From Coverage to Sensing: ISAC meets FR3

TL;DR

This paper explores how future 6G FR3 spectrum networks can integrate sensing functions like localization and detection, proposing new strategies for beam alignment, signal processing, and system design.

Contribution

It introduces a hierarchical beam-alignment strategy, discusses mitigation of array phenomena, and emphasizes the role of digital twins and massive MIMO in FR3 sensing integration.

Findings

Hierarchical FR3 beam-alignment improves sensing and communication capabilities.

Intra- and inter-beam squint phenomena are identified and mitigation approaches are discussed.

FR3 sensing can utilize payload resources beyond pilots, enhancing data extraction.

Abstract

Future 6G systems are expected to exploit upper midband spectrum in frequency range 3 (FR3) not only for high throughput communications, but also for sensing services such as localization, detection, and situational awareness. The following paper develops a concrete path from today's coverage-oriented deployments to FR3 networks that treat sensing as a native function. We first show how existing FR2 radars can be time-multiplexed and coordinated under a $6$G medium access control as radar-as-a-service, forming a bridge between legacy sensing and network-managed integrated sensing and communications (ISAC). We then propose a hierarchical FR3 beam-alignment strategy in which coarse access occurs at lower frequencies and refinement occurs at upper FR3, and quantify the resulting sensing and communication capabilities via range-angle Cram{\'{e}}r-Rao bounds in the near field. We identify intra- and inter-beam squint phenomena specific to wideband FR3 arrays, and discuss design approaches to mitigate them. On the signal-processing side, we argue that FR3 sensing cannot rely solely on pilot resources and discuss how much sensing information can be extracted from payload resource elements. We further highlight the role of calibrated FR3 channel simulators and real-time models as the core of wireless digital twins for training and evaluating ISAC algorithms, and discuss how massive MIMO and dense or distributed deployments at FR3 naturally act as large reconfigurable sensor arrays.