Learning to optimize: Training deep neural networks for wireless resource management

By Haoran Sun, Xiangyi Chen, Qingjiang Shi, Mingyi Hong, Xiao Fu, N. Sidiropoulos. International Workshop on Signal Processing Advances in Wireless Communications, 2017

357 citations recorded by the source corpus.

Subfields: Computational Mechanics, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/499de1eea80e58150a621ef70b755a19d2b26db4

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In Computational Mechanics

In Electrical and Electronic Engineering

In Signal Processing

Citations in the corpus

Machine Learning in the Air (2019, neutral): “These algorithms are too complex for practical implementation, but a key idea advocated in [18], [53] is that we can take this complexity ofﬂine by training a DNN to mimic the input-output behavior of the WMMSE algorithm.”
Learning to Optimize: Training Deep Neural Networks for Interference Management (2017, background): “This paper was presented in part at the 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, Sapporo, Japan, July 2017 [1].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “[301] Resource management over wireless networks Imitation learning MLP”
Deep Learning Based Transmit Power Control in Underlaid Device-to-Device Communication (2019, methodology): “It is possible to solve complex non-linear problems efficiently using a back-propagation algorithm [13] and a trained DNN model can be used with low computation time [12].”
A Very Brief Introduction to Machine Learning With Applications to Communication Systems (2018, methodology): “Notably, in [44], a training set is obtained by running a non-convex solver to produce an optimized output power vector for given input channels.”
Team Deep Neural Networks for Interference Channels (2018, background): “This approach is studied in [13] for caching and in [14] for resource allocation in interference channels.”
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks (2018, background): “Before leaving this section, it is worth mentioning the advantages of applying deep reinforcement learning over supervised learning-based deep neural network approaches (such as in [27] and [28]) in dynamic wireless applications.”
Adversarial Attacks on Deep-Learning Based Radio Signal Classification (2018, methodology): “For example, [3] uses a convolutional neural network (CNN) for channel decoding, [4] studies DL-based wireless resource allocation, and [5], [6] use DL for the classical task of radio signal (modulation) classification.”
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey (2018, neutral): “[58] proposed a DL-based wireless resource allocation scheme.”
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, background): “The DNN for real-time radio resource management at the physical layer of communication networks has been proposed in [101].”
Learning to Cooperate in Decentralized Wireless Networks (2018, result): “While the literature on power control is abundant including for decentralized cases [3] [4], and moreover deep learning approaches [5] [6], this research departs in significant ways from past work in that we consider that each decision making agent (i.”