1 Seventy Years of Radar and Communications The Road from Separation to Integration

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Seventy Years of Radar and Communications:

The Road from Separation to Integration

Fan Liu, Member, IEEE, Le Zheng, Senior Member, IEEE, Yuanhao Cui, Member, IEEE,

Christos Masouros, Senior Member, IEEE, Athina P. Petropulu, Fellow, IEEE,

Hugh Grifﬁths, Fellow, IEEE, and Yonina C. Eldar, Fellow, IEEE

Abstract—Radar and communications (R&C) as key utilities of

electromagnetic (EM) waves have fundamentally shaped human

society and triggered the modern information age. Although

R&C have been historically progressing separately, in recent

decades they have been converging towards integration, forming

integrated sensing and communication (ISAC) systems, giving

rise to new, highly desirable capabilities in next-generation

wireless networks and future radars. To better understand the

essence of ISAC, this paper provides a systematic overview on

the historical development of R&C from a signal processing (SP)

perspective. We ﬁrst interpret the duality between R&C as signals

and systems, followed by an introduction of their fundamental

principles. We then elaborate on the two main trends in their

technological evolution, namely, the increase of frequencies and

bandwidths, and the expansion of antenna arrays. We then show

how the intertwined narratives of R&C evolved into ISAC, and

discuss the resultant SP framework. Finally, we overview future

research directions in this ﬁeld.

I. INTRODUCTION

A. Background and Motivation

SINCE the 20th century, the development of human civi-

lization has relied largely upon the exploitation of elec-

tromagnetic (EM) waves. Governed by Maxwell’s equations,

EM waves are capable of travelling over large distances at

the speed of light, which makes them a perfect information

carrier. In general, one may leverage EM waves to acquire

information on physical targets, including range, velocity, and

angle, or to efﬁciently deliver artiﬁcial information, e.g., texts,

voices, images, and videos from one point to another. Among

many applications, EM waves have enabled information acqui-

sition and delivery, which form the foundation of our modern

information era, and have given rise to the proliferation of

radar and communication (R&C) technologies.

While the existence of EM waves was theoretically pre-

dicted by Maxwell in 1865, and experimentally veriﬁed by

Hertz in 1887, its capability of carrying information to travel

long distances was not validated until Marconi’s transatlantic

F. Liu and Y. Cui are with the Southern University of Science and Technol-

ogy, Shenzhen, China. Y. Cui was with the Beijing University of Posts and

Telecommunications, Beijing, China (e-mail: {cuiyh,liuf6}@sustech.edu.cn).

L. Zheng is with the Beijing Institute of Technology (BIT), Beijing, China

(e-mail: le.zheng.cn@gmail.com).

C. Masouros and H. Grifﬁths are with the University College

London, London, WC1E 7JE, UK (e-mail: chris.masouros@ieee.org,

h.grifﬁths@ucl.ac.uk).

A. P. Petropulu is with the Rutgers, the State University of New Jersey, NJ

08854, United States (e-mail: athinap@rutgers.edu).

Y. C. Eldar is with the Weizmann Institute of Science, Rehovot, Israel

(e-mail: yonina.eldar@weizmann.ac.il).

wireless experiment in 1901 [1]. The successful reception

of the ﬁrst transatlantic radio signal marked the beginning

of the great information era. From then on, communication

technology has rapidly grown thanks to the heavy demand for

intelligence, intercept and cryptography technologies during

the two world wars. It is generally difﬁcult to identify a precise

date for the birth of radar. Some of the early records showed

that the German inventor C. H¨

ulsmeyer was able to use radio

signals to detect distant metallic objects as early as 1904. In

1915, the British radar pioneer Robert Watson Watt, employed

radio signals to detect thunderstorms and lightning. The R&D

of modern radar systems was not carried out until the mid

1930s. The term RADAR was ﬁrst used by the US Navy as

an acronym of “RAdio Detection And Ranging” in 1939.

Despite the fact that both technologies originated from the

discoveries of Maxwell and Hertz, R&C have been largely

treated as two separate research ﬁelds due to different con-

straints in their respective applications, and were therefore

independently investigated and developed for decades. His-

torically, the technological evolution of R&C follows two

main trends: a) from low frequencies to higher frequencies

and larger bandwidths [2], and b) from single-antenna to

multi-antenna or even massive-antenna arrays [3], [4]. With

recent developments, the combined use of large-antenna arrays

and Millimeter Wave (mmWave)/Terahertz (THz) band signals

results in striking similarities between R&C systems in terms

of the hardware architecture, channel characteristics, as well

as signal processing methods. Hence, the boundary between

R&C is becoming blurred, and hardware and spectrum con-

vergence has led to a design paradigm shift, where the two

systems can be co-designed for efﬁciently utilizing resources,

offering tunable tradeoffs and unprecedented synergies for

mutual beneﬁts. This line of research is typically referred

to as integrated sensing and communications (ISAC), and is

applicable in numerous emerging areas, including vehicular

networks, IoT networks, and activity recognition [5], [6].

Over the last decade, ISAC has been well-recognized as a

key enabling technology for both next-generation wireless

networks and radar systems [5]. Given the potential of ISAC,

a deeper understanding of the various connections and dis-

tinctions between R&C, and learning from how they evolved

from separation to integration, is important for inspiring future

research.

In Fig. 1 we summarize key milestones achieved in R&C

history, which are split into four categories with different

markers, namely, individual R&C technologies, general tech-

arXiv:2210.00446v2 [eess.SP] 30 Apr 2023

nologies that are useful for both, and ISAC technologies.

In the remainder of the paper, we will discuss how these

key techniques facilitate the development of R&C and ISAC

systems.

B. Summary and Organization of the Paper

In this paper, we provide a systematic overview on the

development and key milestones achieved in the history of

R&C from an SP perspective. We commence by introducing

the fundamental principles and SP theories of both R&C. We

then present the spectrum engineering of R&C, namely, from

narrowband to wideband, and from single-carrier to multi-

carrier systems. Furthermore, we elaborate on the expansion

of R&C systems’ antenna arrays, i.e., from single-antenna

systems, to phased-array, and to MIMO, massive MIMO

(mMIMO), and distributed antenna systems. Following the

above two technological trends, the paths of R&C eventually

move from separation to integration, and give rise to the ISAC

technology, which motivates the detailed discussion on the

SP framework of ISAC. Finally, we summarize the paper and

identify future research directions.

II. FUNDAMENTALS OF RADAR AND COMMUNICATIONS

A. Basic Principles: A Signals-and-Systems Perspective

The basic system setting for both R&C consists of three

parts: a transmitter (Tx), which produces EM waves, a channel,

over which EM waves propagate, and a receiver (Rx), which

receives EM waves distorted by the channel. While commu-

nication Txs and Rxs are usually well separated, radar Txs

and Rxs may either be collocated or separately positioned,

leading to mono-static or bi-static radar settings, respectively.

In more complicated scenarios, multiple Txs and Rxs may be

involved in both applications, which correspond to multi-user

communications and multi-static radar systems.

It is often convenient to represent EM waves by the elec-

trical ﬁeld intensity, as a complex signal as a function of time

t. The core tasks for R&C can then be deﬁned as:

•Information Acquisition for Radar: The aim here is to

extract the target information embedded in the received

signal, given knowledge of the transmit signal.

•Information Delivery for Communications: The aim

here is to recover the useful information contained in the

transmit signal at the communication Rx, with knowledge

of the channel response.

By denoting the signals at the Tx and Rx at time tas s(t)

and y(t), respectively, the propagation of the signal within

the channel can be modeled as a mapping from its input

s(t)to the output y(t). Ideally, if the noise and disturbance

are not considered, such a mapping is linear due to the

physical nature of EM ﬁelds and waves, or equivalently, owing

to the linearity of Maxwell’s equations. Furthermore, if the

channel characteristics remain unchanged within a certain time

period, it can be approximated as a linear time-invariant (LTI)

system, characterized by its impulse response h(t). As such,

the linear mapping is expressed as a convolution integral

y(t)=(s∗h) (t). While the signaling pulses may be of

different forms for R&C, we suppose that a Nyquist pulse is

leveraged such that s(t)is substantially time-limited on a ﬁnite

interval [−T, T ]. Therefore, signal can be sampled in a nearly

lossless manner after passing through the pulse-shaping ﬁlter

at the Rx, expressed as a convolution sum y(n)=(s∗h) (n)

at the nth sampling point. Let s= [s(−N), . . . , s (N)]Tbe

the Tx signal with length 2N+1,h= [h(0) , . . . , h (P−1)]T

be the channel impulse response with length P, and y=

[y(−N), . . . , y (N+P−1)]Tbe the Rx signal with length

2N+P. Then the convolution can be recast as y=Hs, where

H= Toep (h)∈C(2N+P)×(2N+1) is a Toeplitz matrix, with

the nth column being 0T

n−1,hT,0T

2N−n+1T. Alternatively,

one may express yas y=Sh by the commutative property,

where S= Toep (s)∈C(2N+P)×P.

The above duality between interchangeable signals and

systems implies an interesting connection between R&C. From

the communication perspective, the process of the Tx signal

passing through a channel may be viewed as a linear transform

Happlied to s, with the communication task being to recover

the information embedded in sby receiving y. From the radar

perspective, the sensing task is to recover the target parameters

embedded in h, which is viewed as an input “signal”, by

observing y, which is viewed as an output signal linearly

transformed from hthrough a “system” S. This reveals that

the basic SP problems in R&C are mathematically similar.

B. Linear Gaussian Models

Consider the more general linear Gaussian signal model by

taking additive white Gaussian noise (AWGN) into account:

Y=H(η)S(ξ) + Z,(1)

where Yand Sare the sampled receive and transmit signals,

which could be deﬁned over multiple domains, e.g., time-space

or time-frequency domain, His the corresponding channel

matrix (not necessarily Topelitz), and Zis the white Gaussian

noise signal with variance σ2. The channel His a function

of the physical parameters η, e.g., range, angle, and Doppler.

The transmit signal Smay be encoded/modulated with some

information codewords ξ. Model (1) represents many R&C

systems as elaborated below.

•Radar Signal Model: Radar systems aim at extracting

target parameters ηfrom Y. For both radar Tx and Rx, S

is typically a known deterministic signal, in which case

ξcan be omitted since the radar waveform contains no

information. This can be expressed as

Yr=Hr(η)Sr+Zr.(2)

•Communication Signal Model: Communication systems

aim at recovering codewords ξfrom Y. The channel H,

which is sometimes regarded as an unstructured matrix,

can be estimated a priori via pilots. Therefore, knowing

ηmay not be the ﬁrst priority. The resulting model is

Yc=HcSc(ξ) + Zc.(3)

The subscripts (·)rand (·)care to differentiate R&C signals,

channels, and noises, respectively. We highlight that (2) and

(3) describe a variety of R&C signal models. For example, (2)

Radar

Communications

General Tech

ISAC

Maxwell's Equations

Hertz's EM Experiment

Marconi's Transatlantic Experiment

Watt's Thunderstorm Experiment

First wireless voice transmission

Nyquist Sampling Theorem

BBC's TV Broadcast Experiment

Birth of the Acronym "RADAR"

Matched-Filtering

Phased-Array Radar

Cramér-Rao Lower Bound

NP Detection Criterion

Shannon's Information Theory

Swerling Target Models

First ISAC Signaling Scheme

LDPC Code

FFT Algorithm

OFDM Modulation

SFW Waveform

MUSIC Algorithm

ESPRIT Algorithm

AMRFC Project

MIMO Comms Patent

Chirp-based ISAC Signaling

MIMO Radar

Phased-MIMO Radar

OFDM-based ISAC Signaling

Massive MIMO Comms

SSPARC Project

Hybrid Beamforming for MmWave Comms

Perceptive Mobile Network

3GPP MmWave Standardization

Massive MIMO Radar

ISAC Standardization

19011862 1887 1915 19301928 1939 1943 19441945 1947 1948 19541963 19651967 1968 1979 19891994 1996 2003 2010 2013 2014 2017 2019 20222020

Fig. 1. Important milestones for radar and communications signal processing.

can be viewed as the target return of a multi-input multi-output

(MIMO) radar in a given range-Doppler bin, where ηrepre-

sents angles of targets. Similarly, (3) may be considered as a

narrowband MIMO communication signal. Alternatively, both

(2) and (3) can be viewed as orthogonal frequency-division

multiplexing (OFDM) signal models for R&C, respectively. In

the following, we do not specify the signal domain but focus

on generic models (2) and (3). More concrete signal models

will be discussed in Secs. III-IV. In addition to individual R&C

systems, (1) may also characterize the general ISAC signal

model. That is, a uniﬁed ISAC signal serves for dual purposes

of information delivering and target sensing, whereas R&C

channels may differ from each other. More details on ISAC

systems will be discussed in Sec. V.

C. Fundamental Signal Processing Theories

Below we elaborate on the fundamental SP theories of R&C,

and in particular focus on (2) and (3).

1) Signal Detection: Signal detection problems arise from

many R&C applications. One essential task for radar is to

determine whether a target exists by observing Yr, modeled

as a binary hypothesis testing (BHT) problem

Yr=(H0:Yr=Zr,

H1:Yr=Hr(η)Sr+Zr,(4)

where H0represents the null hypothesis, i.e., the radar receives

nothing but noise, and H1stands for the hypothesis where the

radar receives both the target return and noise. To address

the BHT problem above, one may need to design a detector

T(·)that maps the received signal Yrto a real number,

and then compare the output with a preset threshold γ, to

determine which hypothesis to choose as true. A target detector

may, for example, maximize the detection probability PD=

Pr (H1|H1), while maintaining a low false-alarm probability

PF A =Pr (H1|H0), following the Neyman-Pearson (NP)

criterion [7].

Signal detection also plays a critical role at the commu-

nication Rx. In (3), the communication Rx observes Yc=

HcSc(ξ) + Zc, and seeks to yield an estimate ˆ

ξof the

information symbol vector ξ= [ξ1, ξ2, . . . , ξN]T∈ A. This

problem can be solved by leveraging the minimum error

probability (MEP) criterion. That is, to minimize the error

probability Pe=P|A|

i=1 Pr ˆ

ξi6=ξiPr (ξi), where |A| is

the cardinality of A. The MEP criterion can be translated to

the MAP criterion, i.e., the recovered symbols should be the

maximizer of the a posterior probability. Note that the decision

region in the MEP criterion for communication symbols is

determined by their a priori probability, while the decision

thresholds in the NP criterion for radar is determined by the

required false-alarm probability, resulting in different designs

for R&C detectors.

2) Parameter Estimation: Parameter estimation represents

another category of basic SP techniques in R&C systems. For a

radar system, once a target is conﬁrmed to be present, it needs

to further extract its parameters ηfrom Yrby conceiving an

estimator, mapping Yrfrom the signal space to an estimate ˆ

η,

deﬁned as ˆ

η=F(Yr). To measure how accurate an estimator

is, a possible performance metric is the mean squared error

(MSE), expressed as ε=Ekη−ˆηk2. The average may be

over the noise or also over the parameters if they are assumed

random. When the parameters are assumed to be deterministic,

the MSE of any unbiased estimate is lower-bounded by the

Cram´

er-Rao bound (CRB), deﬁned as the inverse of the Fisher

information matrix J[7]

Eh(η−ˆη) (η−ˆη)HiJ−1=−E∂2ln p(Yr;η)

∂η2−1

(5)

where p(Yr;η)is the probability density function (PDF)

of Yrparameterized by η. While the maximum likelihood

estimate (MLE) asymptotically achieves the CRB, attaining

the MLE can be highly computationally expensive. To that

end, low-complexity parameter estimation algorithms, e.g.,

MUSIC and ESPRIT [8], [9], have been widely applied in

practical situations, such as angle of arrival estimation.

In communication systems, the channel Hcshould be

estimated before delivering the useful information. For channel

estimation, the Tx sends pilots to the Rx, which are reference

signals known to both. The Rx then estimates the channel

based on both received signals and pilots. Channel estimation

is mathematically similar to the target estimation problem,

where the to-be-estimated parameters ηare entries of Hc,

which is regarded as an unstructured matrix. We will elaborate

on similarities and differences between estimation tasks for

communication channels and radar targets in Sec. IV.

3) Information Theory: Information theory serves as the

foundation of communication SP. A remarkable result attained

by C. E. Shannon in his landmark paper [10], published

in 1948, states that, for any discrete memoryless channel

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1SeventyYearsofRadarandCommunications:TheRoadfromSeparationtoIntegrationFanLiu,Member,IEEE,LeZheng,SeniorMember,IEEE,YuanhaoCui,Member,IEEE,ChristosMasouros,SeniorMember,IEEE,AthinaP.Petropulu,Fellow,IEEE,HughGrifths,Fellow,IEEE,andYoninaC.Eldar,Fellow,IEEEAbstractRadarandcommunications(R&C)askeyu...

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