Cognition-based networks: Applying cognitive science to multimedia wireless networking
Leonardo Badia, Daniele Munaretto, Alberto Testolin, Andréa Zanella, Marco Zorzi, Michele Zorzi · 2014
Several techniques for wireless networking, such as opportunistic spectrum access, or self-healing networks, may be seen as using a form of cognition, meaning that they mimic reasoning processes of intelligent beings. We propose to expand this cognition-based process by exploiting the parallel processing power of the infrastructure, so as to go beyond cognition as is meant by these approaches. We leverage novel approaches, taken from cognitive science and artificial intelligence, involving not only supervised but also unsupervised learning, and we envision their application to systems for video over wireless. The transmission of multimedia content, and its adaptation to the condition of the communication infrastructure, i.e., the wireless channel or the content delivery network, are envisioned as particularly critical steps for the development of latest generation mobile networks. For this scenario, we propose and evaluate a video classifier based on a Restricted Boltzmann Machine that tries to extract abstract features of videos from the analysis of the sizes of a few coded frames. These features can then be exploited by the communication network itself to optimize video transmission based on its content.