Photonic layer security is demonstrated over 200 km of a loaded dense wavelength-division multiplexing (DWDM) network. The secure channel achieves 100 Gbps throughput with photonic-encrypted coherent transmission and continuously updating photonic keys. The signal is effectively concealed under optical noise, harvest-proof, and lacks offline decryption capabilities, ensuring post-quantum resilience. Additionally, successful coexistence with seven conventional coherent channels, both in-band and out-of-band, is demonstrated in a real-world scenario.

The capability to form a photonic shield by using a unique all-optical transmission scheme incorporating multi-THz coherent spreading, spectral phase encoding (SPE), and negative optical signal-to-noise ratio (OSNR) completely prevents offline deciphering of captured data-in-transit. This photonic shield scheme provides an ultimate solution to the "harvest-now, decrypt later" threat by eliminating unauthorised recording. Thus, no raw data is available for any post-processing, including by quantum computers. Both the full line rate payload and the asymmetric key exchange, are transmitted through the secured channel. This work presents an industry-level demonstration, including real-time client data transmission and seamlessly continuously changing spectral phase encoding (SPE) photonic keys. A 100 Gbps DP-QPSK signal and a 200 Gbps DP-16QAM link are established over 80 km of standard single mode fiber (SSMF).

We report the demonstration of a novel free space optical (FSO) communication scheme utilizing transmitter-side coherent beam combining (CBC) based on an optical phased array (OPA) for adaptive atmospheric turbulence compensation. Coherent combining of 32 amplified laser elements, spatially arranged in a 2D rectangular array, was performed in free space by actuating 32 phase modulators using a high-speed embedded controller. The phase of each element was adjusted in a real-time closed loop to maximize the received beam intensity, thus mitigating turbulence-induced impairments such as beam wander and scintillations. The CBC system was experimentally evaluated over retroreflected FSO links of 2 km and 10 km distance. Alongside a considerable increase in on-axis irradiance, beam steering and tracking abilities were also demonstrated, allowing the main lobe to steer within a spot with a diameter five times wider. In 10 km, CBC pre-compensation proved crucial for detecting the reflected signal, indicating for a considerable gain for atmospheric effects mitigation. A CBC-aided FSO link over 10 km was successfully established, achieving single-channel and polarization data rates of 64 Gbit/s and 100 Gbit/s, with availability rates of 96% and 77%, respectively.

Interlocking of mode-locked lasers (MLLs) has several applications in the optical communication field and beyond. This work uses an on-chip, integrated coherent receiver (ICR) for successfully interlocking two 80GHz MLLs without exploiting the ICR high-speed outputs.

With the progress of generating more compact, efficient, and high bandwidth mode-locked lasers (MLLs), many novel applications have risen. Such advanced applications demand the acquisition of the cross-correlation (XCORR) between two MLLs for examination and interlocking two lasers. As opposed to the common techniques for acquiring the XCORR via non-linear crystals as effective multipliers, here, employment of conventional optical communication on-chip integrated coherent receiver (ICR) peak detector (PKD) circuit is proposed, analyzed, and demonstrated. A rigorous connection between the PKD circuit and the optical XCORR is derived, along with the expected response of the circuit to MLL inputs. Two interlocking techniques are suggested and examined: a power proportional-integral (PI) and extremum seeking (ES). In addition, inclusive experiments with 40 GHZ MLLs verifying the analytical predictions are conducted, and successful interlocking is demonstrated. The presented approach enables the efficient realization of various interlocking and examination applications using low-cost, compact, low-power pulse lasers.

Combining multi-THz optical spectrum spreading, photonic phase encoding, and negative optical signal-to-noise ratio (OSNR) transmission, forms photonic shield that prevents data recording for offline deciphering. This supports post-quantum security by eliminating raw data availability for quantum computers processing.

We have previously suggested a promising approach for optical layer security, incorporating an all-optical spectrum spreading, spectral phase-encoding time-spreading, and noiseprotected coherent communication system. An authorized receiver with the spectral phases key can evoke a multi-homodyne coherent detection (MHCD) to reconstruct the noise-submerged signal. Unless deciphered in real-time, by all-optical means and with the correct phases key mask, an adversary cannot reconstruct the transmitted data, which is permanently lost. This feature prohibits unauthorized offline processing, regardless of the resources and efforts available to the adversary, thus making data-in-transit record-proof and resilient to any computational power, including the quantum computer. In this work, we present a novel security analysis for this approach, where three different types of attacks are proposed and thoroughly studied: Naive, Analytic, and Greedy. These algorithms represent different approaches for all-optical phases key cracking. We formulated a mathematical model for a Naive attacker who trials an arbitrary phase mask. In the Analytic approach, the attacker studies the encoding system by trialing arbitrary test patterns. In contrast, the attacker who employs the Greedy approach maximizes his performance in each step until the desired signal-to-noise ratio (SNR) level is obtained. We analyze these approaches analytically and discuss their cryptanalysis aspects concerning performance, complexity, and the photonic hardware used to decode the phase mask. Our simulations and models suggest a set of conditions for an all-optical transmission system that is impervious to cryptography attacks.

In free-space optical (FSO) feeder links, atmospheric turbulence causes fluctuations that significantly degrade link performance. We present a novel communication technique aimed at mitigating turbulence-induced intensity fluctuations and phase distortions, while enabling the use of dual-polarization high-order coherent modulation schemes. The technique involves transmitting ultra-short pulses through a turbulent atmosphere using a detection scheme known as a dynamic coherent all-optical matched filter. Simulation and experiments have demonstrated proof of concept, showing a dramatic improvement of more than 8.5 dB in the link budget.

The concept of "harvest now, decipher later" presents a major data security concern in light of thriving quantum compuHng, rendering tradiHonal public-key cryptography ineffecHve and undermining even state-of-the-art symmetric block ciphers. Physical layer security (PLS) offers advantages such as enhancing the security of the enHre OSI model, including its metadata, with minimal latency and providing robust protecHon against data harvesHng. MulH-THz, high repeHHon rate mode-locked laser sources have enabled the development of PLS techniques that leverage coherent opHcal spreading and despreading, phase mask encoding, and under-the-noise transmission. Since capturing mulH-THz E-fields is impracHcal, real-Hme all-opHcal decoding is necessary, as offline post-processing yields permanently poor performance (indicated by a negaHve SNR) for unauthorized detecHon. This paper reports the demonstraHons of PLS transmission of 30G and 60G Bauds, using QPSK and 16-QAM modula Hon schemes, achieved over distances of up to 100 km, with a real-Hme DSP ASIC. These achievements pave the way for high-bandwidth point-to-point harvest-immune opHcal connecHvity.IntroducHon

A spectral-efficient physical layer security system is introduced, which enhances capacity by transmitting multiple channels over shared spectrum. Demonstrating that the success probability of an attack is extremely low and further decreases with the inclusion of more channels.

Optical communications systems’ performance is limited by physical layer nonlinearities inherent in some of their transmitter components. In order to overcome some of these limitations, several approaches utilizing artificial neural networks to digitally pre-distort the transmitted signal have been proposed in the last few years, alongside other more classical models. However, their performance was strongly dependent on the specific training sequence used. This work improves the performance of neural network-based direct learning approach for digital pre-distortion applications. The proposed method, which is based on the curriculum learning approach, aims to construct a training paradigm of gradually increasing complexity based on the statistical properties of the input signal, in order to achieve better convergence of the network. We prove that the proposed method defines a proper curriculum regardless of the training data distribution, considering the proposed wide-sense curriculum conditions and the case of binary weighting functions. A comparative analysis examines a pre-distorter for a coherent optical transmitter modelled by a Long Short-Term Memory neural network. An improvement of 4.8dB in terms of normalized mean-square error is demonstrated for the case of a 3-bits linear quantizer, compared to the conventional training scheme.

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.

A novel all-optical stealth and secured transmission is proposed and demonstrated. Spectral replicas of the covert signal are carried by multiple tones of a gain switched optical frequency comb, optically coded with spectral phase mask, and concealed below EDFA's noise. The secured signal's spectrum is spread far beyond the bandwidth of a coherent receiver, thus forcing real time all-optical processing. An unauthorized user, who does not possess knowledge on the phase mask, can only obtain a noisy and distorted signal, that cannot be improved by post-processing. On the other hand, the authorized user decodes the signal using an inverse spectral phase mask and achieves a substantial optical processing gain via multi-homodyne coherent detection. A transmission of 20 Gbps under negative -7.5 dB OSNR is demonstrated here, yielding error-free detection by the eligible user.

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.

A method for optical fiber equalization using neural network is proposed. The method uses digital up-conversion for processing the complex valued signal with significant reduction in network size without impact on equalization performance.

A gradual training paradigm for Recurrent Neural Network-based pre-distorter is proposed. Stochastic decomposition is used to separate nonlinearity and quantization noise features. Performance improvement of more than 6dB is presented.

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-speed optical communication systems may suffer from a combination of impairments such as memory effect and nonlinear behavior of the optoelectronic components. Nonlinear digital pre-distortion (DPD) is one of the well-known technique to alleviate these effects. As typical implementation of Volterra-based DPD is considered complex and consumes high power, more efficient orthogonal-based Volterra series representation has been proposed. Previous works offered ways to perform efficient grading of the most dominant dimensions based on the combination of the dimensions variances and the signal projection. Here, it is shown that normalization of the data dynamic range further improves this method and decreases significantly the number of required dimensions. Using normalization combined with the previous methods, maximizes the DPD performance by means of error vector magnitude (EVM) and bit error rate (BER), while minimizing the DPD complexity in the terms of required series dimensions. Extensive simulation and lab measurements indicate a potential saving of up to 87% in the number of dimensions with a negligible performance penalty.

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.