Distributed Transmission Scheduling and Power Allocation in CoMP
Bibliographic record
Abstract
The performance of wireless networks can be largely enhanced by coordinated multipoint (CoMP). To design an efficient CoMP in multiuser multiple-input multiple-output scenario, conventional transmission scheduling and power allocation are usually performed in a static and centralized manner. In this paper, we focus on dynamic and distributed transmission scheduling and power allocation. We first determine the coordinated base-station sets (defined as CBSs) candidates in each subband by the channel energy (i.e., square Frobenius norm of channel matrix) of each user. Each CBS candidate contains a set of coordination base-stations and edge users. By chordal distance, we can measure the orthogonality between space spanned of users in the same CBS candidate. Then, we propose two heuristic user scheduling algorithms based on channel energy and chordal distance between users to determine the set of users served by each CBS candidate. The first algorithm is based on an open problem, which reveals the philosophy of user scheduling with orthogonalization threshold guarantee. The second one deals with user scheduling by selecting a set of edge users with the largest total channel energy and orthogonalization threshold guarantee. With the total channel energy per CBS candidate, the CBSs and their served edge users can be determined. Then, water-filling power allocation is further applied to CBSs with block diagonalization precoding. Algorithm performance is demonstrated by extensive simulations.
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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 | 0.002 |
| 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 | 0.000 | 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".