WLC06-5: A Novel Nonlinear Joint Transmitter-Receiver Processing Algorithm for the Downlink of Multi-User MIMO Systems
Bibliographic record
Abstract
A novel nonlinear joint transmitter-receiver processing algorithm is proposed for the downlink of multi-user MIMO systems with multiple-antenna mobiles. In this algorithm, linear receiver processing is applied at each mobile, while nonlinear pre-processing is applied at the base station. Using the zero-forcing (ZF) criterion, the transmitter and receiver processing matrices are designed jointly to optimize the performance of each mobile. When used on the downlink of multi-user MIMO systems with multiple-antenna mobiles, this algorithm achieves significantly better performance than the ZF criterion based pre-processing and joint transmitter-receiver processing algorithms known in the literature, because it successfully constrains the transmitted power increase and effectively utilizes the processing capabilities of the mobiles. For the proposed algorithm, it is found that better system performance can be achieved by suitably ordering the channel matrices of different mobiles, and a combined optimal diversity "best-first" (CODBF) ordering method is proposed to perform the ordering.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".