A neural-network-based MF-TDMA MAC scheduler for collaborative wireless networks

In the unlicensed spectrum, many wireless technologies (e.g Wi-Fi, Bluetooth) use the same spectrum for wireless transmission. This often results in cross-technology interference effects, which are hard to address. Without new methods to manage this shared spectrum, wireless communication is increasingly challenging as too many nodes attempt at accessing the same spectrum. Collaboration between different wireless networks that use the same spectrum will be required to handle this massive amount of devices. In this paper, we present two algorithms based on Neural Networks (NNs) to demonstrate that a function approximation can accurately predict free slots in a Multiple Frequencies Time Division Multiple Access (MF-TDMA) network. By observing the spectrum, we are able to do online learning and let the corresponding NN predict the behavior of the spectrum a second in advance using our approach. We are able to reduce the number of collisions by half if the nodes from other networks are sending data following a Poisson distribution. When the nodes of the other network follow a more periodic traffic pattern, a collision reduction of factor 15 could be achieved.