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.

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.

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.

Accurate estimation of the state-of-charge is a crucial need for the battery, which is the most important power source in electric vehicles. To achieve better estimation result, an accurate battery model with optimum parameters is required. In this paper, a gradient-free optimization technique, namely tree seed algorithm (TSA), is utilized to identify specific parameters of the battery model. In order to strengthen the search ability of TSA and obtain more quality results, the original algorithm is improved. On one hand, the DE/rand/2/bin mechanism is employed to maintain the colony diversity, by generating mutant individuals in each time step. On the other hand, the control parameter in the algorithm is adaptively updated during the searching process, to achieve a better balance between the exploitation and exploration capabilities. The battery state-of-charge can be estimated simultaneously by regarding it as one of the parameters. Experiments under different dynamic profiles show that the proposed method can provide reliable and accurate estimation results. The performance of conventional algorithms, such as genetic algorithm and extended Kalman filter, are also compared to demonstrate the superiority of the proposed method in terms of accuracy and robustness.

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.

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.

A W-band corporate-feed 16×16-slot array antenna with low sidelobe level is designed and fabricated. The basic unit of the array is a 2×2-circular-slot subarray with step square cavities and uses an E-plane waveguide as the feeding line. An efficient method to design an unequal power-splitting ratio but equal phase (UPEP) E-plane waveguide T-junction (E-T) is proposed for constructing a 1-to-64 power-tapering feed network, which is the critical part to realize low sidelobe level. The whole array is fabricated with aluminum by milling and bonded by the vacuum brazing process. The measured results demonstrate that the array can achieve a 7.2% bandwidth with VSWR<1.5 and holistic sidelobe levels lower than -23.5dB in E-plane and H-plane from 89GHz ∼ 95.8GHz. The measured gain is higher than 31.7dBi over the working band with the antenna efficiency better than 67.5%.

This paper evaluates a variety of key 5G technologies such as base station (BS) massive multiple-input multiple-output (MIMO) antennas, beamforming and tracking, intra-baseband unit (BBU) hand over (HO), and coverage. This is done in different interesting 5G areas with a variety of radio conditions such as an indoor office building lobby, an outdoor parking area, and a realistic urban deployment of a 5G radio access system with BSs installed in buildings to deploy a 5G trial area in the Tokyo Odaiba waterfront area. Experimental results show that throughput exceeding 10Gbps is achieved in a 730MHz bandwidth using 8 component carriers, and distributed MIMO throughput gain is achieved in various transmission point deployments in the indoor office building lobby and outdoor parking area using two radio units (RUs). In particular, in the outdoor parking area, a distinct advantage from distributed MIMO is expected and the distributed MIMO gain in throughput of 60% is achieved. The experimental results also clarify the downlink performance in an urban deployment. The experimental results show that throughput exceeding 1.5Gbps is achieved in the area and approximately 200 Mbps is achieved at 500m away from the BS. We also confirm that the beam tracking and intra-BBU HO work well compensating for high path loss at 28-GHz, and achieve coverage 500m from the BS. On the other hand, line of sight (LoS) and non-line-of sight (N-LoS) conditions are critical to 5G performance in the 28-GHz band, and we observe that 5G connections are sometimes dropped behind trees, buildings, and under footbridges.

In this paper, a smart dressing system was implemented based on flexible pH sensors that can monitor the infection of a wounded area by tracking the pH value of the area. Motivated by the fabrication process widely used for semiconductors, the flexible pH sensor fabrication process was devised with a polyester (PET) film and a Si wafer, which deposits Au and Ag on a PET film. Because the electrodes are comprised of a working electrode and a reference electrode, the reference electrode was fabricated by synthesizing the Polyaniline (PANI) on Ag/AgCl, while the pH sensor has four channels to evenly measure the pH value in a wide area. The smart dressing system was constructed with four pH sensors, a single temperature sensor, a level shifter, a regulator, an analog-to-digital converter (ADC), and a monitoring PC. The measurement results show that our smart dressing system has a size of 5×5cm2 and can monitor the pH value range found in [3,9] with a sensitivity slope of 50mV/pH.

Hadoop, a distributed processing framework for big-data, is now widely used for multimedia processing. However, when processing video data from a Hadoop distributed file system (HDFS), unnecessary network traffic is generated due to an inefficient HDFS block slice policy for picture frames in video files. We propose a new block replication policy to solve this problem and compare the newly proposed HDFS with the original HDFS via extensive experiments. The proposed HDFS reduces network traffic, and increases locality between processing cores and file locations.

Deep Graphical Model (DGM) based on Generative Adversarial Nets (GANs) has shown promise in image generation and latent variable inference. One of the typical models is the Iterative Adversarial Inference model (GibbsNet), which learns the joint distribution between the data and its latent variable. We present RGNet (Re-inference GibbsNet) which introduces a re-inference chain in GibbsNet to improve the quality of generated samples and inferred latent variables. RGNet consists of the generative, inference, and discriminative networks. An adversarial game is cast between the generative and inference networks and the discriminative network. The discriminative network is trained to distinguish between (i) the joint inference-latent/data-space pairs and re-inference-latent/data-space pairs and (ii) the joint sampled-latent/generated-data-space pairs. We show empirically that RGNet surpasses GibbsNet in the quality of inferred latent variables and achieves comparable performance on image generation and inpainting tasks.

A variety of all-new systems such as a massive machine type communication (mMTC) system will be supported in 5G and beyond. Although each mMTC device occupies quite narrow bandwidth, the massive number of devices expected will generate a vast array of traffic and consume enormous spectrum resources. Therefore, it is necessary to proactively gather up and exploit fractional spectrum resources including guard bands that are secured but unused by the existing Long Term Evolution (LTE) systems. The guard band is originally secured as a margin for high out-of-band emission (OOBE) caused by the discontinuity between successive symbols in the cyclic prefix-based orthogonal frequency division multiplexing (CP-OFDM), and new-waveforms enabling high OOBE suppression have been widely researched to efficiently allocate narrowband communication to the frequency gap. Time-domain windowing is a well-known signal processing technique for reducing OOBE with low complexity and a universal time-domain windowed OFDM (UTW-OFDM) with a long transition duration exceeding the CP length has demonstrated its ability in WLAN-based systems. In this paper, we apply UTW-OFDM to the LTE downlink system and comprehensively evaluate its performance under the channel models defined by 3GPP. Specifically, we evaluate OOBE reduction and block error rate (BLER) by computer simulation and clarify how far OOBE can be reduced without degrading communication quality. Furthermore, we estimate the implementation complexity of the proposed UTW-OFDM, the conventional CP-OFDM, and the universal filtered-OFDM (UF-OFDM) by calculating the number of required multiplications. These evaluation and estimation results demonstrate that the proposed UTW-OFDM is a practical new-waveform applicable to the 5G and beyond.

In full duplex (FD), which improves the system capacity (or cell throughput) and reduces the transmission delay (or latency) through simultaneous transmission and reception in the same frequency band, self-interference (SI) from the transmitter should be suppressed using antenna isolation, an analog SI canceler, and digital SI canceler (DSIC) to a level such that the data or control channel satisfies the required block error rate (BLER). This paper proposes a structure of iterative DSIC with alternating estimate subtraction (AES-IDSIC) for orthogonal frequency division multiplexing (OFDM) using FD. We first present the required SI suppression level considering SI, quantization noise of an analog-to-digital converter, and nonlinear distortion of a power amplifier and RF receiver circuit for a direct conversion transceiver using FD. Then, we propose an AES-IDSIC structure that iterates the generation of the SI estimate, the downlink symbol estimate, and then alternately removes one of the estimates from the received signal in the downlink including SI. We investigate the average BLER performance of the AES-IDSIC for OFDM using FD in a multipath fading channel based on link-level simulations under the constraint that the derived required signal-to-SI ratio must be satisfied.

In compressive sensing theory (CS), the restricted isometry property (RIP) is commonly used for the measurement matrix to guarantee the reliable recovery of sparse signals from linear measurements. Although many works have indicated that random matrices with excellent recovery performance satisfy the RIP with high probability, Toeplitz-structured matrices arise naturally in real scenarios, such as applications of linear time-invariant systems. Thus, the corresponding measurement matrix can be modeled as a Toeplitz (partial) structured matrix instead of a completely random matrix. The structure characteristics introduce coherence and cause the performance degradation of the measurement matrix. To enhance the recovery performance of the Toeplitz structured measurement matrix in multichannel convolution source separation, an efficient construction of measurement matrix is presented, referred to as sparse random block-banded Toeplitz matrix (SRBT). The sparse signal is pre-randomized by locally scrambling its sample locations. Then, the signal is subsampled using the sparse random banded matrix. Finally, the mixing measurements are obtained. Based on the analysis of eigenvalues, the theoretical results indicate that the SRBT matrix satisfies the RIP with high probability. Simulation results show that the SRBT matrix almost matches the recovery performance of random matrices. Compared with the existing banded block Toeplitz matrix, SRBT significantly improves the probability of successful recovery. Additionally, SRBT has the advantages of low storage requirements and fast computation in reconstruction.

Smart-routers develop greatly in recent years as one of the representative products of IoT and Smart home. Different from traditional routers, they have storage and processing capacity. Actually, smart-routers in the same location or ISP have better link conditions and can provide high quality service to each other. Therefore, for the content required services, how to construct the overlay network and efficiently deploy replications of popular content in smart-routers' network are critical. The performance of existing centralized models is limited by the bottleneck of the single point's performance. In order to improve the stability and scalability of the system through the capability of smart-router, we propose a novel intelligent and decentralized content diffusion system in smart-router network. In the system, the content will be quickly and autonomously diffused in the network which follows the specific requirement of coverage rate in neighbors. Furthermore, we design a heuristic node selection algorithm (MIG) and a replacement algorithm (MCL) to assist the diffusion of content. Specifically, system based MIG will select neighbor with the maximum value of information gain to cache the replication. The replication with the least loss of the coverage rate gain will be replaced in the system based on MCL. Through the simulation experiments, at the same requirement of coverage rate, MIG can reduce the number of replications by at least 20.2% compared with other algorithms. Compared with other replacement algorithms, MCL achieves the best successful service rate which means how much ratio of the service can be provided by neighbors. The system based on the MIG and MCL can provide stable service with the lowest bandwidth and storage cost.

Joint transmit and receive antenna selection (JTRAS) for transceive spatial modulation (TRSM) is investigated in this paper. A couple of low-complexity and efficient JTRAS algorithms are proposed to improve the reliability of TRSM systems by maximizing the minimum Euclidean distance (ED) among all received signals. Specifically, the QR decomposition based ED-JTRAS achieves near-optimal error performance with a moderate complexity reduction as compared to the optimal ED-JTRAS method. The singular value decomposition based ED-JTRAS achieves sub-optimal error performance with a significant complexity reduction. Simulation results show that the proposed methods remarkably improve the system reliability in both uncorrelated and spatially correlated Rayleigh fading channels, as compared to the conventional norm based JTRAS method.

In this paper, we study the power allocation (PA) scheme design for energy efficiency (EE) maximization with discrete-rate adaptive modulation (AM) in the downlink distributed antenna system (DAS). By means of the Karush-Kuhn-Tucker (KKT) conditions, an optimal PA scheme with closed-form expression is derived for maximizing the EE subject to maximum transmit power and target bit error rate (BER) constraints, where the number of active transmit antennas is also derived for attaining PA coefficients. Considering that the optimal scheme needs to calculate the PA of all transmit antennas for each modulation mode, its complexity is extremely high. For this reason, a low-complexity suboptimal PA is also presented based on the antenna selection method. By choosing one or two remote antennas, the suboptimal scheme offers lower complexity than the optimal one, and has almost the same EE performance as the latter. Besides, the outage probability is derived in a performance evaluation. Computer simulation shows that the developed optimal scheme can achieve the same EE as the exhaustive search based approach, which has much higher complexity, and the suboptimal scheme almost matches the EE of the optimal one as well. The suboptimal scheme with two-antenna selection is particularly effective in terms of balancing performance and complexity. Moreover, the derived outage probability is in good agreement with the corresponding simulation.