Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework

• URL: http://arxiv.org/abs/2302.02711v2
• Date: Mon, 29 May 2023 14:54:42 GMT
• Title: Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework
• Authors: Van-Dinh Nguyen, Thang X. Vu, Nhan Thanh Nguyen, Dinh C. Nguyen, Markku Juntti, Nguyen Cong Luong, Dinh Thai Hoang, Diep N. Nguyen and Symeon Chatzinotas
• Abstract summary: We jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent steering application in open RAN (O-RAN) Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iv) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization
• Score: 47.57576667752444
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.

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