=Paper=
{{Paper
|id=Vol-3382/Paper23
|storemode=property
|title=Constructing Radio Environment Map Model for a Cognitive
Communication System Based on LTE
|pdfUrl=https://ceur-ws.org/Vol-3382/Paper23.pdf
|volume=Vol-3382
|authors=Yahor Adamovskiy,Rykhard Bohush,Valery Chertkov
|dblpUrl=https://dblp.org/rec/conf/dtesi/AdamovskiyBC22
}}
==Constructing Radio Environment Map Model for a Cognitive
Communication System Based on LTE==
https://ceur-ws.org/Vol-3382/Paper23.pdf
Constructing Radio Environment Map Model for a Cognitive
Communication System Based on LTE
Yahor Adamovskiy1, Rykhard Bohush1, and Valery Chertkov1
1
Polotsk State University, Blohina St., 29, Novopolotsk, 211440, Republic of Belarus
Abstract
The telecommunication systems actual development direction is the use of cognitive radio
technology and dynamic spectrum access to solve the spectrum scarcity problem. Storage and
processing of spatiotemporal data should be carried out using a radio environment map
(REM). A two-level REM generation model for the LTE system and an algorithm for
implementing the model are presented. A method for calculating REM at a grid node is
shown, taking into account losses during signal propagation. The software implementation of
the model was made using the MatLab package. Approaches to increasing the speed of its
operation are described. The results of the REM generation software implementation
algorithm are demonstrated, which confirm the correctness of the developed model.
Keywords 1
Radio environment map, Long-Term Evolution, imitation model
1. Introduction
The extension of telecommunication systems is the problem of the spectrum scarcity [1]. Primary
Users (PUs) use dedicated frequencies, but with normal traffic only partial spectrum usage. Currently,
the radio frequency spectrum is distributed among various services, so solving the problem spectrum
scarcity is an actual direction in the communication systems development.
It is proposed to use the technology of Dynamic Spectrum Access (DSA) [2] to solve the spectrum
scarcity problem. The licensed frequency band is opened to Secondary Users (SU) at times when
channels are not used by PUs.
The Cognitive Radio (CR) is used to implement DSA. The CR device can perceive radio
environment and adapt the communication parameters using previous experience. The main task of
CR is to identify white spaces in the spectrum for the use of free frequencies. The white spaces
configuration can be predicted based on radio environment probe. The obtained data about radio
environment can improve the CR system efficiency. At the moment, there are communication systems
implementations uses cognitive radio conception [3, 4].
For storage and processing of CR data, a radio environment map (REM) is used. REM is a
spatiotemporal database of all network radio activities [5]. The REM task is the constant information
exchange with cognitive devices. REM construction is carried out by direct and indirect methods [2].
REM construction basic principles and its structure: storage and acquisition unit, REM manager,
measurement capable devices [1, 6].
The generated REM quality indicators can be quantified using RMSE – the difference between the
power values of the calculated and actual REM levels [1]. The radio environment state is calculated
and predicted based on the REM data. Using the software simulation model output, optimal REM
parameters can be extracted.
Proceedings of the 7th International Conference on Digital Technologies in Education, Science and Industry (DTESI 2022), October 20–21,
2022, Almaty, Kazakhstan
EMAIL: e.adamovsky@psu.by (Yahor Adamovskiy); r.bogush@psu.by (Rykhard Bohush); v.chertkov@psu.by (Valery Chertkov)
ORCID: 0000-0003-1044-8741 (Yahor Adamovskiy); 0000-0002-6609-5810 (Rykhard Bohush); 0000-0002-2603-9873 (Valery Chertkov)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
The purpose of this paper is the REM database formation model implementation of the 4G LTE
communication system for the state predicting possibility of the radio environment by a cognitive
system.
2. REM model for LTE system
A model is proposed for generating information about the dynamics of a frequency resource use in
the REM form. The model implements the 4G LTE communication system functioning with some
simplifications:
1. Signals propagate in an urban area, they contain only user information.
2. The radiation power of stations and devices does not change; radiation directivity is uniform.
3. Signal phase, Doppler effect, fast fading and shading effects, antenna suspension height are
also not taken into account.
The multilevel model structure [7] includes:
1. Global level – model initialization information; object output data aggregator.
2. Local level – objects, parameters and signals: eNodeB (Base Station) and UE (User Equipment)
objects, REM grid.
Frequency bands provided by the LTE TS 36.101 specification are licensed. Each band is
characterized by transmission mode (frequency and time division, FDD and TDD), lower and upper
frequencies. For downlink, Orthogonal Frequency-Division Multiplexing (OFDM) is used, and for
uplink - Single-Carrier FDMA (SC-FDMA).
The bands are divided into carriers from 1.4 to 20 MHz, which corresponds to 6 to 100 resource
blocks (RB). One RB is 180 kHz in 15 kHz steps and consists of resource elements (RE) or OFDM
symbols.
In the simplified model, the frequency resource is divided into 𝑓𝑟𝑒𝑞_𝑛𝑢𝑚 subcarriers, starting at
𝑓𝑟𝑒𝑞_𝑠𝑡𝑎𝑟𝑡 and stepping at 𝑓𝑟𝑒𝑞_𝑠𝑡𝑒𝑝 in the 𝑓𝑟𝑒𝑞_𝑏𝑎𝑛𝑑 (kHz) vector.
The signal attenuation depends on the frequency 𝑓 (MHz) and the distance to the source 𝑑 (km).
The loss equation 𝐿 is used, it includes the coefficient 𝛼 and the constant 𝑙0 [8], their possible values
are given in Table 1.
𝐿(𝑑𝐵) = 𝑙0 + 20 log10 𝑓 + 𝛼 log10 𝑑 (1)
Table 1
Possible parameter values for the signal loss equation (1)
Parameter
Description
Coefficient 𝛼 Constant 𝑙0
20.0 20.0 Free space [8]
40.2 27.7 Suburban area [2]
35.0 38.4 Urban area [2]
The simplified model uses a single attenuation factor 𝑓𝑟𝑒𝑞_𝑎𝑡𝑡 for the middle frequency of the
modeled range.
A matrix of attenuation coefficients (𝑎𝑡𝑡) is formed (2) based on (1), the size of which is [(2𝑁𝑥 −
1); (2𝑁𝑦 − 1)] elements, where the values 𝑁𝑥 and 𝑁𝑦 are their number horizontally and vertically on
REM in 𝑔𝑟𝑖𝑑_𝑠𝑖𝑧𝑒 with 𝑔𝑟𝑖𝑑_𝑠𝑡𝑒𝑝.
2
𝑙 +𝑓𝑟𝑒𝑞𝑎𝑡𝑡 +𝛼 log10 √|𝑁𝑥 +1−𝑥|2 +|𝑁𝑦 +1−𝑦| (2)
𝑎𝑡𝑡𝑥𝑦 = 10 0
In practice, receivers have limited sensitivity and cannot receive signals that are too weak. In our
model, the threshold noise level 𝑝𝑜𝑤_𝑡ℎ𝑟𝑒𝑠 is implemented.
The global level contains the initial data for the model presented in Table 2.
Table 2
Model global parameters
Parameter Data type Unit Sample data Description
freq_num - 2002 Number of subcarriers
freq_start Uint 1720000 Frequency band initial value
freq_step kHz 15 Subcarrier spacing
freq_band uint [freq_num] 104 × [172 … 183.5] List of LTE subcarriers
freq_att Float 9.6×10-11 Attenuation factor
-
grid_size Uint [2] [20 20] REM grid size
grid_step Float m 250.0 REM grid step
pow_thres Float dBm -85.0 Interference threshold
Float [9.6×10-11
attenuation [2×grid_size(1)-1] - … Attenuation matrix
[2×grid_size(2)-1] 1.9×10-10]
eNodeB forms a cell with a local identifier. In the simplified model, each eNodeBS is assigned a
unique 𝑒𝑁𝑜𝑑𝑒𝐵𝑆. 𝑁𝐶𝑒𝑙𝑙𝐼𝐷 number and a string name 𝑒𝑁𝑜𝑑𝑒𝐵𝑆. 𝑛𝑎𝑚𝑒. An eNodeBS object is
characterized by a position in space (two coordinates 𝑒𝑁𝑜𝑑𝑒𝐵𝑆. 𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛).
In the model, eNodeB can operate in a given frequency range (𝑒𝑁𝑜𝑑𝑒𝐵𝑆. 𝑓𝑟𝑒𝑞_𝑏𝑎𝑛𝑑), which
determines the operating frequencies from the 𝑓𝑟𝑒𝑞_𝑏𝑎𝑛𝑑 set.
Abonents are connected to the eNodeB and constantly keep in touch. Each UE is registered upon
connection and is identified by a temporary data (M-TMSI, S-TMSI, GUTI) and a static global
identifier (IMSI). In a simplified model, each eNodeB stores connected UEs identifiers
𝑒𝑁𝑜𝑑𝑒𝐵𝑆. 𝑈𝐸_𝑅𝑁𝑇𝐼 and allocated resource blocks for downlink 𝑒𝑁𝑜𝑑𝑒𝐵𝑆. 𝑈𝐸_𝐷𝐿_𝑅𝐵 and for
uplink 𝑒𝑁𝑜𝑑𝑒𝐵𝑆. 𝑈𝐸_𝑈𝐿_𝑅𝐵.
In the simplified model, the eNodeB emits with a constant power (𝑒𝑁𝑜𝑑𝑒𝐵𝑆. 𝑝𝑜𝑤𝑒𝑟𝐵𝑆).
The eNodeB object parameters are presented in Table 3.
Table 3
eNodeB object parameters for the model (local level)
Parameter Data type Unit Sample data Description
eNodeBS.name String Station#1 Station name
eNodeBS.NCellID Uint 1 Station ID
-
eNodeBS.position Uint [2] [9 12] Station cell coordinates
eNodeBS.freq_band Uint 2 Station band number
eNodeBS.powerBS Float dBm 43 Station radiation power
eNodeBS.UE_RNTI Uint [𝑁𝑈𝐸 ] [3 … 2] Connected UEs
eNodeBS.UE_UL_RB Uint [𝑁𝑈𝐸 ] [ ] - [3…16] [2…11] Uplink RB array for UEs
eNodeBS.UE_DL_RB Uint [𝑁𝑈𝐸 ] [ ] [3…166] [2…241] Downlink RB array for UEs
The UE has a unique IMSI number assigned to its SIM card. In the simplified model, UEs are
assigned sequence numbers 𝑈𝐸. 𝑅𝑁𝑇𝐼 and names 𝑈𝐸. 𝑛𝑎𝑚𝑒 by analogy with the eNodeB.
The UE searches for a communication channel with the best reception and connects to the station.
Synchronization is carried out using the Primary Synchronization Signal (PSS) and Secondary
Synchronization Signal (SSS). For the uplink, the common PUSCH is used to transmit user data. The
control channel PUCCH is transmitted independently and is used to transmit the channel quality
indicator CQI, request to obtain a available resources schedule. The random access channel PRACH
is used to request the communication initialization upon transition to the active mode.
In the simplified model, the UE must know the number of its own workstation 𝑈𝐸. 𝑁𝐶𝑒𝑙𝑙𝐼𝐷 and
its carrier frequency number 𝑈𝐸. 𝑓𝑟𝑒𝑞_𝑏𝑎𝑛𝑑. Information about neighboring stations is contained in
the 𝑈𝐸. 𝑏𝑎𝑠𝑒. The device receives and stores information about the allocated resources in 𝑈𝐸. 𝑅𝐵.
The UE is in one of two states: idle mode and data transmission, parameter 𝑈𝐸. 𝑠𝑡𝑎𝑡𝑢𝑠.
Devices may change the 𝑈𝐸. 𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 parameter over time. The movement schedule is set using
an array 𝑈𝐸. 𝑡𝑟𝑎𝑗𝑒𝑐𝑡𝑜𝑟𝑦 of coordinates with a time reference. The 𝑈𝐸. 𝑑𝑎𝑡𝑎𝑇𝑟𝑎𝑛𝑠𝑓𝑒𝑟 list data
transfer sessions is formed with the start time and the session duration.
In the simplified model, the device radiation power is fixed by the parameter 𝑈𝐸. 𝑚𝑎𝑥𝑃𝑜𝑤𝑒𝑟.
UE device parameters are presented in Table 4.
Table 4
UE object parameters for the model (local level)
Parameter Data type Unit Sample data Description
UE.name String UE#1 UE name
UE.RNTI Uint 1 UE ID
UE.position Uint [2] [7 1] Current UE cell coordinates
-
UE.NCellID 1 Workstation ID
Uint
UE.freq_band 1 Workstation band number
UE.RB Uint [ ] [3 … 13] RB numbers allocated by the workstation
UE.maxPower Float dBm 26 UE radiation power
UE.status Bool true UE data transfer status
-
UE.base Int [ ] [1 3 4] List of nearest stations
UE.trajectory (Int [3]) [ ] time-cell [0 9 2; …] Device movement schedule
UE.dataTransfer (Int [2]) [ ] time-time [9 69; …] Device session schedule
The scheme and objects relationships in the simulation model are shown in Figure 1. PU devices
(UE1) move on the grid, choose the nearest eNodeB for connection and use the allocated frequency
resource. SU devices (SU1, SU2) for communication must determine the white spaces configuration
by REM cells analyzing.
Figure 1: A cognitive system simulation model representation based on LTE
The output data is presented as a cell array. The cell contains the total signals matrix received at
the corresponding point, taking into account their attenuation with distance. Based on information
about the emitting objects coordinates and their signals, all cells are filled.
3. Model software implementation
The model is implemented in MatLab (version R2020b) according to the algorithm in Figure 2.
The algorithm includes component initialization, UEs schedule generation, memory allocation for
REM. At each iteration, the model is reconfigured: moving and reconnecting the UEs to the eNodeB
with the best reception, recalculating and interpolating (by Kriging [9]) the REM and saving to file.
Figure 2: REM generation algorithm
The model parameters are chosen based on existing communication systems descriptions. Files are
generated only when the state of at least one object changes to reduce the output data amount.
The 1800 Hz band is used because it is the most common for 4G networks. The transmission mode
is FDD, the channel width is 75 MHz. To reduce computational complexity, 25 RB (5 MHz) are used
each for the uplink and downlink, which corresponds to 𝑓𝑟𝑒𝑞_𝑛𝑢𝑚 = 2002 subcarriers.
The used map size 𝑔𝑟𝑖𝑑_𝑠𝑖𝑧𝑒 is 20×20 cells with a 𝑔𝑟𝑖𝑑_𝑠𝑡𝑒𝑝 = 250 m, which gives enough
accuracy to calculate the UE movement between eNodeB cells. The distance between stations is
1 km. The number of simulated UEs may vary depending on the scenario.
Various behavior scenarios are assigned to the UEs, examples are presented in Table 5.
Table 5
Implemented movement and session scenarios
Scenario Description Parameter Values
modem Stationary device - -
ta – activity start 0 … 20 h
A mobile phone moves twice
tb – activity end ta … 24 h
between the points "home"
[xh, yh] – "home" point
Worker and "work" on weekdays
coordinates [1..grid_size(1),
Move- (5/2). Stays at home for
[xw, yw] – "work" point 1..grid_size(2)]
ment weekends.
coordinates
Mobile phone constantly ta – activity start 0 … 20 h
moving during working hours tb – activity end ta … 24 h
Courier
on weekdays (2/2). Stays at
td – time between sessions 100 … 1000 s
home for weekends.
The device continuously ta – activity start 0 … 20 h
Hub transmits data at a certain
tb – activity end ta … 24 h
interval of time every day.
Session ta – activity start 0 … 20 h
The device performs sessions
tb – activity end ta … 24 h
User with variable intervals and
td – time between sessions 3 … 30 min
durations.
tl – session duration 6 … 600 s
An example of implemented schedules is shown in Figure 3. Figure 3(a) shows the movement key
points of one of one UE during the day. Figure 3(b) shows the device communication sessions.
(a) (b)
Figure 3: UE scenarios: position change according to the "Courier" scenario (a), sequence of sessions
according to the "User" scenario (b)
At each iteration, the UE is checked and reconnected. The decision is made by the maximum
signal level, which is calculated as the average value of the reference OFDM symbols [1]:
∑𝑁𝑅𝑆𝑅𝑃
𝑖=1 𝑃𝑅𝑆𝑅𝑃𝑖
𝑃𝑒𝑁𝑜𝑑𝑒𝐵 = (3)
𝑁𝑅𝑆𝑅𝑃
where 𝑃𝑅𝑆𝑅𝑃𝑖 – OFDM reference symbol level, 𝑁𝑅𝑆𝑅𝑃 – the reference OFDM symbol index in the
signal from the eNodeB.
The connection is carried out by distributing the eNodeB downlink resource
(𝑒𝑁𝑜𝑑𝑒𝐵𝑆. 𝑈𝐸_𝐷𝐿_𝑅𝐵) between the connected UEs. The station has 𝑁𝐷𝐿 = 250 RB for downlink, 25
RB per subframe. The eNodeB downlink resource is calculated by random permutation, function
𝑟𝑎𝑛𝑑𝑝𝑒𝑟𝑚():
𝑅𝐵𝐷𝐿 = 𝑟𝑎𝑛𝑑𝑝𝑒𝑟𝑚(𝑁𝐷𝐿 , 𝑁𝐷𝐿_𝑈𝐸 , 𝐶𝑁𝑇𝑈𝐸 ) (4)
where 𝑁𝐷𝐿 – total resource blocks number (250 RB) for each eNodeB; 𝐶𝑁𝑇𝑈𝐸 – total UEs number;
𝑁𝐷𝐿_𝑈𝐸 – the RB number allocated for each UE:
𝐷𝐿 𝑁 −1
𝑁𝐷𝐿_𝑈𝐸 = [𝐶𝑁𝑇 ] (5)
𝑈𝐸
where [∙] – the integer part.
Each device is randomly allocated an equal number of RB, which depends on the connected UEs
total number:
𝑅𝐵𝐷𝐿 (𝑗), 𝑗 = 𝑁𝐷𝐿_𝑈𝐸 × (𝑖 − 1) + 1 при 𝑈𝐸. 𝑠𝑡𝑎𝑡𝑢𝑠 = 𝑓𝑎𝑙𝑠𝑒
𝑒𝑁𝑜𝑑𝑒𝐵𝑆. 𝑈𝐸_𝐷𝐿_𝑅𝐵(𝑖) = { , 𝑖 = ̅̅̅̅̅̅̅̅̅̅̅̅
1, 𝐶𝑁𝑇𝑈𝐸 (6)
𝑅𝐵𝐷𝐿 (𝑗), 𝑗 = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
𝑁𝐷𝐿_𝑈𝐸 × (𝑖 − 1) + 1, 𝑁𝐷𝐿_𝑈𝐸 × 𝑖 при 𝑈𝐸. 𝑠𝑡𝑎𝑡𝑢𝑠 = 𝑡𝑟𝑢𝑒
where i – UE index.
The uplink resource distribution (𝑒𝑁𝑜𝑑𝑒𝐵𝑆. 𝑈𝐸_𝑈𝐿_𝑅𝐵) between the connected UEs is carried out
differently, because a frequency diversity multiplexing scheme with transmission on a SC-FDMA is
used.
𝑅𝐵𝑈𝐿 (𝑗), 𝑗 = 𝑁𝑈𝐿_𝑈𝐸 × (𝑖 − 1) + 1 при 𝑈𝐸. 𝑠𝑡𝑎𝑡𝑢𝑠 = 𝑓𝑎𝑙𝑠𝑒
𝑒𝑁𝑜𝑑𝑒𝐵𝑆. 𝑈𝐸_𝑈𝐿_𝑅𝐵(𝑖) = { , 𝑖 = ̅̅̅̅̅̅̅̅̅̅̅̅
1, 𝐶𝑁𝑇𝑈𝐸 (7)
𝑅𝐵𝑈𝐿 (𝑗), 𝑗 = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
𝑁𝑈𝐿_𝑈𝐸 × (𝑖 − 1) + 1, 𝑁𝑈𝐿_𝑈𝐸 × 𝑖 при 𝑈𝐸. 𝑠𝑡𝑎𝑡𝑢𝑠 = 𝑡𝑟𝑢𝑒
where 𝑅𝐵𝑈𝐿 – vector of RB numbers in subframe, which is formed similarly (4).
Next, the resource grids generation for all objects is performed. Signals are placed on the REM in
cells with the corresponding parent objects coordinates.
To generate eNodeB resource grids, the function of the LTE Waveform Generator package
𝑙𝑡𝑒𝑅𝑀𝐶𝐷𝐿𝑇𝑜𝑜𝑙(), is used. The eNodeB configuration (Table 2) and information bits are transmitted
to it, the data transmission status of each UE is taken into account. Figure 4 shows an example of a
downlink resource grid.
Figure 4: eNodeB downlink resource grid
4. Results
The presented model version includes 7 eNodeB and 30 UE objects. The stations form a hexagonal
structure, as shown in Figure 5. The model formed a REM for 30 days, which is 259.2×106 frames. As
a result, 54246 files (145 GB) were created, the total operating time was 72 hours on a computer with
an Intel Core i9-10900/2.80 GHz CPU, 64 GB RAM.
Figure 5: An example of the eNodeB and UE objects initial location on the map in the model
REM is formed at the end of loop iterations. An example of the resulting map cell structure is
shown in Figure 6(a) (REM element composition far from the stations) and Figure 6(b) (close to
eNodeB).
(a) (b)
Figure 6: Network traffic at the edge of the map (a); near the station (b): 1 – no signal (below -120
dBm); 2 - weak level, UE shutdown (up to -83 dBm); 3 – medium level (up to -20 dBm); 4 - strong
level (above -20 dBm).
Figure 6 analysis shows that near the eNodeB with many connected UEs the resource is limited -
there are fewer free subcarriers. Moving away from stations makes the spectrum freer, and the free
frequencies number increases.
The generated data comparison in frames was performed to confirm the correctness of the model.
The generated REM analysis in frame 100524000 is presented, the eNodeBs and UEs location is
shown in Figure 7.
Figure 7: Schematic location of eNodeB and UE objects on the REM and radio visibility area at the
point (6, 10)
Figure 7 shows three eNodeBs (B1, B2, B4). Station B1 occupies the second downlink band and
the second uplink band, B2 occupies the third band, and B4 occupies the first band. All UEs do not
transmit data and use one allocated RB in each direction. Figure 8 shows the REM cell state inside the
labeled area in Figure 7 (coordinates (6, 10) - at the A22 location) in frame 100524000.
Figure 8: Radio environment between the UE and the workstation at the point (6, 10)
Figure 8 analysis showed that station resources are evenly distributed to connected devices, which
confirms the model correctness. In Figure 8, the second downlink band uses RB number 190 for A23.
Connected UEs to B2 (third band): A6, A11, A16. For these UEs, an equal number of blocks are
allocated, but only the first ones are used: 170, 24, 36. This confirms the relationship correctness
between objects current parameters and the radio environment activity state.
The RMSE values were calculated to assess the generated REM quality: the difference between the
estimated and actual eNodeB location (meters); the difference between the actual and expected signal
level emitted by one UE (dBm) [2]. The average calculated value of RMSE parameters was 15.11 m
and 7.83 dBm. The obtained RMSE values correspond to a sufficiently accurate REM for further
application and are consistent with the results in [2, 6].
5. Conclusion
The paper presents the developed simulation model of REM generation for the LTE system. Model
software implementation was made using the MatLab application package.
The results of the REM generation with a quality assessment are presented: the average RMSE
values were 15.11 m and 7.83 dBm, these values suitable for further application. The one output file
average size (1 frame) was 3 MB, each frame processing lasted about 1 ms.
The obtained results confirm the correctness of the developed model. The results of using the
model is intended for use in the design and complex modeling of cognitive communication systems
based on LTE.
6. References
[1] M. Pesko, Radio Environment Maps: The Survey of Construction Method, KSII Transactions on
Internet and Information Systems 8.11 (2014) 3789–3809. doi:10.3837/tiis.2014.11.008.
[2] S. Alfattani, A. Yongacoglu, Indirect Methods for Constructing Radio Environment Map, in:
Proceedings of the IEEE Canadian Conference on Electrical & Computer Engineering, 2018.
doi:10.1109/CCECE.2018.8447654.
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