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Today, we are evident to the revolution of the automotive industry and its demand for high bandwidth sensor fusion. Multiple high bandwidth sensors are connected, generating tremendous amount of data to be transferred in real-time in harsh environment, i.e., multi-gigabit intra-vehicle networks. Plastic optical fiber (POF) is an attractive medium for these multi-gigabit intra-vehicle networks due to its inherent EM compatibility, galvanic isolation, low weight, high tolerance to mechanical vibrations, low bending loss, and easy handling. However, commercial off-the-shelf PMMA based POFs are bandwidth-length product limited. Here, we suggest increasing the spectral efficiency using spatial-division multiplexing (SDM). In this article, we have experimentally demonstrated a low cost and eye-safe 3 × 3 multiple-input multiple-output (MIMO) SDM system over a 15 meters multicore (MC) POF using commercial optical components, e.g., RC-LEDs and Si-PIN photodiodes. A 3 × 1 spot-based cores coupler spatial multiplexer was developed to couple the optical components with the MC-POF. This system achieved raw data rates of 1.5 Gb/s and 2.4 Gb/s using offline-processed MIMO equalization. The proposed system enables a flexible and scalable optical MIMO system that can meet the requirements of the automotive industry.

Analog distortion compensation based on digital signal processing methods are widely applied for transmitter or receiver impairments in various digital communication systems. Recently, several neural network methods were developed for distortion compensation. However, while digital communication systems are typically complex-valued, neural networks are mostly designed to work with real-valued inputs. Thus, adaptations of the network architecture or input data should be applied. In this article a method for using a single-input real-valued neural network for digital communication-based complex-valued signals without any modifications to the neural network is proposed. The method transforms the complex-valued signal to a real-valued one by taking the real component of a complex frequency offset applied through digital up-conversion, without affecting the distortion, therefore allowing standard neural network-based functionality with significant reduction in size. The method is tested with a multi-layer perceptron and gated recurrent unit architectures applied over a generic Wiener-Hammerstein model for the case of equalization of a coherent optical system. A reduction of about 7% and 26% in network size is shown for the gated recurrent unit-based and multi-layer perceptron-based architectures respectively, without any significant change in performance. This size reduction capability shows the high potential in applying the proposed method in neural network-based equalization and pre-distortion operations.

High throughput coherent optical transmitters are key components in future optical communication infrastructure. However, these transmitters are often distorted with the nonlinearity of their components. A potential approach for compensating nonlinearity is by applying digital pre-distortion methods based on the Volterra series or one of its derivatives. However, the Volterra series-based solution is complex to implement, difficult to scale, and its simplified versions may not yield the desired performance. Recently digital pre distortion solutions based on neural networks were proposed, which may benefit from the generality of neural networks and can be more easily scaled. These solutions are often based on non-standard neural network architectures which require complex neurons-based architectures or being based on indirect training approach which suffer from noise enhancement. In this article, a novel method for neural network-based pre-distortion with direct learning is proposed. The direct learning with neural network does not assume a specific transmitter model and does not suffer from noise enhancement. The method assumes standard neural network inference architecture and is applied to a coherent nonlinear optical transmitted with long-short-term memory neural network. The overall performance and complexity of the direct learning method is compared with the indirect approach and with the Volterra series-based solution, showing significant advantage in performance, especially in cases of severe nonlinearity and noise conditions.

DSP techniques that enhance the effective resolution of digital-to-analog converters (DACs) are presented. First, DACs that are limited by physical number of bits (PNOB) are addressed. An improved digital-resolution-enhancer (DRE) algorithm, which incorporates an optimized non-uniform quantization scheme is analytically analyzed and experimentally demonstrated. Improvement of up to 8 dB is shown, that enables 16QAM and 64QAM transmission using only 2 and 3 bits DACs, respectively. Furthermore, the DRE concept is elaborated to include additional practical DAC impairments such as timing jitter. Based on a generalized DAC model, we suggest an improved algorithm that increases the tolerance to jitter noise, termed as jitter-aware DRE (JA-DRE). The method is validated in numerical simulations, indicating substantial OSNR gain of $\sim \!5$ dB over non DSP enhanced DACs, and up to 2 dB over the previously proposed DRE algorithm.

A stealthy and encrypted transmission is demonstrated in a dense-WDM network. The residual gap between two neighboring commercial channels is utilized for the transmission of the secured channel, hidden below the amplifier noise at negative −10 dB/0.1nm OSNR. The stealthy and encrypted channel is generated by a digital replication of the baseband signal together with a digital spectral phase mask. This technique provides encryption and steganography as it exclusively allows the eligible user to obtain a considerable processing-gain, and to recover extremely low OSNR signals. In this work the secured channel is explored under stringent network conditions. A stealth transmission was tested between two high OSNR public channels, separated 50 GHz apart: Unshaped 25 Gbps NRZ channel and digitally shaped coherent PM-QPSK 112 Gbps channel. Although it is significantly overlapped by adjacent channels and covered by the ASE noise, we demonstrate error-free detection of the secured channel, without significant effect on the commercial channels.

Digital signal processing (DSP)-based system is a common architecture for high-speed communication links. One of the most important components for DSP-based architectures is the analog-to-digital convertor (ADC). Most of the high-speed ADCs are implemented as time interleaved ADC (TI-ADC). One of the major challenges in TI-ADC is the timing mismatch between the parallel sub-ADCs. The calibration of this mismatch increases system cost and complexity. In this article we propose a new background calibration method based on existing clock and data recovery (CDR) circuits. The timing mismatch can be calibrated using a simple and unique CDR per sub-ADC. Using the existing phase error detector and pulse gain estimator of the regular CDR, the calibration circuit becomes very low-cost, requiring only a first order loop, and a slow numerically controlled oscillator (NCO). VCSEL-based lab experiments are performed, which demonstrate the proposed method efficiency, performance and robustness.

We present a novel 128 Gbps SP-16QAM FSOC experiment, conducted in a retro-reflected setup over 10 km through the atmosphere. The optical setup is presented, and results are further discussed along with the limiting factors.

An analytic model for the chromatic dispersion (CD) effect in coherent broad-band optical transmission, involving mixing of two mode-locked semiconductor lasers, is proposed and analyzed. The severe effects of CD and the temporal signal-LO misalignment are simulated and quantified.

An analog fractionally spaced equalizer (FSE) is an efficient solution for high-speed communication system without a clock and data recovery circuit. However, the FSE might become unstable when a sign–sign least mean square tuning algorithm is used, especially when the transmitter pre-distortion is used. Here, an analytical model of the instability is derived, and a low-cost and efficient stabilization process based on dithering is proposed. The analytical instability model is verified in an experimental setup based on a vertical cavity surface emitting laser. Furthermore, it is experimentally demonstrated that in the case of the instability of the FSE, a clear convergence is achieved when applying the proposed stabilization scheme.

Digital pre-compensation (DPC) is an indispensable block of state-of-the-art optical transceivers, and a key enabler for high-order modulation formats (HOMFs) transmission. A crucial component, which enables the transmission of the precompensated HOMFs, is the digital-to-analog-converter (DAC). However, as data and symbol rates grow, the implementation of such devices becomes highly challenging in terms of performance, power consumption, and costs. In this paper, a digital-resolution-enhancer (DRE) algorithm is discussed, conjoined with high-end DPC methods. Simulation results demonstrate that the DRE reduces the effect of DAC quantization noise power by more than 8 dB for the considered cases of 400G with 64QAM. The proposed scheme is experimentally verified by transmitting a 4-bit DP-64QAM 400 Gbit/s signal in a WDM scenario over 95 km of single mode fiber.