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image of A Reinforcement Learning Inspired Approach for Efficient Cognitive Radio Network Routing

Abstract

Introduction

One fundamental characteristic of Cognitive Radio Networks (CRNs) is their dynamic operating environment, where network conditions, such as the activities of Primary Users (PUs), undergo continuous changes over time. While Secondary Users (SUs) are engaged in communication, if a PU reappears on an SU's channel, the SU is required to vacate the channel and switch to another available channel. Thus, finding a stable route that minimizes frequent channel switches is a challenging task in CRNs.

Method

Existing solutions to reduce PU interference often overlook the energy consumption of nodes when forming clusters, focusing solely on the minimum number of common channels in a cluster. Consequently, these schemes suffer from frequent channel switches due to PU appearances. The proposed Cognitive Radio Network Routing (CRNR) approach aims to minimize frequent channel switches by employing a Reinforcement Learning (RL) technique called Q-Learning to select stable routes with channels exhibiting higher OFF-state probabilities.

Result

This strategy ensures that selected routes avoid rerouting by prioritizing channels with higher off-state probabilities. Experimental studies demonstrate that the CRNR approach enhances network throughput and reduces interference when compared with existing techniques. CRNR introduces a novel application of AI, use of Q-Learning, a reinforcement learning technique in wireless networks.

Conclusion

This bridges the gap between machine learning and network design, showcasing how intelligent algorithms can optimize communication decisions in real-time, which could inspire further exploration of AI-driven techniques in network management and beyond.

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2025-01-28
2025-07-12

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References

  1. Amjad M. Rehmani M.H. Mao S. Wireless multimedia cognitive radio networks: A comprehensive survey. IEEE Commun. Surv. Tutor. 2018 20 2 1056 1103 10.1109/COMST.2018.2794358
    [Google Scholar]
  2. Singh J.S.P. Rai M.K. CROP: Cognitive radio ROuting Protocol for link quality channel diverse cognitive networks. J. Netw. Comput. Appl. 2018 104 48 60 10.1016/j.jnca.2017.12.014
    [Google Scholar]
  3. Zhong X. Li L. Zhang S. Lu R. ECOR: an energy aware coded opportunistic routing for cognitive radio social internet of things. Wirel. Pers. Commun. 2020 110 1 1 20 10.1007/s11277‑019‑06708‑0
    [Google Scholar]
  4. Osman M.M.A. Syed-Yusof S.K. Abd Malik N.N.N. Zubair S. A survey of clustering algorithms for cognitive radio ad hoc networks. Wirel. Netw. 2018 24 5 1451 1475 10.1007/s11276‑016‑1417‑6
    [Google Scholar]
  5. Hou J. Qiao J. Han X. Energy-saving clustering routing protocol for wireless sensor networks using fuzzy inference. IEEE Sens. J. 2022 22 3 2845 2857 10.1109/JSEN.2021.3132682
    [Google Scholar]
  6. Prajapat R. Yadav R.N. Misra R. Energy-efficient k-hop clustering in cognitive radio sensor network for internet of things. IEEE Internet Things J. 2021 8 17 13593 13607 10.1109/JIOT.2021.3065691
    [Google Scholar]
  7. Saleem Y. Yau K.L.A. Mohamad H. Ramli N. Rehmani M.H. SMART: A SpectruM-Aware ClusteR-based rouTing scheme for distributed cognitive radio networks. Comput. Netw. 2015 91 196 224 10.1016/j.comnet.2015.08.019
    [Google Scholar]
  8. Saleem Y. Yau K.L.A. Mohamad H. Ramli N. Rehmani M.H. Ni Q. Clustering and reinforcement-learning-based routing for cognitive radio networks. IEEE Wirel. Commun. 2017 24 4 146 151 10.1109/MWC.2017.1600117
    [Google Scholar]
  9. Oh J. Hessel M. Czarnecki W.M. Xu Z. van Hasselt H.P. Singh S. Silver D. Discovering reinforcement learning algorithms. Adv. Neural Inf. Process. Syst. 2020 33 1060 1070
    [Google Scholar]
  10. Vidhya S. Sasilatha T. Secure data transfer using multi layer security protocol with energy power consumption AODV in wireless sensor networks. Wirel. Pers. Commun. 2018 103 4 3055 3077 10.1007/s11277‑018‑5994‑9
    [Google Scholar]
  11. Cheon J. Hwang H. Kim D. Jung Y. IEEE 802.15. 4 ZigBee-based time-of-arrival estimation for wireless sensor networks. Sensors (Basel) 2016 16 2 203 10.3390/s16020203 26861331
    [Google Scholar]
  12. Li X. Zekavat S.A.R. Number of clusters formed in an emergency cognitive radio network and upper bound of network simultaneous transmission capacity. IET Commun. 2014 8 14 2516 2527 10.1049/iet‑com.2014.0084
    [Google Scholar]
  13. Naeem A. Rizwan M. Alsubai S. Almadhor A. Akhtaruzzaman M. Islam S. Rahman H. Enhanced clustering based routing protocol in vehicular ad‐hoc networks. IET Electr. Syst. Transp. 2023 13 1 e12069 10.1049/els2.12069
    [Google Scholar]
  14. Wang W. Liu X. List-coloring based channel allocation for open-spectrum wireless networks. VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, 2005 Dallas, TX, USA, 28-28 Sep 2005, pp. 690-694. 10.1109/VETECF.2005.1558001
    [Google Scholar]
  15. Jadoon M.A. Kim S. Relay selection Algorithm for wireless cooperative networks: a learning‐based approach. IET Commun. 2017 11 7 1061 1066 10.1049/iet‑com.2016.1046
    [Google Scholar]
  16. Kudo T. Ohtsuki T. Cell selection using distributed Q-learning in heterogeneous networks. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing Vancouver, BC, Canada, 26-31 May 2013, pp. 3148-3152. 10.1109/APSIPA.2013.6694368
    [Google Scholar]
  17. Chen J. Gao Z. Xu Y. Opportunistic spectrum access with limited feedback in unknown dynamic environment: a multi-agent learning approach. The 2014 5th International Conference on Game Theory for Networks Beijing, China, 25-27 Nov 2014, pp. 1-6. 10.1109/GAMENETS.2014.7043715
    [Google Scholar]
  18. Darabkh K.A. Al-Khazaleh H.F. Al-Zubi R.T. Alnabelsi S.H. Salameh H.B. Efficient routing protocol for optimal route selection in cognitive radio networks over IoT environment. Wirel. Pers. Commun. 2023 129 1 209 253 10.1007/s11277‑022‑10093‑6
    [Google Scholar]
  19. Dai C.Q. Liao G. Chen Q. Service‐oriented routing with Markov space‐time graph in low earth orbit satellite networks. Trans. Emerg. Telecommun. Technol. 2021 32 7 e4072 10.1002/ett.4072
    [Google Scholar]
  20. Kim W. Gerla M. Oh S.Y. Lee K. Kassler A. CoRoute: a new cognitive anypath vehicular routing protocol. Wirel. Commun. Mob. Comput. 2011 11 12 1588 1602 10.1002/wcm.1231
    [Google Scholar]
  21. Sefati S. Abdi M. Ghaffari A. Cluster‐based data transmission scheme in wireless sensor networks using black hole and ant colony algorithms. Int. J. Commun. Syst. 2021 34 9 e4768
    [Google Scholar]
  22. Dutta N. Sarma H.K.D. Primary user supported routing protocol for cognitive radio ad hoc networks in search of higher throughput. Int. J. Commun. Syst. 2023 36 11 e5511 10.1002/dac.5511
    [Google Scholar]
  23. Jyothi V. Subramanyam M.V. An enhanced routing technique to improve the network lifetime of cognitive sensor network. Wirel. Pers. Commun. 2022 127 2 1241 1264 10.1007/s11277‑021‑08575‑0
    [Google Scholar]
  24. Zheng M. Wang C. Song M. Liang W. Yu H. SACR: A stability-aware cluster-based routing protocol for cognitive radio sensor networks. IEEE Sens. J. 2021 21 15 17350 17359 10.1109/JSEN.2021.3076995
    [Google Scholar]
  25. Beltagy I. Youssef M. El-Derini M. A new routing metric and protocol for multipath routing in cognitive networks. 2011 IEEE Wireless Communications and Networking Conference Cancun, Mexico, 28-31 March 2011, pp. 974-979. 10.1109/WCNC.2011.5779268
    [Google Scholar]
  26. Dai Y. Wu J. Boundary Helps: Efficient Routing Protocol Using Directional Antennas in Cognitive Radio Networks. 2013 IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems Hangzhou, China, 14-16 Oct 2013, pp. 502-510. 10.1109/MASS.2013.65
    [Google Scholar]
  27. Singhal D. Garimella R.M. Cognitive cross-layer multipath probabilistic routing for cognitive networks. Wirel. Netw. 2015 21 4 1181 1192 10.1007/s11276‑014‑0847‑2
    [Google Scholar]
  28. Huang X.L. Wang G. Hu F. Kumar S. Stability-capacity-adaptive routing for high-mobility multihop cognitive radio networks. IEEE Trans. Vehicular Technol. 2011 60 6 2714 2729 10.1109/TVT.2011.2153885
    [Google Scholar]
  29. Shakhov V. Yurgenson A. Reliability modeling for industrial edge computing systems. 2021 17th International Asian School-Seminar "Optimization Problems of Complex Systems (OPCS) Novosibirsk, Russian Federation, 13-17 Sep 2021, pp. 108-112. 10.1109/OPCS53376.2021.9588751
    [Google Scholar]
  30. Wang J. Li C. A weighted energy consumption minimization-based multi-hop uneven clustering routing protocol for cognitive radio sensor networks. Sci. Rep. 2022 12 1 14039 10.1038/s41598‑022‑18310‑9 35982096
    [Google Scholar]
  31. Raj R.N. Nayak A. Sathish Kumar M. QoS-aware routing protocol for Cognitive Radio Ad Hoc Networks. Ad Hoc Netw. 2021 113 102386 10.1016/j.adhoc.2020.102386
    [Google Scholar]
  32. Joon R. Tomar P. 2022
  33. Huang X.L. Li Y.X. Gao Y. Tang X.W. Q-learning-based spectrum access for multimedia transmission over cognitive radio networks. IEEE Trans. Cogn. Commun. Netw. 2021 7 1 110 119 10.1109/TCCN.2020.3027297
    [Google Scholar]
  34. Wang J. Ge Y. A radio frequency energy harvesting-based multihop clustering routing protocol for cognitive radio sensor networks. IEEE Sens. J. 2022 22 7 7142 7156 10.1109/JSEN.2022.3156088
    [Google Scholar]
  35. Paul A. Maity S.P. Reinforcement learning based q-routing: Performance evaluation on cognitive radio network topologies. Wirel. Pers. Commun. 2022 125 2 1425 1441 10.1007/s11277‑022‑09612‑2
    [Google Scholar]
  36. Ahmed Elsmany E.F. Omar M.A. Wan T.C. Altahir A.A. EESRA: Energy efficient scalable routing algorithm for wireless sensor networks. IEEE Access 2019 7 96974 96983 10.1109/ACCESS.2019.2929578
    [Google Scholar]
  37. Kumar R. Shekhar S. Garg H. Kumar M. Sharma B. Kumar S. EESR: Energy efficient sector-based routing protocol for reliable data communication in UWSNs. Comput. Commun. 2022 192 268 278 10.1016/j.comcom.2022.06.011
    [Google Scholar]
  38. Arat F. Demirci S. Channel switching cost-aware energy efficient routing in cognitive radio-enabled internet of things. Mob. Netw. Appl. 2022 27 4 1531 1550 10.1007/s11036‑022‑02039‑w
    [Google Scholar]
  39. Arun J. Karthikeyan M. Optimized cognitive radio network (CRN) using genetic algorithm and artificial bee colony algorithm. Cluster Comput. 2019 22 S2 Suppl. 2 3801 3810 10.1007/s10586‑018‑2350‑5
    [Google Scholar]
  40. Xia F. Yu S. Liu C. Li J. Lee I. Chief: Clustering with higher-order motifs in big networks. IEEE Trans. Netw. Sci. Eng. 2021 9 3 990 1005 10.1109/TNSE.2021.3108974
    [Google Scholar]
  41. Joon R. Tomar P. Kumar G. Balusamy B. Nayyar A. Unequal clustering energy hole avoidance (UCEHA) algorithm in cognitive radio wireless sensor networks (CRWSNs). Wirel. Netw. 2024 ••• 1 23 10.1007/s11276‑024‑03801‑6
    [Google Scholar]
  42. Teng Y. Zhang Y. Niu F. Dai C. Song M. Reinforcement learning based auction algorithm for dynamic spectrum access in cognitive radio networks. 2010 IEEE 72nd Vehicular Technology Conference - Fall Ottawa, ON, Canada, 06-09 Sep 2010, pp. 1-5. 10.1109/VETECF.2010.5594301
    [Google Scholar]
/content/journals/rascs/10.2174/0126662558356720250104133002
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A Reinforcement Learning Inspired Approach for Efficient Cognitive Radio Network Routing
Bentham Science Publishers ; https://doi.org/10.2174/0126662558356720250104133002
/content/journals/rascs/10.2174/0126662558356720250104133002
/content/journals/rascs/10.2174/0126662558356720250104133002
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  • Article Type:     Research Article
Keywords: Wireless Networks ; Cognitive Radio Network (CRN) ; Channel Capacity Metric ; Reinforcement Learning (RL) ; Q Routing

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