Deep Neural Networks for wireless localization in indoor and outdoor environments

By Wei Zhang, Kang Liu, W. Zhang, Youmei Zhang, Jason Jianjun Gu. 2016

255 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Ocean Engineering, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Ala Al-Fuqaha
2. M. Mohammadi
3. Paul Patras
4. Mohsen Guizani
5. JingJing Wang
6. Sameh Sorour
7. Lajos Hanzo
8. Chunxiao Jiang

In Ocean Engineering

1. Ala Al-Fuqaha
2. Paul Patras
3. Mohsen Guizani
4. Lajos Hanzo
5. Hamed Haddadi
6. Chaoyun Zhang
7. M. Mohammadi

In Signal Processing

1. Ala Al-Fuqaha
2. Paul Patras
3. Yong Ren
4. Mohsen Guizani
5. JingJing Wang
6. Sameh Sorour
7. Lajos Hanzo
8. Chunxiao Jiang

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “use of DL models to predict the location [92]–[94].”
• Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “[97] Indoor and outdoor localization Stacked AE Operates under both indoor and outdoor environments”
• Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “As for learning from the environment, we are able to harness DNNs trained by the data gleaned over the air for the sake of channel estimation [312], interference identification [313], localization [314]–[318], etc.”
• Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services (2018, background): “Researchers have studied different machine learning approaches in this context including: SVM, KNN, Bayesianbased filtering, transfer learning, and neural networks [27].”