The document proposes a new dynamic channel allocation scheme called EBP-HOP that uses error back propagation and Hopfield neural networks. It aims to improve quality of service in mobile multimedia networks by reducing call handoff dropping and blocking probabilities. The scheme first uses an error back propagation neural network model to analyze traffic mobility patterns and predict future handoff locations. It then uses a Hopfield neural network to dynamically allocate channels based on the traffic mobility information and prioritize handoff calls over new calls. The authors claim this approach can better manage network resources and maintain optimal call dropping and blocking probabilities compared to other static and dynamic channel allocation methods.

1. Procedia Computer Science 89 (2016) 107 – 116
Available online at www.sciencedirect.com
1877-0509 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of organizing committee of the Organizing Committee of IMCIP-2016
doi:10.1016/j.procs.2016.06.015
ScienceDirect
Twelfth International Multi-Conference on Information Processing-2016 (IMCIP-2016)
Dynamic Channel Allocation in Mobile Multimedia Networks Using
Error Back Propagation and Hopfield Neural Network (EBP-HOP)
Sanjeev Kumara,∗, Krishan Kumara and Anand Kumar Pandeyb
aFaculty of Technology, Gurukul Kangri University, Haridwar, India
bSchool of Electronics Engineering, Shobhit University, Meerut, India
Abstract
In mobile multimedia communication systems, the limited bandwidth is an issue of serious concern. However for the better
utilization of available resources in a network, channel allocation scheme plays a very important role to manage the available
resources in each cell. Hence this issue should be managed to reduce the call blocking or dropping probabilities. This paper gives the
new dynamic channel allocation scheme which is based on handoff calls and traffic mobility using hopfield neural network. It will
improve the capacity of existing system. Hopfield method develops the new energy function that allocates channel not only for new
call but also for handoff calls on the basis of traffic mobility information. Moreover, we have also examined the performance of
traffic mobility with the help of error back propagation neural network model to enhance the overall Quality of Services (QoS) in
terms of continuous service availability and intercell handoff calls. Our scheme decreases the call handoff dropping and blocking
probability up to a better extent as compared to the other existing systems of static and dynamic channel allocation schemes.
© 2016 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of organizing committee of the Twelfth International Multi-Conference on Information
Processing-2016 (IMCIP-2016).
Keywords: Call Dropping and Blocking Probability; Dynamic Channel Allocation; Error Back Propagation Model; Hopfield
Neural Networks; Mobile Multimedia Network.
1. Introduction
Recently, the demands of mobile users are varying everyday due to the portability and the availability of mobile
system. But the radio spectrum is limited for this purpose as compare to mobile users. Therefore the most efficient
utilization of the radio spectrum is the dynamic channel allocation schemes which improve the overall quality, capacity
and performance of the wireless networks. The prime objective of the dynamic channel allocation (DCA) is to improve
the capacity of mobile multimedia communication networks where the traffic load is unpredictable i.e. randomly
distributed, has been proposed and explain very well in1–4. So far several DCA schemes have been developed and
proposed to use various techniques. In recent years Hopfield neural network or HNN based dynamic channel allocation
or DCA schemes have been discussed frequently2,25,27. HNN is fast and parallel optimizer which is very efficient
neural network model for channel allocations. The optimization of the HNN can be seen in its energy function which
minimizes the cost and channel allocation problem in mobile networks. This paper uses the HNN model, which plays
∗Corresponding author. Tel.: +91 -9759567382.
E-mail address: sanjeevnmc83@gmail.com
© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of organizing committee of the Organizing Committee of IMCIP-2016

2. 108 Sanjeev Kumar et al. / Procedia Computer Science 89 (2016) 107 – 116
a very important role for the prediction of traffic mobility as part of DCA schemes. The literature survey shows that
neural network based DCA schemes are normally focused only on the channel allocation of new call and to compute
the time of convergence or blocking probability of new calls. But it doesn’t give more attention about the influence of
traffic mobility on performance in mobile networks.
In wireless communication users want to access the network services “any place at anytime” which is controlled by
mobility prediction and dynamically channel allocation during handoff management5. In wireless mobile network the
geographical region is divided into numerous hexagonal cells, having number of channels that are used by the handoff
calls and new calls. Each cell contains a base station that provides the services for the mobile users within the vicinity
of the cell. Whenever a Mobile User (MU) wish to communicate with other mobile user (MU) in the network, then the
message is transferred between MU and base station (BS) by radio signals within the range of BS. But when mobile
users switch from the coverage area of one BS to another BS then a seamless handoff process comes into the existence
which maintains the call dropping probability and QOS6.
When handoff occurs it may be happen that, there is not enough resources available to accept the handoff calls in
the new serving cell. In this situation a handoff is dropped which is more annoying than the call blocking from the
user point of view as well as QoS point of view. The main characteristic of traffic mobility is to identify the next
location or a cell of mobile users where the next handoff is performed. In some conditions handoff can lead to forced
call termination that will effect on the overall QoS and performance of the system. Mobility prediction minimizes the
call dropping probability due to the reservation of resources dynamically or in advance. So the higher priority is set on
the handoff calls for the allocation of channels as compared to new call which minimizes the handoff call terminations
and maintain the QoS. Therefore, a group of resources are reserved in each cell to handle the handoff calls only.
However, these resources might not be used. So the reservation of resources must be done carefully which could avoid
the situation of degradation of QoS for both currently served mobile users and mobile user performing handover. The
benefit of this process is that it makes communication system strong in maintaining continuous calls in opposition to
call dropping, while on the other hand due to the limited channel availability the rate of new call blocking probability
increase. To solve this problem call admission control (CAC) with traffic mobility and dynamic channel allocation
schemes are required to maintain the QoS provision and dropping & blocking probabilities of calls.
Usually there are two common schemes for channel reservation of handoff calls: a) guard channel policy and
2) fractional guard channel policy7–9. More practically; it is not possible to absolutely remove handoff call dropping.
Therefore, one of the best ways to maintain the QoS of the communication system is to keep the probability of
handoff dropping (Phd) below a threshold value. The second important issue for evaluating CAC algorithms is to keep
the probability of new calls blocking (Pnb) below a certain threshold value for the maximum resource utilization6.
This paper proposes the dynamic channel allocation policy which maintains a most favourable equilibrium between
call dropping and call blocking HNN model. In addition, the performance of traffic mobility is also analyzed for the
intelligent DCA techniques using error back propagation neural network (EBP-NN).
2. Related Work
Since the couple decades, there has been a lot of research done in the field of traffic mobility and channel allocation
schemes1–8,33. Most of the traffic mobility prediction and channel allocation unique techniques are unique have been
proposed. Usually schemes for traffic mobility are based on movement pattern, location history and velocity. The
fluid flow and random walk model are the two most commonly methods are used for the traffic mobility in wireless
network10. Conceptually, the shadow clustering concept is used to determine the number of resources of future
based on user mobility information in microcellular wireless networks11 A new handoff prediction scheme using data
compression technique implemented by EBP-NN method to predict when and where the next handoff will occur in
order to maintain the handoff dropping probability (Phd) and optimum resource utilization28.
In2,25 the author proposed a new dynamic channel allocation and handoff channel allocation based on traffic
mobility using HNN which provides an optimum radio resource allocation for new call and handoff calls in wireless
mobile networks. The hidden Markov model (HMM) modelling technique is also used in mobile network to determine
the exact future position of the mobile users in the geographical area where the network is deployed12,13. A threshold
base statistical bandwidth multiplexing using channel reservation protocol for mobile network is proposed in14,

3. 109Sanjeev Kumar et al. / Procedia Computer Science 89 (2016) 107 – 116
to achieve the required amount of resource will be required in a cell to provide the service for handoff call in future.
Here there is no traffic mobility prediction technique is used but a predictor is attached to each cell. A real time
position based predictive channel reservation (PCR). The main concept of this scheme is that all MUs and the base
station (BS) or may be both, measure and find out its position by itself. With the help of measurement BS predicts the
future location and cell for each MU. In15, the author proposed mobility prediction techniques to improve the handoff
performance by reserving bandwidth. In1 the detailed survey of channel allocation techniques on the bases of fixed,
dynamic and hybrid channel allocation schemes based on distributed or centralized controls and adaptively to traffic
mobility conditions.
In18,34 a new channel allocation model using a discrete HNN is proposed that formulate energy function for
appropriate channel allocation to keep the blocking and dropping probabilities of calls at minimum threshold value,
which a measure research issue in the field of wireless network. A QoS based real time (dynamic) call admission
control (CAC) method for mobile multimedia network is proposed using HNN4 which offers maximum resource
utilization in distributed wireless area network and maintain a QoS vector that shows the QoS level for all connections
i.e. new calls and handoff calls. A novel for DCA for distributed CDMA wireless network is proposed in3 to enhance
the utilization capacity of available resources. There are various for dynamic channel allocation technique have been
developed so far as discussed in1–4 and in25,27,33,34. Furthermore, many call admission control and resource allocation
policy have been proposed using soft computing techniques to improve the probability of handoff dropping (Phd) and
new calls blocking (Pnb)16. In this paper we propose a new dynamic channel allocation schemes using artificial neural
network model based on traffic mobility. Here traffic mobility is finding out by error back propagation neural network
and reservation of resource is made by HNN after a handoff.
3. Traffic Mobility Distribution
Mobility prediction is used to identify the next location or a cell of a MU in distributed geographical area where
the network is deployed. To maintain the quality of services mobility management play important role for sorting
and updating the mobility information of MUs. Here, uniform and non-uniform traffic distributions are taken for
the performance estimation of the system. Call arrival rates follow a Poisson distribution in under uniform traffic
distribution where as in non-uniform traffic distribution we give more attentions to obtain the result and we use some
algorithms to choose the traffic distribution2. First method follows learning process while the second method is based
on some specific algorithm and network architecture18. Here the traffic mobility information is updated after every
handoff and stored in home Location Register (HLR) and Vertical Location Register (VLR)19. In this paper we have
implemented error back propagation neural network to find out the traffic mobility based on the information received
from HLR and VLR registers and on the basses of traffic mobility, dynamic resource reservation is made by HNN
model.
4. Overview of Artificial Neural Network
Any interconnected group of artificial neurons that send the message to each other is known as artificial neural
network or simply ANN. Basically, this is the composition of various tiny processing units called neurons that work
simultaneously in a nonlinear fashion to complete a specific task. ANN is the nonlinear statistical data modeling tool
inspired by biological neural networks. It was developed by McCulloch and Pitts in 1943. Each neuron contains four
basic components known as dendrites which collect the input signals from various other sources, soma the cell body
which processes the input, axon the long and thin, tubular structure that transfer input signal into output signals,
and last one the synapse to represent the electrochemical connection between two neurons. ANN is an engineering
approach that is used to calculate the behaviour of biological neurons and their interconnections on the bases of
training data. Each connection link is associated with a weight which represents the input information that is used by
the other neurons to solve a particular problem. Basically ANN is the framework of interconnected layers and various
tiny processing units, which operates in parallel manner and configured in regular architecture20,24,28.
Basically the models of ANN are trained with supervised and unsupervised methodology. In supervised method
both the input and output should be known in advance while on the other hand in unsupervised method only input

4. 110 Sanjeev Kumar et al. / Procedia Computer Science 89 (2016) 107 – 116
pattern is know in advance. After each operation the target output is compared with the obtained output, if there is
any difference between calculated output and target output then error is back propagated to input layer. This process is
known as the mean square error of the network19,22. One of the main advantages of ANN is the capability of learning
which is used to update network architecture and connection weights in the network to improve the performance. Due
to this reason ANN plays a very important role to predict the future movement of MUs.
4.1 Error Back Propagation Neural Network (EBP-NN)
The back propagation neural network algorithm or simply error back propagation network model (BPN) is applied
in layered feed forward neural networks. It is a commonly used supervised learning algorithm that organizes the
artificial neurons in layers and then sends their input signals “forward” and the errors are propagated backward. This
network is easy to understand and design. It changes the weights on the basis of positive or negative errors cased during
the training of network. It is also known as multilayer feed-forward network consists of various input layer, hidden
layer, and various output layers. Each layer consists of a number of neurons that are connected to its adjacent layer
neurons with different weights. The general concept is based on gradient-descent method. After that the network is
computed and then error is calculated which the difference between calculated output and desired output. The concept
of BPN is used to reduce this error in hidden layer until the ANN learns the training data. The BPN network is trained
in three steps forward the input signals, compute and propagate error backward and last update the weights21.
The main characteristics of BPN are the self-learning and self-organizing which is very useful in the field of traffic
mobility. In the beginning, the number of layers and associated neurons are required to be declared. Weights among
neurons are also declared initially for the first iteration. Secondly, the transfer function i.e. sigmoid and linear function
between the neurons needs to be decided and last important decision is made by the learning algorithm to train the
networks28. According to algorithm the patterns are studied and output is calculated from input layer to output layer.
After that the weights are updated between the different layers. In the next step checked the updated weights for all
layers, if there is difference between calculated output and desired output then calculate the mean square error for all
layers. Finally, the near optimal weights are obtained depending on the reduction of error in the network so that the
network can achieves the target output19–23.
4.2 Training in error back propagation neural network
Step 1: Apply the input and initial weights.
Step 2: Compute output for each node (forward pass) using the following equation.
Output = 1/(1 + exp − Wi j Xi (1)
Step 3: If the calculated output is different from desired output then calculate the error of the pattern using the following
equation.
Etotal =
1
2
(target output-calculated output) (2)
Backward propagation (Weight updating)
Step 4: Now the output error by using the following equation is calculated
(output layer) = output(1-output)(desire output-output) (3)
The “Output (1-Output)” term is necessary for Sigmoid Function – only for a threshold neuron i.e. (Target – Output)
Step 5: Updates the weights of output layer and applied to hidden layer
W(n + 1) = W(n) + η (output layer) output (hidden layer) (4)
Let W(n + 1) is the new weight and W (n) is the starting weight.

5. 111Sanjeev Kumar et al. / Procedia Computer Science 89 (2016) 107 – 116
Fig. 1. Architecture of EBP-NN28.
Step 6: In this step calculate error for every neurons in hidden layer and output layer in backward order, followed by
weight adjustments. So the error for the every node in the hidden layer is computed from the following equation.
(hidden layer) = output (1 − output) (output layer) W (update weights) (5)
Step 7: After calculating the Error the weights are updated between input and hidden layer using the following equation
W(n + 1) = W(n) + η (hidden layer) output (input layer) (6)
For every the input pattern steps from 2 to 7 will be repeated until the mean square error (MSE) of output layer is
minimized. The error for every training pattern is calculated by the following equation19,28.
Error (MSE) = Error (p) (7)
5. Call Admission Control and Dynamic Channel Allocation Concept
Call admission control (CAC) is a basic procedure used for QoS in mobile networks. Basically, main objective of
the CAC is to decide whether a call is accepted or rejected by the network. The acceptance and rejection of a new call
and handoff call is depends on the availability of the network resources16,17. In this paper a CAC is used to manage
the resource according to the traffic mobility for efficient handling of dynamic channel allocation (DCA) schemes.
Generally priority is set higher for handoff calls as compare to new calls. So from the total numbers of the channels
some channels are reserved for handoff calls only. If no such condition exists or no one channel is available then the
handoff cannot be accepted so the probability of handoff call is increase. To remove this problem of dropping the
handoff calls, we propose a new HNN-based DCA algorithm for the fair resource allocation between the handoff calls
and new calls. It also maintains the stability between blocking and dropping of calls in mobile network2,4. We have
applied Hopfield neural network (HNN) for Dynamic Channel Allocation with a handoff prioritization scheme based
on traffic mobility information. We have abbreviated this approach as HNN-DCA. The main focus of this proposed
model is to minimize the call dropping rate and improving the QoS level of the existing system. In this with the help
of HNN-DCA algorithm, we proposed a new dynamic channel scheme based on the performance criterion of traffic
mobility in order to maintain requirements of new call-blocking and handoff call-dropping in mobile multimedia
networks.
5.1 HNN model for dynamic channel allocation
The Hopfield neural networks (HNN) were firstly introduced by John Hopfield in 1982. Normally it maintain the
connection between the neurons and physical system. Conceptually a set of N neurons are fully interconnected to all

6. 112 Sanjeev Kumar et al. / Procedia Computer Science 89 (2016) 107 – 116
other neurons excluding themselves because HNN don’t makes self feedback in the network. A connection between
two neurons e.g. Ni and Nj can be represented using symmetric weight matrix as Wi j. Each neuron can be defined by
two possible states denoted by V j. Here, the total numbers of input received the other neurons and threshold operations
calculate the value of each neuron state. Therefore, total number of input neurons denoted by Ui
Ui = Wi jVj + Ii (8)
where Wi j denotes the connection weights between neurons Ni to neurons N j Ii is the external input. The states of
each neurons update automatically with the help of neuron threshold rule with threshold THD as shown by
Vi =
1, if U > T H D
0, otherwise
(9)
In HNN model it (threshold0 rule) can be applied in both modes i.e asynchronously or synchronously21. Here the
formulation of energy function E represents the effectiveness of HNN model, with respect to determine the problems
of channel allocation dynamically. The new formulation of energy function is represented as19–32.
E =
1
2
xT
Wx + bt
x (10)
where
x Input vector for channel assignment;
b Bias vector identified by constraints;
W Symmetric weight matrix for HNN.
The HNN model in dynamic channel allocation (DCA) is used to formulating and capturing the channel allocation
problem as shown in equation (10) in22. One of the HNN-DCA modelling concepts presented and describes in23,
it shows that how an energy function can represent the channel allocation problem in wireless mobile networks when
the traffic mobility is not distributed uniformly. To address problem of traffic mobility one of the author already
introduced mobility based call admission control and resource estimation using BPN and queuing model for the
allocation of channels according to the mobility or handoff calls in28. The channel allocation schemes require reserving
some no of “guard channel” in order to maintain the handoff calls over new arrival calls. The guard channel sachems
improve the QoS level by reducing the call dropping probability during traffic mobility after a handoff occurs. So the
above hard and soft channel allocation policy are the converted into a new energy function which address the problem
of channel allocation for wireless DCA schemes. Here a new formulation of energy function represented as follow
E =
A
2
C
j=1
M
i=1
i=i∗
(V∗
i , j − Ai, j − Interf(i, i∗
)) +
B
2
⎡
⎣
M
j=1
(V∗
i , j − Traf(i∗
))
⎤
⎦
−
C
2
C
j=1
M
i=1
i=i∗
V∗
i , j − Ai, j,
1 − Interf(i, i∗)
Dist(i, i∗)
−
D
2
M
j=1
(V ∗
i , j.A∗
i , j)
−
F
2
C
j=1
M
i=1
i=i∗
(V∗
i , j.A(i, j.)[1 − Res(i, i∗
)]) −
G
2
C
j=1
[Free j.(1 − V ∗
i , j) − H] (11)

7. 113Sanjeev Kumar et al. / Procedia Computer Science 89 (2016) 107 – 116
The symbols used in the equation (11) are as follows
M Total cells used in the system.
C Total available channels in the system.
H Total available handoff guard channels.
Ai, j Allocation table elements, denote 1 if channel j is assigned to cell i, 0, otherwise.
i∗ Number of cells used in a call dropping or arrival.
Traf(i∗) Available requested channel at cell i∗.
Vi∗, j Assignment of the cell of interest.
Dist(i, i∗) Adjacent cell distance between cell i and i∗.
Interf(i, i∗) Channel compatibility matrix set to 1 if the assignment of a channel in cell i is not compatable with
the same channel in cell i∗, 0, otherwise.
Res(i, i∗) Channel reuse matrix (its value is 1 if cells i and i∗ belongs to the same result pattern, 0, otherwise).
In the above formulation freej represents the number of channels in a free channel vectors. The value of free is set to
1 if the j channel is being used by any cell otherwise 0. The intrinsic nature of the new energy function is to absorbing
the required no of channels for the current serving calls, so that higher priority is set to handoff calls over the new calls.
Here in our formulation, extra qualities like channel vector free, term, and constant term or number of guard channels
are added to the new energy function for the proposed HNN-DCA model. This model increases the performance of
the energy function if some additional unused channels are available for assignments. It limits the assignment of the
channels for new calls if the request is satisfied with the previous resource allocation in the system. This HNN-DCA
make sure that a few numbers of channels are reserved in advance to handle the handoff calls. But, the problem of
previous DCA schemes so far is that while some channels are always being restricted for handoff calls then it can be
happen that, the blocking probability of new calls may increase due to the shortage of channels for new arrival calls
in the wireless system. We propose the HNN-DCA policy based on neural model in order to maintain fair resource
allocation between new call and handoff call and to reduce the reduce the call blocking and call dropping probabilities.
We employ the error back propagation to effectively explore the geographical area (where the network is deployed)
for traffic mobility and Hopfield neural network for dynamic resource allocation. Hence, the proposed algorithm
will focus to improve the QoS levels of the existing calls in order to reserve some channels for new or handoff
calls. Simulation results show the efficiency and effectiveness of the model with respect to traffic mobility and fairly
allocation of resources.
6. Simulation Result
For the mobility prediction we have used the five input layers, ten hidden layers, and one output layer in error
back propagation neural network method. The training sets are made from the recorded direction data of a MU in a
uniform and random time interval and from the previous history of a MU. During the training phase the mobility of a
user is predicted and based on the prediction HNN model, will check the availability of radio resources and allocate
to the handoff calls. So the proposed scheme provides the guarantee of keeping handoff dropping probability (Phd)
and call blocking probability (Pnb) below a threshold value. All the results are simulated using the MATLAB. These
results show the behaviour of the neural networks and other components of the performance evaluation model. We
have considered a 10 ∗ 10 hexagonal shaped wireless network of radius 2 km. This module will require 100 neurons in
Hopfield neural network to identify the channel allocation problem.
The Fig. 3 shows the location prediction result of the MUs according to the training data available. Simulation is
done using MATLAB to improve the accuracy of prediction 90% to 97%. Figure 3 shows the simulation percentage
result of call blocking probability based on the number of available channels in busy random traffic. The simulation
shows that blocking probability in the proposed model decreases when the number of channels increases in comparison
to FCA and DCA. The percentage of resource utilization is almost 90 to 95%. But at a certain point the percentage
of resource utilization is increase due to the lack of radio resources. Figure 4 shows the percentage result of handoff
dropping probability based on the number of channels available in random traffic. It is not possible to completely

8. 114 Sanjeev Kumar et al. / Procedia Computer Science 89 (2016) 107 – 116
Fig. 2. Architecture of Hopfield Neural Network. Fig. 3. Performance Analysis of Traffic Mobility Prediction.
Fig. 4. Channels vs New Call Blocking Probability. Fig. 5. Channels vs Handoff Call Dropping Probability.
eliminate the handoff dropping percentage but it can be controlled up to a certain threshold value. According to Fig. 5
the percentage of handoff dropping call decreases when the numbers of channels are increased. Consequently, our
proposed model HNN-DCA keeps the percentage of handoff dropping calls between 3 to 5 percent.
7. Conclusions
In this paper the Hopfield neural network and back propagation neural network are used in order to maintain
fairly resource allocation based on traffic mobility. In the first phase, impact of mobility prediction and performance
estimation of the traffic mobility is done using error back propagation model. In the second phase, Hopfield based
dynamic channel allocation scheme has been applied to improve the capacity of existing dynamic channel allocation
schemes. This scheme predicts the uniform or non-uniform mobile users who usually move in the cellular networks.
On the basis of traffic mobility, when and where the next handoff will occur, a dynamically resource allocation model
have been developed using Hopfield, which not only enhance the capacity of resource utilization but also significantly
reduce the blocking and dropping probabilities. Moreover, our approach also maintain the fairness in resource sharing

9. 115Sanjeev Kumar et al. / Procedia Computer Science 89 (2016) 107 – 116
while considering acceptable QoS levels to the new calls and handoff calls. The results show the significant effect in
terms of reducing the blocking and dropping probabilities and increasing proper utilization of resources. Furthermore,
in future our model can also be implemented for other soft computing approach or combination of soft computing
approaches like genetic-neuro, fuzzy-neuro, or fuzzy-genetic-neuro.
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