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New Multi-Modulus Algorithms for Blind Decision- Feedback Equalization of High-Order QAM Signals

Feng Liu, Lindong Ge, Jiaming Li, Shigang Liu

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Abstract

Two new multi-modulus algorithms (MMA)-the dual-mode MMA and the stop-and-go dual-mode MMA for blind decision-feedback equalization of highorder quadrature amplitude modulation (QAM) signals are proposed. Simulations using a fractionallyspaced decision-feedback equalization (DFE) setting are used to compare the proposed scheme with the recently introduced multi-modulus algorithm. The proposed blind equalizers are shown to have faster convergence speed and improved steady-state mean square error, compared with the MMA.

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What this paper is about

Two new multi-modulus algorithms (MMA)-the dual-mode MMA and the stop-and-go dual-mode MMA for blind decision-feedback equalization of highorder quadrature amplitude modulation (QAM) signals are proposed. Simulations using a fractionallyspaced decision-feedback equalization (DFE) setting are used to compare the proposed scheme with the recently introduced multi-modulus algorithm. The proposed blind equalizers are shown to have faster convergence speed and improved steady-state mean square error, compared with the MMA.

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Available abstract

Two new multi-modulus algorithms (MMA)-the dual-mode MMA and the stop-and-go dual-mode MMA for blind decision-feedback equalization of highorder quadrature amplitude modulation (QAM) signals are proposed. Simulations using a fractionallyspaced decision-feedback equalization (DFE) setting are used to compare the proposed scheme with the recently introduced multi-modulus algorithm. The proposed blind equalizers are shown to have faster convergence speed and improved steady-state mean square error, compared with the MMA.

Key concepts: Quadrature amplitude modulation, Blind equalization, QAM, Equalization (audio), Computer science, Convergence (economics), Algorithm, Dual mode

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