Matlab code

Matlab code

implement a heterogeneous network consisting of one macrocell base station and multiple femtocell base stations and multiple mobile requirements. The mobile requirements moving in the coverage area of the base stations and handing over to the base station with the strong signal, i.e. the mobile switch from cell to cell during its mobility according to the received signal from the target cell.
Specification of matlab code:
1-well commented
2-plotting all outputs

Novel Handoff Decision Algorithm in
Hierarchical Macro/Femto-Cell Networks
Jung-Min Moon∗ and Dong-Ho Cho†
Department of Electrical Engineering
Korea Advanced Institute of Science and Technology (KAIST)
Email: jmmoon@comis.kaist.ac.kr∗, dhcho@ee.kaist.ac.kr†
Abstract—Hierarchical macro/femto-cell networks are considered
as a promising technology for the improvement of indoor
coverage and network capacity. In these emerging networks,
handoff procedures for a mobile station moving from a macrocell
to a femtocell should be provided to maximize the advantages
of femtocells with respect to user satisfaction and system performance.
Therefore, we propose an efficient handoff decision
algorithm that can be utilized in the situation where a user
enters the coverage area of the femtocell. The main idea of the
proposed algorithm is to combine the values of received signal
strength from a serving macro BS and a target femto BS in
the consideration of large asymmetry in their transmit powers.
Numerical results show that there is a significant gain in view
of the probability that the user will be assigned to the femtocell
while keeping the same level of the number of handoffs.
I. INTRODUCTION
The development of hierarchical macro/femto-cell networks
has been widely regarded as one of the key technologies to
provide better service quality for indoor mobile users. In these
emerging networks, a hierarchical cell structure is formed as
a large number of low-power femto base stations (BSs) are
deployed in the coverage area of macro BSs. By implementing
these femto BSs in a systematic manner, indoor mobile users
are able to experience a higher signal to interference and noise
ratio (SINR) from the femto BSs compared to that from the
macro BSs. In addition, enhanced radio resource management
can be performed by the macro BSs, because data traffic in the
femto BSs is absorbed into wired backhaul links such as cable
and DSL [1]. As a result, network capacity, or equivalently,
the total number of active users in the service area will be
increased. However, several problems related to interference
mitigation and handoff in the hierarchical cell structure are
still remaining to be solved.
In this paper, we are interested in the design of a handoff
decision algorithm, especially, that can be utilized by a mobile
station (MS) moving from a macrocell to a femtocell. Here, it
is assumed that the MS has a capability to detect neighboring
femtocells. With this assumption, our discussion is concentrated
in a handoff procedure. In hierarchical macro/femtocell
networks, there are two interesting requirements about
mobility management. First, an MS gives higher priority to a
femto BS over a macro BS when the MS selects its serving BS.
A reason for this requirement is not only the high utilization
of femtocells but also the usage of different billing models
between two types of cells [2]. Thus, performing handoff
from a macrocell to a femtocell efficiently can be seen as a
way of increasing user satisfaction. Second, the deployment
of femtocells should not cause drastic changes on mobility
management procedures used in conventional macrocell
networks. It means that conventional methods, such as cell
scanning and handoff, can also be applied to the hierarchical
macro/femto-cell networks. In the aspect of fulfilling these
requirements, various handoff algorithms based on received
signal strength (RSS) with hysteresis and threshold [3] have a
common and critical drawback: that is, a criterion for handoff
from a macrocell to a femtocell is hard to be satisfied when
the femto BS is installed in the center or inner region of the
macrocell. This phenomenon is caused by much lower transmit
power of the femto BS compared to that of the macro BS.
The typical values of the transmit power are 20 dBm for the
femto BS and 46 dBm for the macro BS, respectively [4].
Therefore, it is necessary to design a suitable handoff decision
algorithm for the situation where a user’s call is handed off
from a macrocell to a femtocell.
Many works have been done on the development of handoff
algorithms. The main objective in these works is to decide
an optimal connection with respect to user or system performance,
while minimizing handoff latency and the number of
handoffs. The most commonly used algorithm is based on
the comparison of RSS’s and the concept of hysteresis and
threshold [3]. Note that the threshold sets the minimum level
of the RSS from a serving BS and the hysteresis adds an extra
margin to the RSS from a serving BS compared to that from a
target BS. As applications of this algorithm, Moghaddam et al.
[5] studied optimum combination of hysteresis and threshold
to improve a handoff initiation phase. Moreover, Lee et al.
[6] proposed an adaptive hysteresis algorithm to adjust the
hysteresis according to user mobility and Zahran et al. [7]
proposed a signal threshold adaptation algorithm where service
requirements including RSS are reflected on determining the
threshold. In addition to these handoff algorithms using RSS,
various handoff criteria based on distance [8], bit error rate
[9] and achievable bandwidth [10], were suggested. Even
though their efficiency was verified by both numerical and
simulation results, the environment where a large number of
femto BSs using extremely low transmit power are deployed
in the coverage area of macro BSs was not taken into account.
Therefore, we propose a new RSS based handoff algorithm
for a handoff scenario where a user moves from a macrocell
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the WCNC 2010 proceedings.
978-1-4244-6398-5/10/$26.00 ©2010 IEEEto a femtocell. The main idea of the proposed algorithm is to
combine the values of RSS from a serving macro BS and a
target femto BS to derive a reasonable handoff criterion. In
order to provide an efficient process for this combination, the
concept of a combination factor is introduced and we analyze
its performance in terms of cell assignment probabilities and
the number of handoffs. Numerical results show that there is
a significant advantage in view of the assignment probability
to the femtocell while keeping the same level of the number
of handoffs.
The rest of the paper is organized as follows. In Section
II, a system model and several notations used in this paper
are explained. In Section III, the problem of a conventional
RSS based handoff algorithm and the overall description
of a proposed algorithm with its performance analysis are
presented. Next, numerical results are discussed in Section IV
and the paper is concluded in Section V.
II. SYSTEM MODEL
Here, we describe a system model and several notations
used in this paper. As shown in Fig. 1, our attention is focused
on the situation where an MS performs handoff from a serving
macro BS to a target femto BS. The distance between these
two BSs is set to a variable. Thus, the effects of different RSS
levels from the macro BS on the performance of the handoff
will be examined.
Let sm[k] and sf [k] denote the values of RSS from the
macro BS and the femto BS at time k, respectively. Note that
the subscript m and f indicate the macro BS and the femto BS,
respectively. With the values of transmit power Pm,tx, Pf,tx
and path loss P Lm[k], P Lf [k], the values of RSS from the
macro BS and the femto BS can be expressed as follows:
sm[k] = Pm,tx − P Lm[k] − um[k]
sf [k] = Pf,tx − P Lf [k] − uf [k] (1)
where um[k] and uf [k] represent the log-normal shadowing
with mean zero and variance σ2
m and σ2
f , respectively. It is
assumed that um[k] and uf [k] are independent each other and
each of them has an exponential form of correlation function
[11] with a correlation distance d0.
In order to avoid abrupt variation of the RSS, an exponential
window function w[k] is applied to sm[k] and sf [k], as studied
in [8][9]. This operation can be expressed as follows:
s¯m[k] = w[k] ∗ sm[k] and s¯f [k] = w[k] ∗ sf [k]
where w[k] = 1
d1
exp −kds
d1

(2)
In (2), ds represents the distance between two adjacent measurement
locations and d1 represents a window length that
decides the shape of the window.
According to the study in [8], the correlation coefficient
between two RSS samples and the variance of the shadowing
when the correlated shadowing and the exponential window
function are considered can be written as follows:
1st Femto BS
2nd Macro BS
Outer region
1st Macro BS Of macrocell
2nd Femto BS
Inner region
of macrocell
Fig. 1. Handoff scenario of MS moving from macrocell to femtocell
ρc = 1
d0 − d1

d0 exp
−|ds|
d0

− d1 exp
−|ds|
d1
(3)
σ2
mw = d0σ2
m
d0 + d1
and σ2
fw = d0σ2
f
d0 + d1
(4)
III. PROPOSED HANDOFF DECISION ALGORITHM
In this section, we point out the inadequacy of a conventional
RSS based handoff algorithm for applying to hierarchical
macro/femto-cell networks. Then, we present the overall
description of a proposed algorithm and analyze its performance,
especially, in terms of cell assignment probabilities
and the number of handoffs to investigate its suitability.
A. Problem Definition
In current mobile communication systems, such as 3GPP
LTE, many mobility management procedures including handoff
and cell (re)-selection are performed by using various RSS
based criteria [2]. The most general form among them is the
comparison method of RSS’s in which hysteresis and threshold
are used [3]. If this criterion is applied to the handoff scenario
illustrated in Fig. 1, a criterion for handoff from a macrocell
to a femtocell can be expressed as follows:
s¯m[k] < sm,th and s¯f [k] > s¯m[k]+Δ (5)
where sm,th and Δ represent the minimum RSS level from a
serving macro BS and the value of hysteresis, respectively.
Because of large difference in transmit powers of the macro
BS and the femto BS, whose values are approximately 46
dBm and 20 dBm, respectively, the above criterion in (5) can
hardly be satisfied. Particularly when the femto BS is installed
in the center or inner region of the macrocell, the macro
BS easily has the first priority as a target BS for handoff
even though signal degradation due to walls is considered.
Therefore, the need for enhancement is raised and we propose
a new RSS based handoff decision algorithm to overcome such
a drawback of the conventional algorithm.
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the WCNC 2010 proceedings.

Handover Study Concerning Mobility
in the Two-hierarchy Network
Wu Shaohong1
, Zhang Xin1
, Zheng Ruiming1
, Yin Zhiwei2
, Fang Yinglong1
and Yang Dacheng1
1
Wireless Theories and Technologies Lab (WT&T), Beijing University of Posts and Telecommunications 2
School of Telecommunication Engineering, Beijing University of Posts and Telecommunications
Beijing, P.R.China
nancywsh26@gmail.com
Abstract—Femtocell is an emerging technology aiming for a
better indoor coverage. Due to its low costs, high grade of service,
promotion of fixed-mobile convergence (FMC) and no change of
core network, it has drawn close attention from the
standardization organizations and the industrial field. In the twohierarchy
networks composed of traditional macro cellular
networks and the embedded femtocell hotspots, moving mobiles
will face up to the continual handovers when they pass along
buildings. In this paper, handover algorithms considering signal
strength and velocity are proposed using the mathematics
concept of “set”. Different handover methods are discussed and
simulated. The results show that the proposed algorithms can
obviously cut down unnecessary handovers compared with
conventional soft handover on the same handover signal level
limit. What’s more, throughput of high speed mobiles and the
total throughput of macro base station are increased.
Keywords- femtocell; macrocell; handover; velocity; signal
I. 0BINTRODUCTION
Obvious interference will be engendered when an indoor
mobile connected to the outdoor base station due to thick walls
and metal windows. Several FMC (fixed-mobile convergence)
technologies have developed in the past few years trying to
enhance indoor coverage, such as 802.11, WLAN, WiFi.
However, these technologies operate on an unlicensed
spectrum, and they are exposed to the attack from hackers.
Recently, a new technology called 3G femtocell has been
emerging. Its main purpose is to increase indoor coverage for
voice and high speed data service. A femtocell is deployed
indoor serving as an indoor cellular base station. It works on
the licensed spectrum and connects to the operator’s core
network over the Internet via broadband backhaul. Therefore, it
guarantees low costs, high quality services, security and no
change for the core network. These advantages make 3G
femtocell a seductive technology in indoor coverage as well as
in FMC. [1] – [2] present an overview of femtocell technology,
evaluate its performance and bring forward the pending issues.
However, there are still some problems in the process of
carrying out the new technology. As femtocells are deployed
randomly by users, these plug-and-play residential base station
will be on or off self-willed and are possible to move anywhere
at any moment. Such issue will lead to an unknowable change
of the network topology. Therefore the handover scheme
between femtocells and macrocells is very crucial. In order to
improve user’s QoS and maintain a low outage rate, lower
handover rate is absolutely necessary. On the other hand, a low
handover rate will enhance the throughputs of femtocells and
macrocells.
In [3], an auto-configuration method due to signal control is
proposed to reduce the macrocell users’ call dropping by autoconditioning
femtocell’s transmission and pilot power levels.
Simulation results show the handover probability as a function
of the distance from a femtocell for the users traveling from the
macrocell to the femtocell. [4] – [5] evaluate different handover
algorithms including soft handover in a picocell, while [6]
provides a analytical model for handover schemes. In [7], a
moving mobile is taken into account and a handover algorithm
based on the estimates of location and velocity of the mobile is
brought forward. A real GSM system is used to evaluate the
algorithm and the results show an obvious cut of unnecessary
handovers with the proposed scheme. [8] studies the optimal
moving speed access and suggests a mobility management
scheme for multi-hop networking in hybrid networks. [9]
describes a coverage adaptation method by maintaining a
certain number of mobility events around the femtocell so as to
optimize the femtocell coverage. However, these papers havn’t
combined signal and mobility together when discussing
handover issues in the two-hierarchy network.
This paper aims at analyzing the handover algorithm and
the whole system’s performance in a real scenario when mobile
stations moving with respective velocity in macro cellular cells
which have numbers of femtocells deployed in. In Section II,
the system model of macrocells with embedded femtocell base
stations is presented. Section III brings forward two improved
handover algorithms considering the mobility of mobiles in the
severe multi-path fading environment. Simulation results and
several discussions are presented in Section IV. Finally, the
conclusions of this study are given in Section V.
II. 1BSYSTEM MODEL
A. 5BDeploying Model
In the practical system, femtocells are deployed randomly
in macro cellular cell coverage area. As the handover analysis
978-1-4244-2517-4/09/$20.00 ©2009 IEEE 1is carried in the whole system considering the mobility of
mobile stations (MS), the two-dimensional environment is
adopted to investigate. The sketch map of the deploying model
is shown in Fig. 1 in regard to [10]. There are 7 macrocells
with 3 sectors each shown in blue asterisks and several hundred
femtocells (12m*12m) deployed randomly within the
macrocells shown in red squares. As shown in Fig. 1, the
distance between sites is 1000m. Femtocells are inhibitive
overlapped and they are neighboring deployed according to
uniform distribution.
B. 6BPath Loss Model
In this study, femtocells are granted to use the same
frequency as the macrocells in the two-hierarchical network.
Hereby, the complicated interferences should be carefully
calculated. The path loss models for various scenarios
expressed in [11] and [12] are adopted.
1) Outside Mobiles
If the MS is outside a house, its path loss to the macro BTS
in [12] is
_ 10 15.3 37.6log PL d G PL PL macro MSout ant addi shad =+ −+ + , (1)
where d is the distance from the macrocell to the MS in
meters, Gant is the antenna gain according to its pattern,
PLaddi is some additional path loss, PLshad denotes the
lognormal shadow fading which has 8db standard deviation. It
is an 3GPP macrocell model and can be simplified as follows,
_ 10 128.1 37.6log PL d macro MSout = + , where d is the distance
between the macrocell and the MS in kilometers.
If the MS is outside a house, its path loss to the femtocell in
[12] is
_
max(15.3 37.6log ,37 20log ) 10 10
femto MSout
ow
PL
= + + ++ d d qW L , (2)
where d is the distance between the femtocell and the MS in
meters, W is the wall partition loss and is set to be 5dB, Low is
the outdoor penetration loss and the value is 10dB with
probability 0.8 and 2dB with probability 0.2, q is the number
of walls between the MS and the femtocell chosen from the set
ˆ 0,1,…,
w
d
d
⎧ ⎫ ⎪ ⎪ ⎢ ⎥ ⎨ ⎬ ⎢ ⎥ ⎪ ⎪ ⎩ ⎭ ⎢ ⎥ ⎣ ⎦
with equal probability in which ˆ
d is the
portion of d inside the house and w d is set to be 2m.
2) Inside Mobiles
If the MS is inside a house, its path loss to the macro BTS
in [12] is
PL PL R qW L macro MSin Virt macro MS ow _ __ = ++ + α . (3)
-1500 -1000 -500 0 500 1000 1500 -1500
-1000
-500
0
500
1000
1500
X (m)
Y (m)
Deployment Figure
Figure 1. Deployment of femtocells within macrocells
To figure out the path loss in this instance, four virtual MSs
are assumed locating at the edges of the house. Equation (3) is
calculated for each virtual MS.

In (3), PLVirt macro MS _ _ is the path loss between the macro
BTS and the virtual MS which can be calculated by (1), α is
the attenuation coefficient set to be 0.8dB/m, R is the distance
between the macro BTS and the virtual MS, q is the number of
walls between the macro BTS and the virtual MS chosen from
the set 0,1,…,
w
R
d
⎧⎪ ⎢ ⎥⎫⎪ ⎨ ⎢ ⎥⎬ ⎪⎩ ⎭ ⎣ ⎦⎪
with equal probability. Other parameters
have the same meaning as in (2) and get their value similarly.
Finally, the smallest one from the four calculated numerical
values is chosen to be the result.
If the MS is inside the same house as the femtocell, the path
loss between them in [12] is
_ _ 10 37 20log PL d qW femto MSin Same =+ + , (4)
where the parameters represent the same signification as in (2).
If the MS is inside a different house from the femtocell, the
path loss between them in [12] is modified to be
_ _ 10
(1) (2)
10
max(15.3 37.6log ,
37 20log )
femto MSin Diff
ow ow
PL d
d qW L L
= +
+ + ++
, (5)
where (1) Low and (2) Low are the penetration losses for the two
houses, q is a random number chosen from the set
1 2
ˆ ˆ 0,1,…,
w
d d
d
⎧ ⎢ + ⎥⎫ ⎪ ⎪ ⎨ ⎢ ⎥⎬
⎩ ⎭ ⎪ ⎢⎣ ⎥⎦⎪
where 1
ˆ
d and 2
ˆ
d are the portions of d
inside the two houses.
2III. 2BPROPOSED HANDOVER ALGORITHM
A. 7BBrief Review of Previous Handover Algorithms
In [5], a sum-up of several handover schemes is discussed.
For soft handover algorithm, a minimum received signal level
limit 0 S is set. When the received pilot signal from the current
base station is below the level 0 S and there is another base
station that sends a pilot signal above the level 0 S , handover
happens. For decision-avoided algorithm, there are two signal
levels 0 S and m S where 0 S is the minimum received signal
level and m S is no lower than 0 S . A decision-avoided
handover trigger mechanism includes not only the conditions
that satisfy the soft handover, but also a sufficient pilot signal
received from second base station which is higher than m S . As
there is severe multi-path fading in femtocell indoor systems,
the merit of suppressing the ping-pong effect is really
important.
However, when it applies to the macro cellular cells with
numbers of femtocells deployed in, the previous proposed
handover algorithms unfold some deficiency. As a macro MS
moving besides the femtocells, it will undergo frequent
handovers. For example, when a moving car passes through a
block deployed of femtocells showed in Fig. 2, its chief service
base station will switch continuously due to the fixed handover
signal levels of previous handover algorithms. High handover
probability results in poor quality of service, a high dropping
probability. As the drop probability keeps a fixed proportion to
the handover probability, the handover algorithm has to be
improved to reduce unnecessary handovers for the special
scenario.
B. 8BProposed Handover Algorithm Considering MS’s Moving
Activity
For simplification, analysis processes in one macro cellular
cell. When it comes to multi macrocells, common soft
handover algorithm is used firstly to find a main serving
macrocell and then use the proposed algorithm.
Define a set No N M . 1, 2,…, , = { } where N is the total
number of the femtocells, M defines the macrocell base
station. In order to avoid confusion, set M > N . Every
element in the set is a marker of its coherent base station.Using
if-then rules, the proposed handover algorithm is described as
follows,
Figure 2. Handover scenario of a moving car in the two-hierarchy network
0
0 0
00 0
1: , ( )
2: , ( ) , ( )
3 : , ( ) ( ) ,
( )
4: ( ) , ( )
5:
ms ms
ms ms ms
ms ms ms
ms
ms m ms
Rule if V V then BS BS M
Rule if V V and S i S then BS BS i
Rule if V V S i S and S j S
then BS BS j
Rule if S j S then BS BS i
Rule i
≥ =
< ≥=
<< ≥
=
< =
( ), interchange ms f BS BS i then i and j =
.(6)
In (6), the rules are evaluated sequentially. Vms is the
velocity of the MS, V0 is the maximum handover velocity level
limit, ( ) ms S i is the received pilot signal from the i th femtocell
base station, 0 S is the minimum received signal level, m S is
the handover signal level limit which is higher than 0 S , B ms S
is the MS’s chief serving base station, ( ) BS M is the macro
cellular cell base station, ( ) BS i is the i th femtocell base
station. In the rules, i j No N M , . 1,2,…, , ∈ = { } and i is the
marker of the MS’s current serving base station.
This proposed handover algorithm considers the velocity
and the received signal strength of MS and it is named velocity
and signal handover algorithm accordingly which is
abbreviated as VSHO. As velocity is taken into account and
evaluated firstly, the frequent handovers of those high speed
MSs are avoided. What’s more, it can be prefigured that the
high velocity MSs will receive a better service, such as a lower
drop-off rate and larger system capacity.
C. 9BProposed Handover Algorithm Considering the
Difference of Received Signal Levels from Macro BTS and
Femtocell BTS
Generally, the MS will receive higher signal strength from
femtocells in a house than from the macrocells outside. In [3],
simulation results reveal that the femtocell’s coverage leaks
outside for round about ten meters with a signal about -80dBm
to -70dBm. Considering this issue, VSHO should make some
change to include the difference between macrocell base
stations and femtocell base stations. The improved handover
algorithm is called unequal handover algorithm with the
abbreviation UHO for short. For multi macrocells, the
conventional soft handover algorithm is employed firstly and
then the modified proposed algorithm is performed.
Define two set No M 1. = { } and No N 2. 1,2,…, = { } .
Unequal handover algorithm (UHO) is described in sequential
rules as follows
0
0 0|
0 0| 0|
|
1: , ( )
2: ( ) , ( )
3: , ( ) ( ) ,
( )
4: ( ) , ( )
5:
ms ms
ms ms i ms
ms ms i ms j
ms
ms m j ms
Rule if V V then BS BS M
Rule if V V and S i S then BS BS i
Rule if V V S i S and S j S
then BS BS j
Rule if S j S then BS BS i
Rule
≥ =
<≥ =
<< >
=
< =
( ), interchange ms if BS BS j then i and j =
. (7)
3In (7), i j No No M N , 1. 2. 1,2,…, ∈ = ∪ ∪ { } { } . If i No ∈ 1. ,
then 0| 0 i S S = , mi m | S S = . If i No ∈ 2. , then 0| 0 i d S SS = + ,
mi m d | S SS = + where 0 d S ≥ . In other words, UHO sets a
higher signal level limit for femtocells than macrocell to serve
as a chief base station. Femtocells have relatively good
coverage. As a result, a higher signal level limit won’t
influence much in the house when reducing the handover
probability besides the house.
IV. 3BSIMULATION RESULT
System level simulations are performed to evaluate the
proposed algorithms. As Fig. 1 shows, femtocells are randomly
deployed in 7 macrocells with 3 sectors each and they are
neighbouring according to uniform distribution. Assume 70%
of mobiles are indoor users and 30% are outdoor subscribers
while 10% of the total mobiles are entering in/out houses to
keep a dynamic balance. Velocity distribution of outdoor
mobiles submits to normal school with an expectation of
100km/h while indoor mobiles’ velocity expectation is 3km/h.
EV-DO Rev.A platform is introduced to evaluate following
the cdma2000 evaluation methodology in [13]. The main
simulation assumptions and parameters are shown in Table. 1.
Channel model of multipath fading is elected according to [13]
considering user’s velocity. Assuming that a period of 150 ms
is needed for a handover to carry through and it will last for at
least 200 ms once it occurs.
A. 10BHandover Probability Discussion
Fig. 3 shows the handover probabilities for conventional
soft handover, the VSHO and the UHO schemes. In the twohierarchy
networks, the handover probability for soft handover
climbs all along with the increased velocity. Since the velocity
limit for the two proposed handover algorithms is 20km/h,
handover probabilities have an instantaneous falling on the
velocity limit point. This is due to that the handover in one
macrocell in the proposed algorithms only occurs to the
mobiles whose moving velocity are lower than the velocity
limit. Above the speed limit, handover probabilities of the two
TABLE I. SIMULATION PARAMETERS
Carrier frequency 2 GHz
Slot duration 1.667 ms
Band width 1228.8 KHz
Cell layout Wrap-around macrocells with 3 sectors each
and femtocells bedded
Distance between sites 1000 m
Antenna pattern 2
3
( ) min[12( ) , ] m
dB
A A θ θ
θ = − ,
where 3 65 deg , 20 dB m θ = = rees A dB
Macrocell BTS Height 20 m
Transmit power 43 dBm
Antenna gain 14 dBi
Noise figure 5 dB
Femtocell Transmit power 0 dBm
Antenna gain 0 dBi
Noise figure 9 dB
Path loss model See in Section Ⅱ
0 10 20 30 40 50 60 70 80 90 100 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Velocity (km/h)
Handover Probability
Soft HO
VSHO
UHO
Figure 3. Handover probability as a function of mobile’s velocities for the
three algorithms with V0 = 20km/h, S0 = Sm = -70dBm, Sd = 4dBm

 

 

 

Is this the question you were looking for? If so, place your order here to get started!