On
the
Influence
of
User
Behaviour
and
Admission
Control
on
System
Performance
in
HS-DSCH
Mats
Folke
and
Ulf
Bodin
Division
of
Systems
and
Interaction
Department
of
Computer
Science
and
Electrical
Engineering
Lule'a
University
of
Technology
Email:
{mats.folke,
ulf.bodin}@ltu.se
Abstract-
In
this
paper
we
investigate
the
need
for
admission
control
for
the
high-speed
downlink
shared
channel
(HS-DSCH)
through
the
evaluation
of
two
admission
control
mechanisms.
One
mechanism
uses
the
number
of
active
users
in
a
cell
as
a
metric
and
the
other
one
uses
the
mean
downlink
throughput
of
a
user.
We
also
introduce
a
model
for
user
behaviour
in
which
the
goodput
of
a
completed
file
download
decides
if
further
downloads
are
made.
In
order
to
measure
user-experienced
quality
we
use
a
utility
function
for
transforming
per-flow
goodput
into
a
user
satisfaction
index.
System
performance,
measured
by
total
user
satisfaction
and
total
goodput,
is
evaluated
for
a
range
of
session
arrival
rates
and
admission
control
limits.
This
evaluation
is
done
using
the
ns-2
simulator,
together
with
extensions
of
our
own.
If
the
objective
is
to
maximise
goodput,
our
results
show
that
no
admission
control
is
needed.
Maximising
user
satisfaction
benefits
from
an
admission
control.
We
also
note
that
the
impact
of
user
behaviour
is
not
insignificant.
I.
INTRODUCTION
The
High-Speed
Downlink
Shared
Channel
(HS-DSCH)
in
Wideband
CDMA
(WCDMA)
release
6 has
theoretical
peak
bitrates
for
data
services
of
14
Mbps
[1],
[2].
Moreover,
delays
considerably
shorter
than
for
other
shared
data
channel
technologies
in
previous
releases
of
WCDMA
are
possible.
HS-DSCH
is
primarily
shared
in
the
time
domain,
where
users
are
assigned
time
slots
according
to
a
scheduling
algorithm
that
runs
independently
at
each
Node
B.
The
short
Transfer
Time
Interval
(TTI)
of
2
ms
enables
fast
link
adaptation,
fast
scheduling
and
fast
Hybrid
Automatic
Repeat
reQuest
(HARQ).
The
channel
is
designed
for
bursty
Internet
traffic,
such
as
web
traffic.
Four
traffic
classes
are
defined
for
HSDPA.
Conversational
is
for
streaming
audio
(i.e.
VoIP),
which
requires
low
delays
and
strict
requirements
for
minimum
bandwidth.
Streaming
is
for
streaming
video,
which
also
requires
low
delays
but
higher
and
slightly
more
varying
bandwidth
demands
than
the
conversational
class.
The
interactive
class
is
for
interactive
traffic
(i.e.
web
surfing).
The
demand
for
bandwidth
is
elastic,
and
there
are
no
tight
bounds
in
delay.
The
final
class
is
background.
This
is
for
traffic
with
low
demands
on
delay,
such
as
e-mails.
In
this
paper
we
investigate
the
need
for
admission
control
in
the
interactive
traffic
class
to
improve
and
optimise
total
goodput
and
user
satisfaction.
These
metrics
represent
the
total
value
provided
by
the
system,
but
do
not
capture
the
quality
perceived
by
individual
users.
The
variance
in
quality
experienced
by
users
can
however
be
kept
sufficiently
low
through
a
properly
weighted
proportional
fair
scheduler.
We
therefore
exclude
the
aspect
of
per-user
quality
in
this
study.
TCP
reduces
the
send
rate
upon
congestion
and
users
not
experiencing
high
enough
bit-rates
are
likely
to
become
dissat-
isfied
and
quit
ongoing
sessions
prematurely.
Users
finishing
sessions
prematurely
decrease
the
system
load
in
a
similar
manner
as
a
pre-emption
mechanism,
which
would
drop
users
with
low
experienced
quality.
We
evaluate
whether
or
not
these
existing
load
control
features
provided
by
TCP
and
the
users
themselves
are
sufficient
to
optimise
the
system
utilisation'.
For
this
evaluation
we
examine
the
performance
of
two
admission
control
mechanisms
in
combination
with
a
simple
model
of
user
behaviour.
The
metrics
used
to
judge
this
performance
are
average
total
goodput
and
user
satisfaction.
In
addition
to
optimise
system
utilisation
admission
control
can
be
used
to
ensure
the
stability
of
the
system.
An
admis-
sion
control
mechanism
ensuring
stability
typically
limits
the
maximum
number
of
users
in
a
cell.
We
use
this
approach
for
the
first
mechanism
tested.
The
second
admission
control
mechanism
keeps
a
running
average
for
the
mean
throughput
in
a
cell
and
denies
new
users
when
this
metric
drops
below
a
given
limit.
For
the
evaluation
we
use
an
application
model
based
on
user
experienced
quality.
Depending
on
the
goodput
of
a
completed
file
transfer
a
user
may
choose
to
download
additional
files.
In
order
to
estimate
user
quality
we
employ
a
utility
function.
Utility
functions
have
been
widely
used
to
model
user
behaviour
in
both
wired
and
wireless
networks
(e.g.,
in
analysing
reservations
vs.
best-effort
[3]
and
for
studies
of
fairness
in
wireless
networks
[4],
[5]).
For
our
evaluation
we
study
the
two
admission
control
mechanisms
for
various
loads
from
a
system
perspective
as
well
as
from
a
user
perspective.
In
[6]
Hosein
presents
an
algorithm
that
provides
guaranteed
levels
of
throughput.
Multiple
such
levels
are
maintained
through
a
combined
scheduler
and
admission
control
mecha-
nism.
While
the
guaranteed
levels
of
throughput
may
meet
the
needs
of
the
conversational
and
streaming
traffic
classes,
they
are
not
sufficient
for
the
interactive
class.
This
is
because
users
'We
assume
all
interactive
traffic
to
use
TCP.
0-7803-9392-9/06/$20.00
(c)
2006
IEEE
378
of
this
class
typically
become
increasingly
satisfied
with
the
increasing
goodput
instead
of
immediately
being
satisfied
only
when
an
expected
goodput
is
obtained.
Hence,
maintaining
high
system
throughput
and
user
satisfaction
are
important
for
the
interactive
class.
This
paper
consists
of four
sections.
Section
I
contains
the
introduction.
It
is
followed
by
Section
II
which
describes
the
method
and
metrics
we
have
used.
Section
III
contains
our
results
and
the
paper
is
concluded
in
Section
IV.
II.
METHOD
In
this
section
our
simulation
environment
is
presented.
This
includes
the
radio
model,
the
user
mobility
model
and
the
load
model.
After
the
simulation
environment
is
described
we
present
the
metrics
used
for
the
evaluation.
A.
Simulation
Environment
1)
Radio
model:
We
have
implemented
a
model
of
HS-
DSCH
in
the
Network
Simulator
version
2.28
(ns-2)
[7].
The
radio
model
includes
lognormal
shadow
fading
with
a
standard
deviation
of
8
dB
and
exponential
path
loss
with
a
propagation
constant
of
3.5.
Self
interference
is
assumed
to
be
10
percent
and
the
interference
from
simultaneous
transmissions
within
a
user's
own
cell
is
approximated
to
40
percent.
The
interference
from
transmissions
in
other
cells
than
a
user's
own
cell
is
dampened
through
distance.
Wrap-around
for
interference
is
supported.
All
cells
have
omnidirectional
antennas
and
a
radius
of
500
m.
Code
multiplexing
for
up
to
three
users
in
the
same
time
slot
for
a
given
cell
is
supported.
The
available
coding
and
modulation
combinations
are
accounted
for
in
Table
I.
Block
errors
are
uniformly
distributed.
When
SIR
is
less
than
-3.5
dB
approximately
every
second
block
is
received
in
error,
for
better
SIR
conditions
the
block
error
rate
is
10
percent.
The
choice
of
scheduling
algorithm
is
important.
We
have
implemented
both
a
Round-Robin
(RR)
scheduler
and
a
SIR
scheduler.
In
this
investigation
we
only
use
the
RR
scheduler.
It
distributes
the
transmission
slots
fairly
among
users.
No
fast
HARQ
is
implemented;
instead,
damaged
radio-blocks
are
im-
mediately
retransmitted.
Multi-path
fading
is
not
implemented.
Both
of
these
mechanisms
would
give
variations
in
RTT
if
modelled.
We
expect
however
these
variations
to
be
too
small
to
have
an
impact
on
the
system-level
properties.
TABLE
I
LINK
ADAPTATION
PARAMETERS.
SIZE
REFERS
TO
THE
RADIO-BLOCK
SIZES.
Coding
(rate)
0.25
0.50
0.38
0.63
Modulation
(type)
QPSK
QPSK
16QAM
16QAM
SIR
(dB)
-3.5
0.0
3.5
7.5
Bitrate
(Mbps)
1.44
2.88
4.32
7.20
Size
(bytes)
360
720
1080
1800
2)
User
mobility:
When
starting
a
simulation
the
UEs
are
randomly
distributed
according
to
a
uniform
distribution
for
the
x-axis
and
the
y-axis
on
a
cell
plan
consisting
of
seven
cells
each
having
a
radius
of
500
m.
Traffic
sources
are
at
equal
distance
(50
ms)
from
the
Node
Bs
and
the
UEs
are
associated
with
the
closest
Node
B.
The
links
connecting
the
Node
Bs
with
the
traffic
sources
are
overprovisioned,
hence
packets
will
only
be
dropped
due
to
congestion
at
the
IP-buffers.
During
each
session
the
UE
moves
with
a
speed
drawn
from
a
low-
speed
mobility
model
[8].
All
directions
are
equally
likely
to
be
taken
when
beginning
a
new
session.
Wrap-around
for
the
moving
users
is
supported.
This
means
that
a
UE
moving
off
the
cell
plan
re-appears
at
the
other
end.
3)
Application
and
load
model:
A
session
generator
uses
a
Poisson
arrival
process
to
initiate
new
sessions.
When
a
session
begins,
the
UE
starts
downloading
a
file.
Upon
completion
the
goodput
of
the
transfer
is
calculated.
If
the
goodput
is
above
a
certain
threshold,
the
user
is
satisfied
and
will
begin
a
new
download.
However,
if
the
goodput
is
less
than
the
predetermined
threshold,
the
user
is
dissatisfied
and
will
not
download
any
more
files.
At
most
four
file
transfers
will
be
performed
in
a
session.
The
file
sizes
are
drawn
from
a
Pareto
distribution
with
a
mean
of
30458
bytes
and
the
shape
parameter
set
to
1.7584
[9].
At
the
endpoints
we
use
TCP
Sack
[10],
as
implemented
using
the
TCPSackl
agent
in
ns-2.
This
includes
support
for
limited
transmit
[11]
and
a
variant
of
SACK
loss
recovery,
as
specified
in
[12].
Upon
completion
of
a
transfer,
the
endpoints
are
reset,
thus
no
teardown
is
performed,
but
connection
establishment
takes
place
for
every
flow.
When
a
session
is
started
a
UE
is
said
to
be
active
and
when
the
session
is
ended,
the
UE
is
inactive.
The
load
of
the
system
is
varied
by
setting
the
arrival
rate
of
new
users
initiating
sessions.
This
value
ranges
from
10
to
40
new
sessions
per
second.
Our
system
also
employs
two
algorithms
for
admission
control.
The
first
is
based
on
the
maximum
number
of
simultaneously
active
users
per
cell,
which
ranges
from
10
to
40.
The
other
algorithm
is
based
on
mean
user
throughput.
The
throughput
of
a
cell
is
calculated
using
a
moving
average
filter.
When
a
new
user
arrives
at
the
cell
the
mean
user
throughput
is
calculated
by
dividing
the
total
throughput
with
the
number
of
active
users.
If
this
mean
user
throughput
is
greater
than
a
predefined
threshold,
the
new
user
is
admitted.
The
threshold
ranges
from
100
to
400
kbit/s.
We
also
use
a
moving
average
filter
to
keep
track
of
the
utilisation2
of
a
cell.
Regardless
of
admission
control
algorithm,
a
user
is
admitted
to
the
system
if
the
utilisation
is
less
than
98%.
A
data
transfer
not
admitted
gets
a
goodput
of
0
kbit/s
and
thus
a
satisfaction
index
equal
to
0.
Apart
from
varying
the
load,
we
also
experiment
with
the
user
behaviour.
We
model
this
as
a
threshold
mechanism
in
goodput.
We
have
selected
two
values,
0
kbit/s
and 100
kbit/s.
0
kbit/s
means
that
the
user
will
perform
four
file
transfers
in
2We
define
the
utilisation
over
a
period
of
time
as
the
ratio
of
used
timeslots
over
the
total
number
of
timeslots.
379
each
session,
no
matter
what
the
goodput
is.
The
other
value,
100
kbit/s,
means
that
the
user
will
abort
its
session
if
the
goodput
of
a
file
transfer
is
below
that
value.
A
satisfied
user
will
however
not
perform
more
than
four
file
transfers
before
ending
its
session.
Each
scenario
is
run
five
times
with
different
initial
values
for
the
positions
and
velocities
of
the
users
as
well
as
the
start-
ing
times
of
the
session
and
the
sizes
of
the
file
transfers.
The
simulation
runs
for
150
simulated
seconds.
For
the
analysis,
the
first
20
and
final
20
seconds
of
the
simulation
is
discarded.
B.
Evaluation
metrics
We
use
two
metrics
for
this
evaluation.
Goodput
is
defined
as
the
ratio
of
file
size
over
transmission
time.
An
unnecessary
retransmission
may
prolong
the
transmission
time
and
may
thus
affect
the
goodput
negatively.
We
study
goodput
from
a
system
perspective
(total
goodput).
We
use
a
utility
function
to
transform
the
goodput
of
a
file
transfer
to
an
index
describing
the
level
of
user
satisfaction.
The
function
can
be
seen
in
Equation
1.
This
Satisfaction
Index
is
calculated
for
each
completed
file
transfer
and
is
summed
up
for
all
transfers
in
a
session.
Thus,
the
satisfaction
index
for
a
session
will
be
between
0
and
4.
The
Satisfaction
Index
of
a
file
transfer
will
be
0
if
the
goodput
of
the
transfer
is
below
100
kbit/s.
If
the
goodput
is
above
400
kbit/s
the
Satisfaction
Index
will
be
set
to
1.
Since
a
denied
user
has
no
goodput
at
all,
the
corresponding
file
transfer
will
receive
a
Satisfaction
Index
of
0.
0
Ozx
<
100000
f(x)
=
100000
100000
<
x
<
400000
(1)
t
1
x
>
400000
As
a
support
for
these
two
metrics
we
also
present
the
mean
number
of
active
users
at
any
given
time
during
a
simulation
run.
This
metric
can
be
used
to
see
if
the
admission
control,
which
seeks
to
limit
the
number
of
users,
performs
the
way
it
should.
Studying
admission
control
algorithms
over
different
loads,
employing
user
models,
in
order
to
maximise
the
potential
revenue
of
3GPP
system
is
desirable.
We
analyse
whether
the
optimal
load
differs
between
optimising
the
system
for
maximal
total
goodput
and
for
maximal
total
user
satisfaction
respectively.
This
analysis
is
performed
with
and
without
the
impact
from
user
behaviour
(i.e.,
users
finishing
their
sessions
prematurely
or
not).
III.
RESULTS
In
this
section
we
present
and
discuss
our
results.
All
results
are
averaged
over
five
simulations.
Though
variance
is
not
shown,
we
have
calculated
it
and
judged
it
sufficiently
small
and
will
therefore
not
discuss
it
further.
We
begin
with
presenting
the
results
without
modelling
user
behaviour.
A.
Without
user
behaviour
modelled
In
Figure
1
we
see
the
resulting
metrics
depicted
for
various
loads
and
limits
in
admission
control.
The
total
goodput
does
not
increase
with
increasing
arrival
rates
above
20
arriving
sessions
per
second,
though
some
increase
can
be
seen
as
the
maximal
number
of
users
is
increased.
The
reason
for
this
is
obvious
when
looking
at
Figure
l(c)
which
shows
that
the
number
of
active
users
does
not
increase
with
increased
arrival
rates
for
loads
above
that
level.
In
fact,
the
limit
in
maximum
number
of
users
is
reached
when
the
arrival
rate
is
increased
above
20
new
sessions
per
second.
This
has
an
effect
in
total
satisfaction
(as
seen
in
Figure
l(b))
which
drops
to
a
minimum
when
the
arrival
rate
and
the
maximum
number
of
users
is
increased.
Since
the
satisfaction
index
is
based
on
per-user
goodput
which
must
decrease
if
the
total
goodput
is
kept
constant
and
the
number
of
users
is
increased
this
behaviour
is
expected.
We
believe
that
this
is
the
reason
that
maximising
total
goodput
and
maximising
total
satisfaction
requires
completely
different
settings
to
the
admission
control.
Thus,
in
order
to
maximise
total
goodput
no
restriction
should
be
set
to
the
number
of
users.
The
influence
of
admission
control
is
obvious
when
looking
at
the
number
of
active
users
and
total
satisfaction,
but
less
so
when
referring
to
total
goodput.
When
we
use
the
minimum
mean
throughput
admission
control
mechanism
and
still
not
model
user
behaviour
we
obtain
the
results
showed
in
Figure
2.
A
comparison
between
Figure
2(a)
and
Figure
l(a)
shows
that
for
both
setups,
the
system
is
saturated
in
total
goodput
for
loads
above
20
ar-
riving
sessions
per
second.
The
total
goodput
seems
slightly
less
when
the
admission
control
is
based
on
minimal
mean
throughput
though
(Figure
2(a)).
This
fact
is
further
supported
as
Figure
2(c)
reveals
that
the
number
of
active
users
does
not
increase
with
loads
above
20
arriving
sessions
per
second.
This
also
shows
that
the
admission
control
mechanisms
works
in
that
way
that
it
limits
the
number
of
active
users
in
the
system.
Setting
the
minimum
mean
throughput
to
a
low
value
maximises
total
goodput.
Comparing
Figures
2(c)
and
l(c)
we
see
that
the
latter
accepts
many
more
users.
This
suggests
that
the
range
for
the
minimum
mean
throughput
mechanism
could
have
included
values
below
100
kbit/s
in
order
to
accept
more
users.
B.
With
user
behaviour
modelled
When
user
behaviour
is
modelled
the
goodput
is
higher
than
when
user
behaviour
is
not
modelled.
This
can
be
seen
when
comparing
Figures
l(a)
and
3(a).
This
effect
is
expected
since
users
experiencing
poor
radio
conditions
will
end
their
sessions
prematurely, giving
resources
to
users
with
better
radio
conditions.
The
user
behaviour
is
actually
working
as
a
pre-emption
mechanism,
which
raises
the
question
whether
implementing
such
a
mechanism
in
the
Node
B
is
needed,
given
that
our
model
of
user
behaviour
is
accurate
enough.
In
Figure
3(b)
there
is
a
"ridge"
when
the
maximum
number
of
users
is
set
to
15.
When
the
maximum
number
of
users
is
set
to
10
the
users
are
too
few
to
generate
enough
satisfaction
380
Total
satisfaction
4000
3000
2000
t
201
Max
users
3025ate
(b)
Active
users
300-
200-
100
0
4
ate~
(c)
Fig.
1.
The
images
show
the
total
goodput
(in
bits/s),
the
total
user
satisfaction
and
the
mean
number
of
active
users
during
a
simulation.
These
are
all
averaged
over
all
of
the
simulations
when
the
admission
control
is
based
on
the
maximum
number
of
users
and
no
user
behaviour
is
modelled.
Figure
l(b)
is
rotated
to
increase
the
readability.
Total
satisfaction
(a)
(b)
Active
users
(c)
Fig.
2.
The
images
show
the
total
goodput
(in
bits/s),
the
total
user
satisfaction
and
the
mean
number
of
active
users
during
a
simulation.
These
are
all
averaged
over
all
of
the
simulations
when
the
admission
control
is
based
on
the
minimum
mean
throughput
with
no
user
behaviour
modelled.
Figure
2(b)
is
rotated
to
increase
the
readability.
and
when
the
limit
is
set
above
15
the
increased
competition
immediately
influences
the
total
satisfaction
negatively.
This
ridge
also
appears
in
Figures
2(b)
and
4(b)
around
300
kbit/s
but
not
as
prominent.
We
think
that
if
we
had
set
the
maximum
number
of
users
below
10,
the
ridge
might
have
appeared
in
Figure
l(b)
as
well.
If
the
objective
is
to
maximise
user
satisfaction,
a
minimum
mean
throughput
of
300
kbit/s
seems
like
a
good
setting.
Figure
3(c)
tells
us
that
the
admission
control
only
affects
the
number
of
active
users
for
low
limits
(less
than
a
maximum
of
25
users)
and
high
loads
(arrival
rates
above
25-30
sessions
per
second).
The
reason
that
this
figure
does
not
look
like
Figure
l(c)
is
because
of
the
user
behaviour
which
dampens
the
number
of
active
users
by
shortening
the
sessions
of
the
users
with
poor
goodput.
Minimum
mean
throughput
has
been
used
as
the
admission
control
algorithm
to
produce
the
results
shown
in
Figure
4.
In
the
first
figure,
Figure
4(a),
we
can
see
that
the
total
goodput
is
as
high
as
in
Figure
3(a).
Clearly,
the
choice
of
admission
control
mechanism
does
not
affect
total
goodput
much.
Instead,
the
choice of
including
a
model
for
user
behaviour
or
not
has
a
far
greater
impact
on
total
goodput.
We
also
notice
that
the
system
reaches
a
saturation
point
for
high
loads
and
the
minimum
mean
throughput
set
high.
This
can
also
be
seen
in
Figure
4(c).
For
a
minimum
mean
throughput
of
250
kbit/s
the
number
of
active
users
does
not
increase
beyond
arrival
rates
of
30
new
sessions
per
second.
Looking
at
both
Figures
2(a)
and
4(a)
we
notice
that
the
admission
control
has
a
larger
impact
on
total
goodput
during
higher
loads
than
during
lighter
loads.
This
effect
is
the
result
of
our
limit
in
utilisation.
For
lighter
loads
the
utilisation
never
reaches
98%
for
longer
periods
of
time,
which
is
the
limit
for
the
admission
control
to
be
employed.
The
ridge
in
total
satisfaction
appears
for
this
setup
as
well.
In
Figure
4(b)
we
can
see
that
the
total
satisfaction
is
kept
relatively
constant
when
the
minimum
mean
throughput
is
set
to
300
kbit/s.
The
reason
behind
this
is
the
same
as
before.
For
all
three
figures
we
note
that
admission
control
helps
keep
the
total
user
satisfaction
high,
but
its
effect
on
total
goodput
is
questionable.
IV.
CONCLUSIONS
This
paper
investigates
the
need
for
admission
control
in
the
interactive
traffic
class
for
HS-DSCH
to
improve
and
optimise
total
goodput
and
user
satisfaction.
We
test
through
simulations
the
assumption
that
existing
load
control
features
provided
by
TCP
and
the
users
themselves
are
sufficient
to
optimise
the
system
utilisation.
Goodput
is
measured
over
completed
file
transfers
and
user
satisfaction
is
computed
using
a
simple
utility
function.
The
simulations
show
that
admission
control
may
not
im-
prove
the
total
system
goodput.
Instead,
in
our
simulations,
more
users
in
the
system
result
in
higher
total
goodput.
It
should
however
be
noted
that
optimising
the
total
goodput
alone
is
not
feasible
since
the
average
goodput
will
then
drop
below
what
is
acceptable
for
the
users
of
the
system.
381
(a)
Total
goodput
I
2.5e+07
7'1
2e+07
1.5e+071
le+071
4~~~~~~~~~~4
(a)
Total
satisfaction
4000-
3000
2000-
1000
15
10
1
2
ate
(b)
Active
users
300-
200
100
0
3540
4
ate~
(c)
Fig.
3.
The
images
show
the
total
goodput
(in
bits/s),
the
total
user
satisfaction
and
the
mean
number
of
active
users
during
a
simulation.
These
are
all
averaged
over
all
of
the
simulations
when
the
admission
control
is
based
on
the
maximum
number
of
users
with
user
behaviour
modelled.
Figure
3(b)
is
rotated
to
increase
the
readability.
Total
goodput
Total
satisfaction
Active
users
2.5e+07
300
2e+07
400200-
3000
1
.5e+07
2000
100
le+07
1000
0
30540
402510
540~
10
20ate
400
10
(a)
(b)
(c)
Fig.
4.
The
images
show
the
total
goodput
(in
bits/s),
the
total
user
satisfaction
and
the
mean
number
of
active
users
during
a
simulation.
These
are
all
averaged
over
all
of
the
simulations
when
the
admission
control
is
based
on
the
minimum
mean
throughput
with
user
behaviour
modelled.Figure
4(b)
is
rotated
to
increase
the
readability.
In
contrast
to
total
goodput,
the
total
user
satisfaction
in
an
HS-DSCH
system
can
be
optimised
using
admission
control
as
illustrated
by
the
simulations.
Hence,
our
assumption
that
existing
load
control
features
provided
by
TCP
and
the
users
themselves
are
sufficient
to
optimise
the
system
utilisation
is
wrong.
Users
finish
their
sessions
prematurely
due
to
low
transmis-
sion
quality
means
that
the
number
of
users
experiencing
bad
radio
conditions
is
reduced.
Consequently,
the
system
goodput
is
higher
when
this
user
behaviour
is
modelled
compared
to
when
users
always
finish
their
sessions.
This
means
for
the
evaluated
admission
control
mechanisms
that
admission
limits
should
be
set
to
accept
more
users
when
it
can
be
assumed
that
users
experiencing
low
goodput
finish
before
sessions
are
fully
completed.
Our
model
for
user
behaviour
is
probably
not
the
correct
one,
but
we
think
it
is
reasonable
and
thus
it
serves
to
prove
that
knowledge
about
user
behaviour
is
essential.
A
proportional
fair
scheduler
weighted
to
account
for
the
SIR
of
each
individual
user
would
offer
higher
variance
in
throughput
over
time
and
thus
render
different
results
for
the
two
admission
control
mechanisms
tested
herein.
We
are
currently
implementing
such
a
scheduler
and
intend
to
include
new
results
analysing
this
issue
in
the
future.
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