On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control
By Fengxiao Tang, Bomin Mao, Zubair Muhammad Fadlullah, Nei Kato, Osamu Akashi, Takeru Inoue, Kimihiro Mizutani. 2018
236 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computer Networks and Communications
Citations in the corpus
- In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning (2018, methodology): “, a typical 5G node is expected to have 2000 or more configurable parameters, a recent new trend is to optimize wireless communication by Artificial Intelligence (AI) techniques [4] [5], include but not limited to the application of AI for Physical Layer (PHY), Data Link Layer, and traffic control [”
- Model-Driven Deep Learning for Physical Layer Communications (2018, background): “Recently, it has been applied to wireless communications, such as physical layer communications [1]–[4], resource allocation [5], [6], and intelligent traffic control [7].”
- Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “DNNs also play a critical role in supporting a variety of compelling upper layer applications, such as traffic prediction [327], packet routing [328] and control [329],”
- A Very Brief Introduction to Machine Learning With Applications to Communication Systems (2018, background): “In a similar manner, predicting congestion can be framed as a data-aided classification problem [55].”
- Real-Time Profiling of Fine-Grained Air Quality Index Distribution Using UAV Sensing (2017, background): “Other types of NN [31], [32] are proposed for better classification with more complex structures.”
- Deep Learning in Mobile and Wireless Networking: A Survey (2018, unknown): “[296] Wireless network routing Imitation learning CNN”
- Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey (2021, background): “tion [264], decoding [265], remote sensing [266] and network traffic control to mention a few [267].”
- A Deep Reinforcement Learning Network for Traffic Light Cycle Control (2018, background): “cation [27], [28], but the systems cannot be directly applied in traffic light control scenarios due to different actions and states.”
- Ten Challenges in Advancing Machine Learning Technologies toward 6G (2020, methodology): “…both training and networking efficiency emerges as a new challenge of future networks. the example in networking functions, the traffic patterns in each time slot/interval should exchanged between edge nodes in the distributed ], as well as collected to the controller in the centralized system [8].”
- Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence (2018, methodology): “We utilize the online training method to train the considered CNNs [15].”
- Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding (2019, background): “Some previous works which incorporate deep learning into communications have been investigated, including beam selection, heterogeneous network, non-orthogonal multiple access (NOMA), massive MIMO, and heterogeneous network [13]–[20].”
- AI-Enhanced Offloading in Edge Computing: When Machine Learning Meets Industrial IoT (2019, background): “Traffic control in SDNs [5] Deep CNNs Choose the best path combination for packet forwarding”
- Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches (2020, methodology): “As the traffic flows in the vehicular network are sophisticated and large scaled, ML methods with deep structure, such as ANN, RNN, and CNN, which are capable of high-dimensional data, have been employed in the recent works [91]–[94], [103], [114].”