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Using predictions to reduce complexity and feedback in wireless communications

Sujet proposé par
Directeur de thèse:
Doctorant: Eleftherios LAMPIRIS
Unité de recherche UMR 7102 Laboratoire de recherche d'EURECOM

Domaine: Sciences et technologies de l'information et de la communication

Projet

Wireless communications are currently exhibiting a giant leap in volume and societal impact, but are also facing a massive environmental challenge in the form of a carbon footprint that matches that of global aviation, and which will triple by 2020. This challenge has spurred worldwide research to produce radically new power-efficient high-performance environmentally-friendly communication technologies. However, the efforts have encountered two seemingly insurmountable bottlenecks; the bottleneck of computational complexity corresponding to the need for algorithms that require extreme computing resources, and the bottleneck of feedback corresponding to the need for equally idealistic feedback mechanisms that must disseminate massive amounts of overhead information about the fluctuating states of each link in the network. These bottlenecks drive our theoretical vision: We will provide a never-before-attempted exploration of the crucial interdependencies between computational complexity, feedback and performance in wireless communications. They also drive our technological vision: We will develop algorithms for a new class of mobile-user devices that can participate in properly gathering/disseminating feedback (at the right place and time) as well as in computing solutions to outsourced algorithmic tasks across the network, in an effort which we term as “outsourcing the surgical insertion of bidirectional bits and flops across the network” and which aims to reduce computational complexity and improve performance.

Enjeux

We will take a novel approach, which drives our vision. A recent result of ours has revealed the surprising fact that – for a simple point-to-point setting – a single bit of feedback from the receiver back to the transmitter (properly placed in time, and properly representing the predicted flop count), managed to massively reduce the computational complexity of transceiver algorithms. This reduction was a surprising finding, and it was traced back to the newly-found ability of feedback to `skew’ the statistics of the accumulation of computational load, without negatively skewing the statistics defining performance. Key to our approach will be predictions, which will enable coded caching. Perhaps the strongest reason to jointly consider coded caching and feedback, comes from the possibility that coded caching may be able to alleviate the constant need to gather and distribute CSIT, which
given typical coherence durations
is an intensive task that may have to be repeated hundreds of times per second during the transmission of content. This suggests that content prediction of a predetermined library of files during the night (off peak hours), and a subsequent distribution of parts of this library content again during the night, may go beyond boosting performance, and may in fact offer the additional benefit of alleviating the need for prediction, estimation, and communication of CSIT during the day, whenever requested files are from the library. The idea of exploring the interplay between feedback and coded caching, hence draws directly from this attractive promise that content prediction, once a day, can offer repeated and prolonged savings in CSIT.