Capacity Enhancement in CDMA Cellular Networks using Multi-hop Communication
Bibliographic record
Abstract
The Multi-hop CDMA cellular concept has been proposed to overcome cellular drawbacks, like congestion and load imbalance. Although it is a widely accepted that multi-hopping increases cellular capacity, it has never been quantified. In this paper, the capacity increase in multi-hop CDMA cellular networks is quantified. To this end, and since CDMA networks are interference limited, we derive equations for interference in multi-hop cellular networks at base stations (BSs) and relaying mobile terminals (MTs) in the uplink. The interference formulas are used to verify that capacity can be increased, by increasing either the number of simultaneous calls or data rate. A 10% increase in the number of simultaneous calls is shown to be possible even under worst-case scenarios. This increase is achievable while keeping interference at relaying MTs below acceptable thresholds. The novelty of this paper is that it quantifies, and for the first time, interference levels at MTs and BSs as well as potential capacity enhancements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".