RL models

Applications in 5G

Adoption of Model in healthcare delivery

References

1

RL algorithm based on long short-term memory (RL-LSTM) cells.

In LTE unlicensed (LTE-U) networks, proactive resource allocation is formulated as a non-cooperative game that allows small base stations to learn which unlicensed channel to use based on long-term WLAN behavior and LTE-U traffic loads.

These models can be adopted on electronic medical records of deceased patients to estimate life expectancy.

[35] [37] [39]

2

Gradient follower (GF), the modified Roth-Erev (MRE), and the modified Bush and Mosteller (MBM)

To reduce intra/inter-tier interference, enable femtocells to sense the radio environment and tune their parameters in HetNets autonomously and opportunistically.

These models can be used for a variety of critical care and chronic illness treatment plans, automated medical diagnostics, and many other scheduling or control issues relating to the healthcare system.

[37]

3

RL with Network assisted feedback

Selection of Heterogeneous Radio Access Technologies (RATs).

These models are utilized to create effective solutions in a variety of healthcare settings where diagnosis choices or treatment plans typically involve a long period of time with delayed feedback.

[35]