Active Beam Tracking with Reconfigurable Intelligent Surface
Bibliographic record
Abstract
This paper studies a beam tracking problem in a reconfigurable intelligent surface (RIS)-assisted communication system, in which a single antenna access point (AP) tracks a single-antenna mobile user equipment (UE) through actively reconfiguring the RIS. To maintain beam alignment over time, the mobile UE periodically sends a sequence of pilots to the AP in the uplink, and the AP updates the RIS reflection coefficients for both the subsequent downlink data transmission and uplink pilot reception stages in a sequential fashion. This is an active sensing problem which is analytically intractable. This paper proposes a deep learning framework to solve this problem. We use a neural network architecture based on long short-term memory (LSTM) in which the LSTM cell automatically summarizes the time-varying channel information based on periodically received pilots into a state vector, and the state vector is mapped to the RIS reflection coefficients for subsequent downlink data transmission and uplink pilot reception using two additional deep neural networks (DNNs). Simulation results show that this proposed active sensing approach is able to maintain beam alignment much more efficiently than traditional data-driven methods based only on channel statistics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".