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In this paper, we leverage a multi-agent reinforcement learning (MARL)framework to jointly learn a computation offloading decision and multichannelaccess policy with corresponding signaling. Specifically, the base station andindustrial Internet of Things mobile devices are reinforcement learning agentsthat need to cooperate to execute their computation tasks within a deadlineconstraint. We adopt an emergent communication protocol learning framework tosolve this problem. The numerical results illustrate the effectiveness ofemergent communication in improving the channel access success rate and thenumber of successfully computed tasks compared to contention-based,contention-free, and no-communication approaches. Moreover, the proposed taskoffloading policy outperforms remote and local computation baselines.

Emergent Communication Protocol Learning for Task Offloading in Industrial Internet of Things