A fuzzy-logic-enhanced multi-agent reinforcement learning framework for joint access point selection, precoding, and reconfigurable intelligent surface phase design is shown in simulation to improve energy efficiency in RIS-aided cell-free massive MIMO.
User-centric 5G cellular networks: Resource allocation and comparison w ith the cell-free massive MIMO approach,
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Joint Precoding and AP Selection for Energy Efficient RIS-aided Cell-Free Massive MIMO Using Multi-agent Reinforcement Learning
A fuzzy-logic-enhanced multi-agent reinforcement learning framework for joint access point selection, precoding, and reconfigurable intelligent surface phase design is shown in simulation to improve energy efficiency in RIS-aided cell-free massive MIMO.