Delay Probability Research Articles

Multimodel Approach to Robust Identification of Multiple-Input Single-Output Nonlinear Time-Delay Systems

The robust multimodel solution for multiple-input single-output nonlinear time-delay systems identification with polluted outputs is derived in this article. First, all the local autoregressive exogenous models are preidentified at the working points; then, the global system model is built by interpolating the local models with a smoothing strategy. The outliers and input time-delays which often increase the nonideality of process data are both considered. To cope with the outliers, the Laplace distribution is reutilized to describe the output measurement process and the negative impact resulted from each outlier imposed on parameter estimation can be suppressed through automatically assigned small weight. The parameter estimation procedure is realized with the expectation–maximization algorithm and the joint posterior probability of all input delays is also maximized to calculate the unknown input time-delays. With the verifications on a numerical example and the continuous fermentation process, the validity of the proposed approach is proved.

Learning Automata-Based Access Class Barring Scheme for Massive Random Access in Machine-to-Machine Communications

The machine-to-machine (M2M) communications, which achieve the implementation of Internet of Things (IoT), can be carried over wireless cellular networks. The massive random access (RA) in M2M communications will cause radio access network congestion in the base station (BS), leading to sharp deterioration in access delay and access probability. Access class barring (ACB) that can directly control the flow of machine-type communication (MTC) devices by an ACB factor is an efficient scheme to prevent the BS from traffic overload. In wireless cellular networks, the RA resources (i.e., preambles) are shared by M2M and human-to-human (H2H) devices, and research on ACB scheme ordinarily assumes that a restricted number of preambles are assigned to M2M traffic. However, when suffering from massive access in M2M communications, it is desirable to rapidly satisfy the access requests from MTC devices using all available preambles, especially in time-sensitive IoT scenarios. In this paper, we study the massive access problem in M2M traffic centered scenarios where M2M and H2H traffic can apply for all available preambles without distinction. Utilizing the self-adaptive learning property of learning automata, we further propose a novel learning automata-based ACB (LA-ACB) scheme. Simulation results show that the LA-ACB scheme achieves the performance close to theoretical optimality. The BS equipped with the LA-ACB scheme can effectively control the M2M traffic by dynamically adjusting the ACB factor under the interference of H2H traffic and provide quality services for both M2M and H2H traffic.

Destination buffer analysis for packets rejection obliteration in multi-channel networks

In this paper we study a multi-channel network, each station of which is equipped with a network interface that has a receiver buffer of multiple packets. In this way, each station is able to receive multiple packets per time instant. We adopt a synchronous access protocol which is affected by the collisions over the multiple channels and the destination conflicts. The proposed protocol performance crucially depends on the size of the receiver buffer since it determines the packet rejection probability at destination. An analytical probabilistic model based on a Markovian process is adopted for the performance measures derivation by means of closed mathematical formulas. The proposed protocol is compared to relative ones that either totally ignore the receiver collisions or assume a receiver buffer of a single packet capacity. The numerical results show that the increase of the receiver buffer size improves the performance decisively, resulting in higher throughput and lower delay and rejection probability. Also, it is shown that the appropriate receiver buffer size per station in order for the rejections at destination to be effectively eliminated is not unlimited but is limited to three packets for 0.1% accepted maximum level of rejection probability.

Physician Staffing in Emergency Rooms (ERs): Opening the Black-box of ER Care via a Multi-Class Multi-Stage Network

Problem Definition and Relevance: Characterized by time-varying arrivals, multi-stage service, and multi-class patient population, emergency rooms (ERs) are complex healthcare delivery systems, where optimizing the staffing levels of physicians is a challenge. In collaboration with Mayo Clinic, we study a staffing and an associated routing problem for ER physicians and propose a new staffing rule to meet tail probability of delay (TPoD) type service targets. Methodology: We capture the time-varying patient flow in the ER with a multi-class multi-stage queuing network, describing the ER care as sequences of treatment queues, where physicians serve, and groups of diagnostic medical processes. Treatment stations in ER are busy-server queues, where waiting before service is common, but experience negligible abandonments. Motivated by these queues, we propose a new staffing algorithm, translating the offered load into staffing decisions for efficiency-driven queues with TPoD targets and perfectly patient customers. Results: We analytically show the asymptotic effectiveness of our staffing rule on stabilizing TPoD for M/M/s queues, operating in efficiency-driven mode, and numerically demonstrate its robustness and optimality in various time-varying ER settings via realistic and data-driven simulation experiments. We further show that as the service complexity of an ER increases, hybrid routing rules, using pre-determined (static) priorities and (dynamic) current system state jointly, become necessary to meet TPoD targets. Managerial Implications: Instead of treating the entire patient length of stay as a black-box service model, our multi-stage network model provides more managerial control over the internal components of ER service and is easily implementable in practice.

Statistical delay distribution analysis on high-speed railway trains

The focus of this study is to explore the statistical distribution models of high-speed railway (HSR) train delays. Based on actual HSR operational data, the delay causes and their classification, delay frequency, number of affected trains, and space–time delay distributions are discussed. Eleven types of delay events are classified, and a detailed analysis of delay distribution for each classification is presented. Models of delay probability delay probability distribution for each cause are proposed. Different distribution functions, including the lognormal, exponential, gamma, uniform, logistic, and normal distribution, were selected to estimate and model delay patterns. The most appropriate distribution, which can approximate the delay duration corresponding to each cause, is derived. Subsequently, the Kolmogorov–Smirnov (K–S) test was used to test the goodness of fit of different train delay distribution models and the associated parameter values. The test results show that the distribution of the test data is consistent with that of the selected models. The fitting distribution models show the execution effect of the timetable and help in finding out the potential conflicts in real-time train operations.

Caching in Heterogeneous Ultradense 5G Networks: A Comprehensive Cooperation Approach

Rich multimedia services have significantly increased the traffic load over mobile networks. Emerging mobile content caching techniques can efficiently relieve overloaded network traffic by caching popular contents at intermediate nodes and routers. Furthermore, stimulated by network densification, coordinated multipoint (CoMP) joint transmission (JT) techniques are expected to have a significant role in 5G networks. In this article, we present a system architecture for a cache-enabled heterogeneous ultradense network (HUDN) that consists of CoMP-integrated ultradense cells and cluster-based device-to-device (D2D) networks. We propose three cooperative caching schemes in cellular networks, D2D networks, and cross-tier networks. In the proposed schemes, caching decisions will be made by considering the content?s popularity, the device distribution, the transmission method, and the caching capability. Numerical evaluations reveal that the proposed schemes outperform existing ones in terms of delay and cache hit probability.

Toward Ultrareliable Low-Latency Communications: Typical Scenarios, Possible Solutions, and Open Issues

Ultrareliable low-latency communications (URLLC) is one of three emerging application scenarios in 5G new radio (NR) for which physical layer design aspects have been specified. With 5G NR, we can guarantee reliability and latency in radio access networks. However, for communication scenarios where the transmission involves both radio access and wide-area core networks, the delay in radio access networks contributes to only a portion of the end-toend (E2E) delay. In this article, we outline the delay components and packet loss probabilities in typical URLLC scenarios and formulate the constraints on E2E delay and overall packet loss probability. Then, we summarize possible solutions in the physical, link, and network layers as well as the cross-layer design. Finally, we discuss open issues in prediction and communication codesign for URLLC in wide-area, largescale networks.

Linking demyelination to compound action potential dispersion with a spike-diffuse-spike approach

To establish and exploit novel biomarkers of demyelinating diseases requires a mechanistic understanding of axonal propagation. Here, we present a novel computational framework called the stochastic spike-diffuse-spike (SSDS) model for assessing the effects of demyelination on axonal transmission. It models transmission through nodal and internodal compartments with two types of operations: a stochastic integrate-and-fire operation captures nodal excitability and a linear filtering operation describes internodal propagation. The effects of demyelinated segments on the probability of transmission, transmission delay and spike time jitter are explored. We argue that demyelination-induced impedance mismatch prevents propagation mostly when the action potential leaves a demyelinated region, not when it enters a demyelinated region. In addition, we model sodium channel remodeling as a homeostatic control of nodal excitability. We find that the effects of mild demyelination on transmission probability and delay can be largely counterbalanced by an increase in excitability at the nodes surrounding the demyelination. The spike timing jitter, however, reflects the level of demyelination whether excitability is fixed or is allowed to change in compensation. This jitter can accumulate over long axons and leads to a broadening of the compound action potential, linking microscopic defects to a mesoscopic observable. Our findings articulate why action potential jitter and compound action potential dispersion can serve as potential markers of weak and sporadic demyelination.

Slotted Secondary Transmission With Adaptive Modulation and Coding Under Interweave Cognitive Radio

In this paper, we propose a discrete-time Markov model to characterize slotted secondary transmission process under interweave cognitive implementation. Stationary distribution is solved in closed form to evaluate SU-to-PU collision probability and SU successful transmission probability. Assuming that SU transmission adopts adaptive modulation and coding (AMC), we then carry out the queuing analysis in terms of throughput, queuing delay, and packet drop probability, based on a two-dimensional finite-state Markov chain for small-size packet transmission. The optimal length of secondary slot is solved by maximizing throughput subject to an SU-to-PU collision probability constraint. For large-size packet transmission, the probability mass function of extended delivery time for secondary packet transmission is also derived. Selected numerical results are presented to illustrate the mathematical formulas and to validate our research results.

Prospective Validation of Prediction Model for Kidney Discard.

Many kidneys are discarded every year, with 3631 kidneys discarded in 2016 alone. Identifying kidneys at high risk of discard could facilitate "rescue" allocation to centers more likely to transplant them. The Probability of Delay or Discard (PODD) model was developed to identify marginal kidneys at risk of discard or delayed allocation beyond 36 hours of cold ischemia time. However, PODD has not been prospectively validated, and patterns of discard may have changed after policy changes such as the introduction of Kidney Donor Profile Index and implementation of the Kidney Allocation System (KAS). We prospectively validated the PODD model using Scientific Registry of Transplant Recipients data in the KAS era (January 1, 2015, to March 1, 2018). C statistic was calculated to assess accuracy in predicting kidney discard. We assessed clustering in centers' utilization of kidneys with PODD >0.6 ("high-PODD") using Gini coefficients. Using match run data from January 1, 2015, to December 31, 2016, we examined distribution of these high-PODD kidneys offered to centers that never accepted a high-PODD kidney. The PODD model predicted discard accurately under KAS (C-statistic, 0.87). Compared with utilization of low-PODD kidneys (Gini coefficient = 0.41), utilization of high-PODD kidneys was clustered more tightly among a few centers (Gini coefficient, 0.84 with >60% of centers never transplanted a high-PODD kidneys). In total, 11684 offers (35.0% of all high-PODD offers) were made to centers that never accepted a high-PODD kidney. Prioritizing allocation of high-PODD kidneys to centers that are more likely to transplant them might help reduce kidney discard.

LMI conditions for delay probability distribution dependent robust stability analysis of markovian jump stochastic neural networks with time-varying delays

LMI conditions for delay probability distribution dependent robust stability analysis of markovian jump stochastic neural networks with time-varying delays

Reliable Transmission of Short Packets Through Queues and Noisy Channels Under Latency and Peak-Age Violation Guarantees

This paper investigates the probability that the delay and the peak-age of information exceed a desired threshold in a point-to-point communication system with short information packets. The packets are generated according to a stationary memoryless Bernoulli process, placed in a single-server queue and then transmitted over a wireless channel. A variable-length stop-feedback coding scheme—a general strategy that encompasses simple automatic repetition request (ARQ) and more sophisticated hybrid ARQ techniques as special cases—is used by the transmitter to convey the information packets to the receiver. By leveraging finite-blocklength results, the delay violation and the peak-age violation probabilities are characterized without resorting to approximations based on larg-deviation theory as in previous literature. Numerical results illuminate the dependence of delay and peak-age violation probability on system parameters such as the frame size and the undetected error probability, and on the chosen packet-management policy. The guidelines provided by our analysis are particularly useful for the design of low-latency ultra-reliable communication systems.

Discrete Robustness Optimization on Emergency Transportation Network Based on Prospect Theory

This paper focuses on the discrete robustness optimization of emergency transportation network with the consideration of timeliness and decision behavior of decision-maker under the limited rationality. Based on a situation that the nearer to disaster area, the higher probability of time delay, prospect theory is specially introduced to reflect the subjective decision behavior of decision-maker. Then, a discrete robustness optimization model is proposed with the purpose of the better timeliness and robustness. The model is based on the emergency transportation network with multistorage centers and multidisaster points. In order to obtain the optimal solution, an improved genetic algorithm is designed by introducing a bidirectional search strategy based on a newfangled path cluster to obtain specific paths that connect each storage centers and each disaster points. Finally, a case study is exhibited to demonstrate the reasonability of the model, theory, and algorithm. The result shows that the path cluster with the better timeliness and robustness can be well obtained by using the prospect theory and improved genetic algorithm. The analysis especially reveals that the robustness is correspondent to the risk aversion in prospect theory.

A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning.

The widespread popularity of smartphones makes it possible to provide Location-Based Services (LBS) in a variety of complex scenarios. The location and contextual status, especially the Indoor/Outdoor switching, provides a direct indicator for seamless indoor and outdoor positioning and navigation. It is challenging to quickly detect indoor and outdoor transitions with high confidence due to a variety of signal variations in complex scenarios and the similarity of indoor and outdoor signal sources in the IO transition regions. In this paper, we consider the challenge of switching quickly in IO transition regions with high detection accuracy in complex scenarios. Towards this end, we analyze and extract spatial geometry distribution, time sequence and statistical features under different sliding windows from GNSS measurements in Android smartphones and present a novel IO detection method employing an ensemble model based on stacking and filtering the detection result by Hidden Markov Model. We evaluated our algorithm on four datasets. The results showed that our proposed algorithm was capable of identifying IO state with 99.11% accuracy in indoor and outdoor environment where we have collected data and 97.02% accuracy in new indoor and outdoor scenarios. Furthermore, in the scenario of indoor and outdoor transition where we have collected data, the recognition accuracy reaches 94.53% and the probability of switching delay within 3 s exceeds 80%. In the new scenario, the recognition accuracy reaches 92.80% and the probability of switching delay within 4 s exceeds 80%.

Effects of partial time delays on synchronization patterns in Izhikevich neuronal networks

Synchronization patterns have been observed in neuronal networks and are related to many cognitive functions and information processing and even some pathological brain states. In this paper, we study a ring network of non-locally coupled Izhikevich neurons with electrical synaptic coupling. Since it has been proved that time delays through gap junctions can simplify the synchronization, here we particularly investigate the effects of partial time delays on networks synchronization. By using two control parameters, the time delay and the probability of partial time delay, we show that partial time delays have a significant effect on the synchronization of this network. In particular, partial time delays can either increase or decrease the synchronization and also can induce synchronization transitions between coherent and incoherent states. Thus, partial time delays can cause chimera state, which is a special pattern when both synchronous and asynchronous states coexist and are strongly related to many real phenomena. Furthermore, partial time delays can change the period of synchronized neurons from period-1 to period-2 firing states that have different effects on information transmission in the brain.

Multi-Hop Wireless Transmission in Multi-Band WLAN Systems: Proposal and Future Perspective

Recently, WLANs using multiple frequency bands (e.g., 5 GHz, 2.4 GHz, and 920 MHz) have become popular as a means to increase spectral efficiency. However, during multi-hop transmissions in such multi-band WLANs, the relay node experiences a significant increase in delay and loss of data frames. This happens because of the difference of link rates between the receiving and sending sides of the relay node. In order to address this problem, we consider the channel conditions of each band on either side of the relay. Accordingly, the receiving and sending rates at the relay node are measured and calculated, respectively. Based on the measured receiving rate and calculated sending rate, we propose an algorithm for the relay node to determine the optimal coding rate and modulation method so as to dynamically control the best band and channel selection for the sending side. Simulation results demonstrate the effectiveness of our proposed algorithm in terms of improvement of delay and transmission failure probability.

Performance modeling and analysis of TCP and UDP flows over software defined networks

Software Defined Networking (SDN) decouples the control plane from the data plane, thereby facilitating network virtualization, dynamic programmability and flexibility in network management. Previous studies on SDN modeling focus only on packet-level arrivals. However, if flow-level arrivals are not also considered, the model cannot properly reflect the true probability of packets being sent to the controller. Accordingly, the present study proposes two analytical models for predicting the performance of TCP and UDP flows over SDN, respectively, given the assumption of both flow-level arrivals and packet-level arrivals. In constructing the models, the switch and controller are considered jointly and four-dimensional states are used to evaluate the steady-state probabilities of the states. Analytical formulae are derived for the average packet delay and packet loss probability of the TCP and UDP flows. Simulation results very well match with the analytical ones, thereby validating our analytical models. The results show that TCP significantly outperforms UDP over SDN architectures. In particular, TCP reduces the packet delay by 12 ∽ 50% and the packet loss probability by 25 ∽ 100%.

Maximum likelihood regression tree with two-variable splitting scheme for subway incident delay

ABSTRACTConsidering possible variable interaction effects, this study develops a maximum likelihood regression tree-based (MLRT) model using the proposed two-variable splitting method to describe subway incident delays. A MLRT comprising 13 leaf nodes is built with Hong Kong subway incident data from 2005 to 2012 and a log-logistic distributed accelerated failure time (AFT) model is developed separately for each leaf node. The comparison of model performance indicates that our developed model outperforms traditional AFT models and the tree-based model building based on the traditional single-variable splitting scheme. The probability of subway incident delay being unacceptable can be predicted using our developed model, which can be utilized as a basis for alerting commuters to the necessity of rescheduling their trips in the event of a subway incident.

Optimized State Estimation of Uncertain Linear Time-Varying Complex Networks With Random Sensor Delay Subject to Uncertain Probabilities

This paper is concerned with the optimal state estimation problem for a class of time-varying uncertain dynamical networks with mixed time-delay under uncertain probabilities. Here, the mixed time-delays include the constant time-delay and the random sensor delay, where the random sensor delay is depicted by a Bernoulli distributed random variable and the occurrence probability of the random sensor delay can be uncertain. The major novelty of the paper lies in that a new time-varying state estimation algorithm is given such that, for all parameter uncertainties, mixed time-delays and uncertain probabilities, a locally optimal upper bound of the estimation error covariance is obtained and the desirable estimator parameter of easy-to-implement feature is designed. Moreover, the performance evaluation problem of the presented estimation algorithm is solved, where the monotonicity analysis is shown regarding the trace of the upper bound and the deterministic occurrence probability of random sensor delay. At last, the simulations are given to show the validity and correctness of the proposed time-varying estimation method. In particular, the comparisons are given to show the relationship of the upper bound and occurrence probability.

MPLS Network Parameters Analysis when Changing the Topology

One of the main tasks in network design is to evaluate the probability-time characteristics of the functioning of information and communication networks. Considering that the traffic of modern networks possesses the properties of self-similarity, we observe the difficulties while using the traditional mathematical apparatus of public serve theory. This paper presents us the results of jitter simulation estimating the delay, the probability of traffic loss when traffic processing in MPLS network. It was taken the real multimedia traffic and an on-off flow with a distribution of Pareto periods on and off as the researched flows. As the result, we discovered that when the network load increases or network size increases, jitter usually decreases, while the delay and loss probability increase.