtain ground truth pedestrian counts. Specifically, the number of people passing through the green-highlighted area shown in Figure 4 was counted from the camera footage⁶, enabling simultaneous collection of radio signal data and pedestrian data.

Figure 4 Experimental Environment (Sophia University Yotsuya Campus)

(2) Experimental Results

Figure 5 Trends in Pedestrian Count and Moving Variance of RSSI
Note. The blue line represents the number of pedestrians over time, while the pink line indicates the moving variance of the Received Signal Strength Indicator (RSSI). The left vertical axis corresponds to the pedestrian count, and the right vertical axis corresponds to the RSSI moving variance. Gray-shaded areas indicate class hours, and white-shaded areas indicate break times.

Figure 6 Pedestrian Count Estimation Results

Roles of Each Organization

NTT:
Developed the wireless sensing system to measure synchronization signals transmitted.
From operational commercial base stations. Verified the effectiveness of wireless sensing technology using deep learning.

Sophia University:
Provided the experimental environment (Yotsuya Campus, Sophia University), performed pedestrian counting using cameras, and created the pedestrian count dataset. Evaluated useful RSSI features for estimating pedestrian numbers.

Future Developments

This demonstration successfully showcased the practicality of ISAC (Integrated Sensing and Communications), where sensing can be achieved solely through communication devices within the coverage area of commercial base station signals. Since the wireless sensing system developed in this study can utilize both 4G and 5G signals, ISAC can be demonstrated in real-world environments even before the rollout and widespread adoption of 6G.

Moving forward, we will continue to conduct ISAC demonstration experiments for various real-life use cases using commercial 4G and 5G signals, further advancing ISAC technology research and development. The results obtained will be submitted to 3GPP to promote the standardization and practical implementation of ISAC in 6G. In addition, we plan to explore technologies for integrating and analyzing synchronization signals from base stations of multiple mobile operators, aiming to further enhance sensing accuracy.

Our goal is to verify and explore service applications using ISAC technology toward its realization around 2030, when 6G is expected to be widely deployed.

[Glossary]

¹Wireless Japan × Wireless Technology Park
https://wjwtp.jp/2025/en/

²6G: Abbreviation for the sixth-generation mobile communication system, a next-generation wireless standard expected to offer ultra-high-speed, ultra-low-latency, and massive connectivity.

³ITU-R: The Radiocommunication Sector of the International Telecommunication Union (ITU), a specialized agency of the United Nations. ITU-R is responsible for revising the Radio Regulations (international rules for radio communication), studying technical and operational issues, issuing recommendations, and allocating/registering radio frequencies.

⁴IMT2030 (6G): Stands for International Mobile Telecommunications 2030, the proposed standard for 6G mobile communication technologies.

⁵3GPP: Abbreviation for the 3rd Generation Partnership Project, a global collaborative project that develops international standards for mobile communication systems.

⁶Approved by the Human Research Ethics Committee at Sophia University (Application No.: 2023-152, 2024-202).

⁷Labeled data used for training machine learning models. In this demonstration, observed values (RSSI and CSI) were matched with labels (pedestrian counts derived from camera images) based on timestamps.

⁸Overfitting: A condition in machine learning where the model performs well on the training data but poorly on unseen data. This often occurs when a model is overly complex relative to the amount and variability of the training data.

About NTT

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