MIMO LMMSE Transceiver Design with Imperfect CSI at Both Ends
Bibliographic record
Abstract
This paper presents a new result on minimum total mean squared error (MSE) joint transceiver design for multiple-input multiple-output (MIMO) systems, with imperfect channel state information (CSI) at both ends and subject to a total power constraint. The channel knowledge here is the channel mean and transmit correlation information. The joint design is formulated into an optimization problem, to which the closed-form optimum solution is found. The optimum precoder consists of a linear filter, a matrix which collects effective channel eigenmodes, and a diagonal power allocation matrix. The linear filter balances the suppression of channel noise and the additional noise induced by channel estimation error. Given the precoder, the optimum decoder is simply a linear minimum MSE (LMMSE) data estimator. The performance degradations due to imperfect channel estimation and/or transmit correlation are demonstrated by simulation results. The relation between the minimum total MSE design and the maximum mutual information design is determined as well, under the above mentioned imperfect CSI.
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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".