Tomlinson-Harashima precoding design in MIMO wiretap channels based on the MMSE criterion
Bibliographic record
Abstract
This paper investigates the Tomlinson-Harashima precoding (THP) design for secure communications in broadcast multiple-input multiple-output (MIMO) systems in the presence of a passive eavesdropper. We focus on optimizing the nonlinear transceiver to guarantee a certain Quality-of-Service (QoS) level for the intended receiver in terms of mean-squared-error (MSE). The scheme allocates the transmit power in order to achieve the target MSE for the intended receiver, and then uses the remaining available power to transmit artificial noise (AN) to degrade the eavesdropper's channel. With the geometric mean decomposition (GMD) based THP, the lower MSE bound can be achieved. We convert the nonlinear transmit power minimization problem to a standard convex optimization problem. The solution for the problem is obtained by the waterfilling method via the Karush-Kuhn-Tucker (KKT) conditions. Simulation results demonstrate that the proposed nonlinear scheme outperforms existing linear transceiver designs.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".