Conventional approaches to statistical parametric speech synthesis use context-dependent hidden Markov models (HMMs) clustered using decision trees to generate speech parameters from linguistic features. However, decision trees are not always appropriate to model complex context dependencies of linguistic features efficiently. An alternative scheme that replaces decision trees with deep neural networks (DNNs) was presented as a possible way to overcome the difficulty. By training the network to represent high-dimensional feedforward dependencies from linguistic features to acoustic features, DNN-based speech synthesis systems convert a text into a speech. To improved the naturalness of the synthesized speech, this paper presents a novel pre-training method for DNN-based statistical parametric speech synthesis systems. In our method, a deep relational model (DRM), which represents a joint probability of two visible variables, is applied to describe the joint distribution of acoustic and linguistic features. As with DNNs, a DRM consists several hidden layers and two visible layers. Although DNNs represent feedforward dependencies from one visible variables (inputs) to other visible variables (outputs), a DRM has an ability to represent the bidirectional dependencies between two visible variables. During the maximum-likelihood (ML) -based training, the model optimizes its parameters (connection weights between two adjacent layers, and biases) of a deep architecture considering the bidirectional conversion between 1) acoustic features given linguistic features, and 2) linguistic features given acoustic features generated from itself. Owing to considering whether the generated acoustic features are recognizable, our method can obtain reasonable parameters for speech synthesis. Experimental results in a speech synthesis task show that pre-trained DNN-based systems using our proposed method outperformed randomly-initialized DNN-based systems, especially when the amount of training data is limited. Additionally, speaker-dependent speech recognition experimental results also show that our method outperformed DNN-based systems, by setting the initial parameters of our method are the same as that in the synthesis experiments.

Power line communication (PLC) networks play an important role in home networks and in next generation hybrid networks, which provide higher data rates (Gbps) and easier connectivity. The standard medium access control (MAC) protocol of PLC networks, IEEE 1901, uses a special carrier sense multiple access with collision avoidance (CSMA/CA) mechanism, in which the deferral counter technology is introduced to avoid unnecessary collisions. Although PLC networks have achieved great commercial success, MAC layer analysis for IEEE 1901 PLC networks received limited attention. Until now, a few studies used renewal theory and strong law of large number (SLLN) to analyze the MAC performance of IEEE 1901 protocol. These studies focus on saturated conditions and neglect the impacts of buffer size and traffic rate. Additionally, they are valid only for homogeneous traffic. Motivated by these limitations, we develop a unified and scalable analytical model for IEEE 1901 protocol in unsaturated conditions, which comprehensively considers the impacts of traffic rate, buffer size, and traffic types (homogeneous or heterogeneous traffic). In the modeling process, a multi-layer discrete Markov chain model is constructed to depict the basic working principle of IEEE 1901 protocol. The queueing process of the station buffer is captured by using Queueing theory. Furthermore, we present a detailed analysis for IEEE 1901 protocol under heterogeneous traffic conditions. Finally, we conduct extensive simulations to verify the analytical model and evaluate the MAC performance of IEEE 1901 protocol in PLC networks.

In this letter, we investigate the physical layer security in multi-user multi-relay networks, where each relay is not merely a traditional helper, but at the same time, can become a potential eavesdropper. We first propose an efficient low-complexity user and relay selection scheme to significantly reduce the amount of channel estimation as well as the amount of potential links for comparison. For the proposed scheme, we derive the closed-form expression for the lower bound of ergodic secrecy rate (ESR) to evaluate the system secrecy performance. Simulation results are provided to verify the validity of our expressions and demonstrate how the ESR scales with the number of users and relays.

In order to cope with recent growth of mobile data traffic and emerging various services, world-wide system trials for the fifth-generation (5G) mobile communication system that dramatically extends capability of the fourth-generation mobile communication system are being performed to launch its commercial service in 2020. In addition, research and development of new radio access technologies for 5G evolution and beyond 5G systems are beginning to be made all over the world. This paper introduces our recent activities on 5G transmission experiments that aim to validate Massive MIMO technologies using higher frequency bands such as SHF/EHF bands, that is, 5G experimental trials. Recent results of 5G system trials to create new services and applications in 5G era in cooperation with partners in vertical industries are also introduced.

The fifth generation mobile communication system (5G) is designed to have new radio capabilities to support not only conventional enhanced Mobile Broadband (eMBB) communications but also new machine type communications such as Ultra-Reliable Low-Latency communications (URLLC) and massive Machine Type communications (m-MTC). In such new areas of URLLC and m-MTC, mobile operators need to explore new use cases and/or applications together with vertical industries, the industries which are potential users of 5G, in order to fully exploit the new 5G capabilities. Intelligent Transport System (ITS), including automated driving, is one of the most promising application areas of 5G since it requires both ultra-reliable and low-latency communications. We are actively working on the research and development of truck platooning as a new 5G application. We have developed a field trial system for vehicular-to-network (V2N) communications using 5G prototype equipment and actual large-size trucks in order to assess 5G capabilities, including ultra-low-latency, in automotive test courses in the field. This paper discusses the fundamental performance evaluation required for vehicular communications between platooning trucks, such as low-latency message communication for vehicle control and low-latency video monitoring of following platooning truck vehicles. The paper also addresses the field evaluation results of 5G V2N communications in a rural area. It clarifies the fundamental radio propagation issues at the leading and the following vehicles in truck platooning for V2N communications, and discusses the impact of the radio propagation over a road to the over-the-air transmission performance of 5G V2N communications.

Towards furthering the industrial revolution, the concept of a new cellular network began to be drawn up around 2010 as the fifth generation (5G) mobile wireless communication system. One of the main differences between the fourth generation (4G) mobile communication system Long Term Evolution (LTE) and 5G new radio (NR) is the frequency bands utilized. 5G NR assumes higher frequency bands. Effective utilization of the higher frequency bands needs to resolve the technical issue of the larger path-loss. Massive multiple-input multiple-output (Massive MIMO) beamforming (BF) technology contributes to overcome this problem, hence further study of Massive MIMO BF for each frequency band is necessary toward high-performance and easy implementation. In this paper, then, we propose a Massive MIMO method with fully-digital BF based on two-tap precoding for low super high frequency (SHF) band downlink (DL) transmissions (called as Digital FBCP). Additionally, three intersite coordination algorithms for Digital FBCP are presented for multi-site environments and one of the three algorithms is enhanced. It is shown that Digital FBCP achieves better throughput performance than a conventional algorithm with one-tap precoding. Considering performance of intersite coordination as well, it is concluded that Digital FBCP can achieve around 5 Gbps in various practical environments.

Field trials of the fifth-generation (5G) mobile communication system using 28GHz band at which almost 1GHz bandwidth will be available have been performed all over the world. To realize large coverage with such a high frequency band, beamforming by Massive MIMO (Multiple Input Multiple Output) is necessary to compensate the large path loss. Furthermore, beam tracking which adaptively changes beam direction according to user location, is an important function to support user mobility. In previous works, field trials in subway environment at 25GHz band was carried out, but only fixed beam were employed. On the other hand, the field trials result of 28 GHz-band 5G transmission employing beam tracking in the road environment has been reported. Therefore, we conducted 5G field trials in the actual railway environment using 28GHz band experimental equipment employing beam tracking. This paper reveals the downlink performance achieved by using railway cars traveling at 90km/h. In addition, we show how mobile stations position in the railway car affects the performance of 5G transmission.

This paper presents results of outdoor experiments conducted in the 39-GHz band. In particular, assuming mobile communications such as the fifth generation mobile communications (5G) and beyond, we focus on achieving 1Gbit/s or greater throughput at transmission distances exceeding 1km in the experiments. In order to enhance the data rate and capacity, the use of higher frequency bands above 6GHz for mobile communications is a new and important technical challenge for 5G and beyond. To extend further the benefits of higher frequency bands to various scenarios, it is important to enable higher frequency bands to basically match the coverage levels of existing low frequency bands. Moreover, mobility is important in mobile communications. Therefore, we assume the 39-GHz band as a candidate frequency for 5G and beyond and prepare experimental equipment that utilizes lens antenna and beam tracking technologies. In the experiments, we achieve the throughput values of 2.14Gbit/s at the transmission distance of 1850m and 1.58Gbit/s at 20-km/h mobility. Furthermore, we show the possibility of achieving high throughput even under non-line-of-sight conditions. These experimental results contribute to clarifying the potential for the 39-GHz band to support gigabit-per-second class data rates while still providing coverage and supporting mobility over a coverage area with distance greater than 1km.

In fifth-generation mobile communications systems (5G), grant-free non-orthogonal multiple access (NOMA) schemes have been considered as a way to accommodate the many wireless connections required for Internet of Things (IoT) devices. In NOMA schemes, both system capacity enhancement and transmission protocol simplification are achieved, and an overload test of more than one hundred percent of the transmission samples over conducted. Multi-user shared multiple access (MUSA) has been proposed as a representative scheme for NOMA. However, the performance of MUSA has not been fully analyzed nor compared to other NOMA or orthogonal multiple access schemes. Therefore, in this study, we theoretically and numerically analyze the performance of MUSA in uplink fading environments and compare it with orthogonal frequency division multiple access (OFDMA), space division multiple access-based OFDMA, low-density signature, and sparse code multiple access. The characteristics and superiority of MUSA are then clarified.

This paper proposes frequency domain precoding vector switching (PVS) transmit diversity for synchronization signals to achieve fast physical cell identity (PCID) detection for the narrowband (NB)-Internet-of-Things (IoT) radio interface. More specifically, we propose localized and distributed frequency domain PVS transmit diversity schemes for the narrowband primary synchronization signal (NPSS) and narrowband secondary synchronization signal (NSSS), and NPSS and NSSS detection methods including a frequency offset estimation method suitable for frequency domain PVS transmit diversity at the receiver in a set of user equipment (UE). We conduct link-level simulations to compare the detection probabilities of NPSS and NSSS, i.e., PCID using the proposed frequency domain PVS transmit diversity schemes, to those using the conventional time domain PVS transmit diversity scheme. The results show that both the distributed and localized frequency domain PVS transmit diversity schemes achieve a PCID detection probability almost identical to that of the time domain PVS transmit diversity scheme when the effect of the frequency offset due to the frequency error of the UE temperature compensated crystal oscillator (TCXO) is not considered. We also show that for a maximum frequency offset of less than approximately 8 kHz, localized PVS transmit diversity achieves almost the same PCID detection probability. It also achieves a higher PCID detection probability than one-antenna transmission although it is degraded compared to the time domain PVS transmit diversity when the maximum frequency offset is greater than approximately 10 kHz.

Wavelength-division multiplexing multicore fibers can transmit a large amount of information over one fiber, and high-density core allocations enable a large number of fiber lines to be deployed in limited spaces. However, inter-core crosstalk degrades the signal in these systems. This paper describes the design of a frequency interleaving scheme for a 7-core hexagonal multicore fiber. Interleaving schemes shift signal spectra between neighboring cores to reduce the signal degradation caused by inter-core crosstalk. The channel frequency allocation that most efficiently lowers the bit error rate is numerically determined in this study. The results indicate that the optimum frequency interleaving improves the allowable crosstalk ratio by 6.3 dB for QPSK signals, demonstrating its potential for improving wavelength-division multiplexing multicore fiber transmission systems.

This letter presents a computational complexity reduction technique for space diversity based spectrum sensing when the number of receive antennas is greater than three (NR≥3 where NR is the number of receive antenna). The received signals are combined with phase inversion so as to not attenuate the combined signal, and a statistic for signal detection is computed from the combined signal. Because the computation of only one statistic is required regardless of the number of receive antenna, the complexity can be reduced. Numerical examples and simple analysis verify the effectiveness of the presented technique.

We realize a multicore erbium-doped fiber amplifier (MC-EDFA) with 2dB optical gain improvement (average) by recycling the residual 0.98μm pump light from the MC-EDF output. Eight-channel per core wavelength division multiplexed (WDM) Nyquist PM-16QAM optical signal amplification is demonstrated over a 40-minute period. Furthermore, we demonstrate the proposed MC-EDFA's stability by using it to amplify a Nyquist PM-16QAM signal and evaluating the resulting Q-factor variation. We found that our scheme contributes to reducing the total power consumption of MC-EDFAs in spatial division multiplexing (SDM)/WDM networks by up to 33.5%.

Flash storage suffers from severe performance degradation due to its inherent internal synchronization overhead. Especially, flushing an L2P (logical address to physical address) mapping table significantly contributes to the performance degradation. To relieve the problem, we propose an efficient L2P mapping table management scheme on the flash storage, which works along with a small-sized NVRAM. It flushes L2P mapping table from DRAM to NVRAM or flash memory selectively. In our experiments, the proposed scheme shows up to 9.37× better performance than conventional schemes.

Nowadays, malware is a serious threat to the Internet. Traditional signature-based malware detection method can be easily evaded by code obfuscation. Therefore, many researchers use the high-level structure of malware like function call graph, which is impacted less from the obfuscation, to find the malware variants. However, existing graph match methods rely on approximate calculation, which are inefficient and the accuracy cannot be effectively guaranteed. Inspired by the successful application of graph convolutional network in node classification and graph classification, we propose a novel malware similarity metric method based on graph convolutional network. We use graph convolutional network to compute the graph embedding vectors, and then we calculate the similarity metric of two graph based on the distance between two graph embedding vectors. Experimental results on the Kaggle dataset show that our method can applied to the graph based malware similarity metric method, and the accuracy of clustering application with our method reaches to 97% with high time efficiency.

Urban area suffers severe multipath effects due to its complex infrastructure environment and sector antenna is a popular choice as a base station antenna in those areas. Within sector antennas, omni cell antenna is utilized as supporting antenna to cover low reception areas between them. This paper proposes a slant 45° dual polarized omnidirectional antenna to operate as the omni cell antenna in those environments. The frequency band covers the IMT band, ranging from 1920MHz to 2170MHz with directivity focusing in horizontal plane. The antenna structure consists of a loop slot antenna array as excitation element which is placed inside a cylindrical slot antenna as parasitic element. Good performance is achieved in both S-parameter and directivity results, with a gain of more than 4 dBi and a gain difference of less than 1.5dB. The measurement results also agree well with the simulation results and the final design confirms that the proposed antenna works effectively as a slant ±45 ° dual polarized omnidirectional antenna.

Since software becomes more complex during its life cycle, the verification cost becomes higher, especially for such methods which are using model checking in general and assume-guarantee reasoning in specific. To address the problem of reducing the assume-guarantee verification cost, this paper presents a method to generate locally minimum and strongest assumptions for verification of component-based software. For this purpose, we integrate a variant of membership queries answering technique to an algorithm which considers candidate assumptions that are smaller and stronger first, larger and weaker later. Because the algorithm stops as soon as it reaches a conclusive result, the generated assumptions are the locally minimum and strongest ones. The correctness proof of the proposed algorithm is also included in the paper. An implemented tool, test data, and experimental results are presented and discussed.

Refinement-based formal specification is a promising approach to the increasing complexity of software systems, as demonstrated in the formal method Event-B. It allows stepwise modeling and verifying of complex systems with multiple steps at different abstraction levels. However, making changes is more difficult, as caution is necessary to avoid breaking the consistency between the steps. Judging whether a change is valid or not is a non-trivial task, as the logical dependency relationships between the modeling elements (predicates) are implicit and complex. In this paper, we propose a method for analyzing the impact of the changes of Event-B. By attaching labels to modeling elements (predicates), the method helps engineers understand how a model is structured and what needs to be modified to accomplish a change.

The scale of scientific data generated by experimental facilities and simulations in high-performance computing facilities has been proliferating with the emergence of IoT-based big data. In many cases, this data must be transmitted rapidly and reliably to remote facilities for storage, analysis, or sharing, for the Internet of Things (IoT) applications. Simultaneously, IoT data can be verified using a checksum after the data has been written to the disk at the destination to ensure its integrity. However, this end-to-end integrity verification inevitably creates overheads (extra disk I/O and more computation). Thus, the overall data transfer time increases. In this article, we evaluate strategies to maximize the overlap between data transfer and checksum computation for astronomical observation data. Specifically, we examine file-level and block-level (with various block sizes) pipelining to overlap data transfer and checksum computation. We analyze these pipelining approaches in the context of GridFTP, a widely used protocol for scientific data transfers. Theoretical analysis and experiments are conducted to evaluate our methods. The results show that block-level pipelining is effective in maximizing the overlap mentioned above, and can improve the overall data transfer time with end-to-end integrity verification by up to 70% compared to the sequential execution of transfer and checksum, and by up to 60% compared to file-level pipelining.

The traditional recommendation system (RS) can learn the potential personal preferences of users and potential attribute characteristics of items through the rating records between users and items to make recommendations.However, for the new items with no historical rating records,the traditional RS usually suffers from the typical cold start problem. Additional auxiliary information has usually been used in the item cold start recommendation,we further bring temporal dynamics,text and relevance in our models to release item cold start.Two new cold start recommendation models TmTx(Time,Text) and TmTI(Time,Text,Item correlation) proposed to solve the item cold start problem for different cold start scenarios.While well-known methods like TimeSVD++ and CoFactor partially take temporal dynamics,comments,and item correlations into consideration to solve the cold start problem but none of them combines these information together.Two models proposed in this paper fused features such as time,text,and relevance can effectively improve the performance under item cold start.We select the convolutional neural network (CNN) to extract features from item description text which provides the model the ability to deal with cold start items.Both proposed models can effectively improve the performance with item cold start.Experimental results on three real-world data set show that our proposed models lead to significant improvement compared with the baseline methods.