  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.
