Pairwise error probability evaluation of cooperative mobile femtocells
Bibliographic record
Abstract
Cellular subscribers while travelling in public transportation vehicles, such as streetcars and buses, often experience poor signal reception and low bandwidth when using their cellular devises onboard. Small cell deployment of, for example, femtocells is considered as one of the most promising solutions for cellular operators to enhance coverage and meet the increasing need for capacity and QoS support expected by cellular subscribers. We consider a mobile Femto Base Station (mobFBS) installed in the public transportation vehicle, with an external antenna installed on the roof, to offer enhanced coverage and improved capacity onboard. We investigate the performance gains of a communication scheme in downlink LTE-A networks with mobFBSs. Users are assumed to be travelling using a public transportation vehicle, and the transmission between macroBS and users occurs through a mobFBS. The associated wireless links for this type of fast mobility are characterized by a doubly-selective fading channel. This causes performance degradation in terms of increased error probability. By taking advantage of the more powerful central processing mobFBS, we make use of a precoded technique to overcome the performance degradation that results from the wireless fading channel. We investigate the performance gain in terms of pairwise error probability (PEP) via a derived closed-form expression. Our analytical and simulation results indicate that significant diversity gains are achievable and error rates are tremendously reduced.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".