SPSYSYMay 18

From Coverage to Sensing: ISAC meets FR3

arXiv:2605.1812050.8
Predicted impact top 9% in SP · last 90 daysOriginality Incremental advance
AI Analysis

For 6G network designers, this paper provides a concrete roadmap and technical analysis for transitioning from coverage-oriented deployments to native sensing in FR3, addressing key bottlenecks like beam alignment and resource allocation.

This paper proposes a path for 6G systems to integrate sensing as a native function in FR3 spectrum, including a radar-as-a-service framework, hierarchical beam alignment, and analysis of sensing capabilities via Cramér-Rao bounds. It identifies challenges like beam squint and explores extracting sensing information from payload data.

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{é}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.

Foundations

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