On the Effect of Radio Channel Propagation Models - Oulu

ON THE EFFECT OF RADIO CHANNEL PROPAGATION MODELS
TO THE AD HOC NETWORK PERFORMANCE
Jarmo Prokkola
VTT Technical Research Centre of Finland,
Oulu, Finland
ABSTRACT
In this paper we study the behavior ofad hoc network performance
under different propagation models. Ad hoc networks
are often studied with unrealistic cut propagation
models, where the propagating signal is completely cut to
some predefined distance. The worst drawback in this kind
modeling is that it fails to include the effect of multiple access
interference (MAI). Since cut propagation model is
taken as a point of comparison, free space propagation
model will provide the simplest enhancement to enable
realistic MAI calculation. In addition, we also use a forest
propagation loss model, which provides a more realistic
environment for military scenarios. We study the performance
in two cases. nodes equipped with our BCCA (Bi-
Code Channel Access) and BC-MAC (Bi-Code MAC), and
nodes with IEEE 802.11.
The study shows that there are significant differences in
the absolute performance between scenarios with different
propagation models. This clearly shows the importance of
lower layer modeling, although the focus would be on the
upper layer performance (e.g., on the application layer
Quality of Service (QoS)). The study also shows that BC-
MAC outperforms IEEE 802.11 regardless of the propagation
modeling. Despite the fact that the ranking of the performance
between BC-MAC and 802.11 was unchanged in
this study, the great differences in the absolute performance
questions the generality of the results obtained with
simplified lower layer models. This should be taken into
consideration especially in military related studies, where
the focus often is in the absolute performance of a certain
scenario and not only in the relative difference between
some methods.
INTRODUCTION
In this study, we focus on the effect of radio signal propagation
modeling to the performance of ad hoc networks.
Propagation modeling has been studied a lot, but modeling
in network level simulations, and especially in the case of
ad hoc networks, has been imperfect. Many of the studies
so far use unrealistic cut radio channel model, which fails
to model multiple access interference. Yet, some studies
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Timo Braysy and Teemu Vanninen
Centre for Wireless Communications (CWC),
University of Oulu, Oulu, Finland
for considering the importance of realistic physical layer
in design of ad hoc network routing protocols has been
addressed (e.g., [1]). In [2], for example, a shadowing
model is used in ad hoc network simulations. The fluctuations
in signals strength causes route breakages,
which in turn lead to decreased packet delivery ratio,
increased control traffic and increased packet delay that
cause user perceived QoS to be decreased.
Only few papers address the effect of physical layer parameters
to the performance of routing and MAC layer
functions in wireless ad hoc networks. In [3], a simulation
modeling study has been performed to demonstrate
the possibly significant effect of the different physical
layer properties such as physical layer preamble length,
interference, fading and path loss. The study also addresses
the importance of MAI calculation and discusses
about the ways how different simulation tools model the
radio channel properties. In [4] and [5] a sophisticated
indoor wireless channel model based on ray-tracing and
Markov chains is presented and developed for NS-2
simulation tool.
The novelty in our paper is the extensive simulation
study of propagation modeling impacts to the higher
layer performance. We have set the parameters (e.g.,
transmission power) in a way that the results of different
scenarios are easily comparable to each other. The models
used here are: free space propagation loss (FSL), forest
propagation loss (FPL), and two cut propagation (CP)
models, CP-LP (low power) and CP-HP (high power).
CP-HP is a good point of comparison, since it is quite
similar to the simplified radio channel models used in
several studies. We use mainly our BCCA method and
BC-MAC, but also IEEE 802.11 is used as a reference,
since one of the goals is to compare BC-MAC and
802.1 1 under different propagation models.
SYSTEM DESCRIPTION
A. BCCA and BC-MAC
Bi-Code Channel Access (BCCA) is a dual channel access
method designed especially for the needs of ad hoc
networking. The basic idea is that the ad hoc nodes are

able to track two separate channels simultaneously. In our
spread spectrum approach, nodes are able to track transmissions
with two different spreading codes. One is a
common code channel (C-code), which is used for route
maintenance and broadcast communications. The other is a
receiver based code channel (R-code), which is unique for
each node, unlike the C-code. R-code is used for all directed
transmission (e.g., data) per hop basis. [6]
Since BCCA is a channel access method to enable distributed
CDMA-like (Code Division Multiple Access) communications
for ad hoc networks, a medium access control
(MAC) method is also needed. BC-MAC is designed to be
used with BCCA. It is a straightforward CSMA-based
(Carrier Sense Multiple Access) MAC solution with fast
ARQ (Automatic Repeat Request) implementation. BC-
MAC with BCCA has been shown to have very high performance
in ad hoc environment. [7]
With the use of BCCA, it is required that the R-type
spreading codes of the nodes are known, For the cases
where the codes are unknown, an efficient network layer
spreading code distribution method is being developed.
However, we will use the assumption of known codes in
this paper, since it is a fair assumption in military scenarios.
Hereafter, when we refer to BC-MAC, it is assumed
that BCCA functionality is also implemented to it.
B. IEEE 802.11
The basic IEEE 802.11 (later referred to as 802.11) is considered
as a point of comparison. The modeling of 802.11
is based on the basic OPNET model of 802.11 with some
modifications to enable the functionality with the used
routing protocol (as in our previous studies [6], [7]). Some
modifications were also needed to enable the possibility to
use different propagation models, but no functional
changes to the MAC have been made.
The DSSS (Direct Sequence Spread Spectrum) mode of
802.11 is used, since DS spreading is also used in BC-
MAC. Virtual carrier sensing is not used, because it produces
additional overhead [8], which is not desired (multiple
handshaking transmissions, latency, capacity decrement
under heavy traffic). Carrier sensing is done by
power level measurement with a threshold in both 802.11
and BC-MAC. This threshold is set according to the power
level, where the reception is just more likely to succeed
than fail in theory.
C. The Physical Layer
At the physical layer, channel data rate is set to 1 Mbit/s.
To allow potentiality for interference suppression and to
enhance LPD (Low Probability of Detection) and LPI
(Low Probability of Interception) properties, sufficient
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spreading factor is needed. Spreading code length of Nc
= 63 chip is used. With bit-wise spreading, processing
gain is directly 63 (- 18.0 dB). To make fair comparisons
between BC-MAC and 802.11, the same spreading
factor is also set to 802.11 instead of the standard value
of 11. The frame formats are set to be equal according to
- 802.11. The systems operate on 2.4 GHz ISM (Industrial,
Scientific and Medical) frequency band.
B. The Network Layer
The network layer functionality is kept simple enough so
that it does not provide any extra effects to the results.
The functionality is mainly under control of Ad hoc Ondemand
Distance Vector (AODV) routing protocol with
some basic characteristics of IP (Internet Protocol). But
for example address resolution protocol (ARP) is not
used, instead, it is assumed for simplicity, that the MAC
and network layer share the same address base.
AODV is a reactive pure on-demand type routing protocol,
where routes are formed and updated only when
needed [9]. The used OPNET model of AODV is the
same as in our previous work (e.g., [6], where it is also
described how to enable BCCA functionality with
AODV). AODV hello-messages and passive listening at
MAC layer are not used, because in military scenario,
we want to avoid extra control traffic
C. Transport & Application Layer
In order to keep the focus on lower layer functionalities,
it is not desired that the upper layers will add complexity
and more parameters to the currently already quite large
set of parameters. Thus, transport layer functionalities
are minimized and the layer above network layer is basically
the application layer. In a sense, the transport layer
can be assumed to exist as UDP-lite (Lightweight User
Datagram Protocol, [10]), if one assumes that the UDP
fields exist in the data packet.
Application layer is modeled statistically with traffic
models. VBR-M [6] model is used as a traffic model,
and the data packet length has a constant value of 4096
bits. Because the focus is on lower layer functionalities,
heavy tailed modern data traffic models are not used,
since they also introduce their own effects to the network
performance (e.g., [11]). As a session model, dynamic
connections are used [7].
E. Simulation Scenarios and performance metrics
This study is based on OPNET simulations. A basic ad
hoc scenario is chosen, where the operational area has a
size of 1500 m x 300 m and the nodes have effective
radio range of about 250 m. The area contains 50 nodes,
of which 20 run applications (i.e., generate traffic), but

all the nodes can receive data and route traffic. The simulated
results are each averaged over 32 iterations, which
each have 93750 simulated packets in average. The results
are presented as a function of offered data traffic load,
which is normalized to the channel bit rate. Offered data
traffic load is controlled by modifying mean interarrival
time of data packets.
Unfortunately we do not have enough space in this paper
to consider both mobile and static nodes. We focus only on
static nodes (which could form e.g., a military sensor network).
In this way we can get more information about the
effect of lower layer modeling, since mobility would also
bring its own features to the results. Static nodes here imply
that the positions of the nodes are fixed to random locations
in the beginning of each simulation iteration.
Hence, the effect of node locations will be averaged over
the simulated iterations. Certainly, it would be also interesting
to see the effect of propagation modeling in the case
of mobile nodes. It is, however, presumable that relative
effect of propagation modeling would be similar to the
case of mobile nodes as it is for static nodes. AODV active
route timeout is now set to infinity, since the topology does
not change.
ON THE RADIO CHANNEL MODELING
A. Radio Channel and Propagation Modeling
In ad hoc network studies, a cut propagation model is often
used (e.g., [12], [13]). In this, the signal propagates according
to some law to a certain distance, where the signal is
simply cut (also referred as unit disk graph (UDG) model
[1]). This distance is, therefore, the radio range of the
node. In the even more simplified cut model, no actual
power attenuation is calculated, but the signal just arrives
to the receiver inside certain radius around the node.
Free space loss can be considered as a basic type of a real
attenuation. It originates from the fact that the signal energy
spreads over space in three dimensions as the propagation
distance increases. The forest propagation loss
(FPL) model used in this study is a combination of three
commonly known models: free space, plane earth and
Weissberger foliage attenuation models. The method is the
same as used in the OPNET Path Attenuation Routine
(OPAR) by Mitre Corporation [14]. This model gives attenuation
in leaf-tree forest, presumably fitting well to forest-like
battlefields. At first, in the FPL model, both free
space and plane earth attenuations are calculated as
LPE = 40 logl0(D) -20 logl0 (hJX -20 logl0(hrx), (1)
Lfs = 32.45 + 201oglo (D) + 20 1log, (f), (2)
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where LPE= attenuation by plane-earth model (in dB),
D = total path length (in meters), h,t, hrx = transmit and
receiver antenna heights (in meters), respectively, Lf, =
attenuation by free space model (in dB), f = frequency
(in GHz). Using Weissberger model for foliage obstruction
block, the actual foliage attenuation (in dB) is
1l.33f0284 xD 0588, D1 >14m
weiss O.45f 0284 XDjf0, O

transmission power for desired range when the path-loss
model and all the signal losses are known (in reality this
would be just estimation). When the receiver bandwidth
(B) is 63 MHz and the receiver effective noise temperature
(T) is 290 K, the noise power can be calculated as
PN = kTB 1.379 .23290K 63 106Hz
2.519e-3W
where k is the Boltzmann's constant. Thus, the received
power requirement is
PRX = YbPN 2.30 -2.519 -10 W -92 dBm (6)
Taking into account the 18 dB processing gain, the PRX
drops to about -110 dBm.
With FPL we get path loss for 250 m to be 132.12 dB
when h,t and hrx are assumed to be 2 m andfis 2.4315 GHz
in the ISM band. Since antennas are omnidirectional, antenna
gains are 0 dB, the transmitted power should
be PTX = PX /LWfe =0. 15 W . In the case of FSL, the path loss
for 250 m is 88.13 dB and the corresponding PTX should be
6 [tW. As a point of comparison, we take two cut propagation
models, where the signal propagates according to FSL
until it is cut. In the high power CP-HP model, 100 mW
transmission power is used, and in the low power CP-LP
model, the same power is used as in the FSL case (6 [tW).
C. Interference Modeling
In the simple models, MAI is not actually calculated. The
usual solution is that if two or more signals are seen at the
same receiver, the strongest one (possibly above some
threshold) is selected to be received and the others are considered
lost. This kind of modeling is used at least in the
older versions of ns-2 [16], [3]. In the even simpler model,
no power levels are calculated, but it is just decided (e.g.,
based on the arrival time) which signal is received and
which are lost, or in the pessimistic assumption, all signals
are always considered lost in the case of a collision.
OPNET has an efficient inbuilt link budget calculation system.
It calculates the received signal power level according
to the used propagation model (FSL as default) taking into
consideration all relevant parameters (antenna gains,
transmit power, processing gain, etc.). In addition to background
noise, the interference caused by other transmissions
is also added to the SINR (signal to noise and interference
ratio) calculation. The calculated interference from
other transmissions is added directly to the total noise
power. In spread spectrum modeling, the signal spread
with the correct code is recovered with processing gain,
which is equal to the spreading factor (spreading code rate
/ data rate). Hence, the calculation is done on the power
level basis and no actual correlations with the interfering
(5)
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signals are calculated. This, of course, can bring some
inaccuracy, but assuming a spreading code family with
well known cross correlation properties (like gold sequences
[17]), this simplification is fair enough. The accurate
signal-level calculation is a heavy process and it
would be (currently) almost impossible in bigger network
simulations. Instead, OPNET provides a possibility
to use own SNR-BER behavior curves, which can be
used to model accurate physical level behavior.
As a consequence, OPNET models MAI accurately
enough for our purposes. It can be anticipated that cut
propagation modeling will give too optimistic view of
the performance since it does not consider MAI outside
the defined radio range. Some interesting approaches for
MAI modeling in network level simulations have been
also presented, where nodes have longer interference
range in addition to radio range for successful data
transmission [18]. Although this is a step towards more
accurate modeling, it does not yet give proper idea about
the effect of MAI, since in reality, the signals propagate
to infinity (in the absence of physical barriers). The reason
behind the wide usage of cut propagation modeling
is its simplicity. Accurate MAI modeling is computationally
costly, since the link budget calculations must be
made to every node in a scenario per single transmission.
0
-20
Propagation distance [m]
10 20 300 40 500 600 700 800 900 1000
- 40m
-FPL
2 60
> ~~~~~~~~~~~FSL
ti 80ndetection level
-D _100-
-.,120-
140
160
-180
-200 _LL LL
Figure 1. Received power level as a function of propagation
distance
In FPL model the signal attenuates heavily, which sets
challenges to the communications as compared to free
space propagation. However, in terms of MAI, the situation
is quite interesting. Because of the heavy propagation
loss, the long range MAI is, in fact, clearly weaker
than it is with free space loss. This can be easily seen
from Figure 1, where the received power level is represented
as a function of propagation distance. In the FPL
model, the curve's starting point is naturally higher,
since the transmission power needs to be higher than in
the FSL model. Signal detection level (about -110 dBm)
is also shown in the figure, which of course, intersects
with the other curves at the effective radio range of

250 m. After this distance, the MAI under FSL model is
dominating, since the attenuation under foliage obstruction
is much higher. Take for example the interference level at
the distance of 500 m from the transmitter. Under FPL, the
interference level is about -140 dBm, while under FSL it is
over 20 dB higher (about -1 16 dBm).
SIMULATION RESULTS
First, we present the results of BC-MAC under different
propagation models in detail. Then we turn our attention to
802.11, where we also have some comparison with BC-
MAC. Unfortunately we do not have enough space to analyze
802.11 with the same accuracy as BC-MAC.
A. BC-MAC
Figure 2 presents average number of collisions per received
packet at MAC. Calculated collisions reflect directly
to MAI, because the used metric calculates also simultaneous
transmissions with different spreading codes.
No major differences exist between the two real propagation
models (FSL & FPL) and between the two cut propagation
models. However, under real propagation models,
the number of collisions is constantly higher (- 8 times)
than with the cut propagation models, as anticipated.
Q-
.:
en
_n
a)
10
0,1
O,C
001 OC0,t
0 _
01 .., 0 ' 1
t0W000 0_0::0 0a0000t :100
..i
- BC- AC (FSL)
- -D- BC-MAC (CP-LP)
-BC-MAC (FPL)
BC-MAC (CP-HP)
0,01
Offered data traffic
Figure 2. Average number of collisions per received packet
(BC-MAC) with different propagation models.
A notable discovery is that this has, unlike it is often assumed,
a direct impact to the network layer performance.
Consider e.g., Figure 3, where average number of route
breakages is shown. The basic trend, with all models, is
that the number of route breakages is almost constant at
low and moderate traffic load, but at some point, with increasing
traffic load, the number of route breakages almost
explodes. At this point the network gets congested and finally
collapses with further increase of traffic load.
However, great differences exist in the absolute performance.
As seen, the number of broken routes is constantly
several times higher under real propagation models. The
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performance under CP-HP model is above all others: no
route breakages happen before network congestion (offered
load of 0.6)! The functional difference to the low
power cut propagation model is in the stability of long
links. In CP-LP, the signal is barely receivable near the
limit 250 m, and just a slight extra interference will
cause a long link to fail. In CP-HP, the situation is totally
different, since the signal is still very strong in edge of
the range. Also, the background noise has practically no
effect to the performance, and therefore, processing gain
will mostly be able to handle collisions. In CP-HP, the
retransmissions will always be successful within the retransmission
limits (not shown), and hence, no route
breakages occur. This interesting behavior highlights the
failure of CP modeling, and also, the effect of transmission
power in the cut models, where it is often assumed
meaningless.
cn
4000
3500
3000
a) 2000
z
1500
1000
500 0
0,0001
--BC-MAC (FSL)
- -X- BC-MAC (CP-LP)
BC-MAC (FPL)
BC-MAC (CP-HP)
. - -. . - i-z - - --
0,001 0,01 0,1
Offered data traffic
Figure 3. Number of route breakages (BC-MAC).
Offered data traffic
1 ,OE+00
o,a
1 ,OE-01
0
*p 1 ,OE-02
Cu
-~ec'
1 ,0E-03
1 ,OE-04
1 ,OE-05
l01 01 001 .0
Figure 4. Average packet loss ratio (BC-MAC).
BC-MAC (FSL)
10
1 0
-*- BC-MAC (CP-LP)
BC-MAC (FPL)
BC-MAC (CP-HP)
The great differences in route breakages will definitely
have effect also to the higher layer performance. Figure
4 shows this in the form of packet loss ratio (application
layer). The failure of CP models is, again, seen at its
worst in the CP-HP case, as there is practically no packet
loss at low traffic load. It is interesting to note the behavior
of the network under FPL model. At the low traffic
load, packet loss is even lower than under the CP-LP
model, but it rises with increasing traffic, and finally fol-

lows or exceeds the FSL curve. This kind of behavior can
be also extracted from route breakage figure (Figure 3).
This phenomenon can be explained quite easily, if we recall
the power attenuation behavior. The attenuation under
FPL model is so strong that it effectively behaves nearly
like a CP model, when the network traffic load is very low,
since the interference power far away from the transmitter
is almost negligible. However, as the network traffic is
increased, the total interference power in the network is
also increased, and as a consequence, it makes the CP approximation
to fail with increasing traffic.
Important metric from the application QoS perspective is
the average end-to-end delay, which is presented in Figure
5. Typical network behavior is seen: the delay stays constant
at low traffic loads, but increases heavily when the
network becomes near to the limits of it's capability to
handle traffic. Pushing even more traffic causes buffers to
fill up, which finally leads to buffer overflows (the delay
rise settles). We note that the used propagation model does
not seem to have great impact to average delay, at least not
at the low traffic load. At heavy traffic, there are differences
between the points, where the network starts to become
unstable. CP-HP gives the lowest delay and FPL follows
it at low traffic load. But, as in the case of packet loss
(and route breakages), the delay of FPL starts to approach
FSL as the traffic load is increased.
>c O
>. 0,0
10 I.. ..,
0,01
Offered data traffic
Figure 5. Average data packet delay (BC-MAC).
B. IEEE 802.11 and Comparison to BC-MAC
At the very high traffic loads, the performance differences
between different cases can be clearly seen in throughput
(Figure 6). In this figure, the FSL and CP-HP curves of the
802.11 and BC-MAC are shown. An interesting observation
can be made when considering 802.11 curves. On the
contrary to BC-MAC results, 802.11 FSL gives better performance
than 802.11 CP-HP! This is quite unexpected,
but can be explained: The very nature of this result originates
most likely form the well-known hidden-terminal
problem. CSMA is based on power detection and a single
transmission can be just detected at a distance of 250 m.
10
6 of 7
However, when there are several transmissions simultaneously
at the different portions of the network, the cumulative
power of these transmissions can cause signals
to be detected from farther than 250 m. Therefore, this
basically alleviates hidden-terminal problem as compared
to the CP-HP case, in which the CSMA range is
always 250 m, and shows a better throughput is a result.
The same phenomenon also exists with BC-MAC, but
the effect is only minor, since node-specific spreading
codes and processing gain handle the situation.
0.7
0.6
0.5
D
Ideal random channel access
--802.11 (FSL)
--802.11 (CP-HP)
0.4 -R
_sc'
=3 . BC-MAC (FSL)
s
0.3
BC-MAC (CP-HP)
0.2
0.1
O 1-
0.001 0.01 0.1 1
Offered data traffic
Figure 6. Average throughput (BC-MAC vs. 802.11).
Offered data traffic
1 .vrL nF+nn uu T
0o0 )Q1
,..
0001 00
. _..
1 -1
1 .OE-01 t
, 1.0E-02
-~eou
1.0E-03
1 .OE-04
...- -.,.-802.11(FSL)
-- - 80211(CPHP)
. 1BCMAC 8021 (FSL)
BC-MAC (CP-HP)
1.OE-05
Figure 7. Average packet loss ratio (BC-MAC vs.
802.11).
This interesting phenomenon affects only at the high
traffic load as seen from the packet loss behavior (Figure
7). 802.11 CP-HP clearly dominates the performance
over the 802.11 FSL case at low traffic load. But, as the
traffic load is increased, there will be more and more
simultaneous transmissions at the different parts of the
network, and hence, the phenomenon is enhanced. From
about the traffic load of 0.08 forward, the performance
under FSL is better than under CP-HP. As a consequence,
commonly used CP-HP model clearly fails,
since it gives too optimistic results at the low traffic load
and too pessimistic at the high traffic load.
10

Another important observation is that BC-MAC outperforms
802.11 regardless of the propagation model. In the
maximum throughput, there is about two to three-fold difference
in favor to BC-MAC. Of course, if one makes a
comparison, e.g., BC-MAC under FSL and 802.11 under
CP-HP, the comparison could imply that 802.11 performs
better at the low traffic load (Figure 7). As obvious, such a
comparison is not valid and the comparison between methods
should be always made in a way that all the other variables
are kept unchanged (the propagation model in this
case).
SUMMARY & CONCLUSIONS
In this paper we studied the effect of propagation modeling
to the ad hoc network performance (BC-MAC and 802.11).
As real propagation models, we had free space loss and
forest propagation loss models, and as a point of comparison,
we had high and low power cut propagation models.
CP-HP is idealistic in a sense that due to high power, only
a strong collision can cause a loss at the physical layer.
CP-LP is much more vulnerable, since near to the radio
range, the signal is barely receivable. Already a small interference
power can cause a long link to fail in all but CP-
HP model. However, both CP models fail to model MAI,
and thus, give typically far too optimistic view of the performance.
CP-HP, which is in a way very close to the
models that are often used in ad hoc network studies, gives
totally incorrect results. For example, the packet loss ratio
(with BC-MAC) under real propagation models is of the
order of 10-3, but under CP-HP it is practically zero at low
traffic load. An interesting phenomenon was also discovered
with 802.11: CP-HP at low traffic load gave too optimistic
results, but at high load the effect was reversed.
Also FSL and FPL give quite different view of the performance,
moreover, these differences are not fixed, but
rather the behavior is heavily dependent on network conditions
(e.g., network traffic load). Hence, this study clearly
addresses that careful attention should be put also to lower
layer and especially in radio channel modeling even if the
focus would be in the higher layer performance (e.g., application
layer QoS). This is definitely the case with the
absolute performance, but still it can be asked: can the
relative performances between different higher layer methods
be compared using simplified lower layer models? The
answer is probably dependent on the method under interest.
This study, nevertheless, confirmed that our BC-MAC
outperforms 802.11 regardless of the propagation model.
ACKNOWLEDGEMENTS
This work was supported by ITEA Easy Wireless project
(J. Prokkola) and by the Finnish Defence Forces through
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the Finnish Software Radio Programme
(T. Braysy, T. Vanninen)
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