Cooperative Positioning with the Aid of Reconfigurable Intelligent Surface
Bibliographic record
Abstract
This thesis investigates the integration of reconfigurable intelligent surfaces (RISs) with cooperative positioning (CP) techniques to enhance localization accuracy and enable robust positioning in challenging wireless environments. The research addresses scenarios both with and without access points (APs), presenting novel frameworks and algorithms. Initially, the work establishes foundational channel models for RIS-aided Device-to-Device (D2D) communications in two-dimensional and three-dimensional settings. For AP-present scenarios, a key contribution is an innovative uplink CP framework featuring a resource-efficient model that allows simultaneous multi-user pilot transmissions on orthogonal subcarriers, thereby reducing overhead. This includes a joint optimization of user equipment (UE) scheduling, power allocation, and RIS phase shifts, solved iteratively via block coordinate descent (BCD), to minimize the Cramér-Rao lower bound (CRLB). A two-stage localization algorithm, combining a one-dimensional (1D) search with multidimensional scaling (MDS) for refinement using D2D channel parameters, demonstrates significant performance gains, achieving a 65% improvement over non-cooperative methods. Subsequently, the thesis pioneers AP-free CP, demonstrating its feasibility using only a single RIS and D2D communications, provided a minimum of three half-duplex UEs cooperate. This approach contrasts with prior works, which often require full-duplex UEs or multiple RISs. Practical RIS phase shift designs, including directional codebooks based on estimated spatial frequencies rather than absolute angles, alongside an optimized power allocation strategy, are proposed for this AP-free context. An AP-free localization algorithm, employing a 1D coarse search followed by maximum likelihood estimation (MLE) for refinement, achieves sub-meter accuracy even under multi-path conditions. Finally, the AP-free paradigm is extended to tracking multiple moving UEs using multiple RISs as anchors, where an extended Kalman filter (EKF) maintains high tracking accuracy in the sub-meter range. Overall, this thesis underscores the significant potential of integrating RIS technology with D2D communications to revolutionize wireless positioning systems for 6G and beyond.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".