Guaranteeing User Rates With Reinforcement Learning in 5G Radio Access Networks

The user experience constitutes an important quality metric when delivering high-definition video services in wireless networks. Failing to provide these services within requested data rates, the user perceived quality is strongly degraded. On the radio interface, the packet scheduler is the key entity designed to satisfy the users' data rates requirements. In this chapter, a novel scheduler is proposed to guarantee the bit rate requirements for different types of services. However, the existing scheduling schemes satisfy the user rate requirements only at some extent because of their inflexibility to adapt for a variety of traffic and network conditions. In this sense, the authors propose an innovative framework able to select each time the most appropriate scheduling scheme. This framework makes use of reinforcement learning and neural network approximations to learn over time the scheduler type to be applied on each momentary state. The simulation results show the effectiveness of the proposed techniques for a variety of data rates' requirements and network conditions.

Dynamic Packet Scheduler for Queuing Real Time and Non Real Time Internet Traffic

Real-time traffic or flows also called as inelastic traffic is that which enforces timely delivery of flows within a specified period of time. There are several applications which generate these flows like multimedia, audio-video conferencing, webinar, Interactive gaming, webcam, Internet TV etc. An interactive application demands a speedy delivery of flows. Flows reaching the destination after this deadline are considered useless. Realtime traffic imposes rigid demands for the delivery. All the realtime flows should be timely delivered and accumulated at the destination. The proposed Dynamic Scheduler relies on Dynamic Packet Scheduling ratio which is dynamic in nature and the ratio changes dynamically with respect to the flows accumulated in both the queues. Flows are scheduled based on the maximum flows allowed on the path that is calculated by TP max. Packet scheduling is based on the available throughput of the network. This dynamic scheduling results in guaranteed fair treatment of both real-time traffic and non-real time traffic. In the this paper we propose a Dynamic Scheduler, [DPS] which dynamically works according to the available number of flows in both realtime and non-real-time queues.