Localization with Reconfigurable Intelligent Surface: An Active Sensing Approach
This work addresses localization challenges in wireless communication systems, offering an incremental improvement through active sensing with RISs.
The paper tackles the problem of uplink localization of a remote user using reconfigurable intelligent surfaces (RISs) by proposing an active sensing strategy that adaptively adjusts sensing vectors based on sequential pilot transmissions, resulting in improved performance over non-active methods and showing that a setup with one BS and multiple RISs can outperform multiple BSs.
This paper addresses an uplink localization problem in which a base station (BS) aims to locate a remote user with the help of reconfigurable intelligent surfaces (RISs). We propose a strategy in which the user transmits pilots sequentially and the BS adaptively adjusts the sensing vectors, including the BS beamforming vector and multiple RIS reflection coefficients based on the observations already made, to eventually produce an estimated user position. This is a challenging active sensing problem for which finding an optimal solution involves searching through a complicated functional space whose dimension increases with the number of measurements. We show that the long short-term memory (LSTM) network can be used to exploit the latent temporal correlation between measurements to automatically construct scalable state vectors. Subsequently, the state vector is mapped to the sensing vectors for the next time frame via a deep neural network (DNN). A final DNN is used to map the state vector to the estimated user position. Numerical result illustrates the advantage of the active sensing design as compared to non-active sensing methods. The proposed solution produces interpretable results and is generalizable in the number of sensing stages. Remarkably, we show that a network with one BS and multiple RISs can outperform a comparable setting with multiple BSs.