Optimum scheduling based on beamforming for the fifth generation of mobile communication systems
Bibliographic record
Abstract
In this paper, we present a novel approach for using the smart antennas (SA) to exploit the space diversity for the next generations of mobile communication systems. We propose a combination of the MUltiple SIgnal Classification (MUSIC) and the Least Mean Squares (LMS) algorithms in one algorithm named MLMS. We propose the MUSIC algorithm for finding the direction of the users in the cell while we consider the LMS algorithm for enhancing the accuracy of the obtained Direction of Arrival (DOA) angles, performing the beam generation process, and keep tracking of the users in the cell. Furthermore, we propose a scheduling algorithm that performs the scheduling in terms of the generated beams. The space diversity, together with the time and frequency diversities of LTE (Long Term Evaluation) provide a large capacity enhancement to the next generations of wireless mobile communication systems. Our simulation results show that our novel approach has the capability to converge and completely follow the desired user signal with a very high resolution. The convergence and the accurate tracking of the desired user signal take place after 13 iterations while in the traditional LMS, the convergence needs 85 iterations to take place. This means 84.7% improvement over the traditional LMS algorithm for the same number of calculations in each iteration. In addition, our proposed scheduling scheme based on beamforming shows a gain of around 15% in the total aggregated data rate for each 10° decrease in the beam size.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".