Intelligent 5G: When Cellular Networks Meet Artificial Intelligence
By Rongpeng Li, Zhifeng Zhao, Xuan Zhou, Guoru Ding, Yan Chen, Zhongyao Wang, Honggang Zhang. 2017
535 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Shree Krishna Sharma
- Xianbin Wang
- Sameh Sorour
- Mohsen Guizani
- Ala Al-Fuqaha
- M. Mohammadi
- Ian F. Akyildiz
- H. Vincent Poor
In Electrical and Electronic Engineering
- Andreas F. Molisch
- H. Vincent Poor
- Shuai Nie
- David Gesbert
- Ala Al-Fuqaha
- M. Mohammadi
- Paul de Kerret
- Ian F. Akyildiz
Citations in the corpus
- Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “and accurate solution for complicated configuration problems [171].”
- Machine Learning in the Air (2019, background): “techniques that can sustain the growing demand for ML applications at the edge? We believe that ML and data-driven approaches in general have a lot to offer to all aspects of the communication network architecture, and they have already started to have impact on the higher layers [98]–[100].”
- Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, methodology): “Also, the Non-orthogonal Multiple Access (NOMA) scheme in 5G is seen as an enabler of mMTC connections by allowing grant-free uplink connections to the energy constrained mMTC devices and saving them the control signaling overhead [54].”
- Deep Reinforcement Learning for Resource Management in Network Slicing (2018, background): “so as to obtain satisfactory QoE in each slice at the cost of acceptable spectrum or computing resources [4].”
- 6G and Beyond: The Future of Wireless Communications Systems (2020, background): “Specifically, AI algorithms can help facilitate the following tasks which would yield low efficiency with conventional solutions: identify network anomalies, allocate network resources, perform network management, among others [106].”
- A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, background): “Challenges and opportunities of integrating artificial intelligence into future network designs were likewise discussed [35].”
- A Survey on Green 6G Network: Architecture and Technologies (2019, background): “entity can intelligently adjust the configuration based on multiple predefined options in a different yet deterministic manner [35], which is actually an implementation of perceptual AI without the capability to respond to unintended scenarios.”
- Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions (2019, methodology): “In [80], Q-learning is applied for the fair coexistence between LTE and Wi-Fi in the unlicensed spectrum.”
- Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions (2018, methodology): “Furthermore, authors in [16] recently introduced the fundamental concepts of AI and its relation with 5G candidate technologies.”
- A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning (2021, background): “2) Survey and Tutorial of Deep Learning in Wireless Networks: Deep learning has been considered as one of the key enabling technologies in the intelligent 5G [78] and the beyond 5G cellular networks [79], [80].”
- Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “discuss opportunities and challenges of incorporating artificial intelligence (AI) into future network architectures and highlight the significance of AI in the 5G era [58].”
- Deep Learning with Long Short-Term Memory for Time Series Prediction (2018, background): “One promising solution is to predict the varying pattern of data traffic and the location at which a mobile user will likely demand network service [1].”
- Deep Transfer Learning for Intelligent Cellular Traffic Prediction Based on Cross-Domain Big Data (2019, background): “To meet the diverse requirements of the mobile users, an emerging consensus on the adoption of machine (deep) learning and artificial intelligence (AI) [8]–[10] to the fifth-generation mobile networks (5G) and beyond has been intensively investigated [11]–[14].”
- 5G Key Technologies for Smart Railways (2020, background): “Recently, AI has become popular in the computer field due to its great success in computer vision, natural language processing, automatic speech recognition, and wireless communications [146].”