Efficient design of optimal transmitter for MIMO systems using decision feedback receivers
Bibliographic record
Abstract
The design of optimum transmission precoder for multi-input multi-output (MIMO) communication systems equipped with zero-forcing decision feedback (ZF-DF) receivers usually requires perfect knowledge of the channel state information (CSI) at both the transmitter and the receiver. In wireless communication systems, however, it is often difficult to provide sufficiently timely and accurate CSI feedback from the receiver to the transmitter for such designs to be practically viable. In this paper, we consider the transmission precoder design for a MIMO communication system having M transmitter antennas and N receiver antennas (M ≪ N) in a wireless link in which the channels are assumed to be flat fading and possibly correlated. We assume that full CSI is known at the receiver, but only the first- and second-order statistics of the channels are available at the transmitter. The goal in this paper is to seek an efficient design of the optimal precoder for such a MIMO system by minimizing the total transmitting power of the ZF-DF receiver subject to a constraint on the average arithmetic mean square error (MSE). Utilizing majorization theory, we transform this non-convex optimization problem into a convex geometrical programming problem, which can then be efficiently solved using an interior point method. In the case of the MIMO random channel being uncorrelated, a closed-form solution to the optimization problem has also been obtained.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".