A Distortion Nullforming Precoder in Massive MIMO Systems With Nonlinear Hardware
Bibliographic record
Abstract
In this letter, we propose a novel nullforming precoding scheme to cancel the distortion from multiple-input–multiple-output (MIMO) base stations with nonlinear hardware towards victims that operate either in the same or adjacent frequency bands. The proposed precoder is based first on the design of a nullforming scheme within the same band which steers nulls towards the direction of victims. Then, its power allocation is updated to achieve per-antenna constant envelope (CE) precoding without destructing the nullforming of the desired signal. It is shown analytically that in line-of-sight channels, single-user transmission, CE precoders have the property that the radiation pattern of nonlinear distortion has the same spatial characteristics as that of the in-band desired signal. As a result, the designed nulls are towards the desired directions for both in-band and out-of-band frequencies. Finally, numerical results corroborate the effectiveness of the proposed precoder.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".