Uplink bit combining for multiple base‐stations MIMO with applications to CoMP systems
Bibliographic record
Abstract
Abstract This work considers a simple bit level combining technique, aided by robust bit reliability information, for uplink collaborating multiple‐input multiple‐output (MIMO) base‐stations (also known as macrodiversity MIMO), operating over composite Rayleigh‐lognormal fading channels. Bit reliability weights based on a robust modification of the logarithmic likelihood ratio, combined with instantaneous symbol signal‐to‐noise ratio information, are derived for different local MIMO detection schemes. This bit reliability information is used at the fusion center, together with locally detected data, for combining and producing final information bits delivered to the destination. Computer simulation results confirm that such bit level combining techniques, when used with minimum mean squared error ordered successive interference cancelation and also with sphere decoding maximum likelihood local detectors, provide significant performance improvements over non‐collaborative base‐stations systems. Performance gains are maintained even when these schemes suffer from channel estimation errors and also in the presence of space correlation. Low backhaul overhead and performance advantages make these bit level combining techniques attractive for applications in next generation cellular systems employing coordinated multi‐point (CoMP) technology, as well as for other collaborative MIMO communication schemes.Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".