AbstractAiming at working out the problem that fuzzy logic and neural network based vertical handoff algorithm didn’t consider the load state reasonably in heterogeneous wireless networks, a PSO-FNN-based vertical handoff decision algorithm is proposed. The algorithm executes factors reinforcement learning for the fuzzy neural network (FNN) with the objective of the equal blocking probability to adapt for load state dynamically, and combined with particle swarm optimization (PSO) algorithm with global optimization capability to set initial parameters in order to improve the precision of parameter learning. The simulation results show that the PSO-FNN algorithm can balance the load of heterogeneous wireless networks effectively and decrease the blocking probability as well as handoff call blocking probability compared to sum-received signal strength (S-RSS) algorithm

Nan, Wang

Shaoshuai, Fan

Shuxiang, Liu

Wenxiao, Shi

Elsevier - Publisher Connector 

Procedia Environmental Sciences 11 (2011) 55 – 62
doi:10.1016/j.proenv.2011.12.010
Available online at www.sciencedirect.com
 
Available online at www.sciencedirect.com
 
Procedia 
Environmental 
Sciences Procedia Environmental Sciences  00 (2011) 000–000 
www.elsevier.com/locate/procedia
 
PSO-FNN-based Vertical Handoff Decision Algorithm in 
Heterogeneous Wireless Networks 
Wang Nan, Shi Wenxiao, Fan Shaoshuai, Liu Shuxiang 
College of Communication Engineering Jilin University Changchun, China 
swx@jlu.edu.cn  
 
Abstract 
Aiming at working out the problem that fuzzy logic and neural network based vertical handoff algorithm didn’t 
consider the load state reasonably in heterogeneous wireless networks, a PSO-FNN-based vertical handoff decision 
algorithm is proposed. The algorithm executes factors reinforcement learning for the fuzzy neural network (FNN) 
with the objective of the equal blocking probability to adapt for load state dynamically, and combined with particle 
swarm optimization (PSO) algorithm with global optimization capability to set initial parameters in order to improve 
the precision of parameter learning. The simulation results show that the PSO-FNN algorithm can balance the load of 
heterogeneous wireless networks effectively and decrease the blocking probability as well as handoff call blocking 
probability compared to sum-received signal strength (S-RSS) algorithm. 
 
Keywords: Heterogeneous wireless networks; vertical handoff; fuzzy neural network; load balance; particle swarm optimization 
1. Introduction 
The next-generation wireless networks will be consisted of various mobile and wireless technologies. 
Given the complementary characteristics of different networks, it is necessary to combine them to provide 
ubiquitous wireless access for users. The integration of heterogeneous wireless networks requires the 
design of intelligent vertical handoff decision algorithm to ensure seamless coverage, and provide high 
quality of service for different applications [1]. 
Many vertical handoff decision algorithms have been proposed. Fuzzy logic systems and neural 
network classifiers are good candidates for pattern classifiers due to their non-linearity and generalization 
capability. In [2], the algorithm uses a neural network to forecast the number of users, and adopts a fuzzy 
inference system to make a handoff decision. The algorithm achieves better performance in guaranteeing 
the Quality of Service (QoS). In [3], the fuzzy neural network (FNN) is used to make a vertical handoff 
decision and the learning target value is set to a constant user satisfaction. 
1878-0296 © 2011 Published by Elsevier Ltd.
 Selection and/or peer-review under responsibility of the Intelligent Information Technology Application  Research Association.
Open access under CC BY-NC-ND license.
 
© 2011 Published by Elsevier Ltd.
 Selection and/or peer-review under responsibility of the Intelligent Information Technology Application
 Research  Association.
Open access under CC BY-NC-ND license.
56  Wang Nan et al. / Procedia Environmental Sciences 11 (2011) 55 – 62
Author name / Procedia Environmental Sciences 00 (2011) 000–000 
The vertical handoff strategies mentioned above had their own advantages, but they did not put 
attention on the load conditions of the networks, and the allocation of resources is unreasonable.  
In this paper, we adopt a Particle Swarm Optimization (PSO) algorithm to train the FNN. The proposed 
PSO-FNN-based vertical handoff decision algorithm can make reasonable handoff decision intelligently 
according to the study of network status. This algorithm can reduce the number of unnecessary handoff, 
avoid Ping-Pong effect, balance the load of networks effectively and decrease the access blocking 
probability as well as the handoff call blocking probability. 
2. PSO-FNN-based Vertial Handoff Algorithm 
This paper proposes a PSO-FNN-based vertical handoff decision algorithm, which can adjust the 
parameters according to the study of network status, so as to achieve the expected performance. 
The equal blocking probability of networks indicates that the load conditions of networks are close. So 
the proposed algorithm executes factors reinforcement learning with the objective of the equal blocking 
probability of networks, which can make the access blocking probability of the candidate networks close 
and balance the load of networks effectively. 
2.1Flowchart of Vertical Handoff Decision Algorithm 
In this paper, we consider a heterogeneous wireless environment with the coexistence of UMTS and 
WLAN networks. When the average received signal strength reaches the threshold, it triggers a handoff 
decision. The proposed algorithm is mainly composed of three parts: pre-decision, FNN controller and 
handoff decision. 
As shown in Fig.1, we import the parameters into the pre-decision module. If a handoff decision can be 
made in the pre-decision module directly, then the FNN control module and the handoff decision module 
can be skipped. Otherwise, the parameters are imported into the FNN control module. Finally, handoff 
decision module is used to select the most appropriate network according to the parameter F. 
 
 
 
 
 
 
 
Figure 1 Components of the proposed vertical handoff decision algorithm 
Where Bi (i=1,2) and Ri (i=1,2) are, respectively, the available bandwidth (B) and received signal strength 
(R) of the candidate networks; DB is the difference of available bandwidth between networks; DR is the 
difference of received signal strength between networks; P is the periodical statistics of the access blocking 
probability. 
2.2Handoff Triggering 
In order to avoid “ping-pong” effect, we decide whether to execute a handoff or not according to the 
sum S of the received signal strength in WLAN in a time period T (T=LTs) 
P
F
DR
DB
R2
R1
Vertical 
Handoff 
decision
Pre-
decision
B1
B2 FNN 
controller
Trigger 
handoff 
decision
Decision
Result
57Wang Nan et al. / Procedia Environmental Sciences 11 (2011) 55 – 62
Author name / Procedia Environmental Sciences 00 (2011) 000–000 
 ∑
=
=
K
k
krS
1
                                                                                                                           (1) 
Where K is the times that the received signal strength is higher than the trigger threshold of the handoff R 
in a time period; 1≤K≤L; rk (k=1,2,⋯,K) represent the received signal strength in WLAN at time k; L is the 
times of sampling; Ts is the time interval of sampling. 
The handoff triggering has two cases: 
1) handoff to UMTS: If the received signal strength is lower than R, then start sampling. If S<RL/2, 
then trigger the handoff decision, otherwise reset S. 
2) handoff to WLAN: If the received signal strength is higher than R, then start sampling. If S>RL/2, 
then trigger the handoff decision, otherwise reset S. 
2.3Pre-decision Method 
The pre-decision method is used to filter the users before importing into FNN controller. We use B and 
R of the candidate networks to make a pre-decision. 
If Bi of the target network is less than the threshold BT, no handoff happens; if Bi of the target network 
is larger than the threshold BT, calculate DB and DR, and import the parameters into the FNN controller 
module. The flowchart of the pre-decision is shown in Fig. 2. 
 
 
 
 
 
 
 
 
Figure 2 Flowchart of the pre-decision 
DR is calculated as 
 21 RRD −=R                                                                                                                             (2) 
DB is calculated as 
 21 BBD −=B                                                                                                                            (3) 
2.4FNN Vertical Handoff Controller 
In this paper, we propose a FNN-based vertical handoff algorithm. This algorithm adopts PSO 
algorithm to set initial parameters value and executes factors reinforcement learning with the objective of 
the equal blocking probability of networks. 
3) The structure of FNN 
A four-layer FNN [4], as shown in Fig. 3, comprises the input (Layer1), membership (Layer2), rule 
(Layer3) and output layers (Layer4). This type of FNN is good at acquiring rules, recognizing patterns and 
tuning the membership function shapes [3]. 
 
 
Parameters
 Bi>BT
YN
No 
handoff
PSO-FNN 
module
Pre-decision
Calculate 
DR and DB
58  Wang Nan et al. / Procedia Environmental Sciences 11 (2011) 55 – 62
Author name / Procedia Environmental Sciences 00 (2011) 000–000 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3 Structure of the four-layer FNN 
Layer1: Input Layer 
No computation is required in this layer. xi represents the inputs. x1=DB and x2=DR. 
Layer 2: Membership Layer 
Each node in this layer transforms the value from Layer1 into the linguistic level using the membership 
function. The Gaussian function is adopted as the membership function. The membership function μij of 
the node is represented as 
 ( ) ( )( )( )22exp ijijiiij cxx σμ −−=                                                                                                (4) 
Where cij and σij (i=1,2; j=1,2,3) are, respectively, the mean and the standard deviation of the Gaussian 
function. The total number of the nodes is six. 
Layer3: Rule Layer 
The nodes in Layer3 are the rule nodes, and the links in this layer perform the precondition matching of 
the fuzzy rules. Each node represents a single fuzzy rule. This layer performs a fuzzy “AND” operation 
that sends out the node value according to the incoming membership values from Layer2. Similar to 
Layers1 and 2, there is no weight to be adjusted in this layer. The value amn (m=1,2,3;n=1,2,3) of the node 
is calculated as follows 
 nmmna 21 μμ ×=                                                                                                                            (5) 
Layer4: Output Layer 
Each fuzzy rule from Layer3 is given a weight. wmn is the weight of the fuzzy rule amn.  
By the method of weighted mean defuzzification, we convert the vector into a single value F 
 ⎟⎟⎠
⎞
⎜⎜⎝
⎛
⎟⎟⎠
⎞
⎜⎜⎝
⎛= ∑∑∑∑
= == =
3
1
3
1
3
1
3
1 i j
mn
i j
mnmn aawF                                                                                                (6) 
4) Ascertainment of the initial parameters 
This paper adopts a hybrid training algorithm by combining the PSO and the back-propagation 
algorithm. The PSO is used to optimize the initial parameters of the FNN, and the back-propagation 
algorithm is used to implement the self-adaption modulation for the parameters. 
Layer3
F
x1 x2
w11 w12 w13 w21 w22 w23 w31 w32 w33
Layer1
Layer2
Layer4
59Wang Nan et al. / Procedia Environmental Sciences 11 (2011) 55 – 62
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In PSO, a group of particles, without quality and volume, fly through a N-dimensional space adjusting 
their positions in search space according to their own experience. The position of the particle is represented 
with a position vector pi=(pi1,pi2,…piN), i=1,2,…,m and a velocity vector vi=(vi1,vi2,…viN). In every time 
step t, particle i changes its velocity and position according to the following equations [5] 
 ( ) ( ) ( ) ( ) ( )( ) ( ) ( ) ( )( )txtptrctxtptrctvtv ijgjjijijijijij −+−+=+ 2211 ϕ                                                                  (7) 
 ( ) ( ) ( )11 ++=+ tvtxtx ijijij                                                                                                              (8) 
Where i represents the i-th particle; j represents the j-th dimension of speed or location; vi represents the 
speed of the i-th particle; c1 and c2 represent the study factors; r1j and r2j represent the random numbers 
among 0 and 1; xi represents the current location of the i-th particle; φ is the inertial weight; 
pi=(pi1,pi2,…piN) is the best personal position of particle i which has been visited during the lifetime of the 
particle; pg=(pg1,pg2,…pgN) is the global best position that is the best position of all particles in the swarm. 
The concrete steps of the PSO algorithm are shown [3]: 
Step1: Initialize the positions and velocities of a group of particles randomly. 
Step2: Compute every particle’s fitness value. The fitness function is defined as f=M|di-oi|, di 
represent the realistic value; oi represent the expect value; M is a positive constant. The di and oi are set as 
di=y, oi=y*. The position with lower fitness value is better. 
Step3: Set pi as the current position of the particle i. And set pg as the best position of the initialized 
particles. 
Step4: The positions and velocities of all particles are updated according to (7) and (8). Evaluate each 
new particle’s fitness value. If the current fitness value is better than pi fitness value, replace pi with the 
current position. If the best position of all new particles is better than pg, then pg is updated. 
Step5: Judge the pause conditions. If the conditions are met, stop the iteration, and the positions of 
particles represented by pg is the optimal best value. Otherwise, the process is repeated from step4. 
Step6: Algorithm ends. Take the coordinates of pg as the initial parameters of the FNN. 
5) Reinforcement Learning Algorithm 
To describe the learning algorithm of the FNN using the supervised gradient decent method [6], firstly 
the error function E is defined as 
 ( ) ( )( )2*
2
1 tyytE −=                                                                                                             (9) 
Then, the mean cij, the standard deviation σij of the Gaussian function and the weight wmn of the fuzzy 
rule are updated according to the following equation 
 ))1()(()()1( −−+∂
∂−=+ tZtZ
Z
EtZtZ ijij
ij
ijij αη                                                                                (10) 
Where η is the learning rate; α is the momentum rate; Zij is an universal variable. We can update the 
parameters by using this method. 
2.5Vertical Handoff Decision 
In this phase, we make a vertical handoff decision according to the output parameter F of the FNN. The 
decision method is defined as: 
If F<0.5, handoff to UMTS; 
If F>0.5, handoff to WLAN; 
If F=0.5, handoff to the network in which RSS is better. 
60  Wang Nan et al. / Procedia Environmental Sciences 11 (2011) 55 – 62
Author name / Procedia Environmental Sciences 00 (2011) 000–000 
3. Simulation and Analysis  
In order to evaluate the performance of the proposed PSO-FNN-based vertical handoff decision 
algorithm, we compare the performance of PSO-FNN algorithm to sum-received signal strength (S-RSS) 
algorithm proposed in [7]. 
3.1Simulation scenario 
In our simulation, each UMTS cell overlaps two WLAN cells. Parameter setting is shown in Table 1 . 
Table 1 Simulation Parameter Setting 
Name UMTS WLAN 
Bandwidth 2Mbps 11Mbps 
Cell size 500m 100m 
Transmit power 35dBm 20dBm 
Moving speed of users 18km/h 
Threshold to trigger handoff -100dbm 
Sampling interval 50ms 
3.2Simulation results and discussion 
The results of simulation are shown in Fig. 4-Fig. 6. 
 
 
 
 
 
 
 
 
 
 
Figure 4 Average load 
 
 
 
 
 
 
 
 
 
 
Figure 5 Access blocking probability 
61Wang Nan et al. / Procedia Environmental Sciences 11 (2011) 55 – 62
Author name / Procedia Environmental Sciences 00 (2011) 000–000 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6 Handoff call blocking probability 
Fig. 4-Fig. 6 are the comparisons of PSO-FNN and S-RSS algorithm in one UMTS cell and two 
WLAN cells repeating coverage area. Fig. 4 is the statistics of the normalized load. We can see that the 
PSO-FNN algorithm which executes factors reinforcement learning with the objective of the equal 
blocking probability of networks can balance the load of networks effectively. As the load balancing of 
networks can increase the usage of the whole network resource and make the overload of a network less 
possible. Therefore the access blocking probability and handoff call blocking probability will decrease 
accordingly. 
Fig. 5 shows user blocking probability caused by full network load when users initially get access. We 
can see that the access blocking probability of PSO-FNN algorithm is lower than that of S-RSS algorithm, 
and when the number of users is greater than 180, the access blocking probability decreases 6.37% in 
average. Fig. 6 shows handoff call probability caused by full network load when users switch among 
networks. We can see that the handoff call blocking probability of PSO-FNN algorithm is lower than that 
of S-RSS algorithm, and when the number of users is greater than 180, the handoff call blocking 
probability decreases 2.11% in average. 
4. Conclusions 
This paper proposes a PSO-FNN-based vertical handoff decision algorithm which can make a 
reasonable handoff decision intelligently according to the study of network status. The proposed algorithm 
adopts PSO algorithm to set initial parameters value and executes factors reinforcement learning with the 
objective of the equal blocking probability of networks. Simulation results indicate that the PSO-FNN 
algorithm can balance the load of heterogeneous wireless networks effectively and decrease the blocking 
probability as well as handoff call blocking probability. 
Acknowledgment 
This work was supported by the National Natural Science Foundation of China (No. 60972028). 
62  Wang Nan et al. / Procedia Environmental Sciences 11 (2011) 55 – 62
Author name / Procedia Environmental Sciences 00 (2011) 000–000 
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PSO-FNN-Based Vertical Handoff Decision Algorithm in Heterogeneous Wireless Networks 

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PSO-FNN-Based Vertical Handoff Decision Algorithm in Heterogeneous Wireless Networks

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