Enhancing and Generalizing Position-Velocity Tracking in Imperfect <i>mm</i>Wave Systems Using a Low-Complexity Neural Network
Bibliographic record
Abstract
This work aims to enhance and generalize the joint position-velocity tracking process in millimeter wave (mmWave) systems that suffer from hardware impairments (HWIs), all while considering computational complexity. Initially, we investigate the performance of two widely used traditional trackers: the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). Through this investigation, we identify the strengths and limitations of these trackers. Besides, we evaluate the gap between traditional tracking performance and the theoretical optimum by deriving the Bayesian Cramér-Rao Bound (BCRB) as a benchmark. Our findings reveal a significant disparity between the performance of traditional trackers and the benchmark, with performance being influenced by noise characteristics, initial conditions, and the accuracy of prior knowledge about the transition model. To address these challenges, we propose a neural network (NN)-based approach to achieve accurate and generalized tracking without relying on prior knowledge of the transition model, initial conditions, or noise characteristics. Specifically, our method trains a NN that performs effectively under any noise conditions, without needing to recognize the transition model or initial state. To manage the computational demands of the training phase, we employ a low-complexity algorithm, the Extreme Learning Machine (ELM), which calculates weights and biases through closed-form solution, avoiding complex optimization processes. Finally, we validate the accuracy and generality of the ELM tracker through computer simulations, testing it under various scenarios, including Gaussian and non-Gaussian HWI distortions, as well as systems with known transition models and those involving uncharacterized inputs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".