A Machine Learning Framework for Resource Allocation Assisted by Cloud Computing
This work addresses the problem of meeting increasing QoS requirements with scarce radio resources for users in communication systems, representing an incremental advancement by applying machine learning to a known bottleneck.
The paper tackles the challenge of real-time resource allocation in non-convex optimization problems by proposing a machine learning framework that leverages cloud computing to match current scenarios with historical data, achieving performance improvements over conventional methods in beam allocation for multi-user massive MIMO systems.
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.