  To support the newly introduced multimedia services with ultra-low latency
and extensive computation requirements, resource-constrained end user devices
should utilize the ubiquitous computing resources available at network edge for
augmenting on-board (local) processing with edge computing. In this regard, the
capability of cell-free massive MIMO to provide reliable access links by
guaranteeing uniform quality of service without cell edge can be exploited for
seamless parallel processing. Taking this into account, we consider a cell-free
massive MIMO-enabled mobile edge network to meet the stringent requirements of
the advanced services. For the considered mobile edge network, we formulate a
joint communication and computing resource allocation (JCCRA) problem with the
objective of minimizing energy consumption of the users while meeting the tight
delay constraints. We then propose a fully distributed cooperative solution
approach based on multiagent deep deterministic policy gradient (MADDPG)
algorithm. The simulation results demonstrate that the performance of the
proposed distributed approach has converged to that of a centralized deep
deterministic policy gradient (DDPG)-based target benchmark, while alleviating
the large overhead associated with the latter. Furthermore, it has been shown
that our approach significantly outperforms heuristic baselines in terms of
energy efficiency, roughly up to 5 times less total energy consumption.
