Performance Evaluation of Handoff Detection Schemes
1
P.Marichamy↑, S.Chakrabarti+ and S.L.Maskara*
*Electronics and Electrical Communication Engg. Dept.
+
G.S.Sanyal School of Telecommunication
Indian Institute of Technology, Kharagpur –721 302.
↑
National Engineering College, Kovilpatti – 628 503, India.
E-Mail: [email protected], (saswat, maskara)@ece.iitkgp.ernet.in
Abstract—For successful handoff operation in a cellular
mobile telecommunication, the first and foremost concern is
to detect the handoff requirement without significant delay.
Various handoff procedures have been implemented over
the years to minimize the delay in handoff detection in order
to minimize interruptions in the communication. It is known
that each detection algorithm has its own characteristics and
features to overcome specific problems for which it is
designed. In this paper a new detection algorithm has been
devised which can overcome a problem of occurrence of
handoffs to wrong cells. Performance of the proposed
algorithm is evaluated and compared with a hysteresis based
algorithm.
There are two ways of establishing a radio connection
during handoff and the handoff process is accordingly
termed hard or soft. In case of a hard handoff an MS is
served by only one BS at a time. It makes contact with the
new BS only after breaking its connection with the serving
BS. This is referred to as ‘break before make' connection.
Soft handoff is referred to as 'make before break' connection
as connection to the serving BS is not broken until a
connection to the new BS is made. In soft handoff [2], MS
may be served by more than one BS. The FDMA and
TDMA based cellular systems normally use hard handoff
while cellular CDMA systems use soft handoff techniques.
Various handoff detection algorithms have been
implemented over the years to minimize the delay in
handoff decision. In this paper, various issues involved in
the handoff detection algorithms have been discussed in
Section.2. Performance of a newly proposed detection
algorithm has been discussed in the Section 3. Finally, in
Section 4 conclusions are made.
1. INTRODUCTION
In cellular mobile communications, coverage area is divided
into smaller regions called cells to allow the reuse of the
frequency spectrum to increase the network capacity. Each
cell is controlled by its own transmitter and receiver (or
Base Station) to serve the mobiles within its range. When a
mobile user enters into different cellular regions, the
controls of a call should be transferred from the current base
station to the base station of the cell in which it enters for
continuing the call. This process is termed as ‘Handoff’.
The transfer of controls of a call has to be done fast without
a noticeable break in the communication.
2. HANDOFF DETECTION
The mobile communication channel is known to be a fading
channel. Since, decision for handoff is taken mostly based
on the signal strength measurements, the presence of such
fading cause handoff decisions difficult. Ideally single
handoff is initiated at the cell boundary, where the second
BS signal strength becomes stronger than the first BS.
However, in practice fading causes multiple handoffs (pingpong effect) instead of a required single handoff [1] [3].
These unnecessary handoffs may pose the problems of
i) increase in the network load as each handoff requires
network resources to reroute the call to a new BS, and
ii) shortage in channel resources, leading to call dropping.
So, an efficient handoff detection algorithm is necessary to
avoid such problems in a system. In soft handoff, repeated
handoff refers to the frequent adding and dropping of
probable BSs in the active-set [4]. Signal measured for
handoff decisions are first averaged to minimize the effects
of large scale fluctuations due to short term fading. This
The signal from the serving Base Station (BS) degrades as
the mobile approaches cell boundary. Cellular systems
make use of signal degradation level for determining the
need for handoff. Signal degradation level is measured
mostly by means of a signal strength measurement
performed at BS or Mobile Station (MS) or both. Second
generation mobile systems (e.g, GSM, IS-136 and IS-95)
mostly employ ‘Mobile Assisted Handoff’ (MAHO)
procedure [1], where an MS measures the strength of
signals it receives from adjacent BSs and informs the
controlling BS for handoff decision. The network decides
the handoff from the measurements reported to it by an MS.
0-7803-7651-X/03/$17.00 © 2003 IEEE
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3. PROPOSED RSS-HTNEW ALGORITHM
In cellular systems, there may be handoff to a cell which is
not a right candidate for handoff. It is thought that
occurrence of such wrong cell handoff can be reduced by
delaying the occurrence of handoff until the new BS signal
strength gets sufficiently stronger. To achieve this, an
additional criterion of absolute signal strength (considered
as a threshold) of a candidate or new BS has been involved
in a signal strength based RSS-H algorithm. The resultant
algorithm is termed as RSS-HTnew. This algorithm
improves the performance in the following ways:
Fig.1. A 3 base stations model
i) With proper new BS threshold setting, the number of
unnecessary handoffs to new base station minimized
when the new BS signal strength is not sufficient
enough to serve the call.
ii) Also, with appropriate higher threshold setting, the
number of handoffs occurring to the neighboring
cells which are not intended for handoff can be
minimized.
averaging process reduces many repeated handoffs. Handoff
algorithms are distinguished from one another by the
handoff criteria and the way by which handoff criteria are
incorporated in the detection algorithms. Various criteria [3]
used in making handoff decisions using handoff algorithms
are relative signal strength, signal to interference ratio
(SIR), distance, transmit power, traffic intensity, call and
handoff statistics and mobile velocity etc.
The performance of the proposed RSS-HTnew algorithm
has been evaluated and compared with the RSS-H algorithm
by considering a '3 base station' model.
Handoff Detection Algorithms
Various signal strength based handoff detection algorithms
have been proposed and studied in the literature [5]. They
are
Performance Evaluation
We consider 3 cells served by base stations, BS P, BS Q
and BS R respectively as shown in Fig.1. The mobile is
moving between the BSs P and Q in a straight line path. The
signal received (expressed in dB) from these 3 BSs at the
MS can be written as,
a) Relative Signal Strength (RSS)
b) RSS with absolute Threshold (RSS-T)
c) RSS with relative threshold (hysteresis) (RSS-H)
d) RSS with hysteresis and absolute threshold of serving BS
(RSS-HTser), etc.
p(d ) = µ − η log(d ) + u (d )
q(d ) = µ − η log( D − d ) + v(d )
In Relative signal strength (RSS) algorithm, handoff
decision is taken when the received signal strength from the
candidate base station is greater than the serving base
station whereas, RSS with absolute Threshold (RSS-T)
algorithm initiates handoff when the averaged signal
strength of the current base station falls below a threshold
value and the candidate base station signal strength is
greater than the serving base station. In the RSS with
relative threshold (hysteresis) (RSS-H) [6] approach
handoff is initiated only if the target base station signal
strength is sufficiently stronger by a hysteresis margin (h)
than the serving base station. This method prevents the
repeated handoffs (ping-pong effect). This algorithm may
results in handoffs even when the serving base station signal
strength is sufficiently strong. Such unnecessary handoffs
can be prevented in RSS with hysteresis and absolute
threshold of serving BS (RSS-HTser) algorithm [7], where
absolute signal strength of the serving BS is used as an
additional criteria. However, there may be wrong cell
handoffs. The above hard handoff algorithms have been
compared qualitatively and shown in Table.1.
2 
2

D
  3 D 

r (d ) = µ − η log  − d  + 
 + w(d )
 2
  3 2  


where, D is the distance between the two BSs, d is the
position of an MS from BS P, µ and η are parameters for
path loss, where µ depends on transmitted power at the base
station and η is ten times the path loss exponent. The
components u(d), v(d) and w(d) represent shadow fading,
which follows log-normal distribution. Equivalently, lognormal distribution is represented by the zero mean
stationary Gaussian random processes in dB. The shadow
fading fluctuations have spatial correlation, which is
assumed to be exponential [7]. The values for the
parameters are assumed as follows: µ=0 dB, η=30, D=2000
m, Correlation distance constant = 20, Averaging constant
= 30 and Standard deviation of the shadow fading = 6 dB.
2
Table.1. A qualitative comparison of some handoff detection algorithms
Fixed
Algorithms
Control
Parameters
Features
RSS
Averaging
interval
Higher averaging interval reduces multiple
handoffs (ping-pong), Higher unnecessary handoffs
RSS-T
Serving BS
absolute signal
strength
(Threshold)
Hysteresis
margin
Prevents unnecessary handoffs when the serving
BS signal power is strong enough
Hysteresis &
Threshold
Reduces unnecessary handoffs than the RSS-H,
Higher handoff delay
RSS-H
RSS-HTser
(serving BS)
Reduces multiple handoffs further, Higher delay in
handoff, may result in unnecessary handoffs and
handoff to wrong cells
A test for handoff need is performed periodically at every
sampling distance, which is assumed to be 1 m. All the
quantities are expressed as functions of distance so that the
results are independent of the velocity of the MS. The
performance of this algorithm is evaluated in terms of
‘mean number of handoffs’ [4] [5], ‘mean number of wrong
cell handoffs’ and ‘Expected average signal strength’
(EASS) of the serving BS [5] to indicate delay and shown in
Fig.2 to 4. Performance comparison is made with that of the
basic RSS-H algorithm evaluated on the same platform and
shown in Table.2. Results show that RSS-HTnew algorithm
at very low threshold (for example, -97 dB) behaves similar
to RSS-H algorithm. Performance better than RSS-H is
obtained at higher threshold (example, -90 dB, -85 dB), in
terms of mean number of handoff and mean number of
wrong cell handoff. However, this is achieved at the cost of
increase in the handoff delay (i.e, decrease in the EASS).
For increasing threshold from -90 dB to -85 dB with a
constant hysteresis margin of 2 dB, the EASS value
decreases from -90.95 dB to -91.17 dB.
Fig.2. Mean number of handoff versus hysteresis margin for
RSS-HTnew algorithm using 3 BS model
Performance of the RSS-HTnew algorithm is also shown
graphically for varying hysteresis and threshold parameters.
Fig.2 shows the mean number of handoffs versus hysteresis
margin. Higher the threshold level, lower is the mean
number of handoffs. Higher threshold value also reduces the
mean number of wrong cell handoffs which is shown in
Fig.3. However, the delay in handing off increases. From
the above figure it is seen that for -90 dB threshold and a
hysteresis margin of 2 dB, the mean number of wrong cell
handoff i.e, number of handoffs to cell R is 0.21. This can
be reduced to 0.03 by increasing the threshold value to -85
dB. This also decreases the mean number of handoff from
3.40 to 1.54. But, this results in increase in the handoff
delay. For optimum parameter setting trade off between the
delay and the mean number of handoffs, the graphs is
shown in Fig.4.
Fig.3. Mean number of wrong cell handoff versus hysteresis
margin for RSS-HTnew algorithm using 3 BS model
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Table.2. Comparison of RSS-H and RSS-HTnew algorithms based on 3 BS network model
EASS
(dB)
Parameters (in dB)
Handoff
Algorithms
Threshold
Hysteresis
RSS-H
-
RSSHTnew
-97
2
5
10
2
5
10
2
5
10
2
5
10
-90
-85
-90.84
-92.53
-94.63
-90.87
-92.53
-94.63
-90.95
-92.98
-94.85
-91.17
-93.28
-94.85
Mean
number
of
handoffs
3.94
1.86
1.05
3.94
1.86
1.05
3.40
1.79
1.05
1.54
1.24
1.02
Number of
wrong cell
handoffs
0.31
0.09
0.01
0.31
0.09
0.01
0.21
0.08
0.01
0.03
0.01
0.00
In Fig.4, for a specified EASS value of -93 dB, optimum
values are the corresponding highest hysteresis and the
threshold values at that EASS of -93 dB. From the figures,
it is seen that the optimum threshold and hysteresis margins
are -85 dB and 7.8 dB respectively. With these optimum
parameters, the obtainable minimum mean number of
handoff and the wrong cell handoff are 1.1 and 0.005
respectively.
4. CONCLUSIONS
Existing handoff detection algorithms of RSS-H and RSSHTser may result in unnecessary handoffs due to handoffs
to wrong cells. A new handoff detection algorithm of RSSHTnew has been proposed, which is capable of preventing
the number of wrong cell handoffs. This algorithm
decreases the mean number of handoffs by preventing
wrong cell handoffs. In our example, the RSS-H algorithm
with 2 dB hysteresis value results in about 30% wrong cell
handoffs. This is reduced to about 20% in RSS-HTnew
algorithm with 2 dB hysteresis and -90 dB threshold. It
reduces further to 3% when the threshold is -85 dB,
however, with a higher handoff delay. This algorithm
behaves similar to RSS-H algorithm at very low threshold
values.
Fig.4. EASS versus mean number of handoff for
RSS-HTnew algorithm using 3 BS model
[3] Gregory P.Pollini, "Trends in handover design", IEEE
Commun.Mag., pp 82-90, Mar, 1996.
[4] P.Marichamy, S.Chakrabarti and S.L.Maskara, "Soft
handoff in CDMA cellular mobile communication systems",
National Communications Conference (NCC-2001), Kanpur,
India, pp 75-80, Jan. 29-30, 2001.
[5] P.Marichamy, S.Chakrabarti and S.L.Maskara, "Overview
of handoff schemes in cellular mobile networks and their
comparative performance evaluation," IEEE Proc. Vehicular
Technology Conference (VTC'99)-Fall, Amsterdam, The
Netherlands, pp.1486-91, Sept.1999.
[6] R.Vijayan and J.M.Holtzman, "A model for analyzing
Handoff Algorithms", IEEE Tran. Vech. Tech., vol 42, no 3,
pp 351-356, Aug'1993.
[7] Ning Zhang and J.M.Holtzman, "Analysis of handoff
algorithms using both absolute and relative measurements",
IEEE Tran. Vech. Tech., vol 45, no 1, pp 174-179,
Feb'1996.
REFERENCES
[1] N.D.Tripathi, J.H.Reed and H.F.Vanlandingham,
"Handoff in Cellular Systems," IEEE Personal
Communications, pp.26-37, December 1998.
[2] D.Wong and T.J.Lim, "Soft handoffs in CDMA mobile
systems," IEEE Perosnal communications, pp.6-17,
Dec.1997.
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