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Joint RAN Slicing and Computation Offloading for 5G-Assisted Autonomous Vehicular Networks Seminar

Thursday, July 8, 2-3 p.m.

Online

Dr. Qiang Ye, Minnesota State University

Faculty candidate seminar

Abstract:

In this seminar, I will first give an introduction on fifth generation (5G) communication networks and network slicing as a new resource orchestration technology. A two-timescale radio access network (RAN) slicing and computing task offloading framework is then presented for a 5G-assisted autonomous vehicular network with layered edge computing. The proposed framework is to jointly maximize the communication and computing resource utilization with diverse quality-of-service (QoS) guarantee for autonomous driving tasks. Specifically, to capture the small-timescale network dynamics, a computing task offloading problem is formulated as a stochastic optimization program, for maximizing the long-term network-wide computation load balancing with minimum task offloading variations. To deal with the problem complexity and information uncertainty, a cooperative multi-agent deep Q-learning (MA-DQL) with fingerprint is employed to solve the problem by learning a set of stationary task offloading policies. Given the offloading decisions, a RAN slicing problem is further studied in a large timescale, which is formulated as a convex optimization program, to maximize the network utility with statistical QoS provisioning. Due to the correlation between the problems of two timescales, a hierarchical optimization framework is established to iteratively determine the optimal radio resource slicing ratios for computation load balancing. Future research directions will also be discussed.

Join: https://mun.webex.com/mun/j.php?MTID=m4f0bc68240a0f8c21d21ccf8e04e8bfd

Presented by Department of Computer Science

Event Listing 2021-07-08 14:00:00 2021-07-08 15:00:00 America/St_Johns Joint RAN Slicing and Computation Offloading for 5G-Assisted Autonomous Vehicular Networks Seminar Dr. Qiang Ye, Minnesota State University Faculty candidate seminar Abstract: In this seminar, I will first give an introduction on fifth generation (5G) communication networks and network slicing as a new resource orchestration technology. A two-timescale radio access network (RAN) slicing and computing task offloading framework is then presented for a 5G-assisted autonomous vehicular network with layered edge computing. The proposed framework is to jointly maximize the communication and computing resource utilization with diverse quality-of-service (QoS) guarantee for autonomous driving tasks. Specifically, to capture the small-timescale network dynamics, a computing task offloading problem is formulated as a stochastic optimization program, for maximizing the long-term network-wide computation load balancing with minimum task offloading variations. To deal with the problem complexity and information uncertainty, a cooperative multi-agent deep Q-learning (MA-DQL) with fingerprint is employed to solve the problem by learning a set of stationary task offloading policies. Given the offloading decisions, a RAN slicing problem is further studied in a large timescale, which is formulated as a convex optimization program, to maximize the network utility with statistical QoS provisioning. Due to the correlation between the problems of two timescales, a hierarchical optimization framework is established to iteratively determine the optimal radio resource slicing ratios for computation load balancing. Future research directions will also be discussed. Join: https://mun.webex.com/mun/j.php?MTID=m4f0bc68240a0f8c21d21ccf8e04e8bfd Online Department of Computer Science