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

Learning Physical-Layer Communication with Quantized Feedback

Data-driven optimization of transmitters and receivers can reveal new modulation and detection schemes and enable physical-layer communication over unknown channels. Previous work has shown that practical implementations of this approach require a feedback signal from the receiver to the transmitter. In this paper, we study the impact of quantized feedback on data-driven learning of physical-layer communication. A novel quantization method is proposed, which exploits the specific properties of the feedback signal and is suitable for nonstationary signal distributions. The method is evaluated for linear and nonlinear channels. Simulation results show that feedback quantization does not appreciably affect the learning process and can lead to similar performance as compared to the case where unquantized feedback is used for training, even with 1-bit quantization. In addition, it is shown that learning is surprisingly robust to noisy feedback where random bit flips are applied to the quantization bits

Regular perturbation on the group-velocity dispersion parameter for nonlinear fibre-optical communications

Communication using the optical fibre channel can be challenging due to nonlinear effects that arise in the optical propagation. These effects represent physical processes that originate from light propagation in optical fibres. To obtain fundamental understandings of these processes, mathematical models are typically used. These models are based on approximations of the nonlinear Schr\uf6dinger equation, the differential equation that governs the propagation in an optical fibre. All available models in the literature are restricted to certain regimes of operation. Here, we present an approximate model for the nonlinear optical fibre channel in the weak-dispersion regime, in a noiseless scenario. The approximation is obtained by applying regular perturbation theory on the group-velocity dispersion parameter of the nonlinear Schr\uf6dinger equation. The proposed model is compared with three other models using the normalized square deviation metric and shown to be significantly more accurate for links with high nonlinearities and weak dispersion

When to Use Optical Amplification in Noncoherent Transmission: An Information-Theoretic Approach

The standard solution for short-haul fiber-optic communications is to deploy noncoherent systems, i.e., to modulate and detect only the light intensity. In such systems, the signal is corrupted with optical noise from amplifiers and with thermal (electrical) noise. The capacity of noncoherent optical links has been studied extensively in the presence of either optical noise or thermal noise. In this paper, for the first time, we characterize the capacity under an average power constraint with both noise sources by establishing upper and lower bounds. In the two extreme cases of zero optical noise or zero thermal noise, we assess our bounds against some well-known results in the literature; improvements in both cases are observed. Next, for amplified fiber-optic systems, we study the trade-off between boosting signal energy (mitigating the effects of thermal noise) and adding optical noise. For a wide spectrum of system parameters and received power levels, we determine the optimal amplification gain. While mostly either no amplification or high-gain amplification is optimal, the best performance is for some parameter intervals achieved at finite gains

Constellation Shaping in Optical Communication Systems

Exploiting the full-dimensional capacity of coherent optical communication systems is needed to overcome the increasing bandwidth demands of the future Internet. To achieve capacity, both coding and shaping gains are required, and they are, in principle, independent. Therefore it makes sense to study shaping and how it can be achieved in various dimensions and how various shaping schemes affect the whole performance in real systems. This thesis investigates the performance of constellation shaping methods including geometric shaping (GS) and probabilistic shaping (PS) in coherent fiber-optic systems. To study GS, instead of considering machine learning approaches or optimization of irregular constellations in two dimensions, we have explored multidimensional lattice-based constellations. These constellations provide a regular structure with a fast and low-complexity encoding and decoding. In simulations, we show the possibility of transmitting and detecting constellation with a size of more than 10^{28} points which can be done without a look-up table to store the constellation points. Moreover, improved performance in terms of bit error rate, symbol error rate, and transmission reach are demonstrated over the linear additive white Gaussian noise as well as the nonlinear fiber channel compared to QAM formats.Furthermore, we investigate the performance of PS in two separate scenarios, i.e., transmitter impairments and transmission over hybrid systems with on-off keying channels. In both cases, we find that while PS-QAM outperforms the uniform QAM in the linear regime, uniform QAM can achieve better performance at the optimum power in the presence of transmitter or channel nonlinearities

Graphene for Electronics

Graphene is an allotrope of carbon consisting of a single layer of atoms arranged in a two-dimensional (2D) honeycomb lattice. Graphene's unique properties of thinness and conductivity have led to global research into its applications as a semiconductor. With the ability to well conduct electricity at room temperature, graphene semiconductors could easily be implemented into the existing semiconductor technologies and, in some cases, successfully compete with the traditional ones, such as silicon. This reprint presents very recent results in the physics of graphene, which can be important for applying the material in electronics

Cumulative index to NASA Tech Briefs, 1970-1975

Tech briefs of technology derived from the research and development activities of the National Aeronautics and Space Administration are presented. Abstracts and indexes of subject, personal author, originating center, and tech brief number for the 1970-1975 tech briefs are presented