Variational Inference-Based Channel Estimation for Reconfigurable Intelligent Surface-Aided Wireless Systems
Bibliographic record
Abstract
We propose a variational inference-based channel estimation method in fully passive reconfigurable intelligent surface (RIS)-aided mmWave single-user single-input multiple-output (SIMO) communication systems. The main goal is to jointly estimate the user equipment (UE)-to-RIS (UE-RIS) and RIS-to-base station (RIS-BS) channels using uplink training signals in a passive RIS setup. Specifically, by using a variational inference framework, we approximate the posterior of the channels with convenient distributions given the received uplink training signals. The parameters of the approximated distributions are generated by deep neural networks trained using variational loss functions derived using a lower bound on the log-likelihood of the received signal. Then, the learned distributions, which are close to the true posterior distributions in terms of Kullback Leibler divergence, are leveraged to obtain the maximum a posteriori (MAP) estimation of the UE-RIS and RIS-BS channels. We evaluate the proposed channel estimation solution under two channel priors. The first channel prior models Rayleigh fading channels with Gaussian prior, whereas the second one represents sparse channels in the angular domain with Laplace prior. The simulation results demonstrate that MAP channel estimates using the approximated posteriors yield a capacity which is close to the one achieved with the true posteriors, thus demonstrating the effectiveness of the proposed method.
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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".