Exploring O-RAN Compression Techniques in Decentralized Distributed MIMO Systems: Reducing Fronthaul Load
Bibliographic record
Abstract
This paper explores the application of uplink fronthaul compression techniques within Open RAN (ORAN) to mitigate fronthaul load in decentralized distributed MIMO (DD-MIMO) systems. With the ever-increasing demand for high data rates and system scalability, the fronthaul load becomes a critical bottleneck. Our method uses ORAN compression techniques to efficiently compress the fronthaul signals. The goal is to greatly lower the fronthaul load while having little effect on the overall system performance, as shown by Block Error Rate (BLER) curves. Through rigorous link-level simulations, we compare our quantization strategies against a benchmark scenario with no quantization, providing insights into the trade-offs between fronthaul data rate reduction and link performance integrity. The results demonstrate that our proposed quantization techniques not only lower the fronthaul load but also maintain a competitive link quality, making them a viable solution for enhancing the efficiency of next-generation wireless networks. This study underscores the potential of quantization in O-RAN contexts to achieve optimal balance between system capacity and performance, paving the way for more scalable and robust DD-MIMO deployments.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".