-This paper presents the simulations of Adaptive Modulation and Coding (AMC) for Mobile Communication Networks. The simulations show that AMC gives higher throughput than the static modulation and coding with a gain of 4dB of the Signal-to-Noise Ratio (SNR). The simulation results are validated using real measurements of the High Speed Packet Access Evolution (HSPA+) mobile network.
The increasing demand of mobile multimedia services
including VoIP, mobile TV, audio and video streaming, video
conferencing, FTP and internet access require intelligent
communication 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 [
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 [
Copyright c 2016 held by the authors. used are QP SK, 16 QAM or 64 QAM . The modulated complex symbols are sent to an Additive White Gaussian Noise channel (AWGN). At the receiver a soft demodulation is applied to get LLR followed by a de-puncturing where zeros are added to the removed symbols during the puncturing phase. Finally symbols are detected in the Turbo decoder Vn. In the following we will describe each part of the system.
The Turbo encoder [
QPSK, 16 QAM and 64 QAM modulations. QP SK with CR = 12 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.
Puncturing [
CR = 31 0 1 1 1 1 1 1 1 1 1 P = @ 1 1 1 1 1 1 1 1 A
1 1 1 1 1 1 1 1
In the M QAM modulation symbols in the inputs are
divided into blocks of length k = log2(M ), where M is
the rank of the modulation. Each block is connected to one
point of the M QAM modulation [
The channel used is AWGN [
AND CODING
The simulations have been run in MATLAB under AWGN
and Rayleigh fading channels [
Figure 9 depicts the Bite Error Rate (BER) in term of SNR with the scenarios of QPSK modulation without and with code rates of CR = 31 , CR = 12 , CR = 23 . It is shown that the best performance occurs for CR = 13 , and the performance decreases with the increase of CR. The figure shows that for a fixed value of BER = 10 4, the gain in SNR with coding, comparing to the modulation without coding, is equal to 5dB, 8dB and 12dB when CR = 23 , CR = 12 , CR = 13 respectively.
Figure 10 depicts the BER in term of SNR with the 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 = 13 , and the performance decreases with the increase of CR. The figure shows that for a fixed value of BER = 10 4, the gain in SNR with coding, comparing to the modulation without coding, is equal to 5dB, 9dB and 13dB when CR = 23 , CR = 12 , CR = 13 respectively.
The figure 12 shows that the performance of 16 QAM with CR = 13 is better than QP SK with CR = 32 , though the number of bits per symbol is 13 in both case. The performance of 64 QAM with CR = 31 is better than 16 QAM with CR = 31 , though the number of bits per symbol is 2 in both cases.
Similar results are obtained in Figure 16 that shows the throughput in the presence of AWGN channel and Rayleigh fading.
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 = 31 replaces QPSK with CR = 23 , and 64 QAM with CR = 31 replaces 16 QAM with CR = 12 . E. Throughput in term of SNR for AWGN and Rayleigh fading
Figure 15 shows the throughput in the presence of AWGN channel. We notice that 16 QAM with CR = 31 gives higher throughput than QP SK with CR = 23 . We also notice that 64 QAM with CR = 31 gives higher throughput than 16 QAM with CR = 13 .
Fig. 16. Throughput in term of SNR in AWGN channel with Rayleigh fading IV. SIMULATION OF THE ADAPTIVE MODULATION AND
CODING
Our simulation of the adaptive modulation and coding is
shown in Figure 17. The block diagram is similar to the Figure
1 with the additional Adaptation block. The demodulator at
the receiver part calculates the SNR and sends it to the
Adaptation block which decides the suitable modulation and
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
for low values of SNR, and matches the curves of 64 QAM
and CR = 32 for high values of SNR. For the medium value
of SNR we notice a gain of 4dB at SN R = 15dB. By
consequence the throughput curve of the adaptive modulation
and coding acts like an envelope of the maximum values of
the throughput of the static modulation and coding.
V. VALIDATION OF THE AMC SIMULATION RESULTS
To validate our simulation results of the AMC, we use Nemo
outdoor and Nemo Analyze tools [
This paper presented the simulations of the adaptive modulation and coding for mobile communication networks. We 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.