ML-Based RF Transceiver for Jamming Resilience
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Solution Overview
Problem
Conventional tactical radios using software defined radio (SDR) solutions are susceptible to jamming and interference in hostile environments, making them ineffective in scenarios with multiple deployments, and they struggle with dynamic resource allocation in contested and congested wireless networks.
Innovation Solution
A system and method utilizing machine learning to dynamically allocate radio frequency resources by evaluating the state of the RF network through a machine learning model, generating communication parameters, and modifying the model based on communication outcomes, enabling adaptive power control and frequency management in tactical and commercial wireless networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional SDR solutions are used in tactical radios, then the system is simple and easy to implement, but the system becomes susceptible to jamming and interference in hostile environments
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning model continuously monitors communication outcomes and spectrum environment, then adjusts communication parameters dynamically. The system evaluates the state of the RF network, generates updated parameters, and modifies the model based on results, creating a closed-loop feedback system that enhances resilience against jamming and interference while maintaining adaptive complexity management.
2Productivity
If dynamic resource allocation is implemented in congested wireless networks, then communication efficiency improves, but computational complexity and processing requirements increase
Solution Approach 1:
The machine learning model operates autonomously to evaluate RF network state, generate communication parameters, and allocate spectrum resources without requiring complex centralized control. The system self-adjusts by modifying the machine learning model based on communication results, enabling dynamic resource allocation that improves productivity while managing computational complexity through automated decision-making.
3Adaptability or versatility
If machine learning models are used for real-time communication parameter generation, then adaptability to changing conditions improves, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary evaluation of the RF network state using the machine learning model before actual communication occurs. By pre-generating communication parameters based on predicted network conditions and spectrum environment, the system reduces real-time processing requirements while maintaining high adaptability to changing conditions during actual communication operations.
Data Source
AI summary
Embodiments of the disclosure provide a system for operating a radio frequency (RF) network having a plurality of communication nodes. A network transceiver communicates with communication nodes in the RF network. A computing device coupled to the network transceiver performs actions including: evaluating a state of the RF network using a machine learning model, based on a spectrum environment and a communication objective, generating a set of communication parameters based on the state of the RF network, causing the network transceiver to communicate with the a communication node using the generated set of communication parameters, and modifying the machine learning model based on a result of causing the network transceiver to communicate with the communication node.