Network packet

Round-robin scheduling

Real-time computing

Two-level scheduling

Scheduling (production processes)

Quality of service

Transmission delay

Economics

Computer science

Mathematical optimization

Distributed computing

Computer network

Network scheduler

Reinforcement learning

Rate-monotonic scheduling

Mathematics

Dynamic priority scheduling

Operations management

Artificial intelligence

Processing delay

Fixed-priority pre-emptive scheduling

Fair-share scheduling

IEEE Transactions on Network and Service Management

Dominated by delay-sensitive and massive data applications, radio resource management in 5G access networks is expected to satisfy very stringent delay and packet loss requirements. In this context, the packet scheduler plays a central role by allocating user data packets in the frequency domain at each predefined time interval. Standard scheduling rules are known limited in satisfying higher quality of service (QoS) demands when facing unpredictable network conditions and dynamic traffic circumstances. This paper proposes an innovative scheduling framework able to select different scheduling rules according to instantaneous scheduler states in order to minimize the packet delays and packet drop rates for strict QoS requirements applications. To deal with real-time scheduling, the reinforcement learning (RL) principles are used to map the scheduling rules to each state and to learn when to apply each. Additionally, neural networks are used as function approximation to cope with the RL complexity and very large representations of the scheduler state space. Simulation results demonstrate that the proposed framework outperforms the conventional scheduling strategies in terms of delay and packet drop rate requirements.

Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management