The Global Navigation Satellite System (GNSS) has become an invisible infrastructure. Smartphones, vehicles, logistics systems, emergency services, and even critical infrastructure are increasingly relying on readily available and reliable satellite positioning services.

However, this premise is facing increasing challenges. When GNSS signals reach the ground, they are already very weak: interference prevents receivers from picking up usable signals, and deception causes them to calculate incorrect positions. Moreover, in indoor areas, in containers, and in densely populated urban streets, satellite visibility is greatly reduced.

This is not just a theoretical risk. In 2025, the European Aviation Safety Agency (EASA) and the International Air Transport Association (IATA) reported that GNSS interference was intensifying in the European, Middle East, and Africa region (EMEA). IATA data showed that GPS signal loss incidents in this region increased by 220% between 2021 and 2024. In April 2024, the approach procedure at Tallinn Airport in Estonia relied on GPS, and two flights of Finnair were unable to land due to this interference. The route was subsequently suspended until the airport introduced a non-GNSS approach procedure. The Baltic Sea maritime authorities also reported that interference affecting ships was becoming increasingly frequent.

Now, location information has become a key input for the Industrial Internet of Things, logistics, and autonomous machine operation. In these scenarios, losing positioning capabilities can affect operational efficiency and even job safety. Therefore, the solution is not to replace GNSS with a single technology, but to no longer consider it as the sole positioning layer.

Transforming cellular networks into a positioning infrastructure

A practical alternative is already at hand: cellular networks. The radio measurement data generated by modern cellular networks can determine position without relying on satellites; it also has a widespread network of base stations, managed spectrum, network timing, and authenticated connections with billions of devices [Editor's note: Authentication refers to verifying whether a user has the right to access the system]. These resources allow wireless operators to provide a practical ground backup for GNSS.

Standardized 4G and 5G positioning signals can calculate position using the timing and geometric relationships of multiple base stations, without relying on GPS or Galileo and other satellite systems. Due to the different transmitters, frequencies, and signal paths used in the ground network, it forms a valuable difference from satellite navigation - when the satellite link fails, there is still another technical path available.

What operators still need to do includes enabling positioning signals, maintaining accurate base station information, and providing measurement or auxiliary data to devices; but the most difficult and costly part - deploying a wide-area wireless network - has already been completed.

For battery-powered IoT devices, cellular positioning is also highly attractive. Trackers often need to work continuously for several years, and they need to periodically determine the location of pallets, trailers, or containers. And activating a GNSS receiver consumes a lot of power. Since the devices are already connected to the network, cellular positioning becomes a more power-efficient alternative path.

From 4G positioning to 6G perception

The emergence of 4G was a significant milestone: LTE standardized dedicated positioning reference signals and provided methods to determine position independently using multiple base stations without relying on GNSS. Subsequent versions further improved these capabilities and extended the positioning function to simpler and less power-consuming devices.

5G continued to expand on this basis, introducing new uplink and downlink positioning signals, round-trip time (RTT) measurements, and angle-based positioning methods. Wider bandwidth, large-scale antenna arrays, and beamforming bring additional information, enabling the network to be utilized in factories, cities, highways, and other environments where GNSS performs poorly.

5G-Advanced continues this evolution, continuously improving in terms of accuracy, latency, integrity, and low-power operation, and adding new features such as sidelink positioning between nearby devices. Meanwhile, 3GPP is expanding research in artificial intelligence, machine learning, and integrated perception (ISAC). Here is a distinction to be made: positioning and perception are two different things; positioning is determining the location of connected devices, while perception is allowing wireless infrastructure to detect movement or objects by interpreting the surrounding radio environment.

Towards 6G, the direction is clear: build a network that integrates communication, positioning, and perception in a collaborative design. Artificial intelligence may increasingly participate in this process, making trade-offs and fusions based on the environment and application scenarios between terrestrial cellular networks, non-terrestrial networks (NTN), GNSS, and local sensor data. Ultimately, the network will no longer merely provide device positioning but will have a more extensive and continuously updated understanding of the surrounding physical environment.

Expanding backup layers with non-terrestrial networks

Non-terrestrial networks extend this model beyond traditional base station coverage. Using satellites and high-altitude platforms, standardized cellular networks can cover oceans, remote transportation corridors, and sparsely populated areas.

Devices can use ground positioning in areas where they can receive signals from multiple base stations; once beyond ground coverage, they switch to NTN measurements.

NTN is not immune to interference - it also relies on signals from space. Its value precisely lies in the differences themselves: satellite constellations, orbital heights, carrier frequencies, and waveforms are all different, so a single failure is unlikely to affect GNSS, terrestrial cellular networks, and NTN simultaneously.

Moving the positioning engine to the edge

Equally important is where these computations are performed. Traditional architectures typically upload the measurement data of devices to cloud platforms for processing; nowadays, the positioning engine is increasingly sinking to the devices themselves, directly using the measurement data from cellular networks to determine location locally.

Local processing not only reduces latency and reduces network traffic but also enables manufacturers and enterprises to better control sensitive location data. Moreover, even if cloud connections experience delays or interruptions, the devices can continue to work. Machine learning helps interpret signal reflections, constantly changing network conditions, and significant differences between indoor and outdoor radio environments; its adaptability is stronger compared to relying solely on static propagation models.

Resilience requires multiple layers of protection

GNSS is still indispensable, but for applications that cannot tolerate positioning interruptions, additional layers of protection are needed. A resilient system will combine GNSS, terrestrial cellular networks, NTN, sidelink, and local sensors based on environmental, accuracy, and power consumption requirements. Autonomous and semi-autonomous systems especially require this continuity: even if one positioning source fails, they must know where they are. For the application layer, the cause of the failure is not important; what matters is whether it can seamlessly switch to another reliable positioning source.

So far, the evolution path is clear: cellular networks are evolving from simple communication infrastructures to platforms that support positioning, perception, and location intelligence simultaneously. Ground networks, NTN, edge computing, and even future 6G will form a positioning layer that is increasingly complementary to GNSS. And most of the required infrastructure is already in place - wireless operators can provide the ground side, and NTN extends this resilience beyond traditional coverage. Therefore, for mission-critical applications, the question is no longer whether GNSS can work properly, but what to do when it fails.

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