Exploring UE Sensing Capabilities for Integrated Sensing and Communication

Sammanfattning

In the realm of wireless communications, the exploration of user equipment (UE) sensing capabilities has emerged as a complementary approach for optimizing the performance of wireless networks. This thesis delves into the potential enhancements achievable by integrating UE sensing capabilities into wireless networks, focusing on detecting Line Of Sight (LOS) and Non Line Of Sight (NLOS) scenarios. While traditional beamforming algorithms have played a pivotal role in network optimization, this research aims to broaden the scope by investigating how UE sensing can complement existing techniques. By harnessing real-time UE sensing data, encompassing channel conditions and interference levels, adaptive adjustments in network parameters can be made to enhance throughput, coverage, and energy efficiency. The effectiveness and use cases of incorporating LOS/NLOS detection using deep-learning models into network optimization strategies are demonstrated through simulation-based evaluations. Notably, the simulations provide valuable insights into the impact of UE direction movement towards or away from the base station (BS), UE arrival time to the LOS region, and the size of the obstacle obstructing the communication between the BS and UE. These findings contribute to the body of knowledge in the field and shed light on the potential of UE sensing in optimizing wireless network performance.