With the development of voice synthesis technology using deep learning, voice synthesis research that expresses the characteristics and emotions of speakers is actively being conducted. Current technology does not satisfactorily express various emotions and characteristics for speakers with very low or high vocal ranges and for speakers with dialects. In this paper, we propose mel-spectrogram image transfer (MIST)-Tacotron, a Tacotron 2-based speech synthesis model that adds a reference encoder with an image style transfer module. The proposed method is a technique that adds image style transfer to the existing Tacotron 2 model and extracts the speaker’s feature from the reference mel-spectrogram using a pre-trained deep learning model. Through the extracted feature, the style such as pitch, tone, and duration of the speaker are trained to express the style and emotion of the speaker more clearly. To extract the speaker’s style independently from the speaker’s timbre and emotion, the ID value for the speaker and the ID value for the emotional state were used as inputs. Performance is evaluated by F0 voiced error (FVE), F0 gross pitch error (F0 GPE), mel-cepstral distortion (MCD), band aperiodicity distortion (BAPD), voiced/unvoiced error (VUVE), false positive rate (FPR), and false negative rate (FNR). The performance of the proposed model was observed to have lower error values than the existing models, GST (Global Style Token) Tacotron and VAE (Variational Autoencoder) Tacotron. As a result of measuring mean opinion score (MOS), the sound quality of the proposed model received the highest score in terms of emotional expression and speaker style reflection.

This paper proposes a neural 5G traffic generation model and a methodology for calculating the spectrum requirements of private 5G networks to provide various industrial communication services. To accurately calculate the spectral requirements, it is necessary to analyze the actual data volume and traffic type of industrial cases. However, because there is currently no suitable traffic model to test loads in private 5G networks, we have developed a generative adversarial network (GAN)-based traffic generator that can generate realistic traffic by learning actual traffic traces collected by mobile network operators. In addition, in the case of industrial applications, probability-based traffic models were used in parallel as there were not enough real data to be learned. The proposed 5G traffic generation model is combined with the proposed 5G spectrum calculation methodology, enabling more accurate spectrum requirements calculation through traffic simulation similar to a real-life environment. In this paper, the spectrum requirements are calculated differently according to two types of duplexing, namely frequency division duplexing (FDD) and time division duplexing (TDD). As a guide for companies aiming to provide advanced wireless connectivity for a wide variety of vertical industries using 5G networks, eight use cases defined in the 5G Alliance for Connected Industries and Automation (ACIA) white paper were simulated. The spectrum requirements were calculated under various simulation conditions considering varying traffic loads, deployment scenarios, and duplexing types. Various simulation results confirmed that a bandwidth of at least 22.0 MHz to a maximum of 397.8 MHz is required depending on the deployment scenario.

With the fast proliferation of mobile Internet, the wireless community has been increasingly looking for a framework that can provide seamless mobility. In this paper, we propose a fast cross-layer handover scheme based on movement prediction in mobile WiMAX environment. Prediction is achieved by linear regression model with keeping track of the signal strength of mobile users. With the help of the prediction, layer-3 handover activities are able to occur prior to layer-2 handover, and therefore, total handover latency can be reduced. The experiments conducted with system parameters and propagation model defined by WiMAX Forum demonstrate that the proposed method predicts the future signal level accurately and reduces the total handover latency.

The recurrent neural network (RNN) model, which is a deep-learning network that can memorize past information, is used in this paper to memorize continuous movements in indoor positioning to reduce positioning error. To use an RNN model in Wi-Fi-fingerprint based indoor positioning, data set must be sequential. However, Wi-Fi fingerprinting only saves the received signal strength indicator for a location, so it cannot be used as RNN data. For this reason, we propose a movement path data generation technique that generates data for an RNN model for sequential positioning from Wi-Fi fingerprint data. Movement path data can be generated by creating an adjacency list for Wi-Fi fingerprint location points. However, creating an adjacency matrix for all location points requires a large amount of computation. This problem is solved by dividing indoor environment by K-means clustering and creating a cluster transition matrix based on the center of each cluster.

To allow rapid growth of the number of base stations, reducing the energy consumption of the stations, as the main energy consumers in cellular networks, has become an important research topic. In this paper, we attempt to find an adaptive cell zooming method to reduce the energy consumption of base stations. The cell zooming mechanism was formulated as an optimization problem with consideration of varying traffic patterns and interference, as well as the service availability of the whole area. Simulations were then conducted to verify the performance of the proposed cell zooming method. The simulations considered varying traffic conditions, both timely and spatially, in traditional 19-cell configuration. The proposed scheme demonstrated reduction of energy consumption of up to 4.72 times for urban environments and 3.78 times for rural environments against traditional static cell operation.