Interference Nulling Using Reconfigurable Intelligent Surface
Bibliographic record
Abstract
This chapter studies the interference nulling capability of reconfigurable intelligent surface (RIS) in a K user interference channel, where K single-antenna transceiver pairs communicate using the same time and frequency resources. Assuming the availability of channel state information (CSI), it can be shown that the RIS is capable of eliminating all the interference if the channels between the RIS and the transceivers are line of sight and the number of RIS elements is sufficiently large. For an arbitrary set of channel realizations, we present an efficient alternating projection algorithm to solve the interference nulling problem. Simulation results show that the alternating projection algorithm can find an interference nulling solution when the number of RIS elements is slightly larger than 2 K ( K − 1 ) . This chapter also addresses the scenario in which the channel is unknown, and a pilot stage is needed to estimate the channel. In this context, we propose a learning-based approach to learn an initial point for the subsequent alternating optimization algorithm. Simulation results show that the learning-based approach can achieve a much lower interference level than the random initialization scheme for a fixed pilot length.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".