TY - JOUR

T1 - Intent-driven Closed-Loop Control and Management Framework for 6G Open RAN

AU - Zhang, J.

AU - Yang, C.

AU - Dong, R.

AU - Wang, Y.

AU - Anpalagan, A.

AU - Ni, Q.

AU - Guizani, M.

PY - 2024/2/15

Y1 - 2024/2/15

N2 - Future mobile networks should provide on-demand services for various industries and applications with the stringent guarantees of quality of experience (QoE), which highly challenge the flexibility of network management. However, the diverse requirements of QoE and the management of heterogeneous networks create significant pressure towards communication service providers (CSPs). In the 6th generation mobile networks, the CSPs should guarantee resilient performance for the communication service consumers with less human involvement. In this work, we turn to intent-driven network and on-demand slice management, and to decrease the complexity and cost in full life cycle slice management, we first present an intent-driven closed-loop (CL) control and management framework that automates the deployment of network slices and manages resources intelligently based on the extended CL architecture. And then, we explore and exploit the deep reinforcement learning algorithm to address the problem of resource allocation, which is formulated as a Markov decision process. Finally, we demonstrate the feasibility of the proposed framework by deploying the open radio access network (RAN) infrastructure in OpenAirInterface platform and realizing the CL control and management with near real-time RAN intelligent controller. The emulation results demonstrate the effectiveness of slicing performance, measured in terms of delay and rate.

AB - Future mobile networks should provide on-demand services for various industries and applications with the stringent guarantees of quality of experience (QoE), which highly challenge the flexibility of network management. However, the diverse requirements of QoE and the management of heterogeneous networks create significant pressure towards communication service providers (CSPs). In the 6th generation mobile networks, the CSPs should guarantee resilient performance for the communication service consumers with less human involvement. In this work, we turn to intent-driven network and on-demand slice management, and to decrease the complexity and cost in full life cycle slice management, we first present an intent-driven closed-loop (CL) control and management framework that automates the deployment of network slices and manages resources intelligently based on the extended CL architecture. And then, we explore and exploit the deep reinforcement learning algorithm to address the problem of resource allocation, which is formulated as a Markov decision process. Finally, we demonstrate the feasibility of the proposed framework by deploying the open radio access network (RAN) infrastructure in OpenAirInterface platform and realizing the CL control and management with near real-time RAN intelligent controller. The emulation results demonstrate the effectiveness of slicing performance, measured in terms of delay and rate.

KW - 5G mobile communication

KW - 6G

KW - 6G mobile communication

KW - Closed-loop control and management

KW - Monitoring

KW - Quality of experience

KW - Quality of service

KW - Radio access networks

KW - Reinforcement learning

KW - Reliability

KW - intent-driven network

KW - open RAN

U2 - 10.1109/JIOT.2023.3312795

DO - 10.1109/JIOT.2023.3312795

M3 - Journal article

VL - 11

SP - 6314

EP - 6327

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

SN - 2327-4662

IS - 4

ER -