Coalitional Game-guided Reinforcement Learning for P2P Resource Trading in Sliced IIoT Networks

Abstract

The industrial Internet of Things (IIoT) and network slicing (NS) paradigms are key enablers of the industrial revolution in current and future mobile networks. However, peer-to-peer (P2P) resource blocks (RBs) exchange to match supply and demand in sliced IIoT networks requires proper incentivization and renegotiations between the service providers (SPs). This paper models the business strategic interactions between seller and buyer SPs as a coalitional game in which sellers form coalitions to set RB prices and buyers join coalitions to determine their best-response RB demand. The aim is to maximize the profit of the seller coalition and minimize the expenses of the buyer coalition while jointly contributing to maximize system RB utilization. Due to the uncertainty of network traffic, we propose a coalitional game-guided multiagent reinforcement learning approach that takes the output of the coalitional game as the starting Nash equilibrium (NE) and computes the optimal price and demand strategies of the coalitions regardless of network condition changes. Simulation results and analysis prove the efficacy of the proposed approach in terms of optimizing seller and buyer coalition payoffs, as well as maximizing the overall RB utilization.