Anti-Interference RIS-Aided Positioning Method Based on PSO Algorithm
Bibliographic record
Abstract
To address the insufficient anti-interference capability of positioning systems in complex wireless environments, this paper proposes a reconfigurable intelligent surface (RIS)-aided positioning scheme based on particle swarm optimization (PSO), and verifies its anti-interference performance improvement via Cramér-Rao lower bound (CRLB) theoretical analysis. First, a RIS phase response model accounting for hardware nonlinearities is constructed, with polynomial expansion characterizing the nonlinear mapping between incident signal intensity and reflection phase. Second, a composite interference model integrating multipath effects, sinusoidal disturbances, and random noise is designed—multipath interference is simulated by time-varying delays, random phases, and attenuation coefficients, while sinusoidal disturbances represent periodic electromagnetic interference. Furthermore, a PSO-RIS joint optimization framework is adopted, taking the minimization of positioning error bound (PEB) as the objective function and introducing L1-norm regularization constraints to enhance solution sparsity and suppress false multipath interference. Experimental results demonstrate that under various interferences, the PSO algorithm achieves higher positioning accuracy than the traditional quasi-Newton algorithm.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".