Analysis of the influence of the Lagrange multiplier on the operation of the algorithm for estimating the signal parameters under a priori uncertainty

The paper considers a recurrent regularizing algorithm for joint estimation of distortions of a $M$-ary quadrature amplitude modulation ($M$-QAM) signal obtained in a direct conversion receiver path. The algorithm is synthesized using a modified least squares method in the form of Tikhonov's functional under conditions of a priori uncertainty about the laws of noise distribution. The resulting procedure can work both on the test sequence and on information symbols after the detection procedure. We analyze the influence of the Lagrange multiplier on the accuracy of the estimation procedure and on the complexity of the algorithm. It is shown that, with the same accuracy, the regularizing algorithm requires significantly fewer iterations than the procedure without the Lagrange multiplier, and therefore has a lower computational complexity.