Channel Allocation Scheme Based On Greedy Algorithm In Cognitive Vehicular Network

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Description

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ABSTRACT

Cognitive radio (CR) concept is turning out as a prominent approach used in the wireless communication network for increasing spectrum efficiency by opportunistically and mutually sharing the spectrum of contemporary networks. CR can bear high traffic loads during emergencies and major disasters by overcoming the limitations like lack of network capacity. The fundamental issues pertaining the implementation of CR network (CRN) are the presence of co-channel interference and adjacent channel interference among CR users; and most importantly interference to primary users. Effective interference mitigation and management in CRN will make it more robust in easing the additional stress because of very high traffic loads during an intense emergency and disaster scenarios. In this study, an approach has been taken to minimize interference among secondary nodes by employing interference index as interference minimization key which in turn maximize the system capacity. To validate the results, the authors thoroughly used an existing distributed greedy algorithm, which, on the introduction of interference index, furnished a gain of 60% in the CR network capacity. Further, a trade-off analysis between the interference index and channel leakage ratio is presented with an interference bound of 10dB, which may form the basis of interference management in CRN.

EXISTING METHOD

  • Energy-efficient RB Allocation Algorithm
  • In network layer advanced cryptographic schemes are availed for data security avoid eavesdropper intervention.

DRAWBACKS

  • Low throughput rate with the UE increase in change also.
  • Secret cryptography and key management should be made with much more advancement enabled this make much complicated of the system

PROPOSED METHOD

  • LTE network with decode and forward relaying strategy with high throughput analysis over existing system and real time system analysis.
  • Eaves dropper analysis.
  • Distributed Greedy Algorithm

BLOCK DIAGRAM

  • PBS Primary base station PU: primary user
  • MSU Macro cell secondary user
  • FBS Femtocell base station
  • FSU ?Femtocell secondary user

ADVANTAGES:

  • The action occurs much more in physical layer hence the data is transfering towards the eavesdropper can be efficiently avoided.
  • The relay selection plays a vital role here and the schemes are less.

APPLICATIONS:

  • Multi carrier signal transmission for video conferencing and high data rate WIMAX system communication
  • Normal multipath fading system analysis networks

SOFTWARE REQUIREMENT

  • MATLAB 2018 or above versions

REFERENCES

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[4] F. Abinader, E. Almeida, F. Chaves et al.. ?Enabling the coexistence LTE and Wi-Fi in unlicensed bands.? IEEE Communications Magazine, vol. 52, pp. 54-61, Nov. 2014.

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[6] M. I. Rahman, A. Behravan, H. Koorapaty, J. Sachs, and K. Balachandran. ?License-exempt LTE systems for secondary spectrum usage:scenarios and ?rst assessment.? in IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), pp. 349-358, 2011.

[7] A. Babaei, J. Andreoli-Fang and B. Hamzeh. ?On the impact of LTEU on Wi-Fi performance.? in 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC), Washington DC, pp. 1621-1625, 2014.

[8] S. Y. Lien, J. Lee and Y. C. Liang. ?Random Access or Scheduling: Optimum LTE Licensed-Assisted Access to Unlicensed Spectrum.? IEEE Communications Letters, vol. 20, pp. 590-593, Mar. 2016.

[9] H. Cui, V. C. M. Leung, S. Li and X. Wang. ?LTE in the Unlicensed Band: Overview, Challenges, and Opportunities.? IEEE Wireless Communications, vol. 24, pp. 99-105, 2017.

[10] A. Mukherjee et al.. ?Licensed-Assisted Access LTE: coexistence with IEEE 802.11 and the evolution toward 5G.? IEEE Communications Magazine, vol. 54, pp. 50-57, Jun. 2016.

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