A Machine Learning Framework for Resource Allocation Assisted by Cloud Computing

Conventionally, the resource allocation is formulated as an optimization problem and solved online with

instantaneous scenario information. Since most resource allocation problems are not convex, the optimal solutions

are very difficult to be obtained in real time. Lagrangian relaxation or greedy methods are then often employed,

which results in performance loss. Therefore, the conventional methods of resource allocation are facing great

challenges to meet the ever-increasing QoS requirements of users with scarce radio resource. Assisted by cloud

computing, a huge amount of historical data on scenarios can be collected for extracting similarities among scenarios

using machine learning. Moreover, optimal or near-optimal solutions of historical scenarios can be searched offline

and stored in advance. When the measured data of current scenario arrives, the current scenario is compared with

historical scenarios to find the most similar one. Then, the optimal or near-optimal solution in the most similar

historical scenario is adopted to allocate the radio resources for the current scenario. To facilitate the application

of new design philosophy, a machine learning framework is proposed for resource allocation assisted by cloud

computing. An example of beam allocation in multi-user massive multiple-input-multiple-output (MIMO) systems

shows that the proposed machine-learning based resource allocation outperforms conventional methods.