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Physical-layer network coding (PNC) is an effective technique for enhancing wireless network throughput. Recently, it has been demonstrated that convolutional autoencoders effectively works in point-to-point communication systems, but their application to wireless relay networks is scarcely explored. In this letter, we propose a convolutional autoencoder for PNC in a two-way relay channel. The constellation mapping and demapping of symbols at each node are determined adaptively through a deep learning technique, such that the bit error rate performance is improved for high-order modulation. Simulation results verify the advantages of the proposed scheme over the conventional PNC scheme for various modulation types.

A separated random linear network coding (SRLNC) technique that separately transmits a coding coefficient (CC) and a coded packet (CP) in different frames is proposed based on cooperative control frames in distributed relay networks. SRLNC technique can improve retransmission performance by reducing retransmission overhead, because either the CC or CP is retransmitted, whereas conventional RLNC retransmits the CC and CP simultaneously. The throughput performance is also analyzed to investigate the impact of additional cooperative control frames, because SRLNC requires little additional cooperative control frames. The simulation results show that the proposed scheme outperforms the conventional RLNC in distributed relay networks.

Recently the idea of using deep learning algorithms in massive multiple-input multiple-output channel state information feedback has been studied in great detail. To use the deep-learning-based feedback schemes over the air, they must be able to operate under extremely large antennas. These schemes also need to be verified under a realistic channel model and propagation environment. Thus, we propose ChannelAttention, a deep-learning-based channel state information feedback scheme that utilizes attention layers and residual blocks. Simulations for a 64-by-64 QuaDRiGa channel model based on the 3GPP 38.901 urban microcell scenario show that ChannelAttention surpasses the normalized mean square error and cosine similarity performance of the conventional CsiNet+ scheme across all compression ratio ranges.

The convenience and energy efficiency of electric trams have resulted in their rapid adoption in many areas of the world. Despite trams’ many advantages, overhead lines installed along the track cause severe problems, such as safety issues, maintenance problems, and difficulty in renovating the urban road system. To solve these problems, a wireless charging electric tram system has been developed to replace the overhead lines, catenary, and pantograph. With wireless charging segments installed underground, a tram can operate freely without any support from overhead lines. In order to successfully commercialize a wireless charging tram system, it is essential to minimize the energy cost of the wireless charging tram system. In this article, a model of a wireless charging electric tram system and an energy cost minimization algorithm are proposed. The Manchester Metrolink tram network is modeled using the Google Maps API, the Google Transit API, and the Metrolink API to simulate the wireless charging system under massive public transportation network. To determine the minimum energy cost, an energy cost minimization algorithm for the wireless charging tram system is proposed based on decentralized multiagent reinforcement learning with feedback. The numerical results show that the proposed algorithm can reduce costs while ensuring stable operation.

Goal: Over the decades, there have been improvements in the sequence alignment algorithm, with significant advances in various aspects such as complexity and accuracy. However, human-defined algorithms have an explicit limitation in view of developmental completeness. This paper introduces a novel local alignment method to obtain optimal sequence alignment based on reinforcement learning. Methods: There is a DQNalign algorithm that learns and performs sequence alignment through deep reinforcement learning. This paper proposes a DQN x-drop algorithm that performs local alignment without human intervention by combining the x-drop algorithm with this DQNalign algorithm. The proposed algorithm performs local alignment by repeatedly observing the subsequences and selecting the next alignment direction until the x-drop algorithm terminates the DQNalign algorithm. This proposed algorithm has an advantage in view of linear computational complexity compared to conventional local alignment algorithms. Results: This paper compares alignment performance (coverage and identity) and complexity for a fair comparison between the proposed DQN x-drop algorithm and the conventional greedy x-drop algorithm. Firstly, we prove the proposed algorithm's superiority by comparing the two algorithms’ computational complexity through numerical analysis. After that, we tested the alignment performance actual HEV and E.coli sequence datasets. The proposed method shows the comparable identity and coverage performance to the conventional alignment method while having linear complexity for the $X$ parameter. Conclusions: Through this study, it was possible to confirm the possibility of a new local alignment algorithm that minimizes computational complexity without human intervention.

This paper presents a novel technique for estimating the path loss parameters of radio channels using monostatic radar. To this end, a two-way path loss model for a scenario using monostatic radar for a sub-terahertz (sub-THz) band channel measurement is expressed as a function of a parameter of a one-way path loss model, and then the path loss parameters are estimated using the maximum-likelihood estimation algorithm based on linear regression models, which is based on single reference distance and dual reference distances. To verify the effectiveness of the proposed method, a path loss measurement experiment is conducted in an indoor environment using a frequency modulated continuous wave (FMCW) radar system with the base frequency of 119.5 GHz and the bandwidth of 5 GHz, and then the estimation results based on the proposed method are analyzed.

Purpose: The aim of this study was to determine the accuracy of 3-dimensional (3D) pelvic floor ultrasonography and compare it with defecography in assessment of posterior pelvic disorders.Methods: Eligible patients were consecutive women undergoing 3D pelvic floor ultrasonography at one hospital between August 2017 and February 2019. All 3D pelvic floor ultrasonography was performed by one examiner. A total of 167 patients with suspected posterior pelvic disorder was retrospectively enrolled in the study. The patients were divided into 3 groups according to the main symptoms.Results: There were 82 rectoceles on defecography (55 barium trapping) and 84 on 3D pelvic floor ultrasonography. Each modality identified 6 enteroceles. There were 43 patients with pelvic floor dyssynergia on defecography and 41 on ultrasonography. There were 84 patients with intussusception on defecography and 41 on 3D pelvic floor ultrasonography. Agreement of the 2 diagnostic tests was confirmed using Cohen’s kappa value. Rectocele (kappa, 0.784) and enterocele (kappa, 0.654) both indicated good agreement between defecography and 3D pelvic floor ultrasonography. In addition, pelvic floor dyssynergia (kappa, 0.406) showed moderate agreement, while internal intussusception (kappa, 0.296) had fair agreement.Conclusion: This study showed good agreement for detection of posterior pelvic disorders between defecography and 3D pelvic floor ultrasonography.

Traffic congestion of a random access (RA) link can be resolved by clustering machine type communication devices (MTCDs) and using an aggregator. However, since a packet needs to wait in the aggregator, clustering may cause an additional delay. To solve this problem, this letter suggests a partially clustered access scheme (PCAS) in which direct-access MTCDs (dMTCDs) and clustered-access MTCDs (cMTCDs) are serviced together. We provided an analytical model and an energy consumption model for PCAS. Numerical and simulation results showed that clustering may improve a success ratio. Furthermore, the delay requirement of dMTCDs and the energy consumption requirement of cMTCDs can be guaranteed by using PCAS.

Gene expression profiling is a useful technique for analyzing cellular function, and gene expression profiles are widely studied in human cancer research. Gene expression data usually consist of a very large number of features and a relatively small number of samples, and extracting a small number of important features from these data is a major challenge of gene expression-based analysis in cancer research. In this paper, we propose an embedded feature selection algorithm using boosted linear regression-based feature selection. The boosting technique is applied to derive the ensemble feature selector and improve the performance of linear regression-based feature selection. The proposed feature selection algorithm, called boosted regression-based feature selection for the multilayer perceptron (BREG-MLP), repeats the boosted feature selection process to extract the smallest feature subset while maintaining good classification performance. We apply the proposed BREG-MLP to some human cancer-related gene expression data sets for the purpose of extracting important features, and we confirm that BREG-MLP offers improved performance compared to single regression-based feature selection methods.

Goal: Individual characteristics are determined through a genome consisting of a complex base combination. This base combination is reflected in the k-word profile, which represents the number of consecutive k bases. Therefore, it is important to analyze the genome-specific statistical specificity in the k-word profile to understand the characteristics of the genome. In this paper, we propose a new k-word-based method to analyze genome-specific properties. Methods: We define k-words consisting of the same number of bases as statistically identical k-words. The statistically identical k-words are estimated to appear at a similar frequency by statistical prediction. However, this may not be true in the genome because it is not a random list of bases. The ratio between frequencies of two statistically identical k-words can then be used to investigate the statistical specificity of the genome reflected in the k-word profile. In order to find important ratios representing genomic characteristics, a reference value is calculated that results in a minimum error when classifying data by ratio alone. Finally, we propose a genetic algorithm-based search algorithm to select a minimum set of ratios useful for classification. Results: The proposed method was applied to the full-length sequence of microorganisms for pathogenicity classification. The classification accuracy of the proposed algorithm was similar to that of conventional methods while using only a few features. Conclusions: We proposed a new method to investigate the genome-specific statistical specificity in the k-word profile which can be applied to find important properties of the genome and classify genome sequences.

Millimeter-wave (mmWave) communication with a large bandwidth can result in a significantly improved data rate in wireless communications. To overcome high path-loss in the mmWave frequency band, beamforming technology is necessary. Especially, there has been widespread interest in development of hybrid beamforming (HB) technologies, in view of reducing cost and power consumption in massive multiple input multiple output (MIMO) systems. Some of existing researches on HB algorithms assumed perfect channel state information (CSI) and the others used beam training process in case of assuming imperfect CSI. When beam training process is used, enough beam training has to be conducted to achieve sufficient system performance in massive MIMO systems, which results in significant training overhead. Thus, it is necessary to reduce beam training complexity. Compared to state-of-the-art technology, we propose a multi-user HB system using codebooks based on a deep neural network (DNN) in this paper. In our proposed scheme, beam codewords for the base station (BS) and all users can be inferred using limited beam training in cases when the channel state information (CSI) is unknown. In order to apply the proposed scheme to situations where the CSI is unknown, reference radio frequency (RF) beamformers were introduced. Also, the proposed DNN structure is designed considering introduced reference RF beamformers. By using the proposed DNN with reference RF beamformers, the proposed system can inferred optimal beam codewords with limited beam training. Results obtained from simulations indicate that the proposed scheme can achieve almost the same performance as a conventional scheme with less beam training complexity. We also show that the performances achieved by the proposed scheme are gradually increased as training epoch is increased, eventually converging to a steady-state value.

Purpose To investigate the correlation of the strain elasticity of breast cancer with histologic features, immunohistochemical markers and molecular subtypes that are known to be factors related to prognosis. Materials and Methods B-mode ultrasound and strain elastography were performed in 123 patients (mean age, 53.4; range, 28–82) with invasive ductal carcinoma (IDC) (mean size, 1.54 cm; range, 0.4–7.0 cm). Histologic grade, lymph node (LN) status, lymphovascular invasion, immunohistochemical biomarkers [estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2 (HER2), CK5/6, epidermal growth factor receptor, and Ki-67] and molecular subtypes were determined from surgical pathology reports. The relationships between these factors and elasticity scores were evaluated. Results LN involvement was associated with a higher elasticity score which was statistically significant (p = 0.042). The tumor size, lymphovascular invasion, histologic grades, immunohistochemical markers and molecular subtypes had no significant correlation with the elasticity score (p > 0.05 for all). However, the IDCs with larger size and a positive lymphovascular invasion tended to have higher elasticity scores. Furthermore, higher histologic grade cancers and the HER2 overexpression-type tended to have lower elasticity scores. Conclusion The elasticity score of IDC had a significant correlation with LN involvement but no statistically significant correlation with the histologic features, immunohistochemical markers or molecular subtypes.

A modified uplink sparse code multiple access (SCMA) scheme with receive beamforming is herein proposed. Although a base station equipped with multiple antennas can support many users simultaneously using multi-beams, the complexity of the optimal SCMA system may be too large to be implemented in view of a practical system. Therefore, a two stage SCMA system with receive beamforming is proposed, based on uplink scheduling and three step layer selection. Analysis and simulation results of the proposed scheme showed the advantage of large complexity reduction with little performance degradation.

In this letter, we propose a new synchronous duplexing scheme for a long distance wireless backhaul system. In conventional time-division duplexing, a backhaul system can transmit its data only after the transmission of the opposite backhaul system is finished. Thus, any propagation delay caused by long distances leads to a long guard time. However, in our proposed scheme, both local and remote backhaul systems transmit their down and up-link data to each other simultaneously. Thus, even for a long propagation delay, the time for which the channel is not used, which is called the feedback delay, can be effectively reduced. We conduct a mathematical analysis to derive the theoretical efficiency of the proposed scheme and show simulation results indicating the superiority of the proposed scheme in terms of synchronization error and propagation delay.

In this paper, we investigate cooperative spectrum sensing (CSS) in a cognitive radio network (CRN) where multiple secondary users (SUs) cooperate in order to detect a primary user, which possibly occupies multiple bands simultaneously. Deep cooperative sensing (DCS), which constitutes the first CSS framework based on a convolutional neural network (CNN), is proposed. In DCS, instead of the explicit mathematical modeling of CSS, the strategy for combining the individual sensing results of the SUs is learned autonomously with a CNN using training sensing samples regardless of whether the individual sensing results are quantized or not. Moreover, both spectral and spatial correlation of individual sensing outcomes are taken into account such that an environment-specific CSS is enabled in DCS. Through simulations, we show that the performance of CSS can be greatly improved by the proposed DCS.

The aim of this study was to determine the incidence of delayed post-hemorrhoidectomy bleeding (DPHB) after hemorrhoidectomy using a semi-closed procedure. We also investigated risk factors associated with DPHB. This retrospective study enrolled a total of 1645 consecutive patients with symptomatic grade II to IV hemorrhoids who underwent a semi-closed procedure at the Seoul Songdo Hospital between September 2018 and May 2019. All patients underwent a semi-closed procedure with submucosal feeding vessel ligation, a method commonly performed at our institution. A total of 1645 patients (mean age: 48.67 (±14.38) years, 823 (50.0%) male/822 (50.0%) female) underwent semi-closed hemorrhoidectomy. Critically, 24 (1.5%) patients experienced DPHB. Of these patients, 13 (0.8%) experienced stump bleeding, whereas 11 (0.7%) experienced marginal bleeding. The mean bleeding period was 8.21±4.45 days. Multivariate analysis showed that male sex, drinking history, more than four hemorrhoid piles, and laxative agents were independent risk factors for DPHB. The risk of stump bleeding was significantly associated with male sex (OR=5.55, 95% CI 1.23-25.14, p=0.026), more than four hemorrhoid piles (OR=5.90, 95% CI 1.62-21.53, p=0.007), and laxative usage (OR=3.92, 95% CI 1.31-11.74, p=0.015). Conversely, the risk of marginal bleeding were significantly associated with drinking history (OR=10.48, 95% CI 1.34-82.03, p=0.025) and more than four hemorrhoid piles (OR=4.71, 95% CI 1.24-17.81, p=0.023). Male sex, drinking history, more than four hemorrhoid piles, and laxative usage were independent risk factors for DPHB in patients undergoing semi-closed hemorrhoidectomy. The risk factors for stump bleeding included male sex, more than four hemorrhoid piles, and laxative usage. In contrast, the risk factors for marginal bleeding were drinking history and more than four hemorrhoid piles.

KAIST (Korea Advanced Institute of Science and Technology) and KU (Khalifa University) in UAE have been progressing joint research of the semi-dynamic wireless power transfer system for autonomous electric vehicle from year 2018. The presented semi-dynamic wireless system consists of short length (1.5m) of power line for static charging and long length (5m) of power line for dynamic charging around vehicle stop. In this paper, several important system parameters are obtained through the magnetic simulation and the prototype system is implemented in the laboratory. Finally, the performance of the presented system is verified through test result, that is, the maximum power efficiency is 90.8% for static charging and is upper 85% for dynamic charging.

This paper presents the far-field characteristics of vortex wave of orbital angular momentum (OAM) with circular array antenna. Planar quasi yagi-uda antenna of 8 elements are arranged in circular array and folded into a hollow cylinder. The OAM radio beam can be generated by changing the sequential phase shift of antenna element inputs for each OAM mode number. A full-wave electromagnetic simulator is used to design circular array antenna and to observe distribution of directivity and axial ratio. The depicted results provide insight for the vortex wave radiation.

This study presents a new TEMPEST threat that an attacker can surreptitiously obtain original plain audio information from a distance by exploiting recently emerging unintentional electromagnetic (EM) radiations. As lightweight sensor-based Internet of things (IoT) services become widespread, a mixed-signal system on chip (MSoC) spontaneously integrates all components, such as digital, analog, and even power circuits, into a single chipset to minimize the size of IoT devices. Accordingly, we pay attention to the accelerated integration of a switching regulator (SWREG), which is one of the typical power circuits and may substantially increase the unintentional EM leakages, re-enabling the audio TEMPEST attack. In this paper, we posit that a root cause of new audio coupled EM leakages is the unavoidable integration of SWREG which innately has strong and low-frequency (i.e., several MHz) switching noises; an audio signal is conductively coupled on the single common substrate of an MSoC with a system clock and the newly emerging the SWREG noises. The unique features of the suggested EM leakages compared to previous leakages are that their frequency distribution is dense (i.e., at frequency intervals of the SWREG noise), wideband (i.e., from several MHz to over 1 GHz), and static (i.e., time-invariant center frequencies). These features make the new TEMPEST attack due to the SWREG noise have a longer attack range and be more robust to interferences. Consequently, the presented TEMPEST attack becomes considerably practical. To verify the new TEMPEST attack due to the SWREG noise, we first perform a feasibility analysis by measuring and analyzing the audio-conveyed EM emanations of the popular MSoCs in an anechoic chamber. Next, we demonstrate how critical and practical the threat is by capturing the leakages from the commercial devices in an office environment. Furthermore, we propose a new signal reinforcement method with the three benefits (dense, wideband, and static) of the suggested radiations: the spectral addition of phase-aligned signals. The experimental results show that the test sweep tones of the Sogou voice recorder (nRF52810 chipset) and Xiaomi earbuds (CSR8640 chipset) can be reconstructed over 10 meters. Additionally, an attack feasibility analysis on digital signal (I2C) is performed in a short-range. The overall results indicate that the new TEMPEST attack becomes more practical than the previous side-channel analysis. Finally, we suggest several technical countermeasures that help to design safe IoT devices.

Various methods have been developed to analyze the association between organisms and their genomic sequences. Among them, sequence alignment is the most frequently used for comparative analysis of biological genomes. However, the traditional sequence alignment method is considerably complicated in proportion to the sequences' length, and it is significantly challenging to align long sequences such as a human genome. Currently, several multiple sequence alignment algorithms are available that can reduce the complexity and improve the alignment performance of various genomes. However, there have been relatively fewer attempts to improve the alignment performance of the pairwise alignment algorithm. After grasping these problems, we intend to propose a new sequence alignment method using deep reinforcement learning. This research shows the application method of the deep reinforcement learning to the sequence alignment system and the way how the deep reinforcement learning can improve the conventional sequence alignment method., Comment: 20pages, 9figures

In energy-limited networks, battery-powered nodes suffer from energy famine, which can reduce network lifetime and affect the robustness of networks. To alleviate an energy problem, it is possible to harvest energy from ambient radio frequency signals. In this paper, we consider a proportional fair energy efficiency, which jointly considers energy efficiency and fairness in energy-harvesting-based wireless networks. We formulate a nonconvex optimization problem for solving subchannel and power allocation in order to maximize proportional fair energy efficiency. Using nonlinear fractional programming, we transform the optimization problem into a tractable convex problem. We also derive the solution of the transformed problem and propose a resource allocation algorithm using an iterative method. In addition, we prove the convergence of the proposed algorithm in view of a suboptimal point. Through intensive simulations, we compare the performance of our proposed algorithm with those of conventional algorithms. It is shown that the proposed algorithm improves fairness considerably while maintaining energy efficiency, compared with conventional algorithms.

In this paper, we try to solve the imbalance problem of communication data service and energy supply among receivers in energy harvesting based wireless networks, with receivers equipped with modules that could either decode information or harvest energy from radio frequency signals. We formulate a biconvex optimization problem and find the solutions for subchannel and power allocation. Based on the obtained solutions, we propose a proportional fair resource allocation algorithm using an iterative method, and also show the convergence of the proposed algorithm to the partial optimum solution. Simulation results show that the proposed algorithm improves, to a remarkable extent, fairness among the receivers in terms of both the data rate and the harvested energy. At the same time, the proposed algorithm is found to lead to a negligible loss of system throughput compared with other conventional algorithms.

To satisfy increased data quantity demanded by users, millimeter-wave (mmWave) communication using large bandwidth and numerous antennas has considerable promise as a new technology for wireless communication. Also, it is possible to increase more data rate by using a dual polarized array antenna (DPAA). On the other hand, beamforming techniques have to be used in mmWave communication to overcome high path-loss. Conventional beamformers, however, have problems of high costs, high power consumption, and difficulties of implementation to be used in mmWave communication. Thus, a hybrid analog radio frequency and digital baseband beamformer is used to solve these problems. Since, it is very hard to achieve perfect channel state information, it is necessary to use limited feedback information based on beam training. In this letter, a hybrid beamforming system based on DPAA considering practical antenna radiation pattern is developed in a multi-user channel environment. Furthermore, two hybrid beamforming algorithms using only limited feedback channel information are proposed and analyzed.

Sparse code multiple access (SCMA) is a promising code-based non-orthogonal multiple-access technique that can provide improved spectral efficiency and massive connectivity meeting the requirements of 5G wireless communication systems. We propose a deep learning-aided SCMA (D-SCMA) in which the codebook that minimizes the bit error rate (BER) is adaptively constructed, and a decoding strategy is learned using a deep neural network-based encoder and decoder. One benefit of D-SCMA is that the construction of an efficient codebook can be achieved in an automated manner, which is generally difficult due to the non-orthogonality and multi-dimensional traits of SCMA. We use simulations to show that our proposed scheme provides a lower BER with a smaller computation time than conventional schemes.

High peak-to-average power ratio (PAPR) has been one of the major drawbacks of orthogonal frequency division multiplexing (OFDM) systems. In this letter, we propose a novel PAPR reduction scheme, known as PAPR reducing network (PRNet), based on the autoencoder architecture of deep learning. In the PRNet, the constellation mapping and demapping of symbols on each subcarrier is determined adaptively through a deep learning technique, such that both the bit error rate (BER) and the PAPR of the OFDM system are jointly minimized. We used simulations to show that the proposed scheme outperforms conventional schemes in terms of BER and PAPR.

We investigate the stability of the transmission efficiency (TE) in nonradiative wireless power transfer (WPT) using a relay resonator. An equivalent circuit model is used to show mathematically that two suppositions are satisfied in an overcoupled region: first, the TE of relay-based WPT systems is always larger than that of conventional two-coil-based WPT systems, and second, as the coupling coefficient increases, the TE of two-coil-based WPT systems decreases severely, while the TE of relay-based WPT systems increases slightly. Consequently, we surmise that an intermediate relay can be used to achieve higher TE and stability, compared with conventional two-coil-based WPT systems. We verify that our analytical results are in good agreement with the experimental ones.

The problem of spectrum scarcity has raised the prospect of coexisting network. For example, long-term evolution-licensed-assisted access (LTE-LAA & Wi-Fi) can operate in the same frequency band. However, interference between the two different radio access technologies can severely reduce the performances of both networks. To mitigate this interference, the concept of an almost blank subframe (ABS) has been proposed. However, because the LTE-LAA macrocell must remain silent, this provides no incentive for its throughput enhancement. In this letter, we propose a transaction between LTE-LAA and Wi-Fi using ABS together with offloading. We model a time-resource utility model by jointly considering the resource consumed for ABS and offloading, and then formulate a coalition game. To achieve the solution of a coalition game, we consider two fairness concepts: min-max and Shapley fairness. We then verify the performance of the proposed game using simulations. It is shown that the proposed game model can improve resource utilization through the joint use of ABS and offloading.

The design of conventional electric vehicles (EVs) is affected by numerous limitations, such as a short travel distance and long charging time. As one of the first wireless charging systems, the Online Electric Vehicle (OLEV) was developed to overcome the limitations of the current generation of EVs. Using wireless charging, an electric vehicle can be charged by power cables embedded in the road. In this paper, a model and algorithm for the optimal design of a wireless charging electric bus system is proposed. The model is built using a Markov decision process and is used to verify the optimal number of power cables, as well as optimal pickup capacity and battery capacity. Using reinforcement learning, the optimization problem of a wireless charging electric bus system in a diverse traffic environment is then solved. The numerical results show that the proposed algorithm maximizes average reward and minimizes total cost. We show the effectiveness of the proposed algorithm compared with obtaining the exact solution via mixed integer programming (MIP).

This study presents analyses of the feasibility for the 28 GHz band with a comprehensive overview of important channel parameters. A channel measurement system with a time synchronisation scheme is also introduced which is a sliding correlator channel sounder using 250 Mcps (Mega-chips-per-second) pseudo-random noise sequences. The system enables the observation of the behaviour of 28 GHz wave propagation not only to use of forming narrow beams in certain directions (classical narrow beam beamforming) but also omni-likely. This novel synchronised system makes it possible to analyse the wideband channel characteristics. The outdoor channel measurements are conducted considering various features such as foliage effects, signal outage, path loss and outdoor-to-indoor conditions. Furthermore, the spatial channel modelling for omni-like observations with temporal characteristics is firstly interpreted through analysing the essential channel modelling parameters, including the number of clusters, excess delay, root-mean-square (RMS) delay spread, power delay spectrum, and angular spread. In the authors’ measurements, the average number of clusters was 2.41 with standard deviation of 1.44. The average RMS delay spread was 36.06 ns with standard deviation of 46.73 ns. Furthermore, the mean values of the RMS azimuth spreads of the angle of departure and arrival were 6.65° and 24.04°, respectively.

Multihop cellular networks offer a cost-effective solution to the problem of achieving higher capacity and extending coverage by using relay stations (RSs) deployed within the coverage area of a base station (BS). We herein consider directional transmission based on adaptive beamforming in which multiple users are served at the same time using multiple directional beams; this represents one promising technology that could improve the capacity of multihop cellular network. In directional transmission, severe intra-BS/RS, inter-BS---RS, and inter-RS interferences can occur due to the concurrent transmission and accordingly, system performance can be degraded severely. In this paper, we propose resource allocation to maximize the system capacity of multihop cellular networks by taking into account these severe interferences caused in multihop cellular network using directional transmission. To alleviate the problem of high computational complexity, we propose a sub-optimal scheme. A heuristic scheme is also proposed which can be performed in distributed manner with lower computational complexity. Through simulation results, we show that the use of our proposed schemes increases system capacity.

Recently, self-healing has been actively investigated for mitigating an unforeseen network failure. In particular, to enable self-healing operations in indoor wireless communications systems, an autonomous mechanism to resolve unforeseen network failure problems should be considered. Therefore, we here address the issue of autonomous self-healing, in which continuous connectivity can be provided to users by resolving unexpected network failures. To overcome this problem, we propose a low-complexity resource allocation algorithm based on an optimization approach with reduced message passing. In the proposed algorithm, normal base stations perform subchannel and power allocations with a minimum amount of information sharing ( $N_{H}(M-1)$ ) to provide reliable service to users in faulty cells autonomously. We also show that the proposed algorithm converges to a unique fixed point in the low-interference region by using a contraction mapping technique. Through simulation results, we demonstrate that the proposed algorithm achieves good performances with respect to the average cell capacity, user fairness, and outage probability while reducing the message passing overhead and computational complexity.

As the number of electric vehicles (EVs) is increasing, a balancing control is needed to decrease the charging delay for EV users and increase the overall number of charged EVs for a charging station (CS) service provider. This paper suggests the local pricing-based decentralized balancing control algorithm to balance the power demand and the number of EVs at each CS. The CSs choose the charging costs according to the power demand, and the probability of sending beacon message is controlled according to the queued ratio. When receiving the beacon messages, each EV chooses the CS with the lowest cost. Simulation results showed that the suggested algorithm could balance the parked pad ratio and the charging power ratio of multiple CSs. Furthermore, the number of completely charged EVs was increased and the quality of service was also increased due to the reduction of charging delay. In conclusion, the suggested algorithm can improve the overall performance at the local area from the perspective of both charging service provider and EV users.

We introduce the concept of self-organizing VCN (virtual cell network). Here self-organizing VCN topology for efficient operation will be configured, and the functions of the each element will be defined. Also, the operation scenarios of VCN will be described. Then, we propose an efficient scheduling algorithm that considers the asymmetry of interference between downlink and uplink to mitigate intercell interference with little computing overhead. The basic concept is to construct scheduling groups that consist of several users. Each user in a scheduling group is affiliated with a different cell. Then, the intercell groups are managed efficiently in the proposed VCNs. There is no need for the exchange of a lot of information among base stations to schedule the users over the entire network.

The need for a new version of a next-generation wireless communication system to handle coinstantaneous downlink (DL) and uplink (UL) traffic is increasing as the asymmetry in DL and UL traffic increases and the traffic flows become more variable. We here propose a DL transmit beamforming algorithm for DL radio remote heads (RRHs), a UL receive beamforming algorithm for UL RRHs, and a power allocation algorithm for UL users. These algorithms are designed to maximize the energy efficiency of a dynamic time division duplex (TDD) based cloud radio access network (C-RAN) system that simultaneously supports DL and UL transmission. Simulation results show that the system developed is more energy efficient to a remarkable degree than conventional algorithms.

To enable the efficient use of energy, the environment where unscheduled receivers can harvest energy from the transmitted signal is considered in next-generation wireless communication systems. In this paper, we propose a scheme for allocating subchannel and power to maximize the system throughput and harvested energy simultaneously using optimization techniques. Through simulations, we verify that the proposed scheme can increase the system throughput and harvested energy harmoniously. In particular, the proposed scheme improves the harvested energy remarkably with a negligible degradation of system throughput, compared with conventional scheme, as a result, energy can be used efficiently in the system.

Heterogeneous network technology is expected to be a core technology for 5G mobile communications. 5G mobile network would be composed of many base stations even have mobility, then the operator should connect base stations through the wireless backhaul technology. This paper presents Ultra Wide Area Wireless Backhaul Network System with massive array antenna. We conducted link budget analysis for Ultra Wide Area Wireless Backhaul Network and performance analysis of massive array antenna system through the transmission simulator based on beamforming technology. In wide area () wireless backhaul system composed of massive antenna, we achieved 5 bps/Hz average spectral efficiency with 1 W transmission power per beam.

To improve the packet success probability, random linear network coding (RLNC) based on non-orthogonal multiple access (NOMA) for multicast services was employed in wireless downlink networks where a source superposes multiple coded packets before transmitting these packets to multiplexing groups of receivers in a power domain. Using a successive interference cancellation (SIC) and a Gauss-Jordan elimination process, receivers can recover their original packets. We derive expressions for the total packet success probability for the case of NOMA (or OMA) with (or without) RLNC, and propose group based power allocation schemes in order to improve performance with a low overhead. Through the results of simulations, we verify our derivations and show that RLNC based on NOMA improves the packet success probability and decreases the packet delay.

Scheduling based on random linear network coding is an effective way of improving system performance in a wireless network. In a time-varying channel environment, it is important to take the effects of outdated channel-state information (CSI) into consideration when scheduling because the CSI used may be different from the real CSI for data transmissions due to mobility and feedback delays. Here, we analyze the outage probability for scheduling based on random linear network coding in an outdated CSI environment. Practical scenarios are assessed so that the outage probability performance can be evaluated reliably.

In this paper, an electric tram system with a wireless power transfer system based on SMFIR technology is presented. The detailed technology of power-line infra, regulator, and pick-up device is described for train application, respectively. Furthermore, implementation and experimental results for wireless power transfer electric tram are presented

In wireless networks, it is important to guarantee the energy efficiency and receiver fairness for satisfying service provider and customer at the same time. In this paper, we propose a resource allocation algorithm which improves energy efficiency as well as receiver fairness based on optimization techniques. In the proposed algorithm, subchannel and power are allocated to receivers iteratively in the consideration of channel state information, amount of dissipated power, and receiver rate, in order to improve energy efficiency and receiver fairness. Through simulation, we show the effectiveness and superiority of the proposed algorithm in terms of energy efficiency and receiver fairness.

The dynamic responses of finite element model do not match with those of the experimental results due to modeling and measurement errors. It is impractical to collect the data for the full set of DOFs of a dynamic system. And there are some cases to have difficulty in measuring the response such as slope. This work presents an analytical method to estimate the frequency response function (FRF) at the unmeasured nodes of the actual system. The method is derived by minimizing the discrepancy between the analytical and actual FRF data sets dividing into the real and imaginary parts. The validity of the proposed method is investigated in a numerical application to estimate the unmeasured slope FRF data of a beam.

Up to now, conventional compact antennas have been designed to acquire as much gain as an ideal 6-port polarimetric antenna can obtain. It was recently shown that an achievable number of degrees of freedom (DOF) can be increased regardless of given antenna space. Here, we propose a compact directional 16-port antenna, with a size of about ${(0.5\lambda)^3}$ and a radiation frequency of 2 GHz, which allows more than a six-DOF gain to be achieved using pattern gain. We analyze the far field of the proposed 16-port antenna using spherical vector-wave modes and show that the ergodic capacity of the proposed 16-port antenna is close to that of an ideal 16-port antenna.

In a multiple-receiver wireless power transfer system, most of the power tends to be transferred to a receiver close to a transmitter. Therefore, it is highly desirable to find a way of providing the required amount of power to each receiver, as well as to improve the overall power transfer efficiency. In this letter, we investigate an impedance matching method of providing adjustable power distribution among receivers while preserving a high overall power transfer efficiency using coupled mode theory and circuit model. We also derive an optimal matching condition and the upper bound of the overall power transfer efficiency, in order to assess the performance of the proposed method. Finally, we perform circuit-level simulations to validate the accuracy of our analysis and the effectiveness of the proposed method.

SUMMARYA study was conducted to compare the effects of an open-field or rain-shelter system on the physiological responses of four Asian pear (Pyrus pyrifolia Nakai) cultivars (‘Hanareum’, ‘Wonhwang’, ‘Manpungbae’, and ‘Niitaka’) in an organic orchard in 2013 and in 2014. The rain shelter was covered with polyethylene film between 31 March–4 July each season, then uncovered during the remainder of each growing season. The incidences of fruit disease and insect pests, and fruit quality were measured in 2013. The rain-shelter system increased leaf chlorophyll contents to a SPAD value of 44.0 and rates of CO2 assimilation to 17.0 μmol m−2 s−1 in all four cultivars. It also increased leaf total nitrogen concentrations and dry weights in ‘Wonhwang’ and ‘Niitaka’ trees compared to the open field. Freezing temperatures were observed in the open field during flowering. The rain-shelter system resulted in a 10.3% delay in completion of current year shoot extension in all cultivars on 7 August 2014. It also signifi...

Wi-Fi networks are widely deployed for provision of Internet-centric data services. Since the inception of the Wi-Fi network in 1997 with its technical specification rooted in the IEEE 802.11 standard, much progress for higher data throughput has been made. Currently popular IEEE 802.11n Wi-Fi network in 2.4/5 GHz can deliver 600 Mb/s over a 40 MHz channel, which works well for most types of Internet-centric data services, and a later version of a Wi-Fi network based on IEEE 802.11ac is able to transmit at about 7 Gb/s. A simple configuration of a Wi-Fi network consisting of an AP and multiple stations for bidirectional data transmission enables low-cost implementation. High data rate provided at low cost as well as the abundance of Wi- Fi-capable mobile stations recently led to dense deployment of Wi-Fi networks, particularly in residential areas, business offices, and indoor/ outdoor hotspots. However, dense deployment of Wi-Fi networks (e.g., Wi-Fi DenseNets) causes significantly increased overall interference, and as a result a significantly lowered achievable data rate. Thus, it is sensible to consider technologies that can resolve or mitigate deteriorated throughput of Wi-Fi DenseNets. In this article, technologies to deal with throughput enhancement of Wi-Fi DenseNets are addressed from three different perspectives: exploiting cellular technology for data transmission, elevating spectral efficiency, and controlling overall interference levels. Evaluation of interference control for Wi-Fi DenseNets is carried out in this article, and it is found that significant per-node throughput enhancement can be achieved.

In this paper, we examined the necessity of a proper communication protocol in WPT and studied the structure of communication protocol based on SAE J2847 standard. Then, we implemented an integrated wireless charging system using SAE J2847-6 based WPT communication and confirmed its normal operation through experiments. In order to communicate, we proposed CAN data format and used it as the interface between EVCC and regulator. With this structure, normal operation was verified using the corresponding format. In addition, since the standard focuses on a single charge, a communication structure suitable for a multi-charge situation is proposed.

In order to extract phylogenetic information from DNA sequences, alignment-free methods and alignment-based methods are used. Alignment-based methods have high complexity and conventional alignment-free methods have low accuracy. In this paper, a new alignment-free method based on the distribution of repeated k-word measure is proposed. This novel measure is based on k-words and its multiple repeated words. We can get higher performance than conventional word count methods in case of using proposed scheme while maintaining total time complexity. The proposed measure shows better performance compared to conventional alignment-free methods with respect to RF distance.