2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDSPCOM-1.6
Paper Title EFFICIENT MIGRATION TO THE NEXT GENERATION OF NETWORKS BASED ON DIGITAL ANNEALING
Authors Mohammad Javad-Kalbasi, Shahrokh Valaee, University of Toronto, Canada
SessionSPCOM-1: Signal Processing for Networks
LocationGather.Town
Session Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Poster
Topic Signal Processing for Communications and Networking: [SPCN-NETW] Networks and Network Resource allocation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract Networks are frequently changing due to new technologies. The growing demand for bandwidth is forcing many carriers to migrate their existing network to a network with a new technology in order to increase network performance. Telecommunication companies are looking for optimization algorithms to efficiently manage their network migration. In this paper, the network migration problem is considered as a set of circuit migration problems in which two technicians simultaneously migrate the two ends of a circuit in order to minimize the total accumulated sites in-service and total technician travels. While total accumulated sites in-service indicates how fast the sites can be upgraded during the migration process, total technician travels estimates the required cost. We first formulate our target problem as a constrained binary quadratic program which is NP-hard in general. Our approach for solving the derived optimization problem is based on converting it to a quadratic unconstrained binary optimization problem (QUBO) using the penalty method. Subsequently, we exploit Digital Annealer which is a massively parallel hardware architecture to minimize the derived QUBO. To evaluate our proposed method, we study extensive network migration instances on the 75-node CONUS network topology.