Abstract-In this paper, we propose a novel and simple soft handoff algorithm That helps fair distribution of network resources among users. Due to centralized soft handoff mechanism, no further computational complexity is added to the mobile sets. Because we use Fuzzy Inference system (FIS) in adapting thresholds of our algorithm, algorithm is able to release the traffic channels at high traffic load for increasing the carried traffic. This paper compares the performance of our algorithms with IS-95A soft handoff algorithm by using following performance indicators: outage probability, new call blocking probability, handoff call blocking probability, expected number of base station in active set, expected number of changes in active set, and network carried traffic. By comparison of all parameters among proposed algorithm and
IS-95A, our algorithm tend to have a higher performance than
IS-95A soft handoff, especially in new call requests blocking probability and network carried traffic.
Keywords- Soft handoff, Fair distribution of network resources,
Selective Soft Handoff
I. Introduction
One of the most important objectives in development of the new generation of cellular systems is the provision of the reliable handoff mechanism to maintain quality of the signal for the mobile subscribers. The process of transferring support of a mobile subscriber from one Base Station(BS) to another is called “Handoff”. It occurs when a mobile subscriber moves from one cell to another so the Received
Signal Strength(RSS) from cell antenna becomes wick . In traditional “hard handoff”, the connection to the current base station is broken, and then a new connection to new base station is made. Therefore, it is called “break-before-make” handoff. Hard Handoff has many disadvantages like “pingpong” effect (frequent handoffs between two neighbor cells because of randomness in the RSS) and that it can not implemented easily in CDMA systems since all the cells in
CDMA mobile communication systems use the same carrier frequency, so it is possible to make the connection to the target base station before interrupting the connection to the current base station. This is called “make-before-break” or
“soft handoff”. The RSS is improved due to the diversity provided by the extra channel path at the cell edge compared with hard handoff. From the network operator point of view, however, more resources are required to support this procedure.
The main objective of the proposed algorithm is to distribute network resources among mobile subscribers evenly, It does not allow one user to have a very good RSS while other users are facing new call clocking or forced handoff termination. It will be shown by simulations that the proposed algorithm increase carried traffic and trunking resource efficiency.
The paper is organized as follows; section two describes an overview of IS-95A SHO, proposed algorithm is described in section 3, Section 4 describes FIS based SHO algorithm and section 5 describes the system model and Section 6 describes computer simulation and results. We draw conclusions in section 7.
II. An overview of IS-95A Soft Handoff
In IS-95A SHO, there are channel lists for MS (mobile stations) whose members include the active set (AS), the candidate set (CS) the neighbor set (NS) and the remaining set (RS). The AS contains the currently used channels,the CS contains channel which are almost as good as those in AS, and one of them can be chosen for SHO as a new member of
AS if needed,The NS is the set of channels which is not included in the AS and CS but is reasonably strong and finally RS contains other channels that are not the members of all other sets [4]. While a MS soft handoffs, it searches for other suitable cells; If the NS pilot strength becomes over
T_ADD, the MS includes the pilot into the CS and removes it from NS. The BS controller instructs the MS to add the new pilot and the MS will add the pilot to the AS, if it receives permission to add the pilot. If the current AS pilot strength decreases below T_DROP for T_TDROP consequents samples, the MS moves the pilot from the AS into the NS if it receives permission to drop the pilot. The AS is limited to have six pilots, while the CS is limited to five [4, 5]. In this algorithm, T_DROP, T_TDROP and T_ADD are constant pre-defined values and do not change during the connection.
III. Proposed Soft Handoff Algorithm
In this section, we propose two mechanisms in order to control the network resources( Id. Est. traffic channels) in our algorithm. In the first mechanism, that we call it
“Selective SHO”, elimination of channel from AS is similar to the IS-95A, but addition of channels to AS is different; we

define a flag, called ADD_FLAG, for each MS. When
ADD_FLAG is set, no new BS is added to AS anymore. If any AS member pilot signal-strength exceeds T_SEL for
T_TADD consequent samples, then the ADD_FLAG is set.
If the pilot signal-strength that is above T_SEL becomes below T_SEL, then the ADD_FLAG is reset. It is obvious that T_SEL should be more than T_ADD. The reason for choosing such a mechanism is that there is no need to waste system resources, when we are sure that there is no need for handoff, because the pilot is strong enough.
AS may contain four BS, but “the more BS in a MS’s AS, the more resources it wastes”. Therefore, we propose another mechanism in order to ensure that the third or four BS improves quality of signal for the MS. This algorithm is similar to previous algorithm as far as there are only two BSs in AS, but if any other BS wants to join the AS, its pilot signal-strength should be DELTA dB more than every BSs’ pilot signal-strength in AS. We call this “Hystersis
Condition”, This is different from Hystersis concept in the
Hard handoff because this condition is imposed only for the
3 rd
and 4 th
member of AS. Therefore, this mechanism ensures that the third BS is the dominant BS in AS and two other BSs may be dropped soon.
IV. FIS Based Soft Handoff Procedure
In the previous algorithm, we propose some methods in order to add or drop BS to AS, but all the thresholds were constants and no feedback was taken from network load(traffic),for example, we one cell has low traffic, it can give its free channels to other users much easier than a cell facing congestion . The SHO algorithm has been changed in
IS-95B and addition and dropping thresholds may change dynamically based on the RSS of BSs in AS. In the previous algorithm, the quality of signal was our design criterion, but the algorithm should consider the network traffic. In [2] there has been proposed an algorithm that uses fuzzy inference system in order to adapt addition and dropping thresholds.
We have applied the FIS method to our algorithm in order to adapt algorithm thresholds. Any adaptation algorithm should consider both quality of signal of mobile subscriber while minimizing number of BS in AS and network traffic in order to distribute the network resources to all users equally and ensure reliable connection for them .
There are three important sub-procedures in FIS-SHO algorithm: Fuzzification, Fuzzy Inference System, and
Defuzzification.
A. Fuzzification.
All of the crisp inputs are changed to fuzzy inputs in this step before using in a rule base by the membership functions.
This algorithm has two inputs and two outputs. The inputs are number of BSs in AS of each subscriber (NOBS) and number of remaining free channels in each BS (CHRM). The outputs of fuzzy system are T_DROP and SHW (Soft
Handoff window, the distance between thresholds of addition and dropping) (Fig. 1, 2, 3, and 4)
B. Fuzzy Inference
This step involves with the definition by using “if-then” rules to be relevant to human information for proper adaptation of the thresholds, for instance:
“If NOBS is LOW and CHRM is HIGH then T_DROP is
LOW”
This means that : when there are few BS in the AS while the cell has some free channels(Id. Est. has low traffic) , lower the dropping threshold in order to keep the RSS of the MS.
Fig. 1. Triangular MF of NOBS
Fig. 2. Triangular MF of CHRM
Fig. 3. Triangular MF of T_DROP

CHRM
NOBS
LOW
MEDIUM T_drop: High T_drop: Medium
HIGH
CHRM
NOBS
LOW
MEDIUM SHW: High SHW: Medium
HIGH
Fig. 4. Triangular MF of SHW
Table 1: Rules for T_DROP Adaptation
LOW
T_drop: High T_drop: Medium
T_drop: High
LOW
SHW: High SHW: Medium
SHW: High
MEDIUM
T_drop: High T_drop: Medium
Table 2: Rules for SHW Adaptation
MEDIUM
SHW: High
HIGH
T_drop: Low
T_drop: Low
HIGH
SHW: Low
SHW: Low
SHW: Medium
There are nine rules from three fuzzy sets of two inputs shown in table 1 and 2 for T_DROP and SHW. Aggregation and composition in max-min composition are the procedures for reasoning [2].
C. Defuzzification
To extract a crisp value that represents a fuzzy set is the defuzzification. In this paper, selected defuzzification scheme is COA(Center of Area).
We have applied proposed algorithm to the algorithm proposed in [2] (FIS-SHO) in this section, so we do not explain about fuzzy inference system much. However, it should be mentioned that applying hystersis and selective handoff with FIS-soft handoff ensures that quality of signal of BS that is added to AS is better than other BSs in AS and addition of BS to AS does not waste other subscriber’s resources in the network.
V. System Model
For analysis and simulation of SHO, we need a model of cellular network with its mobile subscribers. There are many models, but we used the following model that we think is appropriate for SHO analysis and simulation. We divide our model into following four sub-models.
A. Cell model
All cells are hexagons. There are 19 cells that consist of a central cell, the first tire, and the second tire (six cells in the first one and twelve cells in the second one), radius of each cell is 1 kilometer and distance between two adjacent BS is two kilometers. There is an omni-directional antenna placed at the center of each cell having 20 traffic channels .There are no reserved(dedicated)channels for HO and there is no difference between new connection request and HO request.
B. Radio Propagation model
We do not consider fast fading in our signal propagation model for each user because of following reasons: first, its effect can be eliminated by using efficient power control algorithms, and in addition, in simulation, RSS is sampled in each two meters, so the effect of fast fading may be ignored for these scales of distances. We use the following formula in order to measure the RSS from each BS to each user.
RSS(i,j) = K1 – K2 Log (dij) + S (dB)
In the above equation, d ij
is distance between BS and MS,
K1 and K2 are constants whose values depend on antenna radiated-power and environment and S is a zero-mean
Gaussian process, which characterizes the shadowing.
The correlation function of S was shown in [5] to be
E[S(d) S(d+ U d)] =
σ
2
exp( U d/d0)
Where d
0
represents the rapidity of correlation decay with
σ 2 increasing distance and is the shadow fading variance. In simulation, we chose K1=0 and K2=30. The standard deviation of S was 6.5.
C. Traffic Model
The new call arrival process is an independent Poisson process with a mean arrival 6. The calls arriving to the system are assumed to arrive uniformly over the simulation area. The call duration is an exponential process with mean
120 seconds. As a result, the traffic of each user would be as follows:
T user
=
λ ×
A .
H .
T
3600
=
6
×
120
=
3600
200 mErLang
Where A.H.T represents “Average Holding Time”.
D. Mobility model
The initial velocity of mobile is considered a random variable with a truncated Gaussian probability distribution function (its mean is 6 and its standard deviation equals to 10,

if it exceeds 16 or becomes below 0, then another velocity will be re-calculated for the user). We assume a mobile to change its velocity each 10 seconds and at each velocity update, we assume the new speed to be correlated to the previous one.
When a mobile user initiates a call, we assume the initial direction to be a random variable, which is uniformly distributed in the range of 0 to 2
π and that a mobile change its direction each 10 seconds. We also assume the new direction to be correlated to the previous one.
VI. Simulation and Results
In simulation, each user has an AS with capacity of maximum four BSs and a CS with capacity of maximum five
BSs. Information about NS is given to user by program. For comparison of different methods, we have simulated 5 methods in 4 different traffics. The first method is IS-95A,
The second one is selective handoff, The third one is hystersis+selective handoff, The fourth algorithm uses is the third on and uses FIS method in order to adapt the T_DROP .
The last one is similar to the fourth method, but also uses FIS method to adapt the SHW.
As we discussed before, SHO is a trade-off between quality of handoff and sharing of network resources . For the fair comparison of 5 mentioned methods,performance measures used to compare the performance of methods are:
•
Expected Number of BS in AS (Fig. 10)(its reverse, indicates trunking resource efficiency)
•
Probability of blocking of handoff (Fig. 6)
•
Outage Probability (Fig. 8)
•
Probability of blocking of new connection (Fig. 7)
•
Expected Number of changes in AS (Fig. 5)(a measure of network signaling load)
•
Carried Traffic by the network (Fig. 9)
Information needed to calculate the above performance measures are obtained from averaging data in seven central cells. As it can be seen from figures, the performance of proposed SHO algorithm is better than performance of the first method. The blocking probability and carried traffic increase and mean of BS in AS decreases in our method, therefore trunking resource efficiency increases. The main problem of FIS-based methods is that probability of outage and blocking of handoff request are more than the others. The reason of this problem is that in the first method sixty percent of call requests are blocked, So there are unused resources for handoff in the first method, but because FIS-based algorithms uses network resources more efficiently, it is more probable to block new handoff requests. It is obvious that proposed algorithm has superior performance in all the performance indices in comparison to IS-95A and has a acceptable performance in comparison to FIS-based algorithms(in some performance indices it is even better, for example in the outage probability or handoff blocking probability) in spite of its being much more simple mechanism than FIS-based methods.
Fig. 5. AS Updates
Fig. 6. Probability of Handoff Blocking

Fig. 7. Probability Blocking of New Connection Request
Fig. 8. Outage Probability
Fig. 10. Mean of number of BSs in AS
VII. Conclusion
Our aim was to design a simple and feasible soft handoff algorithm in order to share the network resources among all the mobile subscribers fairly. We showed with simulation that our algorithm uses system resources more efficiently, so the blocking probability of new call request decreases 30 percent with comparison to normal handoff algorithm. The proposed algorithm also causes an increase of six Erlangs in the network carried traffic. Another important future of algorithm is that no further computational complexity is added to the mobile sets. All information about RSS and AS of each user are sent to BS by MS, so the hardware of mobile handsets remains unchanged, and only software of BSs should be upgraded. By using FIS we take feedback from network resources states, so algorithm is efficient even in high traffic loads.
References
[1] Moe Rahnema," Overview of The GSM System and Protocol architecture", IEEE Communication Magazine. April 1993
[2] B. Homan, V. Kunsriraksakul, W. Benjapolaku, “ Fuzzi Infrence System based Adaptation of CDMA Doft Handoff Thresholds with Diffrent
Defuzzification Schems”
[3] TRIPATHI, REED," Hand-off in Cellular System". IEE Trans. On
Personal Communication, 1998.
[4] Rao, Mishra," Soft Hand-off in CDMA System", University of TEXAS.
[5] Ning Zhang, J.M.Holtzman, “Analysis of a CDMA Soft Hand-off
Algorithm” IEEE Trans on Vehicular Technology, Vol.97, No2,
May 1998.
[6] Daniel Wong and Teny Joon Lim, “Soft Hand-off in CDMA Mobile
Systems”IEEE Trans. On Personal Communication, Vol.4, No.6, Dec
1997.
[7] Theodore S.Rappaport, “Wireless Communications-Principles &
Practices”, Prentice Hall2002.
[8] Yacob,"Wireless Systems, Standards & Protocols", CRC Publications,
2001.
Fig. 9. Carried Traffic