Resource Orchestration at the Edge: Intelligent Management of mmWave RAN and Gaming Application QoE Enhancement

Millimeter wave (mmWave) is a crucial component in 5G and beyond 5G communications. However, the dense deployment of mmWave transceivers would impose a heavy burden on the management of the radio access network (RAN). This challenge increases the need for leveraging intelligent network management techniques. Thanks to edge computing, machine learning (ML) based network management algorithms and other delay-sensitive user applications can operate at the network edge. But, due to the limited resources on edge servers, developing an orchestration scheme for intelligent network management and user applications is necessary. In this paper, we provide an edge-centric resource management framework for intelligent RAN management and applications with the awareness of the users’ quality of experiences (QoE). Specifically, we consider the scenario of a mmWave communication system equipped with an ML-based mmWave beam tracking algorithm. The users under this system request mobile edge gaming services. We formulate a game QoE aware orchestration problem as a non-linear integer programming and prove its NP-hardness. To reduce the complexity, we decompose the original problem into two subproblems, the service placement problem for mobile edge gaming and the configuration selection and placement problem for mmWave beam tracking. Then, we solve the two subproblems consecutively with heuristic approaches. Simulation results demonstrate the effectiveness of the proposed orchestration scheme.

I. Introduction

For the fifth generation and beyond the fifth generation (5G/B5G) communication systems, millimeter-wave (mmWave) bands are considered as the key enabler by providing huge amounts of available bandwidth. Due to the propagation characteristics of mmWave, the dense deployment of mmWave transceivers in the radio access network (RAN) is required, which makes it more difficult for network operators to manage the network efficiently. Therefore, intelligent network management techniques have become increasingly crucial for enhancing system performance.