# Multiservice-Based Traffic Scheduling for 5G Access Traffic Steering, Switching and Splitting
## 1 Introduction
### Related Work
However, for a multiservice network, when a user requests distinct services simultaneously, the delay-type QoS (DQoS) characteristics that match the service requirements must also be considered when scheduling traffic. Recently, the application of an optimal resource-allocation algorithm, based on the maximization of the effective network capacity, yielded good performance in several networks. The use of Karush-KuhnTucker (KKT) conditions to maximize the effective network capacity for mobile video traffic has been proposed [27]. The use of a semi-Markov model has been proposed to derive the effective network capacity under LWA, which is achieved using the delay constraint to offload the network traffic from a licensed frequency band to an unlicensed one [28]. These algorithms demonstrated the feasibility of fusing licensed spectrum-based 3 GPP networks with unlicensed spectrum-based non-3 GPP networks to realize superior network capacity. With the development of ATSSS technology and the rise of multiservice user applications, major telecommunications operators have invested considerable amounts of resources in the development and verification of NWA technologies, including the scheduling and optimization of resources. Spurred by both economic and operational considerations, and by environmental concerns, energy efficiency has now become a key pillar in the design of communication networks [29]. In [30], a deep reinforcement learning-based power control scheme is proposed for improving the system-level energy EE of two-tier 5G heterogeneous and multi-channel cells. The algorithm aims to maximize the EE of the system by regulating the transmission power of the downlink channels and reconfiguring the user association scheme. By introducing a machine learning (ML) algorithm from classical RL to solve the objective problem, the authors of [31] propose a joint power control and channel allocation scheme, based on combining an RL algorithm with statistical CSI, to reduce interference adaptively. In the future, we will incorporate ML and RL methods into our research by training agents to \"learn\" favorable policies to increase the effective capacity of the system, and thus improve EE. In our previous study [32], we optimized network throughput and various business utility functions to obtain optimal power- and bandwidth-allocation mechanisms. Experimental simulations also confirmed that the use of NWA could effectively improve network throughput and user satisfaction. However, the parameters of the wireless channel are constantly changing with time, frequency, and space making it difficult to deterministically guarantee service quality in an actual wireless communication system. With the continuous development of statistical service quality assurance theories, the resolution of deterministic service quality assurance in traditional wireless networks is improving. On this basis, this study was focused on meeting the various business QoS requirements of users. In wireless communication, end-to-end delay is a parameter that directly affects QoS and user experience. Therefore, delay cannot be ignored in wireless communication channels, and the guarantee of end-to-end delay has become a crucial objective of heterogeneous multi-connectivity networks. Although traditional channel modeling, with the primary objective of maximizing network throughput, does not reflect the delay index, the effective network capacity derived from the effective network bandwidth in a wired network describes the relationships among service delay, bandwidth, power, and data rate.