Vector Perturbation Precoding for Multi-User CoMP Downlink Transmission
Bibliographic record
Abstract
This paper focuses on the design of vector perturbation (VP) precoding for coordinated multi-point (CoMP) multi-user downlink transmission. Precoding is performed by individual base stations (BSs) in a distributed manner using only the downlink channel coefficients and user data local to a BS. A cascade precoder structure with an outer precoder managing the inter-cell interference (ICI) and an inner precoder performing mean-squared-error (MSE) minimization-based VP to mitigate the intra-cell interference is proposed. Three different outer precoding techniques are considered. In the first technique, the outer precoder is designed to fully eliminate the ICI by trading off the degrees of freedom (DoFs) available through multiple antennas. While the proposed technique outperforms existing conventional-VP based designs, a large portion of DoF is consumed by the ICI elimination. To overcome this issue, in the second technique, interference alignment-based outer precoding that minimizes the total leakage interference is proposed. To further improve the system performance, in the third approach, precoding by joint minimization of total leakage interference plus MSE is performed. Numerical results show that the proposed cascade precoding structure is an efficient way to use the DoF of CoMP multi-user downlink transmission.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".