COMPARISON OF MODEL AND PROPAGATION MEASUREMENT-BASED BER PERFORMANCE PREDICTIONS FOR RAKE RECEIVERS IN URBAN MICROCELLULAR WCDMA SYSTEMS
Bibliographic record
Abstract
One of the WCDMA service specifications is the delivery of data rates up to 2 Mbps to a single user on a downlink channel. The transmission of such high data rates in an urban propagation environment, however, poses unique problems not encountered previously in DSSS systems intended primarily for voice and low data rate traffic. At high data rates, both multiple-access interference (MAI) and multipath-induced self-interference (SI) can become significant sources of bit error rate (BER) degradation. This is a consequence of the high cross-correlation sidelobes that result from the use of short spreading sequences to achieve high data rates if the system bandwidth is fixed. Although MAI can be mitigated using an increase in power transmitted to the desired user, reductions in SI cannot be achieved by this means. Adequate modelling and simulation of SI effects is therefore a topic of increasing importance. In addition, when analysing the performance of Rake receivers to be used in WCDMA systems it is often assumed that resolvable multipath groups (i.e. delay line model taps) have equal average power and independent, Rayleigh fading statistics. However propagation mea-surements conducted in downtown Ottawa reveal that these assumptions are not valid. This paper reports a method by which the above-cited problems and simplifying assumptions used to erroneously predict WCDMA link performance can be avoided. A computationally-efficient semi-analytical technique (SAT) will be introduced. This technique extends the work of Lehnert and Pursley [1], which analysed the effects of MAI in non-fading conditions, to accurately account for SI
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".