Sparse Signal Recovery Neural Network With Application to High-Mobility Massive MIMO-OTFS Communication Systems
Bibliographic record
Abstract
A deep learning-based sparse signal recovery network$\text{SSRnet}$is designed. This network is built on the proposed neural network$\rm{PositionNet+}$, which takes the received signal as input and obtains the support of the desired sparse matrix without requiring a sensing matrix. Using PositionNet+, SSRnet is able to recover the sparse signal precisely, outperforming conventional methods, including least-squares (LS) estimation with perfectly known support, by virtue of its denoising behavior, while offering substantially reduced computation. The network is then utilized to perform channel estimation of high-mobility massive multiple-input multiple-output orthogonal time frequency space (MIMO-OTFS) wireless systems which is cast as a sparse signal recovery problem. In OTFS, data is modulated in the delay-Doppler domain to transform a fast time-varying and frequency-selective fading channel into a quasi-static and sparse channel. To maximize performance, OTFS systems require accurate channel estimation and low pilot signaling which are provided by SSRnet. Simulation and computational comparisons demonstrate that the proposed approach enhances performance in terms of bit error rate (BER) and normalized mean squared error (NMSE), reduces pilot symbol overhead, as well as lowers computational complexity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".