Multidomain Transfer Ensemble Learning for Wireless Fingerprinting Localization

Multidomain localization has emerged as an important learning paradigm for wireless fingerprinting localization, which leverages data from multiple related domains, known as source domains, to enhance location prediction accuracy in a target domain. However, it faces challenges due to label sparsity, feature heterogeneity, and domain correlation issues. Specifically, 1) intradomain classification-based localization models can only make predictions based on existing discrete labels, causing large errors in sparsely labeled areas; 2) feature heterogeneity across domains impedes effective interdomain model communication, limiting multidomain knowledge utilization; and 3) low correlation between some source domains and the target domain can result in negative transfer. In this article, we present multidomain transfer ensemble localization (MDTELoc) to tackle these challenges. To manage label sparsity and feature heterogeneity, we present a classification-to-regression (C2R) ensemble localization model. This model estimates continuous position values, addressing label sparsity, and fosters interdomain communication by sharing ensemble model weights, enhancing multidomain knowledge use. To mitigate the domain correlation issue, we design a multidomain transfer method to model and learn domain relationships via a domain covariance matrix, which handles both positive and negative domain correlations and identifies outlier domains. MDTELoc combines intradomain model ensemble and interdomain knowledge transfer in a Bayesian framework, allowing simultaneous estimation of model parameters and domain correlation. Experimental results from the DeepMIMO simulation data set and a real-world library data set demonstrate our framework's superior performance over other state-of-the-art methods, reducing errors by over 12%.