Adaptive Cooperative Transmission Design for Ultra-Reliable Low-Latency Communications via Deep Reinforcement Learning

TL;DR

This paper proposes a deep reinforcement learning-based adaptive transmission scheme for two-hop cooperative wireless systems to meet ultra-reliable low-latency communication requirements, optimizing parameters for reliability and latency.

Contribution

It introduces a dual-agent reinforcement learning algorithm that adaptively configures transmission parameters for each hop in a cooperative system, enhancing URLLC performance.

Findings

Achieves near-optimal reliability under strict latency constraints.

Effectively adapts transmission parameters for each hop in real-time.

Outperforms traditional fixed-parameter schemes in simulations.

Abstract

Next-generation wireless communication systems must support ultra-reliable low-latency communication (URLLC) service for mission-critical applications. Meeting stringent URLLC requirements is challenging, especially for two-hop cooperative communication. In this paper, we develop an adaptive transmission design for a two-hop relaying communication system. Each hop transmission adaptively configures its transmission parameters separately, including numerology, mini-slot size, and modulation and coding scheme, for reliable packet transmission within a strict latency constraint. We formulate the hop-specific transceiver configuration as a Markov decision process (MDP) and propose a dual-agent reinforcement learning-based cooperative latency-aware transmission (DRL-CoLA) algorithm to learn latency-aware transmission policies in a distributed manner. Simulation results verify that the proposed algorithm achieves the near-optimal reliability while satisfying strict latency requirements.