Quadratic congruence code (QCC)-based frequency-hopping and time-spreading (FH/TS) optical orthogonal codes (OOCs), and the corresponding expanded cardinality were recently studied to improve data throughput and code capacity. In this paper, we propose a new FH/TS two-dimensional (2-D) code using the QCC and the cubic congruence code (CCC), named as the QCC/CCC 2-D code. Additionally the expanded CCC-based 2D codes are also considered. In contrast to the conventional QCC-based 1-D and QCC-based FH/TS 2-D optical codes, our analysis indicates that the code capacity of the CCC-based 1-D and CCC-based FH/TS 2-D codes can be improved with the same code weight and length, respectively.

The capacity of optical transport networks has been increasing steadily and the networks are becoming more dynamic, complex, and transparent. Though it is common to use worst case assumptions for estimating the quality of transmission (QoT) in the physical layer, over provisioning results in high margin requirements. Accurate estimation on the QoT for to-be-established lightpaths is crucial for reducing provisioning margins. Machine learning (ML) is regarded as one of the most powerful methodological approaches to perform network data analysis and enable automated network self-configuration. In this paper, an artificial neural network (ANN) framework, a branch of ML, to estimate the optical signal-to-noise ratio (OSNR) of to-be-established lightpaths is proposed. It takes account of both nonlinear interference between spectrum neighboring channels and optical monitoring uncertainties. The link information vector of the lightpath is used as input and the OSNR of the lightpath is the target for output of the ANN. The nonlinear interference impact of the number of neighboring channels on the estimation accuracy is considered. Extensive simulation results show that the proposed OSNR estimation scheme can work with any RWA algorithm. High estimation accuracy of over 98% with estimation errors of less than 0.5dB can be achieved given enough training data. ANN model with R=4 neighboring channels should be used to achieve more accurate OSNR estimates. Based on the results, it is expected that the proposed ANN-based OSNR estimation for new lightpath provisioning can be a promising tool for margin reduction and low-cost operation of future optical transport networks.