Spatial Modulation Link Adaptation: a Deep Learning Approach

被引：0

作者：

Tato, Anxo ^{[1
]}

Mosquera, Carlos ^{[1
]}

机构：

[1] Univ Vigo, atlanTT Res Ctr, Galicia, Spain

来源：

CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS | 2019年

关键词：

Deep Learning; Link adaptation; MIMO; Neural Network; Spatial Modulation;

D O I：

10.1109/ieeeconf44664.2019.9048866

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Spatial Modulation (SM) offers a good balance between energy and spectral efficiency of interest for next generation networks. This, together with the need for only one Radio Frequency (RF) chain, makes SM a good proposal for Internet of Things (IoT) devices. In this work, we present a method based on Deep Learning to select the optimum Modulation and Coding Scheme (MCS) in an adaptive SM system. The deep neural network is trained with supervised learning to perform a mapping between the channel conditions and the MCS from a given set. We provide simulations results for a 4 x 4 SM link which uses several coding rates and three different constellations: QPSK, 8PSK and 16QAM. Results show how the adaptive system has a throughput close to its maximum value and how the outage probability can be reduced easily by applying a back-off margin to the neural network output.

引用

页码：1801 / 1805

页数：5

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