Second Order Rectified Parallel Factor Model Based Cascaded Channel Estimation in IRS-Assisted SWIPT System
Bibliographic record
Abstract
This article investigates cascaded channel estimation in intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) system. Multiple-input multiple-output (MIMO) transceiver structures combined with transmitter active beamforming and IRS passive beamforming are studied. Cascaded channel estimation is transformed into sparse signal reconstruction problem, and compressed sensing (CS) is applied to solve this problem. Only a small amount of training overhead provides reliable channel estimation gains as well as better beamforming gains. Second order rectified parallel factor (PARAFA) model is implemented in IRS-assisted SWIPT system, which is described by tensor decomposition method. The received signal can be represented by PARAFA model with algebraic structure and IRS phase shift. Two cascaded channel estimation approaches, namely, bilinear alternating least squares (BALS) and least squares Khatri-Rao factorization (LSKRF), are proposed respectively. Simulation results show that, compared with orthogonal matching pursuit (OMP) approach, the proposed BALS cascaded channel estimation approach obtains better normalized mean square error (NMSE) and convergence performance with the least parameter constraints.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".