Single-Frequency Network Terrestrial Broadcasting with 5GNR Numerology

L'abstract è presente nell'allegato / the abstract is in the attachmen

Artificial Intelligence Aided Receiver Design for Wireless Communication Systems

Physical layer (PHY) design in the wireless communication field realizes gratifying achievements in the past few decades, especially in the emerging cellular communication systems starting from the first generation to the fifth generation (5G). With the gradual increase in technical requirements of large data processing and end-to-end system optimization, introducing artificial intelligence (AI) in PHY design has cautiously become a trend. A deep neural network (DNN), one of the population techniques of AI, enables the utilization of its ‘learnable’ feature to handle big data and establish a global system model. In this thesis, we exploited this characteristic of DNN as powerful assistance to implement two receiver designs in two different use-cases. We considered a DNN-based joint baseband demodulator and channel decoder (DeModCoder), and a DNN-based joint equalizer, baseband demodulator, and channel decoder (DeTecModCoder) in two single operational blocks, respectively. The multi-label classification (MLC) scheme was equipped to the output of conducted DNN model and hence yielded lower computational complexity than the multiple output classification (MOC) manner. The functional DNN model can be trained offline over a wide range of SNR values under different types of noises, channel fading, etc., and deployed in the real-time application; therefore, the demands of estimation of noise variance and statistical information of underlying noise can be avoided. The simulation performances indicated that compared to the corresponding conventional receiver signal processing schemes, the proposed AI-aided receiver designs have achieved the same bit error rate (BER) with around 3 dB lower SNR

Machine Learning in Digital Signal Processing for Optical Transmission Systems

The future demand for digital information will exceed the capabilities of current optical communication systems, which are approaching their limits due to component and fiber intrinsic non-linear effects. Machine learning methods are promising to find new ways of leverage the available resources and to explore new solutions. Although, some of the machine learning methods such as adaptive non-linear filtering and probabilistic modeling are not novel in the field of telecommunication, enhanced powerful architecture designs together with increasing computing power make it possible to tackle more complex problems today. The methods presented in this work apply machine learning on optical communication systems with two main contributions. First, an unsupervised learning algorithm with embedded additive white Gaussian noise (AWGN) channel and appropriate power constraint is trained end-to-end, learning a geometric constellation shape for lowest bit-error rates over amplified and unamplified links. Second, supervised machine learning methods, especially deep neural networks with and without internal cyclical connections, are investigated to combat linear and non-linear inter-symbol interference (ISI) as well as colored noise effects introduced by the components and the fiber. On high-bandwidth coherent optical transmission setups their performances and complexities are experimentally evaluated and benchmarked against conventional digital signal processing (DSP) approaches. This thesis shows how machine learning can be applied to optical communication systems. In particular, it is demonstrated that machine learning is a viable designing and DSP tool to increase the capabilities of optical communication systems