A perceptually sensitive Markovian model of packet loss processes during voip conversations

Technical reports indicate that wireless and mobile devices will account for 71% of all IP traffic by 2022, an increase of 19% over four years. This increase is related to advances in wireless data communication technologies. Wireless networks have become one of the most important ways to connect devices to the Internet, therein improving productivity and encouraging information sharing. IEEE 802.11, known as Wi-Fi, has become the main standard for wireless local area networks. The most important metrics for measuring the quality of Wi-Fi are delay, jitter, and packet loss. Packet loss occurs when one or more packets fail to reach their destination and can occur for a variety of reasons. Packet loss influences the user's perceived quality of applications over Wi-Fi networks, mainly multimedia and real-time applications. The availability of accurate models for packet loss in Wi-Fi networks enables the development of more efficient methods for performance analysis and network design, as well as better computational simulations. Modeling packet loss in such networks presents a major challenge because packets may be lost for many different reasons, including signal attenuation, noise, multipathing, signal refraction, thermal noise, competition for media access and buffer issues. In this paper, we provide an overview of the causes of packet loss and a comprehensive survey of the available models for packet loss in Wi-Fi networks. The potential benefits of the survey are: (i) the systematic presentation of available packet loss models for Wi-Fi networks, their parameters, and respective packet loss rate evaluation, (ii) comparison of models considering validation scenarios and input parameters, and (iii) description of open issues and future research directions. We hope that our analysis will help researchers understand the most important characteristics of the packet loss process in Wi-Fi networks and the strengths and weaknesses of the main packet loss models.

Errors introduced by a wireless medium are more frequent and profound than contemporary wired media. Some of these errors, which are not corrected by the physical layer, result in Medium Access Control (MAC) layer bit errors and packet losses. Design of wireless protocols and applications can benefit substantially from a thorough understanding of these MAC layer impairments. This paper evaluates and proposes Markov-based stochastic chains to model the 802.11b MAC-to-MAC channel behavior for both bit errors and packet losses. We introduce an Entropy Normalized Kullback-Leibler measure to evaluate the performance of existing and new bit error and packet loss models. Based on the proposed measure, and contrary to recent results for mobile networks, we demonstrate that the traditional two-state Markov chain provides a very suitable model for the 802.11b MAC-to-MAC packet loss process. However, this simple model is not adequate for bit errors observed at the MAC layer of wireless local area networks. Consequently, we evaluate three other Markov-based chains for modeling these errors: full-state, hidden, and hierarchical Markov chains. Among these chains, we illustrate that the full-state Markov bit error model, evaluated under a wide range of order values, renders the best performance.

In recent years, wavelet transforms have been successfully applied to image and video compression. In a distributed, multimedia computing environment, compressed video and image files need to be transmitted to users for real-time replay. Because the network is subject to packet losses, the image packetization scheme should take packet losses into account in their design. In this paper, we present an error concealment algorithm for wavelet-based compressed images over packet-switched networks. Two different packetization schemes, called the intrablock-oriented and interblock-oriented schemes, in conjunction with wavelet coding, are presented. In our approach, adjacent wavelet coefficients are scattered into different packets, so that any packet contains encoded wavelet coefficients at different resolution layers. When a packet is lost due to network congestion or transmission errors, a lost wavelet coefficient is estimated by the receiver through a linear interpolation of its adjacent ones. Our approach is evaluated under two different packet loss models with various packet loss probabilities through simulations driven by actual video sequences.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Accurately modeling the packet loss behavior of network components is a longstanding problem due to the impact of loss on quality of service (QoS) management and optimization strategies for real-time multimedia applications. Although a few models have been proposed in the literature with the purpose of estimating the statistical behavior of packet loss processes, none of them correctly reflect the property of a network to form both bursts of consecutive losses and aggregates of single (and/or small bursts of) losses occurring at short distance from one another. In this paper we propose a new model that incorporates the characteristics of burstiness and aggregateness observable in a sequence of packet loss events. We also provide a comprehensive comparison of it and the current state-of-the- art models with traffic traces routed by a bottleneck node. Our results demonstrate our proposed model's ability to outperform the existing models, due to its capacity to capture this double essence of burstiness and aggregateness.

In this chapter, we will adopt the analog erasure model to describe the communication channel present inside a control loop. This model, also referred to as the packet erasure or packet loss model, can be described as follows [8]. The channel operates in discrete time steps. At every time step, the channel accepts as input a finite dimensional real vector r(k). The value of the output of the channel y(k) is chosen according to an erasure process. At every time step, the erasure process assumes either the value T or the value R. If the value at time k is T , y(k + 1) = r(k) and a successful transmission is said to have occurred. Otherwise, y(k + 1) = φ and an erasure event, or a packet loss, is said to have occurred at time k. The symbol φ denotes that the receiver does not receive any data; however, the receiver is aware that an erasure event has occurred at that time. Note that we have assumed that the channel introduces a constant delay of one time step. The analog erasure model aims to capture the data loss effect due to a communication channel. Due to effects such as interference and fading in wireless channels, congestion in shared networks, or even overload and interrupts at a micro-controller, various parts of a control loop between the sensor and controller, or the controller and actuator, may exhibit information loss. By considering the idealization in which every successful transmission results in the communication of a real vector of a bounded dimension, such situations can be modeled using an analog erasure representation. While an analog erasure model has an infinite capacity in an information theoretic sense, it is often a useful representation for the cases when the communication protocols allow for large data packets to be transmitted at every time step. For instance, the minimum size of an ethernet data packet is 72 bytes. This is much more space for carrying information than usually required inside a control loop. If the data packets allow for transmission of control and sensing data to a high fidelity, the quantization effects are often ignored and an analog erasure model adopted. Various descriptions of the erasure process are possible. The following two models are the most popular:

This paper proposes a fast-convergence scheme to update metrics avoiding loop in Open Shortest Path Fast (OSPF) networks. In original OSPF networks, when metrics are updated to improve the routing performance, packets may be routed in a loop in transient states. This may cause packet loss and inefficiency in network resources. To avoid transient loops, a conventional scheme gives each router its priority, which is called a rank, in terms of time to update metrics. However, when the updated metrics include both larger and smaller values than the ones before the update, two updating processes, each of which corresponds either larger or smaller values, are required. It takes time to converge to update all the metrics in the conventional scheme. The proposed scheme tries to transform a set of the updated metrics into another equivalent set of metrics that are equal to or either larger or smaller values than the ones before the update, while the targeted routes in the proposed scheme are exactly the same as those of the conventional scheme. This requires only one updating process. Numerical results indicate that the proposed scheme reduces the convergence time with 24.7% in average in our examined networks, compared with that of the conventional scheme.

In future broadband networks based on asynchronous transfer mode (ATM) transmission and switching technology, the dominant kind of impairment is expected to be packet (ATM cell) losses. The use of forward error correction (FEC) codes has been proposed as a way to overcome these losses. However, the maximum length of a lost packet string that a packet loss recovery scheme can be designed to recover highly depends upon the temporal behavior between the adjacent packet loss bursts. Thus, a realistic model for describing the packet loss process is needed. The Gilbert model proposed for such purpose turns out to be an unrealistic model, since it fails to consider some important characteristics during the packet loss process. A more realistic model is advanced in this paper.

Video communication is often afflicted by various forms of losses, such as packet loss over the Internet. The paper examines the question of whether the packet loss pattern, and in particular the burst length, is important for accurately estimating the expected mean-squared error distortion. Specifically, we (1) verify that the loss pattern does have a significant effect on the resulting distortion, (2) explain why a loss pattern, for example a burst loss, generally produces a larger distortion than an equal number of isolated losses, and (3) propose a model that accurately estimates the expected distortion by explicitly accounting for the loss pattern, inter-frame error propagation, and the correlation between error frames. The accuracy of the proposed model is validated with JVT/H.26L coded video and previous frame concealment, where for most sequences the total distortion is predicted to within /spl plusmn/0.3 dB for burst loss of length two packets, as compared to prior models which underestimate the distortion by about 1.5 dB. Furthermore, as the burst length increases, our prediction is within /spl plusmn/0.7 dB, while prior models degrade and underestimate the distortion by over 3 dB.

Packet loss patterns over unidirectional satellite link provide useful information for improving the performance of applications like bulk data transfer to many recipients. In this paper, we present our observations and analysis of packet loss patterns on one-to-many multicast sessions taken over an extended period of time. We collected the packet loss data on four multicast receivers in four different geographic locations. The measurements taken demonstrate possible reasons of packet loss in both sender and receiver sides including the burstiness behavior of the channel. It is found in our result that setting priority for multicast traffic in the sender side router is one of the key factors for packet loss. We found a significant amount of bursty or consecutive packet losses contributing to the overall loss percentage while the number of occurrences of bursty loss is relatively small.KeywordsPacket LossForward Error CorrectionPacket Loss RateSatellite NetworkBurst LengthThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Multi-node communication is an important aspect in wireless system development, especially in the context of non-line of sight communications. In this study, we explore the implementation of multi-node communication with bridge nodes to increase distance and reduce packet loss in the NRF24L01 transceiver module. The aim of writing this manuscript is to evaluate the effectiveness of using bridge nodes in improving the quality of multi-node communications on the NRF24L01 module in non-line of sight conditions. This study was carried out through a series of multi-node communication tests with certain configurations, including maximum power usage, 250kbps bit rate, and 5dBi antenna. Tests were carried out at different distances with and without the use of bridge nodes to compare communication performance. Our findings show that a configuration with three client nodes and an NRF24L01 module equipped with a 5dBi antenna is capable of achieving the longest distance of up to 125 m. In non-line of sight conditions, the packet loss rate at a distance of 25-75 m has very good communication quality, with packet loss values ranging from 0-6%. However, at a distance of 100 m, the packet loss percentage exceeds the threshold (15%). The use of bridge nodes succeeded in significantly reducing the packet loss rate, reducing the percentage of packet loss at client nodes C1, C2, and C3 to 6.00%, 6.67%, and 6.33% respectively. Thus, implementing the bridge node has proven effective in improving the quality of communication on the NRF24L01 module in non-line of sight conditions.

Packet loss is a major problem for real-time Internet applications. Markov models of packet loss are often used to develop and evaluate the performance of these applications. Despite their wide use, these models have not been validated in terms of how well they capture the loss conditions experienced by residential Internet users. We evaluate the accuracy of common packet loss models using traces of IPTV-like traffic measured on residential ADSL and Cable links, and find that these models are insufficient to capture the observed packet loss patterns. We introduce a new type of model, incorporating packet delay information, and show improved accuracy over previous models.

The high mobility of trains and complex line environment will lead to poor communication quality, which will seriously affect the stability of the virtually coupled train set (VCTS). Packet loss is one of the important indexes to evaluate communication quality. In this paper, a numerical method is proposed to analyze the influence of random packet loss on the stability of the homogeneous VCTS. Firstly, combining with the characteristics of packet loss design probability model. Based on the longitudinal dynamics model and the constraints of the train interval distance, a train operation control model is formulated with considering random packet loss. Secondly, the stability conditions of the homogeneous VCTS expressed by packet loss probability are derived from the perspective of frequency domain. Finally, the effects of random packet loss on the stability of the homogeneous VCTS are analyzed and the validity of the theoretical results is confirmed via numerical simulations.

Video communication over lossy packet networks such as the Internet or wireless links, is hampered by packet loss. Indeed, video quality severely degrades in presence of lost packets. This paper describes a technique based on Unbalanced Multiple Description Coding (UMD) to improve the robustness of video transmission. The proposed system relies on two descriptions, HR (High-Resolution) and LR (Low-Resolution) where LR description is primarily used as redundancy. HR and LR are produced by using different encoding parameters. The novelty of our approach, compared to other techniques based on UMD, is the use of different intra refresh periods for the produced descriptions. In particular we use shorter intra refresh period for LR description (in order to improve its resilience to bursts of packet losses), and higher intra refresh period for HR description (in order to improve coding efficiency). Moreover, a novel packetization scheme has been introduced in order to optimize network performance. This technique is able to produce acceptable quality even if redundancy percentage is less than packet loss rate. Therefore our UMD approach is suitable for multicast video distribution over WLAN (e.g. in hotspots) where estimating packet loss can be a difficult task because different receivers experience different loss patterns.

The problem of TCP and all its existing variations within MANETs resides in its inability to distinguish between different data packet loss causes. Thus, TCP has not always the optimum behaviour in front of packet losses which might cause network performance degradation and resources waste. Multiple loss differentiation algorithms (LDAs) have been designed to improve TCP performances. They had been optimized for data networks where only the last link is a wireless link. In these LDAs the most common packet losses that are handled are those due to wireless channel errors or congestion. We show in this paper that a third common packet loss cause, link failure, has to be handled in multi-hop wireless networks such as mobile ad hoc networks (MANETs). In order to handle these three packet loss causes, we propose TCP-WELCOME. This latter follows a two-step process: (i) first, distinguish between most common packet loss causes, and (ii) then, triggers the most appropriate packet loss recovery according to the identified loss cause. The performance evaluation shows that TCP-WELCOME optimizes both energy consumption and throughput. Also, TCP-WELCOME does not change the standard as its loss differentiation and recovery algorithms can operate with the already existing TCP variants.

This paper studies the relationship between critical sampling interval and stability of the sampled-data control system with output feedback. The communication between system output measurements and controllers can be divided into two cases: no packet loss and packet loss. Packet loss process can also be divided into two cases: independent and identically distributed process and Markov chain. In the absence of packet loss, the upper bound of sampling interval is given to stabilize the whole system in the sense of mean square. In the case of independent and identically distributed packet loss, we give the range of sampling interval to ensure the mean square stable of the whole system, and determine the upper critical value of the sampling interval when the whole system is stable in the sense of mean square. Finally, in the case of Markov packet loss, the condition on sampling interval is given to ensure the mean square stability of the whole system.