Model-Based Deep Learning Tuning of Reconfigurable Intelligent Surface for OFDM Radar Interference Mitigation

This paper presents a deep learning-based framework for enhancing radar systems in the presence of interference, leveraging Reconfigurable Intelligent Surfaces (RIS). The proposed technique uses a modified MUSIC algorithm to estimate the angles of the target and interference. The core of the method is a deep learning model that optimizes the RIS configuration to reduce the impact of interference while maintaining accurate angle estimates. The model consists of a multi-layer perceptron (MLP) that takes estimated angles as inputs and outputs the configuration of the RIS. A specially designed loss function ensures that the interference is properly suppressed and the target remains detectable. To further enhance performance, a convolution technique is introduced to create a notch at the interference angle, ensuring better separation between the target and interference. Additionally, the method is extended to work over multiple subcarriers, improving robustness and performance in practical scenarios. Simulation results show that the technique enhances the signal-to-interference-plus-noise ratio (SINR) and provides accurate localization estimates, demonstrating its potential for radar systems in complex environments.

Reconfigurable Intelligent Surface for OFDM Radar Interference Mitigation

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.

Reflection Map Construction: Enhancing and Speeding Up Indoor Localization

This paper introduces an indoor localization method using fixed reflector objects within the environment, leveraging a base station (BS) equipped with Angle of Arrival (AoA) and Time of Arrival (ToA) measurement capabilities. The localization process includes two phases. In the offline phase, we identify effective reflector points within a specific region using significantly fewer test points than typical methods. In the online phase, we solve a maximization problem to locate users based on BS measurements and offline phase information. We introduce the reflectivity parameter (\(n_r\)), which quantifies the typical number of first-order reflection paths from the transmitter to the receiver, demonstrating its impact on localization accuracy. The log-scale accuracy ratio (\(R_a\)) is defined as the logarithmic function of the localization area divided by the localization ambiguity area, serving as an accuracy indicator. We show that in scenarios where the Signal-to-Noise Ratio (SNR) approaches infinity, without a line of sight (LoS) link, \(R_a\) is upper-bounded by \(n_r \log_{2}\left(1 + \frac{\mathrm{Vol}(\mathcal{S}_A)}{\mathrm{Vol}(\mathcal{S}_{\epsilon}(\mathcal{M}_s))}\right)\). Here, \(\mathrm{Vol}(\mathcal{S}_A)\) and \(\mathrm{Vol}(\mathcal{S}_{\epsilon}(\mathcal{M}_s))\) represent the areas of the localization region and the area containing all reflector points with a probability of at least \(1 - \epsilon\), respectively.