6 research outputs found
Intelligent Modulation Recognition System and its Implementation using MATLAB
Modulation type is one of the most significant qualities utilized in signal waveform recognizable proof and arrangement. ID of modulation has discovered applications in regions of observation, danger investigation and programming characterized radio. In this paper a calculation to distinguish the modulation plan of approaching sign within the sight of AWGN commotion is proposed. The calculation utilizes momentary highlights of the sign, for example, plentifulness, stage and recurrence. Measurable snapshots of these highlights are utilized to recognize ASK, PSK and FSK. The proposed calculation was reproduced and approved through recreation in MATLAB .The calculation shows a high distinguishing proof execution in satisfactory sign to-commotion proportion (SNR) extend
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
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Adaptive Coded Modulation Classification and Spectrum Sensing for Cognitive Radio Systems. Adaptive Coded Modulation Techniques for Cognitive Radio Using Kalman Filter and Interacting Multiple Model Methods
The current and future trends of modern wireless communication systems place heavy demands on fast data transmissions in order to satisfy end users’ requirements anytime, anywhere. Such demands are obvious in recent applications such as smart phones, long term evolution (LTE), 4 & 5 Generations (4G & 5G), and worldwide interoperability for microwave access (WiMAX) platforms, where robust coding and modulations are essential especially in streaming on-line video material, social media and gaming. This eventually resulted in extreme exhaustion imposed on the frequency spectrum as a rare natural resource due to stagnation in current spectrum management policies. Since its advent in the late 1990s, cognitive radio (CR) has been conceived as an enabling technology aiming at the efficient utilisation of frequency spectrum that can lead to potential direct spectrum access (DSA) management. This is mainly attributed to its internal capabilities inherited from the concept of software defined radio (SDR) to sniff its surroundings, learn and adapt its operational parameters accordingly. CR systems (CRs) may commonly comprise one or all of the following core engines that characterise their architectures; namely, adaptive coded modulation (ACM), automatic modulation classification (AMC) and spectrum sensing (SS).
Motivated by the above challenges, this programme of research is primarily aimed at the design and development of new paradigms to help improve the adaptability of CRs and thereby achieve the desirable signal processing tasks at the physical layer of the above core engines. Approximate modelling of Rayleigh and finite state Markov channels (FSMC) with a new concept borrowed from econometric studies have been approached. Then insightful channel estimation by using Kalman filter (KF) augmented with interacting multiple model (IMM) has been examined for the purpose of robust adaptability, which is applied for the first time in wireless communication systems. Such new IMM-KF combination has been facilitated in the feedback channel between wireless transmitter and receiver to adjust the transmitted power, by using a water-filling (WF) technique, and constellation pattern and rate in the ACM algorithm. The AMC has also benefited from such IMM-KF integration to boost the performance against conventional parametric estimation methods such as maximum likelihood estimate (MLE) for channel interrogation and the estimated parameters of both inserted into the ML classification algorithm. Expectation-maximisation (EM) has been applied to examine unknown transmitted modulation sequences and channel parameters in tandem. Finally, the non-parametric multitaper method (MTM) has been thoroughly examined for spectrum estimation (SE) and SS, by relying on Neyman-Pearson (NP) detection principle for hypothesis test, to allow licensed primary users (PUs) to coexist with opportunistic unlicensed secondary users (SUs) in the same frequency bands of interest without harmful effects. The performance of the above newly suggested paradigms have been simulated and assessed under various transmission settings and revealed substantial improvements