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Deep learning based adaptive modulation and coding for uplink multi-user SIMO transmissions in IEEE 802.11ax WLANs

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Abstract

The IEEE 802.11ax standard defined a set of new specifications to improve spectrum efficiency, power efficiency, and reliability of future wireless local area networks (WLANs). Among these specifications, the uplink multi-user multiple-input, multiple-output (MU-MIMO) remedies the uplink shortcomings of existing WLANs and enables high-efficiency uplink transmissions. This paper considers the problem of adaptive modulation and coding (AMC) for uplink MU-single-input, multiple-output (MU-SIMO) in the upcoming WLANs. Our target is to select the appropriate modulation and coding scheme (MCS) for the existing users so as to maximize the throughput and at the same time guarantee that all users can satisfy a frame error constraint. We adopt a deep learning approach to tackle this problem. We propose to let the access point (AP) leverage the estimated channel state information (CSI) of all users along with the estimated noise standard deviation as the input features to a deep convolutional neural network (DCNN) that is trained to perform AMC. Simulation results reveal that the proposed DCNN for the uplink MU-SIMO AMC outperforms rival machine learning techniques such as fully-connected deep neural network (DNN), k-nearest neighbors (KNN), support vector machines (SVM), Gaussian Naive Bayes (GNB), and random forest (RF) in terms of the throughput and the frame error rate (FER).

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Funding

This work was supported by the Natural Science Fund of Guangdong Province under Grant 2020A1515010708.

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Authors and Affiliations

  1. Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, Italy

    Mohamed Elwekeil

  2. College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China

    Mohamed Elwekeil, Taotao Wang & Shengli Zhang

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  1. Mohamed Elwekeil
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  2. Taotao Wang
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  3. Shengli Zhang
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Correspondence to Taotao Wang.

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This work was done in the period 2019-2020 when Mohamed Elwekeil was affiliated with Shenzhen University and Menoufia University.

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Elwekeil, M., Wang, T. & Zhang, S. Deep learning based adaptive modulation and coding for uplink multi-user SIMO transmissions in IEEE 802.11ax WLANs. Wireless Netw 27, 5217–5227 (2021). https://doi.org/10.1007/s11276-021-02803-y

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