Influence of Behavioral Models on Multiuser Channel Capacity

In order to characterize the channel capacity of a wavelength channel in a wavelength-division multiplexed (WDM) system, statistical models are needed for the transmitted signals on the other wavelengths. For example, one could assume that the transmitters for all wavelengths are configured independently of each other, that they use the same signal power, or that they use the same modulation format. In this paper, it is shown that these so-called behavioral models have a profound impact on the single-wavelength achievable information rate. This is demonstrated by establishing, for the first time, upper and lower bounds on the maximum achievable rate under various behavioral models, for a rudimentary WDM channel model

Capacity of a Nonlinear Optical Channel with Finite Memory

The channel capacity of a nonlinear, dispersive fiber-optic link is revisited. To this end, the popular Gaussian noise (GN) model is extended with a parameter to account for the finite memory of realistic fiber channels. This finite-memory model is harder to analyze mathematically but, in contrast to previous models, it is valid also for nonstationary or heavy-tailed input signals. For uncoded transmission and standard modulation formats, the new model gives the same results as the regular GN model when the memory of the channel is about 10 symbols or more. These results confirm previous results that the GN model is accurate for uncoded transmission. However, when coding is considered, the results obtained using the finite-memory model are very different from those obtained by previous models, even when the channel memory is large. In particular, the peaky behavior of the channel capacity, which has been reported for numerous nonlinear channel models, appears to be an artifact of applying models derived for independent input in a coded (i.e., dependent) scenario

Next-generation optical access networks based on Orthogonal Frequency Division Multiplexing

Orthogonal Frequency Division Multiplexing (OFDM) is a robust modulation and multiplexing format which is at the base of many present communication standards. The interest of the OFDM application in optical fiber deployments is quite recent. As the next generation of Passive Optical Networks (NG-PONs) is envisioned, targeting greater capacity and user counts, the limitations of TDMA (Time Division Multiplexing Access) approaches to meet the expected increase in requirements becomes evident and therefore new technologies are being explored. Optical OFDMA is an emerging technology which can be a promising candidate. The main goal of this Master Thesis is to study the problem of users multiplexing in access networks, using OFDM as a technology to transmit the user information data. This work has focused in the uplink study of the network, because it is the most challenging part of the network to design. The studies have been conducted both in a theoretical way and also by simulating the targeted environments by means of a fiber optics transmission simulation tool. Virtual Photonics Integrated (VPI) is the software selected for the simulations. This tool is specially designed to simulate optical transmission system environments. The analysis of the Optical Beat Interference, which is a critical impairment in optical carrier multiplexing schemes, is the most important part of the user multiplexing study

Neural Network Equalizers and Successive Interference Cancellation for Bandlimited Channels with a Nonlinearity

Neural networks (NNs) inspired by the forward-backward algorithm (FBA) are used as equalizers for bandlimited channels with a memoryless nonlinearity. The NN-equalizers are combined with successive interference cancellation (SIC) to approach the information rates of joint detection and decoding (JDD) with considerably less complexity than JDD and other existing equalizers. Simulations for short-haul optical fiber links with square-law detection illustrate the gains of NNs as compared to the complexity-limited FBA and Gibbs sampling.Comment: Submitted to IEEE Trans. Commun. on January 11, 2024