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.
A comprehensive overview on 5G-and - beyond networks with UA Vs: From communications to sensing a nd intelligence,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.IT 1years
2024 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
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.