Proactive resource allocation: Harnessing the diversity and multicast gains

This paper introduces the novel concept of proactive resource allocation for wireless networks, through which the predictability of user behavior is exploited to balance the wireless traffic over time, and significantly reduces the bandwidth required to achieve a given blocking/outage probability. We start with a simple model in which smart wireless devices are assumed to predict the arrival of new requests and submit them to the network $T$ time slots in advance. Using tools from large deviation theory, we quantify the resulting prediction diversity gain to establish that the decay rate of the outage event probabilities increases with the prediction duration $T$. Remarkably, we also show that, in the cognitive networking scenario, the appropriate use of proactive resource allocation by primary users improves the diversity gain of the secondary network at no cost in the primary network diversity. We also shed light on multicasting with predictable demands and show that proactive multicast networks can achieve a significantly higher diversity gain that scales superlinearly with $T$. Finally, we conclude by a discussion of the new research questions posed under the umbrella of the proposed proactive wireless resource framework.