Flexible Link Adaptation in Fully-Decoupled RAN: A Machine Learning Approach
Bibliographic record
Abstract
Fully decoupled radio access network (FD-RAN), as an emerging radio access architecture through physical uplink-downlink decoupling and control-data decoupling, has potential in flexible spectrum utilization and network cooperation. However, real-time uplink feedback is challenging in FD-RAN due to the complete physical decoupling of control and data base stations. In this paper, we propose a flexible link adaptation mechanism that leverages outdated channel state information (CSI) to determine the appropriate Modulation and Coding Scheme (MCS) for the user in the FD-RAN downlink. Specifically, we first utilizes kernel recursive least squares to predict the CSI at the future moment. We then select the optimal modulation and coding scheme based on the predicted CSI and the frame error rate estimated by a neural network. Simulation results show that the proposed link adaptation mechanism has a significant throughput performance gain in various scenarios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".