Minimum SER Zero-Forcing Transmitter Design for MIMO Channels with Interference Pre-Subtraction
Bibliographic record
Abstract
We consider point-to-point multiple antenna communication systems in which multiple data streams are transmitted simultaneously. We consider systems which use Tomlinson-Harashima (TH) precoding to pre-subtract the interference among these data streams at the transmitter. In a conventional Tomlinson-Harashima precoding system, transmitter feedback and receiver feedforward processing matrices are used for interference pre-subtraction and channel spatial equalization. In addition to these matrices, we consider a transmitter precoding matrix that generalizes the permutation matrix used for ordering the precoded symbols in existing designs. This extra degree of freedom offers the potential for improved performance. In particular, under a mild signal to noise ratio (SNR) constraint, we find an optimum zero-forcing preceding matrix that minimizes the average symbol error rate (SER) of the data streams subject to a transmitter power constraint. We also show that the proposed design is optimal from an average bit error rate (BER) perspective. Simulation studies show significant improvement over conventional zero-forcing Tomlinson-Harashima precoders.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".