Near-ML Detection for MIMO-GFDM

For upcoming 5G networks, new challenges are posed on the physical layer, which go beyond increased data rate. Generalized Frequency Division Multiplexing (GFDM) is proposed as a candidate waveform to combat these challenges. However, inherent self-interference between subcarriers of GFDM hinders the application of standard spatial multiplexing (SM) detection algorithms. We present an algorithm that combines maximum likelihood and successive interference cancellation detection techniques that allows to exploit the inherent frequency diversity of GFDM coming from self-interference. Computer simulations reveal that the proposal outperforms OFDM in terms of symbol error rate in fading multipath channels. These findings prove self- interference to be beneficial and that SM can be successfully applied to GFDM.