Learn to Schedule (LEASCH): A Deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer
This addresses network management challenges in 5G for telecom operators, but it is incremental as it applies an existing AI method to a specific domain problem.
The paper tackles the radio resource scheduling problem in the 5G MAC layer by proposing LEASCH, a deep reinforcement learning model, which shows effectiveness in experimental results compared to conventional baseline methods across key performance indicators.
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.