Enhancing and Generalizing Position-Velocity Tracking in Imperfect <italic>mm</italic>Wave Systems Using a Low-Complexity Neural Network
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: t...
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10812951/ |
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| author | Deeb Assad Tubail Mohammed Zourob Salama Ikki |
| author_facet | Deeb Assad Tubail Mohammed Zourob Salama Ikki |
| author_sort | Deeb Assad Tubail |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-53f315083f4d4502aa6547d54ae4cb5c |
| institution | OA Journals |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-53f315083f4d4502aa6547d54ae4cb5c2025-08-20T02:17:40ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-01623625110.1109/OJCOMS.2024.352218910812951Enhancing and Generalizing Position-Velocity Tracking in Imperfect <italic>mm</italic>Wave Systems Using a Low-Complexity Neural NetworkDeeb Assad Tubail0https://orcid.org/0000-0003-1950-0083Mohammed Zourob1https://orcid.org/0000-0001-8385-0876Salama Ikki2https://orcid.org/0000-0003-3868-4447Electrical and Computer Engineering Department, Lakehead University, Thunder Bay, ON, CanadaWireless Technologies Research and Development Department, CableLabs, Louisville, CO, USAElectrical and Computer Engineering Department, Lakehead University, Thunder Bay, ON, CanadaThis 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.https://ieeexplore.ieee.org/document/10812951/TrackingKalmanneural networkFisher information matrixBayesianCramer-Rao bound |
| spellingShingle | Deeb Assad Tubail Mohammed Zourob Salama Ikki Enhancing and Generalizing Position-Velocity Tracking in Imperfect <italic>mm</italic>Wave Systems Using a Low-Complexity Neural Network IEEE Open Journal of the Communications Society Tracking Kalman neural network Fisher information matrix Bayesian Cramer-Rao bound |
| title | Enhancing and Generalizing Position-Velocity Tracking in Imperfect <italic>mm</italic>Wave Systems Using a Low-Complexity Neural Network |
| title_full | Enhancing and Generalizing Position-Velocity Tracking in Imperfect <italic>mm</italic>Wave Systems Using a Low-Complexity Neural Network |
| title_fullStr | Enhancing and Generalizing Position-Velocity Tracking in Imperfect <italic>mm</italic>Wave Systems Using a Low-Complexity Neural Network |
| title_full_unstemmed | Enhancing and Generalizing Position-Velocity Tracking in Imperfect <italic>mm</italic>Wave Systems Using a Low-Complexity Neural Network |
| title_short | Enhancing and Generalizing Position-Velocity Tracking in Imperfect <italic>mm</italic>Wave Systems Using a Low-Complexity Neural Network |
| title_sort | enhancing and generalizing position velocity tracking in imperfect italic mm italic wave systems using a low complexity neural network |
| topic | Tracking Kalman neural network Fisher information matrix Bayesian Cramer-Rao bound |
| url | https://ieeexplore.ieee.org/document/10812951/ |
| work_keys_str_mv | AT deebassadtubail enhancingandgeneralizingpositionvelocitytrackinginimperfectitalicmmitalicwavesystemsusingalowcomplexityneuralnetwork AT mohammedzourob enhancingandgeneralizingpositionvelocitytrackinginimperfectitalicmmitalicwavesystemsusingalowcomplexityneuralnetwork AT salamaikki enhancingandgeneralizingpositionvelocitytrackinginimperfectitalicmmitalicwavesystemsusingalowcomplexityneuralnetwork |