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The indoor radio propagation channel is typically modeled as a two-state time-variant process, where one of the states represents the channel when the environment is static, whereas the other state characterizes the medium when it is altered by people. In this paper, the aforementioned process is augmented with an additional state. It is shown that the changes in received signal strength are dictated by: 1) electronic noise, when a person is not present in the monitored area; 2) reflection, when a person is moving in the close vicinity of line-of-sight; and 3) shadowing, when a person is obstructing the line-of-sight component of the transmitter–receiver pair. Statistical and spatial models for the three states are derived, and the models are empirically validated. Based on the models, a link line monitoring system is designed, which aims to, first, estimate the temporal state of the channel using a hidden Markov model, and, second, track a person using a particle filter. The results suggest that the presented system outperforms other state-of-the-art systems in terms of localization accuracy while increasing size of the link's sensing region. [ABSTRACT FROM PUBLISHER]

Low-data-rate wireless networks can be deployed for physical intrusion detection and localization purposes. The intrusion of a physical object (or human) will disrupt the radio-frequency magnetic field and can be detected by observing the change in radio attenuation. This gives the basis for the radio tomographic imaging technology, which has recently been developed for passively monitoring and tracking objects. Due to noise and the lack of knowledge about the number and the sizes of intruding objects, multiobject intrusion detection and localization is a challenging issue. This paper proposes an extended variational Bayesian Gaussian mixture model (VB-GMM) algorithm in treating this problem. The extended VB-GMM algorithm applies a Gaussian mixture model to model the changed radio attenuation in a monitored field due to the intrusion of an unknown number of objects and uses a modified version of the variational Bayesian approach for model estimation. Real-world data from both outdoor and indoor experiments (using the radio tomographic imaging technology) have been used to verify the high accuracy and the robustness of the proposed multiobject localization algorithm. [ABSTRACT FROM AUTHOR]

The widespread applications of massive MTC are limited by energy availability, spectrum congestion, and device costs. The emerging AmBC not only addresses these bottlenecks but also opens opportunities for new applications. This article aims to explore AmBC-enhanced future ultra-low-power MTC. In this context, we present the development trends in AmBC prototype designs and discuss potential applications, highlight the specific features of the AmBC technology, and review AmBC receiver designs. Finally, we investigate and outline the future research challenges and trends from the practical aspects of AmBC systems. [ABSTRACT FROM AUTHOR]

In ambient rescatter communications, devices convey information by modulating and rescattering the radio frequency signals impinging on their antennas. In this correspondence, we consider a system consisting of a legacy modulated continuous carrier multiple-input-multiple-output link and a multiantenna modulated rescatter (MRS) node, where the MRS node modulates and rescatters the signal generated by the legacy transmitter. The receiver seeks to decode both the original message and the information added by the MRS. We show that the achievable sum rate of this system exceeds that which the legacy system could achieve alone. We further consider the impact of channel estimation errors under the least squares channel estimation and study the achievable rate of the legacy and MRS systems, where a linear minimum mean square error receiver with successive interference cancellation is utilized for joint decoding. [ABSTRACT FROM PUBLISHER]