Modern counter-UAS systems rely heavily on radar, radio-frequency detection, optical and infrared sensors, and acoustics. These are essential components of any layered defence, yet they share inherent limitations: line-of-sight dependency, restricted coverage, susceptibility to clutter, and difficulty discriminating drones from background activity.

Mobile network signalling analysis—specifically LTE S1 and 5G NG signalling—introduces a complementary detection layer that overcomes many of these constraints. When a drone uses LTE/5G for command, telemetry or video, the mobile network sees it continuously, irrespective of terrain, visibility or RF masking. Integrating this data provides unique opportunities for detection, attribution and reconstruction that conventional C-UAS cannot deliver alone.

The following sections outline the principal operational gaps in conventional C-UAS, and how signalling-level analytics address each one.

Wide-Area and National Coverage

Nationwide monitoring for LTE/5G-connected drones

Gap: Conventional C-UAS sensors operate only where installed; achieving national coverage is impractical.

Advantage: A single integration with major mobile operators provides effective visibility across the entire LTE/5G footprint. The system can surface drone-like devices anywhere in the country, enabling strategic early warning.

Detection in remote or unsurveyed areas

Gap: Valleys, coastline, moorland and unpopulated borders often lack radar, RF or optical sensors.

Advantage: If LTE coverage exists, an LTE-controlled drone appears in network signalling. Signalling analysis exposes these devices without requiring local infrastructure.

Tracking long-range transit between sites

Gap: Drones moving between critical sites drop in and out of isolated sensor coverage.

Advantage: The mobile network records continuous cell-to-cell transitions. Signalling analysis reconstructs the entire journey, identifying origin, route and destination.

Airport and Airspace Protection

Detecting terrain-masked approaches

Gap: Hills, cuttings and river valleys allow drones to approach airports below radar and visual lines-of-sight.

Advantage: Terrain does not hide signalling. Airborne movement is evident well before local sensors gain sight.

Identifying drones staging in urban clutter

Gap: Buildings and infrastructure hide loitering drones from radar and cameras.

Advantage: An LTE device manoeuvring in tight, low-altitude patterns near the perimeter is immediately visible in signalling data.

Recognising unsafe or unauthorised BVLOS flights

Gap: Legacy C-UAS tuned for hobby-drone RF may miss LTE-only commercial or semi-professional drones operating close to controlled airspace.

Advantage: Signalling data highlights any drone-like LTE UE crossing or loitering near protected boundaries.

Detecting drones using LTE for video uplink

Gap: Small multirotors using only LTE uplink are invisible to RF scanners and often undetectable at range.

Advantage: Patterns of sustained uplink combined with airborne mobility provide a clear signature of aerial video transmission.

Post-incident reconstruction

Gap: Local sensors often record only fragments of an incursion.

Advantage: Historical signalling logs allow analysts to replay all drone-like activity during the incident to pinpoint flight paths and potential launch sites.

Critical Infrastructure and High-Value Sites

Stealth reconnaissance using LTE-only drones

Gap: Low, slow reconnaissance flights can evade radars, optical coverage and RF scanners.

Advantage: Signalling profiles reveal abnormal 3D movement and uplink usage around secure sites.

Monitoring long linear assets

Gap: Pipelines, railways and high-voltage lines are too extensive to protect with continuous sensors.

Advantage: Drone movement along the route is evident through successive handovers, enabling centralised tracking without physical deployment.

Operating in high-clutter industrial environments

Gap: Industrial RF noise and structural clutter degrade radar and RF detection.

Advantage: Licensed-band signalling is unaffected by local interference, providing clean detection signals.

Identifying malicious live streaming around events

Gap: Dense stadium environments complicate radar, RF and camera-based detection.

Advantage: Signalling reveals devices showing aerial patterns and disproportionate uplink throughput.

Borders, Maritime and Cross-Border Threats

Cross-border smuggling flights

Gap: Sparse sensors and reliance on line-of-sight create major blind zones.

Advantage: Roaming events, attachment patterns and airborne mobility make cross-border drone activity visible.

Low-altitude flights along river valleys

Gap: Terrain and foliage obscure drones from visual and radar detection.

Advantage: Network handovers along the valley expose the full corridor of movement.

Maritime launches near the coastline

Gap: Small vessels may sit outside radar and camera detection of small UAVs.

Advantage: As soon as the drone attaches to coastal LTE, its offshore and shoreward movement becomes observable.

Coordinated LTE-enabled swarms

Gap: Large numbers of small drones overwhelm local sensors.

Advantage: Clusters of UEs demonstrating synchronised airborne patterns can be identified as collective swarm activity.

Military and Defence Applications

Drones using civilian LTE to evade jamming

Gap: Jamming aimed at ISM bands does not affect LTE-controlled drones.

Advantage: Signalling analysis re-establishes visibility of these threats even where RF countermeasures fail.

Red-team penetration testing

Gap: Exercises relying solely on conventional sensors may miss LTE-based threats, creating a false sense of protection.

Advantage: Signalling provides a ground truth view of all LTE-connected drones during testing.

Detecting ISR drones at standoff range

Gap: Hostile drones operating just beyond radar or optical range may remain undetected.

Advantage: Their LTE signalling remains visible even when physical detection is not.

Monitoring friendly fleets

Gap: Conventional sensors cannot determine whether a friendly drone is deviating from assigned missions.

Advantage: Authorised SIM profiles allow continuous behavioural monitoring for anomalies or hijack indicators.

Detecting aerial cellular relays

Gap: A drone acting as an IMSI catcher or rogue base station can be difficult to classify from radar alone.

Advantage: Signalling anomalies associated with drifting aerial cells provide a strong indication of hostile telecommunications activity.

Urban Policing and Public Safety

Short-burst drone flights in city environments

Gap: Urban canyons defeat radar, occlude cameras and saturate RF space.

Advantage: Even brief ascents and movements above street level appear clearly in signalling data.

Serial offenders using different launch points

Gap: Each incident appears isolated to fixed sensors.

Advantage: Signalling correlates repeated events to the same subscriber identity, supporting targeted investigation.

Contraband drops into prisons

Gap: LTE-only custom rigs may evade prison RF detection and camera coverage.

Advantage: Repetitive, short-duration flight patterns around prison rooftops are unambiguous in network data.

Monitoring compliance of authorised delivery drones

Gap: Conventional sensors struggle to distinguish legitimate from non-compliant flights.

Advantage: Integration of authorised SIMs allows automated detection of geofence breaches and unsafe behaviour.

Telecom-Specific and Analytical Capabilities

Pre-deployment vulnerability mapping

Gap: Sensor placement is often based on theory rather than observed activity.

Advantage: Historical signalling data reveals actual drone routes and hotspots, enabling intelligent C-UAS deployment.

Identifying drone hotspots across a region

Gap: Authorities often lack data on where to focus investment.

Advantage: Region-wide analysis produces heatmaps of drone-like behaviour without installing any field hardware.

Distinguishing drones from ground vehicles

Gap: Radar and RF tools can misclassify high-speed ground movement as aerial activity.

Advantage: Drone-specific mobility models differentiate true 3D flight from horizontal terrestrial motion.

Forensic SIM-based attribution

Gap: Conventional sensors confirm only that a drone was present.

Advantage: Signalling links movement patterns to specific IMSI/IMEI identifiers, enabling lawful investigative follow-up.

Correlating telecom anomalies with drone activity

Gap: Network operators and security teams typically operate in isolation.

Advantage: Joint analysis reveals when drones are causing interference or anomalous load on specific cells.

Detecting aerial IoT gateways or mesh relays

Gap: Malicious aerial relays show minimal RF control signatures.

Advantage: Unusual airborne movement paired with disproportionate connectivity to other devices signals airborne relay behaviour.

Summary

Mobile network signalling analysis is not a replacement for radar, RF, optical or IR sensors. Indeed, there are caveats and limitations with each approach. Instead, it provides a crucial dimension that these systems lack: persistent, infrastructure-wide visibility of any LTE/5G-connected drone, regardless of terrain, weather, RF clutter or line-of-sight.

By closing long-standing detection gaps and enabling both real-time alerts and retrospective analysis, signalling-based detection strengthens national security, protects critical infrastructure, supports aviation safety and enhances the effectiveness of integrated C-UAS operations.

Footnote: User-plane data (e.g. video uplinks) is included here based on being identifiable using signalling analysis.