Sum Rate of p-Sphere Encoding for MIMO Broadcast Channels with Reduced Peak Power
Bibliographic record
Abstract
Vector perturbation is a sum-capacity approaching non-linear precoding technique. Vector perturbation uses sphere encoding to perturb data such that the unscaled sum power is minimized. Unfortunately, this minimization does not guarantee that the peak to average power ratio (PAPR) remains in the acceptable range for the transmitter. A p-sphere encoder was proposed in literature to reduce the PAPR by using p-norm minimization instead of the 2-norm one used typically in vector perturbation and its performance was analyzed in terms of bit error rate (BER). In this paper we focus on the spectral efficiency of the p-sphere encoding and investigate its sum rate for multiple-input multiple-output broadcast channel (MIMO-BC) with multiple-antenna users and uniformly distributed input. The results show that for p=5, the PAPR is reduced by 25%, while the sum rate is just slightly less than that of vector perturbation employing 2-norm sphere encoding (only 1% reduced sum rate).
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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 | 0.000 | 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".