5G as radar: the end of drone invisibility

Invisible infrastructure becomes vigilant

Over the last decade, the commercial narrative surrounding 5G focused almost exclusively on the end-user experience: ultra-fast download speeds and reduced latency for streaming or gaming. However, a recent technical demonstration led by Lockheed Martin, in collaboration with Verizon, Keysight, ODC, and Astris AI, using the Nvidia AI Aerial platform, has shifted this paradigm. 5G infrastructure is no longer a mere data conduit, but a distributed sensor capable of 'seeing' the environment. By monitoring micro-disturbances in radio frequency (RF) signals, cell towers can now identify flying objects, such as drones, with a precision that challenges the need for dedicated radars.

Historically, object detection has relied on dedicated active systems (X or S-band radars) that require expensive infrastructure, specific spectrum permits, and rigorous maintenance. Lockheed Martin's proposal represents a structural shift: leveraging the density of existing 5G networks to create a 'passive radar' at an urban scale. This advancement reflects a trend observed in other technologies, such as the use of WiFi signals to detect human presence, but taken to an industrial, high-precision scale.

How does network-based detection work?

The system, called NetSense, redefines the role of telecommunications infrastructure by treating the network as a bistatic or multistatic sensor. The technical process integrates several levels of advanced computing:

• RF disturbance capture: The 5G network constantly emits electromagnetic waves. Any object passing through these waves causes microscopic changes in the phase, amplitude, and time of arrival of the signal. NetSense captures these variations without the need for a dedicated radar signal.
• AI processing: The volume of data generated is immense. This is where Nvidia AI Aerial comes in, an accelerated computing platform that allows artificial intelligence algorithms to be executed directly within the Radio Access Network (RAN). This AI filters out ambient noise—such as the movement of trees or birds—to isolate the specific radio signature of a drone.
• Predictive tracking at the edge (Edge Computing): By processing data at the network edge, latency is drastically reduced. The system not only identifies the object but calculates its trajectory and speed predictively, allowing for real-time response to potential intrusions in protected spaces.

Industry impact and the future of 6G

This development is the tangible precursor to the ISAC (Integrated Sensing and Communication) standard, one of the cornerstones that will define the architecture of 6G networks. The convergence between sensing and communication allows for the optimization of radio spectrum usage, turning telecommunications assets into high-precision sensors for smart cities.

For companies, the impact is disruptive. Sectors such as logistics, airport security, and critical infrastructure management will be able to forgo additional radars, drastically reducing operational costs (OPEX). However, this evolution poses a security challenge: the same infrastructure that protects a perimeter can be used for espionage or non-consensual surveillance if robust safeguards are not implemented.

Comparatively, this technological leap is reminiscent of the transition from analog to digital radar, but with a key difference: ubiquity. While a traditional radar has a limited range and a fixed position, a dense 5G network acts as an omnipresent surveillance mesh. The telecommunications industry thus becomes a security provider, a business pivot that operators like Verizon are already beginning to monetize through value-added services (VAS).

Considerations and speculations

Despite the technical optimism, the commercial viability of NetSense faces significant obstacles. The main concern lies in privacy. If the network can detect a drone, it could technically detect any moving object, including people, which raises an ethical debate about mass surveillance in public spaces. Although not confirmed, it is speculated that the first implementations will be strictly limited to closed environments, such as airports, industrial plants, and military bases, where the need for security prevails over public privacy.

Furthermore, there is a regulatory challenge: who is responsible if a communications network fails during a security operation? The distinction between a data network and a critical security system is blurred, and aviation authorities will need to establish clear regulatory frameworks. In conclusion, while the technology is already functional, its massive deployment in public commercial networks is a matter of governance and social acceptance, rather than technical limitations.