Neural network algorithm and hardware accelerator for optical-wireless communication systems
Creators: name: Lee, Jeonghun
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 400605
subject: Optical fibre communication systems and technologies
classificationCode: 400607
subject: Signal processing
classificationCode: 510204
subject: Photonics, optoelectronics and optical communications

description: Since conventional radio frequency (RF) spectrum range has been congested by the requirements of high-speed wireless communications, optical and wireless communication systems exploring the higher frequency range have attracted intensive interests. Millimeter-wave (mm-Wave) systems leveraging the radio-over-fiber (RoF) technology and optical wireless communication (OWC) systems have been considered as the potential candidates for meeting the requirements. Using these technologies, over gigabit-per-second (Gbps) wireless communications have been achieved, particularly in short-range indoor applications, such as personal area networks. However, they have suffered from various inherent linear and nonlinear impairments, where the digital signal processing (DSP) technique has been widely applied. However, conventional DSP has limited capability in solving nonlinear impairments, and it typically compensates various impairments independently.
   
To overcome these limitations, ML algorithms have been widely studied in optical and wireless communication systems. Due to the nonlinear compensation capability and the suppressing of multiple impairments simultaneously, better communication performance has been achieved using the ML technique, particularly using neural networks. Previous studies of ML algorithms in optical and wireless communications have mainly focused on the fully-connected neural network (FC-NN), which is one type of neural network with relatively high computation load. In addition, the neural network algorithms have been implemented with GPUs and/or high-end CPU in previous studies, which has high cost and requires high power consumption. Therefore, in this research, we focus on more efficient ML-based optical and wireless communication systems and the hardware implementation
of ML algorithms.

Firstly, we investigate high-speed mm-Wave RoF systems and indoor OWC systems with convolutional neural network (CNN), binary convolutional neural network (BCNN), and recurrent neural network (RNN). Both CNN and BCNN reduces the computation cost of widely studied FC-NN. Results show that compared to FC-NN, similar or even better data transmission performance can be achieved, whilst the computation is significantly reduced. The training dataset size and training iterations can also be reduced. In addition, to further reduce the computation need, we further investigate BCNN by binarizing the intermediate computations in CNN, where the computation is further substantially reduced.

In addition, we further study the FPGA-based neural network hardware accelerators for mm-Wave RoF and indoor OWC systems, which have significantly reduced cost and power consumption compared with the conventional neural network implementation with high-end CPUs or GPUs. FPGA-based CNN, BCNN and RNN hardware accelerators have been proposed and studied. To further reduce the hardware resource requirement, processing latency and power consumption, we also proposed the novel inner-folded computation optimization method for CNN and BCNN hardware accelerators, and the multi-phase clock and stream-in based computation optimization schemes for RNN hardware accelerator. Results show that compared with GPU implementations, the power consumption can be reduced by up to 86% and the processing latency can be improved by up to 96%.