Net Throughput Maximization of Per-Chunk User Scheduling for MIMO-OFDM Downlink
Bibliographic record
Abstract
Per-chunk user scheduling for multiple-input–multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) downlink is considered. By grouping adjacent subcarriers into chunks, the amount of required channel state information feedback is reduced. Based on the net throughput criterion, which accounts for the reduction in sum rate due to the feedback overhead, it is shown that there exists an optimal chunk size that maximizes the net throughput. To reduce the feedback requirement even further, an opportunistic feedback scheme is proposed, and a close approximation for its net throughput is derived. The net throughput of per-chunk user scheduling with optimized chunk size is compared to various other limited-feedback MIMO-OFDM downlink strategies. The results show that increasing the total number of users in the system results in the net throughput of most existing MIMO-OFDM downlink schemes decreasing to zero for moderate-size user pools, whereas the net throughput of per-chunk user scheduling with opportunistic feedback increases with the total number of users, even when that number is very large ($>$1000).
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 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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".