AN OPTIMIZATION MODEL FOR ALLOCATION OF
NETWORK USERS IN MACRO-FEMTO NETWORKS
An Approach based on Energy Efficiency and Quality of Service
Diego L. Cardoso
1
, Marcelino S. Silva
1
, Adamo L. Santana
1
,
Carlos R. L. Francês
1
, Solon V. Carvalho
2
and Nandamudi l. Vijaykumar
2
1
Institute of Technology, Federal University of Pará, Augusto Correa, Belém, Brazil
2
Laboratory of Computing and Applied Mathematics, National Institute for Space Research, São José dos Campos, Brazil
Keywords: Femtocell, Mobile, Optimized Allocation, Markov Decision Process, Quality of Service.
Abstract: The femtocell concept aims to combine fixed-line broadband access with mobile telephony using the
deployment of low-cost, low-power third generation (3G) base stations in the subscribers' homes. While the
self-configuration of femtocells is a plus, it can limit the quality of users and reduce the efficiency of the
network, based on outdated allocation parameters such as signal power level. To this end, this paper
presents a proposal for optimized allocation of users on a macro-femto network, aiming to minimize the
consumption of battery without affecting the quality of service of applications. Markov Decision Process
Theory is used to model the system, which is modeled as observed by the user’s side. So, when the user
needs to connect to make a voice call or a data call, the mobile phone has to decide in which network to
connect, using the information of number of connections, the quality of service parameters and the signal
power level of each network.
1 INTRODUCTION
Studies conducted in recent years have revealed the
explosive growth of wireless communications raised
by technological advances in the telecommunication
industry.
The femtocell technology obtained much
attention from researchers, especially focusing on
how they can be used to improve voice services in
coverage limited locations (Chandrasekhar et al.,
2008); however, broadband data services are an
increasingly significant source and percentage of the
mobile operator’s business.
Femtocells have a strong potential to improve the
capacity of next generation wireless systems since
they offer better link qualities and wider spectrum
resources for connected users. Scheduling in
femtocell networks involves more complications due
to involvement of multiple (typically co-channel)
small-size cells, as well as the macro-cell. In
addition, associating users to appropriate frequency
bands for achieving high capacity and fairness,
intelligent assignment of users to different cells is
also required. These unique problems in femtocell
networks require intelligent scheduling algorithms
that can present a good compromise between
maximization of the fairness and the sum-rate
(Ertürk et al., 2010).
This problem becomes more complex when the
battery consumption of client nodes and the QoS
(Quality of Service) requirements are considered to
decide in which cell the client should connect.
Traditionally, the decision is based on the signal
power (connect on the cell with higher signal power,
whether it is a macro cell or femtocell) without
considering if the output meets the minimum QoS
requirements.
This proposal also targets Green Networking,
which is the practice of selecting energy-efficient
networking technologies and products, and
minimizing resource use whenever possible
(SearchNetworking, 2009). It should be noted that
maximizing the energy efficiency of the nodes is a
key factor, however this is not the only one that
should be considered; The maximization of user
satisfaction should also be pursued.
In such context, planning for the allocation of
users by operators in their cells, macro or femto,
carries critical importance for minimizing
209
Cardoso D., Silva M., Santana A., Francês C., Carvalho S. and Vijaykumar N..
AN OPTIMIZATION MODEL FOR ALLOCATION OF NETWORK USERS IN MACRO-FEMTO NETWORKS - An Approach based on Energy Efficiency
and Quality of Service.
DOI: 10.5220/0003953202090214
In Proceedings of the 1st International Conference on Smart Grids and Green IT Systems (SMARTGREENS-2012), pages 209-214
ISBN: 978-989-8565-09-9
Copyright
c
2012 SCITEPRESS (Science and Technology Publications, Lda.)
interference, maximizing the system capacity,
achieving fairness in femtocell networks and
maximizing network utilization (Ertürk et al., 2010).
Several works in literature has already proposed
how to achieve high capacities with fair scheduling
techniques for conventional cellular architectures.
For example, (Zhang and Letaief, 2004) aims to
maximize the sum-rate of all the users within a
cellular network; however, fairness issues have not
been considered. A maximum fairness technique has
been discussed in (Rhee and Cioffi, 2000), which
essentially tries to maximize the capacity of the user
that has the lowest data rate and achieve similar data
rates for all users.
In terms of capacity overflow, some proposed
architectures and schedulers have been proposed. In
(Chung and Lee, 2005) and (Hu and Rappaport,
1995.), models based on Markov-modulated Poisson
Process (MMPP) were employed for representing
multiservice overflow traffic. However, extensive
computations are required by a MMPP method to
solve multi-dimensional Markov chains for large
scale systems.
Relative to Green communication it can be
highlighted (Liao and Yen, 2009), where it is
proposed the power-saving scheduling of base
stations (BS) considering QoS requirements (delay
and jitter) of the real-time communications in
WiMAX network. Also in (Han et al., 2010) several
radio management scheduling algorithms are
evaluated for the long term evolution (LTE) BSs,
and effectively exploits multi-user diversity in the
time, frequency and space domains for LTE
networks. The works in the area were found to
focused mainly on the energy efficiency of macro
cells and core network, with no much attention
devoted to maximizing the use of battery of client
nodes, considering aspects of QoS and signal level.
2 FEMTOCELL
2.1 Definition and Characteristics
Femtocells are devices used to improve mobile
network coverage in small areas, connected locally
to mobile phones and similar devices through their
2G (GSM), 3G (UMTS) or 4G (WIMAX or LTE)
connections, and then route the connections over a
broadband internet connection back to the carrier,
bypassing the normal cell towers (ERBs or nodeBs).
This technology creates a bridge between mobile
and personal wired networks, using a high speed
internet connection (either personal or enterprise) to
link to the operators macro-network. Doing this, it is
easier to extend access to mobile network, providing
better coverage for the population (especially in
areas where there was no signal or weak signal
arrived), and providing high bandwidth to users.
2.2 Problem Description
Femtocells are typically installed by non-expert
users, which do not consider the network’s
performance; simply connecting a Femtocell Acess
Point to DSL (Digital Subscriber Line) and turning it
on. Femtocell Acess point self-organizes its radio
and system operational parameters (Holger et al.,
2008)(John and Holger, 2009). The node client
automatically tries to associate the Femtocell Acess
Point with strongest signal, however this choice
becomes unfair in two aspects:
1. Considering the capacity of the Femtocell
Access Point, which can become overcrowded and
can not serve new users, maintaining the quality of
service. This can lead to an unbalanced load,
overloading a femtocell against each other;
2. Given the choice of allocation by the
customer, the network setting only by the signal
power level may not meet the quality requirements
of the user, which could obtain a better service
through another network near, even at a higher cost
battery. This fact is aggravated when considering the
diversity of existing applications, which have
different requirements for quality of service.
3 MARKOVIAN MODEL
3.1 Markov Decision Process
The problem is formulated as a Continuous Time
MDP (CTMDP), since it considers that the times
(between requests arrival and that a request stay in
the system) follow an exponential probability
distribution. Also, the problem is formulated as an
Infinite Horizon problem, since it can perform for a
long, undefined period of time.
Briefly, to model a problem as a CTMDP, it is
necessary to define (Puterman, 1994):
The state space S: the set of all possible
conditions of the system;
The sets of actions {A(s) | s
∈
S}: for each
state s
∈
S, there is a set of possible actions
A(s), in which the operator must choose a
single action at every decision time;
SMARTGREENS2012-1stInternationalConferenceonSmartGridsandGreenITSystems
210
The set of costs {c(s, a) | s
∈
S, a
∈
A(s)}: where
c(s, a) is the cost entailed to the system when
it is in s
∈
S and the action a
∈
A(s) is chosen;
A set of transitions probabilities {p
sz
(a)| s,z
∈
S,
a
∈
A(s)}: where p
sz
(a) is the probability that, in
the next decision time, the system is in state
z
∈
S, given that action a
∈
A(s) is chosen when
it is in the state s
∈
S;
{τ(s,a)| s
∈
S, a
∈
A(s)}: expected time until the
next decision time if the action a
∈
A(s) is
chosen in state s
∈
S.
Using these five elements, the stationary optimal
policy R* that minimizes the long-run average cost
per time unity can be calculated by some classical
techniques, e.g. Value Iteration Algorithm and
Policy Iteration Algorithm (Tijms, 1994).
3.2 Network Architectures and Traffic
Assumptions
A typical femto-macro mobile network, with cells
providing wireless access for mobile users through
macrocell or femtocell access points, is assumed.
The architecture used is shown in Figure 1.
The arrivals of calls can be answered by both
networks, which have different distances for the
mobile nodes, different bandwidths, different losses
and different maximum users that can be connected.
When a new call arrives to the system, parameters
such as the energy consumption when connected, the
available bandwidth and packet loss probability of
each networks, are used to decide which network
should be chosen to serve the call. If new calls are
blocked due to capacity limitation, they overflow to
the other network for possible service.
Two service classes access the network: voice
and data. These are formed by new calls and handoff
calls. The requests arrive in the system according to
two Poisson processes, with parameters λvn and λdn,
for voice and data respectively; where n indicates if
the request is to connect to macrocell or femtocell,
or if it is a request that have to be decided to which
cell it should connect.
The service times of voice calls and data packets
follow exponential distributions with parameters μvn
and μdn, respectively. Also, there is no
differentiation between voice and data channels.
It is important to clarify that the system is
modeled as observed by the user. So, when the user
needs to connect to make a voice or a data call, the
mobile phone has to decide in which network to
connect, using the information of number of
connections, the quality of service parameters and
the signal power level of each network. The signal
power level can be obtained directly, but the other
variables need to be enquired to the system
Figure 1: Typical macro-femto scenario that is being taken
into consideration.
3.3 Model Formulation
Each state s
∈
S is defined as:
0 2
0 2
0 1
0 1
},,,,,,,,,,, {
},, {
}2,1, {
}, ..., 2, 1, 0{,
}, ...,
2, 1, 0{,
),,,,,,(
2
2
1
1
222
111
22112211
222
111
2211
>==
>==
>==
>==
≠=
≠=
=≠
==
<+
<+
∈
∈
∈
∈
∈
=
dthenDATAkANDCcif
vthenVOICEkANDCcif
dthenDATAkANDCcif
vthenVOICEkANDCcif
andandevthenDATAkif
andandevthenVOICEkif
DATAorVOICEkthendesccif
desckthendesccif
MaxCRdv
MaxCRdv
ev
DATAVOICEdesck
CCdescc
MaxCRdv
MaxCRdv
tosubject
evkcdvdvs
vuduvu
duduvu
duvudvdvduvudvdv
μλλ
μλλ
μμμμμμλλλλλλ
ANOPTIMIZATIONMODELFORALLOCATIONOFNETWORKUSERSINMACRO-FEMTONETWORKS-An
ApproachbasedonEnergyEfficiencyandQualityofService
211
DATAkthenevif
VOICEkthenevif
dOR
VOICEkORCcORdesccANDdthenevif
vOR
DATAkORCcORdesccANDvthenevif
dOR
VOICEkORCcORdesccANDdthenevif
vOR
DATAkORCcORdesccANDvthenevif
du
vu
d
v
d
v
==
==
>
=====
>
=====
>
=====
>
=
=
=
==
1
)) 1 ( 1 (
1
)) 1 ( 1 (
1
)) 2 ( 1 (
1
)) 2 ( 1 (
2
22
2
22
1
11
1
11
μ
μ
μ
μ
μ
μ
Where:
v
1
and d
1
are the number of voice and data
connections, respectively, on macrocell;
v
2
and d
2
are the number of voice and data
connections, respectively, on fentocell;
c indicates if the user is disconnected or
connected to a macrocell or fentocell;
k is the type of applications;
ev is the last event;
MaxCR
1
and MaxCR
2
are the maximum
number of connections in the macrocell and
femtocell, respectively;
λ
v
1
and
λ
v
2
are the voice calls arrival rates in
macrocell and femtocell, respectively.
λ
d
1
and
λ
d
2
are the data calls arrival rates in
macrocell and femtocell, respectively.
λ
v
u
and
λ
v
u
are the user arrival rates for voice
and data, respectively, which have to be
decide if connect on macrocell or femtocell.
μ
v
1
,
μ
v
2
,
μ
v
u
are the service rates of voice
calls in macrocell, femtocell and that was
allocated by the user, respectively.
μ
d
1
,
μ
d
2
,
μ
d
u
are the service rates of data
requests in in macrocell, femtocell and that
was allocated by the user, respectively.
The set of possible actions A(s) for each state s
∈
S is:
A(v
1
,d
1
,v
2
,d
2
,c,k,ev) =
0 if ev ∉ {
λ
vu ,
λ
du}
OR if ev ∈ {
λ
vu ,
λ
du} AND (v1+ d1= MaxCR
1
) AND (v2+d2 = MaxCR
2
)
1, 2 if ev ∈ {
λ
vu ,
λ
du} AND (v1+ d1< MaxCR
1
) AND (v2+ d2 < MaxCR
2
)
1 if ev ∈ {
λ
vu ,
λ
du} AND (v1+ d1< MaxCR
1
) AND (v2+d2 = MaxCR
2
)
2 if ev ∈ {
λ
vu ,
λ
du} AND (v1+ d1= MaxCR
1
) AND (v2+ d2 < MaxCR
2
)
⎧
⎨
⎪
⎪
⎪
⎩
⎪
⎪
⎪
Where:
0 indicates that there is no action to choose. In
this case, if the last event is not a request from
the user (ev
∉
{
λ
vu,
λ
du}), the decision is to
connect while there is at least one free
channel. Otherwise, if the last event is a
request from the user (ev
∈
{
λ
vu,
λ
du}), but
both networks are full, then reject the request;
1 indicates that the user should connect to the
macrocell;
2 indicates that the user should connect to the
femtocell.
To compute the expected time until the next decision
epoch, transitions probabilities and the costs, the
following algorithm can be used:
Algorithm CalcTransitionProbabilities
Input: state sf and action a.
Output: τ - expect time until the next
decision epoch;
p
sfst
(a) - the probability to move
from state sf to each state st;
cost – the cost entailed when
action a is chosen in state sf.
Begin
pd
Å
0;
τ
Å
0; cost
Å
0;
v1
Å
sf.v1; d1
Å
sf.d1; v2
Å
sf.v2;
d2
Å
sf.d2; c
Å
sf.c; k
Å
sf.k; ev
Å
sf.ev;
if (ev=
λ
v1) AND (v1+d1<MaxCR1) then v1++;
if (ev=
λ
d1) AND (v1+d1<MaxCR1) then d1++;
if (ev=
λ
v2) AND (v2+d2<MaxCR2) then v2++;
if (ev=
λ
d2) AND (v2+d2<MaxCR2) then d2++;
if (ev=
λ
vu) then
if (a=1) then v1++; c
Å
C1; k
Å
VOICE;
if (a=2) then v2++; c
Å
C2; k
Å
VOICE;
if (ev=
λ
du) then
if (a=1) then d1++; c
Å
C1; k
Å
DATA;
if (a=2) then d2++; c
Å
C2; k
Å
DATA;
if (ev=
μ
v1) then v1--;
if (ev=
μ
d1) then d1--;
if (ev=
μ
v2) then v2--;
if (ev=
μ
d2) then d2--;
if (ev=
μ
vu) AND (c=C1) then
v1--; c
Å
desc; k
Å
desc;
if (ev=
μ
vu) AND (c=C2) then
v2--; c
Å
desc; k
Å
desc;
pd
Å
pd+
λ
v1+
λ
d1+
λ
v2+
λ
d2;
if (c=desc) then pd
Å
pd+
λ
vu+
λ
du;
if ((v1=1) AND (c=desc OR c=C2 OR
k=DATA)) OR (v1>1) then
Nmu
Å
v1;
if (c=C1) AND (k=VOICE) then Nmu--;
pd
Å
pd + Nmu*
μ
v1;
if ((d1=1) AND (c=desc OR c=C2 OR
k=VOICE)) OR (d1>1) then
Nmu
Å
d1;
if (c=C1) AND (k=DATA) then Nmu--;
pd
Å
pd + Nmu*
μ
d1;
if ((v2=1) AND (c=desc OR c=C1 OR
k=DATA)) OR (v2>1) then
Nmu
Å
v2;
if (c=C2) AND (k=VOICE) then Nmu--;
pd
Å
pd + Nmu*
μ
v2;
if ((d2=1) AND (c=desc OR c=C1 OR
k=VOICE)) OR (d2>1) then
Nmu
Å
d2;
if (c=C2) AND (k=DATA) then Nmu--;
pd
Å
pd + Nmu*
μ
d2;
if (k=VOICE) then pd
Å
pd +
μ
vu;
if (k=DATA) then pd
Å
pd +
μ
du;
SMARTGREENS2012-1stInternationalConferenceonSmartGridsandGreenITSystems
212
τÅ
1/pd;
for each state st
∈
S do
if (v1=st.v1) AND (d1=st.d1) AND
(v2=st.v2) AND (d2=st.d2) AND (c=st.c)
AND (k=st.k) then
switch (st.ev)
case
λ
v1: p
sfst
(a)
Åλ
v1/pd; break;
case
λ
d1: p
sfst
(a)
Åλ
d1/pd; break;
case
λ
v2: p
sfst
(a)
Åλ
v2/pd; break;
case
λ
d2: p
sfst
(a)
Åλ
d2/pd; break;
case
λ
vu: p
sfst
(a)
Åλ
vu/pd; break;
case
λ
du: p
sfst
(a)
Åλ
du/pd; break;
case
μ
v1: p
sfst
(a)
Åμ
v1/pd; break;
case
μ
d1: p
sfst
(a)
Åμ
d1/pd; break;
case
μ
v2: p
sfst
(a)
Åμ
v2/pd; break;
case
μ
d2: p
sfst
(a)
Åμ
d2/pd; break;
case
μ
vu: p
sfst
(a)
Åμ
vu/pd; break;
case
μ
du: p
sfst
(a)
Åμ
du/pd; break;
default: p
sfst
(a)
Å
0;
for i
Å
1 until 2 then
if (c=Ci) then
cost
Å
cost + energyi*Ecost/SR(st,a);
if (k=VOICE) then
cost
Å
cost + Li*LVcost/SR(st,a);
if (ABi < vi*BV + di*BD) then
cost
Å
cost + BVcost/SR(st,a);
else if (k=DATA) then
cost
Å
cost + Li*LDcost/ SR(st,a);
if (AB1 < vi*BV + di*BD) then
cost
Å
cost + BDcost/SR(st,a);
End
Where:
energy1 and energy2 are the energy
consumptions when connected to macrocell or
femtocell, respectively;
Ecost is the energy costs per time unit;
L1 and L2 are the losses in the macrocell and
femtocell, respectively;
LVcost and LDcost are the loss costs for voice
and data applications, respectively;
AB1 and AB2 are the total available
bandwidths in the macrocell and femtocell,
respectively;
BV and BD are bandwidths used for one voice
and data connection, respectively;
BVcost and BDcost are the voice and data
costs, respectively, entailed when the total
number of connections need more bandwidth
than what is available.
4 NUMERICAL RESULTS
Table 1 shows the numerical values used to perform
the experiments.
Observe that the costs are dimensionless, since
for losses and bandwidth overhead it is not possible
to define monetary costs. These costs are used to
weigh what parameter is more critical.
Table 1: Parameters and numerical values.
Parameter Value
MaxCR1 10 connections
MaxCR2 5 connections
energy1 10.8 J
energy2 7.6 J
Ecost 20
L1 0.5 %
L2 2 %
LVcost 40 - 70
LDcost 20
AB1 1 Mbits/s
AB2 5 Mbits/s
BV 12.2 kbytes/s
BD 144 kbytes/s
BVcost 70
BDcost 30
λ
v
1
2 requests/s
λ
d
1
10 requests/s
λ
v
2
1 request/s
λ
d
2
5 requests/s
λ
v
u
0.5 requests/s
λ
d
u
1 request/s
μ
v
1
=
μ
v
2
=
μ
v
u
0.25 requests/s
μ
d
1
=
μ
d
2
=
μ
d
u
2 requests/s
In this paper, the energy cost was set to 20, while
the cost for voice losses was set for a value between
40 and 70. However, it is important to note that the
energy cost will be multiplied by the value of energy
consumption and the losses cost will be multiplied
by the amount of loss observed. The total weight for
energy sums to 216 for the macrocell and 152 for the
femtocell, while the total weigh for losses on voice
connections will be a value between 80 and 140. The
same analysis can be performed for others costs,
which shows that the energy has been used as the
most critical parameter.
Analyzing the optimal policy it is observed that
when the voice loss cost is 40 (lower value) all
requests from the user (voice or data) should be
serviced by the femtocell; only when the femtocell is
full the requests should be serviced by macrocell.
Increasing the loss cost it is observed that the
data connections should be serviced by the femtocell
and the voice calls should be serviced according the
femtocell congestion. While the congestion is low,
the connection is preferred, otherwise, connection to
macrocell is preferred.
For a loss cost of 70 (the highest value observed)
the optimal policy indicates that voice and data
should be connected to macrocell. Only for
ANOPTIMIZATIONMODELFORALLOCATIONOFNETWORKUSERSINMACRO-FEMTONETWORKS-An
ApproachbasedonEnergyEfficiencyandQualityofService
213
congestion exceeding 80% the data requests should
be serviced by femtocell.
Increasing the loss cost means reducing the
battery consumption importance. However, Figure 2
shows that the average battery consumption has a
limit, increasing up to 20%.
Figure 2: Average battery consumption X voice loss cost.
5 CONCLUSIONS
Through an optimized allocation, this work sought
to provide users the minimum levels of service
quality, maximizing battery lifetime at client node.
However, one must consider that the traffic used
(voice and data) have specific characteristics (such
as bandwidth, minimum levels of QoS, transmission
cost), which generates different behavior at the time
of transmission.
It can be seen, from the results, that voice
connections should be designed to macrocells,
which, despite having smaller bandwidths, can meet
a higher number of voice calls, have a greater
coverage area and lower levels of loss (due to
congestion and interference). The data traffic should
be directed to the femtocells, which have higher
bandwidth, and that, even with a loss of data, can
meet the minimum QoS of this particular
application; mainly due to existing correction
protocols in TCP/IP.
Thus, the following contributions can be seen as
results of this work: (a) the proposal of a Markov
optimization model for optimal allocation of users in
macro-femto network, considering the type of traffic
to be transmitted, (b) different from studies in
literature, the model was built considering crosslayer
aspects (bandwidth, signal strength) and energy
efficiency (battery level);
As limitations, it is emphasized that the model
was implemented in a general way, not realizing
specific studies, such as (a) Costs associated with
handoffs (between macro and femto cell), (b) Cost
associated, with each new call, to choose which
network to connect.
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