Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning

• URL: http://arxiv.org/abs/2405.11816v1
• Date: Mon, 20 May 2024 06:23:22 GMT
• Title: Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning
• Authors: Anastasios Foliadis, Mario H. Castañeda, Richard A. Stirling-Gallacher, Reiner S. Thomä,
• Abstract summary: deep learning (DL) techniques for radio-based positioning of user equipment (UE) through channel state information (CSI) fingerprints have demonstrated significant potential. This paper proposes a novel DL model structure consisting of two parts, where the first part aims at identifying features that are independent from any specific environment, while the second part combines those features in an environment specific way with the goal of positioning. Our findings indicate that employing the MEML approach for initializing the weights of the DL model for a new unseen environment significantly boosts the accuracy of UE positioning in the new target environment as well the reliability of its uncertainty estimation.
• Score: 1.1763850077553188
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Utilizing deep learning (DL) techniques for radio-based positioning of user equipment (UE) through channel state information (CSI) fingerprints has demonstrated significant potential. DL models can extract complex characteristics from the CSI fingerprints of a particular environment and accurately predict the position of a UE. Nonetheless, the effectiveness of the DL model trained on CSI fingerprints is highly dependent on the particular training environment, limiting the trained model's applicability across different environments. This paper proposes a novel DL model structure consisting of two parts, where the first part aims at identifying features that are independent from any specific environment, while the second part combines those features in an environment specific way with the goal of positioning. To train such a two-part model, we propose the multi-environment meta-learning (MEML) approach for the first part to facilitate training across various environments, while the second part of the model is trained solely on data from a specific environment. Our findings indicate that employing the MEML approach for initializing the weights of the DL model for a new unseen environment significantly boosts the accuracy of UE positioning in the new target environment as well the reliability of its uncertainty estimation. This method outperforms traditional transfer learning methods, whether direct transfer learning (DTL) between environments or completely training from scratch with data from a new environment. The proposed approach is verified with real measurements for both line-of-sight (LOS) and non-LOS (NLOS) environments.

Related papers

• A Meta-learning based Generalizable Indoor Localization Model using Channel State Information [8.302375673936387]
  We propose a new meta-learning algorithm, TB-MAML, intended to further improve generalizability when the dataset is limited. We evaluate the performance of TB-MAML-based localization against conventionally trained localization models and localization done using other meta-learning algorithms.
  arXiv  Detail & Related papers  (2023-05-22T19:54:59Z)
• Multi-Environment based Meta-Learning with CSI Fingerprints for Radio Based Positioning [2.541166904313997]
  We propose a DL model consisting of two parts: the first part aims to learn environment independent features. The second part combines those features depending on the particular environment. We show that for positioning in a new environment, initializing a DL model with the meta learned environment independent function achieves higher UE positioning accuracy.
  arXiv  Detail & Related papers  (2022-10-26T06:43:42Z)
• Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning [97.28779163988833]
  Multiple pre-training objectives fill the vacancy of the understanding capability of single-objective language modeling. We propose textitMOMETAS, a novel adaptive sampler based on meta-learning, which learns the latent sampling pattern on arbitrary pre-training objectives.
  arXiv  Detail & Related papers  (2022-10-19T04:38:26Z)
• Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration [83.96729205383501]
  We introduce prompt-based learning to achieve fast adaptation for language embeddings. Our model can adapt to diverse vision-language navigation tasks, including VLN and REVERIE.
  arXiv  Detail & Related papers  (2022-03-08T11:01:24Z)
• Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective [52.497514255040514]
  This work develops a new approach that enables data-driven methods to continuously learn and optimize resource allocation strategies in a dynamic environment. We propose to build the notion of continual learning into wireless system design, so that the learning model can incrementally adapt to the new episodes. Our design is based on a novel bilevel optimization formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2021-05-03T07:23:39Z)
• Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment [55.91291559442884]
  This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. We propose to build the notion of continual learning into the modeling process of learning wireless systems. Our design is based on a novel min-max formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2020-11-16T08:24:34Z)
• Transfer Meta-Learning: Information-Theoretic Bounds and Information Meta-Risk Minimization [47.7605527786164]
  Meta-learning automatically infers an inductive bias by observing data from a number of related tasks. We introduce the problem of transfer meta-learning, in which tasks are drawn from a target task environment during meta-testing.
  arXiv  Detail & Related papers  (2020-11-04T12:55:43Z)
• Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement [56.40587594647692]
  We propose a novel transfer learning algorithm, introducing the idea of Target-awareness REpresentation Disentanglement (TRED) TRED disentangles the relevant knowledge with respect to the target task from the original source model and used as a regularizer during fine-tuning the target model. Experiments on various real world datasets show that our method stably improves the standard fine-tuning by more than 2% in average.
  arXiv  Detail & Related papers  (2020-10-16T17:45:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.