Efficient Recovery of Linear Predicted Coefficients Based on Adaptive Steepest Descent Algorithm in Signal Compression for End-to-End Communications

Abstract

The efficiency of recovery and signal decoding efficacy at the receiver in end-to-end communications using linearly predicted coefficients are susceptible to errors, especially for highly compressed signals. In this paper, we propose a method to efficiently recover linearly predicted coefficients for high signal compression for end-to-end communications. Herein, the steepest descent algorithm is applied at the receiver to decode the affected linear predicted coefficients. This algorithm is used to estimate the unknown frequency, time, and phase. Subsequently, the algorithm facilitates down-conversion, time and carrier recovery, equalization, and correlation processes. To evaluate the feasibility of the proposed method, parameters such as multipath interference, additive white Gaussian noise, timing, and phase noise are modeled as channel errors in signal compression using the software-defined receiver. Our results show substantial recovery efficiency with noise variance between 0 and $y$ × 10E − 3, where $y$ lies between 0 and 10 using the modeled performance metrics of bit error rate, symbol error rate, and mean square error. This is promising for modeling software-defined networks using highly compressed signals in end-to-end communications.