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IEEE INTERNET OF THINGS JOURNAL, VOL. XX, 20XX 1
Ultra-Narrowband Waveform for IoT Direct
Random Multiple Access to GEO Satellites
Christian A. Hofmann, Member, IEEE, and Andreas Knopp, Senior Member, IEEE
Abstract—Direct access of small terminals in the Internet
of Things (IoT) to geostationary satellites may provide wide
coverage and almost 100% availability for remote locations with-
out access to terrestrial networks. However, existing waveforms
and IoT solutions do not close the link to the geostationary
earth orbit (GEO) for massively deployed small devices. We
present a novel modulation and signaling scheme based on chirp-
spread spectrum (CSS) that enables reliable transmission at ultra
low bit-rate. The proposed structure for the transmit signal
applies unipolar codes in a novel manner, which allows random
multiple access to a common channel for a large number of
devices. Further, the transmit signal is designed to allow robust
signal detection with low effort even at high carrier frequency
offsets. We propose a system concept for the receiver including
synchronization, and provide the results of extensive simulations
carried out on system and link levels. As a result, we demonstrate
that the proposed scheme, referred to as Unipolar-coded Chirp-
Spread Spectrum (UCSS), enables true random multiple access
for a very large number of devices, closing the challenging link
between IoT devices and satellites in the GEO even at high carrier
frequencies from C-band to Ka-Band.
Index Terms—Efficient Communications and Networking,
Machine-to-Machine Communications, Medium Access Control
Protocols, Satellite Communication, Spread Spectrum Commu-
nication, Chirp Modulation, Satellite Direct Access.
I. INTRODUCTION
MACHINE type communication (MTC) or machine-to-
machine (M2M) communication has gained an enor-
mous interest throughout industry and academia. Low power
wide area network (LPWAN) solutions enable the transmission
of small data packets from very small devices at minimum
power consumption over distances of several kilometers. They
are, therefore, attractive for a vast variety of applications in
the Internet of Things (IoT) [1]. If such devices are used
for the tracking of mobile objects or devices, a coverage
of the employed IoT service over the area of interest is
required. Satellite based systems guarantee ubiquitous service,
especially for objects in remote locations and areas without
terrestrial infrastructure. Generally, a terrestrial connection for
massive machine type communication (mMTC) is the first
choice. The terrestrial service delivers sufficient coverage and
availability in most applications. Nevertheless, the satellite
enabled access to the network serving as an additional and
alternative connection can provide connectivity in cases when
Manuscript received 08-Jul-2019; accepted 31-Jul-2019. Date of publication
xxxx xx, 20xx; The authors are with the Signal Processing Group of the
Institute of Information Technology, Bundeswehr University Munich, 85579
Neubiberg, Germany (e-mail: papers.sp@unibw.de). Copyright (c) 2019 IEEE.
Personal use of this material is permitted. However, permission to use this
material for any other purposes must be obtained from the IEEE by sending
a request to pubs-permissions@ieee.org.
the terrestrial network is not available. For many services
where the guaranteed network connection is a must, the
information could be transmitted via satellite if the IoT device
moves outside the service area of the terrestrial system, or
if the terrestrial system is temporarily not available. For
the tracking of wildlife, objects in very remote locations,
small aerial or maritime vehicles, a novel low-rate satellite
service may be the first choice. Novel applications for sending
emergency messages in any region may rely on such satellites
services, especially in the mountains or in the oceans. Finally,
some services use satellite networks instead of terrestrial ser-
vices for security reasons. Further examples for satellite-based
narrowband (NB) IoT services are, for example, presented in
[2] and [3]. Moreover, the direct access of NB IoT devices to
non-terrestrial networks or satellite networks in low earth orbit
(LEO) and geostationary earth orbit (GEO) is also a topic of
the future research for 5G as recently stated by 3GPP in [4].
The access of an IoT device to a satellite system ensures a
wide coverage, while the direct access to the satellites makes
the devices independent of further terrestrial infrastructure [3].
Satellite constellations in LEO can provide global coverage
and are usually preferred for NB IoT systems due to lower path
loss to the satellite [5], [6]. Nevertheless, for an instantaneous
access 100% of time, a large number of costly satellites
with an inter-satellite network is required. In contrast, GEO
satellites ensure continuous service over a specified coverage
area and can provide global access outside the polar regions
with only three satellites. Usually, GEO satellite systems at
higher frequencies apply directive earth station antennas with
a high gain to close the link. At lower frequencies, such as
UHF or S-band [7], GEO systems allow communication with
omnidirectional antennas.
For MTC via satellite, so-called supervisory control and
data acquisition (SCADA) systems have been developed that
apply scrambled coded multiple access (SCMA) or interleave
division multiple access (IDMA) together with spreading to
enable low-rate communication and multiple access [7]. These
so-called non-orthogonal multiple access (NOMA) schemes
employ a low-rate forward error correction (FEC) and different
scramblers or interleavers for user separation. A joint mul-
tiuser decoding with interference cancellation allows multiple
users accessing the network non-orthogonally. However, the
available spectrum at UHF or S-band is very limited and the
available services cannot handle a huge number of devices for
mMTC.
In this work, we focus on the direct access of preferably
small terminals to GEO satellites in frequencies of the C-
band or above. We consider ultra low data rates with only a
IEEE INTERNET OF THINGS JOURNAL, VOL. XX, 20XX 2
couple of bits per second and a burst transmission with random
multiple access. This is sufficient for many applications, such
as the transmission of sensor values, positioning data, or
emergency messages. In seeking for transceivers that are as
small as possible and universally deployable, their antenna
characteristic is usually non-directive or even omnidirectional.
The available transmit power is limited and the transmit spec-
tral power density is restricted due to regulatory limitations. In
that case, at a given carrier frequency, the available transmit
power and the satellite figure of merit mainly determine
the achievable data rate of the system. Here, link budget
calculations indicate data rates in the range of only a few
bit/s. Therefore, it is a big challenge today to close this link
to a GEO satellite.
If we allow the IoT device to transmit data in a random
multiple access scheme, to enable, for example, emergency
messages, the most challenging task at the receiver side is
to detect the short transmitted bursts. Most probably, the IoT
transmitter’s oscillator is a low-power, low-cost device with
low accuracy and stability. If the narrowband transmission
comprises only of a very small RF-bandwidth (a few Hertz),
the effort for searching the signal (acquisition) at any possible
carrier frequency offset (CFO) in a range of several kHz is
very high [8]. If alternatively a spread spectrum technique is
applied to lower the transmitted power density and/or enable
an easier detection at the Rx, a limited timespan for coherent
correlation, due to the low oscillator stability, must be accepted
[9]. Thus, the synchronization is considered the most important
limitation for ultra-low bit-rate transmissions, and the overhead
to achieve synchronization is large. To the authors’ best
knowledge, no system or technology has been published that
can communicate using omni-antennas to GEO satellites in
C-Band or above. Existing satellite IoT waveforms [5], [10]–
[12] are designed to close the link either for communication
via LEO satellites or for the operation at L- or S-band. In
this work, we present an approach for a modulation scheme
that enables robust synchronization with reduced overhead via
GEO satellites at frequency bands above L- or S-band. The
proposed modulation scheme applies a chirp-spread spectrum
(CSS) technique for a robust transmission and a simplified
signal acquisition. The modulated chirps are separated in
time according to a unipolar code enabling a non-orthogonal
code division multiple access (CDMA) to a shared wireless
transmission channel. Unipolar codes are used for CDMA in
optical transmissions and are also known as optical orthogonal
codes (OOCs). Therefore, we refer to the proposed modulation
scheme as Unipolar Coded Chirp-Spread Spectrum (UCSS).
The codewords of the OOC are constructed here to enable
asynchronous communication of multiple users with random
access (RA) to the channel. Thus, the proposed modulation
scheme allows the application of unslotted ALOHA [13] on
the medium access control (MAC) layer. If UCSS is, for
example, applied in a system with small sensors transmitting
to a common gateway via satellite, the sensors may operate in
simplex mode and do not require a receive entity. The multiple
access for machine type communication (MTC) in satellite
networks has been investigated by various groups; [14] gives
an overview about the available RA techniques. Similar to
gateway
devices
GEO satellite relay
xm(t)
downlink
Fig. 1. Scenario considering the return channel from numerous devices to a
common gateway station
spread spectrum ALOHA (SSA) introduced in [10], we apply
a spreading by using CSS, but we further introduce a unipolar
code to minimize the collisions between the transmitted sym-
bols. If UCSS is used in the return link towards a common
gateway, then a successive interference cancellation (SIC)
technique might be applied to further enhance the throughput
as shown in [15].
As a main contribution, this paper presents UCSS as a
novel modulation and signaling scheme in close combination
with the synchronization concept to allow direct random
multiple access of numerous small devices to GEO satellites at
higher frequency bands. In section II, the satellite scenario is
described and an exemplary link budget is calculated. Section
III contains a detailed description of the proposed modula-
tion format and demonstrates how random multiple access
is achieved for a large number of users. A suitable receiver
design is presented in section IV. Here, we indicate how the
signal structure enables the acquisition and synchronization
without high overhead. In Section V, a performance evaluation
of UCSS is performed. Section VI provides a comparison of
our results to related work. The paper is concluded in section
VII.
II. SATE LL IT E LINK BUDGET
The proposed UCSS might be applied in the up- and
downlink of a small terminal transmitting and receiving signals
from a satellite at any orbit. In the downlink, the CDMA
properties of the waveform allow a transmission towards
multiple small terminals. The application of CSS enables an
immediate signal acquisition at the terminal. In the return
channel, i.e. the uplink from the terminals towards the satellite,
asynchronous random access is enabled for terminals with low
power and low cost hardware due to relaxed requirements
put on the frequency accuracy and stability. Furthermore, the
modulation format might be applied for instance to sensor
systems without downlink, since the multiple access scheme
is truly asynchronous and no synchronization information is
required to be received from the satellite.
As the transmit power of a small mobile device is the most
limiting factor in the link budget of the considered satellite
link, we concentrate on the user uplink towards the satellite.
In the following, we consider the return channel with a large
number of IoT devices transmitting via a transparent GEO
IEEE INTERNET OF THINGS JOURNAL, VOL. XX, 20XX 3
satellite and a gateway station serving as a common receiver
as depicted in Fig. 1.
The m-th user transmits the signal xm(t)at effective
isotropic radiated power (EIRP) EIRPTx,m. The uplink signal
is received by the satellite antenna with gain Gsat. The total
path loss introduced by the channel is Gp,m. The carrier power
over the noise density S
N0UL,min dB for the uplink is given
by S
N0UL,m
=10 lg EIRPTx,mGp,mGsat
N0(1)
=10 lg EIRPTx,mGp,m
k+G
Tsat
(2)
where N0denotes the noise power density of the additive white
Gaussian noise (AWGN) in the uplink at the receiver (Rx).
Furthermore, kstands for the Bolzmann constant and G
Tsat
is the figure of merit of the satellite in dB, which is the receive
antenna gain over the noise temperature. The signal-to-noise
power ratio (SNR) S
NUL,mis given by
S
NUL,m
=S
N0UL,m−10 lg {Bn}(3)
where Bnstands for the noise bandwidth of the system. If the
contribution of the downlink to the overall SNR is negligible,
or if the satellite is regenerative, the overall SNR of the m-th
user is S
Nm≈S
NUL,m.
Note, that the other users transmitting at the same time
cause interference to the desired user m. The mean interference
power Imis calculated from the signals of all Nu−1users,
transmitting in the same time interval [t1,t2]as the target user
m. If we assume mutually uncorrelated transmit signals for all
the users, then interference power Imfor user mis calculated
from the following equation:
Im=
1
t2−t1
t2
∫
t1
Nu
Õ
i=1,i,m|xi(t)|2dt.(4)
The signal-to-interference and noise power ratio (SINR) is
given by
S
I+Nm
="S
Nm−1
+S
Im−1#−1
(5)
where S
Imis the signal-to-interference power ratio (SIR) for
user m.
If the earth station comprises of a non-directional antenna,
the satellite channel becomes a fading channel due to multipath
propagation. We assume a fading process with lognormal
distributed channel coefficients characterized by the standard
deviation σch to cover the propagation effects in our calcula-
tions.
To give an indication for the achievable data rate, we provide
a link budget for a link at C-band1to a GEO satellite’s global
1The calculated link budget is roughly also applicable to higher frequency
bands like Ku-band or Ka-band, if we assume that the receive dish-antenna
at the satellite has the same diameter.
beam with typical G
Tsat =−5 dB/Kat center of coverage. We
assume an IoT device2with EIRPTx,m=23 dBm transmitting
at 6 GHz, and a path loss of Gp,m=−201 dB.
For the example considered, the signal power over the noise
power density comes out to be S
N0m=15.6 dB/Hz which
gives a channel capacity of 29.9 bit/sin a bandwidth of 20 Hz.
If we assume a binary phase-shift keying (BPSK) transmission
with code rate 0.5 in 20 Hz, it would theoretically be possible
to transmit with a user bit-rate of 10 bit/sat Eb/N0=5.6 dB.
This means a device might transmit its position, which has
been source coded to a size of, for example, 40 bits, in a
timespan of 4 s. However, in practice, it is hardly possible to
use BPSK at such a low data rate. This is because the applied
matched filtering at the Rx has such a low bandwidth that
the CFO, which is not constant over the duration of several
seconds, shifts the receive signal out of the receiver bandwidth.
Usually, in that case, spread spectrum is applied in order
to widen the utilized bandwidth. As a consequence, the signal
power density is reduced, shifting the received signal below the
noise floor. The acquisition of such a signal at an unknown
timing and an unknown CFO requires huge effort which is
also known from global navigation satellite systems (GNSSs).
If the IoT transmitter is only transmitting in short blocks for
a low percentage of time, the acquisition has to be repeated
for every transmitted block. This would require a very high
computational effort at the gateway station which is receiving
the signals of many devices. The presented modulation format
solves the highlighted set of problems, as it is designed to
enable ultra narrowband (uNB) transmissions in an effective
manner with low hardware requirements at the transmitter
and low effort for the acquisition and synchronization at the
receiver.
III. PROP OS ED MODULATION SCHEME (UCSS)
A. Overview
In the following, we give a brief overview of the proposed
UCSS modulation and synchronization format, while the re-
mainder of this section presents the detailed description.
Referring to Fig. 2 we consider the input data as bits in
blocks to be transmitted. The data block is encoded with a FEC
code before it is modulated by a differential phase-shift keying
(DPSK) modulator. Each modulated symbol is multiplied by a
Zadoff-Chu or constant amplitude auto-correlation (CAZAC)
chirp sequence (CS) which is referred to as a CSS technique.
This spreading provides us with a correlation gain proportional
to the length of the CS. In the next step, before the signal
is transmitted, we insert short pauses into the time signal
between the chirps. The pause times are chosen according
to a unipolar code, where each codeword represents one user.
Different users employing different codewords are supposed to
transmit asynchronously at the same time in a random-access
scheme.
2The transmit EIRP of the IoT device is assumed to be 23 dBm, as
this would most probably be the maximum transmitted power within 4 kHz
that is allowed when following all regulations issued by the International
Telecommunication Union (ITU). Furthermore, the same transmit power is
assumed for IoT devices with direct satellite access by 3GPP in [4].
IEEE INTERNET OF THINGS JOURNAL, VOL. XX, 20XX 4
FEC
encoder
M-DPSK
modulator
CSS-
Spreading
chirp
unipolar
encoder
codeword
DAC+
Tx RF
Rx RF
+ADC corr 1
chirp
M-DPSK
demod
FEC
decoder
corr 2
codeword
sync.
Fig. 2. Flowgraph of the proposed modulation scheme (UCSS) with transmitter and receiver
At the receiver, where the transmitted signals of all users
interfere, the received signal is first correlated with the CS,
which can be considered as a CSS despreading. Hence, if all
users share a single CS, only one CSS despreader is required
at the gateway to despread the signals of all transmitting users.
The correlation result contains a correlation peak for every
symbol transmitted by any of the users. A collision at the Rx
occurs when two or more symbols have been received at the
same time. The unipolar coding at each Tx ensures that the
number of collisions between two Tx stations is limited to a
maximum number, which is a design parameter of the used
OOC. To identify the users that have been transmitting, the
result of the first correlator is non-coherently correlated a sec-
ond time with a reference signal that contains the information
about the unipolar codeword. This second correlation could
be implemented with a low effort. It is performed for each
of the potentially transmitting users, while the first correlation
is only required once for each CS used. A transmitted signal
is detected if the correlation result of the second correlator
exceeds a certain threshold. In the next step the timing is
recovered and the DPSK symbols are localized within the
output of the CSS despreader and differentially demodulated.
After the decoding, we obtain the transmitted bit-sequence as
the FEC corrects errors due to noise, interference, and signal
collisions.
B. Chirp-Spread-Spectrum
The history of the use of spread spectrum systems goes back
to World War II, and the application of a chirp was one of the
first attempts for the spreading of signals [16]. Applications
of CSS are found in radar systems, military communications,
ranging systems, and robust communication at low power
spectral density. Signals spread by a chirp occupy a broad
band of the spectrum and provide enhanced protection against
interference, unwanted detection, multipath propagation, fre-
quency shifts from Doppler shifts, or oscillator inaccuracy
[17]–[19]. CSS is often the choice for applications requiring
high power efficiency and low data rates. In particular, IEEE
802.15.4a specifies CSS as a technique for use in Low-Rate
Wireless Personal Area Networks (LR-WPAN) [20]. A chirp
is a sinusoidal signal whose frequency increases or decreases
linearly over a certain time span. The modulation of the chirps
in order to transmit data is achieved in different ways. In
[21], [22] for example, the frequencies which the individual
chirp signals start from are varied to encode information.
Phase modulation of chirps is performed in [23] where the
transmitted chirps are modulated by a DPSK symbol. To
enable multiple access to a shared medium it is proposed to
use chirps with different slopes for different users in [24].
The IEEE 802.15.4a standard also enables multiple access by
the use of so-called bi-orthogonal chirps in combination with
phase modulation of chirps and subchirps. In [25] orthogonal
chirps are applied to increase the spectral efficiency of CSS.
In this work, we propose phase modulation of identical
chirps as described in section III-D, while we use the location
of the chirps within the signal for the separation of multiple
users transmitting at the same time and at the same carrier
frequency.
C. Unipolar Coding
The application of CDMA to fiber optical channels has
driven the development of unipolar codes to separate several
users communicating asynchronously over a shared optical
fiber.
The so called OOCs are unipolar sequences consisting of
Zero-Bits and One-Bits, with good auto- and cross-correlation
properties [26]. In the following we use the construction
method in [27] for constructing the OOCs due to its low
complexity. As the variety of available codes in the literature
is large, there is room for the optimization of the code used
depending upon the application. Especially the number of
transmitted One-Bits in relation to the number of different
available orthogonal codewords can be optimized to fit the
requirements of the application in terms of transmit energy
and number of users.
According to [27] a codeword c(n)is calculated using the
so-called quadratic placement operator yµ(ξ)for the µ-th user
defined as
yµ(ξ) ≡ µξ(ξ+1)
2mod β(6)
with 0≤ξ≤β−1and 1≤µ≤β−1. The parameter β
is a prime number and defines the length Looc of a codeword
which is Looc =β2. Furthermore, the number of One-Bits Ns
within every codeword is Ns=β. The µ-th codeword out of
Nooc =β−1possible codewords is given by
cµ(n)=
1if yµ(ξ)+ξ β =n
for µ=1,2, ..., β −1
and ξ=jn
βk
for n=1,2, ..., β2−1
0elsewhere.
(7)
IEEE INTERNET OF THINGS JOURNAL, VOL. XX, 20XX 5
TABLE I
CODEWORDS FOR β=5
user codeword
1 10000 01000 00010 01000 10000
2 10000 00100 01000 00100 10000
3 10000 00010 00001 00010 10000
4 10000 00001 00100 00010 10000
Table I shows exemplary codewords for the Nooc =4users
of a code with β=5. The auto-correlation of a codeword is
defined by
Rc,c(k)=
Looc−1
Õ
n=0
c(n)c(n−k)(8)
and the cross-correlation of two codewords c1(n)and c2(n)is
given by
Rc1,c2(k)=
Looc−1
Õ
n=0
c1(n)c2(n−k)(9)
with −Looc +1≤k≤Looc −1. In [27] it is proven
that the auto-correlation of any codeword excluding the zero
shift has strictly values of λa=max
k,0Rc,c(k)≤2. The
cross-correlation of any two codewords is always λk=
max Rc1,c2(k)≤4.
While we use the described code according to [27], there
exist further codes and design methods for codewords that
enable the modification of λaand λkas well as the number
of codewords and the number of One-Bits [28]. This leaves
room for an optimization of the applied code with respect to
the parameters of the intended communication system.
D. Transmit Signal
This subsection describes the calculation of the transmit sig-
nal. Here, OOC and CSS are combined to create unique signals
for different users. A detailed block diagram description of
the transmitter is given in Fig. 3. We assume a block based
transmission, where a block consists of Nbit data bits xb(n)for
n=1, ..., Nbit that are encoded as a first step using a FEC.
The code rate Rc=Nbit
Ncgives the number Ncof coded data
bits xc(n). The coded bits are the input of a DPSK modulator
with modulation order M. The Nm=Nc/ld{M}modulated
symbols xm(n)are multiplied either by a chirp sequence s(n)
or by the complex conjugate chirp s∗(n). We use s(n)and
s∗(n)alternating as spreading sequences to enable a robust raw
CFO estimation as presented in section IV-C. As a spreading
sequence, we use a CAZAC sequence, which is also known as
Zadoff-Chu sequence. It has initially been proposed in [29] and
[30]. In [31] it has been shown that its cyclic auto-correlation
function (ACF) Rs,s(k)for arbitrary length Lsof the sequence
is zero for k,0, i.e. it is equal to the delta function. A
CAZAC sequence s(n)of length Lsis denoted as column
vector s=[s(0), ..., s(n), ..., s(Ls−1)]>and is generated by
s(n)=ejπMsn2
Lsfor Lseven, and n=0,1, ..., Ls−1(10)
s(n)=ejπMsn(n+1)
Lsfor Lsodd, and n=0,1, ..., Ls−1.(11)
Here, Lsand Msare two integer numbers that are relatively
prime.
In the next step we introduce pause times between the chirps
by inserting dummy samples into the signal. As the number of
inserted dummy samples is chosen according to a codeword of
the OOC, we refer to this step as OOC-encoding. A transmitted
block contains a pair of chirps per One-Bit in the OOC.
Between the pairs of chirps, we insert one dummy sample per
Zero-Bit in the OOC. Here, we have to ensure that the number
of One-Bits (Ns) within the codeword is sufficient to encode
all Nmtransmit symbols3. As we transmit two chirps for every
One-Bit in the OOC, we need an OOC with Ns≥dNm/2e.
The dummy samples between the correlation sequences are
set to 1 in order to ensure the constant envelope property of
the radio frequency (RF) signal4.
By that we create coded correlation signals that are dis-
tinguishable at the receiver by applying one correlation per
codeword.
Assume that ˜pµ(l)for l=1...Nscontains the indices of all
One-Bits within the µ-th codeword, i.e. ˜pµ(l)is the index of
the l-th One-Bit in cµ(n). Then
zµ,l=˜pµ(l) − ˜pµ(l+1) − 1(12)
gives the numbers of Zero-Bits between two One-Bits within
the codeword of the OOC.
The transmit signal vector xis different for each codeword
µand, thus, denoted by xµ
xµ=[xm(1) · s>,xm(2) · sH,o>(zµ,1),
xm(3) · s>,xm(4) · sH,o>(zµ,2),
... ,
xm(2l−1) · s>,xm(2l) · sH,o>(zµ,l),
... ,
xm(Nm−1) · s>,xm(Nm) · sH,]>
(13)
where o(zµ,l)stands for a vector containing zµ,lOnes. The
construction of the transmit signal according to (13) is also
sketched in Fig. 4 and the upper diagram in Fig. 7 shows
an example for the real-part of a base-band transmit signal
calculated from (13) with Ls=100 and Ms=1.
Every frame contains Nspairs of correlation sequences, i.e.
2·Nsindividual correlation sequences. The number of symbols
after spreading ( Nframe) of the transmitted frame is dependent
upon the chosen OOC as well as upon Lsand is given by
Nframe =2·Ns·Ls+N2
s−Ns(14)
where the first part of the sum is the length of all correlation
sequences within one block and the second part is the length
of all pause times according to the OCC. Equation (14) is valid
if the code from [27] is used as described in section III-C.
E. User Separation for Random Multiple Access
The proposed UCSS allows a random multiple access to
the channel due to the unipolar encoding of the used CS,
3In fact the number Nshas to be even larger, because later we insert training
symbols for carrier frequency offset estimation.
4Instead of 1 any other complex value with absolute value equal to 1 would
also fulfill the condition for a constant envelope signal.
IEEE INTERNET OF THINGS JOURNAL, VOL. XX, 20XX 6
xb(n)
data
FEC
encoder
M-DPSK
modulator
xc(n)xm(n)
s(n)
s∗(n)
unipolar (OOC)
encoder
code word for
µ-th user: pµ
DAC +
Tx RF
xs(n)x(n)
Fig. 3. Detailed flow graph of the proposed transmitter
Tx symbols xm(1) · sxm(2) · s∗11 xm(3) · sxm(4) · s∗111 ... xm(Nm−1) · sxm(Nm) · s∗
OOC codeword
for user µ100 1000 1
...
zµ,1zeros zµ, 2zeros
correlation
sequence (CAZAC)
data symbols
ss∗ss∗ss∗
xm(1)xm(2)xm(3)xm(4)xm(Nm−1)xm(Nm)
Fig. 4. Generation of the Tx symbols from data symbols by chirp spread spectrum and unipolar coding
0.2 0.40.60.8 1
·104
2
4
6
8·10−2
CAZAC sequence length Ls
CCF /Ls
max CCF value
mean value over all CCF
cyclic CCF upper bound: √Ls
Fig. 5. Maximum and mean cross-correlation values for all combinations of
two different CAZAC sequences over different sequence length Ls
comparable to a CDMA scheme as described in section III-C.
If the number of existing OOC words is not sufficient to serve
all desired users for a satellite IoT system, we can of course
increase the system capacity by enabling orthogonal multiple
access in the frequency, time or spatial domain. Nevertheless it
is possible to increase the number of user codes by alternating
the used CS and, hence, increase the number of separable users
at the same frequency and in the same timeslot. Because of
this we are able to generate a very large number of codes that
can be assigned to a large number of users. Certainly, if all
users would transmit at the same time, the SINR will be very
low and the user detection is impossible due to interference.
However, we are able to design a system where we can
guarantee random multiple access to a very large number
of users, where only a subset of all users is transmitting at
the same time, given the up-times of the individual users are
limited in time and mutually uncorrelated.
The properties of the used CAZAC sequences as family of
correlation sequences with good cross correlation properties
are investigated in [32] and [33]. Let us denote a single
CAZAC sequence s(n)with length Lsand parameter Msby
s(n,Ls,Ms). Since Lsand Msare integers that have to be
relatively prime, we are able to maximize the number of
CAZAC sequences per family to Ls−1if Lsis prime. It
can be shown that √Lsis an upper limit for the cyclic cross
correlation function (CCF) for any two s(n,Ls,i),s(n,Ls,j)
for i,j. In our case, the CCF at the receiver does not fulfill
the requirements for a cyclic CCF as we inserted dummy
samples (Ones) between consecutive sequences. Thus, the
values for the CCF results exceed √Lsand are analyzed in
the following. To analyze all possible CCF results, simulations
have been performed and the results are presented in Fig. 5.
Here, the maximum value and the mean value for the CCF
for all possible combinations of CAZACs with different Ms
are shown over Ls. The figure reveals that the mean values
of the CCF strictly exceed the upper bound from the cyclic
CCF. Nevertheless, the maximum values are at about 2.3√Ls
which is only a limited degradation of the cross correlation
properties.
Consequently, the families of CAZAC sequences with prime
values for Lsare used here to enlarge the number of orthogonal
sequences. In fact, we can assign Nu,max =(Ls−1) · Nooc
different codes to the users.
This number can be further increased by finding OOCs with
larger number of codewords Nooc or by using longer CS. If
IEEE INTERNET OF THINGS JOURNAL, VOL. XX, 20XX 7
we further allow different lengths Lsfor the CS of different
users, we can generate even more codes.
With the methods described so far, we are able to design
a very large number of different combinations of OOC code-
words and CS parameters. We call each combination of the
parameters "user identification" (UID) in the following.
The most important benefit of this technology is the re-
duction of the transmitted data. Each user is identified by its
UID and can be separated at a gateway that is receiving the
signals of several users at the same time. Hence, no ID needs
to be transmitted within the frame which reduces the overhead
and leaves more bits for user data. Furthermore, if every user
has an individual permanent UID, we do not need to assign
orthogonal resource blocks (time slots, spectrum, or spread
codes) to users dynamically. Thus no signaling information
needs to be transmitted and true random multiple access is
enabled.
IV. RECEIVER
A. Coherent and Non-Coherent Correlation
At the receiver side we obtain the digitized receive signal,
which is denoted by r(n). It may contain the superposition
of different individually delayed transmit signals xµ(n). More-
over, we assume an AWGN channel with a given SNR and a
CFO ∆fc.
We assume that the receiver applies a sufficiently high
oversampling to enable asynchronous sampling. Nevertheless,
for the ease of notation, we use signals at oversampling factor
1. In the first step, the received signal is correlated with s(n)
and s∗(n). The correlation result Rr,s(k)for the CCF of r(n)
and s(n)is given by
Rr,s(k)=
Nmax−1
Õ
n=0
r∗(n) · s(n+k)(15)
where Nmax is the number of recorded samples. The correlation
results Rr,s(k)and Rr,s∗(k)for s(n)and s∗(n), respectively,
are sequences of peaks. For the ease of notation we define
u1(k):=Rr,s(k)and u2(k):=Rr,s∗(k). As an example the
result for a correlation with s(n)and s∗(n)is depicted in Fig. 7
for Ls=100 in the absence of noise and only one transmitting
user.
The expected locations of the peaks within u1(k)are given
by
˜q1(l)=˜pµ(l)+(l−1) · 2Ls.(16)
Accordingly, ˜q2(l)contains the indices of the expected
correlation peaks in u2(k)
˜q2(l)=˜pµ(l)+(2l−1) · Ls.(17)
To perform the non-coherent correlation, a reference signal
q1(n)is defined by
q1(n)=1if n∈˜q1
0else.(18)
A reference signal q2(n)for the non-coherent correlation of
the complex conjugate signals is defined by
q2(n)=1if n∈˜q2
0else.(19)
The non-coherent correlation is calculated as
Ru1,q1(k)=
Nmax−1
Õ
n=0|u1(n)|·q1(n+k)(20)
where Ru1,q1(k)is the non-coherent correlation result for s.
The non-coherent correlation result Ru2,q2(k)for the complex
conjugate correlation sequences s∗is calculated as
Ru2,q2(k)=
Nmax−1
Õ
n=0|u2(n)|·q2(n+k).(21)
Again, for the ease of notation, we denote v1(k):=Ru1,q1(k)
and v2(k):=Ru2,q2(k)in the following. The calculated outputs
of the correlators are used in the following subsection to detect
the transmitted frames in noise. Examples for the output of the
second correlator are shown in the lower part of Fig. 7
B. Frame Detection
At the receiver the most challenging task is the detection of
the short, narrowband signal in noise, as the signal is expected
to be several 10 dBs below the thermal noise floor and the
exact CFO is unknown. The proposed UCSS produces one
correlation peak at the output of each of the two non-coherent
correlators that are calculated using equations (20) and (21).
As this correlation is performed twice, we obtain two results
that can be used for the detection of the transmitted signal.
The peaks are sufficiently high to detect a signal at any CFO.
The problem at hand is the detection of a peak in noise, which
is described in [34] together with the optimum solution. We
first concentrate on the detection of one peak in one of the
correlation results. If the detection is repeated for the second
correlation, the confidence about the presence of a transmitted
signal might be further enhanced.
In the following we derive the probability distribution of the
correlation results v1(k)and v2(k)and determine a criterion
for the detection of a transmitted frame in a noisy receive
signal.
The noise at the receiver contains the thermal noise and
the interference from many other users5and is described by
a complex Gaussian random variable with η=ηQ+jηIand
ηQ, ηI∈ N(0, σ)such that σ2
η=2·σ2.
Let us assume that the constant amplitude of the desired
signal at the receiver is α. We are interested in the peaks within
the first correlation result u1(k). In the absence of noise each
peak has the magnitude
eu1(k=kpeak)=˜upeak =α·Ls.(22)
The noise part ˇupeak within the correlation result is the sum
of Lscomplex Gaussian random numbers with power σ2
ˇu=
Ls·σ2
η. Hence, the peak values upeak are given by upeak =
˜upeak +ˇupeak, and the magnitude |upeak |of the peak is a random
number with Ricean distribution [35, pp. 50-52].
The values of the first correlation outside the peak positions
u1(k,kpeak)=unoise (23)
5Here, we assume that the sum signal of several different users transmitting
simultaneously is complex Gaussian random variable due to the central limit
theorem.
IEEE INTERNET OF THINGS JOURNAL, VOL. XX, 20XX 8
˜η(t)
Rx RF +
ADC
r(n)
corr.
corr.
s(n)
s∗(n)
u1(k)
u2(k)
correct
CFO
correct
CFO
non-coh.
correlation
non-coh.
correlation
q1(n)
q2(n)
peak-
detection
peak-
detection
v1(k)
v2(k)
CFO
estimaton
timing
estimation
M-DPSK
demod.
ˆx(n)FEC
decoder
ˆxc(n)ˆxb(n)
Fig. 6. Detailed flow graph of the proposed receiver for the detection of one transmitting user
−1
0
1
R{x(n)}
0
50
100
|u1|
0
50
100
|u2|
0
500
|v1|
100 200 300 400 500 600 700 800 900 1,000
0
500
|v2|
transmit signal (real part):
first correlation of rwith s:
first correlation of rwith s∗:
second correlation of |u1|with OOC codeword:
second correlation of |u2|with OOC codeword:
Fig. 7. Examples for the transmit signal and correlation results with Ls=100 and Ns=7
are also complex Gaussian random numbers with power
Ls·σ2
η, and their magnitude |unoise |is a random number with
Rayleigh distributed values.
The magnitude of the first correlation result is the input of
the second correlator. In the following the result of the second
correlation is presented and analyzed.
The main peak within the result v1(k)of the second corre-
lator is referred to as v1(k=kpeak)=vpeak . Several additional
peaks exist within v1(k)which are neglected for the moment.
The value of vpeak is the sum of Nspeaks |upeak |. We are
interested in the mean value µv,peak and the standard deviation
σv,peak of vpeak. The mean µv,peak is Nstimes the mean value
of |unoise |and is given according to the mean of a Ricean
distributed random number [35, p. 52] by
µv,peak =Ns·µu,peak =
=Ns·σpLsπ
2e−κ/2(1+κ)I0κ
2+κI1κ
2(24)
where µu,peak is the mean value of the peaks in the first
correlation result, κ=(α·Ls)2
2σ2is the Rice factor, and Iiis
the modified Bessel function of the first kind and order i. The
variance σ2
v,peak of vpeak is given by
σ2
v,peak =Ns·σ2
u,peak =
Ns·2Lsσ2+(α·Ls)2−µ2
u,peak.(25)
If we assume that the number of chirps Nsis sufficiently
large, we can apply the central limit theorem and assume
that vpeak follows a Gaussian distribution. We are then able
to calculate the probability P(vpeak <vth)that vpeak exceeds a
certain threshold vth by
P(vpeak <vth)=Qvth +µv,peak
σv,peak (26)
where Q(·) is the Q-function. The value P(vpeak <vth)is the
probability for not detecting a peak in the second correlation
result with a threshold vth. Hence, Pd=1−P(vpeak <vth)is
the probability that the peak would be detected.
IEEE INTERNET OF THINGS JOURNAL, VOL. XX, 20XX 9
If the threshold vth is exceeded by noise, a peak is falsely
detected. To evaluate the probability of a false detection, we
analyze the distribution of the second correlation result v1(k)
outside the peak position v1(k,kpeak)=vnoise .
The signal vnoise is dominated by a strong part which results
from noise and also contains secondary peaks dependent upon
the ACF and CCF of the used OOC. It is described by the
mean value µv,noise and the variance σ2
v,noise. The noise part
unoise within the first correlation result is zero mean with
variance σ2
u,noise =Ls·σ2
η. Its magnitude |ˇunoise |is a Rayleigh
distributed number with mean value pπ
2σand variance 4−π
2σ2.
Outside the main peak and any secondary peaks, the output
of the second correlation is a sum of Nssignals |unoise |. Hence
the mean value µv,noise and the variance σv,noise of vnoise are
given by
µv,noise =Ns·rσ2Ls
π
2,(27)
σ2
v,noise =Ns·4−π
2σ2Ls.(28)
If the second correlation "collects" one or more peaks of the
first correlation, a secondary peak is obtained. The maximum
number of peaks Npeak that are summed up during the second
correlation is given by the parameter λaof the used OOC. In
general the mean and the variance of vnoise are given by
µv,noise(Npeak)=(Ns−Npeak) ·rσ2Ls
π
2+Npeak ·µu,peak,(29)
σ2
v,noise(Npeak)=(Ns−Npeak)· 4−π
2σ2Ls+Npeak ·σ2
u,peak.(30)
Additional secondary peaks due to multiple users transmitting
at the same time will occur in a real transmission. This is
analyzed by simulation in the sequel.
For the calculation of the probability Pfthat the noise
exceeds the threshold vth, which means that a peak is falsely
detected, we use the worst case value for µv,noise(Npeak )and
σv,noise(Npeak). This occurs for Npeak =λa. As we assume that
vnoise follows a Gaussian distribution, we can calculate Pfwith
Pf=P(vnoise >vth)=Qvth −µv,noise(λa)
σv,noise(λa).(31)
Fig. 8 depicts the calculated probability of detection over the
SNR for different values of vth for Ls=1000 and Ns=70. We
use a normalized threshold vth,n that is given by vth,n =vth
√Ls·Ns
for the ease of notation. For the design of a communication
system, a desired value for Pfneeds to be chosen according to
Fig. 8. The value of Pfascertains the required value for vth,n
and further determines Pd. A lower chosen value of Pfleads
to a lower value of Pdfor a given SNR. In other words, the
lower Pf, the higher is the required SNR to achieve a certain
Pd.
Fig. 9 presents results for the required SNR to achieve the
given Pf=10−6and Pd>0.999 for different values of Nsand
Ls. From the figure, for example, it is revealed that by using
Ns=60 correlation sequences with Ls=100 000 symbols, a
reliable detection at the SNR at the receiver input of −50 dB
is enabled.
−36 −34 −32 −30 −28
0.9999
0.999
0.99
0.9
0
SNR in dB @ Rx input
detecteion probability Pd
Ls=1000,Ns=70
vth,n =1,Pf=0.0467
vth,n =1.05,Pf=0.00787
vth,n =1.1,Pf=0.00081
vth,n =1.15,Pf=5.02e−05
vth,n =1.2,Pf=1.86e−06
vth,n =1.25,Pf=4.07e−08
vth,n =1.3,Pf=5.26e−10
vth,n ↑;Pf↓
Fig. 8. Probability of detection over the signal-to-noise ratio (SNR) for
different threshold levels vth,n and the resulting probability of false detection
Pf;Ls=1000,Ns=70
0 20 40 60 80 100 120 140 160 180 200
-50
-40
-30
-20
number of correlation sequences Ns
SNR for Pf=1e−06 and Pd>0.999
Ls=100
Ls=1000
Ls=10000
Ls=100000
Fig. 9. Required signal-to-noise ratio (SNR) in dB to achieve Pf=10−6
and Pd>0.999 over the number of correlation sequences Nswith different
lengths Ls
C. Carrier Frequency Recovery
For the correct demodulation of the data symbols it is
required to estimate and remove the CFO in the received
signals. Our proposed approach applies an estimation of the
CFO in two steps according to [36]. In a raw estimation the
correlation peak positions within v1(k)and v2(k)are evaluated.
This enables a coarse estimation of high frequency errors.
The fine CFO estimation is based on the evaluation of two
consecutive peaks within u1(k)or u2(k).
The coarse CFO estimation relies on a special property
of the used CAZAC sequences which was first introduced
in [37]. The authors show that a frequency shift of the
CAZAC sequence results in a time-shifted version of the
original sequence. We can make use of this property for
CFO estimation, as the location of the main peak within the
correlation result will be a function of the CFO. Unfortunately
we cannot directly measure the time offset due to the absence
of a time reference. Therefore, we also transmit a complex-
conjugate version of the CAZAC and use the fact that the peak
IEEE INTERNET OF THINGS JOURNAL, VOL. XX, 20XX 10
of the conjugate signal is shifted into the opposite direction.
Hence the distance between the two peaks is a measurable
value that indicates the CFO. In particular, we estimate the
CFO by maximizing the CCF of v1(k)and v2(k)and calculate
the CFO for Ms=1according to6
\
∆fc,raw =
1
2Ls·arg max
k(
Nmax
Õ
n=−Nmax
v1(n) · v2(n+k))(32)
where Nmax is an integer limiting the correlation length to
reasonable values for ∆fc,raw. Here, the CFO is normalized by
the symbol rate fs=1/Tswith
∆fc=∆fHz
c/fs(33)
where ∆fHz
cdenotes the CFO in Hertz and Tsis the symbol
duration.
The lengths Ls,1and Ls,2of the CAZAC sequences de-
termine the resolution of the estimation. The accuracy of
the estimated CFO is limited to 1/max(Ls,1,Ls,2). Thus, the
accuracy increases with increasing Ls.
This estimation method is able to measure very high CFOs.
The upper limit ∆fc,max for the CFO that could be determined
without ambiguity is ∆fc,max=0.5−1/max(Ls,1,Ls,2). This is
almost independent of the length Lsof the CAZAC sequences.
Instead, the upper limit for the CFO in Hertz is determined
by the symbol rate fsof the transmitted CAZAC sequences
given by
∆fHz
c,max=0.5−1
max(Ls,1,Ls,2)·fs.(34)
As the accuracy of this coarse estimation is limited, we add
training symbols to the transmitted data and can obtain an
accurate CFO estimation over a wide range for the frequency
error [36].
D. Symbol Detection and Demodulation
According to equations (16) and (17) we are aware of
the locations ˜q1(l)of the peaks within the first correlation
results u1(k)and u2(k). Thus, evaluating u1(k)and u2(k)at
the locations ˜q1(l)and ˜q2(l), respectively delivers the received
symbol vector ˆx(n). After the demodulation of the signal by
the M-DPSK demodulator, the estimated coded bits ˆxc(n)are
handed over to the decoder, which delivers the decoded user
bits ˆxb(n).
V. PERFORMANCE EVALUATION
A. Multiple Access
In section IV-B the detection of signals from single users in
AWGN has been investigated theoretically and the required
SNR has been determined to detect a transmitted frame
with a given probability of detection. If multiple users are
transmitting simultaneously, the interference within the desired
user signal is measured by the SINR defined in (5).
6For Ms>1the shift of peak positions within the correlation result cannot
be calculated from the CFO using equation (32). In that case the CFO is, for
example, calculated using a lookup table.
0 100 200 300 400 500
-30
-25
-20
number of active users Nu
SINR in dB @ Rx input
SNR = ∞
SNR = -20 dB
SNR = -22 dB
SNR = -24 dB
SNR = -26 dB
SNR = -28 dB
SNR = -30 dB
Fig. 10. Signal-to-interference and noise ratio (SINR) over the number of
users transmitting simultaneously for different values of the signal-to-noise
ratio (SNR); Ls=1009,Ns=71
−32 −30 −28 −26 −24 −22
0
0.2
0.4
0.6
0.8
1
SNR in dB @ Rx input
detection probability Pd
Nu=2
Nu=100
Nu=200
Nu=300
Nu=400
Nu=500
−32 −30 −28
0
0.2
0.4
0.6
0.8
1
SINR in dB @ Rx input
Fig. 11. Detection probability Pdover the signal-to-noise ratio (SNR) for
different numbers of users Nutransmitting simultaneously (left); detection
probability Pdover the signal-to-interference and noise ratio (SINR) for
different numbers of users transmitting simultaneously (right)
An analysis of the detection probability in a multiple access
scenario is performed here by Monte Carlo simulations. We
set the number of symbols within a CS to Ls=1009 and used
Ns=71 CS for the transmitted frame. We were then able to
choose one codeword cµfor µ=1...Nooc out of Nooc possible
codewords. Furthermore, we could choose the parameter Ms
for the CS between 1 and Ls−1.
We used the OOC according to [27] and obtain Nooc =
Ns−1different codewords. Thus the maximum number of
distinguishable user codes (UIDs) Nu,max =Nooc · (Ls−1)=
66 528.
From the available user codes we randomly picked Nucode-
words and assigned them to Nuusers transmitting at the same
time. The signals were randomly shifted in time and added to
a sum signal with random, uniformly distributed phase. We
now consider a single user mout of the Nutransmitting users,
and determine the SINR, after adding AWGN at a given SNR
for the m-th user. Fig. 10 shows the results for the SINR over
Nufor different values of the SNR. As expected the SINR
decreases with increased number of transmitting users. If we
IEEE INTERNET OF THINGS JOURNAL, VOL. XX, 20XX 11
TABLE II
SIMULATION PARAMETERS
parameter value
chirp sequence (CS) length Ls1009 symbols
CSS correlation gain ≈30 dB
nr. of chirp pairs per frame Ns67
nr. of chirps per frame 2·Ns134
modulation 2-DPSK
FEC coding rate 1/2BCH
user bits per frame Nbit 64
frame length Nframe incl. dummy symb. 139 494 symbols
now want to detect a specific user within the received sum
signal, we need to evaluate the result of the second correlation
as described in section IV-B. The probability Pdof the correct
detection of a transmitted frame has been calculated from the
results of Monte Carlo simulations. We again set Ls=1009
and Ns=71 and the threshold for the detection of the
correlation peak was vth,n =1.3ensuring a false detection
probability Pf≤5.26 ·10−10.
The results for Pdover the SNR for different numbers of
transmitting users Nuare depicted in the left sub-figure of Fig.
11. The figure shows that with more users Nutransmitting si-
multaneously, a higher SNR is required to ensure the detection
of an individual user. Hence, for the system design, the link
budget needs to incorporate the self interference from other
users. This is also well known from CDMA. If we consider the
detection performance over the SINR, as depicted in the right
sub-figure of Fig. 11, we can estimate the detection probability
from the SINR independently of the number of transmitting
users Nu. Thus we are able to predict the number of users
that can be detected at given probability Pdby combining the
results of Fig. 9 and Fig. 10. If we are, for example, using
Ns=71 correlation sequences of length Ls=1009, we can
obtain from Fig. 9 that the required SNR is −28 dB for for
Pf=10−6and Pd>0.999. Assuming that the interference of
a large number of users is a white Gaussian noise signal, than
we can use this information to predict the detection probability
in the noise and interference channel. From Fig. 10 we read
the number of users at SINR of −28 dB and obtain that about
250 users can be detected at an SNR of −26 dB, 400 users can
be detected at an SNR of −24 dB, and more than 500 users
can be detected at an SNR of −22 dB.
It is possible to design a system that is able to detect
a larger number of simultaneously transmitting users, if Ls
is increased. Doing so, the correlation gain is higher and
the detection is possible at lower SNR as shown in Fig. 9.
Hence, UCSS has a large potential to be adapted to different
requirements imposed by the link budget, the data rate, and
the number of users for random multiple access. The biggest
benefit is achieved if the average transmission rate of a single
terminal is low, e.g. only one packet per hour or per day.
In that case we can assign different UIDs to a very large
number of users. As long as the number of users transmitting
simultaneously stays below a specific limit, we can detect all
users transmitting without collisions.
−28 −26 −24 −22 −20 −18 −16 −14 −12
10−4
10−3
10−2
10−1
100
SNR in dB @ Rx input
FER
Nu=1
Nu=100
Nu=250
Nu=400
Nu=500
Fig. 12. Frame error rate (FER) over the signal-to-noise ratio (SNR) for
different numbers of users transmitting simultaneously; Ls=1009,Ns=67;
2-DPSK with rate 1/2-BCH coding; without fading
−28 −27 −26 −25 −24 −23 −22
10−4
10−3
10−2
10−1
100
SINR in dB @ Rx input
FER
Nu=1
Nu=100
Nu=250
Nu=400
Nu=500
Fig. 13. Frame error rate (FER) over the signal-to-interference and noise
ratio (SINR) for different numbers of users transmitting simultaneously; Ls=
1009,Ns=67; 2-DPSK with rate 1/2-BCH coding; without channel fading
B. Frame Error Rate
In the following a performance analysis is presented based
on simulations of UCSS in a satellite channel with lognormal
fading. The simulation parameters are given in Table II.
If we use these parameters for a system with a link budget
according to the one calculated in section II, and transmit at
a sample rate of fs=10 kHz. Then the resulting SNR will be
−24.4 dB. The entire frame is transmitted within 13.95 s at a
user bit-rate of 4.59 bit/s.
Fig. 12 shows the simulation results for the frame error
rate (FER) over the SNR. Here, the SNR at the receiver
input is considered for a single user before despreading.
Interference from multiple users who transmit simultaneously
is not included in the SNR, rather in the SINR. The simulation
results were produced without channel fading. If the number
of users transmitting simultaneously equals One (Nu=1),
an FER of 1·10−3is achieved at an SNR of −25 dB. Since
more users transmitting at the same time add interference, the
required SNR for the correct detection of a frame is increased,
as derived from the curves in the figure with Nu>1.
IEEE INTERNET OF THINGS JOURNAL, VOL. XX, 20XX 12
0 100 200 300 400 500 600
0
100
200
300
400
transmission rate λu
throughput %
SNR=-10dB
SNR=-15dB
SNR=-20dB
SNR=-23dB
SNR=-25dB
Fig. 14. Throughput over the transmission rate; Monte Carlo simulation with
Poisson distributed channel access; Ls=1009,Ns=67; 2-DPSK with rate
1/2-BCH coding; without fading
The results for the FER over the SINR are presented in
Fig. 13 for different numbers Nuof transmitting users Nu. For
Nu=1the SNR equals the SINR and the results match with
those from Fig. 12. Please note that the abscissa of Fig. 12
and Fig. 13 are scaled differently. If only one user is active,
the transmission is limited by the thermal noise, then with
more users transmitting at the same time, the interference
power increases and the transmission gets more and more
interference-limited. It is observed in Fig. 13 that the required
SINR to achieve a given FER is lower for larger numbers
of transmitting users Nu. This means that with interference-
limited transmission, the proposed modulation scheme per-
forms better than for the noise-limited cases due to the used
orthogonal CAZAC sequences.
The results show that we are able to receive, demodulate
and decode frames at very low SNR in a random multiple
access scheme. The operating SNR can further be reduced if
the correlation gain is increased by choosing higher values for
Ls. The performance over the SNR for a given Lsmight be
enhanced by the application of a different FEC code7.
C. System Throughput
If UCSS is applied to a MTC system with numerous devices,
the throughput of the entire system is of interest. Generally, the
channel access behavior of the users is modeled by a Poisson
distributed random number [14].
The Poisson distribution gives the probability P(i, λs)that
an event occurs itimes in a given time interval, where λsis
the mean number of events. The probability P(i, λs)is given
by
P(i, λs)=λi
s
i!e−λs;i=0,1,2, ..., ∞.(35)
We define the mean number λuof users starting their trans-
mission during a time interval tframe that is equal to the length
7During our analysis we applied LDPC codes, polar codes, and BCH codes,
which all showed similar performance for the very short block length used
here. Nevertheless, the optimization of the FEC may provide a small gain in
performance, but is not in focus of this paper.
of one frame. Here a frame consists of Nframe transmitted
symbols. If we run a simulation with sampled baseband signals
at oversampling factor νin discrete time, we need to calculate
the probability that a new user starts its transmission at a given
discrete time bin. It is given by (35) using
λs=λu
Nframe ·ν.(36)
We refer to λuas transmission rate in the following. The
throughput of the system is measured by the number of
successfully detected and error free transmitted frames within
a time interval. We define the throughput %as the number of
transmitted frames within tframe. Thus, %=λuindicates that no
frame has been lost. The number of erroneous or lost frames
is used to calculate the frame loss ratio (FLR).
We performed Monte Carlo simulations modeling the user
activity according to a Poisson distribution with transmission
rate λu. The parameters used for the simulation were also
taken from Table II as already introduced in the previous
subsection. Fig. 14 presents the results for the throughput %
over the transmission rate λufor different values of the SNR
in the absence of fading. The figure, for example, shows that
at SNR =−20 dB, the transmission is possible with low FLR
almost at a rate λu=300.
The throughput is reduced, if individual fading is added
to the transmitted frames as derived from Fig. 15, where the
throughput is presented for different values of the Lognormal
fading. The same simulation results are presented in a different
way in Fig. 16, which shows the results for the FLR over
the transmission rate for a fixed SNR of −20 dB for different
values of σch. For transmission rates λu<450 and with
increasing values of σch, the throughput decreases and the
FLR increases. This behavior is due to the fading of user
signals, which are received with lower power and the increased
interference from signals with higher power. At the same time,
the fading process also increases the power of a portion of user
signals. These signals are detected correctly even at higher
transmission rates λu>450.
In the literature it has been shown that unequal receive
powers of superimposed signals from different users in a mul-
tiple access scenario enhance the performance of interference
cancellation approaches [15]. If the proposed UCSS is applied
to the return link of a satellite network, where many users
transmit towards a common gateway, then interference cancel-
lation methods like SIC or iterative interference cancellation
(IIC) could be applied. In that case, the achieved throughput
of the entire system is increased considerably.
D. Practical Verification
The feasibility of ultra-low-rate communication with UCSS
has been demonstrated by an over-the-air (OTA) demon-
stration. Therefore, we implemented the waveform into a
software defined radio (SDR) based testbed. We transmitted 64
bits within blocks using DBPSK modulated chirps of length
Ls=1009 and a rate 1/2 BCH code as FEC. The symbol rate
after spreading has been set to 12.5 kHz, which corresponds
to a user bit rate of 5.7 bit/s. As transmitter we used an
Ettus B205 USRP SDR and a standard block upconverter
IEEE INTERNET OF THINGS JOURNAL, VOL. XX, 20XX 13
0 100 200 300 400 500 600
0
100
200
300
transmission rate λu
throughput %
σch = 0 dB
σch = 1 dB
σch = 2 dB
σch = 3 dB
Fig. 15. Throughput over the transmission rate at mean SNR for each user of
−20 dB for different values for the standard deviation σch of the lognormal
fading process; Monte Carlo simulation with Poisson distributed channel
access; Ls=1009,Ns=67; 2-DPSK with rate 1/2-BCH coding
100 200 300 400 500 600
10−2
10−1
100
transmission rate λu
frame loss rate (FLR)
σch = 0 dB
σch = 1 dB
σch = 2 dB
σch = 3 dB
Fig. 16. FLR over the transmission rate at mean SNR for each user of −20 dB
for different values for the standard deviation σch of the lognormal fading
process
(BUC) to shift the signal to the carrier frequency of 8 GHz.
A rectangular patch with dimensions 18.7 mm ×14.7 mm as
depicted in Fig. 17 served as transmit antenna. The signal has
been received by a transparent bent-pipe GEO satellite and
relayed towards the ground station antenna of the Munich
Center for Space Communications in Neubiberg, Germany.
The signal has been received by a 4.9 m dish antenna, down-
converted and sampled by an Ettus B210 SDR. After the
receive-signal processing, which includes detection, clock and
frequency recovery, demodulation and decoding, we evaluate
the transmitted information bits. From the received signal we
further estimate the SNR from the correlation peak vpeak by
S
N=
vpeak
(Ls·Ns)2·σ2
η
.(37)
Here, we assume that the signal part within the received signal
r(n), which is at least 20 dB below the noise floor, is negligible
against the noise part. We are then able to calculate the noise
power σ2
ηby
σ2
η=En|r(n)|2o.(38)
Fig. 17. SDR based hardware demonstrator used as transmitter for the
practical verification with X-band patch antenna
TABLE III
TES T RES ULTS
SNR in dB transmitted Nr. of error
(measured) blocks free blocks
<-24 2634 961
-24 1874 1633
-23 1995 1942
-22 2136 2125
-21 1652 1651
-20 1132 1132
>-20 1353 1353
From the received data we evaluated all blocks transmitted
during a total time of 48 h with clear sky conditions. We varied
the transmit EIRP between −15 dBW and −5 dBW to achieve
a certain statistic for the SNR values at the receiver. The
measured values for the SNR were rounded to integers to allow
a compact presentation of the results in table III. The table
shows the number of transmitted data blocks and the number
of blocks that have been received without error for a given
SNR at the receiver input. The results provide evidence for the
successful data transmission from a low-power terminal with
very compact antenna via a satellite in the GEO. However, we
recall that the presented results are a first feasibility test of the
waveform. No optimization of the transmission system or the
user antenna has been performed so far. This is left for future
work.
VI. COMPARISON TO RELATE D WORK
In this section, the proposed transmission scheme is com-
pared to existing, related work. It is important to note that none
of the MTC waveforms available in the literature is specified
for rates as low as required for the access to GEO satellites
at higher frequency bands. Of course, it is possible to adopt
the signaling parameters to reduce the symbol rate of existing
waveforms. But this does not imply that the synchronization
of the receiver can be achieved and maintained for a reliable
communication with small devices. In the following section,
we will analyze the main drawbacks of adopting existing
waveforms for ultra low-rate mMTC.
For low-rate MTC via satellite, SCADA systems applying
SCMA or IDMA as physical layer technology are an existing
solution for direct access to GEO satellites. However, they
close the link to the GEO only at lower frequencies bands,
where the available spectrum is not sufficient for massive
MTC.
IEEE INTERNET OF THINGS JOURNAL, VOL. XX, 20XX 14
Among the elaborated LPWAN schemes available for terres-
trial communication [38], we have chosen LoRa to benchmark
our results. LoRa is a patented wireless communication tech-
nology with proprietary physical layer protocol [12], [39]. Like
the proposed UCSS, LoRa also applies CSS for the modulation
and enables communication at very low rate for terrestrial
services.
A. Correlation Gain in the Presence of Phase Noise
As derived in section I, the most challenging task in
conjunction with the envisaged GEO link is the acquisition
of the signal in noise. The link budget calculated in section
II provides us with a S
N0=15.6 dB/Hz, which is very
low and enables communication with positive SNR only in
a bandwidth of a few Hertz. In practice this is not feasible, as
the implemented oscillator’s phase noise will shift the signal
away from its center frequency and outside of the matched
filter at the receiver. For this reason, most of the LPWAN
protocols apply spreading. After spreading, the signal occupies
a larger bandwidth. The despreading at the receiver makes use
of the correlation gain in order to detect the signal. In the
absence of phase noise, the correlation gain is theoretically
not limited, while for practical systems, the phase noise limits
the correlation gain, and therefore limits the maximum time
for coherent correlation.
Here, for the analysis of the achievable correlation gain, we
use a phase noise model taken from [40], where the noisy
signal8r(n)is calculated from the noise-free signal x(n)by
r(n)=x(n) · ejπφ(n).(39)
According to [40], the phase noise φ(n)is a sum of three
independent noise processes:
φ(n)=φ1(n)+φ2(n)+φ3(n)(40)
Each of the three phase noise contributions is modeled with
following variances
σ2
PN =σ2
PN,1 =100 ·σ2
PN,2 =10 ·σ2
PN,3.(41)
The model was applied and Monte Carlo simulations were
performed for different values of σ2
PN. The results presented
in Fig. 18 show the reduced correlation gain with increasing
phase noise. For UCSS we applied a coherent correlation over
chirp sequences with length Ls=1009 and an incoherent
correlation over Ns=67 sequences. A similar correlation
gain is achieved using a single chirp with length Ls=70 000
symbols. It is derived from the figure that with increasing
phase noise, UCSS gains about 5 dB correlation gain over
conventional chirp correlation.
Further we applied a BPSK modulated pseudo random
number (PRN)-sequence as used by SCMA and IDMA with
Ls=70 000 symbols as preamble. As indicated in Fig. 18,
the PRN-sequence loses another 4 dB compared to the chirp.
To conclude, the results demonstrate that UCSS is superior to
conventional techniques for signal acquisition in the presence
8Here, we concentrate on the effects of phase noise and omit any other
noise, such as thermal noise or interference.
10−810−710−610−510−4
40
45
50
phase noise variance σ2
PN
correlation gain in dB
UCSS
Chirp
PRN Code
Fig. 18. Correlation gain over the variance of the modeled phase noise process
of phase noise. The link to GEO satellites considered here
requires a comparably long time for signal acquisition, where
the phase noise is a limiting parameter.
B. Overhead for Acquisition, Synchronization, User Separa-
tion
In many modern communication systems the signal acqui-
sition is achieved by a preamble. The length of the preamble
must be sufficient to enable its detection in noise. It is a
design parameter, which is usually kept as low as possible
to minimize the overhead. With communications at ultra low
rate, the length of the preamble becomes a considerable time
span. To provide an example we consider the optimal detection
of a signal in Gaussian noise by the Newman-Pearson-Test
as derived in [34, Sec. 7.2]. From the calculations there, we
can derive the required energy of a signal over the noise
power density E
N0for given probabilities of detection Pdand
false alarm Pf. For example, an E
N0>15.05 dB is required
for Pf=10−6and Pd>0.999. Thus, the length of the
preamble is approximately 1 second if a link budget with S
N0
=15.6 dB/Hz is assumed as calculated in section II. Not only
that this is a very long time, which is not available for data
transmission, it is furthermore most likely that the stability of
the applied oscillators does not allow such a long coherent
integration.
To overcome this issue, we designed UCSS in a way to use
every transmitted chirp for acquisition and data transmission
at the same time. In particular, the amplitude of the chirps
contributes to the acquisition, while the data is modulated onto
the chip phases. By introducing short pause times according to
the OOC, we are able to even improve the acquisition and are
able to separate and identify different users transmitting at the
same time. The non-coherent integration of the chirps enables
acquisition even with unstable oscillators. In Fig. 19 the signal
structure of UCSS is sketched and the chirps containing user
data are in white color labeled with "D". The signal parts
colored in gray indicate the overhead, which consists of the
pause times and the dummy chirps for CFO estimation. The
IEEE INTERNET OF THINGS JOURNAL, VOL. XX, 20XX 15
legend: overhead data
UCSS (proposed)
C C D D D D D D D D D D D D D D D ...
LoRa CSS
CCCCCCCCCCCC DDDDDD...
preamble based (SCMA/IDMA)
preamble data ...
Fig. 19. Packet structure of different technologies for low-rate communication
with highlighted overhead
exact overhead is dependent upon the applied OOC and the
length Ls.
In comparison, for acquisition and synchronization, LoRa
uses 12.25 chirps as preamble, which wastes transmission time
and energy. Furthermore, as the preamble is the same for all
users, the information about the transmitters ID has to be sent
in the data part.
SCMA and IDMA either apply a unique word (UW) [11]
or a chirp sequence [41] as preamble. Different UWs or chirps
are used for the signals of different users to enable a separation
within the sum signal at the receiver. Here the preamble
enables acquisition, synchronization and user separation, but
still uses a considerable part of transmission time and energy,
which becomes unavailable for data transmission.
To give an example, we calculated the communication
overhead of Lora, SCMA and UCSS in comparison and
present the results9in Fig. 20. Here, we assume Ls=1000 and
choose Ns=67 accordingly to support the desired number of
transmitted bits per block. The code rate is 1/2 for all schemes.
For Lora modulation, we set the spreading factor to 13, which
requires similar SNR as UCSS with Ls=1000. For SCMA we
used a preamble length of 1 s. In that case, the probabilities
of detection of SCMA and UCSS are comparable. For the
data part of SCMA we assume a successful transmission at an
Eb/N0of 3 dB and calculated the corresponding length of the
data part at S
N0=15.6 dB/Hz.
As expected from intuition, the figure reveals that the
percentage of overhead of Lora and SCMA decreases with the
block length. The overhead of UCSS increases for larger sizes
of the blocks, as longer pause times between the chirps are
necessary for the OOC. Hence, UCSS requires less overhead
compared to preamble based schemes for short block lengths
and is outperformed with increasing lengths of the blocks.
In our example UCSS is outperformed by SCMA, if blocks
are carrying more than 190 transmitted user data bits. This is
well in line with our intention to create a waveform for ultra-
low rate and very short messages to be relayed over a GEO
satellite.
VII. CONCLUSION
In this work we presented a novel Unipolar Coded Chirp-
Spread Spectrum (UCSS) modulation scheme to enable the
9Please note that the results for LoRa and SCMA only present the calculated
overhead for the signal acquisition with the link budget provided in section II.
It has not been analyzed if the communication using these modulation formats
at ultra low rate is feasible at all.
0 100 200 300 400 500
0
20
40
60
80
number of user bits per block
overhead in %
UCSS
LoRa
SCMA
Fig. 20. Overhead for acquisition and synchronization in percent over the
number of user bits transmitted per block
direct access of small devices to satellites in the geostationary
earth orbit (GEO) for ultra narrowband (uNB) communica-
tions. The chirp-spreading of the information symbols allows
the instantaneous acquisition and detection of the signal at the
receiver even at unknown larger carrier frequency offsets. After
despreading, we combine the results non-coherently to gain a
sufficiently high SNR for the correct detection of transmitted
frames in presence of noise, since a coherent correlation over
longer periods of time fails due to oscillator instabilities. By
further inserting short pauses between the transmitted bits, we
shape a kind of unipolar coding and enable a random multiple
access of many transmitters using different code sequences or
different chirps. With a very large number of such individual
signal properties, we can assign one signal property to only
one user/device and are able to determine the transmitting
user without wasting information bits for the assignment of
a user identification. UCSS has been analyzed by exhaustive
simulations on system and link level. An over-the-air test
successfully verified the ability of UCSS to transmit data with
a very compact antenna via a satellite already existing in the
GEO.
The proposed UCSS is able to transmit a small number of
bits within a frame and achieves acquisition and synchroniza-
tion without any preamble or header. It enables random mul-
tiple access for a very large number of devices transmitting at
only a low percentage of time without any signaling channel or
any medium access control entity. By this the communication
overhead is reduced dramatically, making UCSS a very power
efficient modulation. Thus, UCSS is ideal for applications with
small amounts of transmitted data like small sensors, GPS
trackers or emergency pagers.
REFERENCES
[1] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and
M. Ayyash, “Internet of things: A survey on enabling technologies,
protocols, and applications,” IEEE Communications Surveys & Tutorials,
vol. 17, no. 4, pp. 2347–2376, 2015.
[2] R. J. F. Fang, M. Eroz, N. Becker, R. J. F. Fang, L. Fellow, and
M. Eroz, “Data collection and SCADA over GEO-MSS satellites using
Spread SCMA,” in 2009 Int. Work. Satell. Sp. Commun. IEEE, Sep
2009, pp. 441–445.
[3] M. De Sanctis, E. Cianca, G. Araniti, I. Bisio, and R. Prasad, “Satellite
communications supporting internet of remote things,” IEEE Internet
Things J., vol. 3, no. 1, pp. 113–123, Feb 2016.
[4] P. Merias and J. M. Meredith, “3GPP TR 38.811V1.0.0: Study on NR
to support non-terrestrial networks,” 3GPP, Tech. Rep., 2018.
IEEE INTERNET OF THINGS JOURNAL, VOL. XX, 20XX 16
[5] G. Cocco and C. Ibars, “On the Feasibility of Satellite M2M Systems,”
in 30th AIAA Int. Commun. Satell. Syst. Conf. Reston, Virigina:
American Institute of Aeronautics and Astronautics, Sep 2012.
[6] Z. Qu, G. Zhang, H. Cao, and J. Xie, “LEO Satellite Constellation for
Internet of Things,” IEEE Access, vol. 5, pp. 18391–18 401, 2017.
[7] R. J. F. Fang, M. Eroz, N. Becker, R. J. F. Fang, L. Fellow, and M. Eroz,
“Data collection and SCADA over GEO-MSS satellites using Spread
SCMA,” in IWSSC’09 - 2009 Int. Work. Satell. Sp. Commun. - Conf.
Proc., 2009, pp. 441–445.
[8] M. Anteur, V. Deslandes, N. Thomas, and A. L. Beylot, “Ultra narrow
band technique for low power wide area communications,” in 2015
IEEE Glob. Commun. Conf. GLOBECOM 2015. IEEE, Dec 2015, pp.
1–6.
[9] M. Nebel and B. Lankl, “Oscillator phase noise as a limiting factor
in stand-alone GPS-indoor navigation,” in 2010 5th ESA Work. Satell.
Navig. Technol. Eur. Work. GNSS Signals Signal Process. IEEE, Dec
2010, pp. 1–8.
[10] O. Del Rio Herrero and R. De Gaudenzi, “High efficiency satellite
multiple access scheme for machine-to-machine communications,”
IEEE Transactions on Aerospace and Electronic Systems, vol. 48,
no. 4, pp. 2961–2989, Oct 2012.
[11] N. Becker, S. Kay, L.-n. Lee, and M. Eroz, “Spread Asynchronous
Scrambled Coded Multiple Access (SA-SCMA) - A New Efficient
Random Access Method,” in 2016 IEEE Glob. Commun. Conf. IEEE,
Dec 2016, pp. 1–6.
[12] U. Raza, P. Kulkarni, and M. Sooriyabandara, “Low power wide area
networks: A survey,” IEEE Commun. Surv. Tutorials, vol. 19, no. 2,
2017.
[13] The ALOHA System-Another Alternative for Computer Communications,
vol. 37, 1970.
[14] R. De Gaudenzi, O. Del Rio Herrero, G. Gallinaro, S. Cioni, and
P.-D. Arapoglou, “Random access schemes for satellite networks, from
VSAT to M2M: a survey,” Int. J. Satell. Commun. Netw., 2016.
[15] R. De Gaudenzi, O. del Rio Herrero, and G. Gallinaro, “Enhanced
spread aloha physical layer design and performance, enhanced spread
aloha physical layer design and performance,” International Journal of
Satellite Communications and Networking, vol. 32, no. 6, pp. 457–473,
Nov 2014.
[16] M. K. Simon, J. K. Omura, and R. A. Scholtz, Spread Spectrum
Communications Handbook. McGraw-Hill, 2002.
[17] A. Berni and W. Gregg, “On the utility of chirp modulation for digital
signaling,” IEEE Transactions on Communications, vol. 21, no. 6, pp.
748–751, Jun 1973.
[18] R. C. R. C. Dixon, Spread spectrum systems : with commercial
applications. Wiley, 1994.
[19] A. Springer, W. Gugler, M. Huemer, L. Reindl, C. C. Ruppel, and
R. Weigel, “Spread spectrum communications using chirp signals,”
in IEEE/AFCEA - EUROCOMM 2000 Inf. Syst. Enhanc. Public Saf.
Secur. IEEE, 2000, pp. 166–170.
[20] E. Karapistoli, F.-N. Pavlidou, I. Gragopoulos, and I. Tsetsinas, “An
overview of the ieee 802.15.4a standard,” IEEE Communications
Magazine, vol. 48, no. 1, pp. 47–53, Jan 2010.
[21] A. R. Martin, “Chirp communication system,” 1974.
[22] O. B. A. Seller and N. Sornin, “Low power long range transmitter,”
2014.
[23] A. Springer, W. Gugler, M. Huemer, R. Keller, and R. Weigel, “A
wireless spread spectrum communication system using saw chirped
delay lines,” IEEE Trans. Microw. Theory Tech., vol. 49, no. 4, pp.
754–760, Apr 2001.
[24] G. A. Waschka and R. Boyd, “Chirp slope multiple access,” 2005.
[25] X. Ouyang, O. A. Dobre, Y. L. Guan, and J. Zhao, “Chirp Spread
Spectrum Toward the Nyquist Signaling Rate - Orthogonality Condition
and Applications,” IEEE Signal Process. Lett., vol. 24, no. 10, pp.
1488–1492, Oct 2017.
[26] F. R. K. Chung, J. A. Salehi, and V. K. Wei, “Optical orthogonal codes:
Design, analysis, and applications,” IEEE Trans. Inf. Theory, vol. 37,
no. 5, pp. 595–604, May 1989.
[27] S. Maric, Z. Kostic, and E. Titlebaum, “A new family of optical code
sequences for use in spread-spectrum fiber-optic local area networks,”
IEEE Transactions on Communications, vol. 41, no. 8, pp. 1217–1221,
1993.
[28] H. Ghafouri-Shiraz and M. M. Karbassian, Optical CDMA Networks,
Principles, Analysis and Applications. John Wiley & Sons, Ltd, Mar
2012.
[29] R. C. Heimiller, “Phase shift pulse codes with good periodic correlation
properties,” IEEE Trans. Inf. Theory, vol. 7, no. 4, pp. 254–257, Oct
1961.
[30] R. L. Frank and S. A. Zadoff, “Phase shift pulse codes with good
periodic correlation properties,” IEEE Trans. Inf. Theory, vol. IT-8, no. 8,
pp. 381–382, Oct 1962.
[31] D. Chu, “Polyphase codes with good periodic correlation properties
(Corresp.),” IEEE Trans. Inf. Theory, vol. 18, no. 4, pp. 531–532, Jul
1972.
[32] D. V. Sarwate, “Bounds on Crosscorrelation and Autocorrelation of
Sequences,” IEEE Trans. Inf. Theory, vol. 25, no. 6, pp. 720–724, Nov
1979.
[33] W. Alltop, “Complex sequences with low periodic correlations
(corresp.),” IEEE Transactions on Information Theory, vol. 26, no. 3,
pp. 350–354, May 1980.
[34] H. L. V. Trees, Detection, Estimation, and Modulation Theory -
Radar-Sonar Processing and Gaussian Signals in Noise. John Wiley,
2001.
[35] J. G. Proakis and M. Salehi, Digital Communications, 5th ed. Boston:
McGraw-Hill, 2008.
[36] S. Boumard and A. Mammela, “Robust and accurate frequency and
timing synchronization using chirp signals,” IEEE Transactions on
Broadcasting, vol. 55, no. 1, pp. 115–123, Mar 2009.
[37] R. L. Frank, “Polyphase codes with good nonperiodic correlation
properties,” IEEE Trans. Inf. Theory, vol. IT-4, pp. 43–45, Jan 1963.
[38] J. Chen, K. Hu, Q. Wang, Y. Sun, Z. Shi, and S. He, “Narrowband
internet of things: Implementations and applications,” IEEE Internet of
Things Journal, vol. 4, no. 6, pp. 2309–2314, Dec 2017.
[39] H. Mroue, A. Nasser, B. Parrein, S. Hamrioui, E. Mona-Cruz, and
G. Rouyer, “Analytical and Simulation study for LoRa Modulation,” in
2018 25th Int. Conf. Telecommun. ICT 2018. IEEE, Jun 2018, pp.
655–659.
[40] M. R. Khanzadi, D. Kuylenstierna, A. Panahi, T. Eriksson, and
H. Zirath, “Calculation of the performance of communication systems
from measured oscillator phase noise,” IEEE Transactions on Circuits
and Systems I Regular Papers, vol. 61, no. 5, pp. 1553–1565, May
2014.
[41] P. Li, G. Cui, and W. Wang, “Asynchronous flipped grant-free scma
for satellite-based internet of things communication networks,” Applied
Sciences, vol. 9, no. 2, p. 335, Jan 2019.
