The proposed technology can reduce network costs by allowing optimized use of radio resources in mobile wireless networks. The system provides reliable and high-capacity channels, while the wireless part allows user mobility. Solving the problem involves an attempt to determine an optimal distribution of radio resources to the RAUs (radio allocation unit) and network cells can be split or merged dynamically to optimize this distribution as a function of the fluctuation of demands.

1. Available online at https://www.ijasrd.org/
International Journal of Advanced Scientific
Research & Development
Vol. 06, Iss. 04, Ver. I, Apr’ 2019, pp. 29 – 32
Cite this article as: Sudha, S., Priyanka, S., Nithya, G., & Gigija, L., “A Novel Implementation for Radio Resource Allocation
on Mobile Access Based on Cognitive Radio Network”. International Journal of Advanced Scientific Research & Development
(IJASRD), 06 (04/I), 2019, pp. 29 – 32. https://doi.org/10.26836/ijasrd/2019/v6/i4/60406.
* Corresponding Author: S. Priyanka, spriyankatnj1@gmail.com
e-ISSN: 2395-6089
p-ISSN: 2394-8906
A NOVEL IMPLEMENTATION FOR RADIO RESOURCE
ALLOCATION ON MOBILE ACCESS BASED ON COGNITIVE
RADIO NETWORK
S. Sudha1, S. Priyanka2*, G. Nithya2, and L. Gigija2
1 Assistant Professor, Dept., of ECE, St. Joseph’s College of Engineering and Technology, Thanjavur.
2 UG students, Dept., of ECE, St. Joseph’s College of Engineering and Technology, Thanjavur.
ARTICLE INFO
Article History:
Received: 12 Mar 2019;
Received in revised form:
14 Apr 2019;
Accepted: 14 Apr 2019;
Published online: 10 May 2019.
Key words:
RAU,
Radio Allocation Unit,
RRM,
Radio Resource Management,
BSC,
Base Station Controller.
ABSTRACT
The proposed technology can reduce network costs by
allowing optimized use of radio resources in mobile wireless
networks. The system provides reliable and high-capacity
channels, while the wireless part allows user mobility.
Solving the problem involves an attempt to determine an
optimal distribution of radio resources to the RAUs (radio
allocation unit) and network cells can be split or merged
dynamically to optimize this distribution as a function of the
fluctuation of demands.
Copyright © 2019 IJASRD. This is an open access article distributed under the Creative Common Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original
work is properly cited.
INTRODUCTION
Wireless communication is among technology’s biggest contributions to mankind.
Common wireless technologies use radio.
Figure – 1: Wireless Communication
• RF signals are transmitted over fiber to provide broadband wireless access
• An emerging very hot area
• Underground

2. A Novel Implementation for Radio Resource Allocation on Mobile Access Based on Cognitive Radio
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• Olympics London
• Niagara Tunnel
Figure – 2: Underground Wireless Transmission
EXISTING SYSTEM
Optimization is the positioning of base stations for optimal use of radio resources.
Solutions for this traditional problem are usually based on static methods and can lead to
resource waste in dynamic networks such as those involving mobile users.
The authors compare two different algorithms, one distributed and the other
centralized Both algorithms, however, are based on a greedy approach and do not provide
optimal results.
2.1 Drawback
• Not appropriate for mobile users since clustering of users is disregarded.
• Not consider the hotspot region
EXISTNG SYSTEM MODEL
Figure – 3: System Model
PROPOSED SYSTEM
In this architecture, the RAUs are organized in a hierarchical structure making
possible the deployment of cells with different radii in a multi-tier fashion Deploying static
cells with a fixed size would lead to resource waste, since user density in the network varies
and congestion can “migrate” from one region to another in the network.
Merging number of BSs used, as this will reduce network costs. In this formulation,
it is not guaranteed the coverage for all MSs, but it is assured for at least the minimum
required percentage.

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SYSTEM ARCHITECTURE
5.1 Transmitter Section
Figure – 4: Transmitter Section
Micro
Controller
Power Supply
Temperature Sensor Zig – Bee Transmitter
LDR Cooling Fan
RELAY LCD Display
User
Button
5.2 Receiver Section
Figure – 5: Receiver Section
ATMEGA 328
Power Supply
Zig – Bee Receiver Temperature Sensor
LDR Cooling Fan
RELAY LCD Display
EXPERIMENTAL RESULTS
1) In our system design, transmitter side of the system consists of Atmega 328, LDR,
temperature sensor, LCD, Zigbee transmitter, user button
2) Zigbee acts as transmitting antenna and the system has 3 user allocations and if the
additional user comes to the antenna, the subscribers will be shared to the conjoint
antenna to handle the subscribers to it.
3) 3) The receiver antenna accepts the subscribers who are unavailable to be
connected in any conjoined antenna.
4) Hence the proposed system explains that by the system can share the users’ calls to
the available conjoined spectrum to the available spectrum to avoid the call waiting.
5) In maximum 5 user can be added
Figure – 6: When Spectrum is High for Shared by Another Network

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CONCLUSION
A centralized resource optimization model, which involves dynamic cell splitting and
merging executed for multi-tier RoF infrastructures of RAUs. The optimization problem
aims at three different objectives and uses relaxation-based integer linear programming
function that simultaneously reduces the network operational cost and maximizes the
operator revenue. The relaxation-based algorithm also implemented in mixed combination
of minimizes the number of base stations and maximize the number users. The results are
obtained using FICO Xpress simulation suite.
FUTURE ENHANCEMENT
Use Li-Fi technique in future enhancement, it reduces a hardware complexity and
cost.
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