Joint Estimation of Direct and RIS-assisted Channels with Tensor Signal Modelling
Bibliographic record
Abstract
We consider a narrowband multi-user MIMO reconfigurable intelligent surface (RIS)-assisted wireless communication system and use tensor signal modelling techniques to jointly estimate all communication channels including the RIS-assisted link and the direct-path link between the access point and user equipment. We model the received signal as a third-order tensor comprising two additive CANDECOMP/PARAFAC (CP) decomposition terms corresponding to the direct-path and the RIS-assisted links. Based on this model we propose an enhanced iterative alternating least squares (E-ALS) algorithm to simultaneously estimate both the direct-path and RIS channels, and we derive the corresponding Cramér-Rao Bounds (CRB). Numerical results show that compared to recent previous works which estimate the direct-path and RIS links during separate training stages, the E-ALS method provides a more accurate estimate by efficiently using all pilots transmitted throughout the full training duration without turning the RIS OFF. For a sufficient number of transmitted pilots, the E-ALS method’s accuracy comes close to the CRB for the RIS channels and attains the CRB for the direct-path 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 | 0.000 | 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".