| 摘要 |
A network system providing highly reliable transmission quality for delay-sensitive applications with online learning and cross-layer optimization is disclosed. Each protocol layer is deployed to select its own optimization strategies, and cooperates with other layers to maximize the overall utility. This framework adheres to defined layered network architecture, allows layers to determine their own protocol parameters, and exchange only limited information with other layers. The network system considers heterogeneous and dynamically changing characteristics of delay-sensitive applications and the underlying time-varying network conditions, to perform cross-layer optimization. Data units (DUs), both independently decodable DUs and interdependent DUs, are considered. The optimization considers how the cross-layer strategies selected for one DU will impact its neighboring DUs and the DUs that depend on it. While attributes of future DU and network conditions may be unknown in real-time applications, the impact of current cross-layer actions on future DUs can be characterized by a state-value function in the Markov decision process (MDP) framework. Based on the dynamic programming solution to the MDP, the network system utilizes a low-complexity cross-layer optimization algorithm using online learning for each DU transmission.
|
