Extreme Learning Machine-Based Feature Refinment for Channel Estimation in RIS-ISAC Systems
Bibliographic record
Abstract
Integrated sensing and communication (ISAC) systems have emerged as a key enabler for future wireless networks, aiming to optimize spectral resource utilization for both sensing and communication tasks. The incorporation of reconfigurable intelligent surfaces (RIS) with ISAC enables more efficient utilization of resources, improving the quality of communication and the accuracy of sensing. A critical aspect of deploying such systems reliably is accurate channel estimation. Traditional deep learning methods, though effective, often struggle with the intricate characteristics of communication channel matricies. This work introduces an innovative two-stage channel estimation approach for RIS-ISAC systems. The first stage focuses on feature refinement using an extreme learning machine framework to process the received signals and extract essential channel features. The second stage employs these refined features to estimate the desired channels through dedicated neural networks specifically designed for sensing and communication tasks. The numerical simulations demonstrate that the proposed two-stage approach significantly outperforms the existing techniques across various system configurations. Moreover, the proposed method achieves a remarkable computational complexity reduction as compared to the state-of-the-art works. The results prove the robustness and efficiency of the proposed approach, facilitating more robust RIS-assisted ISAC deployments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".