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Research Topics in Deep Learning for Intelligent Wireless Networks

Research Topics in Deep Learning for Intelligent Wireless Networks

    Deep learning is a potent tool in integrating intelligence to the wireless network with large-scale analysis and complex network conditions. Wireless networks yield highly complicated features, such as communication signal characteristics, channel quality, channel interference, queueing state of each node, and path congestion situation. Deep Learning analyses extremely complex wireless networks with neural network layers to achieve a sharp feature extraction from high-dimensional raw data. The merits of applying Deep learning in the intelligent wireless network are high prediction accuracy, complexity reduction, and no pre-processing of input data required.
   The main significance of deep learning for Intelligence Wireless Networks are tolerance of incomplete input raw data, the capability of handling a large amount of input data, and the capability of making control decisions. Deep learning applications in a wireless network are the physical layer, data link layer, network layer, upper layer for network feature extraction, and advantage in network security. The most commonly used deep learning algorithms in a wireless network are the convolutional neural network and long short-term memory neural network. Deep learning-based big data transmission in smart city and healthcare management, deep learning-based network swarming, deep learning with software-defined networks, and cloud computing security using deep learning are the future advances in intelligent wireless networks.