Smart antenna self-adapting interference suppression method based on least square-lowest mean square

Abstract

The invention relates to a smart antenna self-adapting interference suppression method based on least square-lowest mean square, which is an interference suppression algorithm based on training sequences; and by combining the least square algorithm with the lowest mean square algorithm, the method increases the velocity of convergence of the least mean square algorithm. The method includes the following steps: radio-frequency signals received by array antenna are converted into IF signals by a down converter; the IF signals are processed by the operations of analogue-to-digital conversion and digital down-conversion to obtain zero IF digital signals; the zero IF digital signals obtained in the step 2; and the local training sequence are utilized for carrying out corresponding processing to obtain local reference signals by computation delay; the low snapshot least square algorithm is utilized for calculating the initial weight vector of an aerial array; the weight vector calculated in the step 4 is used as the initial weight vector of the least mean square algorithm, and the least mean square algorithm is utilized for updating the aerial array weight vector; and the array weight vector calculated in the step 5 is adopted for the interference suppression of user data. The method achieves the purposes of increasing the availability of frequency spectrum of the system and reducing the complexity of the system.

Description

technical field [0001] The invention relates to an intelligent antenna interference suppression algorithm. In particular, it relates to a training sequence-based least square-least mean square smart antenna adaptive interference suppression method. Background technique [0002] In the early 1990s, array signal processing technology was introduced into mobile communication, and soon formed a new research hotspot - smart antenna. Smart antennas are defined as antenna arrays with direction finding and beamforming capabilities, and are generally divided into two categories: multi-beam smart antennas and adaptive array smart antennas, referred to as multi-beam antennas and adaptive antennas. The direction of each beam of the multi-beam antenna is fixed. It determines the direction of arrival of the signal through detection technology, and then selects the corresponding beam by adjusting the weighting coefficient of each array element. The structure is simple. However, as the us...

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Problems solved by technology

[0004] The Least Mean Squares (LMS) adaptive interference method based on the training sequence is widely used in communication systems, but because the LMS algorithm is sensitive to the initial value, the convergence speed is slow, and the convergence performance is related to the interference environment, so A long training sequence is required to achieve the ideal interference suppression effect, which seriously wastes precious frequency resources

Although the least squares (LS) optimal beamforming has high interference suppression performance and the interference suppression performance is independent of the interference environment, the algorithm has a large amount of computation, which is not conducive to the actual system implementation

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