Self-organizing networks (SONs) can help manage the severe interference in dense heterogeneous networks (Het-Nets). Given their need to automatically configure power and other settings, machine learning is a promising tool for data driven decision making in SONs. In this paper, a HetNet is modeled as a dense two-tier network with conventional macro cellsoverlaid with denser small cells (e.g. femto or pico cells). First,a distributed framework based on multi-agent Markov decision process is proposed that models the power optimization problem in the network. Second, we present a systematic approach for designing a reward function based on the optimization problem.Third, we introduce Q-learning based distributed power allocation algorithm (Q-DPA) as a self-organizing mechanism that enables ongoing transmit power adaptation as new small cells are added to the network. Further, the sample complexity of the Q-DPA algorithm to achieve -optimality with high probability is provided. We demonstrate, at density of several thousands femtocells per km2, the required quality of service of a macrocelluser can be maintained via the proper selection of independent or cooperative learning and appropriate Markov state models.
To View the Base Paper Abstract Contents
Now it is Your Time to Shine.
Great careers Start Here.
We Guide you to Every Step
Success! You're Awesome
Thank you for filling out your information!
We’ve sent you an email with your Final Year Project PPT file download link at the email address you provided. Please enjoy, and let us know if there’s anything else we can help you with.
To know more details Call 900 31 31 555
The WISEN Team