Bibliographic record
Abstract
This thesis investigates some important issues involving the smart antenna array and its integration with software radio technology in TDMA cellular systems.Third-generation (3G) wireless systems need strategies to further improve performance, increase data rates and at the same time provide flexible and affordable support for multi-services and multi-standards.Software radio technology is promising to provide the required flexibility in radio frequency (RF), intermediate frequency (IF) and baseband signal processing stages.The smart antenna is one of the attractive advanced processing techniques used to greatly improve the system performance.With smart antennas, the capacity can be enhanced by making use of spatial processing, exploiting the spatial directivity of the smart antenna and reducing co-channel interference.This paper address two main points: (i) the capacity gain analysis of the smart antenna in GSM-like TDMA systems, (ii) the software radio architecture design for the TDMA system base station with a smart antenna.The multiple beam smart antenna, also known as switched beam antenna, is used in our analysis.Technologies such as frequency hopping (FH), perfect power control (PC) and discontinuous transmission (DTX) are considered in our study.The performance is analyzed and compared with the sectorization-only application.Analytical results are given and can be easily extended and applied to any other TDMA systems such as IS-136 or next generation systems such as UMTS.One software radio architecture for a base station with smart antenna is proposed and analyzed.In this architecture, the smart antenna algorithms can be dynamically reconfigured according to different environment requirements and the baseband processing can also be dynamically reconfigured according to different standard requirements.In this way, the need for flexibility is satisfied.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".