Toward Experience-Driven Traffic Management and Orchestration in Digital-Twin-Enabled 6G Networks

Abstract

The envisioned 6G networks are expected to support extremely high data rates, low-latency, and radically new applications empowered by machine learning. The futuristic 6G networks require a novel framework that can be used to operate, manage, and optimize its underlying services such as ultra-reliable and low-latency communication, and Internet of everything. In recent years, artificial intelligence (AI) has demonstrated significant success in optimizing and designing networks. The AI-enabled traffic orchestration can dynamically organize different network architectures and slices to provide quality of experience considering the dynamic nature of the wireless communication network. In this paper, we propose a digital twin enabled network framework, empowered by AI to cater the variability and complexity of envisioned 6G networks, to provide smart resource management and intelligent service provisioning. Digital twin paves a way for achieving optimizing 6G services by creating a virtual representation of the 6G network along with its associated communication technologies (e.g., intelligent reflecting surfaces, terahertz and millimeter communication), computing systems (e.g., cloud computing and fog computing) with its associated algorithms (e.g., optimization and machine learning). We then discuss and review the existing AI-enabled traffic management and orchestration techniques and highlight future research directions and potential solutions in 6G networks.

AI and 6G Into the Metaverse: Fundamentals, Challenges and Future Research Trends

“…One of such community-driven platforms is Botto. 14 Some creations from Botto, GauGAN2, and DALL-E are shown in Figure 9. 15 [114], [116] The use of AIART-E provides an economic sustainability by creating circular economy ecosystem.…”

“…Metaverse services can benefit from RL optimization techniques if they are well designed because they enable the ability of self-healing, self-optimizing, and self-organizing for the 6G networks. For instance, Tariq et al propose a DRLdriven approach for proactive resource management and intelligent service provisioning in 6G networks for a digitaltwin application [14]. However, this approach can suffer from a significant deficiency in the performance of AI models.…”

“…This trend poses a number of constraints on existing 5G communication systems, making them less efficient and unreliable. For example, considering the 0.1ms delay requirements of haptic-based Metaverse applications such as teleportation or teleoperations, 5G can only provide the air interface latency of <1ms, which becomes inconsiderable for such applications [12], [13], [14], [15]. Moreover, according to Cisco [16], mobile data traffic has grown 17-fold over the past five years and is expected to continue to grow.…”

Evolution of Wireless Communication to 6G: Potential Applications and Research Directions

“…The author of [88] developed a DL-based 'radar-aided beam prediction' approaches for mmWave/sub-THz systems. The author of [89] proposed an AI-based digital twin enabled network framework. The authors of [90] suggested 'Graph Attention Q-learning (GAQ) algorithm' for 'tilt optimization'.…”

“…RL 7 [86] Proposed a learning-driven detection scheme using lightweight CNN. DL 8 [88] A ML approach for intermodulation interference detection in 6G ML 9 [89] Computer vision-aided beam tracking in millimeter wave deployment DL 10…”