Approximate bounds for limited backhaul uplink multicell processing with single-user compression
Bibliographic record
Abstract
This paper studies single-user compression for the uplink multi-cell processing (MCP) model where multiple base-stations are connected to a central processor via noiseless backhaul links with a sum capacity constraint. The proposed scheme is based on the virtual multiple-access channel (V-MAC) architecture, where the base-stations quantize the received signals and send the quantized bits to the central processor, and at the decoder side the central processor first recovers the quantized signals then decodes the user messages in a successive manner. This paper focuses on the use of single-user compression in the V-MAC architecture, which is significantly simpler to implement than Wyner-Ziv compression. The main result of this paper is that single-user compression can achieve the sum capacity of the MCP model to within a constant gap under two specific scenarios. First, we define a diagonally dominant channel criterion and show the constant-gap result for the case when the covariance matrix of the received signals across the base-stations is κ-strictly diagonally dominant for some constant κ > 1. Second, we show that the constant-gap result holds for a special class of Wyner channel models in the weak interference regime.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".