An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Modelação não-linear de transistores de potência para RF e microondas

Doutoramento em Engenharia Electrotécnic

Modelação comportamental e pré-distorção digital de transmissores de rádio-frequência

Doutoramento em Engenharia ElectrotécnicaNos atuais sistemas de telecomunicações, os transmissores de rádio-frequência são desenvolvidos tendo maioritariamente em conta a eficiência da conversão da potência fornecida da fonte em potência de rádio-frequência. Este tipo de desenho resulta em amplificadores de potência com características de transmissão não-lineares, que distorcem severamente o envelope de informação no processo de amplificação, gerando distorção fora da banda. Para corrigir este problema utiliza-se um processo de compensação não linear, sendo que a pré-distorção digital se tem favorecido pela sua flexibilidade e precisão. Este método é tipicamente aplicado de uma forma cega, por força bruta até se obter a compensação desejada. No entanto, quando o método se mostra ineficaz, como se verificou em amplificadores de potência baseados em transístores de nitreto de gálio, é difícil saber o que modificar nos sistemas para os tornar de novo úteis. De forma a compreender e desenhar sistemas de pré-distorção digital robustos é necessário, por um lado, perceber o comportamento dos amplificadores de rádio-frequência, por outro, perceber as limitações e relações entre os modelos digitais e o comportamento real do amplificador. Nesse sentido, esta tese explora e descreve estas relações de forma a suportar a escolha de modelos de pré-distorção, desenvolve novos modelos baseados no comportamento dos transístores, e propõe métodos de caracterização para os amplificadores de RF.In current telecommunication systems, the main concern when developing the radio frequency transmitter is power efficiency. This type of design generally leads to a highly nonlinear transmission characteristic, mainly due to the radio frequency power amplifier. This nonlinear transmission severely distorts the information envelope, leading to spectral regrowth, out-of-band distortion. To correct this problem a nonlinear compensation process is employed. For this application, digital predistortion is generally favored for its flexibility and accuracy. Digital predistortion is mostly applied in a blind manner, using brute force until the desired compensation is achieved. Because of this, when the method fails, as it has in gallium nitride based power amplifiers, it is difficult to modify the system to achieve the desired results. To understand and design robust predistortion systems, it is both necessary to have knowledge of the power amplifiers’ behavior, on one hand, and understand the limitations and relations between the digital models and these behaviors, on the other. To do this, this thesis explores and describes these relationships, granting support to the digital predistortion model choice, it further develops new predistortion models based on the physics of the transistors’ behaviors, and it proposes methods for the characterization of radio frequency power amplifiers

Compensation of Physical Impairments in Multi-Carrier Communications

Among various multi-carrier transmission techniques, orthogonal frequency-division multiplexing (OFDM) is currently a popular choice in many wireless communication systems. This is mainly due to its numerous advantages, including resistance to multi-path distortions by using the cyclic prefix (CP) and a simple one-tap channel equalization, and efficient implementations based on the fast Fourier and inverse Fourier transforms. However, OFDM also has disadvantages which limit its use in some applications. First, the high out-of-band (OOB) emission in OFDM due to the inherent rectangular shaping filters poses a challenge for opportunistic and dynamic spectrum access where multiple users are sharing a limited transmission bandwidth. Second, a strict orthogonal synchronization between sub-carriers makes OFDM less attractive in low-power communication systems. Furthermore, the use of the CP in OFDM reduces the spectral efficiency and thus it may not be suitable for short-packet and low-latency transmission applications. Generalized frequency division multiplexing (GFDM) and circular filter-bank multi-carrier offset quadrature amplitude modulation (CFBMC-OQAM) have recently been considered as alternatives to OFDM for the air interface of wireless communication systems because they can overcome certain disadvantages in OFDM. Specifically, these two systems offer a flexibility in choosing the shaping filters so that the high OOB emission in OFDM can be avoided. Moreover, the strict orthogonality requirement in OFDM is relaxed in GFDM and CFBMC-OQAM which are, respectively, non-orthogonal and real-field orthogonal systems. Although a CP is also used in these two systems, the CP is added for a block of many symbols instead of only one symbol as in OFDM, which, therefore, improves the spectral efficiency. Given that the performance of a wireless communication system is affected by various physical impairments such as phase noise (PN), in-phase and quadrature (IQ) imbalance and imperfect channel estimation, this thesis proposes a number of novel signal processing algorithms to compensate for physical impairments in multi-carrier communication systems, including OFDM, GFDM and CFBMC-OQAM. The first part of the thesis examines the use of OFDM in full-duplex (FD) communication under the presence of PN, IQ imbalance and nonlinearities. FD communication is a promising technique since it can potentially double the spectral efficiency of the conventional half-duplex (HD) technique. However, the main challenge in implementing an FD wireless device is to cope with the self-interference (SI) imposed by the device's own transmission. The implementation of SI cancellation (SIC) faces many technical issues due to the physical impairments. In this part of research, an iterative algorithm is proposed in which the SI cancellation and detection of the desired signal benefit from each other. Specifically, in each iteration, the SI cancellation performs a widely linear estimation of the SI channel and compensates for the physical impairments to improve the detection performance of the desired signal. The detected desired signal is in turn removed from the received signal to improve SI channel estimation and SI cancellation in the next iteration. Results obtained show that the proposed algorithm significantly outperforms existing algorithms in SI cancellation and detection of the desired signal. In the next part of the thesis, the impact of PN and its compensation for CFBMC-OQAM systems are considered. The sources of performance degradation are first quantified. Then, a two-stage PN compensation algorithm is proposed. In the first stage, the channel frequency response and PN are estimated based on the transmission of a preamble, which is designed to minimize the channel mean squared error (MSE). In the second stage the PN compensation is performed using the estimate obtained from the first stage together with the transmitted pilot symbols. Simulation results obtained under practical scenarios show that the proposed algorithm effectively estimates the channel frequency response and compensates for the PN. The proposed algorithm is also shown to outperform an existing algorithm that implements iterative PN compensation when the PN impact is high. As a further development from the second part, the third part of the thesis considers the impacts of both PN and IQ imbalance and proposes a unified two-stage compensation algorithm for a general multi-carrier system, which can include OFDM, GFDM and CFBMC-OQAM. Specifically, in the first stage, the channel impulse response and IQ imbalance parameters are first estimated based on the transmission of a preamble. Given the estimates obtained from the first stage, in the second stage the IQ imbalance and PN are compensated in that order based on the pilot symbols for the rest of data transmission blocks. The preamble is designed such that the estimation of IQ imbalance does not depend on the channel and PN estimation errors. The proposed algorithm is then further extended to a multiple-input multiple-output (MIMO) system. For such a MIMO system, the preamble design is generalized so that the multiple IQ imbalances as well as channel impulse responses can be effectively estimated based on a single preamble block. Simulation results are presented and discussed in a variety of scenarios to show the effectiveness of the proposed algorithm

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, machine learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing, and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude this paper proposing new possible research directions