Virtualizing the Senses: Enabling High-Precision ISAC on Commercial Cellular Infrastructure

TL;DR

This paper proposes a computational virtualization framework to enable high-precision sensing in legacy cellular networks, transforming them into effective radar sensors for 6G applications.

Contribution

It introduces a unified full stack virtualization approach that upgrades existing networks with minimal hardware changes for advanced sensing capabilities.

Findings

Virtualized signal generation enhances aperture and bandwidth.

Environmental maps enable virtual array reflections.

Sub Nyquist strategies bypass sampling bottlenecks.

Abstract

Integrated sensing and communication (ISAC) is poised to be a defining feature of 6G networks, promising to transform cellular base stations (BSs) into ubiquitous radar sensors. However, a significant gap exists between the theoretical promise of ISAC and the commercial reality of legacy cellular communication infrastructure. Existing communication networks are constrained by fragmented spectrum, blockage-prone environments, and cost-prohibitive high-rate analog-to-digital converters (ADCs). These limitations stifle the high-resolution sensing required for emerging applications. This article advocates a shift from dependence on physical resources to computational synthesis and introduces a unified full stack virtualization framework that upgrades legacy networks with minimal hardware changes, spanning signal generation, propagation, and acquisition. Specifically, we virtualize signal generation via space-time -frequency synthesis across distributed BSs to synthesize a larger effective aperture and a wider effective bandwidth. We then virtualize signal propagation by leveraging environmental multipath and digital maps to reinterpret reflections as massive virtual arrays. Finally, we virtualize signal acquisition using sub Nyquist strategies to bypass sampling bottlenecks. We demonstrate that by trading computation for hardware, commercial networks can achieve fine-grained sensing without expensive retrofitting.