We propose a new optical amplitude multiplexing technique that consists of merging two wavelength multiplexed on-off keying (OOK) signals into a single quaternary amplitude shift keying signal. This technique is based on four-wave mixing in a single-mode optical fiber and requires power offset from the OOK signals. Simulation results indicate that the multiplexing may be performed between signals of the same or different bit rates. As a consequence, the proposed technique may be used not only for increasing spectral efficiency but also for optical labeling.

Recent work suggests that fiber four-wave mixing may be used to merge two wavelength multiplexed binary channels into a single quaternary signal. In this letter, we investigate the application of this technique to the optical packets generation considering that a 10-Gb/s payload is optically multiplexed to a 2.5-Gb/s label. Computer simulations are used to analyze the propagation of the generated quaternary optical packet in the dominance of amplified spontaneous emission noise. Results suggest that the optical packet may be propagated through metropolitan area networks length scales and that an average power around 0 dBm should be used to minimize the packet bit-error rate.

In this paper we show that the adoption of a simple weighted ordering heuristic strategy to find fixed shortest path routes in a topology can improve load-balancing and, consequently, network performance. We compared our fixed routing strategies against the $k$-shortest paths ($k$-SP) fixed-alternate routing strategy for three different mesh topologies considering fixed grid networks (FGNs) and elastic optical networks (EONs). The results show that our simple fixed routing strategies can improve network performance for an optical network system, even compared with the fixed alternate routing and always using the shortest path. Results also show that such easy strategies act mainly in longer routes, increasing their probability of use and balancing the link load distribution.

A novel nonlinear equalizer based on a multiple-input multiple-output (MIMO) deep neural network (DNN) is proposed and experimentally demonstrated for compensation of inter-subcarrier nonlinearities in a 40 Gb/s coherent optical orthogonal frequency division multiplexing system. Experimental results reveal that MIMO-DNN can extend the power margin by 4 dB at 2000 km of standard single-mode fiber transmission when compared to linear compensation or conventional single-input single-output DNN. It is also found that MIMO-DNN outperforms digital back propagation by increasing up to 1 dB the effective Q -factor and reducing by a factor of three the computational cost.

In order to meet the increasing capacity requirements, network operators are extending their optical infrastructure closer to the end-user while making more efficient use of the resources. In this context, long reach passive optical networks (LR-PONs) are attracting increasing attention.Coherent LR-PONs based on high speed digital signal processors represent a high potential alternative because, alongside with the inherent mixing gain and the possibility of amplitude and phase diversity formats, they pave the way to compensate linear impairments in a more efficient way than in traditional direct detection systems. The performance of coherent LR-PONs is then limited by the combined effect of noise and nonlinear distortion. The noise is particularly critical in single channel systems where, in addition to the the elevated fibre loss, the splitting losses should be considered. In such systems, Kerr induced self-phase modulation emerges as the main limitation to the maximum capacity. In this work, we propose a novel clustering algorithm, denominated histogram based clustering (HBC), that employs the spatial density of the points of a 2D histogram to identify the borders of high density areas to classify nonlinearly distorted noisy constellations. Simulation results reveal that for a 100 km long LR-PON with a 1:64 splitting ratio, at optimum power levels, HBC presents a Q-factor 0.57 dB higher than maximum likelihood and 0.21 dB higher than k-means. In terms of nonlinear tolerance, at a BER of 2×10 ${}^{-3}$ , our method achieves a gain of ∼2.5 dB and ∼1.25 dB over maximum likelihood and k-means, respectively. Numerical results also show that the proposed method can operate over blocks as small as 2500 symbols.