Orthogonal chirp division multiplexing: performance evaluation in the presence of phase noise

In Beyond 5G technologies, Terahertz communications will be used: frequency bands between 100 GHz and 10 THz will be exploited in order to have higher throughput and lower latency. Those frequency bands suffer from several impairments, and it is thought that phase noise is one of the most significant. Orthogonal Chirp Division Multiplexing (OCDM) might be used in Beyond 5G communications, thanks to its robustness to multipath fading: it outperforms Orthogonal Frequency Division Multiplexing (OFDM) systems. The aim of this thesis is to find a suitable model for describing phase noise in Terahertz communications, and to study the performance of an OCDM system affected by this impairment. After this, a simple compensation scheme is introduced, and the improvement that it provides is analysed. The thesis is organized as follow: in the first chapter Terahertz communications and Beyond 5G are introduced, in the second chapter phase noise is studied, in the third chapter OCDM is analysed, and in the fourth chapter numerical results are presented.

Predicting corticospinal excitability from pre-TMS EEG using convolutional neural networks

The amplitude of motor evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) of the primary motor cortex (M1) shows a large variability from trial to trial, although MEPs are evoked by the same repeated stimulus. A multitude of factors is believed to influence MEP amplitudes, such as cortical, spinal and motor excitability state. The goal of this work is to explore to which degree the variation in MEP amplitudes can be explained by the cortical state right before the stimulation. Specifically, we analyzed a dataset acquired on eleven healthy subjects comprising, for each subject, 840 single TMS pulses applied to the left M1 during acquisition of electroencephalography (EEG) and electromyography (EMG). An interpretable convolutional neural network, named SincEEGNet, was utilized to discriminate between low- and high-corticospinal excitability trials, defined according to the MEP amplitude, using in input the pre-TMS EEG. This data-driven approach enabled considering multiple brain locations and frequency bands without any a priori selection. Post-hoc interpretation techniques were adopted to enhance interpretation by identifying the more relevant EEG features for the classification. Results show that individualized classifiers successfully discriminated between low and high M1 excitability states in all participants. Outcomes of the interpretation methods suggest the importance of the electrodes situated over the TMS stimulation site, as well as the relevance of the temporal samples of the input EEG closer to the stimulation time. This novel decoding method allows causal investigation of the cortical excitability state, which may be relevant for personalizing and increasing the efficacy of therapeutic brain-state dependent brain stimulation (for example in patients affected by Parkinson’s disease).