arXiv (All)
open access
QoS-Aware RACH Preamble Slicing via Quota-Projected Branching Deep Reinforcement Learning
Quality-of-service (QoS)-aware random access requires adaptive allocation of a finite random access channel (RACH) preamble budget across heterogeneous traffic and access procedures. This paper proposes QP-BD3QN-RACH, a quota-projected branching deep reinforcement learning controller for mixed two-s
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Deep Weighted Bellman Residual Minimization for $Q^*$ Estimation#1128856
- KKTCode: Asymptotically Optimal Linear Codes for Noisy Feedback AWGN Channels#1116449
- Distribution of Link-State and Traffic Engineering Information Using BGP#399465
- Sample Efficient Hierarchical Reinforcement Learning via Best Policy Identification#422271
- Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML#1130430
Record · ID 985575
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive