Adaptive Digital Predistortion based on MC-FQRD-RLS Algorithm using Indirect Learning Architecture
Multichannel fast QR decomposition recursive leastsquares (MC-FQRD-RLS) algorithms are well known for their good numerical properties and low computational complexity. There are tow distinct ways to obtain the expended input vector, sequential-type method, and block-type one. The latter one, despite exhibiting a higher computational burden as compared to the former one, has some attractive features, e.g., suitability for parallel implementation. Predistortion techniques for linearizing power amplifier (PA) nonlinearity with indirect learning architecture (IDLA) are widely used. The benefit of the IDLA leaves unnecessary the assumption of a model for PA, corresponding parameters estimation and inverse construction. In this paper we present a new technique for predistortion using block-type MC-FQRD-RLS algorithm with IDLA, in which the predistorter is constructed by simplified volterra model. Simulations results verify that the proposed techniques have good convergence property.
predistortion MC-FQRD-RLS Algorithm block-type IDLA volterra model
You Li Xiaolin Zhang
School of Electronic and Information Engineering Beihang University Beijing China
国际会议
The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)
沈阳
英文
240-242
2010-03-27（万方平台首次上网日期，不代表论文的发表时间）