Multilayer probability extreme learning machine for device-free localization

Device-free localization (DFL) is becoming one of the new techniques in wireless localization field, due to its advantage that the target to be localized does not need to attach any electronic device. One of the key issues of DFL is how to characterize the influence of the target on the wireless links, such that the target’s location can be accurately estimated by analyzing the changes of the signals of the links. Most of the existing related research works usually extract the useful information from the links through manual approaches, which are labor-intensive and time-consuming. Deep learning approaches have attempted to automatically extract the useful information from the links, but the training of the conventional deep learning approaches are time-consuming, because a large number of parameters need to be fine-tuned multiple times. Motivated by the fast learning speed and excellent generalization performance of extreme learning machine (ELM), which is an emerging training approach for generalized single hidden layer feedforward neural networks (SLFNs), this paper proposes a novel hierarchical ELM based on deep learning theory, named multilayer probability ELM (MP-ELM), for automatically extracting the useful information from the links, and implementing fast and accurate DFL. The proposed MP-ELM is stacked by ELM autoencoders, so it also keeps the very fast learning speed of ELM. In addition, considering the uncertainty and redundant links existing in DFL, MP-ELM outputs the probabilistic estimation of the target’s location instead of the deterministic output. The validity of the proposed MP-ELM-based DFL is evaluated both in the indoor and the outdoor environments, respectively. Experimental results demonstrate that the proposed MP-ELM can obtain better performance compared with classic ELM, multilayer ELM (ML-ELM), hierarchical ELM (H-ELM), deep belief network (DBN), and deep Boltzmann machine (DBM)

Machine learning for wireless signal learning

Wireless networks are vulnerable to adversarial devices by spoofing the digital identity of valid wireless devices, allowing unauthorized devices access to the network. Instead of validating devices based on their digital identity, it is possible to use their unique physical fingerprint caused by changes in the signal due to deviations in wireless hardware. In this thesis, the physical fingerprint was validated by performing classification with complex-valued neural networks (NN), achieving a high level of accuracy in the process. Additionally, zero-shot learning (ZSL) was implemented to learn discriminant features to separate legitimate from unauthorized devices using outlier detection and then further separate every unauthorized device into their own cluster. This approach allows 42\% of unauthorized devices to be identified as unauthorized and correctly clustere