Soft capacity analysis of TDMA systems with slow-frequency hopping and multiple-beam smart antennas
Bibliographic record
Abstract
Smart antenna is considered as one of the most effective means for enhancing wireless system capacity. When fractional loading is accompanied with slow-frequency hopping (SFH), soft capacity can be realized in time-division multiple access (TDMA) wireless networks. Then, the interference reduction due to smart antennas, power control, and discontinuous transmission can be directly translated into capacity gain. This paper addresses the capacity gain due to multiple-beam (MB) smart antennas in TDMA wireless systems with soft capacity. The system capacity is determined analytically and by simulation. MB smart antennas with practical antenna pattern are used in this study. Perfect power control and discontinuous transmission are assumed in the simulation and the theoretical analysis. A novel call admission control algorithm is proposed to enhance the system capacity without degrading the signal quality. The TDMA system is assumed to be global system for mobile communications (GSM)-like, however, the analysis can be extended and applied to other TDMA systems.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".