BPRIA: Crosstalk-Avoided Bi-Partitioning-Based Counter-Propagation Resource Identification and Allocation for Spectrally-Spatially Elastic Optical Networks
Bijoy Chand Chatterjee, Imran Ahmed, Abdul Wadud, Mukulika Maity, Eiji Oki
Abstract
Signal transmission using counter-propagation nowadays is adopted to enhance resource utilization in spectrally-spatially elastic optical networks (SS-EONs), where inter-mode and inter-core crosstalks always degrade signal quality and become bottlenecks for high transport capacity. In this paper, we propose BPRIA for the first time, a Bi-Partitioning-based crosstalk-avoided Resource Identification and Allocation scheme in SS-EONs to enhance resource utilization while suppressing both inter-mode and inter-core crosstalks. We introduce a bi-partitioning optimization problem with vertex elimination to maximize the number of non-adjacent cores and modes in each partition of the bipartite graph; two distinct sets of cores and modes are used in a counter-propagation manner for lightpath allocation. The optimization problem is formulated as an integer linear programming (ILP) problem, and we prove that the optimization problem is NP-complete. An algorithm for core-mode-spectrum allocation is developed considering two distinct sets of cores and modes obtained by ILP to serve lightpath requests while avoiding inter-mode and inter-core crosstalks. For dynamic scenarios, we present core-mode-spectrum allocation. Numerical results reveal that the blocking probability is reduced by BPRIA in SS-EONs, and it enhances traffic admissibility in the network.