Markov Chain Model For Analysis Research Articles

A Markov chain model for analysis of physician workflow in primary care clinics.

This paper studies physician workflow management in primary care clinics using terminating Markov chain models. The physician workload is characterized by face-to-face encounters with patients and documentation of electronic health record (EHR) data. Three workflow management policies are considered: preemptive priority (stop ongoing documentation tasks if a new patient arrives); non-preemptive priority (finish ongoing documentation even if a new patient arrives); and batch documentation (start and finish documentation when the desired number of tasks is reached). Analytical formulas are derived to quantify the performance measures of three management policies, such as physician's daily working time, patient's waiting time, and documentation waiting time. A comparison of the results under three policies is carried out. Finally, a case study in a primary care clinic is carried out to illustrate model applicability. Such a work provides a quantitative tool for primary care physicians to design and manage their workflow to improve care quality.

Performance Evaluation of Coexistence of WiFi and LTE Licensed-Assisted Access to Unlicensed Spectrum using Markov chain Analytical Model

LTE with the inclusion MIMO renders high data rate, we introduce a model for system performance evaluation considering typical coexistence of LAA LTE and WiFi. Currently, as the need for high bandwidth to satisfy high data rate demanding services increased significantly over the years, the operators are thinking about various answers for incrementing system limit. As the authorized range has been costly, utilizing LTE in the unlicensed spectrum will be a hopeful strategy to oblige trade requirement for versatile information. Expecting that LAA eNBs perform CCA as a similar way to WiFi CSMA, we utilize Markov chain to help assess the influenced execution of WiFi as well as the connection between the joint framework execution and LAA most extreme transmission time. Nonetheless, LTE needs to exist together with different heterogeneous system, particularly WiFi, prior in the unlicensed medium.

Enabling Green Wireless Sensor Networks: Energy Efficient T-MAC Using Markov Chain Based Optimization

Due to the rapidly growing sensor-enabled connected world around us, with the continuously decreasing size of sensors from smaller to tiny, energy efficiency in wireless sensor networks has drawn ample consideration in both academia as well as in industries’ R&D. The literature of energy efficiency in wireless sensor networks (WSNs) is focused on the three layers of wireless communication, namely the physical, Medium Access Control (MAC) and network layers. Physical layer-centric energy efficiency techniques have limited capabilities due to hardware designs and size considerations. Network layer-centric energy efficiency approaches have been constrained, in view of network dynamics and available network infrastructures. However, energy efficiency at the MAC layer requires a traffic cooperative transmission control. In this context, this paper presents a one-dimensional discrete-time Markov chain analytical model of the Timeout Medium Access Control (T-MAC) protocol. Specifically, an analytical model is derived for T-MAC focusing on an analysis of service delay, throughput, energy consumption and power efficiency under unsaturated traffic conditions. The service delay model calculates the average service delay using the adaptive sleep wakeup schedules. The component models include a queuing theory-based throughput analysis model, a cycle probability-based analytical model for computing the probabilities of a successful transmission, collision, and the idle state of a sensor, as well as an energy consumption model for the sensor’s life cycle. A fair performance assessment of the proposed T-MAC analytical model attests to the energy efficiency of the model when compared to that of state-of-the-art techniques, in terms of better power saving, a higher throughput and a lower energy consumption under various traffic loads.

License assisted access-WiFi coexistence with TXOP backoff for LTE in unlicensed band

License assisted access (LAA) was proposed as a solution to the challenge of satisfying high throughput requirement in cellular network with limited licensed band. LAA uses long term evolution (LTE) carrier aggregation technique to combine licensed and unlicensed bands, specifically 5 GHz WiFi band, in order to achieve high data rates. However, LAA has several critical concerns for services provision on 5 GHz unlicensed band. In this article, an overview of the techniques concerning the coexistence of LTE and WiFi on the same unlicensed band is presented. We first present several general approaches including power control of LTE evolved Node B (eNB), carrier selection, LTE duty cycling, self clear to send (CTS) and request to send/CTS (RTS/ CTS), and listen before talk (LBT). Then, we focus on LBT techniques for LAA-WiFi coexistence and discuss how LBT based coexistence solutions can dynamically respond to the variations in network load and ensure fair coexistence in a distributed manner. Based on the LBT mechanism, we also present a fairness-aware LAA-WiFi coexisting scheme with TXOP (transmitted in a single transmission opportunity) backoff for LAA eNB. Finally, the performances of various LBT solutions for LAA-WiFi coexistence are evaluated using Markov chain analytical model.

Performance of Virtual Full-Duplex Relaying on Cooperative Multi-path Relay Channels

We consider a cooperative multi-path relay channel (MPRC) where multiple half-duplex relays assist in the packet transmissions from a source to its destination. A virtual full-duplex (FD) relaying scheme is proposed that allows the source to transmit a new packet simultaneously with the selected best relay, with the rest of the relays attempting to decode this new packet. Thus, a new source packet can be served in each time slot, as in FD relay systems. Taking into account the effect of inter-relay interference (IRI) that is caused by simultaneous relay and source transmissions, a Markov chain analytical model is used to characterize the decoding performance at the relays, based on which the overall outage probability of MPRC is obtained in closed-form expressions. The asymptotic performance analysis reveals that in low rate scenarios, a close-to-full diversity order is achieved by the proposed scheme while substantially improving the spectrum efficiency. In high rate scenarios, the decoding performance of relays is limited by IRI and the system outage performance experiences an error floor. Simulation results demonstrate the performance gains of the proposed scheme by comparisons with existing half-duplex and FD relay systems in the literature.

Semantically Enriched Variable Length Markov Chain Model for Analysis of User Web Navigation Sessions

The rapid growth of the World Wide Web has resulted in intricate Web sites, demanding enhanced user skills to find the required information and more sophisticated tools that are able to generate apt recommendations. Markov Chains have been widely used to generate next-page recommendations; however, accuracy of such models is limited. Herein, we propose the novel Semantic Variable Length Markov Chain Model (SVLMC) that combines the fields of Web Usage Mining and Semantic Web by enriching the Markov transition probability matrix with rich semantic information extracted from Web pages. We show that the method is able to enhance the prediction accuracy relatively to usage-based higher order Markov models and to semantic higher order Markov models based on ontology of concepts. In addition, the proposed model is able to handle the problem of ambiguous predictions. An extensive experimental evaluation was conducted on two real-world data sets and on one partially generated data set. The results show that the proposed model is able to achieve 15–20% better accuracy than the usage-based Markov model, 8–15% better than the semantic ontology Markov model and 7–12% better than semantic-pruned Selective Markov Model. In summary, the SVLMC is the first work proposing the integration of a rich set of detailed semantic information into higher order Web usage Markov models and experimental results reveal that the inclusion of detailed semantic data enhances the prediction ability of Markov models.

Hopping control channel MAC protocol for opportunistic spectrum access networks

Opportunistic spectrum access (OSA) is considered as a promising approach to mitigate spectrum scarcity by allowing unlicensed users to exploit spectrum opportunities in licensed frequency bands. Derived from the existing channel-hopping multiple access (CHMA) protocol, we introduce a hopping control channel medium access control (MAC) protocol in the context of OSA networks. In our proposed protocol, all nodes in the network follow a common channel-hopping sequence; every frequency channel can be used as control channel and data channel. Considering primary users' occupancy of the channel, we use a primary user (PU) detection model to calculate the channel availability for unlicensed users' access. Then, a discrete Markov chain analytical model is applied to describe the channel states and deduce the system throughput. Through simulation, we present numerical results to demonstrate the throughput performance of our protocol and thus validate our work.

Performance of collaborative codes in CSMA/CD environment

AbstractA new medium access control scheme is proposed for implementing collaborative codes in a system using carrier sense multiple access with collision detection protocol (CC‐CSMA/CD). We also propose a new backoff algorithm which is simple to implement and to analyse. A discrete‐time Markov chain analytical model is developed for CC‐CSMA/CD. The resulting model describes the regular CSMA/CD as a special case. Protocol performance measures were studied such as throughput, packet acceptance probability, average packet delay and channel utilisation. It is found that CC‐CSMA/CD offers improvements over a system that uses CSMA/CD in terms of throughput, packet acceptance probability, delay and channel utilisation. Copyright © 2006 AEIT.

A Markov Chain Model for Medical Record Analysis

This paper presents a Markov chain model for the analysis of a centralized medical record system in a large general hospital. The model is used to describe the dynamic behaviour of the age of records associated with patients' visits to a hospital and provides a method for the long-term planning of the medical record storage facilities.

A Markov Chain Model for Medical Record Analysis

This paper presents a Markov chain model for the analysis of a centralized medical record system in a large general hospital. The model is used to describe the dynamic behaviour of the age of records associated with patients' visits to a hospital and provides a method for the long-term planning of the medical record storage facilities.

A Markov chain model for analysis and operation in learning control systems

A Markov chain model for analysis and operation in learning control systems