arXiv subjects

Tatsuaki Kimura

Publications and source records attributed to Tatsuaki Kimura.

10 recordsLinked to original sources

arXiv2023

Periodic handover skipping in cellular networks: Spatially stochastic modeling and analysis

Handover (HO) management is one of the most crucial tasks in dense cellular networks with mobile users. A problem in the HO management is to deal with increasing HOs due to network densification in the 5G evolution and various HO skipping techniques have so far been studied in the literature to suppress excessive HOs. In this paper, we propose yet another HO skipping scheme, called periodic HO skipping. The proposed scheme prohibits the HOs of a mobile user equipment (UE) for a certain period of time, referred to as skipping period, thereby enabling flexible operation of the HO skipping by adjusting the length of the skipping period. We investigate the performance of the proposed scheme on the basis of stochastic geometry. Specifically, we derive analytical expressions of two performance metrics -- the HO rate and the expected downlink data rate -- when a UE adopts the periodic HO skipping. Numerical results based on the analysis demonstrate that the periodic HO skipping scenario can outperform the scenario without any HO skipping in terms of a certain utility metric representing the trade-off between the HO rate and the expected downlink data rate, in particular when the UE moves fast. Furthermore, we numerically show that there can exist an optimal length of the skipping period, which locally maximizes the utility metric, and approximately provide the optimal skipping period in a simple form. Numerical comparison with some other HO skipping techniques is also conducted.

cs.IT↗

arXiv2022

A subgeometric convergence formula for finite-level M/G/1-type Markov chains: via a block-decomposition-friendly solution for the Poisson equation of deviation matrix

This paper studies the subgeometric convergence of the stationary distribution in taking the infinite-level limit of a finite-level M/G/1-type Markov chain, that is, in letting the upper boundary level go to infinity. This study is performed through the fundamental deviation matrix, which is a block-decomposition-friendly solution for the Poisson equation of the deviation matrix. The fundamental deviation matrix yields a difference formula for the respective stationary distributions of the finite-level chain and the corresponding infinite-level chain. The difference formula plays a crucial role in deriving the main result of this paper: a subgeometric convergence formula for the infinite-level limit of the stationary distribution of the finite-level chain.

math.PR↗

arXiv2020

Time-based Handover Skipping in Cellular Networks: Spatially Stochastic Modeling and Analysis

Handover (HO) management has attracted attention of research in the context of wireless cellular communication networks. One crucial problem of HO management is to deal with increasing HOs experienced by a mobile user. To address this problem, HO skipping techniques have been studied in recent years. In this paper, we propose a novel HO skipping scheme, namely, time-based HO skipping. In the proposed scheme, HOs of a user are controlled by a certain fixed period of time, which we call skipping time. The skipping time can be managed as a system parameter, thereby enabling flexible operation of HO skipping. We analyze the transmission performance of the proposed scheme on the basis of a stochastic geometry approach. In the scenario where a user performs the time-based HO skipping, we derive the analytical expressions for two performance metrics: the HO rate and the expected data rate. The analysis results demonstrate that the scenario with the time-based HO skipping outperforms the scenario without HO skipping particularly when the user moves fast. Furthermore, we reveal that there is a unique optimal skipping time maximizing the transmission performance, which we obtain approximately.

cs.NI↗

arXiv2020

DeepSIP: A System for Predicting Service Impact of Network Failure by Temporal Multimodal CNN

When a failure occurs in a network, network operators need to recognize service impact, since service impact is essential information for handling failures. In this paper, we propose Deep learning based Service Impact Prediction (DeepSIP), a system to predict the time to recovery from the failure and the loss of traffic volume due to the failure in a network element using a temporal multimodal convolutional neural network (CNN). Since the time to recovery is useful information for a service level agreement (SLA) and the loss of traffic volume is directly related to the severity of the failures, we regard these as the service impact. The service impact is challenging to predict, since a network element does not explicitly contain any information about the service impact. Thus, we aim to predict the service impact from syslog messages and traffic volume by extracting hidden information about failures. To extract useful features for prediction from syslog messages and traffic volume which are multimodal and strongly correlated, and have temporal dependencies, we use temporal multimodal CNN. We experimentally evaluated DeepSIP and DeepSIP reduced prediction error by approximately 50% in comparison with other NN-based methods with a synthetic dataset.

cs.NI↗

arXiv2020

Distributed Collaborative 3D-Deployment of UAV Base Stations for On-Demand Coverage

Deployment of unmanned aerial vehicles (UAVs) performing as flying aerial base stations (BSs) has a great potential of adaptively serving ground users during temporary events, such as major disasters and massive events. However, planning an efficient, dynamic, and 3D deployment of UAVs in adaptation to dynamically and spatially varying ground users is a highly complicated problem due to the complexity in air-to-ground channels and interference among UAVs. In this paper, we propose a novel distributed 3D deployment method for UAV-BSs in a downlink network for on-demand coverage. Our method consists mainly of the following two parts: sensing-aided crowd density estimation and distributed push-sum algorithm. The first part estimates the ground user density from its observation through on-ground sensors, thereby allowing us to avoid the computationally intensive process of obtaining the positions of all the ground users. On the basis of the estimated user density, in the second part, each UAV dynamically updates its 3D position in collaboration with its neighboring UAVs for maximizing the total coverage. We prove the convergence of our distributed algorithm by employing a distributed push-sum algorithm framework. Simulation results demonstrate that our method can improve the overall coverage with a limited number of ground sensors. We also demonstrate that our method can be applied to a dynamic network in which the density of ground users varies temporally.

cs.NI↗

arXiv2019

Spatio-Temporal Correlation of Interference in MANET Under Spatially Correlated Shadowing Environment

Correlation of interference affects spatio-temporal aspects of various wireless mobile systems, such as retransmission, multiple antennas and cooperative relaying. In this paper, we study the spatial and temporal correlation of interference in mobile ad-hoc networks under a correlated shadowing environment. By modeling the node locations as a Poisson point process with an i.i.d. mobility model and considering Gudmundson (1991)' s spatially correlated shadowing model, we theoretically analyze the relationship between the correlation distance of log-normal shadowing and the spatial and temporal correlation coefficients of interference. Since the exact expressions of the correlation coefficients are intractable, we obtain their simple asymptotic expressions as the variance of log-normal shadowing increases. We found in our numerical examples that the asymptotic expansions can be used as tight approximate formulas and useful for modeling general wireless systems under spatially correlated shadowing.

cs.NI↗

arXiv2019

Theoretical Performance Analysis of Vehicular Broadcast Communications at Intersection and their Optimization

In this paper, we propose an optimization method for the broadcast rate in vehicle-to-vehicle (V2V) broadcast communications at an intersection on the basis of theoretical analysis. We consider a model in which locations of vehicles are modeled separately as queuing and running segments and derive key performance metrics of V2V broadcast communications via a stochastic geometry approach. Since these theoretical expressions are mathematically intractable, we developed closed-form approximate formulae for them. Using them, we optimize the broadcast rate such that the mean number of successful receivers per unit time is maximized. Because of the closed form approximation, the optimal rate can be used as a guideline for a real-time control-method, which is not achieved through time-consuming simulations. We evaluated our method through numerical examples and demonstrated the effectiveness of our method.

cs.PF↗

arXiv2018

Theoretical Framework for Estimating Target-Object Shape by Using Location-Unknown Mobile Distance Sensors

This paper proposes a theoretical framework for estimating a target-object shape, the location of which is not given, by using mobile distance sensors the locations of which are also unknown. Typically, mobile sensors are mounted on vehicles. Each sensor continuously measures the distance from it to the target object. The proposed framework does not require any positioning function, anchor-location information, or additional mechanisms to obtain side information such as angle of arrival of signal. Under the assumption of a convex polygon target object, each edge length and vertex angle and their combinations are estimated and finally the shape of the target object is estimated. To the best of our knowledge, this is the first result in which a target-object shape was estimated by using the data of distance sensors without using their locations.

eess.SP↗

arXiv2016

Subexponential Asymptotics of the Stationary Distributions of GI/G/1-Type Markov Chains

This paper considers the subexponential asymptotics of the stationary distributions of GI/G/1-type Markov chains in two cases: (i) the phase transition matrix in non-boundary levels is stochastic; and (ii) it is strictly substochastic. For the case (i), we present a weaker sufficient condition for the sub exponential asymptotics than those given in the literature. As for the case (ii), the subexponential asymptotics has not been studied, as far as we know. We show that the subexponential asymptotics in the case (ii) is different from that in the case (i). We also study the locally subexponential asymptotics of the stationary distributions in both cases (i) and (ii).

math.PR↗

arXiv2013

Light-tailed asymptotics of stationary tail probability vectors of Markov chains of M/G/1 type

This paper studies the light-tailed asymptotics of the stationary tail probability vectors of a Markov chain of M/G/1 type. Almost all related studies have focused on the typical case, where the transition block matrices in the non-boundary levels have a dominant impact on the decay rate of the stationary tail probability vectors and their decay is aperiodic. In this paper, we study not only the typical case but also atypical cases such that the stationary tail probability vectors decay periodically and/or their decay rate is determined by the tail distribution of jump sizes from the boundary level. We derive light-tailed asymptotic formulae for the stationary tail probability vectors by locating the dominant poles of the generating function of the sequence of those vectors. Further we discuss the positivity of the dominant terms of the obtained asymptotic formulae.

math.PR↗