Beam Refinement for THz Extremely Large-Scale MIMO Systems Based on Gaussian Approximation
Bibliographic record
Abstract
Beam refinement is a key technology to overcome the problem of limited resolution in beam training. However, most existing works on beam refinement are not suitable for the emerging extremely large-scale multiple-input-multiple-output (XL-MIMO) due to the differences in the channel characteristics. To fill in the gap, in this paper, beam refinement for XL-MIMO systems is investigated. Inspired by the similarities between the Taylor series of the Gaussian function and that of the beam gain, we propose to approximate the beam gain by the Gaussian function. Then, a low-complexity beam refinement based on the Gaussian approximation (BRGA) scheme, which quantizes the narrowed intervals after beam training into several samples and performs additional channel tests on the quantized grids, is proposed to improve the estimation accuracy of the beam training. Based on the measurements in the beam refinement stage, the BRGA-based least square (BRGA-LS) estimator is developed for high-resolution channel parameter estimation. To avoid the noise amplification effects of the BRGA-LS, the BRGA-based weighted least square (BRGA-WLS) estimator is further developed. Simulation results verify the effectiveness of the proposed scheme and show that the proposed BRGA scheme can greatly improve the accuracy of beam training with only a few additional channel tests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".