Abstract
An Improved Minor Component Analysis Algorithm Based on Convergence Analysis of 5G Multi-Dimensional Signals
Highlights
5G technology brings drastic improvements as well as challenges almost everywhere
(11), (31) and (33) indicate that when the learning process is close to convergence, Feng’s Minor component analysis (MCA) neural network algorithm has a smaller q1(t) and higher convergence speed compared with OJAn, AMEX, and OJAm algorithms
Characteristic analysis of 5G multi-dimensional signals is challenging and MCA is a powerful approach to leverage
Summary
Since the pioneering work of [8] on MCA neural network algorithms, many adaptive MCA neural network learning algorithms have been developed [11], [26]. Selecting an initial weight vector with a large norm will increase the convergence speed of Feng’s MCA neural network algorithm. We use the AMEX algorithm (25) to extract the minor component of R1.In this simulation, learning step μ = 0.1 and four different initial weight vectors wj(0)(j = 1, 2, 3, 4) are selected, where w1(0) = 0.001 ∗, w2(0) = 0.1 ∗, w3(0) = ∗, and w4(0) = 100 ∗. A simulation is conducted to illustrate the convergence speeds of OJAn, AMEX, and OJAm algorithms In this simulation, the three MCA learning algorithms are used to extract the minor component of correlated matrix R1. The experimental results shown in the two figures are identical to the analysis result
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