Current students


Section: Telecommunications

Major Research topic:
V2X performance enhancement through RIS deployment

Connected Autonomous Vehicles (CAV) is one of the critical vertical industries of 6G that demands various service qualities such as extremely high throughput, extremely high Reliability, and extremely low latency. mmWave communication is one of the main technology to satisfy the envisioned KPIs. However, mmWave is prone to high propagation loss and blockage due to obstacles. To tackle high propagation loss, massive Multiple Input Multiple Output (mMIMO) is deployed. Additionally, Reconfigurable Intelligent Surface (RIS) has recently emerged to tackle wireless channel fading impairment and interference issues. One of the main goals of RIS to provide a virtual Line of Sight (LOS)  to bypass the obstacle between transmitter and receiver. For example, there is a building that blocks the LOS between Base Station (BS) and a car. In this scenario RIS can be exploited to reflect the beam toward the receiver. To achieve the performance gain provided by RIS, accurate acquisition of Channel State Information (CSI) is essential. So far, to acquire CSI, training sequences (Known as pilot sequences) are used. However, they impose a large overhead on the system. Therefore, in this research, we investigate the blind channel estimation to reduce the training phase overhead and improve the system capacity.