A DDPG-Based Procedure for Mitigating Pilot Contamination in Massive MIMO RSMA Systems
Bibliographic record
Abstract
The growing demand for improved spectral efficiency is one of the main challenges for the upcoming beyond fifth-generation wireless mobile communications networks. While massive multiple-input multiple-output (MIMO) technology has been demonstrating its potential in achieving higher spectral efficiency, the persistent problem of pilot contamination poses a significant hurdle for these systems. To address this issue, the Rate-Splitting Multiple Access (RSMA) framework has emerged as a potential solution. In this paper, we present a novel approach that leverages reinforcement learning (RL) with the Deep Deterministic Policy Gradient (DDPG) algorithm to maximize the sum spectral efficiency (SUM-SE) in a massive MIMO system implementing the RSMA framework with all users sharing a single pilot. The numerical results indicate that the proposed DDPG-based method is a competitive tool for optimizing the SUM-SE in massive MIMO scenarios employing the RSMA framework.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".