Joint beamforming design and base‐station assignment in a coordinated multicell system
Bibliographic record
Abstract
This study is concerned with the downlink beamforming designs in a coordinated multicell system with dynamic base‐station (BS) assignment. At each cell, a multiple‐antenna BS employs linear beamforming to send multiple data streams to its assigned mobile‐stations (MSs). Exploiting multicell coordination, the multiple BSs jointly optimise the beamformers and the BS‐MS assignments to enhance the overall system performance. With per‐BS power constraints, considered are the coordinated beamforming problems under the following two design criteria: (i) minimising the transmit power margin at the BS with a set of target signal‐to‐interference‐plus‐noise ratios (SINR) at the MSs and (ii) jointly maximising the minimum SINR margin at the MSs. As the original problem formulations are shown to be non‐convex integer programs, which are combinatorially hard, the authors propose an efficient convex relaxation approach to solve the problems with low complexity. Simulations show that the convex relaxation‐based assignment schemes significantly outperform heuristic fixed assignment schemes.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".