Description
Date depot: 3 octobre 2024
Titre: Sensing-aided Channel Estimation on mmWave Band
Directeur de thèse:
Farid NAIT-ABDESSELAM (LIPADE)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Systèmes et réseaux
Resumé: Context and Motivation:
Millimeter-Wave (mmWave) communication is considered to be a key component of the next generation of mobile communication technologies (e.g., 5G and 6G cellular systems). Its advantage lies on its capability to support multi-Gbps throughput at higher operating frequencies (i.e., 30GHz∼300GHz). One of the major challenges is that RF signals propagating in the mmWave frequency band experience significant path loss, penetration and reflection loss. Despite these disadvantages, by packing many antenna elements in a single array, mmWave system can compensate the high propagation loss through beamforming techniques. However, the gains stemming from these multiple antenna techniques hinge on the ability to accurately estimate the channel state information (CSI). Capitalizing on the high angular resolution of mmWave MIMO, the sensing/location information of the targets can be clearly identified by exploiting the radar echo signals from different directions. As such, we are inspired to integrate radar sensing into the mmWave channel estimation.
Research Objectives:
In this context, we aim to address the problem of channel estimation for mmWave MIMO system by leveraging sensing information obtained from a co-located radar at the base station, primarily including the location information represented by the time of flight (ToF) and angle of arrival (AoA), to reduce the pilot overhead. In addition, estimating channel coefficients over a wideband and across multiple antennas incurs significant resource overhead in terms of resources occupied for sending pilot symbols. However, it has been observed that the mmWave channel exhibits asparse behavior with only a few resolvable multi-paths inangle and delay domain. By leveraging such sparsity, we also aim to adopt compressed sensing (CS) based approaches for channel estimation in mmWave MIMO systems.
Résumé dans une autre langue: Context and Motivation:
Millimeter-Wave (mmWave) communication is considered to be a key component of the next generation of mobile communication technologies (e.g., 5G and 6G cellular systems). Its advantage lies on its capability to support multi-Gbps throughput at higher operating frequencies (i.e., 30GHz∼300GHz). One of the major challenges is that RF signals propagating in the mmWave frequency band experience significant path loss, penetration and reflection loss. Despite these disadvantages, by packing many antenna elements in a single array, mmWave system can compensate the high propagation loss through beamforming techniques. However, the gains stemming from these multiple antenna techniques hinge on the ability to accurately estimate the channel state information (CSI). Capitalizing on the high angular resolution of mmWave MIMO, the sensing/location information of the targets can be clearly identified by exploiting the radar echo signals from different directions. As such, we are inspired to integrate radar sensing into the mmWave channel estimation.
Research Objectives:
In this context, we aim to address the problem of channel estimation for mmWave MIMO system by leveraging sensing information obtained from a co-located radar at the base station, primarily including the location information represented by the time of flight (ToF) and angle of arrival (AoA), to reduce the pilot overhead. In addition, estimating channel coefficients over a wideband and across multiple antennas incurs significant resource overhead in terms of resources occupied for sending pilot symbols. However, it has been observed that the mmWave channel exhibits asparse behavior with only a few resolvable multi-paths inangle and delay domain. By leveraging such sparsity, we also aim to adopt compressed sensing (CS) based approaches for channel estimation in mmWave MIMO systems.
Doctorant.e: Wang Dongyun