Using Beamforming for Dense Frequency Reuse in 5G
Bibliographic record
Abstract
Implementing an efficient frequency reuse (FR) plan is significantly important to meet the demand on high data rates and the required quality of service for 5G. In this paper, we use the direction of arrival algorithms and the correlator to determine the directions of the desired user and the interferers in the cell. Then, we use the beamformer to produce a beam towards the desired user and nulls in the direction of the interferers. Moreover, we implement the synthesizer to smartly form the desired beam shape and make the nulls deeper. We take the advantage of the smart antennas, beamforming capabilities, and the radiation pattern synthesizing techniques to build up an efficient FR plan for 5G. In addition, we develop a formula for calculating the signal to interference and noise ratio (SINR) in terms of the desired and the interferers directions. Our objective is to maintain the SINR at the minimum levels required for data calls with accepted quality while reducing the beam sizes, and hence increase the FR factor. Our simulation results show that with a uniform linear antenna of 11 elements, we can achieve the desirable SINR levels using beams of 10° width, which raises the FR factor from 1 to 18 and subsequently increases the number of mobile users by 18 times.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".