=Paper=
{{Paper
|id=Vol-1712/p07
|storemode=property
|title=Adaptive Modulation and Coding Simulations for Mobile Communication Networks
|pdfUrl=https://ceur-ws.org/Vol-1712/p07.pdf
|volume=Vol-1712
|authors=Nizar Zarka,Amoon Khalil,Abdelnasser Assimi
}}
==Adaptive Modulation and Coding Simulations for Mobile Communication Networks==
https://ceur-ws.org/Vol-1712/p07.pdf
Adaptive Modulation and Coding Simulations for
Mobile Communication Networks
Nizar Zarka, Amoon Khalil and Abdelnasser Assimi
Higher Institute for Applied Sciences and Technology
Communication Department
Damascus, Syria
Email: nizar.zarka@hiast.edu.sy
Abstract—This paper presents the simulations of Adaptive used are QP SK, 16 − QAM or 64 − QAM . The modulated
Modulation and Coding (AMC) for Mobile Communication Net- complex symbols are sent to an Additive White Gaussian
works. The simulations show that AMC gives higher throughput Noise channel (AWGN). At the receiver a soft demodulation is
than the static modulation and coding with a gain of 4dB
of the Signal-to-Noise Ratio (SNR). The simulation results are applied to get LLR followed by a de-puncturing where zeros
validated using real measurements of the High Speed Packet are added to the removed symbols during the puncturing phase.
Access Evolution (HSPA+) mobile network. Finally symbols are detected in the Turbo decoder Vn . In the
following we will describe each part of the system.
I. I NTRODUCTION
The increasing demand of mobile multimedia services in-
cluding VoIP, mobile TV, audio and video streaming, video
conferencing, FTP and internet access require intelligent com-
munication systems able to adapt the transmission parameters
based on the link quality. Changing the modulation and
coding scheme yield a higher throughput by transmitting with
high information rates under favorable channel conditions
and reducing the information rate in response to degradation
effects of the channel [1]. The idea behind the Adaptive
Modulation and Coding (AMC) is to dynamically change the
modulation and coding scheme to the channel conditions. If
good Signal-to-Noise Ratio (SNR) is achieved, system can
switch to the highest order modulation with highest code rates
(e.g. 64−QAM with code rate CR = 34 ). If channel condition
changes, system can shift to other low order modulation with
low code rates (e.g. QP SK with CR = 12 ) [3].
The aim of this paper is to understand how AMC works, to
develop our own simulation tools and validate the simulation
results with the real measurements of the HSPA+ mobile
network [2]. The paper is organized as follows; first we
present the design of the proposed system model for mobile
communication networks, followed by the simulation results
of the static and the adaptive modulation and coding and the
validation from real measurements and finally a conclusion.
II. P ROPOSED S YSTEM M ODEL
Figure 1 represents the simulation model used in our simula- Fig. 1. AMC Proposed System Model
tion program. It is composed of a transmitter, a communication
channel and a receiver. At the transmitter Un data are encoded A. Turbo Encoder
to get Cn using Turbo code with rate 31 . Some symbols are The Turbo encoder [5] is composed of two identical Recur-
removed in the puncturing block to give Dn depending on the sive Systematic Convolution (RSC) as it shows in Figure 2.
code rate CR = 13 , CR = 12 or CR = 23 [4]. The modulation The two coders receive the same input data Uk with reordering
via the interleaver. The outputs are composed of three symbols
Copyright c 2016 held by the authors. Uk , Pk1 and Pk2 . The code rate of the turbo encoder is
CR = 31 .
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QPSK, 16−QAM and 64−QAM modulations. QP SK with
CR = 21 gives 2 bits per symbol, 16-QAM with CR= 34 gives
4 bits per symbol and 64 − QAM with CR = 34 gives 6 bits
per symbol.
Fig. 2. The Turbo Encoder Fig. 6. Distribution of symbols in QP SK
B. Puncturing
Puncturing [6] is applied at the outputs of the turbo encoder,
to groups of turbo code symbols to reduce the coding rate of
the transmitter. The puncturing Matrix contains 1 and 0 to
give different code rate CR = 13 , CR = 12 and CR = 32 as it
shows in Figure 3, Figure 4 and Figure 5.
CR = 13
1 1 1 1 1 1 1 1
P = 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1
Fig. 3. Puncturing matrix with CR = 13
Fig. 7. Distribution of symbols in 16 − QAM
CR = 12
1 1 1 1 1 1 1 1
P = 1 0 1 0 1 0 1 0
0 1 0 1 0 1 0 1
Fig. 4. Puncturing matrix with CR = 12
CR = 23
1 1 0 1 1 0 1 1
P = 0 0 0 0 1 1 1 1
0 1 1 1 0 1 0 1
Fig. 5. Puncturing matrix with CR = 23
C. Modulation Fig. 8. Distribution of symbols in 64 − QAM
In the M − QAM modulation symbols in the inputs are
divided into blocks of length k = log2 (M ), where M is D. The Receiver
the rank of the modulation. Each block is connected to one The channel used is AWGN [8]. At the receiver symbols
point of the M − QAM modulation [7]. Figure 6, Figure 7 are demodulated and sent to the decoder which is composed
and Figure 8 show respectively the distribution of symbols in of de-puncturing and Turbo decoder. The de-puncturing uses
37
the same puncturing matrix to redistribute the symbols in the
correct place before the puncturing. The deleted symbols are
replaced with zeros. The de-puncturing gives the values to the
turbo decoder which concludes the sent symbols [9]. In the
following we will explain the results of the simulations.
III. S IMULATION R ESULTS OF THE S TATIC M ODULATION
AND C ODING
The simulations have been run in MATLAB under AWGN
and Rayleigh fading channels [10]. We first present the perfor-
mance of different types of modulations and coding, then we
conclude the table of the threshold of the SNR for the each
modulation and coding. Finally we present the throughput in Fig. 10. 16 − QAM Performance
term of SNR in AWGN channel and Rayleigh fading.
A. The Performance of QPSK C. The Performance of 64-QAM
Figure 9 depicts the Bite Error Rate (BER) in term of SNR
with the scenarios of QPSK modulation without and with code Figure 11 depicts the BER in term of SNR with the
rates of CR = 13 , CR = 12 , CR = 23 . It is shown that the scenarios of 64 − QAM modulation without and with code
best performance occurs for CR = 31 , and the performance rates of CR = 13 , CR = 12 , CR = 23 . It is shown that the
decreases with the increase of CR. The figure shows that best performance occurs for CR = 31 , and the performance
for a fixed value of BER = 10−4 , the gain in SNR with decreases with the increase of CR. The figure shows that
coding, comparing to the modulation without coding, is equal for a fixed value of BER = 10−4 , the gain in SNR with
to 5dB, 8dB and 12dB when CR = 23 , CR = 21 , CR = 13 coding, comparing to the modulation without coding, is equal
respectively. to 5dB, 10dB and 15dB when CR = 23 , CR = 12 , CR = 31
respectively.
Fig. 9. QP SK Performance
Fig. 11. 64 − QAM Performance
B. The Performance of 16-QAM
Figure 10 depicts the BER in term of SNR with the D. Comparison of Performances
scenarios of 16 − QAM modulation without and with code
rates of CR = 31 , CR = 12 , CR = 23 . It is shown that the
best performance occurs for CR = 31 , and the performance The figure 12 shows that the performance of 16 − QAM
decreases with the increase of CR. The figure shows that with CR = 31 is better than QP SK with CR = 32 , though the
for a fixed value of BER = 10−4 , the gain in SNR with number of bits per symbol is 13 in both case. The performance
coding, comparing to the modulation without coding, is equal of 64 − QAM with CR = 13 is better than 16 − QAM with
to 5dB, 9dB and 13dB when CR = 23 , CR = 21 , CR = 13 CR = 13 , though the number of bits per symbol is 2 in both
respectively. cases.
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Fig. 12. Performances of different modulation with different code rates Fig. 15. Throughput in term of SNR in AWGN channel
Figure 13 shows the Frame Error Rate (FER) in term of
SNR for different modulations and coding. Similar results are obtained in Figure 16 that shows the
throughput in the presence of AWGN channel and Rayleigh
fading.
Fig. 13. Frame Error Rate in term of SNR
Figure 14 shows the minimum thresholds of SNR that
allows to choose the respective modulation and coding for
F ER = 10−2 . We notice that 16 − QAM with CR = 13
Fig. 16. Throughput in term of SNR in AWGN channel with Rayleigh fading
replaces QPSK with CR = 23 , and 64 − QAM with CR = 13
replaces 16 − QAM with CR = 12 .
IV. S IMULATION OF THE A DAPTIVE M ODULATION AND
bpsym SN R(dB) CR M odulation C ODING
2/3 − 1/3 QP SK
1 3 1/2 QP SK
4/3 4 1/3 16 − QAM
Our simulation of the adaptive modulation and coding is
2 9 1/3 64 − QAM
shown in Figure 17. The block diagram is similar to the Figure
8/3 12.5 2/3 16 − QAM
1 with the additional Adaptation block. The demodulator at
3 14.5 1/2 64 − QAM
the receiver part calculates the SNR and sends it to the
4 18.5 2/3 64 − QAM
Adaptation block which decides the suitable modulation and
Fig. 14. The Minimum threshold for modulation and coding coding. Figure 18 shows the curves of the throughput in term
of SNR for the adaptive modulation and coding with Rayleigh
fading. The curve matches the curve of QP SK and CR = 13
E. Throughput in term of SNR for AWGN and Rayleigh fading
for low values of SNR, and matches the curves of 64 − QAM
Figure 15 shows the throughput in the presence of AWGN and CR = 23 for high values of SNR. For the medium value
channel. We notice that 16−QAM with CR = 13 gives higher of SNR we notice a gain of 4dB at SN R = 15dB. By
throughput than QP SK with CR = 23 . We also notice that consequence the throughput curve of the adaptive modulation
64 − QAM with CR = 31 gives higher throughput than 16 − and coding acts like an envelope of the maximum values of
QAM with CR = 31 . the throughput of the static modulation and coding.
39
Fig. 19. Throughput of AMC
Figure 19 shows the measured and the simulated throughput
in term of SNR for the adaptive modulation and coding. We
noticed that most of the points of the measurements are located
near the throughput of the adaptive modulation and coding.
Some points deviate because of the difference between the
real channel and the simulated channel.
VI. C ONCLUSION
This paper presented the simulations of the adaptive mod-
ulation and coding for mobile communication networks. We
Fig. 17. Throughput of AMC with Rayleigh channel
concluded that the throughput could be increased by increasing
the modulation order and the coding rate with the increase
of Signal-to-Noise Ratio. The AMC gives higher throughput
by changing the modulation and coding in function of the
Signal-to-Noise ration at the receiver with a gain of 4dB. The
simulation results are validated via drive test measurements of
HSPA+ mobile network.
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40