  Network management tools are usually inherited from one generation to
another. This was successful since these tools have been kept in check and
updated regularly to fit new networking goals and service requirements.
Unfortunately, new networking services will render this approach obsolete and
handcrafting new tools or upgrading the current ones may lead to complicated
systems that will be extremely difficult to maintain and improve. Fortunately,
recent advances in AI have provided new promising tools that can help solving
many network management problems. Following this interesting trend, the current
article presents LEASCH, a deep reinforcement learning model able to solve the
radio resource scheduling problem in the MAC layer of 5G networks. LEASCH is
developed and trained in a sand-box and then deployed in a 5G network. The
experimental results validate the effectiveness of LEASCH compared to
conventional baseline methods in many key performance indicators.

Networking and Internet Architecture;Machine Learning;Signal Processing