Empirical Aspects of Environment and Intent Specific Adaptive PHY via Deep Learning
Abstract
The design of error-correcting codes for reliable transmission in noisy channels is a fundamental building block of physical layer communications. Traditional code and decoder designs are optimized for fixed environments such as AWGN or fading channels. However, real-world scenarios are dynamic, with changing factors like fading processes, interference patterns, and spectrum availability. Additionally, evolving communication needs influenced by application and command layer decisions, such as varying data rates and latency, present further challenges. Current PHY layer adaptations to these changes are slow and inefficient, often hampered by inflexible encoding and decoding designs and the separation between different layers. In this project, we apply data-driven learning to adapt the channel decoders (and encoders) to varying environmental conditions and the evolving intent of upper command and application layers. Specifically, our approach involves leveraging machine learning to enhance PHY adaptivity through low-dimensional parameterization and fine-tuning using data from various channel conditions and upper-layer needs, aiming to design a PHY layer that is closely aligned with the environment and command intent and demonstrate the proposed PHY in an over-the-air environment. The challenge lies in creating efficient, lightweight, and data-adaptive PHY schemes. This proposal expands upon our recent success in developing efficient and reliable data-driven PHY using deep learning techniques.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 08, 2024
- Source ID
- N000142412542
Entities
People
- Hyeji Kim
Organizations
- Office of Naval Research
- United States Navy
- University of Texas at Austin