  In this paper, we propose a joint radio and core resource allocation
framework for NFV-enabled networks. In the proposed system model, the goal is
to maximize energy efficiency (EE), by guaranteeing end-to-end (E2E) quality of
service (QoS) for different service types. To this end, we formulate an
optimization problem in which power and spectrum resources are allocated in the
radio part. In the core part, the chaining, placement, and scheduling of
functions are performed to ensure the QoS of all users. This joint optimization
problem is modeled as a Markov decision process (MDP), considering time-varying
characteristics of the available resources and wireless channels. A soft
actor-critic deep reinforcement learning (SAC-DRL) algorithm based on the
maximum entropy framework is subsequently utilized to solve the above MDP.
Numerical results reveal that the proposed joint approach based on the SAC-DRL
algorithm could significantly reduce energy consumption compared to the case in
which R-RA and NFV-RA problems are optimized separately.
