Cooperative cache in cognitive radio networks: A heterogeneous multi-agent learning approach

Abstract

Deploying distributed cache in cognitive radio networks (CRNs), which spreads popular contents to the edge of network during the off-peak time through spectrum sharing, can reduce the deliver delay to users nearby without causing severe interference to the primary network. However, due to the un-predicable contents requirement as well as the band occupation of primary users, it is non-trivial to optimize the cache storage and contents fetching strategy of users dynamically. The letter proposes a heterogeneous multi-agent deep deterministic policy gradient (MADDPG) approach, which takes users and cache servers as two different types of agents to learn the cooperation and competition for mutual benefits. The numeral simulation demonstrates that comparing with the other single or homogeneous deep reinforcement learning (DRL) approaches, the proposed heterogeneous MADDPG can further reduce the delivery delay of users and enhance the cache efficiency of SBSs.

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