Channel Estimation and Data Detection for MIMO Systems under Spatially and Temporally Colored Interference
Bibliographic record
Abstract
The impact of interference on multiple-input multiple-output (MIMO) systems has recently attracted interest. Most studies of channel estimation and data detection for MIMO systems consider spatially and temporally white interference at the receiver. In this paper, we address channel estimation, interference correlation estimation, and data detection for MIMO systems under both spatially and temporally colored interference. We examine the case of one dominant interferer in which the data rate of the desired user could be the same as or a multiple of that of the interferer. Assuming known temporal interference correlation as a benchmark, we derive maximum likelihood (ML) estimates of the channel matrix and spatial interference correlation matrix, and apply these estimates to a generalized version of the Bell Labs Layered Space-Time (BLAST) ordered data detection algorithm. We then investigate the performance loss by not exploiting interference correlation. For a MIMO system undergoing independent Rayleigh fading, we observe that exploiting both spatial and temporal interference correlation in channel estimation and data detection results in potential gains of 1.5 dB and 4 dB for an interferer operating at the same data rate and at half the data rate, respectively. Ignoring temporal correlation, it is found that spatial correlation accounts for about 1 dB of this gain.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".