Research on Concurrent Transmission Control of Heterogeneous Wireless Links Based on Adaptive Network Coding

ZHAO Su; WANG Wei; ZHU Xiaorong; NI Qinyin

doi:10.11999/JEIT210520

Volume 44 Issue 8

Aug. 2022

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ZHAO Su, WANG Wei, ZHU Xiaorong, NI Qinyin. Research on Concurrent Transmission Control of Heterogeneous Wireless Links Based on Adaptive Network Coding[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2777-2784. doi: 10.11999/JEIT210520

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ZHAO Su, WANG Wei, ZHU Xiaorong, NI Qinyin. Research on Concurrent Transmission Control of Heterogeneous Wireless Links Based on Adaptive Network Coding[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2777-2784. doi: 10.11999/JEIT210520

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Research on Concurrent Transmission Control of Heterogeneous Wireless Links Based on Adaptive Network Coding

doi: 10.11999/JEIT210520 cstr: 32379.14.JEIT210520

Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunication, Nanjing 210003, China

Funds: The National Natural Science Foundation of China (92067101, 61871237), The “Blue Project” of Universities in Jiangsu Province and the Key R&D Program of Jiangsu Province (BE2021013-3)

Received Date: 2021-06-04
Rev Recd Date: 2022-03-10

Available Online: 2022-04-14

Publish Date: 2022-08-17

Abstract

Abstract

With the continuous rise of high-speed services such as high-definition video live broadcast and virtual reality, it is difficult for a single network to meet the business needs of users. Using a variety of heterogeneous links to achieve concurrent transmission can effectively aggregate bandwidth resources and improve the quality of service. However, in heterogeneous wireless networks, due to the complex link conditions and the different quality of multiple links, the existing multi-path concurrent transmission algorithms can not make the optimal decision adaptively according to the complex network conditions. In this paper, a multi-path concurrent transmission control algorithm based on adaptive network coding is proposed. The Asynchronous Advanced Actor Critical (A3C) reinforcement learning is introduced. Through adaptive network coding, the coding packet size and redundancy size can be intelligently selected according to the current network conditions, so as to solve the problem of disordered packet. Simulation results show that the algorithm can improve the transmission rate by about 10% and improve the user experience.
- Wireless network,
- Concurrent transmission,
- Network coding,
- Reinforcement learning

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References(15)

References

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