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.

Java is one of important program language today. In Java, in order to build sound software, we have to carefully implement two fundamental methods hashCode and equals. This requirement, however, is not easy to follow in real software development. Some existing studies for ensuring the correctness of these two methods rely on static analysis, which are limited to loop-free programs. This paper proposes a new solution to this important problem, using software analysis workbench (SAW), an open source tool. The efficiency is evaluated through experiments. We also provide a useful situation where cost of regression testing is reduced when program refactoring is conducted.

We present a seven-bit multilayer true-time delay (TTD) circuit operating from 1 to 7GHz for wideband phased array antennas. By stacking advanced substrates with low dielectric loss, the TTD with PCB process is miniaturized and has low insertion loss. The signal vias with surrounding ground vias are designed to provide impedance matching throughout the band, allowing the overall group delay to be flat. The standard deviation of the TTD for all states is below 19ps, which is 1.87% of the maximum group delay. The maximum delay is 1016ps with resolution of 8ps. The implemented TTD is 36.6×19.4mm2 and consumes 0.65mW at 3.3V supply for all the delay states. The measured input/output return loss is better than 12.1dB for the band of 1-7GHz.

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.

We introduce a method to estimate the attractiveness of a food photo. It extracts image features focusing on the appearances of 1) the entire food, and 2) the main ingredients. To estimate the attractiveness of an arbitrary food photo, these features are integrated in a regression scheme. We also constructed and released a food image dataset composed of images of ten food categories taken from 36 angles and accompanied with attractiveness values. Evaluation results showed the effectiveness of integrating the two kinds of image features.

Speech captured by an in-ear microphone placed inside an occluded ear has a high signal-to-noise ratio; however, it has different sound characteristics compared to normal speech captured through air conduction. In this study, a method for blind speech quality enhancement is proposed that can convert speech captured by an in-ear microphone to one that resembles normal speech. The proposed method estimates an input-dependent enhancement function by using a neural network in the feature domain and enhances the captured speech via time-domain filtering. Subjective and objective evaluations confirm that the speech enhanced using our proposed method sounds more similar to normal speech than that enhanced using conventional equalizer-based methods.

Heat map is an important tool for eye tracking data analysis and visualization. It is very intuitive to express the area watched by observer, but ignores saccade information that expresses gaze shift. Based on conventional heat map generation method, this paper presents a novel heat map generation method for eye tracking data. The proposed method introduces a mixed data structure of fixation points and saccades, and considers heat map deformation for saccade type data. The proposed method has advantages on indicating gaze transition direction while visualizing gaze region.

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.

Dynamic sharing of limited radio spectrum resources is expected to satisfy the increasing demand for spectrum resources in the upcoming 5th generation mobile communication system (5G) era and beyond. Distributed real-time spectrum sensing is a key enabler of dynamic spectrum sharing, but the costs incurred in observed-data transmission are a critical problem, especially when massive numbers of spectrum sensors are deployed. To cope with this issue, the proposed spectrum sensors learn the ambient radio environment in real-time and create a time-spectral model whose parameters are shared with servers operating in the edge-computing layer. This process makes it possible to significantly reduce the communication cost of the sensors because frequent data transmission is no longer needed while enabling the edge servers to keep up on the current status of the radio environment. On the basis of the created time-spectral model, sharable spectrum resources are dynamically harvested and allocated in terms of geospatial, temporal, and frequency-spectral domains when accepting an application for secondary-spectrum use. A web-based prototype spectrum management system has been implemented using ten servers and dozens of sensors. Measured results show that the proposed approach can reduce data traffic between the sensors and servers by 97%, achieving an average data rate of 10 kilobits per second (kbps). In addition, the basic operation flow of the prototype has been verified through a field experiment conducted at a manufacturing facility and a proof-of-concept experiment of dynamic-spectrum sharing using wireless local-area-network equipment.

The fifth-generation (5G) mobile communication system is being researched and developed for launch as a commercial service in 2020. The 5G mobile network will include many radio access technologies, such as LTE, 5G NR, and WLAN. Therefore, a user equipment (UE) will be connected to different types of base stations as it moves within a 5G heterogeneous network. Accordingly, it is assumed that the throughput will change with each change in the serving cell. The 5G mobile network is expected to serve large capacity contents, such as 4K videos. However, a conventional video streaming method cannot effectively use the available bandwidth in a 5G heterogeneous network. In this study, we propose a sending rate adaptation method based on predictions for the available bandwidth. In the proposed method, the available bandwidth is predicted from the communication log data. The communication logging database, including past throughput with its location, is created by a UE. A UE refers to the communication log data for predictions when the serving cell is likely to change. We develop a video streaming device that implements the proposed method and evaluates its performance. The results show that the proposed method can change the sending rate and resolution according to the available bandwidth. The proposed method increases the probability of transmitting high-resolution video, which is not possible with conventional methods. Moreover, we performed subjective evaluation of the transmitted video by the proof-of-concept test. The result of the subjective evaluation shows that the proposed method improves the quality of experience for video streaming.

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.

Apache Hadoop and its ecosystem have become the de facto platform for processing large-scale data, or Big Data, because it hides the complexity of distributed computing, scheduling, and communication while providing fault-tolerance. Cloud-based environments are becoming a popular platform for hosting Hadoop clusters due to their low initial cost and limitless capacity. However, cloud-based Hadoop clusters bring their own challenges due to contradictory design principles. Hadoop is designed on the shared-nothing principle while cloud is based on the concepts of consolidation and resource sharing. Most of Hadoop's features are designed for on-premises data centers where the cluster topology is known. Hadoop depends on the rack assignment of servers (configured by the cluster administrator) to calculate the distance between servers. Hadoop calculates the distance between servers to find the best remote server from which to fetch data from when fetching non-local data. However, public cloud environment providers do not share rack information of virtual servers with their tenants. Lack of rack information of servers may allow Hadoop to fetch data from a remote server that is on the other side of the data center. To overcome this problem, we propose a delay distribution based scheme to find the closest server to fetch non-local data for public cloud-based Hadoop clusters. The proposed scheme bases server selection on the delay distributions between server pairs. Delay distribution is calculated measuring the round-trip time between servers periodically. Our experiments observe that the proposed scheme outperforms conventional Hadoop nearly by 12% in terms of non-local data fetch time. This reduction in data fetch time will lead to a reduction in job run time, especially in real-world multi-user clusters where non-local data fetching can happen frequently.

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.

This paper proposes an energy efficiency (EE) maximized resource allocation (RA) algorithm in orthogonal frequency division multiple access (OFDMA) downlink networks with multiple relays, where a novel opportunistic subcarrier pair based decode-and-forward (DF) protocol with beamforming is used. Specifically, every data transmission is carried out in two consecutive time slots. During every transmission, multiple parallel paths, including relayed paths and direct paths, are established by the proposed RA algorithm. As for the protocol, each subcarrier in the 1st slot can be paired with any subcarrier in 2nd slot to best utilize subcarrier resources. Furthermore, for each relayed path, multiple (not just single or all) relays can be chosen to apply beamforming at the subcarrier in the 2nd slot. Each direct path is constructed by an unpaired subcarrier in either the 1st or 2nd slot. In order to guarantee an acceptable spectrum efficiency, we also introduce a minimum rate constraint. The EE-maximized problem is a highly nonlinear optimization problem, which contains both continuous, discrete variables and has a fractional structure. To solve the problem, the best relay set and resource allocation for a relayed path are derived first, then we design an iterative algorithm to find the optimal RA for the network. Finally, numerical experiments are taken to demonstrate the effectiveness of the proposed algorithm, and show the impact of minimum rate requirement, user number and circuit power on the network EE.

A novel adaptive cross-layer optimal power allocation (OPA) scheme over physical layer and data-link layer for two-way relaying system with amplify-and-forward policy (TWR-AF) is proposed in this paper. Our goal is to find the optimal power allocation factors under each channel state information (CSI) to maximize the sum throughput of two sources under total transmit power constraint in the physical layer while guaranteeing the statistical delay quality-of-service (QoS) requirement in the data-link layer. By integrating information theory with the concept of effective capacity, the OPA problem is formulated into an optimization problem to maximize the sum effective capacity. It is solved through Lagrange multiplier approach, and the optimal power allocation factors are presented. Simulations are developed and the results show that the proposed cross-layer OPA scheme can achieve the best sum effective capacity with relatively low complexity when compared with other schemes. In addition, the proposed cross-layer OPA scheme achieves the maximal sum effective capacity when the relay is located in (or near) the middle of the two source nodes, and the sum effective capacity becomes smaller when the difference between two QoS exponents becomes larger.