Abstract
Next-generation networks powered by millimetre wave (mmW)
technology offer improved data rates in the range of a few Gigabits per
second. Heterogeneous networks (HetNets) with mmWave capability must be
reliable, adaptable, and energy-efficient. It is suggested to maximise
the transmission power of small cells to get better performance. This
leads to the use of intelligent techniques namely, Q-learning approach
that will allow seamless connectivity. A higher degree of automation,
cooperation, and intelligence in distributed HetNets are applied using
Q-learning with Markov decision process (MDP) based technique. The first
objective is to perform cooperative online learning scheme to allocate
power based on MDP in the Two-tier HetNet. Maximizing energy efficiency
through effective power distribution is the second objective. The
suggested technique improves the network’s overall energy efficiency and
capacity by optimizing judicious power usage based on the probability of
an MDP state transition. Cooperative learning techniques and the right
Markov state models can enhance both macrocell and femtocell service,
enhancing user experience.
