Next generation >200 Gb/s multicore fiber short-reach networks employing machine learning

This work proposes and evaluates the use of machine learning (ML) techniques on >200 Gb/s short-reach systems employing weakly coupled multicore fiber (MCF) and Kramers-Kronig (KK) receivers. The short-reach systems commonly found in intra data centers (DC) connections demand low cost-efficient direct detection receivers (DD). The KK receivers allow the combination of higher modulation order, such as 16-QAM used in coherent systems, with the low complexity and low cost of DD. Thus, the use of KK receivers allows to increase the bit rate and spectral efficiency while maintaining the cost of DD systems as this is an important requirement in DC. The use of MCF allows to increase the system capacity as well as the system cable density, although the use of MCF induces additional distortion, known as inter-core crosstalk (ICXT), to the system. Thus, low complexity ML techniques such as k-means clustering, k nearest neighbor (KNN) and artificial neural network (ANN) (estimation feedforward neural network (FNN) and classification feedforward neural network) are proposed to mitigate the effects of random ICXT. The performance improvement provided by the k-means clustering, KNN and the two types of FNN techniques is assessed and compared with the system performance obtained without the use of ML. The use of estimation and classification FNN prove to significantly improve the system performance by mitigating the impact of the ICXT on the received signal. This is achieved by employing only 10 neurons in the hidden layer and four input features. It has been shown that k-means or KNN techniques do not provide performance improvement compared to the system without using ML. These conclusions are valid for direct detection MCF-based short-reach systems with the product between the skew (relative time delay between cores) and the symbol rate much lower than one (skew x symbol rate « 1). By employing the proposed ANNs, the system shows an improvement of approximately 12 dB on the ICXT level, for the same outage probability when comparing with the system without the use of ML. For the BER threshold of 10−1.8 and compared with the standard system operating without employing ML techniques, the system operating with the proposed ANNs show a received optical power improvement of almost 3 dB and a ICXT level improvement of approximately 9 dB when the mean BER is analized.Este trabalho propõe e avalia o uso de técnicas de machine learning (ML) em sistemas de curto alcance com ritmo binário superior a 200 Gb/s utilizando receptores Kramers-Kronig (KK) e fibras multinúcleo (MCF). Os sistemas de curto alcance usualmente encontrados em conexões intra-data centers (DC) exigem receptores de deteção direta (DD) de baixo custo. Os receptores KK permitem a combinação de sistemas de modulação de maior ordem, tais como o 16-QAM, usado em sistemas coerentes, com o baixo custo dos receptores DD. Portanto, o uso de receptores KK permite melhorar o ritmo binário e eficiência espectral e manter a eficiência de custo dos sistemas DD, o que é importante em DC. O uso de fibras multinúcleo permite o aumento da capacidade do sistema, bem como a densidade de cabos. No entanto, o uso de MCF introduz uma distorção adicional no sistema conhecida como inter-core crosstalk (ICXT). Para mitigar os efeitos do ICXT aleatório, são propostas e avaliadas técnicas de ML de baixa complexidade como k-means clustering, k nearest neighbor (KNN) e rede neuronais artificiais (ANN). O desempenho associado à utilização de algoritmos de ML (k-means, KNN e duas redes neuronais do tipo feedforward (FNN): uma para estimação e outra para classificação), é avaliado e comparado com o desempenho do sistema obtido sem o uso de ML. A utilização de FNN para estimação e classificação conduziram a uma melhoria significativa no desempenho do sistema, mitigando o impacto do ICXT no sinal recebido. Isso é alcançado com o uso de uma rede neuronal com uma arquitetura muito simples contendo quatro entradas e 10 neurónios na camada escondida. Foi demonstrado que os algoritmos k-means e KNN não proporcionam melhoria de desempenho em comparação com o sistema sem o uso de ML. Essas conclusões são válidas para sistemas DD de curto alcance baseados em MCF com o produto entre o skew (atraso relativo entre os núcleos) e o ritmo de símbolos muito menor que um (skew x symbol rate « 1). Com o uso das ANNs, o sistema apresenta uma melhoria de aproximadamente 12 dB na probabilidade de indisponibilidade quando comparado com o sistema sem o uso de ML. Para o limite de BER de 10−1.8 , e comparado com o sistema padrão sem o uso de técnicas de ML, o sistema com o uso de ANN mostra uma melhoria na potência óptica recebida de quase 3 dB e uma melhoria no nível de ICXT de aproximadamente 9 dB em relação ao BER médio

Design and Analysis of OFDM System for Powerline Based Communication

Research on digital communication systems has been greatly developed in the past few years and offers a high quality of transmission in both wired and wireless communication environments. Coupled with advances in new modulation techniques, Orthogonal Frequency Division Multiplexing (OFDM) is a well-known digital multicarrier communication technique and one of the best methods of digital data transmission over a limited bandwidth. The main aim of this research is to design an OFDM modem for powerline-based communication in order to propose and examine a novel approach in comparing the different modulation order, different modulation type, application of Forward Error Correction (FEC) scheme and also application of different noise types and applying them to the two modelled channels, Additive White Gaussian Noise (AWGN) and Powerline modelled channel. This is an attempt to understand and recognise the most suitable technique for the transmission of message or image within a communication system. In doing so, MATLAB and embedded Digital Signal Processing (DSP) systems are used to simulate the operation of virtual transmitter and receiver. The simulation results presented in this project suggest that lower order modulation formats (Binary Phase Shift Keying (BPSK) and 4-Quadrature Amplitude Modulation (QAM)), are the most preferred modulation techniques (in both type and order) for their considerable performance. The results also indicated that, Convolutional Channel Encoding (CCE)-Soft and Block Channel Encoding (BCE)-Soft are by far the best encoding techniques (in FEC type) for their best performance in error detection and correction. Indeed, applying these techniques to the two modelled channels has proven very successful and will be accounted as a novel approach for the transmission of message or image within a powerline based communication system

Clustering algorithms for Stokes space modulation format recognition

\u3cp\u3eStokes space modulation format recognition (Stokes MFR) is a blind method enabling digital coherent receivers to infer modulation format information directly from a received polarization-division-multiplexed signal. A crucial part of the Stokes MFR is a clustering algorithm, which largely influences the performance of the detection process, particularly at low signal-to-noise ratios. This paper reports on an extensive study of six different clustering algorithms: k-means, expectation maximization, density-based DBSCAN and OPTICS, spectral clustering and maximum likelihood clustering, used for discriminating between dual polarization: BPSK, QPSK, 8-PSK, 8-QAM, and 16-QAM. We determine essential performance metrics for each clustering algorithm and modulation format under test: minimum required signal-to-noise ratio, detection accuracy and algorithm complexity.\u3c/p\u3

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks