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Found 15 papers, 0 papers with code
Hybrid Precoding and Combining for mmWave Full-Duplex Joint Radar and Communication Systems under Self-Interference
Specifically, we introduce a generalized eigenvalue-based precoder design that considers the downlink user rate, the radar gain, and the SI suppression.
Sparse Recovery with Attention: A Hybrid Data/Model Driven Solution for High Accuracy Position and Channel Tracking at mmWave
In this paper, we propose first a mmWave channel tracking algorithm based on multidimensional orthogonal matching pursuit algorithm (MOMP) using reduced sparsifying dictionaries, which exploits information from channel estimates in previous frames.
Learning to Localize with Attention: from sparse mmWave channel estimates from a single BS to high accuracy 3D location
One strategy to obtain user location information in a wireless network operating at millimeter wave (mmWave) is based on the exploitation of the geometric relationships between the channel parameters and the user position.
Separable multidimensional orthogonal matching pursuit and its application to joint localization and communication at mmWave
Greedy sparse recovery has become a popular tool in many applications, although its complexity is still prohibitive when large sparsifying dictionaries or sensing matrices have to be exploited.
Hybrid mmWave MIMO Systems under Hardware Impairments and Beam Squint: Channel Model and Dictionary Learning-aided Configuration
Low overhead channel estimation based on compressive sensing (CS) has been widely investigated for hybrid wideband millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems.
Multidimensional orthogonal matching pursuit: theory and application to high accuracy joint localization and communication at mmWave
Greedy approaches in general, and orthogonal matching pursuit in particular, are the most commonly used sparse recovery techniques in a wide range of applications.
Low complexity joint position and channel estimation at millimeter wave based on multidimensional orthogonal matching pursuit
Compressive approaches provide a means of effective channel high resolution channel estimates in millimeter wave MIMO systems, despite the use of analog and hybrid architectures.
Joint Initial Access and Localization in Millimeter Wave Vehicular Networks: a Hybrid Model/Data Driven Approach
High resolution compressive channel estimation provides information for vehicle localization when a hybrid mmWave MIMO system is considered.
Multidimensional Orthogonal Matching Pursuit-based RIS-aided Joint Localization and Channel Estimation at mmWave
RIS-aided millimeter wave wireless systems benefit from robustness to blockage and enhanced coverage.
Deep Learning-based Link Configuration for Radar-aided Multiuser mmWave Vehicle-to-Infrastructure Communication
Then, a deep network is used to translate features of these radar spatial covariances into features of the communication spatial covariances, by learning the intricate mapping between radar and communication channels, in both line-of-sight and non-line-of-sight settings.
Radar Aided mmWave Vehicle-to-InfrastructureLink Configuration Using Deep Learning
In this paper, we obtain prior information to speed up the beam training process by implementing two deep neural networks (DNNs) that realize radar-to-communication (R2C) channel information translation in a vehicle-to-infrastructure (V2I) system.
A Dynamic Codebook Design for Analog Beamforming in MIMO LEO Satellite Communications
Beamforming gain is a key ingredient in the performance of LEO satellite communication systems to be integrated into cellular networks.
Beamformer Design and Optimization for Full-Duplex Joint Communication and Sensing at mm-Waves
We then also propose new transmitter and receiver beamforming solutions for purely analog beamforming based JCAS systems that maximize the beamforming gain at the sensing direction while controlling the beamformed power at the communications direction(s), cancelling the SI as well as eliminating the potential reflection from the communication direction and optimizing the combined radar pattern (CRP).
Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systems
We design fully-connected layers to optimize channel acquisition and beam alignment.
Site-specific online compressive beam codebook learning in mmWave vehicular communication
Furthermore, based on the CS channel measurements, we develop techniques to update and learn such channel AoD statistics at the BS.
