Data detection in MIMO systems with cochannel interference
Bibliographic record
Abstract
The Bell Labs layered space-time (BLAST) architecture has been proposed to achieve high spectral efficiency on multi-input multi-output (MIMO) channels. Most studies of the BLAST algorithm consider spatially and temporally white noise and interference at the receiver. We study channel estimation and data detection of a MIMO system under both spatially and temporally colored interference. We derive maximum likelihood (ML) estimates of channel and spatial interference correlation matrices. By exploiting known temporal interference correlation, we extend one-time-slot ordered minimum mean-squared error (MMSE) nulling detection to a multi-time-slot version. We evaluate the symbol error rate of an uncoded QPSK MIMO system under independent Rayleigh fading. The results show that by exploiting both spatial and temporal interference correlation, we achieve about 2dB gain in SIR for a 4/spl times/4 MIMO system.
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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".