Starting Date: April 1st, 2023 for a period of 6 months
Location: ENSEA, France
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).
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). An orthogonal matching pursuit (OMP) based channel estimation algorithm can be designed and implemented by simulations in the framework of compressive sensing theory. Advanced machine learning techniques can also be investigated to serve the mmWave beam prediction. In addition, the realistic localization performance could further be evaluated by deploying 60GHz/77GHz mmWave radar sensors in the lab.
All international and local graduates with a passion for research and excellent academic results
are encouraged to apply. MSc level expertise in one of the following areas is highly desirable for this project:
• Good knowledge on wireless communications and signal processing techniques.
• Good PC programming skills (e.g., Python, MatLab or C/C++).
Depending on the performance, a fully-funded Ph.D. opportunity is highly possible after this internship.
How to Apply:
Applications should include: 1) a detailed CV, 2) one-page motivation letter, 3) two academic references. All applications must be submitted directly by email to luan.chen@cell-page-content-manager
Application Deadline: 23:59 March 17th, 2023 (CET)
 Garcia, Nil et al. ”Location-aided mm-wave Channel Estimation for Vehicular Communication.” in IEEE SPAWC, 2016.
 Mundlamuri, Rakesh et al. ”Sensing aided Channel Estimation in Wideband Millimeter-Wave MIMO Systems.” arXiv preprint, 2023.
 Luan Chen et al. ”AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information”, in IEEE Internet of Things Journal, 2020.