DeepMARA - Deep Reinforcement Learning based Massive Random Access Toward Massive Machine-to-Machine Communications
Lead Research Organisation:
Imperial College London
Department Name: Electrical and Electronic Engineering
Abstract
Communication technologies have achieved remarkable success over the last decades - today we can connect almost 7 billion
people at any time from almost anywhere in the world, we can stream YouTube videos on-the-go, or have video conferences on our
mobile devices. These achievements were part of science fiction literature not long time ago. Today, we need to design the
communication technologies that can realise our current dreams: Can we connect over 30 billion intelligent devices over the same
network infrastructure that serves us today? Can we connect all these devices to enable reliable healthcare services to all the people
at any time anywhere in the world, or to regulate traffic flow and to coordinate autonomous cars? Can we, not only stream prerecorded
videos, but provide seamless AR/VR experience on mobile devices? These are only a few applications of massive machineto-
machine (M2M) communications (one of the main enablers of massive Internet of things (IoT)). Laying the theoretical and
algorithmic foundations of these future technologies is the core ambition of this project.
M2M devices are typically only sporadically active and transmit at low data rates. Since it is impossible to coordinate the transmission
of such devices, random access-based solutions are needed to enable their connectivity. With these drastically different requirements,
it is imperative to design novel massive random access (MRA) solutions for use in future M2M communication systems. The ability of
machine learning (ML) approaches such as deep reinforcement learning (DRL) in orchestrating multiple agents to achieve a common
goal in an uncoordinated manner, makes them the right tools to achieve this goal. Specifically, the aim of this project is to devise
smart transmission strategies that combine the collision avoidance capability of DRL-based solutions with collision resolution
capability of some MRA algorithms to make future massive M2M communication systems realisable.
people at any time from almost anywhere in the world, we can stream YouTube videos on-the-go, or have video conferences on our
mobile devices. These achievements were part of science fiction literature not long time ago. Today, we need to design the
communication technologies that can realise our current dreams: Can we connect over 30 billion intelligent devices over the same
network infrastructure that serves us today? Can we connect all these devices to enable reliable healthcare services to all the people
at any time anywhere in the world, or to regulate traffic flow and to coordinate autonomous cars? Can we, not only stream prerecorded
videos, but provide seamless AR/VR experience on mobile devices? These are only a few applications of massive machineto-
machine (M2M) communications (one of the main enablers of massive Internet of things (IoT)). Laying the theoretical and
algorithmic foundations of these future technologies is the core ambition of this project.
M2M devices are typically only sporadically active and transmit at low data rates. Since it is impossible to coordinate the transmission
of such devices, random access-based solutions are needed to enable their connectivity. With these drastically different requirements,
it is imperative to design novel massive random access (MRA) solutions for use in future M2M communication systems. The ability of
machine learning (ML) approaches such as deep reinforcement learning (DRL) in orchestrating multiple agents to achieve a common
goal in an uncoordinated manner, makes them the right tools to achieve this goal. Specifically, the aim of this project is to devise
smart transmission strategies that combine the collision avoidance capability of DRL-based solutions with collision resolution
capability of some MRA algorithms to make future massive M2M communication systems realisable.
