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Capturing, recording, and analyzing LTE signals using USRPs and LabVIEW
Conference Paper · June 2015
DOI: 10.1109/SECON.2015.7132939
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Proceedings of the IEEE SoutheastCon 2015, April 9
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12, 2015
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Fort Lauderdale, Florida
Capturing, Recording, and Analyzing LTE Signals
Using USRPs and Lab VIEW
Nadisanka Rupasinghe and ismail Giiven�
Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174
Email: { rrupaOOl, iguvenc } @fiu.edu
Abstract-Long Term Evolution (LTE) systems are currently
playing a major role in catering the ever increasing traffic
demand. Better performance and better throughput compared
to other mobile communication technologies are main objectives
of LTE deployments. To provide a better quality of experience,
LTE signals from real base stations should be captured and
analyzed, for possibly making adjustments to network operation
parameters, or for deploying new base stations. In this paper, real
LTE signals from the base stations are recorded using universal
software radio peripheral (USRP) devices and NI LabVIEW
software. Then, these recorded LTE signals are processed and an­
alyzed in MATLAB to identify information such as primary cell
identifier (PCI) and master information block (MIB). LabVIEW
and MATLAB graphical user interfaces (GUIs) are provided to
make the system more user friendly.
Index
Terms-LTE,
Universal
software
radio
peripheral
(USRP), primary synchronization signal (PSS), secondary syn­
chronization signal (SSS), software defined radio (SDR), Zadoff­
Chu sequence.
I.
IN TRODUC TION
With the emergence of new wireless applications and de­
vices, the demand for radio spectrum has been dramatically
increasing over the last decade. Cisco has recently predicted an
11-fold increase in global mobile data traffic between 2013 and
2018 [1], while Qualcomm has predicted an astounding 1000x
increase in mobile data traffic in near future [2]. It is obvious
that the 4G broadband Long Term Evolution (LTE) and its
foreseen 5G successor will be the dominant technologies to
keep up with the emerging traffic demand. While satisfying
this increasing traffic demand, it is essential to provide a
better quality of experience to users by optimizing network
parameters. This can be achieved through drive tests [3]-[8],
through careful monitoring of LTE signals in the field, and by
modifying network parameters to improve QoS [9]-[11].
In order to monitor LTE signals, there are tools available
in the market [12], [13]. However most of these tool are very
expensive, and service support for maintenance and software
upgrades adds extra cost. There are also some open source
tools available for analyzing LTE signals [14]-[17]. They use
RTL-SDR, HackRF and BladeRF to capture LTE signals.
In [14], open source software tools to locate and track LTE
cells have been introduced, using very low performance RF
front ends such as RTL-SDR [18] based dongles (E4000,
This research was supported in part by the U.S. National Science Founda­
tion under the grants CNS-1406968 and AST-1443999.
978-1-4673-7300-5/15/$31.00 ©2015 IEEE
R820T, etc.) with a noise figure of 20 dB and only 8 bits in
the AID. However, due to the processing power limitations
of RTL-SDR, it is difficult to further enhance this open
source tool. The gr-lte project implemented in [15] is an
open source software package which aims to provide a GNU
radio LTE receiver to receive, synchronize and decode LTE
signals. There are different modules in this project, to achieve
different functionalities in LTE signal reception. With this
package, capabilities of GNU radio can be extended to LTE
related implementations. libLTE, an open-source LTE library
introduced in [17], does not rely on any external dependencies
or frameworks. This library contains a set of Python tools for
the automatic code generation of modules for popular soft­
ware defined radio (SDR) frameworks, including GNURadio,
ALOE++, IRIS, and OSSIE. However, several incompatibility
issues were encountered when installing these software tools
which limits ease of use and reliability.
In this paper, we introduce a reliable and low cost LTE
signal monitoring tool which can be used either in academia
or industry, with universal software radio peripheral (USRP)
devices and MATLAB. For recording LTE signals, NI USRP
is used. A LabVIEW virtual instrument (VI), LTE Signal
Recorder, is developed to achieve this task. For analyzing
recorded LTE signals, a MATLAB GUI, LTE Signal Analyzer,
is developed to provide user friendly access to the MATLAB
implementation.
The rest of the paper is organized as follows. In Section II,
we provide details about LabVIEW VI implementation for
capturing LTE signals using USRP. Then in Section III,
MATLAB implementation for analyzing various information
in recorded LTE signals, is presented. Section IV discusses
observed results and possible future extensions. Finally, Sec­
tion V provides concluding remarks.
II. LTE
SIGNA L RECORDING USING
USRP
NI USRP [19] is a low cost, reliable solution for advanced
SDR related implementations. In this paper, USRP devices are
used for capturing real LTE signals. As can be seen from Fig. 1
and Fig. 3, using Nl LabVIEW software, a VI is developed
to capture LTE signals using USRPs (LabVIEW code can be
downloaded from [20]). Synchronization signals and master
information block (MIB) are transmitted in central 6 resource
blocks (RBs) in LTE [21] and to identify and attach to a LTE
cell, a user equipment (UE) has to decode these signals.
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session handle out
Filename
IM1
Coerced parameters
o
coerced carrier frequency
r gain
IQrate
coerced
� 10M
r
Carrier frequency
�915M
gain
� 10
Number of samples
active antenna
IRX1
Fig. 1: LabVIEW front panel of LTE Signal Recorder.
Radio Frame: 10 ms
bandwidth of 1.4 MHz. For that much of bandwidth, FFT
size ( N pPT ) of 128 has to be considered. Since N pPT should
be in the form 2X where x E Z, x
7 is selected as the
optimum FFT value for 1.4 MHz bandwidth. If x
6 is
selected, only 64 sub-carriers would be utilized, wasting the
scarce spectrum resources. However, with the NFFT of 128,
actual bandwidth requirement (if all 15 KHz sub-carriers are
transmitting) equals to 128 x 15 KHz
1.92 MHz which
exceeds allocated 1.4 MHz bandwidth. To overcome this issue,
only 72 sub-carriers are utilized and rest of the 56 sub-carriers
are set to zero. This will ensure that there won't be any spectral
leakage to the adjacent bands from the LTE transmission. In
LTE, at least about 200 KHz of bandwidth is padded with
zeros due to spectral mask constraints.
In an OFDM based communication system, time duration
between two adjacent samples in an OFDM symbol (T��ri�n
can be given as,
=
=
=
Dsss
.pss
Slot: 0.5 ms
Fig.
2:
LTE FDD frame structure, and locations of PSS and SSS
signals within the radio frame.
A. Derivation of Sampling Frequency
samples
TOPDM
(fs)
In LTE, with normal cyclic prefix, OFDM symbol duration
(TOPDM) is approximately 71.4 fJs [21]. This is fixed and will
not be changed with different transmissions bandwidths (i.e.,
FFT sizes). For an example, TOPDM is same for FFT sizes of
2048 and 128. From that it can be understood that duration
between two samples in an OFDM symbol varies with FFT
size. Hence, with different bandwidths, it is required to sample
the medium with different sampling frequencies (fs).
When a UE searches for an LTE network, it will consider
only the central 6 RBs that carry the synchronization signals
(see Fig 2). That means that the synchronization signals,
regardless of the transmission bandwidth, are carried in a
_
-
6.1
1
X
NFFT'
where 6.1 is the sub-carrier spacing. In LTE, 6.1
and hence for 1.4 MHz of bandwidth Is is
Is
=
128
x
15 KHz
=
1.92 MHz.
(1)
=
15 KHz
(2)
To capture data in central 6 RBs, the medium has to be sam­
pled with this rate irrespective of the actual system bandwidth.
B. Capturing and Recording LTE Signals with USRP and
Lab VIEW
Fig. 1 and Fig. 3 show the front panel and the block diagram
of the developed LTE Signal Recorder VI in LabVIEW which
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Filename
Device names
session handle out
error out
�
stop
Ig
�
active antenna
Fig. 3: LabVIEW block diagram of LTE Signal Recorder.
is used to capture LTE signals using USRP. In the front panel,
the user has to set desired center frequency, I/Q rate, gain, and
number of samples per fetch. Below, a brief explanation about
each of these parameters are provided.
• File Name: Collected I/Q data are saved in an CSV file.
User can define the name of that file using this parameter.
• Device Name: IP address of the USRP which is used to
record LTE signals.
• I1Q Rate: This is the sampling rate Us) to capture
primary synchronization signal (PSS), secondary syn­
chronization signal (SSS) and physical broadcast channel
signals. As derived in Section II-A, this is 1.92 MHz.
• Carrier frequency: Center frequency of the LTE trans­
mission to be recorded is set using this parameter.
• Gain: This is the gain introduced by the USRP.
• Number of samples: This much of samples will be
fetched from USRP Rx buffer and written to comma
separated values (CSV) files. If the sampling rate is too
high and number of samples fetched during one access
is low, there is a chance of experiencing buffer overflow
issue. Hence, this value should be selected carefully.
• Active antenna: The antenna which is used to record
LTE signals by the USRP.
III.
ANA LY ZING
LTE
SING ALS WITH
MATLAB
In this section, details about PSS and SSS detection and
PBCH decoding processes are briefly explained. After that,
the implementation details of each of these processes are
presented. Fig. 4 shows the developed MATLAB GUI, to make
the system user friendly.
A. PSS/SSS Detection
In order to extract data from LTE reference signals, first
step is to synchronize in to the LTE cell. There are three major
synchronization requirements in LTE [21J.
II
Select Center Frequency
PSS
SSS
PCI
[OJ
[6OJ
�
RBs
�
Fig.
4:
CP
Rx Power level
GmaQ §392""dBJ
Antenna Ports
SFN
IT]
�
MATLAB GUI of LTE Signal Analyzer.
Acquiring symbol and frame timing. So that it is possible
to identify the correct symbol start position.
• Carrier frequency synchronization. This is to reduce or
eliminate the effect of frequency errors arising from a
mismatch of the local oscillators between the transmitter
and the receiver and also due to the Doppler shift caused
by any UE motion.
• Sampling clock synchronization
For synchronization procedure in LTE, two specially de­
signed physical signals are used: 1) Primary synchronization
signal, and 2) Secondary synchronization signal. In order to
achieve time and frequency synchronization with the LTE cell
[22], proper detection of these two signals are very important.
In addition, from this synchronization procedure, it is possible
to detect cyclic prefix length and duplexing mode (frequency
division duplex (FDD) or time division duplex (TDD)) used
by that cell.
1) PSS Sequence Detection [21J: The PSS is generated by
using frequency domain Zadoff-Chu (ZC) sequence of length
63, with the middle element punctured to avoid transmitting
on the d.c. subcarrier [21J. Fig. 5 shows ZC sequence mapping
•
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to sub carriers to generate a PSS sequence.
Fig. 5: PSS mapping of a length-63 ZC sequence into the central 62
sub-carriers in LTE [23].
Fig.
ZC sequence are also know as Generalized Chirp-Like
(GCL) sequences. These ZC sequences are non-binary unit­
amplitude sequences and satisfy the property of Constant Am­
plitude Zero Autocorrelation (CAZAC) [24]. Hence DFT of a
ZC sequence also has a constant amplitude limiting the peak­
to-average-power ratio. Further, ZC sequence of any length has
ideal cyclic autocorrelation. These properties have made ZC
sequences to be selected in achieving LTE synchronization.
Three PSS sequences can be selected in LTE based on the
three possible ZC roots, M E 29,34,25. For a selected ZC
root M, frequency domain length-63 ZC sequence can be
written as,
.
ZCM(n)=exp[-J
63
7rMn(n + 1)
],n=0.1. . . . ,62,
(3)
63
here n is the sub-carrier index where each element of the PSS
will be mapped.
PSS has to be detected without any prior knowledge of the
channel. Hence, a maximum likelihood detector is used for
non-coherent correlation for identifying the timing offset m'M
with the maximum correlation, as follows:
m'M = arg
m�x
l %1
I
Y[i + m]S'M[i] ,
(4)
where i is time index, m is the timing offset, N is the PSS
time-domain signal length, Y[i] is the received signal at time
instant i and SM[i] is the PSS with root M replica signal at
time i which is defined by (3) in the frequency domain.
An important thing to note here is that, ZC sequences show
similar type of correlation behavior both in the frequency
domain and as well as the time domain [25], [26].
2) SSS Sequence Detection {2lj: The SSS sequences are
based on maximum length sequences (also known as M­
sequences), which is generated by cycling through every
possible state of a shift register of length n. Each SSS sequence
is constructed by interleaving, in the frequency domain, two
6: Resource element allocation for PSS, SSS, PBCH and
Reference Signals within 1 ms in one of the 6 central resource blocks.
length-31 BPSK modulated secondary synchronization codes.
Because of these two codes, it is possible to determine the 10
ms radio frame timing from a single observation of an SSS.
SSS detection is carried out after non-coherent PSS detec­
tion. Therefore, by assuming channel is not varying signifi­
cantly, channel state information from PSS can be used for
coherent detection of SSS (S') as follows [21]:
S'= arg
mjn
N
(,8 ly[n]- s[n,n]hnl ) ,
(5)
where y and S represent received signal vector and candidate
SSS sequence matrix (diagonal), respectively, and h represents
estimated channel coefficients from the PSS detection.
B. Determination of Physical Cell ID Using PSS and SSS
There are 504 unique physical-layer cell IDs in the LTE
system. The cell IDs are grouped into 168 unique cell ID
groups, and each group contains three unique sector numbers.
A physical layer cell identity (PID) can be defined as,
ID= 3 NsIDSS
R
X
+
NPSS
ID
(6)
Here, NI�S is the cell ID group which is a value between 0
to 167 and identified through SSS sequence and Nross cell
ID which is a value between 0 to 2 and corresponds to ZC
root indices (25,29,34). Once the ZC root index is identified
through non-coherent detection, that can be used to identify
the corresponding NfDSS [22].
C. Physical Broadcast Channel (PBCH) Decoding
In cellular systems, the basic system information (SI) re­
quired for the other channels in the cell to be configured and
operated is usually transmitted through Broadcast CHannel
(BCH). LTE also utilizes BCH to achieve this task. There are
two categories of SI transmitted by LTE cells [21].
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Proceedings of the IEEE SoutheastCon 2015, April 9
eRe
'----.....,r-L----'
"
MIB with 16 bit eRe
"
1920 bits
,
Four equal-sized individually decodable units
Fig. 7: MIB generation in LTE systems.
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to receive the other parts of MIB within 40 ms time duration.
But if the SIR is low, the UE can receive more parts of the
MIB transmission, and can implement soft-combining of each
part with those received already, until successful decoding is
achieved [21]. The number of transmit antenna ports used by
the eNodeB is also determined by UE through space-frequency
block code (SFBC) decoding which corresponds to different
possible number of transmit antenna ports.
Once 24 MIB bits (IMIB) (14 information bits and 10 spare
bits) are decoded successfully, system bandwidth is determined
as follows:
RefBw
The "Master Infonnation Block" (MIB), which consists
of a limited number of parameters which are essential for
initial access to the cell. This is carried on the Physical
Broadcast CHannel (PBCH) (see Fig. 6).
• The other System Information Blocks (SIBs) which are
transmitted along with the physical downlink shared
channel (PDSCH).
MIB which is broadcasted through PBCH, carries three
essential system information. They are [27]:
• System bandwidth
• System Frame Number (SFN)
• Physical Hybrid Automatic Repeat Request (HARQ) In­
dicator Channel (PRICH) Configuration
PBCH transmission is based on a fixed coding and modula­
tion scheme. After the initial cell search and synchronization
procedure, UE decodes PBCH and extract MIB data. With the
information obtained from the MIB, the UE can decode the
control format indicator (CFI), which indicates the physical
downlink control channel (PDCCH) length. Then, UE uses this
infonnation to decode PDCCH. The presence in the PDCCH
of a downlink control information (DCI) message scrambled
with system infonnation radio network temporary identifier
(SI-RNTI) indicates that a SIB is carried in the same sub­
frame. The SIB is transmitted in the broadcast control logical
channel (BCCH). Generally, BCCH messages are carried on
the downlink shared channel (DL-SCH) and transmitted on the
physical downlink shared channel (PDSCH). The format and
resource allocation of the PDSCH transmission is indicated by
the DCI message on the PDCCH.
Hence, successful decoding of the PBCH is extremely
important to gain access to an LTE cell. As PBCH has to be
detected without prior knowledge of the system bandwidth, it
is mapped to the central 72 subcarriers (central 6 REs) of the
OFDM signal. As can be seen from Fig. 7 that a large number
of overhead bits are added to the 14 bit MIB data. This will
provide a better forward error correction (FEC) capability at
the UE.
This MIB data is segmented in to four individually­
decodable parts as shown in Fig. 7 and transmitted in PBCH
for a time duration of 40 ms. The data rate of the PBCH is
low and equals to 350 bps. If the signal to interference ratio
(SIR) is good and it is possible to decode the MIB correctly
from the transmission less than 40 ms period, there is no need
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=
4
x
IMIB(l) + 2
x
IMIB(2) + IMIB(3),
(7)
where, 1MIB(i) is the ith bit in the MIB, RefBW E
{O, 1,2,3,4, 5 } , and RefBw 0 corresponds to 1.4 MHz or
6 RB LTE transmission. Accordingly, other values in RefBw
correspond to 3,5,10,15,20 MHz of bandwidth respectively.
Based on the value of the 4th MIB bit IMIB(4), PRICH
duration is determined. If IMIB(4)
1, PRICH duration is
Extended; otherwise it is Normal. To identify PHICH resource
type, IMIB(5) and IMIB(6) are used. Finally, SFN is detected
by using 8 MIB bits, from IMIB(7) to IMIB(14).
=
=
D. Extracting Samples from the Recorded Data for PSS/SSS
Detection and MIB Decoding
As discussed in Section III-A, through PSS/SSS detec­
tion, symbol and frame timing acquisition can be achieved.
Using PSS and SSS, PCI can also be identified. Further,
PSS, SSS detection will enable frequency synchronization
and sampling clock synchronization. Once synchronization is
achieved, PBCH decoding can be carried out. As explained in
Section III-C, MIB information is distributed in 40 ms time
duration and hence to decode PBCH and identify MIB data it
is required to extract at least 40 ms of data.
With a sampling rate of 1.92 MHz, number of samples
recorded during slot duration (N:l�,;,ples) is
Nslsaotmples
=
1 · 92
X
106
x
0.5
X
10-3
=
960.
(8)
Hence, number of samples required for capturing 40 ms of
samples) .IS
data (N40
ms
Nl��les
=
960
x
2
x
40
=
76800
(9)
This much of samples (Nl��les) from the recorded data
should to be extracted for decoding MIB. However, in the
paper, 80 ms of data is extracted for better reliability. There­
fore 153,600 samples will be extracted from collected data
for analyzing in MATLAB.
E. Analyzing Recorded Data in MATLAB
In this section, explanation about MATLAB implemen­
tations on how to extract infonnation from recorded data,
is provided. When implementing previously discussed func­
tionalities, MATLAB implementations provided in [14] were
closely followed.
As explained in Section III-D, extracted 153,600 samples
will be correlated with 3 possible PSS sequences. Each PSS
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TABLE I: Information from a detected LTE cell.
Parameter
Center frequency
PSS
SSS
PCI
CP type
Rx Power level
No. of RBs
No. of antenna ports
SFN
Value
739 MHz
o
60
180
Normal
-60.8392 dB
SO
2
698
-5 8
Fig. 8: Setup inside MPACT Lab. LTE signals are captured using NI
USRP and LabVIEW software, and analyzed using MATLAB.
sequence with 15 different frequency offsets will be used to
achieve higher accuracy. PSS is incoherently detected where
no prior knowledge about the channel is assumed. More
details regarding PSS identification can be found in MATLAB
functions xcorr -pss. m and peak_search. m.
After PSS is successfully detected, the MATLAB function
sss_detect. m performs SSS detection. In that, first, channel
response is identified using the detected PSS sequence. After
that, coherent detection to identify transmitted PSS sequence,
is carried out. Finally the MATLAB function sss_detect. m
provides detected PSS sequence, cyclic prefix type and the
starting position of the frame. This starting position is the
starting of the cyclic prefix of that OFDM symbol. Hence, for
FFT operation, samples should be extracted from eliminating
number of cyclic prefix samples from this starting position.
Frequency offset is calculated using the MATLAB function
pss_sssJoe. m after successfully identifying PSS and SSS
sequences of the LTE transmission. With all these information,
time/frequency grid is created while correcting frequency
offset and time offset using MATLAB functions extracctjgm
and tjoec.m, for further processing of collected samples to
identify MIB information.
Finally, by using the MATLAB function decode_mib.m,
MIB data is extracted. Within this function, as described in
Section III-C, first, number of transmit antenna ports are
determined using trial and error approach. This approach in­
volves demodulation, unscrambling, de-rate matching, viterbi
decoding and cyclic redundancy check (CRC) processing.
Detected bit sequence with proper CRC decoding will be
identified as the MIB information (24 bits). After that, as
explained in Section III-C, number of resource blocks in the
transmission, SFN and PHICH information will be extracted.
IV.
RESULTS DISCUSSION AND FUTURE EX TENSIONS
In this paper, AT&T downlink LTE transmission within
the frequency range of 734 - 744 MHz, with the center
frequency of 739 MHz is recorded using USRP as explained in
Section II. System setup inside MPACT Lab, which is located
in the Engineering Center of flU, is shown in Fig 8. Table I
sUlmnarizes information from a detected LTE cell inside the
MPACT Lab. Full detection of PSS, SSS and MIB can be
-60
Detected PSS peak
iii' -62
:!:!.
-64
:;
c.
:; -66
0
'0
-68
.2!
'"
-70
�
0
u
X: 4557
Y:-60.84
-72
-74
1000
Fig.
9:
2000
3000
4000 5000 6000
OFDM sample index
7000
8000
9000
Detected first PSS OFDM location in the recorded LTE
samples.
achieved with the developed MATLAB program. However,
note here that if the signal to interference plus noise ratio
(SINR) is not acceptable it is difficult to decode MIB data.
From Fig. 9, the detected first PSS sequence carrying
OFDM symbol can be identified. Here, the value 4557 is the
starting sample of the 7th OFDM symbol in oth slot of a
sub-frame. Fig. 10 shows coherent detection output for SSS,
as used in (5). As mentioned in Sections III-E and III-A2,
SSS detection is coherent and channel response is determined
through detected PSS sequence.
There are several future extensions that can be introduced
to this paper to make it more useful. Firstly, to make this more
user friendly it is convenient to move everything to LabVIEW.
All the MATLAB codes used here can be implemented using
�
10
30
:!:!.
in 28
26
Fig. 10: SSS coherent detection output.
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Proceedings of the IEEE SoutheastCon 2015, April 9
LabVIEW MathScript RT modules. Once MATLAB imple­
mentations are moved to LabVIEW, it is possible to start
capturing and analyzing LTE signals real time. At the moment,
LTE signal analysis is not done in real time and LTE signals
are recorded first using NI USRP and then analyzed using
MATLAB.
Further, if GPS signal decoding VI can be integrated to
this LabVIEW project, the new system can be used for drive
testing purposes where LTE signals can be captured and
analyzed for mobile equipment. At the moment, this system
can capture only one LTE cell. It can be easily extended to
capture more than one LTE cell simultaneously. Also, the
current version has to provide central frequency of the LTE
transmission which we are interested to analyze. Instead, it can
be extended to automatically scan frequencies within a given
frequency range for ease of operation. After implementing the
features mentioned here and introducing common reference
signals decoding, it is possible to demodulate signal strength
indicators, and make drive tests to obtain a data base of
coverage areas from different LTE base stations. As mentioned
earlier, even though there are products in the market to capture
and analyze LTE signals real time, they are very expensive.
From the solution proposed in the paper, a reliable, cost
effective LTE signal scanner can be developed.
V.
CONCLUDING REM A RKS
In this paper, a novel LTE signal recording and analysis
approach is introduced. To record LTE signals, NI USRP
device is used. Those recorded signals are then analyzed in
MATLAB. PSS, SSS and MIB information could successfully
be decoded from the recorded data. With further extensions, it
is possible to use this system as a drive testing tool to capture
and analyze LTE signals in real time.
ACKNOWLEDGMEN T
The authors would like to thank the REU students Alex Per­
domo and Sami Elkabli for fruitful discussions on LabVIEW
implementation of the LTE signal reception codes.
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