Optimal Power Allocation with Channel Inversion Regularization-Based Precoding for MIMO Broadcast Channels
Bibliographic record
Abstract
Zero-forcing (ZF) precoding scheme can achieve the asymptotic sum capacity as dirty-paper coding (DPC) in multiple-input multiple-output broadcast (MIMO-BC) channel when the number of users, , approaches infinity. However, the gap between ZF and DPC is not negligible in a practical range of , that is, . The capacity loss is partly due to the excessive transmission power penalty incurred by ZF when the channel matrix of the selected user subset is poorly conditioned. To avoid this power penalty, we propose to use a variation of ZF, channel inversion regularization (CIR), as a precoding scheme in MIMO-BC channels. But, unlike the interference-free ZF, the problem of maximizing sum-rate capacity using CIR precoding becomes nonconvex, which cannot be solved by water-filling strategy. Thus, we propose an efficient algorithm based on gradient projection (GP) as the optimal power allocation strategy for selected users, and show that the proposed CIR precoding scheme can achieve asymptotically the optimum sum-rate of the DPC strategy. Moreover, simulation results show that the CIR precoding scheme with the proposed optimal power allocation scheme achieves better sum-rate performance than ZF for a wide range of .
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".