Simultaneous Feedback Reduction and Sum Rate Maximization in Block-Diagonalized Space-Division Multiplexing
Bibliographic record
Abstract
A major hindrance to the adoption of orthogonalized space-division multiplexing (SDM) via block diagonalization (BD) in multi-user MIMO downlinks is the need for channel state information (CSI) feedback from user terminals. Another drawback is lower than optimal sum rates, theoretically achievable with dirty paper coding (DPC). While multi-user diversity could be leveraged via user selection to narrow the sum- rate performance gap, it requires the presence of very large user pools. To help raise the practical feasibility of BD-SDM, we propose a scheme that jointly reduces CSI feedback while approaching optimal DPC sum rates with smaller user-pool sizes. Additionally, BD-SDM offers the flexibility for spatial mode allocation to cater for individual transmission rate requirements. This presents a challenging resource allocation problem because mode selection at one terminal affects the rates achieved at all other terminals and in turn, the overall sum rate. The proposed scheme offers a systematic means for resource allocation, while minimizing rate loss at the overall- and individual levels. It represents a streamlined process that simultaneously reduces CSI feedback while achieving sum rate maximization, user selection and systematic rate-loss minimizing resource allocation.
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 | 0.002 |
| 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 | 0.000 |
| 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".