DFT-Assisted Multimodulus Blind Equalization for GFDM

Abstract

As sixth-generation (6G) network development progresses, research continues to explore various technologies and new waveforms to meet the demands of future communications. Generalized frequency division multiplexing (GFDM) is considered as a promising waveform due to its adaptable structure, reduced latency, and efficient use of the spectrum. Nevertheless, nonorthogonal subcarriers of GFDM induce severe inter-symbol interference (ISI) and inter-carrier interference (ICI), leading to slow convergence and elevated symbol error rate (SER) floors. This study introduces a blind equalization strategy that integrates the multimodulus algorithm (MMA) with discrete Fourier transform (DFT)-based channel estimation. The simulation results show that a GFDM system employing the proposed MMA-DFT blind equalizer with $K=128$ subcarriers and $M=5$ subsymbols over the long-term evolution extended pedestrian A (LTE-EPA) channel closely approaches the theoretical bound within 20 GFDM-block iterations. Moreover, the mean square error (MSE) of MMA-DFT converges within approximately 15–20 GFDM blocks, enabling near-optimal detection performance with a limited number of adaptation iterations. Furthermore, as a result of the higher effective signal-to-noise ratio (SNR) enabled by the reduced cyclic prefix (CP) overhead of GFDM, the MMA-DFT equalizer applied to GFDM achieves a 0.89-dB SER gain compared to the same MMA-DFT equalizer applied to orthogonal frequency division multiplexing (OFDM) under identical parameter settings. A theoretical SER expression incorporating misadjustment, channel frequency response (CFR)-estimation error, and residual ICI is derived and shown to closely match the simulation. The proposed method reduces pilot overhead, improves spectral efficiency, and supports scalable massive multiple-input multiple-output (mMIMO) scenarios, contributing to energy-efficient future networks.