Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB traffic

TL;DR

This paper introduces a deep reinforcement learning approach using PPO to dynamically allocate resources for URLLC traffic over a fully occupied eMBB grid, ensuring low latency and minimal outage.

Contribution

It presents a novel DRL-based resource slicing method that effectively manages URLLC and eMBB coexistence by puncturing eMBB codewords without violating latency.

Findings

DRL policy maintains URLLC latency requirements

Minimizes eMBB codeword outages compared to existing schemes

Uses PPO for dynamic and efficient resource allocation

Abstract

With the advent of 5G and the research into beyond 5G (B5G) networks, a novel and very relevant research issue is how to manage the coexistence of different types of traffic, each with very stringent but completely different requirements. In this paper we propose a deep reinforcement learning (DRL) algorithm to slice the available physical layer resources between ultra-reliable low-latency communications (URLLC) and enhanced Mobile BroadBand (eMBB) traffic. Specifically, in our setting the time-frequency resource grid is fully occupied by eMBB traffic and we train the DRL agent to employ proximal policy optimization (PPO), a state-of-the-art DRL algorithm, to dynamically allocate the incoming URLLC traffic by puncturing eMBB codewords. Assuming that each eMBB codeword can tolerate a certain limited amount of puncturing beyond which is in outage, we show that the policy devised by the DRL agent never violates the latency requirement of URLLC traffic and, at the same time, manages to keep the number of eMBB codewords in outage at minimum levels, when compared to other state-of-the-art schemes.