Multi-service path switching in all-optical GMPLS core network

Abstract

In this paper, we present a novel labeling scheme for Multi-service path switching in the all-optical Generalized MultiProtocol Label Switched (GMPLS) core networks. In this scheme, we employ the Multi-Length Variable-Weight Optical Orthogonal Code (MLVW-OOC) as a multiplexing signature sequence and identifying label. In order to analyze the performance of the network, the probability of error is evaluated for arbitrary number of classes and cross-correlation values. Furthermore, the probability of performance degradation is investigated by considering the activity coefficient of connected paths. To assure the demanded performance, Label Distributed Protocol (LDP) is accommodated by a controlling mechanism which limits the number of connected paths. Finally, a four-class network is designed by the proposed labeling scheme and the probabilities of error and the performance degradation are investigated; results indicate that decreasing the upper bound of the connected paths will reduce the probability of the performance degradation.

Next generation access networks: flexible OCDMA systems and cost-effective chaotic VCSEL sources for secure communications

“…The main drawback of WDMA technology is the need of fine tuning and constant monitoring of wavelengths emitted from each source, as well as monitoring of the central wavelength of each filter to eliminate crosstalk among the distinct wavelengths. Furthermore, the smallest bandwidth granularity in this technique is a WDMA window, which might be inadequately coarse or wider than required [6] , [17], [18]. Each house represents a user ∈ {1, 2, …, U}.…”

Proposal of a new approach for BER evaluation of multirate, multiservice OCDMA systems

“…Further, taking into account the additive and positive properties of the optical channel, no decision error will occur when a data bit "1" is sent, i.e., as it is always decoded correctly [1] Therefore, (28) reduces to (29) Knowing that the binomially distributed MAI converges to a gaussian distribution when the number of users in the system is large enough [45], [48] a gaussian approximation of the BER expression (29) can be obtained as follows [84]…”