Directional Multicast Routing in Mobile Networks Through Tracking of Directional Interference Dynamics

Abstract

This research targets interference-resistant routing in airborne multicast networks with directional antennas (DAs). Multicast communications are important in one-to-many (1:M) message deliveries. Wireless multicast protocol design is more challenging than conventional wired multicast networks, due to the dynamic network topology caused by the frequent node mobility. On the other hand, the deploying of DAs in each node enhances the quality due to its well-focused wireless transmissions. However, when DA is equipped in each node of the multicast tree, the signal interference becomes more difficult to handle than in the case of omnidirectional networks, due to the special features of directional interference (DI), which varies with the parent-children spatial relationship in the multicast tree. The DI becomes more challenging when the multicast tree gets too large (such that it must be decomposed into multiple subtrees). Both intra- and inter-subtree DI cases exist, and different strategies are required to overcome the impacts of the interference. In this research, we propose a predictive multicast DI avoidance scheme, which adopts spatio-temporal deep learning with up/down sampling to perform both spatial and temporal DI evolution prediction. Such predicted DI patterns in intra-/inter-subtrees will be used to design a directional multicast cross-layer (routing/MAC) protocol. Such a cross-layer protocol design is further enhanced with a bio-inspired scheme, i.e., an enhanced ant colony optimization (ACO) algorithm. Our simulation results verify the efficiency of the DI-prediction-based cross-layer design in the directional multicast networks.

Keywords

• Cross-layer design
• directional antennas
• deep learning
• directional interference
• multicast communications