Authors: Wei Rao; Huijun Xu; Jianqiu Zhang
Addresses: Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang 330099, China ' Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang 330099, China ' Key Laboratory for Information Science of Electromagnetic Waves (MoE), School of Information Science and Technology, Fudan University, Shanghai 200433, China
Abstract: It is well known that for constant modulus (i.e., magnitude) signals the famous constant modulus (CM) blind equalisation algorithm implemented in a fractionally spaced equaliser can present a zero steady-state mean square error (MSE), which means completely eliminating the distortions introduced in transmitting signals through channels. But for non-constant modulus signals it suffers a large steady-state MSE. In order to overcome this defect, a segment cost function according to the CM criterion is suggested. The distinctive feature of the segment cost function is that the equalised signals (of the non-constant modulus signals) are divided into three segments to form a ⊓ shape where the ideal signals have a constant modulus instead of non-constant. And then a new blind equalisation algorithm seeks to minimise this segment cost function by applying a stochastic gradient method is proposed. When employing the proposed algorithm to equalise the 4-PAM or 16-QAM non-constant modulus signals, just as using the CM blind equalisation algorithm to equalise the 4-QAM constant modulus signal, a zero steady-state MSE can be obtained, which is derived. Compared to the classical bind equalisation algorithms, such as CMA, MMA, MCMA, or CMA+SDD, the proposed algorithm yields improved performance, especially for higher SNR.
Keywords: adaptive equaliser; blind deconvolution; blind equalisation; CMA; constant modulus algorithm.
International Journal of Wireless and Mobile Computing, 2019 Vol.16 No.4, pp.295 - 304
Accepted: 19 Nov 2018
Published online: 30 May 2019 *