Comparative study of uplink and downlink beamforming algorithms in UTRA/TDD
Bibliographic record
Abstract
The arrival of new multi-media and Internet services to mobile cellular subscribers has led to the development of new techniques, such as antenna array or smart antennas (SA), to improve spectrum efficiency. SA provides a means for spatial filtering for separating users based on angular characteristics, a method also known as beamforming. In this contribution, the performance of various beamforming algorithms is studied for the uplink and downlink of an UTRA/TDD system under realistic deployment and channel models. The algorithms are evaluated in terms of improvement in signal to interference plus noise ratio (SINR) and coded bit error rate (BER) with a RAKE receiver structure and compared to a more complex space-time multiuser detection receiver. We consider three different beamforming algorithms, namely: switched beams (SB), dynamically phased arrays (DPA) and sample matrix inversion (SMI). The results indicate that on the uplink, SMI has the potential to perform better than both DPA and SB but DPA outperforms both SB and SMI on the downlink.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".