A Novel Nonlinear Joint Transmitter-Receiver Processing Algorithm for the Downlink of Multiuser MIMO Systems
Bibliographic record
Abstract
This paper focuses on signal processing algorithms for the downlink of multiuser multiple-input multiple-output (MIMO) systems with multiple-antenna mobiles. A novel nonlinear joint transmitter-receiver processing algorithm is proposed based on the zero-forcing (ZF) criterion. In this algorithm, nonlinear Tomlinson-Harashima precoding (THP) is applied at the base station, whereas linear receiver processing and modulo operation are applied at each mobile. It is first shown that the proposed algorithm effectively decomposes the multiuser MIMO channel into parallel independent single-user MIMO channels, and then, the performance of each mobile can be separately optimized. Subsequently, closed-form expressions for the transmitter and receiver processing matrices are derived to optimize the asymptotic bit error rate (BER) of each mobile. When used on the downlink of multiuser MIMO systems with multiple-antenna mobiles, this algorithm achieves significantly better performance than the ZFcriterion-based nonlinear preprocessing algorithm designed for the multiuser MIMO systems with single-antenna mobiles, because it effectively utilizes the processing capabilities of the mobiles. Moreover, the proposed algorithm achieves a much higher sum capacity at a high signal-to-noise ratio (SNR) than the known block diagonalization technique due to the effective application of the nonlinear preprocessing at the transmitter. When the proposed algorithm is applied, it is found that better system performance can be achieved by suitably ordering the channel matrices of different mobiles, and a combined optimal diversity and best-first (CODBF) ordering method is proposed to perform the ordering. Simulation is used to show the advantages of the proposed algorithm and the CODBF ordering method.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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".