Vehicle clustering for improving enhanced LTE-V2X network performance

By P. Luoto, Medhi Bennis, P. Pirinen, Sumudu Samarakoon, K. Horneman, Matti Latva-aho. 2017

46 citations recorded by the source corpus.

Subfields: Biomedical Engineering, 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

1. Medhi Bennis
2. Merouane Debbah
3. Walid Saad
4. Sumudu Samarakoon
5. Jihong Park
6. Mingzhe Chen
7. Rui Zhang
8. Mohammad Mozaffari

In Electrical and Electronic Engineering

1. Medhi Bennis
2. Merouane Debbah
3. Sumudu Samarakoon
4. Walid Saad
5. Rui Zhang
6. Jihong Park
7. H. Vincent Poor
8. Mingzhe Chen

Citations in the corpus

• Federated Learning for Ultra-Reliable Low-Latency V2V Communications (2018, background): “Assuming that VUEs maintain channel-quality indicators (CQIs), each VUE can estimate the interference I u (t) ≃ Ĩ u (t) based on its past experiences (time average) [18].”
• Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications (2018, background): “…of the drift plus penalty (DPP), As a result, our goal will now be to minimize the following upper bound: o Assuming that VUEs maintain channel-quality indicators (CQIs), each VUE can estimate the interference I nu ( t ) ≃ ˜ I nu ( t ) based on past observations (time averaged interference) [35].”