Collaborative Caching Mechanism of Nodes Based on Cooperative Game and Deep Learning
The collaborative caching between nodes is utilized to achieve content sharing and reduce the network burden in order to solve the increasing contradiction between the user's increasing content demand and limited network resourcesWe model the collaboration caching problem as a cooperative game by considering the factors of interaction costs and individual rationality to optimize the system utility with limited cache space.According to whether the utility between nodes can be transferred,we discuss the cooperative game in two cases.Under the transferable utility game,the conditions for achieving a stable grand coalition are derived.For non-transferable utility game,the rational nodes cannot ensure the formation of a stable grand coalition,and the number of formable coalitions increases dramatically with the number of users.Therefore,to ensure the formation of stable coalitions within a limited time,a deep reinforcement learning-based coalition formation algorithm is proposed.Both theoretical analysis and simulation results demonstrate that the proposed algorithm can converge to a Nash-stable optimal solution or asymptotically optimal solution,which outperforms other known comparison algorithms.
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