In this thesis, we propose a novel architecture for hybrid radio frequency (RF)/free–space optics (FSO) wireless systems. Hybrid RF/FSO systems are attractive since the RF and FSO sub–systems are affected differently by weather and fading phenomena. We give a thorough introduction to the RF and FSO technology, respectively. The state of the art of hybrid RF/FSO systems is reviewed. We show that a hybrid system robust to different weather conditions is obtained by joint bit–interleaved coded modulation (BICM) of the bit streams transmitted over the RF and FSO sub–channels. An asymptotic performance analysis reveals that a properly designed convolutional code can exploit the diversity offered by the independent sub–channels. Furthermore, we develop code design and power assignment criteria and provide an efficient code search procedure. The cut–off rate of the proposed hybrid system is also derived and compared to that of hybrid systems with perfect channel state information at the transmitter. Simulation results show that hybrid RF/FSO systems with BICM outperform previously proposed hybrid systems employing a simple repetition code and selection diversity.

In this thesis, we propose a novel architecture for hybrid radio frequency (RF)/free–space optics (FSO) wireless systems. Hybrid RF/FSO systems are attractive since the RF and FSO sub–systems are affected differently by weather and fading phenomena. We give a thorough introduction to the RF and FSO technology, respectively. The state of the art of hybrid RF/FSO systems is reviewed. We show that a hybrid system robust to different weather conditions is obtained by joint bit–interleaved coded modulation (BICM) of the bit streams transmitted over the RF and FSO sub–channels. An asymptotic performance analysis reveals that a properly designed convolutional code can exploit the diversity offered by the independent sub–channels. Furthermore, we develop code design and power assignment criteria and provide an efficient code search procedure. The cut–off rate of the proposed hybrid system is also derived and compared to that of hybrid systems with perfect channel state information at the transmitter. Simulation results show that hybrid RF/FSO systems with BICM outperform previously proposed hybrid systems employing a simple repetition code and selection diversity.

In this thesis, we propose a novel architecture for hybrid radio frequency (RF)/free–space optics (FSO) wireless systems. Hybrid RF/FSO systems are attractive since the RF and FSO sub–systems are affected differently by weather and fading phenomena. We give a thorough introduction to the RF and FSO technology, respectively. The state of the art of hybrid RF/FSO systems is reviewed. We show that a hybrid system robust to different weather conditions is obtained by joint bit–interleaved coded modulation (BICM) of the bit streams transmitted over the RF and FSO sub–channels. An asymptotic performance analysis reveals that a properly designed convolutional code can exploit the diversity offered by the independent sub–channels. Furthermore, we develop code design and power assignment criteria and provide an efficient code search procedure. The cut–off rate of the proposed hybrid system is also derived and compared to that of hybrid systems with perfect channel state information at the transmitter. Simulation results show that hybrid RF/FSO systems with BICM outperform previously proposed hybrid systems employing a simple repetition code and selection diversity. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

With an increasing demand for rapid high-capacity internet, the telecommunication industry is constantly driven to explore and develop new technologies to ensure stable and reliable networks. To provide a competitive internet service in this growing market, proactive detection and prevention of disturbances are key elements for an operator. Therefore, analyzing network traffic for forecasting disturbances is a well-researched area. This study explores the advantages and drawbacks of implementing a long short-term memory model for forecasting codeword errors in a hybrid fiber-coaxial network. Also, the impact of using multivariate and univariate data for training the model is explored. The performance of the long short-term memory model is compared with a multilayer perceptron model. Analysis of the results shows that the long short-term model, in the vast majority of the tests, performs better than the multilayer perceptron model. This result aligns with the hypothesis, that the long short-term memory model’s ability to handle sequential data would be superior to the multilayer perceptron. However, the difference in performance between the models varies significantly based on the characteristics of the used data set. On the set with heavy fluctuations in the sequential data, the long short-term memory model performs on average 44% better. When training the models on data sets with longer sequences of similar values and with less volatile fluctuations, the results are much more alike. The long short-term model still achieves a lower error on most tests, but the difference is never larger than 7%. If a low error is the sole criterion, the long short-term model is the overall superior model. However, in a production environment, factors such as data storage capacity and model complexity should be taken into consideration. When training the models on multivariate and univariate datasets, the results are unambiguous. When training on all three features, ratios of uncorrectable and correctable codewords, and signal-to-noise ratio, the models always perform better. That is, compared to using uncorrectable codewords as the only training data. This aligns with the hypothesis, which is based on the know-how of hybrid fiber-coaxial experts, that correctable codewords and signal-to-noise ratio have an impact on the occurrence of uncorrectable codewords. / På grund av den ökade efterfrågan av högkvalitativt internet, så drivs telekomindustrin till att konsekvent utforska och utveckla nya teknologier som kan säkerställa stabila och pålitliga nätverk. För att kunna erbjuda konkurrenskraftiga internettjänster, måste operatörerna kunna förutse och förhindra störningar i nätverken. Därför är forskningen kring hur man analyserar och förutser störningar i ett nätverk ett väl exploaterat område. Denna studie undersökte för- och nackdelar med att använda en long short-term memory (LSTM) för att förutse kodordsfel i ett hybridfiber-koaxialt nätverk. Utöver detta undersöktes även hur multidimensionell träningsdata påverkade prestandan. I jämförelsesyfte användes en multilayer perceptron (MLP) och dess resultat. Analysen av resultaten visade att LSTM-modellen presterade bättre än MLP-modellen i majoriteten av de utförda testerna. Men skillnaden i prestanda varierade kraftigt, beroende på vilken datauppsättning som användes vid träning och testning av modellerna. Slutsatsen av detta är att i denna studie så är LSTM den bästa modellen, men att det inte går att säga att LSTM presterar bättre på en godtycklig datauppsättning. Båda modellerna presterade bättre när de tränades på multidimensionell data. Vidare forskning krävs för att kunna determinera om LSTM är den mest självklara modellen för att förutse kodordsfel i ett hybridfiber-koaxialt nätverk.

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