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

衛星受信機のための動的部分再構成型復調器の設計と実装

九州工業大学博士学位論文 学位記番号：工博甲第461号 学位授与年月日：平成30年9月21日1: Introduction|2: Background and Literature Review|3: Dynamic Partial Reconfigurable Demodulation System – Classification|4: DPRDS – DPR|5: ICAP Multiple Access by DPRDS and SEU Mitigation Systems|6: Conclusion and Future Perspective九州工業大学平成30年

Automatic Modulation Classification in Mobile OFDM Systems with Adaptive Modulation

Adaptive modulation is an efficient way to combat the effects of deep fades in broadband orthogonal frequency division multiplexing (OFDM) systems with time-varying multipath channels, where modulation schemes are adapted to the current channel state. Bandwidth efficient modulation schemes are applied on subcarriers with high channel quality, while robust modulation schemes or even no modulation is preferred for subcarriers in deep fades. The resulting benefit in terms of required transmit power was demonstrated for a fixed data rate in the literature, where a gain of 5 · · · 15 dB was recorded for a BER of 0.001 over the OFDM system with a fixed modulation. In literature, several algorithms for adaptive modulation have been proposed with different emphasis on bandwidth efficiency and implemental complexity. In this thesis, the algorithm proposed by Chow is used. A main drawback of adaptive modulation is that it requires the adapted modulation schemes to be provided at the receiver to enable demodulation. Traditionally, this information is provided in forms of explicit signalling, which reduces the bandwidth efficiency due to the signalling overhead. In the thesis, proposals are developed to reduce this undesirable overhead. These proposals exploit the correlation properties inherently existing in the transmission channel in both time and frequency domain, which leads to memory effects in the signalling source. State-dependent Huffman coding schemes are then applied to reduce the redundancy resulting from these memory effects. This signalling overhead can be totally eliminated by automatic modulation classification (AMC). In the past, AMC was mainly of interest in military fields like threat analysis and electronic surveillance, where no prior knowledge about the used modulation scheme is available. The received signal is the single information source for classification. Under such circumstance, maximum likelihood (ML) based AMC provides the optimum solution in the sense that the classification error probability is minimized. Nowadays, AMC is drawing more and more research interest also in civilian applications like systems with adaptive modulation, where certain co-operations are organized as in the system considered in this thesis. These co-operations provide certain prior information, which can be utilized to improve the classification reliability. Consequently, the ML based approach does not deliver the minimum error probability any more. Investigations have to be conducted to verify how much the performance can be improved by incorporating this prior information into the AMC algorithm. As one focus in this thesis, a AMC algorithm is developed, which is potentially able to minimize the classification error probability again. Another focus is to reduce the implemental complexity to enable the application of AMC in systems with high time requirements like real-time systems. In the last part of the thesis, comparisons are performed between these two approaches, namely explicit signalling and signalling-free AMC, in terms of the end-to-end packet error probability. To ensure a fair comparison, the net data rate is always maintained as a constant in both operation modes

Advances in parameter estimation, source enumeration, and signal identification for wireless communications

Parameter estimation and signal identification play an important role in modern wireless communication systems. In this thesis, we address different parameter estimation and signal identification problems in conjunction with the Internet of Things (IoT), cognitive radio systems, and high speed mobile communications. The focus of Chapter 2 of this thesis is to develop a new uplink multiple access (MA) scheme for the IoT in order to support ubiquitous massive uplink connectivity for devices with sporadic traffic pattern and short packet size. The proposed uplink MA scheme removes the Media Access Control (MAC) address through the signal identification algorithms which are employed at the gateway. The focus of Chapter 3 of this thesis is to develop different maximum Doppler spread (MDS) estimators in multiple-input multiple-output (MIMO) frequency-selective fading channel. The main idea behind the proposed estimators is to reduce the computational complexity while increasing system capacity. The focus of Chapter 4 and Chapter 5 of this thesis is to develop different antenna enumeration algorithms and signal-to-noise ratio (SNR) estimators in MIMO timevarying fading channels, respectively. The main idea is to develop low-complexity algorithms and estimators which are robust to channel impairments. The focus of Chapter 6 of this thesis is to develop a low-complexity space-time block codes (STBC)s identification algorithms for cognitive radio systems. The goal is to design an algorithm that is robust to time-frequency transmission impairments

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion