Design of minimum bit error rate precoders for multiuser ofdm systems fitted with MMSE receivers
Bibliographic record
Abstract
In this paper, we consider a multiuser downlink OFDM system having a single transmitting antenna and a single receiving antenna for which the channel state information (CSI) are known to both the transmitter and the receivers. For such a system, we design optimal precoders that minimize the average bit error rate (BER) subject to a total power constraint when minimum mean square error (MMSE) receivers are employed. This problem is solved by a two-stage optimization procedure in which a lower bound on the BER is first minimized, followed by showing that this lower bound is actually achieved by the solution obtained in the first stage. In the first stage, we can transform the formulation of the original non-convex optimization problem into a convex one by optimally allocating the subcarriers based on the largest subchannel gain. Furthermore, an alternative efficient power loading method is proposed here in order to reduce the computation complexity. Simulation results show that for moderate to high SNR, our design achieves a gain of several dBs over several other design methods, including currently available precoder design based on MMSE.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".