Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks

In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical applications. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to "model" the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals. We then represent the trained NN with a factor graph, and design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm. The implementation of a turbo receiver with the proposed Bayesian detector is also investigated. Extensive simulation results demonstrate that the proposed technique delivers remarkably better performance than state-of-the-art methods

Advanced DSP Techniques for High-Capacity and Energy-Efficient Optical Fiber Communications

The rapid proliferation of the Internet has been driving communication networks closer and closer to their limits, while available bandwidth is disappearing due to an ever-increasing network load. Over the past decade, optical fiber communication technology has increased per fiber data rate from 10 Tb/s to exceeding 10 Pb/s. The major explosion came after the maturity of coherent detection and advanced digital signal processing (DSP). DSP has played a critical role in accommodating channel impairments mitigation, enabling advanced modulation formats for spectral efficiency transmission and realizing flexible bandwidth. This book aims to explore novel, advanced DSP techniques to enable multi-Tb/s/channel optical transmission to address pressing bandwidth and power-efficiency demands. It provides state-of-the-art advances and future perspectives of DSP as well

Optics for AI and AI for Optics

Artificial intelligence is deeply involved in our daily lives via reinforcing the digital transformation of modern economies and infrastructure. It relies on powerful computing clusters, which face bottlenecks of power consumption for both data transmission and intensive computing. Meanwhile, optics (especially optical communications, which underpin today’s telecommunications) is penetrating short-reach connections down to the chip level, thus meeting with AI technology and creating numerous opportunities. This book is about the marriage of optics and AI and how each part can benefit from the other. Optics facilitates on-chip neural networks based on fast optical computing and energy-efficient interconnects and communications. On the other hand, AI enables efficient tools to address the challenges of today’s optical communication networks, which behave in an increasingly complex manner. The book collects contributions from pioneering researchers from both academy and industry to discuss the challenges and solutions in each of the respective fields

Optical Transmission Systems based on the Nonlinear Fourier Transformation

Solitons are stable pulse shapes, which propagate linearly and maintain their shape despite the highly nonlinear fiber optical channel. A challenge in the use of these signal pulses in optical data transmission is to multiplex them with high efficiency. One way to multiplex many solitons is the nonlinear Fourier transform (NFT). With the help of the NFT, signal spectra can be calculated which propagate linearly through a nonlinear channel. Thus, in perspective, it is possible to perform linear transmissions even in highly nonlinear regions with high signal power levels. The NFT decomposes a signal into a dispersive and a solitonic part. The dispersive part is similar to spectra of the conventional linear Fourier transform and dominates especially at low signal powers. As soon as the total power of a signal exceeds a certain limit, solitons arise. A disadvantage of solitons generated digitally by the NFT is their complex shape due to, for example, high electrical bandwidths or a poor peak-to-average power ratio. In the course of this work, a scalable system architecture of a photonic integrated circuit based on a silicon chip was designed, which allows to multiplex several simple solitons tightly together to push the complex electrical generation of higher order solitons into the optical domain. This photonic integrated circuit was subsequently designed and fabricated by the Institute of Integrated Photonics at RWTH Aachen University. Using this novel system architecture and additional equalization concepts designed in this work, soliton transmissions with up to four channels could be successfully realized over more than 5000 km with a very high spectral efficiency of 0.5 b/s/Hz in the soliton range

Energy-Efficient Receiver Design for High-Speed Interconnects

High-speed interconnects are of vital importance to the operation of high-performance computing and communication systems, determining the ultimate bandwidth or data rates at which the information can be exchanged. Optical interconnects and the employment of high-order modulation formats are considered as the solutions to fulfilling the envisioned speed and power efficiency of future interconnects. One common key factor in bringing the success is the availability of energy-efficient receivers with superior sensitivity. To enhance the receiver sensitivity, improvement in the signal-to-noise ratio (SNR) of the front-end circuits, or equalization that mitigates the detrimental inter-symbol interference (ISI) is required. In this dissertation, architectural and circuit-level energy-efficient techniques serving these goals are presented. First, an avalanche photodetector (APD)-based optical receiver is described, which utilizes non-return-to-zero (NRZ) modulation and is applicable to burst-mode operation. For the purposes of improving the overall optical link energy efficiency as well as the link bandwidth, this optical receiver is designed to achieve high sensitivity and high reconfiguration speed. The high sensitivity is enabled by optimizing the SNR at the front-end through adjusting the APD responsivity via its reverse bias voltage, along with the incorporation of 2-tap feedforward equalization (FFE) and 2-tap decision feedback equalization (DFE) implemented in current-integrating fashion. The high reconfiguration speed is empowered by the proposed integrating dc and amplitude comparators, which eliminate the RC settling time constraints. The receiver circuits, excluding the APD die, are fabricated in 28-nm CMOS technology. The optical receiver achieves bit-error-rate (BER) better than 1E−12 at −16-dBm optical modulation amplitude (OMA), 2.24-ns reconfiguration time with 5-dB dynamic range, and 1.37-pJ/b energy efficiency at 25 Gb/s. Second, a 4-level pulse amplitude modulation (PAM4) wireline receiver is described, which incorporates continuous time linear equalizers (CTLEs) and a 2-tap direct DFE dedicated to the compensation for the first and second post-cursor ISI. The direct DFE in a PAM4 receiver (PAM4-DFE) is made possible by the proposed CMOS track-and-regenerate slicer. This proposed slicer offers rail-to-rail digital feedback signals with significantly improved clock-to-Q delay performance. The reduced slicer delay relaxes the settling time constraint of the summer circuits and allows the stringent DFE timing constraint to be satisfied. With the availability of a direct DFE employing the proposed slicer, inductor-based bandwidth enhancement and loop-unrolling techniques, which can be power/area intensive, are not required. Fabricated in 28-nm CMOS technology, the PAM4 receiver achieves BER better than 1E−12 and 1.1-pJ/b energy efficiency at 60 Gb/s, measured over a channel with 8.2-dB loss at Nyquist frequency. Third, digital neural-network-enhanced FFEs (NN-FFEs) for PAM4 analog-to-digital converter (ADC)-based optical interconnects are described. The proposed NN-FFEs employ a custom learnable piecewise linear (PWL) activation function to tackle the nonlinearities with short memory lengths. In contrast to the conventional Volterra equalizers where multipliers are utilized to generate the nonlinear terms, the proposed NN-FFEs leverage the custom PWL activation function for nonlinear operations and reduce the required number of multipliers, thereby improving the area and power efficiencies. Applications in the optical interconnects based on micro-ring modulators (MRMs) are demonstrated with simulation results of 50-Gb/s and 100-Gb/s links adopting PAM4 signaling. The proposed NN-FFEs and the conventional Volterra equalizers are synthesized with the standard-cell libraries in a commercial 28-nm CMOS technology, and their power consumptions and performance are compared. Better than 37% lower power overhead can be achieved by employing the proposed NN-FFEs, in comparison with the Volterra equalizer that leads to similar improvement in the symbol-error-rate (SER) performance.</p

Machine Learning Techniques To Mitigate Nonlinear Impairments In Optical Fiber System

The upcoming deployment of 5/6G networks, online services like 4k/8k HDTV (streamers and online games), the development of the Internet of Things concept, connecting billions of active devices, as well as the high-speed optical access networks, impose progressively higher and higher requirements on the underlying optical networks infrastructure. With current network infrastructures approaching almost unsustainable levels of bandwidth utilization/ data traffic rates, and the electrical power consumption of communications systems becoming a serious concern in view of our achieving the global carbon footprint targets, network operators and system suppliers are now looking for ways to respond to these demands while also maximizing the returns of their investments. The search for a solution to this predicted ªcapacity crunchº led to a renewed interest in alternative approaches to system design, including the usage of high-order modulation formats and high symbol rates, enabled by coherent detection, development of wideband transmission tools, new fiber types (such as multi-mode and ±core), and finally, the implementation of advanced digital signal processing (DSP) elements to mitigate optical channel nonlinearities and improve the received SNR. All aforementioned options are intended to boost the available optical systems’ capacity to fulfill the new traffic demands. This thesis focuses on the last of these possible solutions to the ªcapacity crunch," answering the question: ªHow can machine learning improve existing optical communications by minimizing quality penalties introduced by transceiver components and fiber media nonlinearity?". Ultimately, by identifying a proper machine learning solution (or a bevy of solutions) to act as a nonlinear channel equalizer for optical transmissions, we can improve the system’s throughput and even reduce the signal processing complexity, which means we can transmit more using the already built optical infrastructure. This problem was broken into four parts in this thesis: i) the development of new machine learning architectures to achieve appealing levels of performance; ii) the correct assessment of computational complexity and hardware realization; iii) the application of AI techniques to achieve fast reconfigurable solutions; iv) the creation of a theoretical foundation with studies demonstrating the caveats and pitfalls of machine learning methods used for optical channel equalization. Common measures such as bit error rate, quality factor, and mutual information are considered in scrutinizing the systems studied in this thesis. Based on simulation and experimental results, we conclude that neural network-based equalization can, in fact, improve the channel quality of transmission and at the same time have computational complexity close to other classic DSP algorithms

Enabling Technology in Optical Fiber Communications: From Device, System to Networking

This book explores the enabling technology in optical fiber communications. It focuses on the state-of-the-art advances from fundamental theories, devices, and subsystems to networking applications as well as future perspectives of optical fiber communications. The topics cover include integrated photonics, fiber optics, fiber and free-space optical communications, and optical networking