Abstract: In wireless and mobile communications, this progress
provides opportunities for introducing new standards and improving
existing services. Supporting multimedia traffic with wireless networks
quality of service (QoS). In this paper, a grey-fuzzy controller for radio
resource management (GF-RRM) is presented to maximize the number
of the served calls and QoS provision in wireless networks. In a
wireless network, the call arrival rate, the call duration and the
communication overhead between the base stations and the control
center are vague and uncertain. In this paper, we develop a method to
predict the cell load and to solve the RRM problem based on the
GF-RRM, and support the present facility has been built on the
application-level of the wireless networks. The GF-RRM exhibits the
better adaptability, fault-tolerant capability and performance than other
algorithms. Through simulations, we evaluate the blocking rate, update
overhead, and channel acquisition delay time of the proposed method.
The results demonstrate our algorithm has the lower blocking rate, less
updated overhead, and shorter channel acquisition delay.
Abstract: Due to the coexistence of different Radio Access
Technologies (RATs), Next Generation Wireless Networks (NGWN)
are predicted to be heterogeneous in nature. The coexistence of
different RATs requires a need for Common Radio Resource
Management (CRRM) to support the provision of Quality of Service
(QoS) and the efficient utilization of radio resources. RAT selection
algorithms are part of the CRRM algorithms. Simply, their role is to
verify if an incoming call will be suitable to fit into a heterogeneous
wireless network, and to decide which of the available RATs is most
suitable to fit the need of the incoming call and admit it.
Guaranteeing the requirements of QoS for all accepted calls and at
the same time being able to provide the most efficient utilization of
the available radio resources is the goal of RAT selection algorithm.
The normal call admission control algorithms are designed for
homogeneous wireless networks and they do not provide a solution
to fit a heterogeneous wireless network which represents the NGWN.
Therefore, there is a need to develop RAT selection algorithm for
heterogeneous wireless network. In this paper, we propose an
approach for RAT selection which includes receiving different
criteria, assessing and making decisions, then selecting the most
suitable RAT for incoming calls. A comprehensive survey of
different RAT selection algorithms for a heterogeneous wireless
network is studied.
Abstract: Recent communications environment significantly
expands the mobile environment. The popularization of smartphones
with various mobile services has emerged, and smartphone users are
rapidly increasing. Because of these symptoms, existing wired
environment in a variety of mobile traffic entering to mobile network
has threatened the stability of the mobile network. Unlike traditional
wired infrastructure, mobile networks has limited radio resources and
signaling procedures for complex radio resource management. So
these traffic is not a problem in wired networks but mobile networks, it
can be a threat. In this paper, we analyze the security threats in mobile
networks and provide direction to solve it.
Abstract: Next Generation Wireless Network (NGWN) is
expected to be a heterogeneous network which integrates all different
Radio Access Technologies (RATs) through a common platform. A
major challenge is how to allocate users to the most suitable RAT for
them. An optimized solution can lead to maximize the efficient use
of radio resources, achieve better performance for service providers
and provide Quality of Service (QoS) with low costs to users.
Currently, Radio Resource Management (RRM) is implemented
efficiently for the RAT that it was developed. However, it is not
suitable for a heterogeneous network. Common RRM (CRRM) was
proposed to manage radio resource utilization in the heterogeneous
network. This paper presents a user level Markov model for a three
co-located RAT networks. The load-balancing based and service
based CRRM algorithms have been studied using the presented
Markov model. A comparison for the performance of load-balancing
based and service based CRRM algorithms is studied in terms of
traffic distribution, new call blocking probability, vertical handover
(VHO) call dropping probability and throughput.