In the cellular system, the Worst Case User (WCU), whose distances to three nearest BSs are the similar, usually achieves the lowest performance. Improving user performance, especially the WCU, is a big problem for both network designers and operators. This paper works on the WCU in terms of coverage probability analysis by the stochastic geometry tool and data rate optimization with the transmission power constraint by the reinforcement learning technique under the Stretched Pathloss Model (SPLM). In analysis, only fast fading from the WCU to the serving Base Stations (BSs) is taken into the analysis to derive the lower bound coverage probability. Furthermore, the paper assumes that the Coordinated Multi-Point (CoMP) technique is only employed for the WCU to enhance its downlink signal and avoid the explosion of Intercell Interference (ICI). Through the analysis and simulation, the paper states that to improve the WCU performance under bad wireless environments, an increase in transmission power can be a possible solution. However, in good environments, the deployment of advanced techniques such as Joint Transmission (JT), Joint Scheduling (JS), and reinforcement learning is an suitable solution.

In this paper we consider two non-parametric estimation methods for software reliability assessment without specifying the fault-detection time distribution, where the underlying stochastic process to describe software fault-counts in the system testing is given by a non-homogeneous Poisson process. The resulting data-driven methodologies can give the useful probabilistic information on the software reliability assessment under the incomplete knowledge on fault-detection time distribution. Throughout examples with real software fault data, it is shown that the proposed methods provide more accurate estimation results than the common parametric approach.

In this letter we develop a software reliability modeling framework by introducing the Burr XII distributions to software fault-detection time. An extension to deal with software metrics data characterizing the product size, program complexity or testing expenditure is also proposed. Finally, we investigate the goodness-of-fit performance and compare our new models with the existing ones through real data analyses.

Recently, the wavelet-based estimation method has gradually been becoming popular as a new tool for software reliability assessment. The wavelet transform possesses both spatial and temporal resolution which makes the wavelet-based estimation method powerful in extracting necessary information from observed software fault data, in global and local points of view at the same time. This enables us to estimate the software reliability measures in higher accuracy. However, in the existing works, only the point estimation of the wavelet-based approach was focused, where the underlying stochastic process to describe the software-fault detection phenomena was modeled by a non-homogeneous Poisson process. In this paper, we propose an interval estimation method for the wavelet-based approach, aiming at taking account of uncertainty which was left out of consideration in point estimation. More specifically, we employ the simulation-based bootstrap method, and derive the confidence intervals of software reliability measures such as the software intensity function and the expected cumulative number of software faults. To this end, we extend the well-known thinning algorithm for the purpose of generating multiple sample data from one set of software-fault count data. The results of numerical analysis with real software fault data make it clear that, our proposal is a decision support method which enables the practitioners to do flexible decision making in software development project management.

A mobile hotspot is a moving vehicle that hosts an Access Point (AP) such as train, bus and subway where users in these vehicles connect to external cellular network through AP to access their internet services. To meet Quality of Service (QoS) requirements, typically throughput and/or delay, a Call Admission Control (CAC) is needed to restrict the number of users accepted by the AP. In this paper, we analyze a modified guard channel scheme as CAC for mobile hotspot as follows: During a mobile hotspot is in the stop-state, we adopt a guard channel scheme where the optimal number of resource units is reserved for vertical handoff users from cellular network to WLAN. During a mobile hotspot is in the move-state, there are no handoff calls and so no resources for handoff calls are reserved in order to maximize the utility of the WLAN capacity. We model call's arrival and departure processes by Markov Modulated Poisson Process (MMPP) and then we model our CAC by 2-dimensional continuous time Markov chain (CTMC) for single traffic and 3-dimensional CTMC for two types of traffic. We solve steady-state probabilities by the Quasi-Birth and Death (QBD) method and we get various performance measures such as the new call blocking probabilities, the handoff call dropping probabilities and the channel utilizations. We compare our CAC with the conventional guard channel scheme which the number of guard resources is fixed all the time regardless of states of the mobile hotspot. Finally, we find the optimal threshold value on the amount of resources to be reserved for the handoff call subject to a strict constraint on the handoff call dropping probability.

We consider coding for sources that output the symbols according to Poisson process from the viewpoint of real-time transmission. In order to reduce the transmission delay we avoid using input buffers. However, the lack of buffer causes overflow error. The theoretical relation between the transmission rate and the error probability is clarified. It is shown that the optimal code that minimizes the probability of error differs from the code that minimizes the expected codeword length. We also investigate the case of block coding as one of the applications of buffers.

In this paper the problem of determining optimal workload for a load sharing system is considered. The system is composed of total n components and it functions until (n-k+1) components are failed. The works that should be performed by the system arrive at the system according to a homogeneous Poisson process and it is assumed that the system can perform sufficiently large number of works simultaneously. The system is subject to a workload which can be expressed in terms of the arrival rate of the work and the workload is equally shared by surviving components in the system. We assume that an increased workload induces a higher failure rate of each remaining component. The time consumed for the completion of each work is assumed to be a constant or a random quantity following an Exponential distribution. Under this model, as a measure for system performance, we derive the long-run average number of works performed per unit time and consider optimal workload which maximizes the system performance.

Measuring and modeling network traffic is of key importance for the traffic engineering of IP networks, due to the growing diversity of multimedia applications and the need to efficiently support QoS differentiation in the network. Several recent measurements have shown that Internet traffic may incorporate long-range dependence and self-similar characteristics, which can have significant impact on network performance. Self-similar traffic shows variability over many time scales, and this behavior must be taken into account for accurate prediction of network performance. In this paper, we propose a new parameter fitting procedure for a superposition of Markov Modulated Poisson Processes (MMPPs), which is able to capture self-similarity over a range of time scales. The fitting procedure matches the complete distribution of the arrival process at each time scale of interest. We evaluate the procedure by comparing the Hurst parameter, the probability mass function at each time scale, and the queuing behavior (as assessed by the loss probability and average waiting time), corresponding to measured traffic traces and to traces synthesized according to the proposed model. We consider three measured traffic traces, all exhibiting self-similar behavior: the well-known pOct Bellcore trace, a trace of aggregated IP WAN traffic, and a trace corresponding to the popular file sharing application Kazaa. Our results show that the proposed fitting procedure is able to match closely the distribution over the time scales present in data, leading to an accurate prediction of the queuing behavior.

This article theoretically provides the ensemble average and the ensemble variance of membrane potential of an integrate-and-fire neuron, when the neuron receives random spikes from the other neurons. The model assumes that EPSPs rise and fall continuously. Our theoretical result shows good agreement with a numerical simulation.

Asynchronous Transfer Mode (ATM) networks are expected to support a diverse mix of traffic sources requiring different Quality Of Service (QOS) guarantees. This paper initially examines several existing scheduling disciplines which offer delay guarantees in ATM switches. Among them, the Earliest-Due-Date (EDD) discipline has been regarded as one of the most promising scheduling disciplines. The EDD discipline schedules the departure of a cell belonging to a call based on the delay priority assigned for that call during the call set-up. Supporting n delay-based service classes through the use of n respective urgency numbers D0 to Dn-1 (D0D1 Dn-1), EDD allows a class-i cell to precede any class-j (j>i) cell arriving not prior to (Dj-Di)-slot time. The main goal of the paper is to determine the urgency numbers (Dis), based on an in-depth queueing analysis, in an attempt to offer ninety-nine percentile delay guarantees for higher priority calls under various traffic loads. In the analysis, we derive system-time distributions for both high- and low-priority cells based on a discrete-time, single-server queueing model assuming renewal and non-renewal arrival processes. The validity of the analysis is justified via simulation. With the urgency numbers (Dis) determined, we further propose a feasible efficient VLSI implementation architecture for the EDD scheduling discipline, furnishing the realization of QOS guarantees in ATM switches.

This paper presents an alternative traffic model for an ATM multiplexer providing video, voice, image, and data services. The traffic model classifies the input traffic into two types: real-time and non-real-time. The input process for realtime traffic is periodic and correlated, while that for non-realtime traffic is batch Poisson and independent. This multiplexer is assumed to be a priority queueing system with synchronous servers operating on time-frame basis and with separate finite buffers for each type of traffic. State probabilities and performance measures are successfully obtained using a Markov analysis technique and an application of the residue theorem in complex variable. The results can be applied in the design of an ATM multiplexer.

The paper studies a high-ranking node in a broadband integrated services digital network(B-ISDN). The input traffic is classified into two types: real-time and non-real-time. For each type of input traffic, we assume that the message arrival process is a batch Poisson process and that the message size is arbitrarily distributed so as to describe services from narrowband to wideband. We model the high-ranking node by a queueing system with multiple synchronous servers and two separate finite buffers, one for each type of traffic. We derive performance measures exactly by using a two-dimensional imbedded discrete-time Markov chain analysis, within which the transition probabilities are obtained via an application of the residue theorem in complex variables. The performance measures include the blocking probability, delay, and throughput.

Non-homogeneous Poisson Processes (NHPP's) can be applied for analyzing reliability growth models for hardware and/or software. Evaluating the Mean Time Between Failures (MTBF's) for such processes, we can evaluate the present status (the degree of improvement). However, it is difficult to evaluate the MTBF's for such processes analytically except the simplest cases. The so-called instantaneous MTBF's which can be easily evaluated are applied in practice instead of the exact MTBF's. In this paper, we discuss both MTBF's analytically, and derive the conditions for the existence of both exact and instantaneous MTBF's. We further illustrate both MTBF's for the Weibull process and S-shaped reliability growth model numerically.