43 research outputs found
Bounds on the Per-Sample Capacity of Zero-Dispersion Simplified Fiber-Optical Channel Models
A number of simplified models, based on perturbation theory, have been
proposed for the fiber-optical channel and have been extensively used in the
literature. Although these models are mainly developed for the low-power
regime, they are used at moderate or high powers as well. It remains unclear to
what extent the capacity of these models is affected by the simplifying
assumptions under which they are derived. In this paper, we consider single
channel data transmission based on three continuous-time optical models i) a
regular perturbative channel, ii) a logarithmic perturbative channel, and iii)
the stochastic nonlinear Schr\"odinger (NLS) channel. We apply two simplifying
assumptions on these channels to obtain analytically tractable discrete-time
models. Namely, we neglect the channel memory (fiber dispersion) and we use a
sampling receiver. These assumptions bring into question the physical relevance
of the models studied in the paper. Therefore, the results should be viewed as
a first step toward analyzing more realistic channels. We investigate the
per-sample capacity of the simplified discrete-time models. Specifically, i) we
establish tight bounds on the capacity of the regular perturbative channel; ii)
we obtain the capacity of the logarithmic perturbative channel; and iii) we
present a novel upper bound on the capacity of the zero-dispersion NLS channel.
Our results illustrate that the capacity of these models departs from each
other at high powers because these models yield different capacity pre-logs.
Since all three models are based on the same physical channel, our results
highlight that care must be exercised in using simplified channel models in the
high-power regime
Capacity Analysis and Receiver Design in the Presence of Fiber Nonlinearity
The majority of today\u27s global Internet traffic is conveyed through optical fibers. The ever-increasing data demands have pushed the optical systems to evolve from using regenerators and direct-direction receivers to a coherent multi-wavelength network. Future services like cloud computing and virtual reality will demand more bandwidth, so much so that the so called capacity-crunch is anticipated to happen in near future. Therefore, studying the capacity of the optical system is needed to better understanding and utilizing the existing fiber network.The characterization of the capacity of the dispersive and nonlinear optical fiber described by the nonlinear Schr{\"o}dinger equation is an open problem. There are a number of lower bounds on the capacity which are mainly obtained based on the mismatched decoding principle or by analyzing simplified channels. These lower bounds either fall to zero at high powers or saturate. The question whether the fiber-optical capacity has the same behavior as the lower bounds at high power is still open. Indeed, the only known upper bound increases with the power unboundedly. In this thesis, we first study how the fiber nonlinear distortion is modeled in some simplified channels and what is the influence of the simplifying assumptions on the capacity. To do so, the capacity of three different memoryless simplified models of the fiber-optical channel are studied. The results show that in the high-power regime the capacities of these models grow with different pre-logs, which indicates the profound impact of the simplifying assumptions on the capacity of these channels. Next, we turn our attention to demodulation and detection processes in the presence of fiber nonlinearity. We study a two-user memoryless network. It is shown that by deploying a nonlinearity-tailored demodulator, the performance improves substantially compared with matched filtering and sampling. In the absence of dispersion, we show that with the new receiver, unlike with matched filtering and sampling, arbitrarily low bit error rates can be achieved. Furthermore, we show via simulations that performance improvements can be obtained also for a low-dispersion fiber.Then, we study the performance of three different dispersion compensation methods in the presence of inter-channel nonlinear interference. The best performance, in terms of achievable information rate, is obtained by a link with inline per-channel dispersion compensation combined with a receiver that compensates for inter-channel nonlinearities. Finally, the capacity analysis is performed for short-reach noncoherent channel, where the source of nonlinearity is not the fiber but a square-law receiver. Capacity bounds are established in the presence of optical and thermal noises. Using these bounds we show that optical amplification is beneficial at low signal-to-noise ratios (SNRs), and detrimental at high SNRs. We quantify the SNR regime for each case for a wide range of channel parameters
Demodulation and Detection Schemes for a Memoryless Optical WDM Channel
It is well known that matched filtering and sampling (MFS) demodulation
together with minimum Euclidean distance (MD) detection constitute the optimal
receiver for the additive white Gaussian noise channel. However, for a general
nonlinear transmission medium, MFS does not provide sufficient statistics, and
therefore is suboptimal. Nonetheless, this receiver is widely used in optical
systems, where the Kerr nonlinearity is the dominant impairment at high powers.
In this paper, we consider a suite of receivers for a two-user channel subject
to a type of nonlinear interference that occurs in
wavelength-division-multiplexed channels. The asymptotes of the symbol error
rate (SER) of the considered receivers at high powers are derived or bounded
analytically. Moreover, Monte-Carlo simulations are conducted to evaluate the
SER for all the receivers. Our results show that receivers that are based on
MFS cannot achieve arbitrary low SERs, whereas the SER goes to zero as the
power grows for the optimal receiver. Furthermore, we devise a heuristic
demodulator, which together with the MD detector yields a receiver that is
simpler than the optimal one and can achieve arbitrary low SERs. The SER
performance of the proposed receivers is also evaluated for some single-span
fiber-optical channels via split-step Fourier simulations
Scalable Interconnection Scheme for Data Center Multicast Applications
We propose a modular star-coupler-based switch architecture along with a scalable multicast scheduling algorithm to enable all-optical multicasting among data center nodes. With broadcast domain partitioning in a 126-port switch, we achieve up to 24% improvement in the maximum achievable throughput
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Overcoming the Switching Bottlenecks in Wavelength-Routing, Multicast-Enabled Architectures
Modular optical switch architectures combining wavelength routing based on arrayed waveguide grating (AWG) devices and multicasting based on star couplers hold promise for flexibly addressing the exponentially growing traffic demands in a cost- and power-efficient fashion. In a default switching scenario, an input port of the AWG is connected to an output port via a single wavelength. This can severely limit the capacity between broadcast domains, resulting in interdomain traffic switching bottlenecks. An unexplored solution to this issue is to exploit multiple AWG free spectral ranges (FSRs), i.e., to set up multiple parallel connections between each pair of broadcast domains. In this paper, we study, for the first time, the influence of the FSR count on the throughput of a multistage switching architecture and propose a generic and novel analytical framework to estimate the blocking probability. We assess the accuracy of our analytical results via Monte Carlo simulations. Our study points to significant improvements with a moderate increase in the number of FSRs. We show that an FSR count beyond four results in diminishing returns. Furthermore, to investigate the tradeoffs between the network- and physical-layer effects, we conduct a cross-layer analysis, taking into account pulse amplitude modulation and rate-adaptive forward error correction. We illustrate how the effective bit rate per port increases with an increase in the number of FSRs
Data preprocessing for machine-learning-based adaptive data center transmission
To enable optical interconnect fluidity in next-generation data centers, we propose adaptive transmission based on machine learning in a wavelength-routing network. We consider programmable transmitters that can apply N possible code rates to connections based on predicted bit error rate (BER) values. To classify the BER, we employ a preprocessing algorithm to feed the traffic data to a neural network classifier. We demonstrate the significance of our proposed preprocessing algorithm and the classifier performance for different values of N and switch port count
SISO RIS-Enabled Joint 3D Downlink Localization and Synchronization
We consider the problem of joint three-dimensional localization and synchronization for a single-input single-output (SISO) multi-carrier system in the presence of a reconfigurable intelligent surface (RIS), equipped with a uniform planar array. First, we derive the Cram\ue9r-Rao bounds (CRBs) on the estimation error of the channel parameters, namely, the angle-of-departure (AOD), composed of azimuth and elevation, from RIS to the user equipment (UE) and times-of-arrival (TOAs) for the path from the base station (BS) to UE and BS-RISUE reflection. In order to avoid high-dimensional search over the parameter space, we devise a low-complexity estimation algorithm that performs two 1D searches over the TOAs and one 2D search over the AODs. Simulation results demonstrate that the considered RIS-aided wireless system can provide submeter-level positioning and synchronization accuracy, materializing the positioning capability of Beyond 5G networks even with single-antenna BS and UE. Furthermore, the proposed estimator is shown to attain the CRB at a wide interval of distances between UE and RIS. Finally, we also investigate the scaling of the position error bound with the number of RIS elements