A Novel Implementation for Radio Resource Allocation on Mobile Access Based on Cognitive Radio Network
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The document discusses a novel implementation for radio resource allocation in mobile access networks based on cognitive radio technology, aimed at optimizing the use of radio resources and reducing costs. It highlights a centralized resource optimization model involving dynamic cell splitting and merging, which enhances network efficiency while maximizing operator revenue. The authors introduce algorithms and system architecture designed to accommodate varying user densities and minimize resource waste in changing environments.
![A Novel Implementation for Radio Resource Allocation on Mobile Access Based on Cognitive Radio
Network
Volume 06, Issue 04, Version I, Apr’ 2019 32
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.
REFERENCES
[1] L. Xiang, X. Ge, C.-X.Wang, F. Y. Li, and F. Reichert, “Energy efficiency evaluation of
cellular networks based on spatial distributions of traffic load and power
consumption,” IEEE Trans. Wireless Communication., vol. 12, no. 3, pp. 961–973,
Mar. 2013.
[2] X. Ge et al., “Energy efficiency optimization for MIMO-OFDM mobile multimedia
communication systems with QoS constraints,” IEEE Trans. Veh. Technol., vol. 63,
no. 5, pp. 2127–2138, Jun. 2014.
[3] X. Ge et al., “Spectrum and energy efficiency evaluation of two-tier femtocell networks
with partially open channels,” IEEE Trans. Veh. Technol., vol. 63, no. 3, pp. 1306–
1319, Mar. 2014.
[4] S. Verdu, “Spectral efficiency in the wideband regime,” IEEE Trans. Inf. Theory, vol. 48,
no. 6, pp. 1319–1343, Jun. 2002.
[5] I. Ku, C.-X.Wang, and J. S. Thompson, “Spectral-energy efficiency tradeoff in relay-aided
cellular networks,” IEEE Trans. Wireless Commun., vol. 12, no. 10, pp. 4970–4982,
Oct. 2013.
[6] X. Hong, Y. Jie, C.-X. Wang, J. Shi, and X. Ge, “Energy-spectral efficiency trade-off in
virtual MIMO cellular systems,” IEEE J. Sel. Areas Commun., vol. 31, no. 10, pp.
2128–2140, Oct. 2013.](https://image.slidesharecdn.com/60406-190514070344/85/A-Novel-Implementation-for-Radio-Resource-Allocation-on-Mobile-Access-Based-on-Cognitive-Radio-Network-4-320.jpg)
A Novel Implementation for Radio Resource Allocation on Mobile Access Based on Cognitive Radio Network
- 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 Network Volume 06, Issue 04, Version I, Apr’ 2019 30 • 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.
- 3. Sudha et al., (2019) 31 Volume 06, Issue 04, Version I, Apr’ 2019 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
- 4. A Novel Implementation for Radio Resource Allocation on Mobile Access Based on Cognitive Radio Network Volume 06, Issue 04, Version I, Apr’ 2019 32 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. REFERENCES [1] L. Xiang, X. Ge, C.-X.Wang, F. Y. Li, and F. Reichert, “Energy efficiency evaluation of cellular networks based on spatial distributions of traffic load and power consumption,” IEEE Trans. Wireless Communication., vol. 12, no. 3, pp. 961–973, Mar. 2013. [2] X. Ge et al., “Energy efficiency optimization for MIMO-OFDM mobile multimedia communication systems with QoS constraints,” IEEE Trans. Veh. Technol., vol. 63, no. 5, pp. 2127–2138, Jun. 2014. [3] X. Ge et al., “Spectrum and energy efficiency evaluation of two-tier femtocell networks with partially open channels,” IEEE Trans. Veh. Technol., vol. 63, no. 3, pp. 1306– 1319, Mar. 2014. [4] S. Verdu, “Spectral efficiency in the wideband regime,” IEEE Trans. Inf. Theory, vol. 48, no. 6, pp. 1319–1343, Jun. 2002. [5] I. Ku, C.-X.Wang, and J. S. Thompson, “Spectral-energy efficiency tradeoff in relay-aided cellular networks,” IEEE Trans. Wireless Commun., vol. 12, no. 10, pp. 4970–4982, Oct. 2013. [6] X. Hong, Y. Jie, C.-X. Wang, J. Shi, and X. Ge, “Energy-spectral efficiency trade-off in virtual MIMO cellular systems,” IEEE J. Sel. Areas Commun., vol. 31, no. 10, pp. 2128–2140, Oct. 2013.