A methodology for scalable and concurrent real-time implementation of highly recurrent algorithms is presented and experimentally validated using the AWS-FPGA. This paper presents a parallel implementation of a KNN algorithm focused on the m-QAM demodulators using high-level synthesis for fast prototyping, parameterization, and scalability of the design. The proposed design shows the successful implementation of the KNN algorithm for interchannel interference mitigation in a 3 × 16 Gbaud 16-QAM Nyquist WDM system. Additionally, we present a modified version of the KNN algorithm in which comparisons among data symbols are reduced by identifying the closest neighbor using the rule of the 8-connected clusters used for image processing. Real-time implementation of the modified KNN on a Xilinx Virtex UltraScale+ VU9P AWS-FPGA board was compared with the results obtained in previous work using the same data from the same experimental setup but offline DSP using Matlab. The results show that the difference is negligible below FEC limit. Additionally, the modified KNN shows a reduction of operations from 43 percent to 75 percent, depending on the symbol’s position in the constellation, achieving a reduction $47.25\%$ reduction in total computational time for 100 K input symbols processed on 20 parallel cores compared to the KNN algorithm.

A hybrid-coding scheme based on Multi-Pulse Position Modulation (M-PPM) and Pulse Width Modulation (PWM) is proposed and experimentally validated for simultaneous control of illumination and data transmission in a visible light communication system VLC (Visible Light Communications). The implemented VLC system complies with the dual functionality of indoor lighting and data transmission using low cost components. The codification implemented allows data transmission at a constant speed despite the lighting levels supported by the VLC system. The proposed and experimentally validated coding solves the problem of the interdependence between the data transmission rate and the variation of the lighting levels. Such interdependence is caused by the variation of the useful cycle available for data transmission after the lighting control performed by PWM.In this work, the feasibility of the implementation of the proposed scheme is demonstrated using low cost elements and commercial photodetectors. A constant transmission speed of 30 kbit/s was obtained with two 10W RGB LEDs at 0.9 meters from the OPT101 photodetector from Texas Instruments, with a range of illumination ranging from 25\% to 85\% of the possible luminous power of the LED sources.

<!-- *** Custom HTML *** --> We propose two methods based on machine learning algorithms to estimate the level of spectral overlapping in a specific optical channel, without information of adjacent channels in Nyquist-wavelength division multiplexing (WDM) systems. The first method uses the fuzzy c-means (FCM) clustering algorithm to relate the membership degrees of the FCM matrix with the level of spectral overlapping in frames of 10 k symbols that relied on the k-nearest neighbors algorithm. The second method uses the density-based spatial clustering of application with noise algorithm to identify the level of spectral overlapping based on the number of symbols classified as noise (outliers) as well as the number of extra clusters found in a constellation diagram, resulting in an overlapping index. Both methods were experimentally verified in a 3 × 16 Gbaud 16-QAM Nyquist-WDM system with different channel spacing. Knowing a priori the level of OSNR, results showed accuracy percentages up to 91% and up to 100% by the first proposed method in a multiclass and a binary classification, respectively. Moreover, the second method can achieve a percentage estimation up to 100% when optical channels are overlapped more than 12.5%. Thereby, both methods could be implemented in monitoring tools for incoming gridless optical transmission systems.

We present the approach of cognition applied to heterogeneous optical networks developed in the framework of the EU project CHRON: Cognitive Heterogeneous Reconfigurable Optical Network. We introduce and discuss in particular the technologies and techniques that will enable a cognitive optical network to observe, act, learn and optimizes its performance, taking into account its high degree of heterogeneity with respect to quality of service, transmission and switching techniques.

The rising demand for bandwidth in optical communication networks has led to the need for more efficient solutions for spectrum allocation. This article presents a solution to enhance the capacity and efficiency of passive optical networks (PON) using optical microring resonators and dynamic spectrum allocation. The solution relies on wavelength division multiplexing (WDM). It proposes using a support vector machine (SVM) and a Routing, Modulation Level, and Spectrum Assignment (RMLSA) method to manage spectrum allocation based on the bandwidth and distance of multiple requests. The network employs a pulse shaper to physically allocate the spectrum, allowing for the separation of the spectrum generated by the microring resonators into different wavelengths or wavelength ranges (super-channel). Additionally, the SVM and RMLSA algorithms regulate the pulse shaper to execute the allocation. This photonic network achieves improved spectrum utilization and reduces the network blocking probability. Our proposal shows that we successfully addressed 1090 requests with a zero blocking probability, accounting for 81% of the total requests. These request scenarios can simultaneously accommodate up to 200 requests, with a maximum bandwidth of 31 THz. This highlights the efficacy of our approach in efficiently managing requests with substantial processing capacity.

Radio over Fiber (RoF) systems have been proposed as a promising solution for transmitting radiofrequency signals at high data rates over long distances. To reach data rates in the Gbps range, studies indicate using the W-Band (75 -110 GHz). However, in this frequency band, chromatic dispersion becomes an issue that increases the bit-error-rate. This paper presents a novel digital dispersion monitoring technique for RoF systems based on asynchronous histogram analysis. This method quantifies the intensity level of the distortion of a radiofrequency demodulated signal by a dispersion factor. This dispersion factor is calculated using an enhanced clustering ap-proach, which carries out a Gaussian fitting technique through the expectation-maximization algorithm. Dispersion monitoring was performed on radiofrequency transmission simulations using non-return-to-zero and binary phase-shift keying modulated signals over 80 km of optical fiber at 60, 75 and 100 GHz. The bit error rate is estimated and compared to the dispersion factor, showing that the behavior of the dispersion effects are not proportional to the increase of carrier frequency, bit rate and distances. This novel monitoring method can be used to estimate the feasibility of RoF systems for future hybrid networks under specific transmission parameters such as fiber length, modulation format, and carrier frequency.