Learning for Cross-layer Optimization

Cross-layer optimization solutions have been proposed in recent years to improve the performance of network users operating in a time-varying, error-prone wireless environment. However, these solutions often rely on ad-hoc optimization approaches with known environmental dynamics experienced at various layers by a user and violate the layered network architecture of the protocol stack. This paper presents a new theoretic foundation for cross-layer optimization, which allows each layer to autonomously learn the environmental dynamics, while maximizing the utility of the wireless user by optimally determining what information needs to be exchanged among layers. Hence, this cross-layer framework does not change the current layered architecture. The experimental results demonstrate that the proposed layered learning framework achieves nearoptimal performance.