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.

翻译：本文针对配备可重构智能表面（RIS）的6G网络，提出了一种用于无线边缘的节能、低延迟、高精度推理的新型算法。我们考虑一个场景：新数据由一组设备持续生成/收集，并通过动态排队系统进行处理。基于李雅普诺夫随机优化与深度强化学习（DRL）的结合，我们设计了一种动态学习算法，该算法联合优化数据压缩方案、无线资源分配（即功率、传输预编码）、计算资源（即CPU周期）以及RIS反射参数（即相移），旨在满足端到端（E2E）时延和推理精度约束的条件下，实现节能的边缘分类。所提出的策略能够实现系统及无线传播环境的动态控制，在每时隙上进行低复杂度的优化，同时处理时变无线信道和未知统计特性的任务到达。数值结果评估了所提出的RIS赋能边缘推理策略在分类任务的能量、时延与精度之间的权衡性能。