With the development of graph applications, generative models for graphs have been more crucial. Classically, stochastic models that generate graphs with a pre-defined probability of edges and nodes have been studied. Recently, some models that reproduce the structural features of graphs by learning from actual graph data using machine learning have been studied. However, in these conventional studies based on machine learning, structural features of graphs can be learned from data, but it is not possible to tune features and generate graphs with specific features. In this paper, we propose a generative model that can tune specific features, while learning structural features of a graph from data. With a dataset of graphs with various features generated by a stochastic model, we confirm that our model can generate a graph with specific features.

In this paper, we investigate the convergence speed of the Arimoto-Blahut algorithm. For many channel matrices, the convergence speed is exponential, but for some channel matrices it is slower than exponential. By analyzing the Taylor expansion of the defining function of the Arimoto-Blahut algorithm, we will make the conditions clear for the exponential or slower convergence. The analysis of the slow convergence in this paper is new. Based on this analysis, we will compare the convergence speeds of the Arimoto-Blahut algorithm numerically with the values obtained in our theorems for several channel matrices. The purpose of this paper is to obtain a complete understanding of the convergence speed of the Arimoto-Blahut algorithm.

In network evaluation through simulations, accurately modeling traffic of real networks is difficult. Even if accurate traffic modeling is achieved, it is also difficult to accurately estimate a rate of rare packet loss events. For accurate estimations of rare events, Importance Sampling (IS) based on the change-of-measure technique using traffic models has been investigated. However, these studies are inapplicable for traffic traces of real networks since the applicable traffic models are extremely limited. In this paper, we propose a model-less approach to accurately estimate a packet loss rate through a simulation without directly modeling traffic. The change-ofmeasure is achieved based on traffic traces of networks in our model-less approach. We evaluated the applicability of the modelless approach on a G/M/1/K system with a traffic trace of a real network and confirmed that the model-less approach achieves up to 145 times accurate than normal a trace-driven Monte Carlo (MC) simulation.

For the design of delay/loss sensitive applications (e.g., audio/video conferencing, IP telephony, or telesurgery), it is important to accurately measure metrics along an end-to-end path. To improve the accuracy of end-to-end delay measurements, in our previous work, we have proposed a parallel flow monitoring technique. In this technique, delay samples of a target flow increase by utilizing the observation results of other flows sharing the source/destination with the target flow. In this paper, we extend this delay measurement technique to loss measurements and enable it to fully utilize information of all flows including flows with different source and destination. We confirmed that the proposed method reduces the error of loss rate estimations by 57.5% on average in ns-3 simulations.

In our previous research, we have proposed a parallel flow monitoring method in which the end-to-end delay is accurately measured. The method increases delay samples of a target flow by utilizing the observation results of other flows sharing the source/destination with the target flow. In this paper, we extend this method to loss measurement, and enable it to fully utilize information of all flows including flows with different source and destination. Through NS-3 simulations, we confirmed that the proposed method reduces error of loss rate estimations by 57.5% on average.

It is important to accurately model network traffic when we evaluate Quality of Service (QoS) of networks through simulations. However, for traffic in real networks, it is a tough task to select an appropriate traffic model and tune its parameters. Even if the accurate traffic modeling is achieved, it is also difficult to accurately estimate QoS regarding rare events, such as a packet loss rate in the modern Internet. In this paper, we propose a model-less approach to accurately estimate a packet loss rate through a simulation without directly modeling traffic including real network traffic. We also show the effectiveness of the approach in a simple queueing system as a first step in our development.

In this paper, we investigate the convergence speed of the Arimoto algorithm. By analyzing the Taylor expansion of the defining function of the Arimoto algorithm, we will clarify the conditions for the exponential or 1=N order convergence and calculate the convergence speed. We show that the convergence speed of the 1=N order is evaluated by the derivatives of the Kullback-Leibler divergence with respect to the input probabilities. The analysis for the convergence of the 1=N order is new in this paper. Based on the analysis, we will compare the convergence speed of the Arimoto algorithm with the theoretical values obtained in our theorems.

Recently, the fifth-generation mobile communications system (5G) in higher frequency bands has been extensively studied. Massive MIMO is one of the promising technologies to dramatically increase spectral efficiency. The authors have been engaged on R&D into wideband massive MIMO and beam control technologies for high SHF bands. Recently we have developed a massive MIMO system capable of supporting 16-beam transmission at a 28 GHz band and have conducted its experimental trial. In this paper, we present recent indoor experimental trial results using 16-beam spatial multiplexing at 28 GHz with 500 MHz bandwidth. For a single-user MIMO scenario, it has been verified that the downlink throughput of 25.5 Gbps is reached in the anechoic chamber.

In this paper, we propose an accurate parallel flow monitoring method using active probe packets. Although multiple probe flows are monitored to measure delays on multiple paths in parallel for most measurement applications, information of only one probe flow of the multiple probe flows is utilized to measure an end-to-end delay on a path in conventional active measurement. In addition to information observed by the flow along the path, information of other flows is also utilized for the measurement in the proposed method. Delays on a flow are accurately measured by partially converting the observation results of a flow to those of another flow. Simulations are performed to confirm that the observation results of 72 parallel flows of active measurement are appropriately converted between each other in the proposed method. When the 99th-percentile of an end-to-end delay for each flow are measured, the proposed method achieves up to 95 % reduction of the error, and the error of the worst flow among all flows are reduced by 28%.

Traffic matrix (TM) estimation has been extensively studied for decades. Although conventional estimation techniques assume that traffic volumes are unchanged between origins and destinations, packets are often discarded on a path due to traffic burstiness, silent failures, etc. This paper proposes a novel TM estimation method that works correctly even under packet drops. The method is established on a Boolean fault localization technique; the technique requires fewer counters though it only determines whether each link is healthy. This paper extends the Boolean technique so as to deal with traffic volumes with error bounds just by a small number of counters. Along with submodular optimization for the minimum counter placement, we evaluate our method with real network datasets.

For 5G massive MU-MIMO (Multi-User Multiple-Input and Multiple-Output) systems in high frequency bands, a favorable scenario is a line-of-sight (LOS) environment where the direct paths dominate the spatial channels. In the LOS environment, spatial correlation between user equipments (UEs) tend to be high and its capacity decreases, especially in the case where we have densely distributed UEs. The authors have already proposed a nonlinear precoding scheme incorporating block multi- diagonalization (NL-BMD) for highly correlated channel and demonstrated its effectiveness by computer simulation. In this paper, the authors present indoor propagation measurement results using a channel sounding system with 44 GHz active phased array antenna in the condition that UEs are sparsely and closely distributed. In addition, this paper shows the spectral efficiency of NL-BMD based on the measurement results compared with the conventional precoding schemes.

We consider a search algorithm for the output distribution that achieves the channel capacity of a discrete memoryless channel. We will propose an algorithm by iterated projections of an output distribution onto affine subspaces in the set of output distributions. The problem of channel capacity has a similar geometric structure as that of smallest enclosing circle for a finite number of points in the Euclidean space. The metric in the Euclidean space is the Euclidean distance and the metric in the space of output distributions is the Kullback-Leibler divergence. We consider these two problems based on Amari's α-geometry. Then, we first consider the smallest enclosing circle in the Euclidean space and develop an algorithm to find the center of the smallest enclosing circle. Based on the investigation, we will apply the obtained algorithm to the problem of channel capacity.

The active measurement of network quality, in which probe packets are injected into a network, is hindered by the intrusiveness problem, where the load of the probe traffic itself affects network quality. In this paper, we first demonstrate that there exists a fundamental bound on the accuracy of the conventional active measurement of delay. Second, to transcend that bound, we propose INTEST (INTrusiveness-aware ESTimation), an approach that compensates for delays produced by probe packets for wired networks. We show that INTEST enables an accurate high quantile estimation of delay. We do so through two simulations: a single-hop network composed of a router modeled by M/M/1 queuing, and a realistic multi-hop network modeled by a network simulator.

In this correspondence, we give a sufficient condition for the exponential decay of the tail probability of a nonnegative random variable. We consider the Laplace-Stieltjes transform of the probability distribution function of the random variable. We present a theorem, according to which if the abscissa of convergence of the LS transform is negative finite and the real point on the axis of convergence is a pole of the LS transform, then the tail probability decays exponentially. For the proof of the theorem, we extend and apply so-called a finite form of Ikehara's complex Tauberian theorem by Graham-Vaaler.

We propose a buffer management mechanism, called V-WFQ (virtual weighted fair queueing), for achieving approximately fair bandwidth allocation with a small amount of hardware in high-speed networks. The basic process for allocating bandwidth fairly uses selective packet dropping to compare the measured input rate of the flow with an estimated fair bandwidth share. Though V-WFQ is a hardware-efficient FIFO-based algorithm, it can achieve almost ideal fairness in bandwidth allocation. Simulation results show that V-WFQ achieves a good balance between fairness and link utilization under various simulation conditions.

We investigate the importance sampling (IS) simulation for the sample average of an output sequence from an irreducible Markov chain. The optimal Markov chain used in simulation is known to be a twisted Markov chain, however, the previous proofs are very complicated and do not give us a good perspective. We give a simple and natural proof for the optimality of the simulation Markov chain in terms of the Kullback-Leibler (KL) divergence of Markov chains. The performance degradation of the IS simulation by using a not optimal simulation Markov chain, i.e., the difference between the obtained variance and the minimum variance is shown to be represented by the KL divergence. Moreover, we show a geometric relationship between a simulation Markov chain and the optimal one.

Simple statistical hypothesis testing is investigated by making use of the divergence geometric method. The asymptotic behavior of the minimum value of the error probability of the second kind under the constraint that the error probability of the first kind is bounded above by exp(-rn) is looked for, where r is a given positive number. If r is greater than the divergence of the two probability measures, the so-called converse theorem holds. It is shown that the condition under which the converse theorem holds can be divided into two separate cases by analyzing the geodesic connecting the two probability measures, and, as a result, an explanation is given for the Han-Kobayashi linear function f/sub T/(X).>

Hypothesis testing for two Markov chains is considered. Under the constraint that the error probability of the first kind is less than or equal to exp(-rn), the error probability of the second kind is minimized. The geodesic that connects the two Markov chains is defined. By analyzing the geodesic, the power exponents are calculated and then represented in terms of Kullback-Leibler divergence.>

A basic mathematical function that conjoins the two key conceptions of mutual information and Bayes risk is defined. Based on that function, some asymptotic theorems that verify an important implication of mutual information in the context of practical Bayesian decisionmaking are proven.>

The effect of channel losses on the capacity of noiseless optical communication systems utilizing the number state is studied. Based on these findings, the photon efficiency of these systems is evaluated in the presence of losses. Then, by comparing the photon efficiency of the number-state pulse-position modulation (PPM) and the coherent-state PPM under certain realistic conditions, it is found that the former is always superior to the latter from the perspective of photon efficiency regardless of losses.>

Maximizing the mutual information under a linear input constraint is considered from a geometric point of view. Assuming a suitable regularity condition on the channel matrix, it is found that the probability distribution (PD) equidistant from the row PDs of the channel matrix plays an important role, and the maximum is achieved by the projection of that PD onto the set of PDs satisfying the constraint. The PD attaining the capacity-constraint function is obtained by using Lagrange's method of indeterminate coefficients at most (m-2) times.>

A novel geometrical characterization of the capacity of a discrete memoryless channel is proposed. It is based on Csiszar's theorem, which represents the capacity using the Kullback-Leibler discrimination information. A new geometrical capacity computing method is obtained.>