Data-Based Model for Wide Nulling Problem in Adaptive Digital Beamforming Antenna Array
An adaptive digital beamforming(DBF)array is vital in advanced wireless systems.Beamforming problems in DBF ar-rays could be time-consuming and inflexible,as most of them are only solvable by optimization algorithms.In this letter,we propose an improvement to this issue in solving wide nulling problems of an adaptive DBF array by building a data-based wide nulling model using a powerful optimization algorithm—the Bat algorithm(BA)with the general regression neural network(GRNN).The BA ef-ficiently generates the necessary full complex weights as training samples to train the model.The GRNN estimates the data in the gaps of training samples and gives an efficient and flexible esti-mation.A 32-element uniform linear array is used an example to demonstrate the proposed algorithm.Numerical experimental re-sults show that the data-based model is functional with an accept-able performance for all test cases and achieves a much better time efficiency in comparison to using solely the BA in determining the complex weights to wide nulling problems.
Adaptive beamforming antenna array neural networks optimization algorithm
Xiao Xiao Yilong Lu
School of Electrical and Electronics En-gineering,Nanyang Technological University,Singapore 639798
国际会议
深圳
英文
75-79
2021-12-01（万方平台首次上网日期，不代表论文的发表时间）