Network as Sensor (NaS)

Miranda Köhn () and Nisha Prabhu Aravinda Prabhu ()

Abstract

Integrated Sensing and Communication (ISAC) proposes a promising methodology to establish
a working model for network sensing. With the existing infrastructure, both communication and
sensing can be performed thus reducing the need for additional hardware and avoiding the need for
multiple measurements and processing. Using communication networks also as connected sensors
termed as Network Sensing brings whole new world into existence. With this, the idea of spatial
sensing could be brought about whereby entire surrounding of the transmitter could be mapped
out. These solutions will also bring new human experiences such as immersive mixed reality digital
worlds. Single-node sensing improves communication network capability to radar applications such
as line of sight sensing. However, if each node or multiple nodes of the network can communicate
sensed information and perform signal processing together on the sensed information, we can
truly harness potential in the network to build digital twin representation. Multi-node sensing
or distributed sensing offer exciting possibilities to carry out sensing also under non-line-of-sight
(NLOS) conditions. Challenges lie in the synchronizing and coordinated processing in problems
such as removing the clutter. Algorithms for resolving appropriate resource allocation for sensing
when considering multi-cell interference are needed.
This thesis aims to include a study of the channels, their modelling under different sight conditions
using mono-static and bi-static sensing, in addition to targeted sensed environment modeling by
capturing the relevant characteristics of the environment, such as the presence of objects, their
movement, and other properties that can be sensed by the network. Subsequently, it aims at
extracting mathematical models inclining towards finding an automated method to perform sensing
and estimation for any given environment in the future.