A New Theoretic Foundation for Cross-Layer Optimization

TL;DR

This paper introduces a formal, layered Markov decision process framework for cross-layer optimization in wireless networks, enabling autonomous layer decisions while preserving the protocol stack architecture.

Contribution

It provides a new theoretical foundation that allows independent layer optimization through message exchange, avoiding ad-hoc methods and architectural violations.

Findings

Framework unifies existing algorithms as special cases.

Supports both offline and online optimization.

Enhances performance without altering layered architecture.

Abstract

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, which ignore the different environmental dynamics experienced at various layers by a user and violate the layered network architecture of the protocol stack by requiring layers to provide access to their internal protocol parameters to other layers. This paper presents a new theoretic foundation for cross-layer optimization, which allows each layer to make autonomous decisions individually, 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. Specifically, because the wireless user interacts with the environment at various layers of the protocol stack, the cross-layer optimization problem is formulated as a layered Markov decision process (MDP) in which each layer adapts its own protocol parameters and exchanges information (messages) with other layers in order to cooperatively maximize the performance of the wireless user. The message exchange mechanism for determining the optimal cross-layer transmission strategies has been designed for both off-line optimization and on-line dynamic adaptation. We also show that many existing cross-layer optimization algorithms can be formulated as simplified, sub-optimal, versions of our layered MDP framework.