Characterization of Monostatic Base Stations
Sensing Resolution Using 5G Reference Signals
Tanguy Ropitault
∗, †
, Steve Blandino
∗, †
, David Griffith
‡
, Anirudha Sahoo
‡
, Thao Nguyen
‡
, Nada Golmie
‡
∗
Associates, CTL, National Institute of Standards and Technology, Gaithersburg, MD, USA
†
Prometheus Computing LLC, Bethesda, MD, USA
‡
CTL, National Institute of Standards and Technology, Gaithersburg, MD, USA
Email: {tanguy.ropitault, steve.blandino, david.griffith, anirudha.sahoo, thao.t.nguyen, nada.golmie}@nist.gov
Abstract—Sensing is poised to be a crucial feature in 6G,
leading 3GPP to integrate sensing capabilities into the 5G New
Radio (NR) framework. Significant progress has been made,
but extensive work remains in defining architectures, protocol
operations, and deployment strategies. Successful adoption by
operators hinges on balancing cost-effectiveness with anticipated
benefits. We introduce a novel architecture that repurposes
existing 3GPP signals—Synchronization Signal Blocks (SSBs) and
Positioning Reference Signals (PRSs)—for monostatic sensing
at base stations (BSs), leveraging existing infrastructure to
efficiently enhance capabilities.
Given that sensing must coexist with communication in 5G
systems, resource management is critical. It restricts available
resources for sensing, thus limiting resolution. By fusing data
from multiple BSs and processing it centrally, significant enhance-
ments in sensing resolution are possible. This paper quantifies
the resolution of monostatic and multi-monostatic BS systems by
examining the spatial resolution area (SRA)—the region within
which the system cannot differentiate between two objects—and
localization resolution (LR), defined as the maximum distance
error between a target’s estimated and actual positions.
Our results show that in the FR1 band, using multiple
monostatic BSs significantly enhances system resolution over
a single BS setup. Adding a second BS improves SRA by at
least 40 %. Employing larger bandwidths up to 100 MHz for
PRSs achieves sub-1-meter LR with five BSs, while narrower
bandwidths like 7.2 MHz for SSBs approach sub-10-meter LR
with seven monostatic BSs.
Index Terms—3GPP, ISAC, monostatic sensing.
I. INTRODUCTION
Wireless sensing leverages radio frequency interactions with
objects and human activity to detect environmental changes,
offering extensive applications from drone intrusion detection
to smart highways. Integrating wireless sensing into wire-
less networks through Integrated Sensing and Communication
(ISAC) enhances spectral efficiency, optimizes resource use,
and reduces hardware redundancy and network costs [1], [2].
The industry’s growing interest in ISAC is driven by the
potential for operators to offer more services using their
existing networks. By integrating communication and sensing
capabilities, they can enhance situational awareness and de-
liver personalized services while maintaining low operational
costs. Reflecting this interest, the IEEE is developing the
802.11bf specification to incorporate sensing into Wireless
Local Area Networks (WLANs), with finalization expected
by 2025 [3], [4]. This marks a significant advancement in
consumer wireless networks. Concurrently, the 3rd Generation
Partnership Project (3GPP) is embedding sensing within its
standards, with Release 19 introducing a technical report (TR)
on supported use cases [5] and a sensing channel model.
Despite the ongoing standardization efforts, integrating
sensing capabilities within 3GPP standards for 5G New Radio
(NR) involves a complex and lengthy process that requires
the development of new architectures, protocol operations, and
deployment strategies. Operators must balance benefits against
costs, which mandates practical and integrative approaches.
One central design question is whether sensing should be
supported at base station (BS) or user equipment (UE) level.
BS-based sensing, which benefits from centralized processing,
may require full-duplex systems and could incur higher costs.
On the other hand, UE-based sensing allows for distributed
data collection but necessitates hardware upgrades and more
power. In the short term, sensing at the BS level is more
feasible, as upgrades to BS infrastructure are generally simpler
and less disruptive compared to the extensive modifications
required for UEs. This approach not only avoids potential
delays in adoption due to hardware compatibility issues and
the logistical challenges of deploying new equipment to end
users but also inherently supports the deployment of mono-
static sensing solutions, where each BS can independently
perform sensing tasks. Additionally, this approach supports
the integration of data from multiple BSs, enhancing sensing
performance through spatial diversity.
Leveraging existing 3GPP reference signals already trans-
mitted by BSs offers a practical path for operators to adopt
monostatic sensing with minimal infrastructure changes, re-
ducing costs and easing deployment. Sensing can be imple-
mented in either active or passive modes, each with its own
trade-offs. Active sensing, which involves allocating dedicated
resources for sensing, enhances sensing flexibility and perfor-
mance but introduces additional communication overhead. In
contrast, passive sensing relies on signals already in use for
communication, incurring no extra overhead but with limited
flexibility due to its dependence on existing signal timing.
In this paper, we propose to repurpose Positioning Reference
