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Research on Neighbor Discovery Algorithms Based on Reinforcement Learning with Directional Antennas for Ad Hoc Networks

Published: 06 June 2021 Publication History

Abstract

The use of directional antennas in Ad Hoc networks can improve the performance of networks, but it will make neighbor discovery challenging. The neighbor discovery algorithms based on reinforcement learning improve the efficiency of neighbor discovery by learning the experience of the process according to the reward obtained by the nodes without the prior location information of neighbors. Four reinforcement learning algorithms, including Q-Learning, SARSA, Q(λ), and SARSA(λ), are used to simulate the time to discover all neighbors and the neighbor discovery ratio in different conditions. According to the simulation results, compared with the completely random algorithm, the neighbor discovery algorithm based on Q-Learning has the largest increase in efficiency with the 1-way handshake, especially in the network with high node density and low speed movement of nodes.

References

[1]
Zhang, Z., & Li, B. (2008). Neighbor discovery in mobile ad hoc self-configuring networks with directional antennas: algorithms and comparisons. IEEE Transactions on Wireless Communications, 7 (5), 1540-1549.
[2]
Cai, H., & Wolf, T. (2015, April). On 2-way neighbor discovery in wireless networks with directional antennas. In 2015 IEEE Conference on Computer Communications (INFOCOM) (pp. 702-710). IEEE.
[3]
Luo, J., & Guo, D. (2008, September). Neighbor discovery in wireless ad hoc networks based on group testing. In 2008 46th Annual Allerton Conference on Communication, Control, and Computing (pp. 791-797). IEEE.
[4]
Tehrani, A. S., Dimakis, A. G., & Caire, G. (2013, July). Optimal measurement matrices for neighbor discovery. In 2013 IEEE International Symposium on Information Theory (pp. 2134-2138). IEEE.
[5]
Ölçer, G. M., Genç, Z., & Onur, E. (2010, September). Smart neighbor scanning with directional antennas in 60 GHz indoor networks. In 21st annual IEEE international symposium on personal, indoor and mobile radio communications (pp. 2393-2398). IEEE.
[6]
Ramanathan, R., Redi, J., Santivanez, C., Wiggins, D., & Polit, S. (2005). Ad hoc networking with directional antennas: a complete system solution. IEEE Journal on selected areas in communications, 23 (3), 496-506.
[7]
Takai, M., Martin, J., Bagrodia, R., & Ren, A. (2002, June). Directional virtual carrier sensing for directional antennas in mobile ad hoc networks. In Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing (pp. 183-193).
[8]
Tehrani, A. S., Molisch, A. F., & Caire, G. (2015, December). Directional ZigZag: Neighbor discovery with directional antennas. In 2015 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE.
[9]
Wang, Y., Mao, S., & Rappaport, T. S. (2017, June). On directional neighbor discovery in mmwave networks. In 2017 IEEE 37th international conference on distributed computing systems (ICDCS) (pp. 1704-1713). IEEE.
[10]
Vasudevan, S., Kurose, J., & Towsley, D. (2005, March). On neighbor discovery in wireless networks with directional antennas. In Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies. (Vol. 4, pp. 2502-2512). IEEE.
[11]
El Khamlichi, B., El Abbadi, J., Rowe, N. W., & Kumar, S. (2020, February). Adaptive Directional Neighbor Discovery Schemes in Wireless Networks. In 2020 International Conference on Computing, Networking and Communications (ICNC) (pp. 332-337). IEEE.
[12]
Huang, S., Li, M., & Zhao, L. (2013, December). An intelligent neighbor discovery algorithm for Ad Hoc networks with directional antennas. In Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC) (pp. 302-305). IEEE.
[13]
Papoudakis, G., Christianos, F., Rahman, A., & Albrecht, S. V. (2019). Dealing with non-stationarity in multi-agent deep reinforcement learning. arXiv preprint arXiv:1906.04737.
[14]
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.

Cited By

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  • (2024)An Adaptive 3D Neighbor Discovery and Tracking Algorithm in Battlefield Flying Ad Hoc Networks with Directional AntennasSensors10.3390/s2417565524:17(5655)Online publication date: 30-Aug-2024
  • (2024)Intelligent Beam Configuration for Neighbor Discovery in Ad Hoc Networks With Directional AntennasIEEE Transactions on Vehicular Technology10.1109/TVT.2024.343742573:12(18846-18862)Online publication date: Dec-2024
  • (2024)Advancing Networked Airborne Computing with MmWave for Air-to-Air CommunicationsProceedings of the International Symposium on Intelligent Computing and Networking 202410.1007/978-3-031-67447-1_3(34-50)Online publication date: 8-Aug-2024
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cover image ACM Other conferences
ICCBN '21: Proceedings of the 2021 9th International Conference on Communications and Broadband Networking
February 2021
342 pages
ISBN:9781450389174
DOI:10.1145/3456415
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2021

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Author Tags

  1. Ad Hoc network
  2. Directional antenna
  3. Neighbor discovery
  4. Reinforcement learning

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Cited By

View all
  • (2024)An Adaptive 3D Neighbor Discovery and Tracking Algorithm in Battlefield Flying Ad Hoc Networks with Directional AntennasSensors10.3390/s2417565524:17(5655)Online publication date: 30-Aug-2024
  • (2024)Intelligent Beam Configuration for Neighbor Discovery in Ad Hoc Networks With Directional AntennasIEEE Transactions on Vehicular Technology10.1109/TVT.2024.343742573:12(18846-18862)Online publication date: Dec-2024
  • (2024)Advancing Networked Airborne Computing with MmWave for Air-to-Air CommunicationsProceedings of the International Symposium on Intelligent Computing and Networking 202410.1007/978-3-031-67447-1_3(34-50)Online publication date: 8-Aug-2024
  • (2023)Efficient Neighbor Discovery for Wide-Range FANET with Non-Uniform Directional Beams2023 9th International Conference on Computer and Communications (ICCC)10.1109/ICCC59590.2023.10507355(1379-1384)Online publication date: 8-Dec-2023
  • (2023)Intelligent Beam Configuration for Neighbor Discovery in Ad Hoc Networks with Directional AntennasICC 2023 - IEEE International Conference on Communications10.1109/ICC45041.2023.10278642(1843-1849)Online publication date: 28-May-2023
  • (2023)Oblivious neighbor discovery algorithms in airborne networks with directional multi-antennaAd Hoc Networks10.1016/j.adhoc.2022.103074141:COnline publication date: 15-Mar-2023

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