Indoor Signal Focusing with Deep Learning Designed Reconfigurable Intelligent Surfaces
Top Cited Papers
- 1 July 2019
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Reconfigurable Intelligent Surfaces (RISs) comprised of tunable unit elements have been recently considered in indoor communication environments for focusing signal reflections to intended user locations. However, the current proofs of concept require complex operations for the RIS configuration, which are mainly realized via wired control connections. In this paper, we present a deep learning method for efficient online wireless configuration of RISs when deployed in indoor communication environments. According to the proposed method, a database of coordinate fingerprints is implemented during an offline training phase. This fingerprinting database is used to train the weights and bias of a properly designed Deep Neural Network (DNN), whose role is to unveil the mapping between the measured coordinate information at a user location and the configuration of the RIS's unit cells that maximizes this user's received signal strength. During the online phase of the presented method, the trained DNN is fed with the measured position information at the target user to output the optimal phase configurations of the RIS for signal power focusing on this intended location. Our realistic simulation results using ray tracing on a three dimensional indoor environment demonstrate that the proposed DNN-based configuration method exhibits its merits for all considered cases, and effectively increases the achievable throughput at the target user location.Keywords
This publication has 13 references indexed in Scilit:
- Massive MIMO Channel Estimation for Millimeter Wave Systems via Matrix CompletionIEEE Signal Processing Letters, 2018
- CSI Amplitude Fingerprinting-Based NB-IoT Indoor LocalizationIEEE Internet of Things Journal, 2018
- Achievable Rate Maximization by Passive Intelligent MirrorsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- Beyond Massive MIMO: The Potential of Positioning With Large Intelligent SurfacesIEEE Transactions on Signal Processing, 2018
- High-accuracy positioning for indoor applications: RFID, UWB, 5G, and beyondPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Cognitive femtocell networks: an opportunistic spectrum access for future indoor wireless coverageIEEE Wireless Communications, 2013
- Capacity and coverage enhancement in heterogeneous networksIEEE Wireless Communications, 2011
- Focused Microstrip Array Antenna Using a Dolph-Chebyshev Near-Field DesignIEEE Transactions on Antennas and Propagation, 2009
- Multilayer feedforward networks are universal approximatorsNeural Networks, 1989
- A Statistical Model for Indoor Multipath PropagationIEEE Journal on Selected Areas in Communications, 1987