MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) signal blind separation method for increasing natural gradient algorithm convergence speed

Abstract

The invention discloses an MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) signal blind separation method for increasing the natural gradient algorithm convergence speed. The method comprises the following steps: firstly, making improvements on a Flexible ICA algorithm under the constraint of ICA preprocessing in combination with an ICA principle to obtain an improved iterative algorithm; secondly, giving a method for adaptively feeding back and adjusting an iteration step size based on a separation error in combination with a BP neural network feedback thought with the separation error; and lastly, giving a comprehensive ending criterion of algorithm iteration from the aspects of the convergence of a separation matrix, iteration times and the separation error. Compared with the prior art, the method has the advantages that the requirements on the convergence speed and the separation accuracy are met on the basis of combining the MIMO-OFDM system features, and an effective separation effect is achieved.

Description

technical field [0001] The invention belongs to the field of signal blind source separation, relates to multiple input multiple output (MIMO) system, orthogonal frequency division multiplexing (OFDM) technology, natural gradient algorithm (NGA) and independent component analysis (ICA) theory, etc., and is applicable to wireless Blind separation of signals in communications. Background technique [0002] With its unique advantages in frequency band utilization and anti-multipath fading, MIMO-OFDM is applied to next-generation cellular communications (3GPP-LTE, mobile WiMAX, IMT-Advanced), wireless local area networks (IEEE 802.11a, IEEE 802.11n) , a key technology of wireless personal area network (MB-OFDM) and broadcasting (DAB, DVB, DMB). [0003] The theory of blind source separation refers to the theory that when the source signal is unknown and the mixing method of the source signal is unknown, only some statistical characteristics of the source signal are used to resto...

AI Technical Summary

Problems solved by technology

[0006] (1) In the MIMO-OFDM system, the blind source separation theory researched earlier and more is the blind source separation theory based on the natural gradient algorithm. The natural gradient algorithm can quickly realize self-adaptation in a changing environment, but there are The problem of slow convergence speed. At the same time, choosing an appropriate step size is also very critical. If the step size is not selected properly, it may destroy the convergence of the algorithm.

[0007] (2) The Fast ICA algorithm combines the advantages of fixed-point iteration, projection pursuit and Newton's method. Newton optimization algorithm, the initial value of the iteration is sensitive, and the performance is complex, and sometimes there may be problems such as divergence and oscillation that lead to uncertain convergence

Some schemes propose the concept of variable step size, such as using a large step size at the beginning of the algorithm to speed up the convergence speed, and using a small step size at the end of the algorithm to improve the convergence accuracy, taking into account both the convergence speed and convergence accuracy to a certain extent Balanced, but still in a non-adaptive way

There are also some schemes that propose an improved FastICA method, but they are only for a certain type of signal, and do not break through the limitations of this type of algorithm in the MIMO-OFDM system

Images