Energy Efficiency Optimization for SWIPT-Based D2D-Underlaid Cellular Networks Using Multiagent Deep Reinforcement Learning.
Sengly MuyDara RonJung-Ryun LeePublished in: IEEE Syst. J. (2022)
Keyphrases
- energy efficiency
- cellular networks
- multi agent
- energy consumption
- reinforcement learning
- base station
- wireless sensor networks
- power consumption
- energy efficient
- multi hop
- wireless networks
- sensor networks
- data center
- routing protocol
- sensor nodes
- mobile users
- response time
- smart home
- traffic load
- multi agent systems
- cluster head
- mobile networks
- data transmission
- database
- transfer learning
- distributed systems