Parameter Estimation Using Multiple Signal Classification Algorithm for Joint Sensing and Communication System

ABSTRACT Joint sensing and communication (JSAC) technology is anticipated to enable autonomous driving and Extended Reality (XR) in future Wireless Communication Systems (WCS). Pilot signals in wireless communications have excellent passive recognition, anti‐noise and autocorrelation capabilities, which lead to suitability for radar sensing. In this paper, Parameter Estimation using multiple signal classification algorithm for JSAC system (JSAC–MUSIC–PET–OCDM) is proposed. Here, the Multiple Signal Classification (MUSIC) algorithm evaluates radar parameters, demonstrating its effectiveness in simultaneous radar sensing, communication, and positioning. This generates a Cramer–Rao Lower Bound (CRLB) for range and velocity estimation in MUSIC‐based positioning, thereby providing a theoretical framework for performance evaluation. The results show that the OCDM‐MUSIC‐based JSAC system significantly improves the efficiency of radar parameter estimation and the overall system performance. The proposed JSAC‐MUSIC‐PET‐OCDM method is implemented in Python. The JSAC‐MUSIC‐PET‐OCDM attains 28.96%, 33.21%, and 23.89% higher SNR; 22.87%, 31.36%, and 20.34% lower RMSE when compared with existing methods: Fifth generation positioning reference signal‐dependent sensing: a sensing reference signal method for combined sensing and communication system (PRS‐FFT‐JSAC‐OFDM), Radar sensing via OTFS signaling (RS‐PA‐OTFS), and evaluation technique of sensing parameters depending upon Orthogonal Time Frequency Space (MFF‐ISAC‐OTFS) methods respectively.