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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".