Blind deconvolution using compressed sensing in time dispersive MIMO OFDM systems
Bibliographic record
Abstract
In this paper, we propose a blind algorithm for channel identification and signal separation in MIMO OFDM systems with Nyquist sampling at the baseband. To estimate in time the channel and the input signals using compressed sensing, we exploit the sparsity structure of the matrix type channel impulse responses and the Gaussian characteristics of the transmitted OFDM signals. The matching pursuit sparse algorithm is applied in the channel recovery. First, we develop the method for blind deconvolution in SISO systems where after estimating the channel, a zero-forcing (Z-F) equalizer in the frequency domain recovers the transmitted QAM symbols. Then, we apply the method in the MIMO setting. This is accomplished by decomposing the matrix type convolution representing the mixing process in the MIMO time dispersive channel into systems of equations similar to the SISO case. Specifically, in the MIMO system, the SISO type sparse channel estimation is performed independently and in parallel for every receive antenna. The QAM symbol recovery on spatial streams is performed at every subcarrier using a matrix equivalent to the Z-F equalizer. The good estimation convergence of the method and its resilience in different SNR scenarios is verified through extensive simulations.
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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".