Throughput Scaling of MIMO Channels With Imperfect CSIT in the Low-SNR Regime
Bibliographic record
Abstract
We study the effect of channel estimation error on the performance of water-filling in point-to-point multiple-input- multiple-output (MIMO) channels at low signal-to-noise ratios (SNRs). In this regard, we derive the water-filling throughput of a MIMO channel in the presence of imperfect channel state information at the transmitter (CSIT). The asymptotic growth rate for the throughput, i.e., R, is then found and is compared with the asymptotic growth rate for the capacity with perfect CSIT, i.e., CP, as a function of the signal-to-estimation-error ratio (SER). We show that, at low SNR, for moderate values of the SER, water filling based on erroneous channel estimates can still achieve significant throughputs asymptotically.
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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 | 0.001 | 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".