Characterization of Multi-Link Propagation and Bistatic Target Reﬂectivity for Distributed Multi-Sensor ISAC

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Characterization of Multi-Link Propagation and

Bistatic Target Reﬂectivity for Distributed

Multi-Sensor ISAC

Reiner S. Thom¨

a , Carsten Andrich , Julia Beuster , Heraldo Cesar Alves Costa ,

Sebastian Giehl , Saw James Myint , Christian Schneider , Gerd Sommerkorn

Electronic Measurement and Signal Processing Research Group

Technische Universit¨

at Ilmenau, Germany

reiner.thomae@tu-ilmenau.de

Abstract—Integrated sensing and communication (ISAC) qual-

iﬁes mobile radio systems for detecting and localizing of passive

objects by means of radar sensing. Advanced ISAC networks rely

on meshed mobile radio nodes (infrastructure access and/or user

equipment, resp.) establishing a distributed, multi-sensor MIMO

radar system in which each target reveals itself by its bistatic

backscattering. Therefore, characterization of the bistatic reﬂec-

tivity of targets along their trajectories of movement is of highest

importance for ISAC performance prediction. We summarize

several challenges in multi-link modeling and measurement of

extended, potentially time-variant radar targets. We emphasize

the speciﬁc challenges arising for distributed ISAC networks and

compare to the state of the art in propagation modeling for mobile

communication.

Index Terms—Integrated sensing and communication, multi-

sensor ISAC, distributed MIMO radar, bistatic target reﬂectivity,

propagation measurement and modeling.

I. INTRODUCTION

ISAC is considered to be one of the key features of future

6G mobile radio. Despite of different interpretations, we

understand ISAC as a means of radar detection and location

of passive objects (“targets”) that are not equipped with a

radio tag. These targets reveal their existence and position

by radio wave reﬂection only when properly illuminated.

In contrast to well-known radar systems, ISAC exploits the

inherent resources of the mobile radio system on both the

radio access and network level. In its most resource efﬁcient

operational mode, ISAC reuses the signals originally trans-

mitted for communication purposes at the same time also for

target illumination. This scheme resembles and extends the

well-known passive radar principle. We introduced the term

“cooperative passive coherent location (CPCL)” [1], [2] for

it. In case of this communication centric version of ISAC,

the radio access modes deﬁned for communication are also

used to radar sensing. This includes the waveform (usually

OFDM and derivatives), its numerology, multiuser access

(OFDMA, TDMA), pilot schemes, channel state estimation

and synchronization, channel state signaling for predistortion

and link adaptation, and eventually also for resource allo-

cation. With the ubiquitous availability of the mobile radio

access, we immediately have a distributed network of radar

sensors at hand. The same network is also used for data

transport and data fusion. With the computing facilities of

the mobile edge cloud (MEC) we have all resources at our

disposal, which we may need to apply machine learning

(ML) and artiﬁcial intelligence (AI) for adaptive resource

allocation, target parameter estimation, and scene recognition.

This way, ISAC will become a ubiquitous and cognitive radar

sensing network. As we know very well from mobile radio

performance prediction, the knowledge about the multipath

radio propagation is very important. Channel measurement

and modeling always stands at the very beginning of the

deﬁnition and standardization of new radio access schemes. In

this paper, we ask the question: “What are the differences and

challenges of propagation research for ISAC as compared to

plain mobile radio communication?” We will ﬁnd out among

other things that the knowledge about single, i.e., solitaire

objects that are identiﬁed as radar targets, is most important.

This includes bistatic target reﬂectivity, how it evolves if

the target is moving, and how it can be characterized if it

is inherently time-variant. Besides of conceptual issues, we

for the ﬁrst time introduce a new measurement range for

the bistatic reﬂectivity of extended objects up to the size of

a passenger car. This unique measurement range, which we

call BiRa (Bistatic Radar), is capable of real-time wideband

measurements of time-variant targets. Hence, we can analyze

the bistatic micro-Doppler response of extended targets [3].

II. MULTI-LINK ISAC SYSTEM ISSUES

A typical ISAC system consists of either one stand-alone or

several meshed radio nodes acting as transmitter (Tx), receiver

(Rx), or both. In case of an infrastructure based setup, these

can be single or distributed base stations consisting of several

synchronized remote radio units (RRUs). A single base station

case corresponds to a stand alone radar. The gNodeB (gNB)

must be capable of full duplex radio access and needs to be

equipped with an antenna array for direction of arrival (DoA)

estimation. The target bearing line will be a circle around the

gNB and the target location is given by joint DoA and time

of ﬂight (ToF) (resp. range) estimation, see Fig. 1. In radar

terms, this is referred to as “monostatic”. The challenge is

arXiv:2210.11840v2 [eess.SP] 15 May 2023

monosta�c

target echo

target

gNB

communica�ons link

Fig. 1. Infrastructure based sensing using a single base station that is equipped

with an antenna array.

distributed gNB

bista�c

target echo

target

communica�ons link

gNB

direct link

Fig. 2. Infrastructure based sensing using distributed radio heads.

the full duplex operation of the radio interface, which is not

yet standard in communications. The distributed equivalent is

depicted in Fig. 2.

It already resembles the passive radar case and, hence, also

our CPCL scenario [1]. The estimated parameter is the excess

ToF delay of the sensing link relative to the direct line-of-sight

(LoS) link. The resulting target bearing line is as an ellipse

with the Tx and Rx positions as its focal points. Obviously,

we would need multiple measurements, hence additional radio

nodes, to achieve a unique and unambiguous location estimate

bista�c

target echo

target

communica�ons link

mobile ISAC node

gNB

Fig. 3. Including the UE in the sensing network (UL/DL sensing).

in 2D or even 3D. DoA estimation, hence antenna arrays,

are not necessary. However, beamforming can be additionally

applied, e.g., for ﬁltering undesired multipath (clutter). This

distributed Tx/Rx geometry is obvious and self-evident for

communication centric ISAC. In radar terms, it is referred

to as “bistatic”. In this case, we do not need a full duplex

air interface and we may have further advantages in spatial

diversity as will be discussed below.

The ISAC architecture can also comprise multiple units of

mobile UE in the UL or DL, resp., see Fig. 3. This corresponds

to a multiuser scenario, which we call a multisensor scenario

in ISAC terminology (the ﬁgure shows only one UE). The

difference to Fig. 2 is that the sensor may now be mobile which

has inﬂuence on Doppler processing. Moreover, as the sensor

is the UEs, the direct wireless link is necessary for Tx/Rx

synchronization and to make the transmitted signal available at

the sensor as a correlation reference. This is also very close to

passive radar, but in contrast to it, the CPCL receiver is already

prepared by the inherent receiver functionality to generate a

clean replica of the transmitted waveform [1].

The radar architecture made up from multiple, widely

distributed radio nodes is called distributed MIMO radar (as

opposed to co-located) [4]. The generic distributed multiple-

input multiple-output (MIMO) radar setup shown in Fig. 4

involves some issues and challenges.

Obviously, the full #Tx ×#Rx MIMO matrix requires

monostatic radio interfaces at all nodes. Moreover, the multiple

Tx/Rx links would require some coordinated access, which

includes sensor broadcast (with multiple simultaneous DL

measurements at several Tx) and the orthogonal multisen-

sor case that can be used for UL and DL sensing). Joint

transmission can be implemented in the DL with noncoherent

and coherent superposition at the place of the target. More-

over, heterogeneous links (including very different frequency

bands) can make sense. However, further discussion is beyond

the scope of this paper. Obviously, distributed MIMO radar

includes several synchronization issues. It allows unambigu-

ous 3D location and dynamic state vector estimation (which

Rx Tx Rx Tx

Rx Tx

bista�c response

monosta�c response

Fig. 4. Generic distributed MIMO radar architecture consisting of multiple

transmit and receive links.

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CharacterizationofMulti-LinkPropagationandBistaticTargetReectivityforDistributedMulti-SensorISACReinerS.Thom¨a,CarstenAndrich,JuliaBeuster,HeraldoCesarAlvesCosta,SebastianGiehl,SawJamesMyint,ChristianSchneider,GerdSommerkornElectronicMeasurementandSignalProcessingResearchGroupTechnischeUniversit¨at...

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Characterization of Multi-Link Propagation and Bistatic Target Reﬂectivity for Distributed Multi-Sensor ISAC

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