Reconfigurable Intelligent Surface for OFDM Radar Interference Mitigation

TL;DR

This paper presents a neural network-based approach using reconfigurable intelligent surfaces to mitigate interference in OFDM radar systems, significantly improving signal clarity and target detection.

Contribution

It introduces a novel neural network framework for optimizing RIS configurations to suppress interference and enhance radar signal quality.

Findings

Effective interference nulling demonstrated in simulations

Improved signal-to-interference-and-noise ratio (SINR)

Enhanced target detection performance

Abstract

This paper introduces a method to reduce interference in OFDM radar systems through the use of reconfigurable intelligent surfaces (RIS). The method involves adjusting the RIS elements to diminish interference effects and improve the clarity of the desired signal. A neural network framework is established to optimize the configurations of the RIS, aiming to lower the power from unwanted sources while enhancing the target signal. The network produces settings that focus on maximizing the signal at the intended angle. Utilizing a convolution-based approach, we illustrate the effective tuning of RIS elements for interference mitigation and the creation of nulls in the direction of interference, resulting in a better signal-to-interference-and-noise ratio (SINR). Simulations confirm the effectiveness of the proposed method in a radar context, demonstrating its capability to enhance target detection while reducing interference.