Leveraging Generative Artificial Intelligence for Uplink Feedback-Free Transmission in 6G FD-RAN
Bibliographic record
Abstract
Cooperative uplink multi-base station (BS) reception has emerged as a promising technology to enhance received signal strength and improve wireless spectral efficiency. However, realizing the potential performance gains remains challenging due to substantial communication overhead among cooperative BSs and excessive delays in channel state information (CSI) feedback. This paper investigates a CSI feedback-free mechanism that leverages time-invariant physical layer parameters to facilitate cooperative BS reception within a fully-decoupled radio access network (FD-RAN). First, given the dynamically changing characteristics of the wireless environment, we employ conditional variational autoencoder (CVAE), a state-of-the-art generative artificial intelligence (GAI) approach, to generate location-specific representative channels for calculating CSI feedback-free transmission parameters. Subsequently, to maximize the throughput of user equipment (UE), a diffusion model-based deep reinforcement learning (DRL) framework is proposed for jointly selecting cooperative BS reception sets and precoding schemes, utilizing the representative channels generated by CVAE. Extensive simulations conducted on a link-level simulator demonstrate that the proposed CSI feedback-free mechanism for cooperative multi-BS reception can effectively improve spectrum efficiency by 17.3%, which provides a promising design principle for the development of sixth-generation (6G) wireless networks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".