Energy Saving in 5G and Beyond Networks: A Quantum Reinforcement Learning Approach
cs.NI, cs.LG
Submitted: 2026-10-01
Updated: 2026-10-01
Terminology
Sources
- Evolution of UE in Massive MIMO Systems for 6G: From Passive to Active
- Quantum Reinforcement Learning for 6G and Beyond Wireless Networks
- Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning
Related papers
- HiFiNet: Hierarchical Fault Identification in Wireless Sensor Networks via Edge-Based Classification and Graph Aggregation
- PACC: Protocol-Aware Cross-Layer Compression for Compact Network Traffic Representation
- TraceCodec: A Compiler-Backed Neural Codec for Network Traffic Traces
- Embodied AI in 6G Networks: From Intelligent Connectivity to Physical Intelligence
- Lightweight GenAI for Network Traffic Generation: Fidelity, Augmentation, and Classification
- NetRL: Powering Network Decisions via NetGRPO-Driven Reinforcement Learning