Investigation of the Impact of Zero-Forcing Precoding on the Variation of Massive MIMO Transmitters’ Performance With Channel Conditions
Bibliographic record
Abstract
In this letter, the impact of two variants of zero-forcing (ZF) precoding (conventional and minimax) on the performance of massive multiple-input-multiple-output (MIMO) transmitters is investigated through simulations and experiments. In massive MIMO transmitters, the average-power levels across the radio frequency (RF) chains depend on the choice of precoder and the channel conditions. Compared to the conventional ZF precoder, the minimax variant results in significantly less variations in the average-power levels across the RF chains and, consequently, in less disparity between their operating characteristics. Thus, the minimax precoder, albeit requiring more computational resources, reduces the variation of the transmitter's performance with the channel conditions. Experiments performed using the two ZF variants, in conjunction with digital predistortion, on a two-user four-chain MIMO transmitter under 200 different channel realizations revealed that the minimax variant allows for up to 10 dB and 8% reductions in the adjacent-channel power ratio and root normalized mean-square error, respectively, in each chain.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".