Resilient wireless networks and intelligent tactical communications

Background and problem statement

This research addresses the growing need for reliable, secure, adaptive and resilient wireless communications in extreme, dynamic and unpredictable environments. It applies directly to critical fields such as defence, public safety and emergency response.

In these environments, tactical networks must maintain their performance despite intentional jamming, severe interference, high user mobility, partially damaged infrastructure, spectrum scarcity, limited computing resources, intermittent connectivity and cyber-physical threats.

Any communication failure can lead to decision-making delays, degraded situational awareness and reduced operational efficiency, with direct impacts on operational safety.

Research objective

The objective is to design wireless networks capable of automatically adapting to hostile and dynamic environments, while maintaining reliable and high-performance communication for critical systems.

Professor of applied sciences, smiling, wearing a light blazer, in front of a modern building, conveys a welcoming and professional atmosphere.

Georges Kaddoum holds several research chairs and serves as Director of Research at the Resilient Machine Learning Institute (ReMI). He is also a member of the Royal Society of Canada.

Proposed approach

The research develops AI-based resilient wireless network solutions for tactical systems and critical infrastructure.

This approach combines:

Distributed intelligence
Edge learning
Quantum-assisted optimization
Cross-layer design to improve spectrum sensing, channel access, radio resource management, routing and overall network resilience

These mechanisms make it possible to improve spectrum sensing, channel access, radio resource management, routing and overall network resilience.

Innovation and key differentiators

The approach distinguishes itself from conventional architectures through its real-time adaptation capability in dynamic and hostile environments.

Unlike centralized or static systems, the proposed solutions rely on intelligent, distributed mechanisms capable of reacting to interference, jamming, radio channel variations and operational constraints.

They also enable advanced coordination between mobile platforms, notably UAVs (Unmanned Aerial Vehicles) and UGVs (Unmanned Ground Vehicles).

Expected benefits

Improved communication reliability
Reduced latency
Better utilization of the radio spectrum
Increased resistance to intelligent jamming
Improved packet delivery rate
Optimized energy efficiency
Better situational awareness

Target sectors

Defence and national security
Public safety and emergency response
Critical infrastructure
Telecommunications and 6G networks
Autonomous systems (UAV, UGV)
Industrial networks and intelligent transportation

Results and validation

The solutions have been validated through simulation in tactical and critical scenarios. The results notably show:

+6.5% packet delivery rate
-32.5% end-to-end delay
Improved resilience against cross-layer jamming
Detection accuracy greater than 85%
Performance greater than 90% of the maximum throughput in degraded conditions

A portion of the technologies is currently being commercialized through industrial collaborations, while others are in the advanced prototyping phase.

Research challenges

The main challenge consists of validating these systems under realistic operational conditions simultaneously combining:

jamming and interference
high mobility
damaged infrastructure
cyber threats
spectrum constraints
processing power limits
strict latency constraints

Technology readiness level (TRL)

The work covers technologies ranging from advanced prototyping to experimental demonstrations, with potential for testbed validation, field experimentation and industrial transfer within a 1-to-3-year horizon.

Project acceleration needs