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CORR
2008
Springer

Decomposition Principles and Online Learning in Cross-Layer Optimization for Delay-Sensitive Applications

8 years 2 months ago
Decomposition Principles and Online Learning in Cross-Layer Optimization for Delay-Sensitive Applications
In this paper, we propose a general cross-layer optimization framework in which we explicitly consider both the heterogeneous and dynamically changing characteristics of delay-sensitive applications and the underlying time-varying network conditions. We consider both the independently decodable data units (DUs, e.g. packets) and the interdependent DUs whose dependencies are captured by a directed acyclic graph (DAG). We first formulate the cross-layer design as a non-linear constrained optimization problem by assuming complete knowledge of the application characteristics and the underlying network conditions. The constrained cross-layer optimization is decomposed into several cross-layer optimization subproblems for each DU and two master problems. These two master problems correspond to the resource price update implemented at the lower layer (e.g. physical layer, MAC layer) and the impact factor update for neighboring DUs implemented at the application layer, respectively. The propos...
Fangwen Fu, Mihaela van der Schaar
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2008
Where CORR
Authors Fangwen Fu, Mihaela van der Schaar
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