Energy-­efficient massive random access for real­-time distributed autonomous systems

To support the Internet-of-Things (IoT) vision of enabling distributed autonomous systems able to operate in real time, we need a new wireless infrastructure, providing connectivity to a massive number of sporadically active and energy-limited devices, which access the wireless medium in an uncoordinated fashion.

Indeed, current wireless systems are provably unable to provide low-latency, energy-efficient communications in the presence of massive uncoordinated interference.To achieve this crucial and timely objective, we will obtain an information-theoretic characterization of the maximum energy efficiency at which quality-of-service targets that are relevant for real-time decision making can be achieved.

Guided by the insights provided by this characterization, we will then design novel, low-complexity, massive random-access protocols, able to operate close to the predicted theoretical limits.

Our approach involves the following four novel elements: we willexplicitly account, via the use of nonasymptotic tools from information theory, for the small payload size of IoT packets;address practically relevant IoT scenarios, in which the devices have heterogeneous requirements in terms energy efficiency and quality of service;consider new metrics beyond packet error probability, which take into account the value of the information carried by each packet;use techniques from machine learning, to disentangle packet collisions by exploiting commonly-observed traffic patterns.