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We investigate the coexistence of task-oriented and data-orientedcommunications in a IoT system that shares a group of channels, and study thescheduling problem to jointly optimize the weighted age of incorrectinformation (AoII) and throughput, which are the performance metrics of the twotypes of communications, respectively. This problem is formulated as a Markovdecision problem, which is difficult to solve due to the large discrete actionspace and the time-varying action constraints induced by the stochasticavailability of channels. By exploiting the intrinsic properties of thisproblem and reformulating the reward function based on channel statistics, wefirst simplify the solution space, state space, and optimality criteria, andconvert it to an equivalent Markov game, for which the large discrete actionspace issue is greatly relieved. Then, we propose a Whittle's index guidedmulti-agent proximal policy optimization (WI-MAPPO) algorithm to solve theconsidered game, where the embedded Whittle's index module further shrinks theaction space, and the proposed offline training algorithm extends the trainingkernel of conventional MAPPO to address the issue of time-varying constraints.Finally, numerical results validate that the proposed algorithm significantlyoutperforms state-of-the-art age of information (AoI) based algorithms underscenarios with insufficient channel resources.

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