Preamble Selection Probability Optimization in RACH: A Multi-Armed Bandits Approach
Bibliographic record
Abstract
The use of cellular networks for massive machine-type communications (mMTC), is an attractive solution due to the availability of existing infrastructure. However, the sheer number of user equipments (UEs) creates congestion and overloading challenges on the random access channel (RACH). To address this, we develop a multi-armed bandit (MAB)-based reinforcement learning (RL) approach that learns optimal preamble selection strategies without requiring the base station (BS) to know the number of UEs in the network. We first model a two-priority RACH that captures the behavior of UEs through access patterns observed at the BS. This enables us to design a non-uniform preamble selection scheme and formulate an optimization problem that seeks the best preamble selection probabilities to maximize high-priority UE success while constraining low-priority access. Our proposed RL framework uses a discretized and compressed the action space (AS) to improve scalability, and uses cross-entropy methods to efficiently update the MAB solution. In addition, we present a compact AS (CAS) approach that leverages a lookup table of pre-optimized preamble selection probabilities across different network loads. This not only reduces the AS further but also enables implicit network load estimation. Numerical experiments show that the proposed method offers higher throughput for high priority UEs compared to the uniform preamble selection scheme, as well as an access class barring scheme, while maintaining a minimum throughput for low priority UEs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".