10,749 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
A Hybrid Neural Network Framework and Application to Radar Automatic Target Recognition
Deep neural networks (DNNs) have found applications in diverse signal
processing (SP) problems. Most efforts either directly adopt the DNN as a
black-box approach to perform certain SP tasks without taking into account of
any known properties of the signal models, or insert a pre-defined SP operator
into a DNN as an add-on data processing stage. This paper presents a novel
hybrid-NN framework in which one or more SP layers are inserted into the DNN
architecture in a coherent manner to enhance the network capability and
efficiency in feature extraction. These SP layers are properly designed to make
good use of the available models and properties of the data. The network
training algorithm of hybrid-NN is designed to actively involve the SP layers
in the learning goal, by simultaneously optimizing both the weights of the DNN
and the unknown tuning parameters of the SP operators. The proposed hybrid-NN
is tested on a radar automatic target recognition (ATR) problem. It achieves
high validation accuracy of 96\% with 5,000 training images in radar ATR.
Compared with ordinary DNN, hybrid-NN can markedly reduce the required amount
of training data and improve the learning performance
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