Research on Algorithm Architecture and Key Techniques of the Joint Communication and Sensing Based on Big Data from Air Interface
The project explores the spatial consistency of wireless propagation environment and investigates the reconstruction design of global channel state information (CSI). Two CSI reconstructions schemes, including one based on spatial parameter interpolation and the other one based on limited feedback, are designed.
Importance/impact, challenges/pain points
CSI with high accuracy is crucial to harvest the performance gain of many wireless algorithms and improve the overall performance of wireless networks. The acquisition of accurate CSI at lower feedback cost is of great importance in massive MIMO systems. The CSI reconstruction based on spatial consistency can exploit the dataset sensed effectively to boost the accuracy of the reconstructed CSI and reduce the feedback overhead greatly. However, how to utilize the sensing data and how to achieve high reconstruction accuracy while reducing the feedback overhead are very challenging.
By analyzing the spatial consistency of channel parameters, we first propose a CSI reconstruction based on the aligning and spatial interpolation over channel parameters. Furthermore, considering the limited feedback, we propose another reconstruction scheme based on the construction of space basis and the property of code fed back.
Key contribution/commercial implication
The reconstruction scheme based on the spatial interpolation of propagation parameters can get the accurate estimation of most parameters. With the phase information fed back from UE, an averaged NMSE below −15 dB of reconstructed CSI is achieved. The second scheme via the basis construction and the utilization of code property only takes 3−5 rounds of Type I code feedback to exceed the reconstruction performance of Type II code.
Tsung-Hui Chang, Lei Li, Qian Chen