Full Duplex User Pairing via DQN
What it is
A reinforcement learning approach using Deep Q-Networks (DQN) to solve user pairing problems in full-duplex communication systems. It formulates the pairing task as a Markov Decision Process and accelerates training by injecting expert experience into the replay buffer.
The problem it solves
Solving the complex combinatorial optimization of user pairing in full-duplex systems efficiently and effectively.
What you could use it for
- Optimizing resource allocation in full-duplex wireless networks
- Researching RL-based scheduling for uplink/downlink user pairing
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.