• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2020, Volume: 13, Issue: 41, Pages: 4332-4350

Original Article

Interference resilient stochastic prediction based dynamic resource allocation model for cognitive MANETs

Received Date:21 May 2020, Accepted Date:30 June 2020, Published Date:03 December 2020


Background/Objectives: Being dynamic in nature, Mobile Ad-hoc Network (MANET) requires robust resource allocation strategy that can ensure both optimal transmission reliability and resource efficiency to meet Quality of Service (QoS) demands. The objective of this research is to address interference resilience requirement in MANETs which is must due to greedy nature of nodes especially when accessing resource or bandwidth and develop a highly robust stochastic prediction based resource allocation strategy. Methods: The proposed Interference Resilient Stochastic Prediction based Dynamic Resource Allocation model for Cognitive MANET (ISP-DRACM) intends to enable optimal resource allocation under interweave and underlay network setup with instantaneous as well as average interference conditions. It employs a joint power management and resource allocation strategy where it intends to maximize the weighted sum-rate of the secondary users under certain defined conditions like average power and stochastic interference level. Findings/Novelty: Inculcating resource allocation problem as controlled Markov Decision Process using Hidden Markov Model (HMM) and Lagrange relaxation, our proposed model achieves better resource allocation under limited noise or interference condition and hence achieves both costeffectiveness as well as QoS provision. This method has exhibited satisfactory performance towards spectrum allocation to the secondary users without imposing any significant interference for both interweave as well as underlay Cognitive Radio setup.
Keywords: Cognitive mobile ad-hoc network; stochastic prediction; interference resilience; channel state information; dynamic resource allocation; underlay and overlay cognitive MANET 


  1. Liang Z, Feng S, Zhao D, Shen X. Delay performance analysis for supporting real-time traffic in a cognitive radio sensor network. IEEE Transactions on Wireless Communications. 2011;10(1):325–335. Available from: https://doi.org/10.1109/TWC.2010.111910.100804
  2. Bicen AO, Gungor VC, Akan OB. Delay-sensitive and multimedia communication in cognitive radio sensor networks. Ad Hoc Networks. 2012;10(5):816–830. Available from: https://dx.doi.org/10.1016/j.adhoc.2011.01.021
  3. Lin S, Chen K. Improving spectrum efficiency via in-network computations in cognitive radio sensor networks. IEEE Transactions on Wireless Communications. 2014;13(3):1222–1234. Available from: https://doi.org/10.1109/TWC.2014.011514.121905
  4. Spachos P, Hantzinakos D. Scalable Dynamic Routing Protocol for Cognitive Radio Sensor Networks. IEEE Sensors Journal. 2014;14(7):2257–2266. Available from: https://dx.doi.org/10.1109/jsen.2014.2309138
  5. Mesodiakaki A, Adelantado F, Alonso L, Verikoukis C. Energy-efficient user association in cognitive heterogeneous networks. IEEE Communications Magazine. 2014;52(7):22–29. Available from: https://dx.doi.org/10.1109/mcom.2014.6852079
  6. Imtiaz J, Kim D. Energy-Efficient Management of Cognitive Radio Terminals With Quality-Based Activation. IEEE Communications Letters. 2017;21(5):1171–1174. Available from: https://dx.doi.org/10.1109/lcomm.2017.2656910
  7. Chatterjee S, Maity SP, Acharya T. Energy Efficient Cognitive Radio System for Joint Spectrum Sensing and Data Transmission. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 2014;4(3):292–300. Available from: https://dx.doi.org/10.1109/jetcas.2014.2337191
  8. Chen J, Kuo Y, Liu Y, Lv L, Ren C. Energy efficient relay selection and power allocation for cooperative cognitive radio networks. IET Communications. 2015;9(13):1661–1668. Available from: https://dx.doi.org/10.1049/iet-com.2014.1246
  9. Shi Z, Li KH, Tan T, Teh KC. Energy efficient cognitive radio network based on multiband sensing and spectrum sharing. IET Communications. 2014;8(9):1499–1507. Available from: https://dx.doi.org/10.1049/iet-com.2013.0699
  10. Haddad M, Hayel Y, Habachi O. Spectrum Coordination in Energy-Efficient Cognitive Radio Networks. IEEE Transactions on Vehicular Technology. 2015;64(5):2112–2122. Available from: https://dx.doi.org/10.1109/tvt.2014.2339271
  11. Marques AG, Dall'Anese E, Giannakis GB. Cross-Layer Optimization and Receiver Localization for Cognitive Networks Using Interference Tweets. IEEE Journal on Selected Areas in Communications. 2014;32(3):641–653. Available from: https://dx.doi.org/10.1109/jsac.2014.1403009
  12. Saki H, Shikh-Bahaei M. Cross-Layer Resource Allocation for Video Streaming Over OFDMA Cognitive Radio Networks. IEEE Transactions on Multimedia. 2015;17(3):333–345. Available from: https://dx.doi.org/10.1109/tmm.2015.2389032
  13. Mokari N, Azmi P, Saeedi H. Quantized Ergodic Radio Resource Allocation in Cognitive Networks with Guaranteed Quality of Service for Primary Network. IEEE Transactions on Vehicular Technology. 2014;99(8). Available from: https://doi.org/10.1109/TVT.2014.2306133


© 2020 Shashi Raj et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee).


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