Enhanced performance of MIMO multi-branch hybrid neural network in single receiver MIMO visible light communication system

For the single receiver multiple-input-multiple-output (SR-MIMO) visible light communication (VLC) system, the superposing of two transmitters will introduce severe distortion in the time-domain and frequency-domain. In this paper, we first proposed a MIMO multi-branch hybrid neural network (MIMO-MBNN) as the post-equalizer in the SR-MIMO pulse amplitude magnitude eight levels (PAM8) VLC system. Compared with the traditional single-input-single-output least mean square equalizer with Volterra series (SISO-LMS) and SISO deep neural network (SISO-DNN), MIMO-MBNN can achieve at most 3.35 dB Q factor improvement. Furthermore, the operation range of MIMO-MBNN is at least 2.33 times of SISO-DNN and SISO-LMS among the measured signal peak to peak voltage. At last, 2.1 Gbps data rate is achieved by MIMO-MBNN below the 7% hard-decision forward error correction (HD-FEC) threshold. As far as we know, this is the highest data rate in the SR-MIMO VLC system.