Efficient Sum Rate Maximization and Resource Allocation in Block-Diagonalized Space-Division Multiplexing
Bibliographic record
Abstract
For space-division multiplexing (SDM) via block diagonalization on multiuser multiple-input multiple-output (MIMO) wireless downlink, it is shown that receive antenna selection (RAS) is necessary for maximizing the achievable sum rate. This is true even when all receive antennas are equipped with radio frequency (RF) chains and RAS reduces the upper bound on the broadcast sum capacity, and when the orthogonalized channels use optimal processing. Similarly, spatial-mode selection (SMS) is necessary for sum rate maximization when receive-weight matrices are used for spatial-mode allocation. RAS/SMS may release transmission resources that can fully be utilized via additional user scheduling to yield further sum rate gains. Optimal user selection for sum rate maximization is subsumed within an exhaustive RAS/SMS process for multiantenna terminals, and both selection processes become identical for single-antenna terminals. RAS/SMS thus helps reduce the performance gap from the optimal sum capacity even for small user pool sizes. A block antenna/mode selection approach is introduced to help overcome the drawbacks of existing algorithms. Since RAS/SMS involves antenna/mode ranking, a systematic method for resource allocation with sum rate loss minimization is inherently provided. This way, a streamlined process that combines user selection, RAS/SMS, and resource allocation is developed for sum rate maximization of block-diagonalized SDM.
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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 | 0.001 | 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) | 0.002 | 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".