Multi-Layer FEC for Jamming-Resilient Wireless Communications
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current communication systems are vulnerable to signal jamming and interference, leading to bit errors across various OSI layers, which cause data communication loss and are inadequately addressed by existing mitigation solutions focused on modulation and demodulation techniques.
Innovation Solution
A multi-layer forward error correction (FEC) system is implemented, utilizing machine learning models to identify interference types and deploy FEC configurations across multiple layers, including network, physical, and application levels, to mitigate jamming and reduce bit error rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current single-layer mitigation solutions (modulation/demodulation techniques) are used, then device complexity is reduced, but reliability deteriorates under jamming conditions
Solution Approach 1:
The communication system is segmented into multiple OSI layers (physical layer, network layer, application layer), each implementing independent forward error correction mechanisms. This segmentation allows targeted error correction at different protocol levels, improving overall reliability without requiring complete system redesign.
Solution Approach 2:
The patent applies a composite error correction approach by combining multiple FEC schemes across different OSI layers. Each layer contributes a different correction mechanism, creating a composite defense against jamming that is more robust than any single-layer solution alone.
2Reliability
If multi-layer FEC is deployed, then reliability improves under jamming, but device complexity increases
Solution Approach 1:
The machine learning model serves multiple functions: it identifies jamming types, selects appropriate FEC configurations, and optimizes parameters across different OSI layers. This multi-functionality reduces the need for separate control mechanisms, managing complexity while maintaining enhanced reliability.
Solution Approach 2:
The system implements self-service through automated machine learning-based detection and response to jamming conditions. The system autonomously selects and configures appropriate FEC schemes without manual intervention, reducing operational complexity while maintaining high reliability under attack.
3Measurement precision
If machine learning models are used for interference identification, then measurement precision improves, but use of energy increases
Solution Approach 1:
The machine learning model is pre-trained offline with extensive jamming and interference data, performing the computationally intensive learning phase before deployment. During actual operation, the pre-trained model requires minimal energy for inference, achieving high detection precision without excessive real-time energy consumption.
Data Source
AI summary
Interference signals are detected in a multi-layer wireless communication systems, which have a frequency spectrum that is captured by using a spectrum analyzer. Thereafter, an occurring interference type is identified based on image of the frequency spectrum. In response, an interference mitigation solution is engaged which corresponds to the identified interference type. The interference mitigation solution can simultaneously deploy or change one or more forward error correction (FEC) protocols on each of two or more of the layers along with other related system parameters. Related apparatus, systems, techniques and articles are also described.