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Resource Allocation Algorithm for GSM-OSC Cellular Systems
Conference Paper · July 2011
DOI: 10.1109/icc.2011.5963356 · Source: IEEE Xplore
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Resource Allocation Algorithm for
GSM-OSC Cellular Systems
D. Molteni(1,2) , M. Nicoli(1) , M. Säily(2)
di Elettronica e Informazione, Politecnico di Milano, Italy
(2) Nokia Siemens Networks, Säterinportti, Espoo, Finland
e-mail: {molteni, nicoli}@elet.polimi.it, mikko.saily@nsn.com
(1) Dipartimento
Abstract— We consider one of the latest feature included in
the Release 9 of the GSM/EDGE standard: the Orthogonal Sub
Channel (OSC) transmission scheme. OSC aims at doubling the
cell capacity by multiplexing two co-cell users on the same radio
resource. In this work we deal with the challenge of finding the
optimum pairing strategy among co-cell OSC users exploiting the
Adaptive QPSK (AQPSK) modulation in both up- and down-link
scenarios. The aim of the proposed scheduling algorithm is to
i) find the best association among the users and the available
OSC logical channels, and ii) select the optimum transmitting
powers. The criterion for optimization is the minimization of the
overall transmitted power constrained to service quality targets.
The proposed scheduling algorithm is performed locally at the
BS, exploiting channel state information reported by the users.
Numerical results show significant power saving provided by the
algorithm in heterogeneous scenarios with variable cell load.
I. I NTRODUCTION
The GSM/EDGE standard, after almost 20 years, does not
show any sign of obsolescence. The latest release, as a matter
of fact, points strongly in the direction of increasing the
competitiveness with the most recent wireless communication
solutions. For this purpose, a new multiplexing technique,
called the Orthogonal SubChannel (OSC) [1] [2], has been
designed with the aim of doubling the cell capacity by
allowing two users to share the same radio resource. In the
uplink (UL), two GSM mobile stations (MSs) transmit over the
same subcarrier fully interfering each other. In the downlink
(DL), the base station (BS) transmits two overlapped GMSKmodulated streams with a 90 deg phase offset exploiting a
QPSK-like modulator; power allocation for the two users can
be adjusted through the Adaptive Quadrature Phase Shift Keying (AQPSK) feature [3] to compensate unbalanced channel
conditions and/or service requirements for the two users and
provide fair performance. Obviously, for both UL and DL, the
detection of the desired stream must rely on receivers with
interference management capabilities. The Successive Interference Cancellation (SIC) scheme is the decoding strategy
suggested by 3GPP [1], while a family of interference rejection
receivers such as, for instance, the Single Antenna Interference
Cancellation (SAIC) algorithm [4] can be employed in DL. A
new filtering technique, called the Joint OSC Receiver (JOR)
has been proposed in [5] for both the links, combining filters
for out-of-cell interference rejection and multi-user detection
for joint estimation of the two in-cell streams.
To the authors’ knowledge, very few works have been
published on OSC-GSM systems and they all deal with
receiver design for interference management at the physical
layer. An important issue that needs to be addressed now is
resource allocation, as the policy adopted by the BS to select
the users to be served on the same physical channel and the
respective transmitting parameters (power, rate) has a strong
impact on the system performance. In the recent literature user
pairing has been studied for other applications such as partner
selection in cooperative networks [6] [7] or fair scheduling
in multiple-input-multiple-output (MIMO) systems [12]. On
the other hand, for conventional (non OSC) GSM scenarios,
algorithms have been proposed for optimal power allocation
[8], joint power-rate control [9], or power control with service
constraints [10] for the GSM Adaptive Multi-Rate (AMR)
coding scheme [11].
In this work, an algorithm is designed to provide optimal
pairing and power allocation for the medium access control
(MAC) layer of OSC-GSM cellular systems. The optimization criterion is the minimization of the transmitting powers
constrained to service requirements defined for each user as
maximum bit error rate (BER) for a voice quality target. As
a matter of fact, fairness is required by the OSC approach to
guarantee reliable voice transmissions to both users sharing
the radio channel. The proposed algorithm is designed for UL
and DL exploiting for each user channel state information such
as path-loss and interference level. Moreover, for the DL, the
latest AQPSK feature is adopted in the optimization to further
reduce the overall transmitting power. The reduction of the
required transmitting power directly affects the lifetime of the
devices and the overall interference generated on neighbouring
cells, thus providing benefits to the whole multicell environment. Numerical performance analyses are used to evaluate
the power saving provided by the proposed algorithm for both
optimal and suboptimal system configurations.
II. PAIRING PROBLEM FORMULATION
In this work we focus on resource allocation for a communication scenario with N users accessing to a common BS.
Transmissions rely on the GSM-OSC multiplexing technique
[1] which prescribes to associate two users on the same logical
channel to maximize the system capacity. For the sake of
simplicity, N is supposed to be even. The BS is assumed to
978-1-61284-231-8/11/$26.00 ©2011 IEEE
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings
σ i2
Downlink (DL)
Uplink (UL)
Pi
σ
2
j
MSi
MS j
Fig. 1.
di
dj
U
MSi
Pi ,Dj
BS
PjD
MS j
σ 02
di
dj
BS
Down- and up-link OSC scenarios.
pair all users and associate them to K = N/2 GSM-OSC
logical channels according to an optimality criterion1 .
Let N = {1, . . . , N } and K= {1, . . . , K} represent, respectively, the set of users asking for transmission and the set
of available logical channels in the considered cell. Given¡ the
¢
set of all the possible users’ associations Ω, with |Ω| = N2 ,
the target of the scheduling algorithm is to find the optimum
pairing strategy S = {S1 , . . . , SK } ∈ Ω, with Sk = {i, j}
gathering the two users allocated to the kth channel and
i, j ∈ N . Subsets are disjoint, i.e. Sk ∩ Sh =Ø for k 6= h.
The resource allocation algorithm has also to define a power
assignment solution for the optimum pairing strategy S.
III. S YSTEM MODEL
In this section a GSM/EDGE cellular system with OSC
feature is outlined (for a detailed description see [2]). The
ith device can be characterized by its distance di from the
BS, a transmitting power PiU for the UL transmission and
a sensed noise level σ2i (including thermal noise and cocell interference) in DL reception (see Fig. 1). The OSC
modulated signal is composed by two GMSK streams with
equal (or comparable) transmitted powers that fully interfere
each other. The pairing algorithm and the power assignment
solution are derived taking into account the long-term signalto-interference-plus-noise ratio (SINR) experienced by the
users (averaged over the fading statistics). In the following the
SINR is defined considering as useful signal the multiplexed
streams for the two in-cell OSC users and as interference the
signal from the out-of-cell users.
A. Uplink (UL) OSC signal model
The signal received on the kth subchannel by the BS (here
denoted with the subscript 0) is the superimposition of two
GSM-standard compliant bursts from the two users {i, j}
in Sk . The two streams are assumed to be synchronized
through the timing advance mechanism and follow the linearized GMSK modulation [3]. The received complex base-band
signal can be modelled as:
(including the receiving and transmitting filter impulse responses) and {xi , xj } represent the last L transmitted symbols
for the two users. According to the path-loss model, the
received power is assumed to decay with the power η of
the distance. The impairment w0 (n) is the combination of
the GMSK modulated signals from R out-of-cell interferers
and additive white GaussianPbackground noise. The overall
η
2
impairment power is σ20 = R
r=1 Pr /dr + σ bn , where Pr is
the transmitting power of the rth interferer at a distance dr
from the BS and σ 2bn is the power of the background noise2 .
We define the SINR for the compound {i, j}th signal as:
!
Ã
1
PiU PjU
U
U
U
+ η
= PiU · δ U
(2)
γ i,j =
i + Pj · δ j ,
dηi
dj
σ20
¡ η 2 ¢−1
¡ η 2 ¢−1
where δ U
and δ U
represent the UL
i = di σ 0
j = dj σ 0
channel state information (CSI) for users i and j, respectively.
To avoid near-far problems and provide fair performance to
both OSC users, the average powers of the two signals are
U U
required to be equal at the receiver: PiU δ U
i = Pj δ j . Thereby,
the two users are assumed to experience the same SINR and
their BER performance can be expressed as a function of γ U
i,j
as: BERi = BERj = g(γ U
i,j ).
B. Downlink (DL) OSC signal model
In the DL, the BS generates an OSC-compliant stream
comprising two GMSK signals with phase offset π/2 [1]. The
compound signal is obtained using a QPSK modulator where
the ith and jth users occupy, respectively, the first and second
position in the modulation label (see Fig. 2). The MS can
detect the desired signal as a GMSK stream affected by a
dominant interferer. The signal received by the ith user is:
yi (n) =
1 T D
h (P xi (n) + PiD xj (n)) + wi (n),
dηi 0i j
(3)
D
= PiD + PjD is the overall power transmitted by
where Pi,j
the BS, the L × 1 vector h0i represents the DL channel, and
wi (n) is the sum of out-of-cell interference and background
noise as in Sec. III-A. The SINR for the OSC-multiplexed
signal is now:
γD
i =
D
Pi,j
D
= Pi,j
· δD
i ,
dηi σ 2i
(4)
where n denotes the symbol time, the L×1 vectors {hi0 , hj0 }
gather the samples of the frequency-selective radio channels
¡ η 2 ¢−1
where δ D
denotes the CSI for the DL.
i = di σ i
The OSC feature includes the possibility to unbalance the
powers assigned to the two users associated to the same
channel through a deformation of the constellation shape
(AQPSK modulation [3]), as sketched in Fig. 2. Without any
loss of generality, we assume that among the pair {i, j} the ith
user experiences the most unfavorable channel condition, i.e.
D
γD
i ≤ γ j . The AQPSK feature can be employed to compensate
1 We consider that all users have to be paired simultaneously (i.e. no
handovers or active transmissions are already established).
2 Due to frequency hopping, the second order statistics of w (n) are
0
assumed to be independent of the specific OSC logical channel.
y0 (n) =
PjU T
PiU T
h
x
(n)
+
h xj (n) + w0 (n),
i
dηi i0
dηj j0
(1)
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10
(0,1)
(OSC i,OSC j )
(0,0)
sin α
QPSK
AQPSK
α
γ iD ≤ γ Dj
α = 45° - Disabled
α = 50°
MSi
α = 55°
MS j
δδi i
α = 60°
α = 65°
α = 70°
δj
Q
cos α
−1
0
Raw BER
I
BS
1
D
i, j
P =1
Strong user
10
(1,1)
(1,0)
-1
BER j = f s (γ Dj , α )
BER i
AFS 7.4 kb/s
BER j
AFS 12.2 kb/s
Fig. 2. AQPSK constellation: the power assigned to the two channels (I and
Q) can be tuned through the α parameter.
Weak user BERi = f w (γ iD , α )
this channel unbalance by setting PiD ≥ PjD . According to [3],
we can express the relation between the users’ power as:
(
PD
D
≥ 2i,j
PiD = sin2 α · Pi,j
(5)
PD
D
PjD = cos2 α · Pi,j
≤ 2i,j
where the parameter α (with 45 deg < α ≤ 90 deg) aims at
D
adjusting the distribution of the total power Pi,j
among the
two OSC users.
Given a specific receiving algorithm and a transmit power
D
, the BER performances of the two OSC users depend
Pi,j
on the power of both the out-of-cell interference (specified
D
by γ D
i and γ j ) and the in-cell interference (defined by α).
The receiver performance can thus be drawn as a function
D
of {γ D
i , γ j } and α. More specifically, we can define the
performance of the “weak” user (i) as BERi = fw (γ D
i , α)
and that of the “strong” user (j) as BERj = fs (γ D
,
α).
An
j
example is given in Fig. 3 where a single-antenna JOR receiver
(as described in [5]) has been designed for the DL Muros Test
Scenario (MTS) 1 and evaluated for different values of power
unbalancing α. The performance are presented as a function
D
of γ D
i or γ j where the interference is formed by one dominant
out-of-cell GMSK interferer.
C. Service requirements
Higher levels of the protocol stack characterize the service
requirements for each user transmission. These requirements
can be expressed in terms of quality of voice call by specifying
a maximum value of the Frame Error Rate (FER) for the
selected voice codec [11]. Several mapping techniques can
be adopted to associate the FER value with system level
parameters as described in [2] and [13]. Here, since we are
focusing on the pairing problem, we simplify the mapping
by translating the target FER into a required maximum BER
value (BER), which is supposed to hold for both UL and
DL transmission. The corresponding minimum SINR value
required at the receiver depends on the algorithm used for
data detection and can be derived from link level simulations
on realistic testing scenarios. Since MS and BS typically
implement different receivers, two different SINR thresholds
are derived for DL (γ D ) and UL (γ U ), using simulated BER
performances: BER = g(γ U ), BER = fw (γ D
i , α) and
10
-2
0
5
γ iD,opt
10
15
20
γ iD or γ
γ Dj ,opt
D
j [dB]
Fig. 3. Performance of DL single-antenna JOR receiving algorithm [5] for
the MTS 1 scenario. Different values of α are shown.
BERj = fs (γ D
j , α). The BER thresholds relative to 1% FER
for four GSM-standard compliant AMR Adaptive Full-Rate
Speech (AFS) codecs are presented in Table I as obtained
through link-level simulations with a JOR receiver in a Typical
Urban (TU) fading environment.
IV. PAIRING ALGORITHM
In this Section the pairing problem is formulated to minimize the sum-power for all OSC users under performance
constraints. The BS aims at finding the optimal pairing
strategy and the relative transmitting powers that satisfy the
service requirements for all users. The pairing is assumed to
be performed locally by the BS based on the information
collected from the MSs through feedback channels. Within
the GSM/EDGE standard, some measurements of the channel
quality are available at the BS, such as the received signal quality (RxQual), the received signal level (RxLev), an estimate
of SINR and C/I [10]. Furthermore, the BS can estimate some
features of the users’ radio channels. For the sake of simplicity,
U
N
here we assume that the quantities {δ D
i , δ i , BERi }i=1 are
available at the scheduler without any estimation error.
The sum-power optimization problem is the minimization
of the sum of the transmit powers for all users in S,
(6)
Ŝ = arg min P (S)
S∈Ω
TABLE I
R AW BER THRESHOLDS FOR DIFFERENT CODECS AT FER 1%.
Codec AFS [kb/s]
BER
4.75
0.14
5.9
0.12
7.4
0.09
12.2
0.05
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings
under the constraints
½
BERi ≤ BERi , ∀i ∈ N
, (7)
U
D
D
PiU ≤ Pmax
and Pi,j
≤ Pmax
with ∀ {i, j} ∈ N
U
D
and Pmax
denote, respectively, the maximum
where Pmax
transmitting power allowed for UL and DL.
The algorithm can be applied over DL or UL separately or
jointly for both links. Given the candidate pairing set S, the
overall transmitted power for UL can be expressed as
X
P U (i, j),
(8)
P (S) =
(i,j)∈S
where P U (i, j) gathers the transmitting power for the candidate pair {i, j} so that: P U (i, j) = PiU + PjU . On the other
hand the pairing can be performed for DL using:
X
D
Pi,j
.
(9)
P (S) =
(i,j)∈S
D
The power Pi,j
and the value α have to be jointly optimized to
satisfy the performance targets of both users. Notice that this
is a local optimization problem between two candidate users.
In any case, the solution of (6) is independent of the definition of the sum-power metric. The optimal pairing strategy
is the solution of a combinatorial optimization problem, more
precisely it is a weighted (perfect) matching problem in nonbipartite graphs [14]. The vertices are represented by the
users N fully connected by the set of undirected edges E =
{ei,j : (i, j ∈ N ), i 6= j, ∀i, j} of cardinality |Ω|. The weight
of each edge can be represented by w(ei,j ) = P U (i, j) or
D
. Several approaches can be adopted to solve
w(ei,j ) = Pi,j
this problem: an optimal solution is represented by the Gabow
algorithm [7], [15]. We solve the optimization problem by
evaluating the cost function (8) for all the possible pairing
sets Ω and then constructing the graph.
In the following we deal with the definition of the sumpower metric for DL and UL deriving the powers jointly for
the OSC users and casting the performance for typical realistic
GSM-OSC receivers. Sect. IV-A details the pairing problem
with balanced powers (UL case and DL without AQPSK
feature) while Sec. IV-B takes into account the optimization
of the parameter α for the DL scenario.
A. Balanced-powers association
Due to the concavity of the BER function, the constraint
(7) can be easily reformulated in terms of SINRs inequality.
Taking the UL case as an example, the condition becomes
U
U
−1
γU
(BERi,j ) is ©evaluated on the
i,j ≥ γ̄ i,j , where γ̄ i,j = g
ª
minimum BER requirement BERi,j = min BERi , BERj
which represents the condition where both performance targets
are satisfied. Notice that the function g−1 (·) is not linear
and any analytical derivation, besides being not trivial, is not
applicable in practical contexts. Thus, in this work (and in
real-time applications) look-up tables generated with system
level simulations will be used to find the target SINR.
Finally, from (2) we can find the two transmitting powers as
U
U
1 U
U
PiU ≥ 12 γ̄ U
i,j /δ i and Pj ≥ 2 γ̄ i,j /δ j . In this case the solution
is therefore a power control loop which aims at compensating
the path-losses and at receiving the same power from the two
candidate users so as to satisfy the stricter service requirement.
B. Unbalanced-powers association (AQPSK)
In the DL scenario two parameters control the power
allocated to the users paired on an OSC channel: the total
D
transmit power Pi,j
and the value α. Note that the use of
AQPSK represents a further degree of freedom in the optimization since it allows to define two different SINR regimes
for the paired users so as to compensate the differences of
performance targets and channel conditions. The joint α and
power allocation optimization can be formulated as:
© D
ª
D
Pi,j,opt , αopt = arg minPi,j
.
(10)
D ,α
Pi,j
under the performance constraints
½
BERi = fw (γ D
i , α) ≤ BERi
BERj = fs (γ D
j , α) ≤ BERj
(11)
which can be redefined as a function of the SINR thresholds:
D
D
D
γD
i ≥ γ̄ i and γ j ≥ γ̄ j with i 6= j.
The solution of the optimization problem (10) consists of
finding the optimal α value (αopt ) which minimizes the SINRs
D
D
D
γD
i and γ j obtaining the relative values γ i,opt and γ j,opt that
D
satisfy the requirements (11): fw (γ i,opt , αopt ) ≤ BERi and
fs (γ D
j,opt , αopt ) ≤ BERj . Finally the requested minimum
D
power (Pi,j,opt
) is defined so as to guarantee the requested
D
D
D
D
= γD
SINR regime as Pi,j,opt
i,opt /δ i = γ j,opt /δ j . Notice
that the relationship among the two SINRs is fixed and it can
be obtained through (4) as
γD
j =
δD
j
δD
i
γD
i ,
(12)
which is defined by the physical properties of the channels.
A closed form solution of (10) is not trivial since the
performances are strongly not linear with respect to (wrt)
γ D and α. In practical context, the optimization is carried
out as described below and it employs look-up tables of the
performance drawn as a function of the BER targets and
available α values.
To illustrate the optimization procedure we use an example
of allocation scenario and we remap the BER vs SINR
D
performances of Fig. 3 into the γ D
i vs γ j domain in Fig.
4 to ease the discussion. The new figure shows five curves
obtained© from Fig. 3 byª selecting five different pairs of BER
targets BERi , BERj (drawn from three of the codecs in
D
Tab. I). Each curve represents the SINR thresholds {γ̄ D
i , γ̄ j }
as a function of α. Let us, for instance, consider the BER
targets {0.09, 0.05} for the AFS codecs {7.4, 12.2} kb/s.
D
The values {γ̄ D
i , γ̄ j } associated to such targets for α =
45, 50, 55, 60, 65, 70deg (but also for intermediate continuos
values of α) are represented by a black curve with empty circle
markers. The relationship (12) is shown on the same figure in
D
red for, e.g., δD
j /δ i = 3. The optimum α is given by the
intersection of the black curve (the SINRs relationship (12))
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings
γ
BERi - BER j
1000
D
j [dB]
Random Scheduler - No AQPSK (case A)
BER j < BER j
0.14 - 0.05
0.09 - 0.05
α = 65°
with α opt = 50°
0.14 - 0.09
α = 60°
10
α = 55°
8
γD
j ,opt
800
i
j
12
α = 50°
0.14 - 0.14
j
i
α = 45°
6
Optimum Scheduler - No AQPSK (case B)
Random Scheduler - Opt AQPSK (case C)
BER i < BER i
0.05 - 0.05
α = 70°
14
900
BER j < BER j
Average BS Sum-Power [mW]
16
Optimum Scheduler - Opt AQPSK (case D)
RANDOM
CODECS
700
600
500
400
AFS 12.2
300
4
200
2
δ j / δi = 3
γ iD [dB]
0
-4
-2
0
2
4
6
AFS 4.75
0
BER i < BER i
δi = δ j
-2
100
4
6
8
Number of users N
10
12
8
γ iD,opt with α opt = 50°
Fig. 4. Example of optimum α value selection. The optimum value has been
found in the intersection between the black curve and the red one.
and the red line (the BER constraints). The corresponding
values for the two minimum SINRs are denoted as γ D
i,opt and
γD
.
In
case
α
can
assume
only
a
set
of
values,
the
solution
j,opt
is given by that value that provides the minimum γ D
i,opt that
satisfy the BER targets (in the example αopt = 50 deg).
V. N UMERICAL RESULTS
In this Section we study the performance of the proposed
methodology by mean of numerical simulations. For performance comparison, we employ also a non-optimized (random)
pairing strategy and suboptimal configurations of the proposed
methodology. Notice that when two users are randomly selected the transmitting power is chosen as described in Sec. IV-A
based on the stricter SINR requirement. The performance of
the algorithms are drawn in terms of the total transmit power,
i.e. the sum of the powers for all users in the UL scenario and
the power transmitted by the BS to serve all the users in DL.
We consider a cellular layout with reuse factor 7, cell
radius 1000m and with BSs located in the center of the
cells. User positions are selected randomly inside the cell.
U
The maximum transmit powers are set to Pmax
= 28dBm
D
and Pmax
= 30dBm. The path loss exponent is η = 3
and the power spectral density of the background noise is
−174dBm/Hz. Link adaptation is performed using four AMR
codec modes: AFS {4.75, 5.9, 7.4, 12.2} kb/s. The FER target
is set to 1%, the relative BER and SINR thresholds for the
considered codecs are drawn by numerical simulations of the
physical link as discussed in Sec. III-C.
Figure 5 draws the power consumption of the BS as a
function of the number N of users for the DL scenario. Each
point is obtained by averaging the sum-power resulting from
the proposed algorithm over a number of system configurations
with random users’ positions. In the figure, three groups of
curves are highlighted: the solid curves refer to random service
Fig. 5. Average DL sum-power vs number of users for different optimization
strategies and sets of codecs.
requirements (i.e., each MS selects randomly one over the four
available codecs), dashed curves account for a scenario where
only the most robust codec is selected by every user (i.e.,
AFS 4.75 kb/s), while dotted curves employ the less robust
codec (i.e., AFS 12.2 kb/s). As expected, the overall power is
reduced for the latter case, as the threshold is the lowest for
every MS.
The algorithm has been evaluated considering different
degrees of optimization. The most power-expensive solution is
given by the random scheduler (Case A), the optimal pairing
(Case B) is shown to provide a gain wrt the random association; both cases do not exploit the AQPSK feature thus the
power is evaluated as described in Sec. IV-A. Random pairing
with AQPSK optimization (Case C) shows a higher gain wrt
Case A. The maximum power saving solution is provided, as
expected, by the joint optimization of the pairing and of the
unbalancing ratio between the multiplexed users (Case D): the
percentage of saved power wrt the random scheduler ranges
from 45% (accounting for random thresholds) to 35% for the
case with only one codec for every MSs. This fact is motivated
by the diversity introduced by the different codec selection
which can be exploited to improve the gain.
To evaluate the effect of the channel macro-diversity (i.e.,
the variations of the path-loss values over the links) on the
algorithm performance, we now set the number of users
to N = 8 and we restrict the MS positions to a given
area of the cell. Figure 6 shows the performance wrt the
minimum distance (dmin ) from the BS allowed to users (dmin
is normalized wrt the cell radius). As in Fig. 5, three groups
of curves are presented with different codec selections. The
more dmin grows the less is the overall power saving for all
the cases. As a matter of fact, in the extreme case (dmin = 1)
where all the users lie on the cell border and experience
the same path-loss, the macro diversity is reduced to zero.
We can also notice that case C provides a lower gain for
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings
500
1200
Random Scheduler - No AQPSK (case A)
Random Scheduler (case A)
Optimum Scheduler - No AQPSK (case B)
450
Random Scheduler - Opt AQPSK (case C)
1000
AFS 12.2
800
RANDOM
CODECS
600
RANDOM OVER
TWO CODECS
[12.2, 7.4]
400
Average MSs Sum-Power [mW ]
Average BS Sum-Power [mW ]
Optimum Scheduler - Opt AQPSK (case D)
400
350
300
250
200
RANDOM OVER
FOUR CODECS
[12.2, 7.4, 5.9, 4.75]
150
200
100
AFS 4.75
0
AFS 12.2
Optimum Scheduler (case B)
50
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Normalized min distance from the BS - dmin
0.8
0.9
1
4
6
Fig. 7.
Fig. 6.
10
12
Average UL sum-power vs number of users.
Average DL sum-power vs minimum distance from the BS.
increasing distance as the differences between the SINR values
experienced by the users reduce and the granularity of the
parameter α can not exploit such (small) differences. On the
other hand, case B continues to reach better performance wrt
case A. With a single codec the gain at dmin = 1 is due
only to interference diversity while for the case with random
assignment the gain is related to the different BER targets.
For the UL scenario (AQPSK is not allowed) the pairing
simplifies as described in Sec. IV-A. Figure 7 presents the
sum-power of the MSs averaged over different UL system
configurations. Here the comparison is carried out considering
only two curves referred to case A and B and three sets of
codecs. It can be observed from the results in Fig. 7 that the
larger is the gap between the SINR thresholds of different
codecs the higher is the power gain (i.e. the case with four
random codecs outperforms the case with two in terms of
saved power). As a matter of fact, in the UL scheduling of two
candidate users the BS forces the user with the lower SINR
requirement to transmit at an higher power level to guarantee
that the signal from both users is received by the BS with the
same power. Notice that if only one threshold is set for every
MS, no power gain is provided by the optimized scheduling. In
this case, the power control loop only compensates the pathloss
for every user independently by the selected partner.
VI. C ONCLUSIONS
In this work we have considered GSM/EDGE systems with
OSC transmission technique. The problem of pairing the users
is a key aspect for the system level optimization to reduce
the overall transmitting power and, consequentially, the cocell interference. A scheduling approach to solve the pairing
problem has been presented for the DL and UL scenarios.
The proposed algorithm finds an optimal user association
strategy minimizing the overall sum-power at the cell level.
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Number of users N
Furthermore the AQPSK feature has been accounted in the
optimization to further increase the final power saving. The
optimization has been carried out constraining the solution to
satisfy service requirements for each user. Numerical results
have shown a significant power-saving with respect to nonoptimized pairing solution.
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