Achieving High-Capacity Narrowband Cellular Systems by Means of Multicell Multiuser Detection
Bibliographic record
Abstract
Narrowband cellular systems require no bandwidth expansion for spectrum sharing. This attractive property is offset by the need to separate cochannel cells in order to reduce mutual interference. The net effect is a larger cluster size and a smaller system capacity than can be obtained by wideband cellular systems that operate with a cluster size of one. We propose to use a joint maximum-likelihood detection in the uplink of a narrowband system as a method to also allow it to operate with a unit cluster size. Unlike previous research, we jointly perform the detection on the desired and other-cell users. The focus of this multicell multiuser detection is more on cluster-size reduction than additional same-cell users, although the latter is also achieved. We address the problem of computational complexity by including only the strongest interferers in the joint detection. We have shown that, with a small computational cost, a narrowband system can operate with a cluster size of one and, thereby, obtain many times the spatial reuse efficiency of conventional narrowband systems.
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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) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".