Structured-Compressed-Sensing-Based Impulsive Noise Cancelation for MIMO Systems
Bibliographic record
Abstract
In this paper, the powerful signal processing theory of structured compressed sensing (SCS) is exploited to overcome the challenge of impulsive noise (IN) cancelation in multiple-input multiple-output (MIMO) systems. To the best of the authors' knowledge, the SCS theory is adopted for the first time for IN elimination, bridging IN mitigation and MIMO systems for its potential applications in vehicular-related communications. In achieving the SCS-based IN cancelation, the measurements matrix of the IN is first obtained from the null subcarriers in the MIMO system specified by the IEEE 802.11 standards series. The SCS optimization framework is then formulated through the proposed spatially multiple measuring method, by fully exploiting the spatial correlation of the IN signals at different receive antennas. To efficiently reconstruct the IN signal, an enhanced SCS-based greedy algorithm, structured a priori aided sparsity adaptive matching pursuit, is proposed, which significantly improves the accuracy and robustness compared with the state-of-the-art methods. Theoretical analysis is presented to guarantee the convergence and the performance error bound of the proposed greedy algorithm. Computer simulations validate that the proposed scheme outperforms the conventional ones over the wireless MIMO channel.
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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 | 0.001 | 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".