A DRSN-Based Wireless Channel Propagation Scenario Identification

Channel scenario identification plays an important auxiliary role in wireless channel modeling as well as in the design of physical layer and network layer algorithms for wireless communications. Traditional approaches rely on exhaustive channel parameter estimation—including path-specific delays, angles and Doppler shift—a process demanding prohibitive computational resources. To enable real-time and accurate identification of the current propagation scenario, this letter proposes a scenario identification method based on least squares channel estimation and a deep residual shrinkage network (LSE-DRSN). The channel impulse response (CIR) is obtained via the low-complexity least squares (LS) method, while the deep residual shrinkage network (DRSN) ensures the scenario identification accuracy in propagation environments where significant estimation errors exist in LS-based CIR acquisition. The simulation results demonstrate that the identification accuracy of the proposed method outperforms existing scenario identification methods, especially under low signal-to-noise ratio (SNR) conditions.