VHDL Design and FPGA Implementation of a High Data Rate Turbo Decoder based on Majority Logic Codes

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 4, August 2017, pp. 1824~1832
ISSN: 2088-8708, DOI: 10.11591/ijece.v7i4.pp1824-1832  1824
Journal homepage: http://iaesjournal.com/online/index.php/IJECE
VHDL Design and FPGA Implementation of a High Data Rate
Turbo Decoder based on Majority Logic Codes
A. Boudaoud, M. El Haroussi, E. Abdelmounim
ASTI Laboratory, FST Settat, Hassan I University, Settat, Morocco
Article Info ABSTRACT
Article history:
Received Jan 6, 2017
Revised Mar 10, 2017
Accepted Mar 27, 2017
This paper presents the electronic synthesis, VHDL design and
implementation on FPGA of turbo decoders for Difference Set Codes (DSC)
decoded by the majority logic (ML). The VHDL design is based on the
decoding Equations that we have simplified, in order to reduce the
complexity and is implemented on parallel process to increase the data rate.
A co-simulation using the Dsp-Builder tool on a platform designed on
Matlab/Simulink, allows the measurement of the performance in terms of
BER (Bit Error Rate) as well as the decoder validation. These decoders can
be a good choice for future digital transmission chains. For example, for the
Turbo decoder based on the product code DSC (21.11)² with a quantization
of 5 bits and for one complete iteration, the results show the possibility of
integration of our entire turbo decoder on a single chip, with lower latency at
0.23 microseconds and data rate greater than 500 Mb/s.
Keyword:
Error correcting codes
FPGA implementation
Interleaver
ML-DSC codes
Turbo decoding
VHDL language
Copyright © 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Abdelghani Boudaoud,
ASTI Laboratory, FST Settat,
Hassan I University,
B.P: 577, Casablanca Road, Km 3, Settat, Morocco.
Email: boudaoud586@gmail.com
1. INTRODUCTION
The discovery, of Turbo-Codes by C.Berrou [1] in 1993, represents an essential step forward for
information transmission systems. Indeed, most terrestrial and satellite transmission standards have also
adopted them. Thus NASA uses it in all their space probes since 2003; similarly, the European Space Agency
(ESA) lunar probe SMART-1. Turbo-codes are also used as in UMTS, ADSL-2 and in mobile networks 4G-
LTE and LTE-Advanced [20].
Initially, the turbo codes were based on convolutional codes concatenated in parallel. R.Pyndiah in
1994 [2] proposed the turbo block codes (TBC), which are an alternative to turbo convolutional codes. These
TBC used the decoding weighted inputs and outputs (SISO). The iterative decoding process that we use,
follows the model proposed by Pyndiah and built from the “One Step Majority Logic Decodable Codes”
(OSMLD) [3] using the soft-out extension threshold decoding classic of Massey [4-9], [14].
The majority decoding uses a linear combination of a reduced set of syndromes represented by the
orthogonal Equations.
The current digital systems and channel coding especially decoding, require high data rates.
Decoding rate depends on the chosen architecture in the electronic design; the complexity of the circuit (ie its
surface) is often considered as a critical parameter. In this context of very high throughput, the operating
speed must be maximized while minimizing complexity.

IJECE ISSN: 2088-8708 
VHDL Design and FPGA Implementation of a High Data Rate Turbo Decoder …. (A. Boudaoud)
1825
2. CODING AND DECODING OF PRODUCT CODES
2.1. Product Codes Construction
Consider tow systematic block linear codes C1(n1, k1, d1) and C2(n2, k2, d2). Where ni, ki and di are,
in order: the code word length, the information symbols number and the Hamming distance (i = 1,2).
Product code CP=C2C1 built based on C1 and C2, which has the parameters (n1×n2, k1×k2, d1×d2),
is obtained by encoding the k2×k1 information symbols by the C1 code and k2×n1 symbols by C2 code, see
Figure 1. In this work, we choose C1=C2 and, DSC(7,3,1), DSC(21,11,2) and DSC(73,45,4) codes.
2.2. Turbo Decoding Principle
A turbo decoder consists of SISO decoders (generally two decoders) and interleavers as shown in
Figure 2. The channel symbols are received by line machine, by the first decoder which gives soft priori
information, and then the channel information and the extrinsic information are interleaved and supplied
column by column, to the input of the second decoder. A complete iteration consists of activating each
decoder once. Thus, with more iteration, the decoder converges to the right solution, however it requires
more time.
Figure 1. Product code principle
Figure 2. Structure of the turbo decoder
3. THE SISO DECODER
3.1. Threshold Decoding Algorithm
The SISO (Soft In-Soft Out) decoder is the base element of the turbo decoding. We used SISO
decoder for which the threshold decoding algorithm [3], [4], [13] is given below:
For each j = n to 1
a. Calculate the terms Bi and ωi with iє {1,..,M}
b. Calculate B0 and w0
c. Calculate the extrinsic information 

M
i iij ByW 1
)21()( 
d. Calculate
jjj Wyy
Es
yLLR
N

0
4
)(
e. If (LLR(yj)>0)  Hard Decision =1
else  Hard Decision =0
Where:
a. n: Code length;

 ISSN: 2088-8708
IJECE Vol. 7, No. 4, August 2017 : 1824 – 1832
1826
b. M: number of orthogonal Equations;
c. Bi: the orthogonal Equation on the ith
bit, after remove of ith
bit;
d. ωi: proportional to the reliability of ith
parity Equation,
e. W (yj): extrinsic information representing the estimated orthogonal Equations on the symbol yj;
f. LLR (yi) the decision function on the symbol yj;
And:





















nik
kik
ik
nik
kik
ik
L
L
i
,1
,1
)
2
tanh(1
)
2
tanh(1
ln
(1)
3.2. Structural Diagram of SISO Decoder
We have designed, in VHDL, a SISO decoder, which can be used in an iterative process. This
iterative process we use follows the model proposed by Pyndiah [3]; See Figure 3.
The soft input and respectively the soft output of the qth
step (half-iteration) of the iterative decoding
are given by:
)()()1( qWqRqR 
)()(4)(
0
qWqR
N
E
qLLR s

Where R represents the lines or columns of the received and quantified word, W(q) the extrinsic
information calculated by the previous decoder and α is a coefficient which varies with each iteration. In
addition to its threshold output, the decoder has a hard decision output that we used for its validation.
Figure 3. SISO Decoder (elementary cell of turbo decoding)
3.3 FPGA Implementation of the SISO Decoder
To reduce the complexity of the algorithm, we used, to simplify the expression (1), the Equation (2)
below proposed by [12] and applied to the majority logic codes by [13], [11], [21].
The simplified expression of our decoder becomes:
(2)
The architecture of the proposed SISO decoder was described in VHDL and implemented on FPGA
using Quartus II tool from Altera. We used The EPC4CE115F29C7 FPGA type, containing about 115,000



n
k
ikik
ni
k
LsignLi
1
1
)(*min

IJECE ISSN: 2088-8708 
1827
LEs. We studied the complexity evolution of our decoders for different quantization bit number using
C(7,3)C(7,3), C(21,11)C(21,11) and C(73,45)C(73,45) product codes.
The results obtained, after the Quartus II synthesis, are summarized in Table 1. Figure 4 shows a
functional simulation example of our SISO decoder for DSC (7,3) code on which it is reported that the
outputs are updated at each rising edge of the clock, hence the latency is one clock cycle.
Figure 4. Simulation example of our SISO decoder for the DSC (7,3) code, with 5 bits of quantization.
Table 1. The Obtained Data for Different SISO Decoders
4. THE INTERLEAVER
4.1. The Interleaver Principle
An interleaver is a system that receives a sequence of symbols in its input and provides another of
the same alphabet to the output in a completely different order. In our case of product codes, the operation of
the interleaver is summarized in two stages: receipt of all symbols of the product code matrix n2*n1, and
swapping row-column of this matrix.
4.2. FPGA Implementation of the Interleaver
The used interleavers are described in VHDL and implemented on the same FPGA circuit using
Quartus II tool for C(7,3)C(7,3), C(21,11)C(21,11) and C(73,45)C(73,45) product codes. The Figure 5
shows the external structure of the interleaver design for the C(7,3)C(7,3) product code.
SISO Decoders Characteristics
Quantization bit number
4 Bits 5 Bits 6 Bits 7 Bits
DSC(7,3)
Complexity(LE) 714 994 1141 1302
Frequency max. (MHz) 47.62 43.71 41.51 41.46
throughput max.(Gb/s) 0.33 0.31 0.29 0.29
Latency (ns) 21.00 22.88 24.09 24.12
DSC(21,11)
Complexity(LE) 5334 6496 7749 8863
Frequency max. (MHz) 24.99 24.30 23.58 23. 34
Latency (ns) 40.02 41.15 42.41 42.84
DSC(73,45)
Complexity(LE) 52516 63155 78833 90513
Latency (ns) 49.95 53.88 59.31 62.03

 ISSN: 2088-8708
1828
In Figure 6, which illustrates an example of functional simulation of that interleaver, we note that
input lines are recovered as columns on the outputs with latency equals to the length of the base code, seven
clock periods in this case.
Table 2 summarizes the readings of the complexity, the maximum frequency and the maximum
throughput, for different interleavers depending on the quantization bit number.
Figure 5. Interleaver structure for DSC(7,3)C(7,3) product code
Figure 6. Example of functional simulation of the interleaver for DSC(7,3) code on Quartus II software.
Table 2. Caracteristics of the Intereleavers
Interleaver Characteristics
Quantization bit number
4 Bits 5 Bits 6 Bits 7 Bits
7 To7
Symbols
Complexity(LE) 570 708 850 988
Latency (ns) 31.90 32.01 33.47 34.48
21 To 21
Symbols
Complexity(LE) 4643 5799 6962 8113
Latency (ns) 138.16 141.71 150.06 160.59
73 To 73
Symbols
Complexity(LE) 57388 71867 86014 100302
Latency (ns) 865.85 887.86 912.16 948.67

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5. THE TURBO DECODER
5.1. FPGA Implementation of Turbo Decoder
Our turbo decoder consists of two SISO decoder and four interleavers similar to those discussed
above, see Figure 7. Figure 8 shows the evolution of the silicon surface (expressed in logic element LE),
occupied by the components of the Turbo decoder for C(7,3)C(7,3), C(21,11)C(21,11) and
C(73,45)C(73,45) product codes, with a quantization of 5 bits. Figure 9 illustrates this surface with
quantization of 3 to 7 bits for C(21,11)C(21,11) codes.
Figure 7. Detailed structure of our Turbo decoder
Figure 8. Area occupied by different SISO decoders
for quantization of 5 bits
Figure 9. DSC(21,11)² SISO decoders occupied
area for different bits of quantization (3 to 7 bits)
5.2. Validation of our Turbo Decoder
After functional simulation on Quartus II software, our design is also validated on a co-simulation
chain on Matlab / Simulink and DSP Builder.
The co-simulation chain proposed (Figure 10) consists of the main components of a digital
transmission chain: the information to be transmitted is generated in a random manner, coded by a product
coder before being modulated on Binary Phase Shift Keying (BPSK) modulation. The channel, AWGN (Add
white Gaussian Noise) will insert random errors in the information received. The block called in the figure 11
"Turbo decoder" contains the Turbo decoder to test, functions for the quantization and a data adaptation.
Quantization assigns to each symbol a fixed number of bits, according to the decoder. The loop is
closed by a counter of "bit error" in order to measure the BER performance (Bit Error Rate) versus
SNR(Signal-to-Noise Ratio). Figure 11 illustrates the performance for the DSC(21,11) turbo decoder at the
first iteration in the theoretical case and in VHDL with quantization of 4, 5 and 7 bits.
In Figure 12, we presented the BER performance of the same turbo decoder with a quantization of 5
bits for 1st
, 2nd
and 3rd
iteration

 ISSN: 2088-8708
1830
Figure 10: the proposed co-simulation chain for the BER performance measurement of the turbo decoder
Figure 12. BER performances of Turbo decoder for 1st
, 2nd
and 3rd
iteration
5.3. Performances of our Turbo Decoder
Our turbo decoder designed and implemented on the EPC4CE115F29C7 FPGA circuit presents the
performances summarized in Table 3. It is evident from Table 4, that the proposed design has a good
performance compared to other recent architectures; and especially the latency that is nine times lower than
the least one in the other designs.
Table 3. Summary of DSC (21, 11) Turbo Decoder Performances With a Quantization of 5 Bits.
IterationPath*
Throughput
max. (Mb/s)
Latency
min. (µs)
Complexity
(LE)
Occupation
(%)
BER at
SNR=3dB
1 2S+I 510 0.224 24 590 21.38% 4.72 10-2
2 4S+3I 255 0.590 36 188 31.46% 9.91 10-4
3 6S+5I 170 0.956 36 188 31.46% 5.62 10-5
(*) Information path: S for SISO decoder and I for Interleaver
Table 4. Comparaison With Others Turbo Decoder Design
This work [18]
2010
[15]
2005
[16]
2003
[17]
20132nd
iter. 3rd
iter.
Number of Iterations 2 3 5 6 4 NA
Throughput (Mb/s) 255 170 930 758 75.6 346
Latency(µs) 0.59 0.956 5.5 10.5 5.35 NA
Frame length 441 441 5120 5120 432 NA
BER at SNR=3dB 9.9 10-4
5.6 10-5
4 10-6
NA 4 10-7
2 10-3
Modulation BPSK BPSK BPSK NA NA BPSK
Channel type AWGN AWGN AWGN NA NA AWGN
Code rate 0.274 0.274 NA NA 0.33 0.33

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6. CONCLUSION
The approach of the VHDL design using a parallel mode and the FPGA implementation of the
proposed turbo decoder has allowed us a high data rate, low complexity and very low latency. This will
justify its use in future communication channels.
Use of a FPGA circuit with better “Speedgrad” could further increase data rate and reduce latency.
In addition, a review of the interleaver architecture is necessary to reduce more its complexity.
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BIOGRAPHIES OF AUTHORS
Abdelghani BOUDAOUD was born in Azilal Morocco in 1973. He is graduated from High
Normal School of Technical Education (ENSET) in 1996 at Mohammedia Morocco. In 2003, he
had the diploma of aggregation in electrical engineering at ENSET of Rabat Morocco. In 2011 he
obtained the Master in «Laser Instrumentation and Optoelectronic Components” at the Faculty of
Science and Technology (FST) Hassan I University, Settat Morocco. Currently, he is PhD student
in “System Analysis and Information Technology Laboratory “(ATSI) in FST, Hassan I
University, Settat, Morocco.
Mustapha ELHAROUSSI was born in Azilal Morocco in 1974; he received his PhD in Error
Correcting Codes from Mohammed V University Morocco in 2013. In 2014 he joined, as
Professor, applied Physics department of FST, Hassan I University, Settat, Morocco. His current
research interests include hardware design of algorithms applied to information processing and
control of industrial systems at System Analysis and Information Technology Laboratory at
Hassan I University, Settat, Morocco.
Elhassane ABDELMOUNIM was born in Oued-Zem Morocco in 1965; he received his PhD in
Spectral analysis from Limoges University France in 1994. In 1996, he joined, as Professor,
applied physics department of FST, Hassan I University, Settat, Morocco. His current research
interests include digital signal processing and machine learning. He is currently coordinator of a
Bachelor of Science in electrical engineering and he is researcher at System Analysis and
Information Technology Laboratory at Hassan I University, Settat, Morocco.

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