Proactive Resource Allocation: Harnessing the Diversity and Multicast Gains

This paper introduces the novel concept of proactive resource allocation through which the predictability of user behavior is exploited to balance the wireless traffic over time, and hence, significantly reduce the bandwidth required to achieve a given blocking/outage probability. We start with a simple model in which the 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. This model is then generalized to incorporate the effect of the randomness in the prediction look-ahead time T. Remarkably, we also show that, in the cognitive networking scenario, the appropriate use of proactive resource allocation by the primary users improves the diversity gain of the secondary network at no cost in the primary network diversity. We also shed lights on multicasting with predictable demands and show that the proactive multicast networks can achieve a significantly higher diversity gain that scales super-linearly with T. Finally, we conclude by a discussion of the new research questions posed under the umbrella of the proposed proactive (non-causal) wireless networking framework

Matching Theory for Future Wireless Networks: Fundamentals and Applications

The emergence of novel wireless networking paradigms such as small cell and cognitive radio networks has forever transformed the way in which wireless systems are operated. In particular, the need for self-organizing solutions to manage the scarce spectral resources has become a prevalent theme in many emerging wireless systems. In this paper, the first comprehensive tutorial on the use of matching theory, a Nobelprize winning framework, for resource management in wireless networks is developed. To cater for the unique features of emerging wireless networks, a novel, wireless-oriented classification of matching theory is proposed. Then, the key solution concepts and algorithmic implementations of this framework are exposed. Then, the developed concepts are applied in three important wireless networking areas in order to demonstrate the usefulness of this analytical tool. Results show how matching theory can effectively improve the performance of resource allocation in all three applications discussed

Mind the social feedback : effects of tDCS applied to the left DLPFC on psychophysiological responses during the anticipation and reception of social evaluations

The left dorsolateral prefrontal cortex (lDLPFC) is implicated in anticipatory (i.e. during anticipation of emotional stimuli) and online (i.e. during confrontation with emotional stimuli) emotion regulatory processes. However, research that investigates the causal role of the lDLPFC in these processes is lacking. In this study, 74 participants received active or sham transcranial direct current stimulation (tDCS) over the lDLPFC. Participants were told strangers evaluated them. These (rigged) social evaluations were presented, and in 50% of the trials, participants could anticipate the valence (positive or negative) of the upcoming social feedback. Pupil dilation (a marker of cognitive resource allocation) and skin conductance responses (a marker of arousal) were measured. The results indicate that active (compared to sham) tDCS reduced arousal during the confrontation with anticipated feedback but only marginally during the confrontation with unanticipated feedback. When participants were given the opportunity to anticipate the social feedback, tDCS reduced arousal, irrespective of whether one was anticipating or being confronted with the anticipated feedback. Moreover, tDCS reduced cognitive resource allocation during anticipation, which was associated with resource allocation increases during the subsequent confrontation. Altogether, results suggest that the lDLPFC is causally implicated in the interplay between anticipatory and online emotion regulatory processes