The method of moments in the problem of estimating the parameters of a communication channel
Natalia E. Poborchaya, S. A. Zharkikh, E. M. Lobov · T-Comm - Телекоммуникации и Транспорт · 2024
This paper introduces a synthesis of an algorithm for estimating the parameters of a communication channel based on the observed M-QAM signal with a known information sequence. The proposed algorithm leverages the method of moments, expressed in the form of the Tikhonov A.N. functional, which allows for the efficient estimation of various channel parameters. These parameters include the amplitude, phase, and frequency shift of the received signal. The phase noise is also incorporated into the phase model to provide a more accurate reflection of real-world channel conditions. The estimation process was carried out under conditions of additive white Gaussian noise (AWGN), as well as in scenarios where the noise followed a lognormal probability distribution. The phase was assumed to follow a uniform distribution. To evaluate the performance of the proposed algorithm, a computational experiment was conducted, focusing on the accuracy of parameter estimation for a 64-QAM signal. The results obtained using this method were compared with those achieved through advanced Kalman filtering, a well-known approach for parameter estimation in communication systems. Additionally, a rough analysis of the computational complexity of the algorithms was performed, comparing the method of moments to recursive nonlinear filtering algorithms. Experimental curves depicting the interference immunity of 64-QAM signal reception were obtained using both the synthesized algorithm and the advanced Kalman filtering method. These results provide valuable insights into the effectiveness and practical feasibility of the proposed approach for parameter estimation in communication channels, particularly in noisy environments. To further validate the robustness of the proposed algorithm, different noise levels and channel conditions were tested in the simulations. This allowed for a comprehensive assessment of the algorithm's adaptability and accuracy under varying signal-to-noise ratios (SNR), ensuring its applicability across a wide range of practical communication scenarios.