Coexistence between Task- and Data-Oriented Communications: A Whittle's Index Guided Multi-Agent Reinforcement Learning Approach

TL;DR

This paper introduces a novel multi-agent reinforcement learning approach guided by Whittle's index to optimize coexistence of task- and data-oriented communications in IoT systems, effectively managing shared channels and improving performance metrics.

Contribution

It develops a Whittle's index guided multi-agent proximal policy optimization algorithm for joint scheduling, addressing large action spaces and time-varying constraints in IoT communication systems.

Findings

The proposed WI-MAPPO algorithm outperforms existing AoI-based methods.

The approach effectively manages shared channels in resource-constrained IoT scenarios.

Numerical results demonstrate significant performance improvements.

Abstract

We investigate the coexistence of task-oriented and data-oriented communications in a IoT system that shares a group of channels, and study the scheduling problem to jointly optimize the weighted age of incorrect information (AoII) and throughput, which are the performance metrics of the two types of communications, respectively. This problem is formulated as a Markov decision problem, which is difficult to solve due to the large discrete action space and the time-varying action constraints induced by the stochastic availability of channels. By exploiting the intrinsic properties of this problem and reformulating the reward function based on channel statistics, we first simplify the solution space, state space, and optimality criteria, and convert it to an equivalent Markov game, for which the large discrete action space issue is greatly relieved. Then, we propose a Whittle's index guided multi-agent proximal policy optimization (WI-MAPPO) algorithm to solve the considered game, where the embedded Whittle's index module further shrinks the action space, and the proposed offline training algorithm extends the training kernel of conventional MAPPO to address the issue of time-varying constraints. Finally, numerical results validate that the proposed algorithm significantly outperforms state-of-the-art age of information (AoI) based algorithms under scenarios with insufficient channel resources.