  The rapid development of mobile networks proliferates the demands of high
data rate, low latency, and high-reliability applications for the
fifth-generation (5G) and beyond (B5G) mobile networks. Concurrently, the
massive multiple-input-multiple-output (MIMO) technology is essential to
realize the vision and requires coordination with resource management functions
for high user experiences. Though conventional cross-layer adaptation
algorithms have been developed to schedule and allocate network resources, the
complexity of resulting rules is high with diverse quality of service (QoS)
requirements and B5G features. In this work, we consider a joint user
scheduling, antenna allocation, and precoding problem in a massive MIMO system.
Instead of directly assigning resources, such as the number of antennas, the
allocation process is transformed into a deep reinforcement learning (DRL)
based dynamic algorithm selection problem for efficient Markov decision process
(MDP) modeling and policy training. Specifically, the proposed utility function
integrates QoS requirements and constraints toward a long-term system-wide
objective that matches the MDP return. The componentized action structure with
action embedding further incorporates the resource management process into the
model. Simulations show 7.2% and 12.5% more satisfied users against static
algorithm selection and related works under demanding scenarios.
