Practical Guidelines for Approaching the Implementation of Neural Networks on FPGA for PAPR Reduction in Vehicular...

Area of Science:

• Wireless Sensor Networks
• Vehicular Communications
• Signal Processing

Background:

• Wireless sensor networks are expanding, with vehicular communications (V2V) offering potential for accident reduction via early warnings.
• The ECMA-368 standard, using Multiband Orthogonal Frequency Division Multiplexing (MB-OFDM), is proposed for V2V networks.
• Orthogonal Frequency Division Multiplexing (OFDM) signals exhibit large power envelope fluctuations, limiting High Power Amplifier (HPA) efficiency and causing nonlinear distortion, critical for mobile and vehicular networks.

Purpose of the Study:

• To implement and evaluate a novel architecture for reducing Peak to Average Power Ratio (PAPR) in MB-OFDM signals for vehicular communications.
• To address the complexity and implementation challenges of existing PAPR reduction algorithms.
• To provide guidelines for implementing efficient PAPR reduction techniques in Field Programmable Gate Array (FPGA) chips for V2V applications.

Main Methods:

• Implementation of a multilayer perceptron artificial neural network architecture on Field Programmable Gate Array (FPGA) chips.
• Evaluation of the proposed architecture on Xilinx and Altera FPGA platforms.
• Analysis of performance metrics including PAPR, distortion, Bit Error Rate (BER), resource consumption, and maximum operating frequency.

Main Results:

• Significant reduction in Peak to Average Power Ratio (PAPR) and signal distortion.
• Improved Bit Error Rate (BER) performance compared to existing methods.
• Demonstrated lower complexity and minimal resource consumption on FPGA implementations.
• Achieved higher maximum operating frequencies on tested FPGA platforms.

Conclusions:

• The proposed multilayer perceptron artificial neural network architecture offers an efficient solution for PAPR reduction in wireless vehicular communications.
• FPGA implementation provides a practical and low-complexity approach for enhancing the power efficiency and performance of V2V communication systems.
• The study provides valuable guidelines for the development of next-generation wireless sensors in vehicular networks.