Automatic Learning-based MANET Cross-Layer Parameter Configuration

Mobile ad hoc networks (MANETs) operate in highly dynamic environments with limited resources. Current approaches to network configuration are static and ad-hoc, and therefore frequently perform extremely poorly. We describe our approach to network configuration control that relies on automatically learning the relationships among configuration parameters and maintains near-optimal configurations adaptively, even during highly dynamic missions. We present a case study demonstrating the feasibility of the approach.