Abstract-Narrowband multiple -input -multiple -output (MIMO) measurements using 16 transmitters and 16 receivers at 2.11 GHz were carried out in Manhattan. High capacities were found for full, as well as smaller array configurations, all within 80% of the fully scattering channel capacity. Correlation model parameters are derived from data. Spatial MIMO channel capacity statistics are found to be well represented by the separate transmitter and receiver correlation matrices, with a median relative error in capacity of 3%, in contrast with the 18% median relative error observed by assuming the antennas to be uncorrelated. A reduced parameter model, consisting of 4 parameters, has been developed to statistically represent the channel correlation matrices. These correlation matrices are, in turn, used to generate matrices with capacities that are consistent within a few percent of those measured in New York. The spatial channel model reported allows simulations of matrices for arbitrary antenna configurations. These channel matrices may be used to test receiver algorithms in system performance studies. These results may also be used for antenna array design, as the decay of mobile antenna correlation with antenna separation has been reported here. An important finding for the base transmitter array was that the antennas were largely uncorrelated even at antenna separations as small as two wavelengths.

We propose a convolutional-recurrent channel equalizer and experimentally demonstrate 1dB Q-factor improvement both in single-channel and 96×WDM, DP-16QAM transmission over 450km of TWC fiber. The new equalizer outperforms previous NN-based approaches and a 3-steps-per-span DBP.

Spatial-division multiplexing (SDM) and band-division multiplexing (BDM) have emerged as solutions to expand the capacity of existing C-band wavelength-division multiplexing (WDM) optical systems and to deal with increasing traffic demands. An important difference between these two approaches is that BDM solutions enable data transmission over unused spectral bands of already-deployed optical fibers, whereas SDM solutions require the availability of additional fibers to replicate C-band WDM transmission. On the other hand, to properly design a multiband optical line system (OLS), the following fiber propagation effects have been taken into account in the analysis: (i) stimulated Raman scattering (SRS), which induces considerable power transfer among bands; (ii) frequency dependence of fiber parameters such as attenuation, dispersion, and nonlinear coefficients; and (iii) utilization of optical amplifiers with different doping materials, thus leading to different characteristics, e.g., in terms of noise figures. This work follows a two-step approach: First, we aim at maximizing and flattening the quality of transmission (QoT) when adding L- and -bands to a traditional WDM OLS where only the C-band is deployed. This is achieved by applying a multiband optimized optical power control for BDM upgrades, which consists of setting a pre-tilt and power offset in the line amplifiers, thus achieving a considerable increase in QoT, both in average value and flatness. Second, the SDM approach is used as a benchmark for the BDM approach by assessing network performance on three network topologies with different geographical footprints. We show that, with optical power properly optimized, BDM may enable an increase in network traffic, slightly less than an SDM upgrade but still comparable, without requiring additional fiber cables.

In this paper, a new methodology is proposed that allows for the low-complexity development of neural network (NN) based equalizers for the mitigation of impairments in highspeed coherent optical transmission systems. In this work, we provide a comprehensive description and comparison of various deep model compression approaches that have been applied to feed-forward and recurrent NN designs. Additionally, we evaluate the influence these strategies have on the performance of each NN equalizer. Quantization, weight clustering, pruning, and other cutting-edge strategies for model compression are taken into consideration. In this work, we propose and evaluate a Bayesian optimization-assisted compression, in which the hyperparameters of the compression are chosen to simultaneously reduce complexity and improve performance. Next, this paper presents four distinct metrics (RMpS, BoP, NABS, and NLGs) that are discussed here that can be used to evaluate the amount of computing complexity required by various compression algorithms. These measurements can serve as a benchmark for evaluating the relative effectiveness of various NN equalizers when compression approaches are used. In conclusion, the trade-off between the complexity of each compression approach and its performance is evaluated by utilizing both simulated and experimental data in order to complete the analysis. By utilizing optimal compression approaches, we show that it is possible to design an NN-based equalizer that is simpler to implement and has better performance than the conventional digital back-propagation (DBP) equalizer with only one step per span. This is accomplished by reducing the number of multipliers used in the NN equalizer after applying the weighted clustering and pruning algorithms. Furthermore, we demonstrate that an equalizer based on NN can also achieve superior performance while still maintaining the same degree of complexity as the full electronic chromatic dispersion compensation block. We conclude our analysis by highlighting open questions and existing challenges, as well as possible future research directions.

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