Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered
  by Reconfigurable Intelligent Surfaces
Kyriakos Stylianopoulos Mattia Merluzzi Paolo Di Lorenzo George C. Alexandropoulos
In this paper, we propose a novel algorithm for energy-efficient,
low-latency, accurate inference at the wireless edge, in the context of 6G
networks endowed with reconfigurable intelligent surfaces (RISs). We consider a
scenario where new data are continuously generated/collected by a set of
devices and are handled through a dynamic queueing system. Building on the
marriage between Lyapunov stochastic optimization and deep reinforcement
learning (DRL), we devise a dynamic learning algorithm that jointly optimizes
the data compression scheme, the allocation of radio resources (i.e., power,
transmission precoding), the computation resources (i.e., CPU cycles), and the
RIS reflectivity parameters (i.e., phase shifts), with the aim of performing
energy-efficient edge classification with end-to-end (E2E) delay and inference
accuracy constraints. The proposed strategy enables dynamic control of the
system and of the wireless propagation environment, performing a low-complexity
optimization on a per-slot basis while dealing with time-varying radio channels
and task arrivals, whose statistics are unknown. Numerical results assess the
performance of the proposed RIS-empowered edge inference strategy in terms of
trade-off between energy, delay, and accuracy of a classification task.