1
Interference-Aware Self-Deploying Femto-Cell
Weisi Guo, Siyi Wang
Department of Electronic and Electrical Engineering
University of Sheffield, United Kingdom
Email: {w.guo, siyi.wang}@sheffield.ac.uk
Abstract—Femto-cells have been proposed as a throughput
boosting solution for indoor users, but a key challenge is how
to resolve the interference between neighboring cells. This paper
proposes a distributed self-deployment solution to improve the
indoor throughput. This investigation considers where to optimally place a femto-cell in a multi-room indoor environment. The
novelty of the research is that a closed-form Femto-cell placement
expression has been derived, which can maximize the throughput
of the indoor building given knowledge of a few key statistical
network parameters. The benefit compared to blind placement is
that it can achieve a throughput improvement of 20 to 50%. The
solution has a high placement error tolerance of 10% of building
size. Another benefit is that the optimal solution can maintain
an approximately constant level of throughput, irrespective of the
indoor building size.
I. I NTRODUCTION
In recent years, there is increased interest in Femto-cell
Access Points (FAPs), as an integrated heterogeneous cellular network solution to providing capacity in areas of poor
coverage and high user density. Typically this is for indoor
areas, where over 70% of the mobile data traffic occurs. In
order to maximize indoor throughput, a key challenge is how
to reduce cellular interference. Typically, this interference is
strongest for adjacent FAPs and between FAPs and a nearby
outdoor basestation (BS). Existing research on self-organizingnetworks (SON) has focused on inter-cell coordination techniques, which generally require inter-cell interfaces. There
are significant challenges to implementing inter-cell interfaces,
due to the lack of operator control over FAP deployment, their
closed subscriber group (CSG) nature, and the diverse number
of hardware vendors.
A. Review
Whilst the location and transmission of outdoor BSs are
controlled by operators to meet throughput and coverage
targets, there is less understanding and control on where indoor
access-points (APs) can be placed. Conventionally, indoor APs
are located in areas of convenience. Indeed, the end-user can
not always arbitrarily decide where an AP can be placed, but
this paper shows that given a choice of regions in a room, there
are regions which are more beneficial than others. Optimal
placement of wireless nodes have been previously investigated
in [1] [2], whereby iterative computational techniques were
used to find the optimal location of multiple nodes. Given
that the diverse variations in buildings, such a model requires
knowledge of: the building structure, electromagnetic properties of materials, and user locations. This is not a practically
Fig. 1. System setup for indoor FAP placement with respect to a building
and interference parameters.
distributable solution for homes and small enterprise scenarios.
To our knowledge, given a general building structure, no
explicit rule has been derived for where an AP should be
placed, as a function of statistical network parameters and cochannel interference.
B. Contribution
The paper proposes a SON deployment solution, whereby
the FAP advises the end-user on approximately where to
deploy it. The analysis shows that the optimal region that
maximizes the indoor throughput is a function of a few
statistical network parameters, which can easily be detected.
Whilst, this solution is independent of signal processing and
radio resource management techniques, it can be used in
conjunction with it to further enhance performance. This is
done by the following steps:
1) Deriving a closed-form signal-to-interference ratio (SIR)
expression for an indoor user. This can then be used to
derive the worst indoor cell-edge throughput.
2) The optimal FAP location that maximizes the indoor
cell-edge throughput can be found.
The paper will show that the throughput benefits can be up
to 50% and this can be employed in conjunction with existing
signal-processing techniques. The envisaged solution is that
this proposed algorithm can be embedded in FAPs and upon
knowledge of the static network environment, it can advise the
end-user on where to place the FAP. Furthermore, the authors
show that the solution has a high tolerance to placement error.
II. S YSTEM S ETUP
The performance of a network is often governed not by
the mean throughput, but rather by the maximum achievable
throughput for a majority percentile of users [3], known
2
as the Quality-of-Service (QoS) of the network. The paper
considers an OFDMA based system, where the downlink QoS
is optimized. The system setup considers a single FAP in
a single floored building. There is a dominant interference
source, either from a neighbouring FAP or an outdoor cellsite (BS). For demonstrative purposes, the authors define the
dominant interference source as that from an outdoor cell.
The challenge is to find where the optimal location of the
indoor serving FAP is, with respect to the interference strength,
propagation and the building parameters. As shown in Fig. 1,
the following key parameters (Table I) are defined as:
• The transmit power of the serving FAP is PS and the
transmit power of the dominant interference cell is PI .
The distance between the interference cell and the nearest
wall of the building is D.
• The total length of the building is L. The indoor serving
FAP is located d away from the wall that faces the most
dominant interference source. A user is at a distance x
away from the serving FAP.
• The outer wall has a penetration loss of W0 and the indoor
wall has a loss of Win . The sum of the walls on either side
of the FAP have a loss of Wj and Wk respectively, with
M
the total indoor wall loss for M walls being WΠ = Win
.
The width of the building is not considered, as for any given
value of x, the signal strength variation is assumed to be
uniform across the associated width. This is true when the
interference distance is greater than the building size (D L).
Typically the SIR quality is poor at the cell-edge, and for
small SIR (γ) values, the following approximation holds true:
γ
log2 (1 + γ) ≈ log(2)
. This justifies taking the expectation of
the multipath and shadow fading distributions. Therefore, the
indoor throughput at the cell-edge x is:
C(x) ≈
The paper defines the indoor SIR at a location x away from
the FAP at location d as:
PS HS Kin x
γ(x) =
PI HI Kout (D + x +
−αin
Wj
d)−αout W
0 Wj Wk 10
− x+d
20
, (1)
where the AWGN power and is regarded as negligibly small
compared to the interference power. The parameter H contains
the effects of multipath fading (∼ X 2 ) and log-normal shadow
fading, which when combined is a modified log-normal distributed with probability density function [4]:
fH (s; σ
e, µ
e) =
1
√
se
σ 2π
e−
(log10 s−µ)
e 2
2σ
e2
(2)
where the modified values are µ
e = −0.58 and σ
e2 = 0.48(σ 2 +
5.57) are given in [4]. The combined distribution of the
S
signal and interference powers ( H
HI ) can be found to yield
an expectation:
E[
2
2
HS
] = e0.48(σin +σout +11.1) ,
HI
(3)
S
and the paper defines a different = ζE[ H
HI ] for the different
locations 1 and 2 shown in Fig. 1 to account for differences
in building clutter (ζ). The specific values of parameters used
are given in Table I.
The system level throughput of a user at location x is
defined as: C(x) = min[log2 (1+γ), Cs ], where the throughput
saturation level is typically Cs = 5.6 bit/s/Hz for LTE,
which is unlikely to be exceeded at the room boundary.
log(2)PI Kout (D + x + d)−αout W0 Wk 10−
x+d
20
, (4)
IV. O PTIMIZATION F ORMULATION
Figure 1 illustrates that the QoS throughput is dictated by
the worst downlink throughput location, which is either at
locations 1 or 2. In order to maximize the QoS with respect to
the location of the FAP (d), the throughput levels at location
1 and 2 must be equal. Otherwise, if the throughput at either
location lesser or greater than the other, it will create a lower
QoS provisioning than the parity case.
The paper considers the general case of a FAP serving a
building with M + 1 equally spaced rooms, as well as the
special case of a FAP serving a single room. In Lemma 1 of
the Appendix, the paper shows that in order to maximize the
QoS throughput, the optimal FAP location is:
d∗ (M ) ≈
L
1 + ΨΩNLOS
L
L αout
ΩNLOS = 10 20αin,NLOS (1 + ) αin,NLOS
D
1
d(M +1)
2 2 − αin,NLOS
Ψ =( Wk )
and Wk = 10b 10L c .
1
(5)
where:
where:
III. T HROUGHPUT
PS Kin x−αin
For non-uniformly placed rooms, the indoor wall spacing
parameter (Wk ) needs to be adjusted. This is beyond the scope
of this paper, but is straightforward from (5).
For a single room, the propagation is predominantly Lineof-Sight (LOS) based, and expression (5) is reduced to:
d∗ (1) ≈
L
1 + ΩLOS
(6)
out
L ααin,LOS
where: ΩLOS = 10
(1 + )
.
D
The theoretical conclusions drawn from expressions (5) and
(6) have been validated by matching simulation results shown
in Fig. 2 for a single and multiple-rooms. Figure 2 shows that
the intuitive solution of placing the FAP in middle of the room
deviates significantly from the optimal solution for large buildings. Furthermore, a tolerance of 80% interference distance
estimation error is tolerable for a 3% loss in performance.
By substituting values from Table I into expression (5), the
following bounds hold true:
 L
small 1-room building (ΩLOS ∼ 1)

 2

L

small M -room building† (L < 10)
L 0.9
1+Ψ(1+ D )
.
d∗ ≈
L
L
 1+Ψ10
weak interference§ ( D
∼ 0)
L/87


 L
both † and § (ΩNLOS ∼ 1)
1+Ψ
(7)
L
20αin,LOS
The novel insights obtained are:
3
Fig. 2. Optimal FAP location (d) results as a function of building length (L) with D = 250m. An estimation error of D is shown to vary from 5% to 95%.
Results are for a) single-room, b) multi-room. Simulation results in symbols and theory in lines.
The maximum throughput solution is a function of the
length of the room (L), distance from the interference
source (D), indoor wall penetration loss (Win ) and the
pathloss exponents. All of these parameters are quasistatic statistical parameters and therefore the solution
does not require constant adjustment.
• The optimal solution is highly tolerant to mis-estimation
of the distance from the dominant interference source
(D). A 50% estimation error only yields less than 6%
error in the solution.
• In a multi-room building, the FAP should always be
placed in the first or second room closest to the interference source, as shown in theory and matching
simulation results in Fig. 2b. if the indoor walls have
a low penetration loss (Ψ → 1), the multi-room (5) and
single room (6) solutions converge.
L
• For a single small room ( 34 ∼ 0), the FAP should be
deployed near the centre of the room. Otherwise, the
FAP should be deployed near the wall closest to the
interference source.
The solution is independent of the transmit power of the
serving-FAP and interference source. This is because the
optimization maximizes the QoS and not the mean throughput.
Therefore, by optimizing the placement, the FAP’s transmit
power can be reduced so that the interference impact on
outdoor network is reduced. This holds true up to the extent
that the network performance is interference limited, i.e.,
the interference power is greater than the AWGN power.
Therefore, under the realistic circumstances considered, the
optimal placement algorithm doesn’t degrade the outdoor
network performance.
•
V. T HROUGHPUT R ESULTS
A. Sensitivity Analysis
The results in Fig. 3 show the throughput improvement of
optimal placement compared to 2 reference deployments:
• Corner of Room/Building for convenience.
• Centre of Room/Building, in absence of interference
knowledge.
By adopting the proposed optimal deployment solution, the
resulting improvement in throughput QoS is ¡20% for small
buildings and 30-50% for large buildings. The definition of
small and large building is given by expression (7).
In reality, the FAP might not be able to be placed at
the desired location even if it was known. Therefore, the
paper examines the margin of error that is acceptable and the
QoS degradation that it can cause. Two forms of error are
introduced, a percentage and a fixed placement error (2-8m).
The results in Fig. 3 show that up to a 10% percentage error
can be tolerated before the QoS is reduced to below the middle
of building solution. Another interesting result is that, if the
FAP is deployed in the centre or the corner of the building, the
QoS drops as the size of the building increases. This is because
the propagation to the furthermost users increases. However,
optimal placement achieves a different trend, whereby the QoS
achieved stays between a range of values. This is because the
FAP location changes with the building size. The results show
the following key benefits:
• Constant Throughput: the QoS does not degrade with
increased building size.
• Tolerance to Error: a placement error of 10% of building
size can be tolerated to guarantee a better QoS than
both reference deployments. An interference distance
estimation error of 50% can be tolerated for a 10%
throughput degradation.
B. Practical Implementation and Discussions
To achieve this distributed FAP placement solution in reality, the paper proposes that each FAP employs an in-built
algorithm that estimates the following parameters:
• Dominant interference strength (reflected by D): either
via pre-knowledge of where the nearest outdoor cell is,
or alternatively via a walk-and-scan method to locate the
strength and direction of the nearest co-channel interference source is also sufficient.
• Pathloss Model’s distance exponents (α): can be estimated from known literature by knowing the type of
environment the building is in.
4
TABLE I
M ODELING PARAMETERS [6]
Fig. 3. Multi-room QoS achieved as a function of building length (L): all
results are theoretical. Margin of error considered are: 10% proportional error,
and 2-8m fixed error.
Length of Building or Room (L) and the wall penetration
loss (W ): can be derived by associating the type of wall
material and known literature on their penetration loss.
Whilst, this does require some degree of effort from the
customer, the closed-form optimal placement expressions have
shown that this is a one-off and rapid process that has been
demonstrated to yield improvements to the throughput(up to
50%).
It is also worth noting that the hand-over boundary between
the indoor and outdoor cell can be inside the building. In such
a case, referring to Fig. 1, cell-edge point 2 should be moved
inwards so that it is at the handover boundary. Whilst, the
paper has presented a general framework, this modification is
relatively straightforward. Generally speaking, literature has
found that the handover zone is outside the building [5].
Parameter
Operating Frequency
Serving FAP Tx Power
Interference Source Tx Power
Pathloss Model
Building Length
Interference Distance
Outdoor Pathloss Constant
Outdoor Pathloss Exponent
Outdoor Shadow sdv.
Indoor LOS Pathloss Constant
Indoor LOS Pathloss Exponent
Indoor NLOS Pathloss Constant
Indoor NLOS Pathloss Exponent
Indoor Shadow sdv.
Indoor Clutter
Indoor-Outdoor Wall Loss
Indoor Wall Loss
Number of Indoor Walls
This paper has shown that the challenge of optimizing
indoor Femto-cell placement can be solved using a novel set
of theoretical expressions, which is supported by matching
simulation results. The deployment guideline is to identify
the direction of the strongest interference source and place
the Femto-cell accordingly. This has been proven for both
a single room scenario and for multi-room buildings, and
the proposed solution can be implemented as an embedded
automated-solution in the hardware.
The benefit is that a throughput gain of 20% for small and
50% for large buildings can be achieved compared to blind
placement strategies. Unlike blind placement, the proposed
solution’s throughput doesn’t continuously decrease as the
building size increases. A sensitivity analysis has shown that
the optimal solution has a placement error tolerance of 10%
of the building size and an interference estimation tolerance
of 50%. The solution can be viewed as a distributed selforganizing solution that doesn’t require CSI or inter-cell
interfaces. The solution doesn’t cause additional interference
to the outdoor network can be used in conjunction with other
techniques.
L
D
Kout
αout
σout
Kin,LOS
αin,LOS
Kin,NLOS
αin,NLOS
σin
ζ
W0
Win
M
Value
2600 MHz
100 mW
10 W
3GPP [6]
2-40 m
20-500 m
4.48×10−4
3.67
3 dB
7.76×10−5
1.69
1.05×10−2
4.33
4 dB
0-4 dB
1×10−2
1×10−1
5
A PPENDIX
•
VI. C ONCLUSIONS
Symbol
f
PS
PI
A. Lemma 1
In order to maximize the QoS, the throughput at locations
1 and 2 in Fig. 1 should be equal, with the FAP location d =
d∗ . By assuming that the the AWGN power (N ) is negligible
compared to the interference strength, the following can be
found:
log2 (1 + γ(x = L − d∗ , 1 )) = log2 (1 + γ(x = d∗ , 2 ))
L
1 L − d∗ −αin
D + L −αout
)
)
(
≈ Wk2 10− 20 (
∗
(8)
2
d
D
L
d∗ =
,
1 + ΩΨ
where the AWGN noise power is assumed to be negligible compared to the interference power. The parameters
αout
1
L
−
L αin
Ω = 10 20αin (1 + D
) , and Ψ = ( 12 Wk2 ) αin,NLOS . For
a multi-room building of M internal walls, the parameter
d(M +1)
Wk = 10b 10L c . For a single room, there are no internal
walls (Wk = 1) and the value of the parameter Ψ = 1.
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