Intelligent Reflecting Surfaces and Classical Relays: Coexistence and Co-Design

This paper investigates a multiuser downlink communication system with coexisting intelligent reflecting surface (IRS) and classical half-duplex decode-and-forward (DF) relay. In this system, the IRS and the DF relay interact with each other and assist transmission simultaneously. In particular, active beamforming at the base station (BS) and at the DF relay, and passive beamforming at the IRS, are jointly designed to maximize the sum-rate of all users. The sum-rate maximization problem is nonconvex due to the coupled beamforming vectors. We propose an alternating optimization (AO) based algorithm to tackle this complex co-design problem. Numerical validation and discussion on the superiority of the coexistence system and the tradeoffs therein are presented.

Analysis and Visualization of Deep Neural Networks in Device-Free Wi-Fi Indoor Localization

Device-free Wi-Fi indoor localization has received significant attention as a key enabling technology for many Internet of Things (IoT) applications. Machine learning-based location estimators, such as the deep neural network (DNN), carry proven potential in achieving high-precision localization performance by automatically learning discriminative features from the noisy wireless signal measurements. However, the inner workings of DNNs are not transparent and not adequately understood especially in the indoor localization application. In this paper, we provide quantitative and visual explanations for the DNN learning process as well as the critical features that DNN has learned during the process. Toward this end, we propose to use several visualization techniques, including: 1) dimensionality reduction visualization, to project the high-dimensional feature space to the 2D space to facilitate visualization and interpretation, and 2) visual analytics and information visualization, to quantify relative contributions of each feature with the proposed feature manipulation procedures. The results provide insightful views and plausible explanations of the DNN in device-free Wi-Fi indoor localization using channel state information (CSI) fingerprints.