Estimation of Nonlinear Wireless Channel Based on Incremental Spline Adaptive Filtering

Channel estimation in a wireless sensor network is imperative to error-free information dissemination and data collection. The estimation procedure is challenging if there exists a nonlinear distortion to the communication signal due to the radio-frequency components in the transmitting or receiving entity. It has drawn attention to nonlinear system modeling for channel estimation, where lately, one of the most important methods has been spline adaptive filter (SAF). The necessity of updating both linear filter coefficients and nonlinear control points makes the adaptation process slow. Hence, we propose an incremental spline adaptive filter using the least mean square algorithm (ISAF-LMS), which acquires faster convergence while estimating non-linearity along with linear filter coefficients. The steady-state performance of the proposed method is carried out by following the energy conservation approach. The simulation result shows faster convergence in the distributed case than in non-cooperative estimation. Further, the performance is compared with diffusion SAF and incremental version of conventional Volterra adaptive filter-based nonlinear channel estimation (IVLMS). The proposed algorithm performance is better than IVLMS.