A Hybrid Millimeter-wave Channel Simulator for Joint Communication and Localization Junquan Deng

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A Hybrid Millimeter-wave Channel Simulator for

Joint Communication and Localization

Junquan Deng

National University of Defense Technology, China, Email: jqdeng@nudt.edu.cn

Abstract—Joint communication and localization (JCL) is envi-

sioned to be a key feature in future millimeter-wave (mmWave)

wireless networks for context-aware applications. A map-based

channel model considering both site-speciﬁc radio environment

and statistical channel characteristics is essential to facilitate

JCL research and to evaluate the performance of various JCL

systems. To this end, this paper presents an open-source hybrid

mmWave channel simulator called OmniSIM for site-speciﬁc

JCL research, which uses digital map, network layout and user

trajectories as inputs to predict the channel responses between

users and base stations. A fast shooting-bouncing rays (FSBR)

algorithm combined with Computational Electromagnetic, has

been developed to generate channel parameters relevant to

JCL, considering mmWave reﬂection, diffusing, diffraction and

scattering.

Index Terms—Millimeter-wave, joint communication and lo-

calization, ray-tracing, shooting-bouncing rays

I. INTRODUCTION

Future wireless networks feature the use of large antenna

arrays and wide-band radio access techniques in high radio

frequency range, especially in the millimeter-wave (mmWave)

frequencies. 3GPP’s 5G New Radio (NR) in Frequency Range

2 (FR2) and IEEE 802.11ay are two representative standards

for mmWave communications. Large number of antennas and

signal bandwidth adopted in these mmWave networks not

only increase the communication capacity, but also equip

the networks with radio sensing and positioning functions of

high accuracies. On the other hand, the built-in localization

functionality facilitate various context-aware applications

including Internet-of-Things, intelligent transportation system,

crowd sensing, assets tracking and advanced radio resource

management (RRM).

The performances of a mmWave joint communication and

localization (JCL) system heavily depends on the site-speciﬁc

mmWave channel characteristics, including LOS condition,

powers of multi-path components (MPC), angular and tem-

poral distributions of MPCs and Doppler shifts. On the one

hand, mmWave communication relies on line-of-sight (LOS)

or prominent reﬂection paths for beamforming or multiple-

input-multiple-output (MIMO) transmission, and the channel

state information (CSI) needs to be estimated for designing the

baseband pre-coder and combiner. On the other hand, various

geometrical mmWave localization algorithms also rely on

This work was supported by the National Science Foundation of China

under grant 61901497, 62131005, 62231012 and Research Project of National

University of Defense Technology under grant ZK 19-09. The channel

simulator is available at https://github.com/dengjunquan/OmniSIM.

the accurate estimations of the direction-of-arrivals (DOAs)

and/or delays for prominent MPCs. For ﬁngerprinting-based

localization methods, channel spatial consistency is important,

which describes the smooth variations of channels when the

user equipment (UE) moves in the geographic space. In order

to effectively support the design and evaluation of a mmWave

JCL system, accurate network-level channel characterization

and modeling considering massive user equipments (UEs) are

of great importance.

According to how the clusters of MPCs are generated,

mmWave channel models can be categorized as stochastic,

deterministic and their hybrid ones [1], [2]. Stochastic models,

including the widely used geometry-based stochastic channel

model (GSCM) [3]–[5], use randomly dropped scatters to

generate MPC clusters, they are suitable for evaluating multi-

antenna communication performances, but cannot be used

directly for designing and evaluating localization techniques.

Deterministic channel models are based on detailed site-

speciﬁc electromagnetic environment information, using either

full-wave solutions such as method of moments (MoM), ﬁnite-

difference time domain (FDTD), or asymptotic technique

like geometric optics (GO) and uniform theory of diffrac-

tion (UTD) to predict the radio channels. As compared, a

hybrid model [1] uses a simpliﬁed geometric description of

the propagation environment and a ray-tracing method to

generate realistic large-scale spatial channel properties, and

adds some stochastic factors to model the small-scale fading

effects.

There are several mmWave channel simulators available for

academia and industrial research, including QuaDRiGa [5],

NYUSIM [6], NIST Q-D Realization [7], Altair WinProp [8]

and Remcom Wireless InSite [9]. QuaDRiGa and NYUSIM

are based on GSCM, while Q-D Realization, WinProp and

Wireless InSite are based on ray tracing. QuaDRiGa extends

the popular GSCM channel model with new features that

allow the generation of channel traces with temporal evolution

and scenario transitions. It dose not use an exact geometric

representation of the environment but distributes the positions

of scatters randomly in the simulated scenario. NYUSIM

provides an accurate rendering of actual channel impulse

responses in both delay and spatial spaces based on parameters

derived from measurement data. The open-source NIST Q-D

Realization software implements ray tracing to capture the

deterministic specular rays, and integrate the deterministic

channel description with stochastic models for diffuse rays.

arXiv:2210.11422v1 [eess.SP] 4 Oct 2022

Its ray tracing is based on backtracking algorithm and the

method of images and has high computational complexities

with a larger number of facets in the simulated scenario, and

diffraction and scattering are not implemented. WinProp is a

commercial wireless propagation and radio network planning

software, and supports reﬂection, diffraction and mobile

scattering in the simulation. It employs techniques including

the dimension reduction, space partitioning, intelligent ray

tracing (IRT) and dominant path model (DPM) [2] to

accelerate the simulation and can be used in a large-scale

scenario with thousands of facets. Wireless Insite is another

popular commercial software uses ray tracing coupled with

empirical models for a frequency range from 50 MHz to

100 GHz. It employs dimension reduction and ray launching

acceleration algorithms, as well as GPU and multi-threaded

CPU hardware acceleration.

This paper presents an open-source mmWave network-level

hybrid channel simulator (OmniSIM) for JCL research. It

combines a customized deterministic ray-tracing model with

stochastic small-scale modeling methods to generated UE-

location-dependant channel responses. It models the diffuse

reﬂections by building surfaces using GO, the diffractions by

wedges using UTD and the scattering effects by trees using

the radiative energy transfer (RET) function. The core part of

OmniSIM is a fast shooting-bouncing rays (FSBR) algorithm

which can ﬁnd propagation paths between BS and massive

UE locations with low computation complexities. OmniSIM

is available at https://github.com/dengjunquan/OmniSIM.

II. MMWAVE CHANNEL MODEL

We consider a generic mmWave orthogonal frequency-

division multiplexing (OFDM) baseband channel model,

assuming a total bandwidth of

W=N∆

with subcarrier

spacing

∆

, the frequency-domain channel response between a

transmitter (TX) with

antennas and a receiver (RX) with

antennas at subcarrier

n∈ {0,··· , N −1}

and symbol

duration s∈ {0,··· , S −1}is [10], [11]

Hn,s =

l=1

αlp(sT −τl)ar(θl)aH

t(φl)ej2π(sT νl−n∆τl),(1)

where

denotes the number of propagation paths,

αl

a complex channel gain for the

-th path,

denotes the

symbol duration,

p(τ)

is a ﬁlter that includes the effects

of pulse-shaping and other low-pass ﬁltering evaluated at

Furthermore,

ar(θ)∈CNR

is the RX antenna array response

as a function of the DoA

θ∈R2

in azimuth and elevation

domains,

at(φ)∈CNT

is the TX array response as a function

of the direction of departure (DoD)

φ∈R2

. Finally,

τl

is the

time of arrival (ToA) and

νl

is the Doppler shift, related to

the l-th path.

The channel models at mmWave differs from conven-

tional sub-6GHz frequencies because of smaller wavelengths.

Diffraction is not signiﬁcant due to the reduced Fresnel zone,

scattering is higher as the wavelengths are comparable to

effective roughness of radio reﬂectors and scatters, and the

penetration losses are much larger. A large amount of channel

measurement results have conﬁrmed that MPCs for typical

3D digital map

BS and UE

positions

Building surfaces

Scatter position

and sizes

Electromagnetic

parameters

Shoot rays from a

BS position

Find reflection

surfaces

Find diffraction

wedges

Generate

diffraction paths

Generate

reflection paths

Find LoS scatters

Generate

scattering rays

Find paths

interacting with

UEs

Find LoS ray

Generate

reflection and

diffraction rays

Compute channel

coefficients

Fig. 1. OmniSIM ﬂowchart

outdoor and indoor mmWave channels come with cluster

structures in both the delay and angular domains [12]–[15],

and the channel power is dominated by LoS and/or specular

reﬂection paths.

In a mmWave JCL system, the baseband RX signal is of

the form

yn,s =WsHn,sxn,s +nn,s,(2)

where

Ws∈CNR×MR

is the RX combiner, using

MR≤NR

RF chains,

xn,s

is the

-th radiated signal vector from the

TX antenna array, with

E{kxn,sk2}=P/W

where

the average transmit power, and

nn,s

is noise after the

combining. For channel estimation and localization purposes,

the transmit signals

xn,s

are generally known based on

predeﬁned pilots, and the channel

Hn,s

can be estimated

via

yn,s

and a reconstruction algorithm. If the antenna

array responses

ar(θ)

at(φ)

are known, it would be possible

to estimate the DoAs, delays of dominant paths using

MUSIC [16] or other algorithms for positioning purpose. In

the case that array responses are unknown, advanced machine

learning ML methods can be utilized for localization based

on ﬁngerprinting and a channel similarity metric [17].

III. OMNISIM FRAMEWORK

Different from conventional sub-6GHz BSs, mmWave BSs

should be deployed below the rooftops and probably at the

same height as the UEs [18]. Notice that, we consider using

the vertical-plane-launch (VPL) [19] method to reduce the

simulation complexity. Different from a full 3D SBR method,

in which rays are launched in the 3D space, the VPL method

is a dimension reduction method. OmniSIM ﬁrst treats vertical

building surfaces as 2D segments and generates the rays in

the azimuth domain, then maps the 2D rays to 3D space

by considering ground reﬂections and the possible ceiling

reﬂections.

A mmWave channel simulator for JCL should generate

realistic MPCs and related path parameters including complex

path gains

{αl}

, delays

{τl}

, DoAs

{θl}

, DoDs

{φl}

and

Doppler shifts

{νl}

for massive number of BS-UE links.

The direct LoS, specular & diffuse reﬂections from building

surfaces, diffractions by wedges and scattering by irregular

scatters (e.g. vegetation) need to be appropriately modeled.

For this purpose, OmniSIM uses GO, UTD, radiative energy

transfer (RET) models to compute reﬂection, diffraction and

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AHybridMillimeter-waveChannelSimulatorforJointCommunicationandLocalizationJunquanDengNationalUniversityofDefenseTechnology,China,Email:jqdeng@nudt.edu.cnAbstractJointcommunicationandlocalization(JCL)isenvi-sionedtobeakeyfeatureinfuturemillimeter-wave(mmWave)wirelessnetworksforcontext-awareapplicati...

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A Hybrid Millimeter-wave Channel Simulator for Joint Communication and Localization Junquan Deng

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