# Blind Source Separation-Enabled Joint Communication and Sensing in IBFD MIMO Systems

## TL;DR

This paper introduces a blind source separation framework using FastICA for IBFD MIMO systems that enables simultaneous self-interference cancellation and environmental sensing, improving performance without dedicated radar signals.

## Contribution

It presents a novel BSS-based approach for joint communication and sensing in IBFD MIMO systems, leveraging SI signals for sensing without additional hardware.

## Key findings

- Effective separation of SI and SOI signals demonstrated.
- Improved sensing and communication performance with larger signal frames.
- Framework enables simultaneous signal recovery and environmental sensing.

## Abstract

This paper addresses the challenge of joint communication and sensing (JCAS) in next-generation wireless networks, with an emphasis on in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) systems. Traditionally, self-interference (SI) in IBFD systems is a major obstacle to recovering the signal of interest (SOI). Under the JCAS paradigm, however, this high-power SI signal presents an opportunity for efficient sensing. Since each transceiver node has access to the original SI signal, its environmental reflections can be exploited to estimate channel conditions and detect changes, without requiring dedicated radar waveforms. We propose a blind source separation (BSS)-based framework to simultaneously perform self-interference cancellation (SIC) and extract sensing information in IBFD MIMO settings. The approach applies the Fast Independent Component Analysis (FastICA) algorithm to separate the SI and SOI signals while enabling simultaneous signal recovery and channel estimation. Simulation results confirm the framework's effectiveness, showing improved sensing and communication performance as signal frame size increases.