End-to-End Multi-Band Adaptive Optimization Autoencoder Framework for Bandwidth-Limited Optical Wireless Fronthaul

Fronthaul solutions based on optical wireless communication (OWC) have emerged as a promising technology for 6G communications. OWC systems offer advantages such as flexible deployment and low construction costs, but face significant challenges due to nonlinear distortions and turbulence. Efficiently optimizing these joint impairments is particularly crucial for large-scale deployment. Additionally, various mobile network architectures typically have distinct requirements, and different applications may necessitate a certain level of isolation. In this paper, we propose a multi-band adaptive optimization autoencoder framework (MBAE) to achieve end-to-end (E2E) optimization for OWC-based fronthaul systems. The impairments from the multiple transceiver systems and the propagation link can be jointly optimized. Simulation results show that MBAE can obtain an average Q factor gain of 3 dB compared with the multi-band carrier-less amplitude and phase (CAP) modulation with neural network based post-equalization (NN) and decision feedback equalization (DFE). The experimental results show that MBAE can achieve a 1.9 dB received optical power (ROP) sensitivity gain compared with CAP using NN and DFE at a transmission rate of 36 Gbps under the 7% hard decision-forward error correction (HD-FEC) threshold of 3.8×10^-3 utilizing a low-cost PD with 10G-class bandwidth and support a link budget of 44.7 dB. In the performance test under turbulent conditions, MBAE also exhibits superior average performance, achieving a mean Q factor gain of 1.41 dB compared with CAP using NN and DFE. The proposed method can be a promising solution to meet the high-speed and low-cost requirements of future optical wireless fronthaul systems.