Naga VishnuKanth Irukulapati

Design and Experimental Validation of a Cooperative Driving System in the Grand Cooperative Driving Challenge

In this paper, we present the Cooperative Adaptive Cruise Control (CACC) architecture, which was proposed and implemented by the team from Chalmers University of Technology, Göteborg, Sweden, that joined the Grand Cooperative Driving Challenge (GCDC) in 2011. The proposed CACC architecture consists of the following three main components, which are described in detail: 1) communication; 2) sensor fusion; and 3) control. Both simulation and experimental results are provided, demonstrating that the proposed CACC system can drive within a vehicle platoon while minimizing the inter-vehicle spacing within the allowed range of safety distances, tracking a desired speed profile, and attenuating acceleration shockwaves.

Stochastic Digital Backpropagation

In this paper, we propose a novel detector for single-channel long-haul coherent optical communications, termed stochastic digital backpropagation (SDBP), which takes into account noise from the optical amplifiers in addition to handling deterministic linear and nonlinear impairments. We discuss the design approach behind this detector, which is based on the maximum a posteriori (MAP) principle. As closed-form expressions of the MAP detector are not tractable for coherent optical transmission, we employ the framework of Bayesian graphical models, which allows a numerical evaluation of the proposed detector. Through simulations, we observe that by accounting for nonlinear signal-noise interactions, we achieve a significant improvement in system reach with SDBP over digital backpropagation (DBP) for systems with periodic inline optical dispersion compensation. In uncompensated links with high symbol rates, the performance difference in terms of system reach for SDBP over DBP is small. In the absence of noise, the proposed detector is equivalent to the well-known DBP detector.

On nonlinearly-induced noise in single-channel optical links with digital backpropagation

In this paper, we investigate the performance limits of electronic chromatic dispersion compensation (EDC) and digital backpropagation (DBP) for a single-channel non-dispersion-managed fiber-optical link. A known analytical method to derive the performance of the system with EDC is extended to derive a first-order approximation for the performance of the system with DBP. In contrast to the cubic growth of the variance of the nonlinear noise-like interference, often called nonlinear noise, with input power for EDC, a quadratic growth is observed with DBP using this approximation. Finally, we provide numerical results to verify the accuracy of the proposed approach and compare it with existing analytical models.

Stochastic Digital Backpropagation With Residual Memory Compensation

Stochastic digital backpropagation (SDBP) is an extension of digital backpropagation (DBP) and is based on the maximum a posteriori principle. SDBP takes into account noise from the optical amplifiers in addition to handling deterministic linear and nonlinear impairments. The decisions in SDBP are taken on a symbol-by-symbol (SBS) basis, ignoring any residual memory, which may be present due to nonoptimal processing in SDBP. In this paper, we extend SDBP to account for memory between symbols. In particular, two different methods are proposed: a Viterbi algorithm (VA) and a decision directed approach. Symbol error rate (SER) for memory-based SDBP is significantly lower than the previously proposed SBS-SDBP. For inline dispersion-managed links, the VA-SDBP has up to 10 and 14 times lower SER than DBP for QPSK and 16-QAM, respectively.

On maximum likelihood sequence detectors for single-channel coherent optical communications

Two different detectors that account for the nonlinear signal-noise interaction in a single-channel coherent optical link are compared. The results indicate that accounting for the correlation between the samples leads to improved performance over stochastic digital backpropagation.

On the use of factor graphs in optical communications

Factor graphs and message passing allow the near-automated development of algorithms in many engineering disciplines, including digital communications. This paper gives an overview of their possible use in optical communications.

Backward particle message passing

Particle methods are an established way to represent messages and perform message passing in factor graphs. Despite their common use, there are several cases for which messages are hard to compute, even in linear models. Building on results from Gaussian message passing, we demonstrate how backward particle-based messages can be computed and describe a practical application in the context of fiber-optical communications.

Methodology for Power-Aware Coherent Receiver Design

We describe a methodology to design and evaluate DSP hardware for a coherent receiver. Important parameters that can be assessed include DSP power consumption and chip area.

Improved Lower Bounds on Mutual Information Accounting for Nonlinear Signal--Noise Interaction

In fiber-optic communications, evaluation of mutual information (MI) is still an open issue due to the unavailability of an exact and mathematically tractable channel model. Traditionally, lower bounds on MI are computed by approximating the (original) channel with an auxiliary forward channel. In this paper, lower bounds are computed using an auxiliary backward channel, which has not been previously considered in the context of fiber-optic communications. Distributions obtained through two variations of the stochastic digital backpropagation (SDBP) algorithm are used as auxiliary backward channels and these bounds are compared with bounds obtained through the conventional digital backpropagation. Through simulations, higher information rates were achieved with the SDBP, which can be explained by the ability of SDBP to account for nonlinear signal–noise interactions.