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Exploring UE Sensing Capabilities for Integrated Sensing and Communication

Samadifar, Pooriya LU and Kurapati, Anveeksha LU (2024) EITM02 20241
Department of Electrical and Information Technology
Abstract
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... (More)
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. (Less)
Popular Abstract
The Fifth Generation (5G) technology heralds a significant revolution in wireless communication, characterized by notable advancements in data rates, latency, connectivity, and reliability. Through its ability to deliver substantially higher data rates and ultra-low latency, 5G facilitates rapid access to large data files and immersive multimedia experiences. The technology's capacity for massive connectivity, achieved through techniques like massive MIMO which means very large number of antennas at the Base Station (BS) with many users, unlocks the potential for extensive implementation of the Internet of Things (IoT). IoT refers to devices equipped with sensors, processing capabilities, software, and other technologies, enabling them to... (More)
The Fifth Generation (5G) technology heralds a significant revolution in wireless communication, characterized by notable advancements in data rates, latency, connectivity, and reliability. Through its ability to deliver substantially higher data rates and ultra-low latency, 5G facilitates rapid access to large data files and immersive multimedia experiences. The technology's capacity for massive connectivity, achieved through techniques like massive MIMO which means very large number of antennas at the Base Station (BS) with many users, unlocks the potential for extensive implementation of the Internet of Things (IoT). IoT refers to devices equipped with sensors, processing capabilities, software, and other technologies, enabling them to connect and share data with other devices and systems via the Internet or other communication networks. Additionally, 5G's enhanced reliability and expanded coverage ensure consistent connectivity, even in challenging environments. The far-reaching applications of 5G encompass various sectors, fostering innovation in areas such as smart cities, healthcare, transportation, and industrial automation. This transformative technology empowers emerging fields like autonomous vehicles, telemedicine, smart grids, and precision agriculture, reshaping industries and revolutionizing societal interactions.
Imagine a world where your smartphone not only connects you to the internet but also helps monitor your surroundings. This vision is becoming a reality with the advancement of 5G technology. In particular, researchers are exploring the potential of integrating sensing capabilities into 5G communication networks. This means that the same infrastructure that enables your phone, calls, and internet browsing, can also be used for tasks like environmental monitoring or healthcare applications. One promising approach involves utilizing a signal called Uplink Sounding Reference Signal (ULSRS), which is already used in 5G networks for communication and sensing purposes. By cleverly analyzing this signal, researchers can gather valuable information about the surrounding environment or specific phenomena. This integration of sensing and communication opens up exciting possibilities for a wide range of applications, making our world smarter and more connected than ever before. (Less)
Please use this url to cite or link to this publication:
author
Samadifar, Pooriya LU and Kurapati, Anveeksha LU
supervisor
organization
course
EITM02 20241
year
type
H2 - Master's Degree (Two Years)
subject
keywords
ISAC, WINNERII, MIMO, ULSRS, SRS, CNN, Artificial Intelligence, Machine Learning, Neural Networks, Deep Learning, Integrated Sensing and Communication
report number
LU/LTH-EIT 2024-981
language
English
id
9160433
date added to LUP
2024-06-11 14:01:20
date last changed
2024-06-11 14:01:20
@misc{9160433,
  abstract     = {{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.}},
  author       = {{Samadifar, Pooriya and Kurapati, Anveeksha}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Exploring UE Sensing Capabilities for Integrated Sensing and Communication}},
  year         = {{2024}},
}