Drop Probability Research Articles

Quantifying the Impact of Guard Capacity on Session Continuity in 3GPP New Radio Systems

Dynamic blockage of millimeter-wave (mmWave) radio propagation paths by dense moving crowds calls for advanced techniques to preserve session continuity in the emerging New Radio (NR) systems. To further improve user performance by balancing the new and ongoing session drop probabilities, we investigate the concept of guard capacity – reserving a fraction of radio resources at the NR base stations exclusively for the sessions already accepted for service. To this aim, we develop a detailed mathematical framework that takes into account the key effects in mmWave systems, including the heights of communicating entities, blocker geometry and mobility, modulation and coding schemes and antenna array geometry, as well as radio propagation and queuing specifics. Using our framework, which enables sessions to change their resource requirements during service, we demonstrate that reserving even a small fraction of bandwidth (less than $10\%$ ) exclusively for the sessions already accepted by the system allows to enhance session continuity at the expense of a slight growth in the new session drop probability as well as a small decrease in the resource utilization (approximately $5\hbox{--}7\%$ ). Furthermore, guard capacity is shown to perform better in overloaded conditions and with sessions having high data rate requirements, thus making it particularly useful for the NR systems. Our results indicate that guard capacity is a viable option for improving session continuity that can be used by the network operators in combination with other techniques, such as multi-connectivity, to maintain user experience.

Generalized TCP-RED dynamical model for Internet congestion control

Abstract Adaptive management of traffic congestion in the Internet is a complex problem that can gain useful insights from a dynamical approach. In this paper we propose and analyze a one-dimensional, discrete-time nonlinear model for Internet congestion control at the routers. Specifically, the states correspond to the average queue sizes of the incoming data packets and the dynamical core consists of a monotone or unimodal mapping with a unique fixed point. This model generalizes a previous one in that additional control parameters are introduced via the data packet drop probability with the objective of enhancing stability. To make the analysis more challenging, the original model was shown to exhibit the usual features of low-dimensional chaos with respect to several system and control parameters, e.g., positive Lyapunov exponents and Feigenbaum-like bifurcation diagrams. We concentrate first on the theoretical aspects that may promote the unique stationary state of the system to a global attractor, which in our case amounts to global stability. In a second step, those theoretical results are translated into stability domains for robust setting of the new control parameters in practical applications. Numerical simulations confirm that the new parameters make it possible to extend the stability domains, in comparison with previous results. Therefore, the present work may lead to an adaptive congestion control algorithm with a more stable performance than other algorithms currently in use.

The study of environmental and human factors affecting aquifer depth changes using tree algorithm

In recent years, more attention has been paid to water resources due to the development of agriculture and proper planning for better management of aquifers. Considering the effect of different factors on aquifer depth changes, in this study, human and environmental factors affecting the depth of aquifer changes in Qazvin plain were used. The Classification And Regression Tree (CART) algorithm was adopted to investigate and predict changes in aquifer depth. According to the results, the highest probability of the aquifer drop observed in July, August and September was 86.5%, and the highest probability of uprising aquifer depth arisen in December, January, February and March was 71.2%. According to sensitivity analysis by CART algorithm, the most important human and environmental factors affecting the number of aquifer depth changes in Qazvin plain were groundwater withdrawal from the agricultural abstraction well and the air temperature, respectively. Therefore, predicting the amount of aquifer depth changes by CART model, planning and managing groundwater resources is possible.

Remote Estimation Over a Packet-Drop Channel With Markovian State

We investigate a remote estimation problem in which a transmitter observes a Markov source and chooses the power level to transmit it over a time-varying packet-drop channel. The channel is modeled as a channel with Markovian state where the packet drop probability depends on the channel state and the transmit power. A receiver observes the channel output and the channel state and estimates the source realization. The receiver also feeds back the channel state and an acknowledgment for successful reception to the transmitter. We consider two models for the source---finite state Markov chains and first-order autoregressive processes. For the first model, using ideas from team theory, we establish the structure of optimal transmission and estimation strategies and identify a dynamic program to determine optimal strategies with that structure. For the second model, we assume that the noise process has unimodal and symmetric distribution. Using ideas from majorization theory, we show that the optimal transmission strategy is symmetric and monotonic and the optimal estimation strategy is like Kalman filter. Consequently, when there are a finite number of power levels, the optimal transmission strategy may be described using thresholds that depend on the channel state. Finally, we propose a simulation based approach (Renewal Monte Carlo) to compute the optimal thresholds and optimal performance and elucidate the algorithm with an example.

A GLRT-Based Mechanism for Detecting Relay Misbehavior in Clustered IoT Networks

Clustering Internet of Things (IoT) networks, to alleviate the network scalability problem, provides an opportunity for an adversary to compromise a set of nodes by simply compromising the relay they are associated with. In such scenarios, an adversary who has compromised the relay can affect the network’s performance by deliberately dropping the packets transmitted by the IoT devices and/or by corrupting the packets to be forwarded by the relay. In this way, the adversary can successfully mimic a bad radio channel between the IoT devices and the relay, thereby requiring the IoT devices to retransmit more frequently. Such a strategy increases the processing load on the IoT devices and will drain their batteries at a faster rate. To detect such an attack, we present hybrid intrusion detection systems that rely on the monitoring of uplink and downlink packets transmitted between IoT devices and the relay. Specifically, we compare the observed packet drop probabilities against their long-term expected values. The detection rules proposed originate from the generalized likelihood ratio test, where the adversary parameters are estimated using maximum likelihood estimation. A semi-analytical approach to obtain the expressions for the false alarm probability is presented in order to determine the decision thresholds. Results presented show the effectiveness of the proposed detection systems, demonstrate the impact of the choice of adversary parameters on them, and validate the expressions obtained for the false alarm probability.

A route stability-based multipath QoS routing protocol in cognitive radio ad hoc networks

Secondary user (SU) mobility, primary user activity, and spectrum sensing inaccuracy are the main challenges to the stability of a quality of service (QoS) route in cognitive radio ad hoc networks (CRAHNs). In this work, a new routing protocol for CRAHNs, referred to as the route stability based multipath QoS routing protocol (SMQRP), is proposed. The proposed protocol finds the optimal primary and alternative paths between an SU source and an SU destination and guarantees the use of the optimal primary and alternative channels along the paths. A proposed routing metric (SMQ) used for path and channel decisions is applied to the proposed protocol. The main idea is to use different routing metrics to select QoS paths with higher stability. The SMQRP was compared in this category with other routing protocols and was evaluated based on CRAHNs parameters. The results of the simulations show significant improvements with respect to the average end-to-end delay, packet drop probability, and throughput.

HRED, An Active Queue Management Algorithm for TCP Congestion Control

Background: Active Queue Management (AQM) is a TCP congestion avoidance approach that predicts congestion before sources overwhelm the buffers of routers. Random Early Detection (RED) is an AQM strategy that keeps history of queue dynamics by estimating an average queue size parameter avg and drops packets when this average exceeds preset thresholds. The parameter configuration in RED is problematic and the performance of the whole network could be reduced due to wrong setup of these parameters. Drop probability is another parameter calculated by RED to tune the drop rate with the aggressiveness of arriving packets. Objective: In this article, we propose an enhancement to the drop probability calculation to increase the performance of RED. Methods: This article studies the drop rate when the average queue size is at the midpoint between the minimum and maximum thresholds. The proposal suggests a nonlinear adjustment for the drop rate in this area. Hence, we call this strategy as the Half-Way RED (HRED). Results: Our strategy is tested using the NS2 simulator and compared with some queue management strategies including RED, TD and Gentle-RED. The calculated parameters are: throughput, link utilization and packet drop rate. Conclusion: Each performance parameter has been plotted in a separate figure; then the robustness of each strategy has been evaluated against these parameters. The results suggest that this function has enhanced the performance of RED-like strategies in controlling congestion. HRED has outperformed the strategies included in this article in terms of throughput, link utilization and packet loss rate.

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.

Exploiting Self-Reserving Spectrum to Reduce Service Dropping Probability in Cognitive Radio Systems

Exploiting Self-Reserving Spectrum to Reduce Service Dropping Probability in Cognitive Radio Systems

An Evolutionary Scheme for Secondary Virtual Networks Mapping onto Cognitive Radio Substrate

The Fifth Generation (5G) of wireless communication is envisioned to comprise heterogeneous applications, different radio access technologies (RATs), and a large demand for mobile traffic. In this respect, Wireless Virtualization (WV) and Cognitive Radio (CR) are put forward as 5G enablers for providing additional spectrum resources through dynamic spectrum access (DSA) techniques, besides dealing with heterogeneity with no hardware modification. By empowering the synergy between CR and WV, we visualize an environment denoted as Cognitive Radio Virtual Networks Environment (CRVNE) that encompasses VWNs with different access priorities, called Primary Virtual Networks (PVNs) and Secondary Virtual Networks (SVNs) that may be deployed in an overlay manner. In this scenario, the SVNs users (SUs) access the resources opportunistically, which naturally raises challenges towards the SVN mapping. In this paper, we revisit our previous letter that models the interactions between PUs and SUs in a CRVNE and analyzes a proposed formulation for collision probability during the SVN mapping process. The current work is pioneer as it presents a comprehensive approach to the SVNs mapping problem; models, validates, and analyzes additional performance metrics such as SU blocking and SU dropping probabilities and joint utilization; formulates the SVNs mapping as a multiobjective problem; and proposes an evolutionary scheme based on Genetic Algorithms (GAs) to solve it. The results show that the proposed scheme outperforms the alternative method in terms of collision, SU dropping, SU blocking probabilities, and joint utilization under different primary and secondary loads.

Cost-utility analysis of primary liver transplant versus salvage liver transplant after hepatectomy for hepatocellular carcinoma

Background: Liver transplantation and hepatectomy remain the main curative therapies for hepatocellular carcinoma (HCC). Although transplant offers better disease-free survival than resection, the shortage of organs and current donor utilization policies limit its utilization in the setting HCC. Herein, we aim to determine the cost-utility between primary liver transplant (PLT) and salvage liver transplant (SLT) after resection for HCC. Methods: A Markov cohort model was created to simulate a cohort of 55-year-old patients, diagnosed with early-stage HCC within Milan criteria, eligible to undergo transplant or hepatectomy. The patients transitioned through different health states in 1-month cycles and were followed for a time horizon of 10 years. The primary outcome was quality-adjusted-life-years (QALY) gained. The ceiling ratio of US$100.000/QALY was adopted. Our analysis assumed the perspective of a third-party payer. Probabilities and utilities were obtained from literature review, and costs from Medicare database and Agency for Health Research and Quality. Results: The base-case analysis revealed SLT after hepatectomy for HCC as the dominant strategy. Primary liver transplant (PLT) had an incremental cost of US$ 535.530,00 when compared to SLT. In addition, it generated 0.23 QALYs less than SLT, therefore PLT was dominated. Sensitive analysis showed the most sensitive variables, that could affect the results of our analysis, were the rate of recurrence after hepatectomy and the probability of drop out while in the waiting list for SLT. When the recurrence rate after resection was more than 90% in 5-years (monthly rate > 4.2%), PLT was no longer dominated. Likewise, when the probability of drop out from the waiting list for SLT was more than 48% in 6 months (monthly probability > 10.3%), SLT was no longer dominated. On both scenarios the Incremental Cost-Effectiveness Ratio (ICER) was above the ceiling ratio of US$100.000/QALY, therefore SLT remained the more cost-effective strategy. Conclusion: In this study, Salvage Liver Transplant after Hepatectomy for HCC within Milan criteria was more cost-effective than Primary Liver Transplant.

Integrated Use of Licensed- and Unlicensed-Band mmWave Radio Technology in 5G and Beyond

The 3GPP standardization rapidly moves forward with studies of a wide-bandwidth waveform as well as an adaptation of the emerging 5G new radio (NR)-based access to the unlicensed spectrum (NR-U). One of the basic architectures for NR-U involves carrier aggregation of an anchor-licensed-NR carrier and a secondary carrier in unlicensed spectrum, which altogether allows for seamless traffic offloading in scenarios where multi-gigabit data rates are required. While today's research on NR-U addresses mostly physical- and protocol-layer aspects, a system-level performance of the NR-U offloading mechanisms has not been investigated thoroughly. In this paper, we develop a mathematical queuing-theoretic framework that is mindful of the specifics of millimeter-wave (mmWave) session dynamics and may serve as a flexible tool for the analysis of various strategies for the integrated use of licensed and unlicensed mmWave bands in terms of the session drop probability and system utilization. To illustrate this, we select three distinct strategies (based on sequential service, probabilistic offloading, or proportional splitting), and complement our mathematical models with a detailed performance evaluation in a representative massive augmented/virtual reality scenario. Based on this quantitative analysis of the selected schemes, we conclude that proportional splitting of traffic between the two mmWave bands leads to a better performance. We believe that the contributed mathematical analysis can become an important building block in further system development and service optimization across many usage scenarios.

Analysis of an approach to reducing drops of secondary user on primary user emulation attack

In this paper, we propose a method for a Cognitive Radio Network (CRN) to reduce the drop probability of Secondary User (SU) due to the Primary User Emulation Attack (PUEA) of Malicious User (MU). Instead of abandoning the current channel, a novel method called a Pause Approach is used if Primary User (PU) or MU accesses the same channel. This method helps SU to find the attack behaviour of MU and increases the network throughput performance. Also, we analyse the channel states with a Continuous Time Markov Chain (CTMC). The simulation results validate the proposed method based on Matlab.

Cloudification of Virtual Reality Gliding Simulation Game

Cloud gaming provides cloud computing-based game as a service. In this paper we describe the development of a virtual reality base gliding game as a proof-of-concept. In the cloud, a cloud gaming platform is hosted on cloud servers with two principal components: game logic engaged in the implementation of game mechanics and game interactions, and video renderer that generates the game frames in real-time. The virtual gliding game was realized in the Unity gaming engine. To ensure smooth playability, and access for remote players, the computationally-intensive parts of the game were offloaded to a physically remote cloud server. To analyze the efficiency of the client-cloud interaction, three cloud servers were setup. The results of cloudification were evaluated by measuring and comparing computation offloading performance, network traffic, the probability of service drop, perceptual quality and video quality.

Adaptive heading prediction of moving path for user-centric optimal cell selection toward 5G cooperative cellular communication

Toward 5G, 3GPP specifies the cooperative communications in Long Term Evolution-Advanced (LTE-A Pro) to form a cooperative set of eNBs or cell sections for each UE. Clearly, for a UE near cell edge still anchoring at the original serving eNB before performing handover, the cooperative communication certainly reduces transmission delay and increases reliability. However, 5G cooperative communications suffers from some critical issues, including (1) how to dynamically form the set for a moving UE, (2) yielding high computing complexity for RAN (Radio Access Network) to compute the cooperative set for each UE, (3) when to initiate the cooperative communication, (4) increasing RB allocation cost significantly, etc. Thus, this paper proposes the UE heading Prediction Optimal Cooperative set selection approach (POC) that consists of three phases: (1) the phase of QoS-based Priority Function (QPF), (2) the phase of Adaptive UE Heading Prediction of moving path (HP), (3) the phase of Adaptive Optimal Cooperative mode determination and RB allocation (AOC). Numerical results show that the proposed approach outperforms the compared approaches in system capacity, access delay, average dropping probability, reward and net-profit. Furthermore, the computation complexity of the proposed approach is analyzed, i.e., T(n)150=O0.07233⋅n3, which is less than that of the compared approaches without any predictive heading of a moving UE, i.e., T(n)150<T(n)360=On3.

End-user traffic policing for QoS assurance in polyservice RINA networks

Looking at the ever-increasing amount of heterogeneous distributed applications supported on current data transport networks, it seems evident that best-effort packet delivery falls short to supply their actual needs. Multiple approaches to Quality of Service (QoS) differentiation have been proposed over the years, but their usage has always been hindered by the rigidness of the TCP/IP-based Internet model, which does not even allow for applications to express their QoS needs to the underlying network. In this context, the Recursive InterNetwork Architecture (RINA) has appeared as a clean-slate network architecture aiming to replace the current Internet based on TCP/IP. RINA provides a well-defined QoS support across layers, with standard means for layers to inform of the different QoS guarantees that they can support. Besides, applications and other processes can express their flow requirements, including different QoS-related measures, like delay and jitter, drop probability or average traffic usage. Greedy end-users, however, tend to request the highest quality for their flows, forcing providers to apply intelligent data rate limitation procedures at the edge of their networks. In this work, we propose a new rate limiting policy that, instead of enforcing limits on a per QoS class basis, imposes limits on several independent QoS dimensions. This offers a flexible traffic control to RINA network providers, while enabling end-users freely managing their leased resources. The performance of the proposed policy is assessed in an experimental RINA network test-bed and its performance compared against other policies, either RINA-specific or adopted from TCP/IP. Results show that the proposed policy achieves an effective traffic control for high QoS traffic classes, while also letting lower QoS classes to take profit of the capacity initially reserved for the former ones when available.

Performance Analysis of Cellular Networks with D2D communication Based on Queuing Theory Model

In this paper, we develop a spatiotemporal model to analysis of cellular user in underlay D2D communication by using stochastic geometry and queuing theory. Firstly, by exploring stochastic geometry to model the user locations, we derive the probability that the SINR of cellular user in a predefined interval, which constrains the corresponding transmission rate of cellular user. Secondly, in contrast to the previous studies with full traffic models, we employ queueing theory to evaluate the performance parameters of dynamic traffic model and formulate the cellular user transmission mechanism as a M/G/1 queuing model. In the derivation, Embedded Markov chain is introduced to depict the stationary distribution of cellular user queue status. Thirdly, the expressions of performance metrics in terms of mean queue length, mean throughput, mean delay and mean dropping probability are obtained, respectively. Simulation results show the validity and rationality of the theoretical analysis under different channel conditions.

Distributed Power Control for Multi-Hop Energy Harvesting Links With Retransmission

In this paper, we consider an energy harvesting (EH) node that periodically takes a measurement and conveys it to a destination over multiple EH relays operating in the decode-and-forward fashion, using the automatic repeat request protocol. Packets that are not delivered to the destination before the next measurement is taken are dropped . We seek to design an online retransmission-index based power control policy (RIP) for each node which minimizes the packet drop probability (PDP). To this end, we first derive an expression for the PDP in terms of the RIPs at the nodes. Next, when the energy cost for decoding a packet is negligible, we obtain closed form expressions for the optimal RIPs. We also extend the results to the case where the peak transmit power is constrained. When the energy cost of decoding is non-negligible, we present a geometric programming based iterative algorithm to obtain near-optimal RIPs. In both the scenarios, in order to obtain insight into the impact of channel coherence time, we design the RIPs for both slow and fast fading channels. Through Monte Carlo simulations, we show that the proposed policies significantly outperform state-of-the-art solutions.

Assessing the risk profile of dairy farms: application of the Income Stabilisation Tool in Italy

PurposeThis paper bridges the gap between theory and practice in the application of the Income Stabilisation Tool (IST). With an application to the dairy sector, the purpose of this paper is to propose methodology for the quantification of reference income when farm structural change occurs and estimate the role of farm attributes on the probability of income loss, offering an ex ante evaluation of farm resilience to risk.Design/methodology/approachBased on a balanced Farm Accountancy Network farm-level panel ranging from 2008 to 2014, three hypotheses of reference income calculation are tested to assess whether farms structural changes over the years significantly affect the level of IST indemnification. The role of farm characteristics on the probability of an income reduction is then evaluated by estimating a multinomial logit model.FindingsResults show that farms’ structural changes significantly affect IST indemnities and need to be considered in calculating the reference income. The estimated model suggests that farm characteristics significantly affect the probability of a severe income drop and hence risk resilience. Extensive livestock systems seem to reduce the probability of an income drop, while farms in upland areas managed by young farmers seem to experience increased risk exposure.Originality/valueThe research provides one of the first attempts to define risk profile of dairy farms by modelling the probability of an income reduction on observable attributes. Indeed, among different sectors, dairy farms emerge as the main candidates for the application of the IST.

A fuzzy priority based congestion control scheme in wireless body area networks

Congestion management is one of the major challenges in wireless body area network (WBAN) applications. This study proposes a technique similar to the random early detection (RED) active queue management (AQM) to indicate congestion. A two-input-single-output fuzzy logic system is used to dynamically adjust the maximum drop probability (maxp) parameter of the RED algorithm, then two adaptive values the minimum threshold (minth) and the maximum threshold (maxth) are used to estimate the congestion level of each sensor nodes. We adjust send rate of the parent node with a fuzzy logical controller (FLC) based on the child's node traffic load, considering the different amounts of data being transmitted. The simulation results show that the proposed protocol achieves high performance as compared to PCCP and PHTCCP, in terms of packet loss, end-to-end delay and energy.