Using Reflective Intelligent Surfaces for Indoor Scenarios: Channel Modeling and RIS Placement

Abstract

In the recent year, people had to work from home due to the outbreak of the COVID-19 pandemic. When the majority of the family members are working online, the bitrate experienced by the average user may drop, especially if some members have to work in rooms that suffer from weak coverage. Benefiting from the emerging concept of Reflective Intelligent Surfaces (RIS), the network coverage in our houses can be greatly improved. This paper presents a study of an RIS-assisted system for an indoor scenario operating at 2.4GHz. We propose an RIS placement approach that is based on minimizing the pathloss of the channel, to enhance the rate of bad coverage rooms, while taking into consideration their user occupancies. The proposed approach, which we refer to as the Weighted RIS Placement, is modeled and simulated for a single RIS. The problem is then extended to a two-RIS scenario. Our results show that the Weighted RIS placement provides significant rate gains. Also, this is the first work that models the communication channels for the individual rooms, using corresponding Rician K-factor values that reflect the indoor layout.