Performance comparison of data-sharing and compression strategies for cloud radio access networks
Bibliographic record
Abstract
This paper provides a system-level performance comparison of two fundamentally different transmission strategies for the downlink of a cloud radio access network. The two strategies, namely the data-sharing strategy and the compression-based strategy, differ in the way the limited backhaul is utilized. While the data-sharing strategy uses the backhaul to carry raw user data, the compression strategy uses the backhaul to carry compressed beamformed signals. Although these strategies have been individually studied in the literature, a fair comparison of the two schemes under practical network settings is challenging because of the complexity in jointly optimizing user scheduling, beamforming, and power control for system-level performance evaluation, along with the need to optimize cooperation clusters for the data-sharing strategy and quantization noise levels for the compression strategy. This paper presents an optimization framework for both the data-sharing and compression strategies, while taking into account losses due to practical modulation in terms of gap to capacity and practical quantization in terms of gap to rate-distortion limit. The main conclusion of this paper is that the compression-based strategy, even with a simple fixed-rate uniform quantizer, outperforms the data-sharing strategy under medium to high capacity backhauls. However, the data-sharing strategy outperforms the compression strategy under low capacity backhauls primarily because of the large quantization loss at low backhaul capacity with compression.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".