On the real time hardware implementation feasibility of joint radio resource management policies for heterogeneous wireless networks

On the Real-Time Hardware Implementation Feasibility
of Joint Radio Resource Management Policies for
Heterogeneous Wireless Networks
ABSTRACT:
We design of Joint Radio Resource Management (JRRM) techniques is a key and challenging aspect in future
heterogeneous wireless systems where different Radio Access Technologies (RAT) will physically coexist.
In this system, the total available radio resources need to be used in a coordinated way to guarantee adequate
satisfaction levels to all users, and maximize the system revenues. In addition to carry out an efficient use of the
available radio resources, JRRM algorithms need to exhibit good computational performance to guarantee their
future implementation viability.
We proposes novel JRRM techniques based on linear programming techniques, and investigates their
computational cost when implemented in DSP platforms commonly used in mobile-based stations. The obtained
results demonstrate the feasibility to implement the proposed JRRM algorithms in future heterogeneous
wireless systems.
JRRM techniques have been implemented following the JRRM server approach discussed in the 3GPP
standards. This approach considers a centralized architecture that places the JRRM functionality in a node that
collects information of all available RATs.1
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Since the JRRM techniques use utility functions to estimate the users’ QoS demands, only updated information
about each RAT’s load must be transmitted to the JRRM server. Using this information, the implemented
JRRM techniques manage the available radio resources to maximize the percentage of satisfied users.
INTRODUCTION:
The evolution of mobile and wireless communication systems is being characterized by the coexistence of
diverse Radio Access Technologies (RAT) with different, but sometimes complementary, technical
characteristics. In parallel, novel user applications are continuously appearing with diverse Quality of Service
(QoS) requirements. Despite the appearance of novel RATs with increasing performance, the research
community agrees that future mobile and wireless communication systems will be composed of heterogeneous
RATs physically coexisting and offering mobile services to a wide range of QoS-demanding users in a
coordinated manner.
Future heterogeneous wireless systems are the coordinated management of heterogeneous radio resources,
usually referred as Joint Radio Resource Management (JRRM) or Common Radio Resource Management
(CRRM). The Third Generation Partnership Project (3GPP) defines the JRRM concept and describes different
supporting network architectures that ensure the interoperability between the different access technologies.
JRRM policies need then to be designed so that the total available radio resources are efficiently distributed
among active users in order to maximize the system revenue and provide the QoS levels demanded by
users/services in multimedia environments to carry out the most efficient use of the total available resources.
JRRM policies must decide for each incoming call the RAT over which it will be conveyed (RAT selection) and
the number of radio resources within the selected RAT (intra-RAT RRM) that will be necessary to satisfy the
user/service QoS requests. JRRM policy and resulting radio resource assignments should be capable to
dynamically adapt to the current operating conditions, for example system load and active multimedia services.
We describes the framework over which JRRM algorithms can be developed, and proposes some basic
techniques to address the initial RAT selection dilemma based on pre-established service-to-RAT assignments
and user location. Other studies have investigated how to exploit multi technology terminals capability to switch
between RATs in order to free the capacity required to accept new calls from single-mode terminals.

For example, the JRRM load balancing mechanism reported in aims at achieving a uniform traffic distribution
between the available RATs. As the authors point out, such uniform distribution is desirable in order to
maximize the trucking gain and minimize the probability of making unnecessary Vertical Handovers (VHO) of
multi technology terminals between RATs. For Non real-time services, the load balancing is performed based
on the measured buffer delay, while the authors propose a mechanism based on load thresholds for real time
services.
Proposals to jointly address the RAT selection and intra-RAT RRM dilemmas have also been reported. For
example, a Joint Call Admission Control (JCAC) algorithm that combines the RAT selection and Call
Admission Control (CAC) mechanisms in order to reduce the call blocking and dropping probabilities, and
ensure a fair radio resource allocation.
We proposed novel JRRM policies that simultaneously assign to each user an adequate combination of RAT
and number of radio resources within such RAT to guarantee the user/service QoS requirements. The proposed
JRRM techniques are based on linear programming and optimization techniques.
QoS performance that can be achieved by novel JRRM techniques, but have not investigated their
computational cost and implementation feasibility in evaluation of the computational efficiency of new
proposals is widely conducted in other research fields like audio and video real-time compression, where the
time spent by the algorithm to process the data is crucial to provide a good performance to the end user.
The result of this study is of relevance to the research community since it demonstrates the feasibility of
implementing complex JRRM policies, and provides the first indications on their hardware computational
performance in time requirements are not as demanding as for audio and video compression techniques, JRRM
decisions in mobile networks should be made as quickly as possible in order to be able to efficiently adapt the
use of the radio resources networks.

LITRAURE SURVEY:
Joint Call Admission Control Algorithm for Fair Radio Resource Allocation in Heterogeneous Wireless
Networks Supporting Heterogeneous Mobile Terminals
Author: O.E. Falowo and H.A. Chan, pp. 1-5, Jan. 2010
This paper proposes a joint call admission control (JCAC) algorithm to reduce this problem of unfairness. The
proposed JCAC algorithm makes call admission decisions based on mobile terminal modality (capability),
network load, and radio access technology (RAT) terminal support index. The objectives of the proposed JCAC
algorithm are to reduce call blocking/ dropping probability, and ensure fairness in allocation of radio resources
among heterogeneous mobile terminals in heterogeneous networks. We develop an analytical model to evaluate
the performance of the proposed JCAC scheme in heterogeneous wireless networks and derive expression for
call blocking / dropping probability. The performance of the proposed JCAC algorithm is compared with that of
other JCAC algorithm. Results show the proposed algorithm reduces call blocking/ dropping probability in the
networks, and ensure fairness in allocation of radio resources among heterogeneous terminals.
Common Radio Resource Management Policy for Multimedia Traffic in Beyond 3G Heterogeneous
Wireless Systems
Author: M.C. Lucas-Estan˜ , J. Goza´lvez, and J. Sa´nchez-Soriano, pp. 1-5, Sept. 2008.
Beyond 3G wireless systems will be composed of a variety of radio access technologies (RATs) with different,
but also complementary, performance and technical characteristics. To exploit such diversity while
guaranteeing the interoperability and efficient management of the different RATs, common radio resource
management (CRRM) techniques need to be defined. This work proposes and evaluates a CRRM policy that
simultaneously assigns to each user an adequate combination of RAT and number of radio resources within
such RAT to guarantee its QoS requirements. The proposed CRRM technique is based on linear objective
functions and programming tools.
H.263 Video Traffic Modelling for Low Bit Rate Wireless Communications
Author: O. Lazaro, D. Girma, and J. Dunlop, pp. 2124-2128, Sept. 2005.
Video traffic exhibits a greater complexity than traditional services such as voice. Therefore, it is necessary to
identify the relevant statistical properties, which characterise this type of traffic and provide the tools to
properly model them so that networks could be efficiently dimensioned. This work presents on one hand a
detailed analysis of the statistical features, which characterise H.263 video traffic streams coded at bit rates

suitable for wireless communications. On the other hand, the paper presents and evaluates an on-line video
traffic model, which captures the most significant features observed.
A Perspective on Radio Resource Management in B3G
Author: O. Sallent, pp. 30-34, Sept. 2006.
Beyond 3G usually refers to heterogeneous scenarios where different radio access technologies (RATs) coexist
and operate in a coordinated way. This brings a new challenge to offer services to the users over an efficient and
ubiquitous radio access. In this way, the user can be served through the RAT that fits better to the terminal
capabilities and service requirements, and also a more efficient use of the radio resources can be achieved. This
challenge calls for the introduction of new radio resource management (RRM) algorithms operating from a
common perspective that take into account the overall amount of resources offered by the available RATs. In
this context, this paper presents the framework for developing RRM algorithms in the B3G scenarios, including
some possible approaches.
EXISTING SYSTEM:
Existing of the benefits of common radio resource management (CRRM) for traffic management in an
environment where several different radio access technologies co-exist with cells on several hierarchical layers.
Load balancing, i.e., the capacity gains from CRRM concept are studied by dynamic simulations for both real-
time and non-real-time traffic. The results show that CRRM improves the conversational and streaming
capacity by 11% with 144 kbps and the interactive capacity up to 70-90% when 5 s delays is required with 80-
85% probability.
Previous work on CRRM and RAT selection has mainly focused on the development of solutions aimed at
maximizing the overall system capacity, the design of strategies from the QoS-provision viewpoint has received
less attention.
CRRM work reported in focused on the development of new CRRM techniques designed to efficiently
distribute heterogeneous traffic among the available RATs in order to provide appropriate user/service QoS
levels while adequately exploiting the available radio resources of each node distribute multimedia traffic in
heterogeneous RAT.

PROPOSED SYSTEM:
JRRM techniques, initially proposed in aimed at providing the highest possible homogeneous user satisfaction
levels to all service types by exploiting the QoS/resource flexibility offered by different services present in a
multimedia framework.
For example, email users do not require the same number of radio resources than a video conferencing session
to obtain the same user satisfaction levels. In this work, the user satisfaction is represented by utility values
identifying the radio resources needed per service class to achieve certain user QoS satisfaction levels.
Implemented JRRM techniques, this work considers a heterogeneous wireless environment where the General
Packet Radio Service (GPRS), Enhanced Data rates for GSM Evolution (EDGE), and High Speed Downlink
Packet Access (HSDPA) RATs physically coexist. The JRRM techniques have been implemented following the
JRRM server approach discussed in the 3GPP standards.
This approach considers a centralized architecture that places the JRRM functionality in a node that collects
information of all available RATs.1 since the JRRM techniques use utility functions to estimate the users’ QoS
demands, only updated information about each RAT’s load must be transmitted to the JRRM server. Using this
information, the implemented JRRM techniques manage the available radio resources to maximize the
percentage of satisfied users.
Advanced JRRM techniques for heterogeneous wireless networks implemented and evaluated techniques are
based on linear programming and optimization algorithms, and have been shown to achieve good system
performance under multimedia traffic conditions. To evaluate their implementation feasibility, the JRRM
techniques have been implemented in a DSP simulator software using open source linear programming solvers.

HARDWARE & SOFTWARE REQUIREMENTS:
HARDWARE REQUIREMENTS:
 Processor - Pentium –IV
 Speed - 1.1 GHz
 RAM - 256 MB (min)
 Hard Disk - 20 GB
 Floppy Drive - 1.44 MB
 Key Board - Standard Windows Keyboard
 Mouse - Two or Three Button Mouse
 Monitor - SVGA
SOFTWARE REQUIREMENTS:
 Operating System : Windows XP
 Front End : Java JDK 1.7
 Scripts : Java Script.

IMPLEMENTATION:
The proposed, implemented and evaluated techniques are based on linear programming and optimization
algorithms, and have been shown to achieve good system performance under multimedia traffic conditions. To
evaluate their implementation feasibility, the JRRM techniques have been implemented in a DSP simulator
software using open source linear programming solvers.
Demonstrated the feasibility of implementing the proposed JRRM techniques in real mobile communication
systems using powerful hardware and software tools in the computational execution cost can be further reduced
at the cost of eliminating the optimality condition in the radio resources distribution.
However, the obtained results show that both JRRM proposals satisfy their various objectives.
 The majority of services achieve their minimum QoS level, and only when such level is guaranteed,
resources are additionally assigned to higher priority users.
 The number of served users is the maximum possible satisfying the system and service constraints.
 The service priorities criterion defined in correctly applied under radio resources shortage conditions.
MODULES:
HETEROGENEOUS WIRELESS SYSTEMS:
RADIO ACCESS TECHNOLOGIES (RAT):
JRRM SERVER CLIENT MODULE:
JOINT RADIO RESOURCE MANAGEMENT (JRRM):
JRRM PERFORMANCE ANALYSIS:

REFERENCES:
[1] 3GPP, Improvement of Radio Resource Management (RRM) across RNS and RNS/BSS, Technical Report
25.881, v5.0.0, Jan. 2002.
[2] 3GPP, Improvement of Radio Resource Management (RRM) Across RNS and RNS/BSS Post-Rel-5,
Technical Report 25.891, v0.3.0, June 2003.
[3] J. Perez-Romero, O. Sallent, and R. Agusti, “Policy-Based Initial RAT Selection Algorithms in
Heterogeneous Networks,” Proc. Seventh IFIP Int’l Conf. Mobile and Wireless Comm. Networks (MWCN
’05), Sept. 2005.
[4] S.J. Lincke, “Vertical Handover Policies for Common Radio Resource Management,” Int’l J. Comm.
Systems, vol. 18, no. 6, pp. 527-543, Aug. 2005.
[5] A. Tolli, P. Hakalin, and H. Holma, “Performance Evaluation of Common Radio Resource Management
(CRRM),” Proc. IEEE Int’l Conf. Comm. (ICC ’02), vol. 5, pp. 3429-3433, Apr. 2002.
[6] O. Cabral, F.J. Velez, J. Rodriguez, V. Monteiro, A. Gameiro, and N.R. Prasad, “Optimal Load Suitability
Based RAT Selection for HSDPA and IEEE 802.11e Information Theory, and Aerospace and Electronic
Systems Technology (Wireless VITAE ’09), pp. 722-726, May 2009.
[7] Y. Guang, C. Jie, Y. Kai, Z. Ping, and V.O.K. Li, “Joint Radio Resource Management based on the Species
Competition Model,” Proc. IEEE Wireless Comm. and Networking Conf. (WCNC ’06), pp. 54-57, Apr. 2006.
[8] O.E. Falowo and H.A. Chan, “Joint Call Admission Control Algorithm for Fair Radio Resource Allocation
in Heterogeneous Wireless Networks Supporting Heterogeneous Mobile Terminals,” Proc. IEEE Seventh
Consumer Comm. and Networking Conf. (CCNC), pp. 1-5, Jan. 2010.
[9] L. Giupponi, R. Agusti, J. Pe´rez-Romero, and O. Sallent, “A Novel Approach for Joint Radio Resource
Management based on Fuzzy Neural Methodology,” IEEE Trans. Vehicular Technology, vol. 57, no. 3, pp.
1789-1805, May 2008.

[10] M.C. Lucas-Estan˜ , J. Goza´lvez, and J. Sa´nchez-Soriano, “Common Radio Resource Management Policy
for Multimedia Traffic in Beyond 3G Heterogeneous Wireless Systems,” Proc. IEEE 19th Int’l Symp. Personal,
Indoor, and Mobile Radio Comm., pp. 1-5, Sept. 2008.
[11] J. Goza´lvez, M.C. Lucas-Estan˜ , and J. Sa´nchez-Soriano, “Joint Radio Resource Management in Beyond
3G Heterogeneous Wireless Systems,” Proc. 11th Int’l Symp. Wireless Personal Multimedia Comm. (WPMC),
pp. 1-5, Sept. 2008.
[12] E.A. Yavuz and V.C.M. Leung, “Computationally Efficient Method to Evaluate the Performance of Guard-
Channel-Based Call Admission Control in Cellular Networks,” IEEE Trans. Vehicular Technology, vol. 55, no.
4, pp.1412-1424, July 2006.
[13] 3GPP, Services and Service Capabilities, Technical Specification 22.105, v6.3.0, 2005.
[14] P. Barford, M. Crovella, “Generating Representative Web Workloads for Network and Server Performance
Evaluation,” Proc. Int’l Conf. Measurement and Modeling of Computer Systems
(SIGMETRICS/PERFORMANCE ’98), pp. 151-160, June 1998.
[15] J. Ho, Y. Zhu, and S. Madhavapeddy, “Throughput and Buffer Analysis for GSM General Packet Radio
Service (GPRS),” Proc. IEEE Wireless Comms. and Networking Conf. (WCNC 1999), pp. 1427-1431, Sept.
1999.

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