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A learning approach to link adaptation based on multi-entities Bayesian network

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Abstract

Adaptive modulation and coding (AMC) is widely used in modern communications which attempts to predict the best available rate and select the most suitable modulation and coding scheme (MCS) by estimating the real-time channel quality to obtain higher throughput of communication system. However, due to the characteristics of wireless channel fading, there are a lot of uncertainties in the communication process, which makes deviation between the channel estimate and the true value and can affect performance of AMC system. Bayesian network is an important tool to research uncertainty. This paper considers learning with the multi-entities bayesian network (MEBN) as a new framework for adaptive modulation and coding which avoids the flaw of flexibility in traditional Bayesian network (BN). Simulation results show that our algorithm has more validity in the selection MCS and lower bit error rate (BER) by considering estimate deviation in MEBN-AMC system. We also provide the further simulation results by using Bayesian structure learning and parameter learning.

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Acknowledgements

The authors would like to thank the editors and the anonymous referees for their valuable comments and suggestions. This work was supported by the National Science Foundation of China under Grant Number 61471090.

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Authors and Affiliations

  1. National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China

    Cui Zhang, Xia Lei & Yannan Yuan

  2. College of Communication Engineering, Chengdu University of Information Technology, Chengdu, China

    Lijun Song

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  1. Cui Zhang
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  2. Xia Lei
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  3. Yannan Yuan
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  4. Lijun Song
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Correspondence to Lijun Song.

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Zhang, C., Lei, X., Yuan, Y. et al. A learning approach to link adaptation based on multi-entities Bayesian network. Cluster Comput 22 (Suppl 4), 8463–8473 (2019). https://doi.org/10.1007/s10586-018-1878-8

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  • DOI: https://doi.org/10.1007/s10586-018-1878-8

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