Bibliographic record
Abstract
In this thesis, we analyze the performance of a variable spreading factor (VSF) OFCDM employed in femtocells, with OFDM used in macrocells in a hybrid heterogenous network. Orthogonal subcarriers are assigned to macro users and for femtocell users, non-contiguous subcarrier grouping is employed. We derive the analytic expression of the BER for uplink VSF-OFCDM femto and OFDM macro users for the case of maximal ratio combining receiver. We evaluate the performance of femto/macro users in VSF-OFCDM system through numerical and Monte Carlo simulation studies. Improvement in BER of the femtocell users is also noted. The relationship between the femto spreading factor and femto/macro BER is analyzed. We present, the relationship between the channel load and optimum spreading factor employed by femtocell users for the energy efficient performance of macro users. Femto wall penetration loss, that is the important parameter to evaluate the femto performance, is also taken into account. Also, effect of femto wall penetration on macro BER is evaluated for various spreading factors. Following our study, we find that interference-limited system favors increased time spreading especially when number of subcarriers is limited and noise-limited system favors increased frequency domain spreading. When large number of subcarriers are available, optimum spreading (from macro perspective) favors increased frequency domain spreading regardless of the femto-macro loads, or whether operating environment is noise or interference limited. Once the optimum spreading factor is determined, increase or decrease in the femto Eb/No does not matter. Also, femto wall penetration factor not only effects the femto BER directly, but also reduce the potential interference faced by macro user equipment (UEs). As a result macro BER is improved, but the choice of optimal spreading factor for macro UEs remain unaffected with the variation in femto wall penetration loss.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".