Int. J. Communications, Network and System Sciences, 2009, 2, 704-713
doi:10.4236/ijcns.2009.28081 blished Online November 2009 (http://www.SciRP.org/journal/ijcns/).
Copyright © 2009 SciRes. IJCNS
Pu
Service Adaptable 3G Turbo Decoder for Indoor/Low
Range Outdoor Environment
Costas CHAIKALIS, Nicholas S. SAMARAS
Department of Informatics & Telecommunications, TEI of Larissa, Larissa, Greece
Email: kchaikalis@teilar.gr
Received July 28, 2009; revised September 6, 2009; accepted October 7, 2009
Abstract
For the well-known 3G mobile communications standard UMTS, four different service classes have been
specified. Considering two turbo decoding algorithms, like SOVA and log-MAP, it would be desirable to use
an efficient turbo decoder. In this paper this decoder is shown to adapt dynamically to different service sce-
narios, considering parameters like performance and complexity for indoor/low range outdoor operating en-
vironment. The scenarios show that for streaming service class real-time class applications the proposed de-
coding algorithm depends on data rate; for the majority of scenarios SOVA is proposed, whereas log-MAP is
optimal for increased data rates and medium-sized frames. On the other hand, conversational service class
real-time applications cannot be established. For the majority of non real-time applications (interactive and
background service classes) either algorithm can be used, while log-MAP is proposed for medium data rates
and frame lengths.
Keywords: Reconfigurable Systems, Turbo Decoder, UMTS, Flat Reyleigh Fading, Indoor/Low Range Out-
door Operating Environment
1. Introduction and UMTS Data Flow
Channel coding is a critical signal processing element in
modern mobile communications systems. Turbo codes
[1] represent a powerful channel coding technique. Uni-
versal Mobile Telecommunications System (UMTS)
belongs to the third generation (3G) of mobile commu-
nication systems. Turbo codes have been incorporated as
a channel coding scheme in UMTS for data rates higher
or equal to 28.8 kbps [2]. They also provide high coding
gains in flat fading channels with the use of outer block
interleaving [3,4]. Soft-input/soft-output (SISO) decoder
is part of a turbo decoder and two candidate algorithms
to be used in a SISO decoder are soft output Viterbi al-
gorithm (SOVA) and log maximum a-posteriori (log-
MAP) algorithm [2,5–7].
A reconfigurable turbo decoder can be derived ac-
cording to the common operations of the two algorithms,
optimal in terms of performance and latency [8,9,10].
We consider just SOVA and log-MAP and not other
turbo decoding algorithms like max-log-MAP or MAP,
because SOVA is better in terms of delay, while log-
MAP is better in terms of performance [3,5].
SOVA and log-MAP algorithms share common opera-
tions which have been addressed in [8–10]. These com-
mon operations form a turbo decoder which can be re-
configured and choose the suitable turbo decoding algo-
rithm for different applications (reconfigurable SOVA/
log-MAP turbo decoder). In [8] and [10] is also shown
that in a reconfigurable SOVA/log-MAP turbo decoder
scaling of the extrinsic information is possible with a
common scaling factor, which is constant and independ-
ent of signal-to-noise ratio for additive white Gaussian
noise (AWGN) channels. In [9] it is shown that in the
case of a flat Rayleigh fading channel for a reconfigur-
able SOVA/log-MAP decoder a common scaling factor
with value 0.7 is the optimal choice.
Nowadays, UMTS represents the dominant 3G system
in the mobile communications market. According to
UMTS specifications, a transport channel transfers data
over radio interface from Medium Access Control sub-
layer of layer 2 to physical layer and is characterized by
its transport format set, which consists of different
transport formats. They must have the same type of
channel coding and time transmission interval (TTI),
while the transport block set or data frame size can vary.
The transport block set determines the number of input
bits to the channel encoder and can be transmitted every
C. CHAIKALIS ET AL. 705
k
n
TTI, with possible values for TTI of 10, 20, 40 and 80
msec [2,11]. After channel coding, outer block inter-
leaving is performed, and since the frame duration in
UMTS is 10 msec, the number of columns of the outer
block interleaver can be 1, 2, 4 or 8, depending on TTI
value. Therefore, the TTI values and the number of col-
umns of the outer block interleaver are interrelated. Fur-
thermore, every transport channel is assigned a radio
access bearer with a particular data rate, which provides
the transfer of the service through the radio network. A
mobile terminal may use several parallel transport chan-
nels simultaneously, each having its own characteristics
(transport format set).
UMTS radio interface transfers multiple applications.
Parameters like bit error rate (BER) performance and
delay are assigned to these applications. Four different
service traffic classes are defined: conversational,
streaming, interactive and background. For real-time
conversational and streaming classes BER has to be less
than 10-3, while for non-real time interactive and back-
ground classes BER has to be less than 10-5. The maxi-
mum acceptable delay for conversational class is 80
msec, for streaming it is 250 msec, for interactive it is 1
sec, while for background it is higher than 10 sec [2,11].
2. Simulation Parameters
The discrete representation of flat Rayleigh fading chan-
nel is given by the following equation:
kkk
yx
 (1)
where is an integer symbol index,
kk
x
is a binary
phase shift keying (BPSK) symbol amplitude
1
,
is a Gaussian random variable and is a noisy re-
ceived symbol. The fading amplitude is a sample
from a correlated Gaussian random process with zero
mean and is generated using the Sum of Sines or Jakes-
model, which is described in [12]. This model is based
on summing 9 sinusoids whose frequencies are chosen as
samples of the Doppler spectrum. The properties of Jakes
model are further analysed in [13].
k
n
k
y
k
a
For the simulation model a carrier frequency 2
c
f
GHz is considered. It is also assumed that 1000000 bits
are transmitted and grouped into frames whose length
must be and
f
k405114
, according to UMTS
specifications [2,14]. For a particular transport channel,
every TTI the data with the characteristics specified in a
transport format of the transport channel ( bits), is
turbo encoded (constraint length
f
k
4
K
and rate
13
c
r
) at the transmitter. Furthermore, each time in-
stant it is assumed that the two recursive systematic
convolutional encoders of the turbo encoder start encod-
ing from all-zero state. After turbo coding and block in-
terleaving using the UMTS parameters, the bits are
BPSK modulated and transmitted through the mobile
channel. At the receiver, outer block deinterleaving and
turbo decoding is performed. The received values are not
quantized which means that floating point arithmetic is
used. The receiver is also assumed to have exact esti-
mates of the fading amplitudes (perfect channel estima-
tion without side information), while eight iterations are
used in the turbo decoder.
Table 1 illustrates eight different UMTS dedicated
transport channels with different transport format sets,
which represent different implementation scenarios of
the reconfigurable turbo decoder. The transport format
set for each transport channel consists of different exam-
ple transport formats and also of dynamic and semi-static
parts. The semi-static part (turbo encoder parameters,
TTI) is the same for all transport formats of the transport
format set, while the dynamic part (frame size) differs [2,
11,15]. Moreover, as published simulation results have
shown in [3,4] for flat Rayleigh fading channels, data
rate, outer block interleaving (thus TTI) and signal-to-
noise ratio (SNR) greatly affect BER performance: for
each scenario of Table 1 these three parameters differ
considering also the examples presented in [15].
Table 1. Implementation scenarios.
Transport format set
Dynamic part Semi-static part
Turbo encoder parameters
Transport
channel type
Transport block set or frame sizes (bits) K Code rate
TTI
(msec)
Data rate
Rb (kbps)
SNR
(dB) Scenario
576, 1152 4 1/3 40 28.8 32 1
576, 1152, 1728, 2304 4 1/3 40 57.6 30 2
336, 672, 1008, 1344 4 1/3 20 64 30 3
336, 672, 1344, 2688 4 1/3 20 128 30 4
336, 672, 1344, 2688, 3024 4 1/3 20 144 28 5
168, 336, 672, 1344, 2016, 2688, 3360, 40324 1/3 20 384 28 6
2560 4 1/3 40 64 30 7
Dedicated
channel
336, 1344, 2688, 4032, 4704 4 1/3 40 2000 40 8
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Table 2. Quality of service and proposed decoding algorithm for scenarios 1, 2 and 3 of Table 1.
Frame size
(bits)
td using SOVA
(msec)
td using log-MAP
(msec)
Max latency
(msec)
Log-MAP
BER SOVA BER BER
range
Proposed decoding
algorithm
576 240 528 80 0.0004720.000523 <10-3
Conv. class 1152 400 976 80 0 0 <10-3 Cannot be applied
576 240 528 250 0.0004720.000523 <10-3 SOVA
Strea
ming class 1152 400 976 250 0 0 <10-3 Cannot be applied
576 240 528 0.0004720.000523 <10-5 Cannot be applied
Scenario
1
Non-real
time classes 1152 400 976
Up to 1 sec
interactive, >10
sec background0 0 <10-5 Log-MAP or SOVA
576 160 304 80 0.0018360.002096 <10-3
1152 240 528 80 0.0009880.001036 <10-3
1728 320 752 80 0.0005820.000634 <10-3
Conv. class
2304 400 976 80 0 0 <10-3
Cannot be applied
576 160 304 250 0.0018360.002096 <10-3
1152 240 528 250 0.0009880.001036 <10-3
1728 320 752 250 0.0005820.000634 <10-3
Strea
ming class
2304 400 976 250 0 0 <10-3
Cannot be applied
576 160 304 0.0018360.002096 <10-5
1152 240 528 0.0009880.001036 <10-5
1728 320 752 0.0005820.000634 <10-5
Cannot be applied
Scenario
2
Non-real
time classes
2304 400 976
Up to 1 sec
interactive, >10
sec background
0 0 <10-5 Log-MAP or SOVA
336 82 157.6 80 0.0034850.003888 <10-3
672 124 275.2 80 0.001460.00183 <10-3
1008 166 392.8 80 0.0007790.000984 <10-3
Conv. class
1344 208 510.4 80 0.0005190.000538 <10-3
Cannot be applied
336 82 157.6 250 0.0034850.003888 <10-3
672 124 275.2 250 0.001460.00183 <10-3 Cannot be applied
1008 166 392.8 250 0.0007790.000984 <10-3 SOVA
Strea
ming class
1344 208 510.4 250 0.0005190.000538 <10-3 SOVA
336 82 157.6 0.0034850.003888 <10-5
672 124 275.2 0.001460.00183 <10-5
1008 166 392.8 0.0007790.000984 <10-5
Scenario
3
Non-real
time classes
1344 208 510.4
Up to 1 sec
interactive, >10
sec background
0.0005190.000538 <10-5
Cannot be applied
Table 3. Quality of service and proposed decoding algorithm for scenarios 4 and 5 of Table 1.
Frame size
(bits)
td using SOVA
(msec)
td using log-MAP
(msec)
Max latency
(msec)
Log-MAP
BER SOVA BER BER
range
Proposed decoding
algorithm
336 61 98.8 80 0.003465 0.004047 <10-3
672 82 157.6 80 0.001584 0.001713 <10-3
1344 124 275.2 80 0.000796 0.000934 <10-3
Conv. class
2688 208 510.4 80 0 0 <10-3
Cannot be applied
336 61 98.8 250 0.003465 0.004047 <10-3
672 82 157.6 250 0.001584 0.001713 <10-3 Cannot be applied
1344 124 275.2 250 0.000796 0.000934 <10-3 SOVA
Strea
ming class
2688 208 510.4 250 0 0 <10-3 SOVA
336 61 98.8 0.0034650.004047 <10-5
672 82 157.6 0.0015840.001713 <10-5
Scenario
4
Non-real time
classes
1344 124 275.2
Up to 1 sec
interactive,
>10 sec
background 0.000796 0.000934 <10-5
Cannot be applied
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C. CHAIKALIS ET AL. 707
2688 208 510.4 0 0 <10-5 Log-MAP or SOVA
336 58.6 92.26 80 0.005771 0.006268 <10-3
672 77.3 144.5 80 0.003005 0.003287 <10-3
1344 114.6 249.06 80 0.000704 0.0010007 <10-3
2688 189.3 458.13 80 0 3.091e-05 <10-3
Conv. class
3024 208 510.4 80 0 0 <10-3
Cannot be applied
336 58.6 92.26 250 0.005771 0.006268 <10-3
672 77.3 144.5 250 0.003005 0.003287 <10-3 Cannot be applied
1344 114.6 249.06 250 0.000704 0.0010007 <10-3 Log-MAP
2688 189.3 458.13 250 0 3.091e-05 <10-3 SOVA
Strea
ming class
3024 208 510.4 250 0 0 <10-3 SOVA
336 58.6 92.26 0.0057710.006268 <10-5
672 77.3 144.5 0.0030050.003287 <10-5
1344 114.6 249.06 0.0007040.0010007 <10-5
Cannot be applied
2688 189.3 458.13 0 3.091e-05 <10-5 Log-MAP
Scenario
5
Non-real time
classes
3024 208 510.4
Up to 1 sec
interactive,
>10 sec
background
0 0 <10-5 Log-MAP or SOVA
Table 4. Quality of service and proposed decoding algorithm for scenarios 6 and 7 of Table 1.
Frame size
(bits)
td using SOVA
(msec)
td using
log-MAP (msec)
Max latency
(msec)
Log-MAP
BER SOVA BER BER
range
Proposed decoding
algorithm
168 43.5 49.8 80 0.0073 0.008 <10-3
336 47 59.6 80 0.0065 0.007 <10-3
672 54 79.2 80 0.0051 0.0062 <10-3
1344 68 118.4 80 0.0022 0.0028 <10-3
2016 82 157.6 80 0.0006540.001307 <10-3
2688 96 196.8 80 0 0 <10-3
3360 110 236 80 0 0 <10-3
Conv. class
4032 124 275.2 80 0 0 <10-3
Cannot be applied
168 43.5 49.8 250 0.0073 0.008 <10-3
336 47 59.6 250 0.0065 0.007 <10-3
672 54 79.2 250 0.0051 0.0062 <10-3
1344 68 118.4 250 0.0022 0.0028 <10-3
Cannot be applied
2016 82 157.6 250 0.0006540.001307 <10-3 Log-MAP
2688 96 196.8 250 0 0 <10-3 Log-MAP or SOVA
3360 110 236 250 0 0 <10-3 Log-MAP or SOVA
Strea
ming class
4032 124 275.2 250 0 0 <10-3 SOVA
168 43.5 49.8 0.0073 0.008 <10-5
336 47 59.6 0.0065 0.007 <10-5
672 54 79.2 0.0051 0.0062 <10-5
1344 68 118.4 0.0022 0.0028 <10-5
2016 82 157.6 0.0006540.001307 <10-5
Cannot be
applied
2688 96 196.8 0 0 <10-5 Log-MAP or SOVA
3360 110 236 0 0 <10-5 Log-MAP or SOVA
Scenario
6
Non-real time
classes
4032 124 275.2
Up to 1 sec
interactive,
>10 sec back-
ground
0 0 <10-5 Log-MAP or SOVA
Conv. class 2560 400 976 80 0 0 <10-3 Cannot be applied
Strea
ming class 2560 400 976 250 0 0 <10-3 Cannot be applied
Scenario
7
Non-real time
classes 2560 400 976
Up to 1 sec
interactive,
>10 sec back-
ground
0 0 <10-5 Log-MAP or SOVA
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Table 5. Quality of service and proposed decoding algorithm for scenario 8 of Table 1.
Frame size
(bits)
td using SOVA
(msec)
td using log-MAP
(msec)
Max latency
(msec)
Log-MAP
BER SOVA BER BER
range
Proposed decoding
algorithm
336 81.34 83.76 80 0.0018150.0019574 <10-3
1344 85.37 95.05 80 0.0014140.0016548 <10-3
2688 90.75 110.1 80 9.97e-07 9.97e-07 <10-3
4032 96.12 125.1 80 0 0 <10-3
Conv. class
4704 98.81 132.68 80 0 0 <10-3
Cannot be applied
336 81.34 83.76 250 0.0018150.0019574 <10-3
1344 85.37 95.05 250 0.0014140.0016548 <10-3 Cannot be applied
2688 90.75 110.1 250 9.97e-07 9.97e-07 <10-3 Log-MAP or SOVA
4032 96.12 125.1 250 0 0 <10-3 Log-MAP or SOVA
Strea
ming class
4704 98.81 132.68 250 0 0 <10-3 Log-MAP or SOVA
336 81.34 83.76 0.0018150.0019574 <10-5
1344 85.37 95.05 0.0014140.0016548 <10-5 Cannot be applied
2688 90.75 110.1 9.97e-079.97e-07 <10-5 Log-MAP or SOVA
4032 96.12 125.1 0 0 <10-5 Log-MAP or SOVA
Scenario
8
Non-real
time classes
4704 98.81 132.68
Up to 1 sec
interactive,
>10 sec back-
ground
0 0 <10-5 Log-MAP or SOVA
Total max delay using SOVA: According to [2] and [11], three different operating
environments have been specified for UMTS:
2f
d
b
k
tTTI N
R

 

Rural outdoor operating environment with maxi-
mum supported mobile terminal speed 500 km/h and
maximum data rate of 144 kbps. Here, it has to be men-
tioned that a speed of 500 km/h corresponds to high
speed vehicles (e.g. trains). More typical value for this
environment is 300 km/h.
(2)
Total max delay using log-MAP:
2
f
d
b
k
t TTIN
R

 

2.8
(3)
Urban or suburban outdoor operating environment
with maximum supported mobile speed 120 km/h and
maximum data rate of 384 kbps. where is the total delay, is the frame size,
is the data rate of the radio bearer assigned to the trans-
port channel and is the number of turbo decoder
iterations. In these equations the higher complexity of
log-MAP compared to SOVA (2.8 times) is also consid-
ered.
d
tf
kb
R
N
Indoor or low range outdoor operating environ-
ment with maximum supported mobile speed 10 km/h
and maximum data rate of 2 Mbps.
In [9] the approach is similar, but we considered the
first two operating environments: a terminal speed of 300
km/h for a rural outdoor environment and a terminal
speed of 100 km/h for an urban/suburban outdoor envi-
ronment. In this paper we focus on the last operating
environment and we choose a low terminal speed of 4
km/h. This means that the maximum data rate of 2 Mbps
can be considered. A terminal speed of 4 km/h is a typi-
cal common value and it is important to be explored:
represents walking human speed. In other words, each
implementation scenario of the reconfigurable decoder of
Table 1 is applied to indoor or low range outdoor oper-
ating environment. Moreover, similarly to [8,9,10], for
the calculation of total maximum delay per frame for
SOVA and log-MAP we use the following equations
assuming a pipeline turbo decoder architecture and a
processor that runs at the same rate for both SOVA and
log-MAP:
3. Simulation Results
The suitable decoding algorithm for each scenario is
chosen according to performance and delay. Therefore,
for each scenario of Table 1 all four service classes are
applied to determine the quality of service profile pa-
rameters for different applications. Delay is calculated
for each algorithm using Equations (2) and (3), while the
simulated BER for each scenario is given in the follow-
ing subsections together with a brief analysis of the re-
sults. Particularly, Table 2 shows quality of service for
the different frame lengths of scenarios 1, 2, 3, while
Tables 3 and 4 present quality of service for scenarios 4,
5 and 6, 7, respectively. Finally, Table 5 presents quality
of service for scenario 8.
C. CHAIKALIS ET AL. 709
Figure 1. BER vs Eb/No for scenario 1.
Figure 2. BER vs Eb/No for scenario 2.
Figure 3. BER vs Eb/No for scenario 3.
3.1. Scenario 1
The simulated BER for this scenario is shown in Figure 1
assuming a symbol rate
s
of 86.4 Kbaud, normalised
fade rate 0.000085
ds
fT
with Doppler frequency
7.407
d
f
Hz. Two frame lengths are considered in
this scenario: 576 and 1152 bits, as Table 2 illustrates.
3.1.1. Conversational Service Class
At a SNR of 32 dB, the conversational class cannot be
considered for this scenario because even though the
BER criterion is satisfied, latency is too high for all
frame lengths for either SOVA or log-MAP.
3.1.2. Streaming Service Class
For this class only a frame length of 576 bits can be ap-
plied. In this case SOVA satisfies both requirements,
while log-MAP exceeds the maximum acceptable delay
limit. For a frame of 1152 bits delay for SOVA and
log-MAP is too high to achieve the limit for this class.
3.1.3. Interactive/Background Service Classes
For a frame length of 576 bits neither algorithm can be
used because of the low BER criterion, while both re-
quirements are achieved from both algorithms for a
frame length of 1152 bits. Thus, a 576 bit frame service
can not be applied, whereas in an 1152 bit frame service
either SOVA or log-MAP can be used.
3.2. Scenario 2
The simulated BER results for this scenario are shown in
Figure 2 assuming a symbol rate
s
R
42
of 172.8 Kbaud,
normalised fade rate 0.0000
ds
fT
and a SNR of 30
dB.
3.2.1. Conversational Service Class
According to Table 2, for this class the four different
frame lengths cannot be applied because of the tight de-
lay limit (80 msec).
3.2.2. Streaming Service Class
Similarly, as illustrated in Table 2, the four frame lengths
are not applicable. Particularly, for frame lengths of 576
and 1152 bits SOVA satisfies the delay criterion, but
does not satisfy BER criterion. On the other hand, the
use of log-MAP gives unacceptable delay. For frame
lengths of 1728 and 2304 bits although BER is satisfied
from both algorithms, maximum acceptable delay is ex-
ceeded.
3.2.3. Interactive/Background Service Classes
For these service classes it is well-known that BER must
be low and latency limits are not very strict. Thus, the
first three frame lengths cannot be applied due to not
acceptable BER. For a frame length of 2304 bits the two
criteria are achieved by both decoding algorithms: either
SOVA or log-MAP can be used.
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3.3. Scenario 3
Figure 3 presents the simulation results for this scenario
using the following parameters: Kbaud,
and a SNR of 30 dB.
192
s
R
0.000038
ds
fT
3.3.1. Conversational Service Class
According to the analysis of Table 2, the four frame
lengths give too high delay. Thus, their application is not
possible for SOVA or log-MAP.
3.3.2. Streaming Service Class
The analysis of Table 2 clearly shows that for all frame
lengths SOVA satisfies the delay limit of 250 msec at 30
dB. On the other hand the BER limit is not achieved for
the small frames of 336 and 672 bits. Thus, SOVA can
be used for frames of 1008 and 1344 bits. For log-MAP
and frames of 672, 1008, 1344 bits the delay limit cannot
be achieved. For a small frame of 336 bits the delay limit
is achieved, but the BER limit is not achieved.
3.3.3. Interactive/Background Service Classes
For these non-real time service classes and for all four
frames the achieved BER is lower than the acceptable
limit. Therefore, although the delay limit is achieved the
four frames can not be applied.
3.4. Scenario 4
Figure 4 presents the simulated BER for this scenario
using the following parameters: Kbaud,
with Hz and a SNR of 30
dB.
384
s
R
0.000019
ds
fT 185.1
d
f
3.4.1. Conversational Service Class
Again, for this class the four frames cannot be applied
Figure 4. BER vs Eb/No for scenario 4.
because of high delay. For a frame of 336 bits although
delay is acceptable for SOVA, BER criterion is not satis-
fied. According to Table 3 it is obvious that this service
scenario is not possible to be implemented.
3.4.2. Streaming Service Class
The analysis of Table 3 clearly shows that for all frame
lengths SOVA satisfies the delay limit of 250 msec at 30
dB. On the other hand the BER limit is not achieved for
the small frames of 336 and 672 bits. Thus, SOVA is the
proposed turbo decoding algorithm for frames of 1344
and 2688 bits. For log-MAP and frames of 1344, 2688
bits the delay limit cannot be achieved. For small frames
of 336 and 672 bits the delay limit is achieved, but the
BER limit is not achieved.
3.4.3. Interactive/Background Service Classes
According to Table 3, for these classes and for the first
three frames the achieved BER is lower than the accept-
able limit. Therefore, although the delay limit is achieved
these frames can not be applied. On the other hand, for a
frame of 2688 bits the two parameters (BER, delay) are
satisfied by both algorithms.
3.5. Scenario 5
For Figure 5 the following parameters are assumed:
432
s
R
Kbaud, 0.000017
ds
fT
and a SNR of 28
dB. Figure 5 shows BER performance for the five dif-
ferent frame lengths specified in Table 1 for this sce-
nario.
3.5.1. Conversational Service Class
For this class (Table 3) for all five frames the delay crite-
rion is too low to be achieved from both algorithms.
There is an exception for the small frames of 336 and
672 bits, where the delay criterion is achieved for SOVA
but BER criterion is not. It is obvious that the constraints
Figure 5. BER vs Eb/No for scenario 5.
Copyright © 2009 SciRes. IJCNS
C. CHAIKALIS ET AL. 711
of the two parameters cannot be achieved by both algo-
rithms.
3.5.2. Streaming Service Class
The analysis of Table 3 identifies three cases:
Small frames of 336 and 672 bits. Here, the de-
lay limit is achieved, but the BER limit is not for SOVA
and log-MAP. This means that these frames cannot be
implemented.
Medium frame of 1152 bits. Here, the delay
limit is achieved by both algorithms. Log-MAP is the
proposed choice because it can achieve the BER limit as
well. SOVA cannot achieve the BER limit. Thus,
log-MAP represents the proposed algorithm.
Large frames of 2688 and 3024 bits. Here,
SOVA is the algorithm that can be implemented. The rea-
son is the following: BER limit is achievable by both al-
gorithms, whereas delay limit is achieved only by SOVA.
3.5.3. Interactive/Background Service Classes
According to Table 3, the delay limit is achieved by both
SOVA and log-MAP for all frames. Furthermore, for the
first three frames the BER limit is not achieved, but for
2688 bits frame it is achieved only by log-MAP. In this
case log-MAP is proposed. For a frame of 3024 bits the
limits of the two parameters are achieved by both algo-
rithms.
3.6. Scenario 6
Figure 6 illustrates the simulated BER of the different
frame lengths for this scenario using the following pa-
rameters: Kbaud, and a
SNR of 28 dB.
1152
s
R0.0000064
ds
fT
3.6.1. Conversational Service Class
For this class (Table 4) for the first three frames although
delay criterion is satisfied, BER criterion is not satisfied.
Figure 6. BER vs Eb/No for scenario 6.
For the next four frames either BER, or delay limits are
not achieved for SOVA and log-MAP. Thus, this service
class is not possible to be implemented for all frames.
3.6.2. Streaming Service Class
The analysis of Table 4 identifies four cases:
Frames of 168, 336, 672 and 1344 bits. Here,
the delay limit is achieved, but the BER limit is not
achieved for SOVA and log-MAP. This means that these
frames cannot be implemented.
Frame of 2016 bits. Here, the delay limit is
achieved by both algorithms. Log-MAP is the proposed
choice because it can achieve the BER limit as well,
while SOVA cannot achieve the BER limit. Thus, log-
MAP represents the proposed algorithm.
Frames of 2688 and 3360 bits. Here, SOVA and
log-MAP achieve both limits. Therefore, both algorithms
can be used.
Frame of 4032 bits. Here, SOVA is the algo-
rithm that can be implemented. The reason is the follow-
ing: BER limit is achievable by both algorithms, whereas
log-MAP gives unacceptable delay.
3.6.3. Interactive/Background Service Classes
According to Table 4, the delay limit is achieved by both
SOVA and log-MAP for all frames. Furthermore, for the
first five frames the BER limit is not achieved. Thus,
they cannot be implemented. For frames of 2688, 3360
and 4032 bits the limits of the two parameters are
achieved by both algorithms.
3.7. Scenario 7
In Figure 7 BER performance for the different frame
lengths for this scenario can be seen using the following
parameters: 192
s
R
Kbaud, and a
SNR of 30 dB.
0.000038
ds
fT
Figure 7. BER vs Eb/No for scenario 7.
C
opyright © 2009 SciRes. IJCNS
C. CHAIKALIS ET AL.
712
Figure 8. BER vs Eb/No for scenario 8.
3.7.1. Conversational/Streaming Service Classes
The analysis of Table 4 clearly shows that the frame of
2560 bits gives unacceptable delay for both real time
classes and both decoding algorithms. Therefore, they
cannot be implemented.
3.7.2. Interactive/Background Service Classes
For non-real time classes both limits are achieved by
both algorithms, which mean that they are both suitable
for this application.
3.8. Scenario 8
Figure 8 presents BER performance of the different
frame lengths for this scenario using the following pa-
rameters: Kbaud, and a
SNR of 40 dB.
6000
s
R0.0000012
ds
fT
3.8.1. Conversational Service Class
For all five frames the calculated delay, according to
Table 5, is too high. Thus, this scenario cannot be im-
plemented for this service class.
3.8.2. Streaming Service Class
Here, delay criterion is achieved by both algorithms and
for all frames. Furthermore, for frames of 336 and 1344
bits the BER limit is not achievable by the two algo-
rithms. This means that these two frames cannot be im-
plemented. On the other hand, for frames of 2688, 4032
and 4704 bits the two criteria are satisfied by both algo-
rithms: they are equally suitable.
3.8.3. Interactive/Background Service Classes
From Table 5 it can be seen that the analysis is similar to
the previous section: the first two frames cannot be es-
tablished, whereas for the last three frames either SOVA
or log-MAP can be used.
4. Conclusions
In this paper we have presented possible reconfiguration
scenarios applied to an important receiver technique,
namely, channel decoding. It has been shown that recon-
figurability is a desirable feature towards the implemen-
tation of energy efficient receivers without performance
sacrifices.
For a UMTS turbo decoder SOVA and log-MAP cor-
respond to the main decoding algorithms. Considering
performance and complexity or delay, SOVA is the best
choice in terms of complexity, while log-MAP is the best
choice in terms of performance. The similarities in the
data-flow of the two algorithms support the idea of a
reconfigurable SOVA/log-MAP turbo decoder [8,9,10].
Moreover, according to [3] at low terminal speeds BER
is worse than at higher terminal speeds. For UMTS some
applications require the lowest possible delay, while for
others the lowest possible performance is sufficient.
Having in mind the results of [9] it is observed that at
rural and urban/suburban outdoor operating environ-
ments more frames can be established compared to in-
door/low range outdoor environment. Thus, for indoor/
low range outdoor environment there are many applica-
tions which cannot be established.
Our results for indoor/low range outdoor environment
show that for all implementation scenarios real time
conversational class cannot be established. The reason is
the low terminal speed which gives high BER. Compar-
ing with urban/suburban environment in [9], this class
can be applied to medium sized frames and high data
rates, whereas in rural outdoor operating environment
this class can be applied to small frames and low or me-
dium data rates.
For real time streaming class the proposed algorithm
choice depends on data rate. For low data rates all frames
cannot be applied, except for small frames where SOVA
is optimal. For medium data rates (64 kbps, 128 kbps)
small frames cannot be applied, while for medium-sized
frames SOVA is proposed. For 144 kbps again SOVA is
proposed for larger frames, while for medium-sized
frames log-MAP is optimal. For high data rates (384
kbps) small frames cannot be considered, for medium
frames log-MAP is proposed, while for large frames
SOVA is proposed. For the other frame lengths either
algorithm is proposed. For very high data rates (2 Mbps)
small frames cannot be established: for the other frames
either SOVA or log-MAP can be used. On the other
hand, in [9] for streaming class applications urban/subur-
ban and rural outdoor operating environments SOVA is
optimal for the scenarios that can be established. It is
remarkable that, similarly to [9], as data rate increases
more and larger frames can be applied.
For non-real time applications performance is the pri-
ority and delay requirements are looser. We observe that
Copyright © 2009 SciRes. IJCNS
C. CHAIKALIS ET AL.
Copyright © 2009 SciRes. IJCNS
713
for all scenarios small frames cannot be applied due to
tight BER. For larger frames both algorithms are equally
suitable. Furthermore, for medium data rates and me-
dium frames log-MAP is the proposed algorithm choice.
For urban/suburban outdoor environment the conclusions
are similar in [9], whereas for rural outdoor environment
log-MAP is optimum for the small frames and the two
algorithms are equally suitable for larger frames.
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