Blind Symbol Rate Estimation of Faster-than-Nyquist Signals Based on Higher-Order Statistics

Both faster-than-Nyquist (FTN) and cognitive radio go towards an efficient use of spectrum in radio communications systems at the cost of an added computational complexity at the receiver side. To gain the maximum potential from these techniques, non-data-aided receivers are of interest. In this paper, we use fourth-order statistics to perform blind symbol rate estimation of FTN signals. The estimator shows good performance results for moderate system's densities beyond the Nyquist rate and for a reasonable number of received samples

機械学習を用いたコグニティブ無線における変調方式識別に関する研究

The current spectrum allocation cannot satisfy the demand for future wireless communications, which prompts extensive studies in search of feasible solutions for the spectrum scarcity. The burden in terms of the spectral efficiency on the radio frequency terminal is intended to be small by cognitive radio (CR) systems that prefer low power transmission, changeable carrier frequencies, and diverse modulation schemes. However, the recent surge in the application of the CR has been accompanied by an indispensable component: the spectrum sensing, to avoid interference towards the primary user. This requirement leads to a complex strategy for sensing and transmission and an increased demand for signal processing at the secondary user. However, the performance of the spectrum sensing can be extended by a robust modulation classification (MC) scheme to distinguish between a primary user and a secondary user along with the interference identification. For instance, the underlying paradigm that enables a concurrent transmission of the primary and secondary links may need a precise measure of the interference that the secondary users cause to the primary users. An adjustment to the transmission power should be made, if there is a change in the modulation of the primary users, implying a noise oor excess at the primary user location; else, the primary user will be subject to interference and a collision may occur.Alternatively, the interweave paradigm that progresses the spectrum efficiency by reusing the allocated spectrum over a temporary space, requires a classification of the intercepted signal into primary and secondary systems. Moreover, a distinction between noise and interference can be accomplished by modulation classification, if spectrum sensing is impossible. Therefore, modulation classification has been a fruitful area of study for over three decades.In this thesis, the modulation classification algorithms using machine learning are investigated while new methods are proposed. Firstly, a supervised machine learning based modulation classification algorithm is proposed. The higher-order cumulants are selected as features, due to its robustness to noise. Stacked denoising autoencoders,which is an extended edition of the neural network, is chosen as the classifier. On one hand stacked pre-train overcomes the shortcoming of local optimization, on the other, denoising function further enhances the anti-noise performance. The performance of this method is compared with the conventional methods in terms of the classification accuracy and execution speed. Secondly, an unsupervised machine learning based modulation classification algorithm is proposed.The features from time-frequency distribution are extracted. Density-based spatial clustering of applications with noise (DBSCAN) is used as the classifier because it is impossible to decide the number of clusters in advance. The simulation reveals that this method has higher classification accuracy than the conventional methods. Moreover, the training phase is unnecessary for this method. Therefore, it has higher workability then supervised method. Finally, the advantages and dis-advantages of them are summarized.For the future work, algorithm optimization is still a challenging task, because the computation capability of hardware is limited. On one hand, for the supervised machine learning, GPU computation is a potential solution for supervised machine learning, to reduce the execution cost. Altering the modulation pool, the network structure has to be redesigned as well. On the other hand, for the unsupervised machine learning, that shifting the symbols to carrier frequency consumes extra computing resources.電気通信大学201

Post Conversion Correction of Non-Linear Mismatches for Time Interleaved Analog-to-Digital Converters

Time Interleaved Analog-to-Digital Converters (TI-ADCs) utilize an architecture which enables conversion rates well beyond the capabilities of a single converter while preserving most or all of the other performance characteristics of the converters on which said architecture is based. Most of the approaches discussed here are independent of architecture; some solutions take advantage of specific architectures. Chapter 1 provides the problem formulation and reviews the errors found in ADCs as well as a brief literature review of available TI-ADC error correction solutions. Chapter 2 presents the methods and materials used in implementation as well as extend the state of the art for post conversion correction. Chapter 3 presents the simulation results of this work and Chapter 4 concludes the work. The contribution of this research is three fold: A new behavioral model was developed in SimulinkTM and MATLABTM to model and test linear and nonlinear mismatch errors emulating the performance data of actual converters. The details of this model are presented as well as the results of cumulant statistical calculations of the mismatch errors which is followed by the detailed explanation and performance evaluation of the extension developed in this research effort. Leading post conversion correction methods are presented and an extension with derivations is presented. It is shown that the data converter subsystem architecture developed is capable of realizing better performance of those currently reported in the literature while having a more efficient implementation