Signal Modulation Identification Based on Deep Learning in FBMC/OQAM Systems

Signal modulation identification (SMI) has always been one of hot issues in filter-bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM), which is usually implemented by the machine learning-based feature extraction. However, it is difficult for conventional methods to extract the signal feature, resulting in a limited probability of correct classification (PCC). To tackle this problem, we put forward a novel SMI method based on deep learning to identify FBMC/OQAM signals in this paper. It is noted that the block repetition is employed in the FBMC/OQAM system to achieve the imaginary interference cancelation. In the proposed deep learning-based SMI technique, the in-phase and quadrature samples of FBMC/OQAM signals are trained by the convolutional neural network. Subsequently, the dropout layer is designed to prevent overfilling and improve the identification accuracy. To evaluate the proposed scheme, extensive experiments are conducted by employing datasets with different modulations. The results show that the proposed method can achieve better accuracy than conventional methods.

Improved Salp Swarm Optimization Algorithm: Application in Feature Weighting for Blind Modulation Identification

In modulation identification issues, like in any other classification problem, the performance of the classification task is significantly impacted by the feature characteristics. Feature weighting boosts the performance of machine learning algorithms, particularly the class of instance-based learning algorithms such as the Minimum Distance (MD) classifier, in which the distance measure is highly sensitive to the magnitude of features. In this paper, we propose an improved version of the Salp Swarm optimization Algorithm (SSA), called ISSA, that will be applied to optimize feature weights for an MD classifier. The aim is to improve the performance of a blind digital modulation detection approach in the context of multiple-antenna systems. The improvements introduced to SSA mainly rely on the opposition-based learning technique. Computer simulations show that the ISSA outperforms the SSA as well as the algorithms that derive from it. The ISSA also exhibits the best performance once it is applied for feature weighting in the above context.

Task-dependent effects of nicotine treatment on auditory performance in young-adult and elderly human nonsmokers

Electrophysiological studies show that nicotine enhances neural responses to characteristic frequency stimuli. Previous behavioral studies partially corroborate these findings in young adults, showing that nicotine selectively enhances auditory processing in difficult listening conditions. The present work extended previous work to include both young and older adults and assessed the nicotine effect on sound frequency and intensity discrimination. Hypotheses were that nicotine improves auditory performance and that the degree of improvement is inversely proportional to baseline performance. Young (19–23 years old) normal-hearing nonsmokers and elderly (61–80) nonsmokers with normal hearing between 500 and 2000 Hz received nicotine gum (6 mg) or placebo gum in a single-blind, randomized crossover design. Participants performed three experiments (frequency discrimination, frequency modulation identification, and intensity discrimination) before and after treatment. The perceptual differences were analyzed between pre- and post-treatment, as well as between post-treatment nicotine and placebo conditions as a function of pre-treatment baseline performance. Compared to pre-treatment performance, nicotine significantly improved frequency discrimination. Compared to placebo, nicotine significantly improved performance for intensity discrimination, and the improvement was more pronounced in the elderly with lower baseline performance. Nicotine had no effect on frequency modulation identification. Nicotine effects are task-dependent, reflecting possible interplays of subjects, tasks and neural mechanisms.