Reconfigurable Intelligent Surface-Assisted Millimeter-Wave Wireless Communications

The development of 6th Generation (6G) wireless communication technologies mark a new era in global connectivity, characterized by ultra-fast data transmission, near-zero latency, and the ability to support massive device density. Central to the 6G vision is the development of an intelligent communication environment, where machine learning, artificial intelligence, and edge computing enable real-time adaptive control and decision-making. Through technologies like reconfigurable intelligent surfaces (RIS), the environment can be tailored in real time to adapt to changes in network conditions, user mobility, and spatial configurations.

A reflecting RIS is an advanced technology used to enhance wireless communication by actively controlling the way electromagnetic waves propagate. Composed of numerous small, programmable elements, a reflecting RIS can reflect or absorb incoming signals to optimize their path toward a receiver. However, existing research has not adequately addressed the optimization of the quantity and deployment locations of RISs for effectively serving a mobile robot. To address this research gap, I explore the application of RIS-assisted millimeter-wave (mmWave) communications for a mobile robot operating within an indoor industrial environment containing fixed obstacles. I minimize the transmission energy consumption of the access point (AP) by jointly optimizing the number, positions, and phase shifts of the RISs, and the beamforming vectors of the AP. Simulation results indicate that the proposed algorithm converges efficiently and successfully identifies the optimal configuration of RISs, significantly reducing the transmission energy consumption of the AP.

Unlike reflecting RIS, which merely reflect signals, an intelligent omni-surface (IOS) can simultaneously reflect and refract signals while modifying their properties, such as phase shifts and amplitude. However, existing research has not sufficiently investigated the optimization of the number and deployment locations of IOSs for serving multiple indoor users. To tackle this research gap, I investigate IOS-assisted outdoor-to-indoor mmWave communications. With a fixed total number of refracting elements, I maximize the downlink energy efficiency of the outdoor base station (BS) by jointly optimizing the number, locations, and phase shifts of the IOSs, along with the beamforming vectors of the BS. Simulation results show that the proposed algorithms efficiently determine the optimal number and placement of IOSs, significantly improving the energy efficiency of the outdoor BS.

In conclusion, the utilization of RIS/IOS significantly enhances the performance of wireless communications, and further improvements can be achieved through the optimization of deployment strategies and on/off state management. In the future, I shall extend RIS deployment to 3D space to enable more complex strategies, incorporate real-time optimization with adaptive algorithms, and explore advanced configurations, such as integrating RIS with urban landscapes, aerial platforms, and coordinating multiple RISs for managing complex electromagnetic environments.