Performance analysis for multi-carrier system based on stochastic network calculus

Stochastic network calculus is an evolving theory for network performance guarantee analysis. Although many theoretical results of this theory have been developed, there still lack applicable examples to demonstrate how it may be used. This paper exemplifies applying stochastic network calculus to delay analysis of the IEEE 802.11 distributed coordination function (DCF). Analyzing and obtaining the stochastic characteristics of the single packet service time is the primary task. Then the stochastic behavior of the DCF is characterized by a time-domain server model, which describes the cumulative service time provided to an arrival flow using a probabilistic bound. Based on this server model, we obtain delay bounds for different arrival processes. In addition, the delay bounds also take buffer size into account. The analytical bounds are further discussed using numerical results. Through these, we present a stochastic network calculus approach to delay evaluation of the DCF.

High-speed railway (HSR) wireless communication network has won a great development in recent years. However, due to its unique characteristics, such as high speed, fixed mobility track, special traffic, frequent handover and bandwidth constraints, it is quite a challenge to carry out the performance analysis on HSR. In this paper, we derive the backlog and delay bounds for HSR communication network by using stochastic network calculus (SNC) with moment generating functions (MGFs). SNC is a highly useful tool in packet switched network performance analysis since 1990s. Different from the previous studies about SNC which mostly concentrate on general low-speed, traditional public communication network, we take the varying small-scale fading and large-scale fading into consideration by means of a novel multi-dimension finite-state Markov channel (FSMC) model. We elaborate the differences in packet-level channel model, traffic model of train control information and performance metrics in HSR communication network. Particularly, the backlog and delay bounds of HSR with different packet arrival rates and violation probabilities are derived when train travels from the cell center to the cell edge. What is more, the numerical results can help to provide a guideline for designing the proper violation probabilities and arrival rates to different types of traffic in HSR communication network.

Code-division multiple-access (CDMA) has the potential to support traffic sources with a wide range of quality of service (QoS) requirements. The traffic carrying capacity of CDMA channels under QoS constraints (such as delay guarantee) is, however, less well-understood. In this work, we propose a method based on stochastic network calculus and large system analysis to quantify the maximum traffic that can be carried by a multiuser CDMA network under the QoS constraints. At the physical layer, we have linear minimum-mean square error receivers and adaptive modulation and coding, while the channel service process is modeled by using a finite-state Markov chain. We study the impact of delay requirements, violation probability and the user load on the traffic carrying capacity under different signal strengths. A key insight provided by the numerical results is as to how much one has to back-off from capacity under the different delay requirements.

Providing end-to-end network delay guarantees in packet-switched networks such as the Internet is highly desirable for mission-critical and delay-sensitive data transmission, yet it remains a challenging open problem. Due to the looseness of the deterministic bounds, various frameworks for stochastic network calculus have been proposed to provide tighter, probabilistic bounds on network delay, at least in theory. However, little attention has been devoted to the problem of regulating traffic according to stochastic burstiness bounds, which is necessary in order to guarantee the delay bounds in practice. We propose and analyze a stochastic traffic regulator that can be used in conjunction with results from stochastic network calculus to provide probabilistic guarantees on end-to-end network delay. Numerical results are provided to demonstrate the performance of the proposed traffic regulator. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> This work was supported in part by the U.S. National Science Foundation under Grant No. 1717033.

Providing end-to-end network delay guarantees in packet-switched networks such as the Internet is highly desirable for mission-critical and delay-sensitive data transmission, yet it remains a challenging open problem. Since deterministic bounds are based on the worst-case traffic behavior, various frameworks for stochastic network calculus have been proposed to provide less conservative, probabilistic bounds on network delay, at least in theory. However, little attention has been devoted to the problem of regulating traffic according to stochastic burstiness bounds, which is necessary in order to guarantee the delay bounds in practice. We design and analyze a stochastic traffic regulator that can be used in conjunction with results from stochastic network calculus to provide probabilistic guarantees on end-to-end network delay. Two alternative implementations of the stochastic regulator are developed and compared. Numerical results are provided to demonstrate the performance of the proposed stochastic traffic regulator.

In this paper, we use stochastic network calculus to analyze the performance bounds for a cognitive radio network with two classes of input traffic. First, stochastic service curves for primary users and secondary users are obtained based on the system model and stochastic network calculus. Then, we derive the general expressions of backlog and delay bounds for both primary and secondary users under two methods, i.e., min-plus convolution and independent case analysis. Finally, numerical results as well as simulation results are compared and discussed.

Underwater wireless sensor networks (UWSNs) are critical for monitoring environmentally sensitive areas, and operation of such networks is however, very much energy constrained. Conventional deterministic methods do not accurately capture the random and time-varying properties of the underwater acoustic environment. This paper presents a novel routing paradigm, temperature-aware SNC for underwater wireless sensor networks (T-SNC UWSN), which is the combination of stochastic network calculus (SNC) with temperature-based analysis and piezoelectric energy harvesting (PEH) and mobile approaches to improve network flexibility and robustness. Variation in temperature affects the efficiency of energy harvesting and has an immediate impact on power availability at the sensor nodes as well, indicating the occurrence of such underwater catastrophes as seismic events or tsunamis. By incorporating the temperature fluctuation into the SNC model, our model is capable of precisely revealing thermal influence on harvested energy and network stability, which enables efficient adaptive routing and enhanced disaster detection. The performance is analysed through simulations on packet delivery ratio (PDR), end-to-end delay, network throughput and path loss. It is demonstrated that SNC with temperature-aware modelling and mobile technologies manages to improve energy sustainability and disaster preparedness as well as the robustness of the network in unforeseen aquatic environments. This probabilistic model is helpful to practical systems of energy-efficient UWSNs, early warning systems, mobility-assisted monitoring, climate-resilience solutions and so forth.

Chapter 5 - Stream Sessions: Stochastic Analysis

Stochastic network calculus provides an elegant way to characterize traffic and service processes. However, little effort has been made on applying it to multi-access communication systems such as 802.11. In this paper, we take the first step to apply it to the backlog and delay analysis of an 802.11 wireless local network. We found the derived bounds are very loose in comparison to ns-2 simulation results especially for heavy arriving traffic, indicating that improvements are needed for the current version of stochastic network calculus.

The stochastic network calculus (SNC) has become an attractive methodology to derive probabilistic performance bounds. So far the SNC is based on (tacitly assumed) exact probabilistic assumptions about the arrival processes. Yet, in practice, these are only true approximately-at best. In many situations it is hard, if possible at all, to make such assumptions a priori. A more practical approach would be to base the SNC operations on measurements of the arrival processes (preferably even on-line). In this paper, we develop this idea and incorporate measurements into the framework of SNC taking the further uncertainty resulting from estimation errors into account. This is a crucial step towards a statistical network calculus (StatNC) eventually lending itself to a self-modelling operation of networks with a minimum of a priori assumptions. In numerical experiments, we are able to substantiate the novel opportunities by StatNC.

In this paper, stochastic network calculus is relied on to analyze the guaranteed quality of service in a cognitive radio network, mainly from the viewpoint of delay. Each user is assumed to carry an ON-OFF source with same average rate but random phase. Then the aggregated flow can be approximated by a Gaussian process, based on which specific delay distribution bound is derived. Numerical results are obtained and discussed from different aspects, including the capacity under certain delay constraint, impact of sensing error probability, and system admission region.

Weibull fading channel has a wide range of applications in wireless fading channels as we know today. In this paper, an FPGA-based wireless channel emulator is presented. We investigate the quality of service (QoS) performance of wireless channels subject to weibull fading channel on an FPGA platform based on stochastic network calculus. In order to analyze the performance, we aim at deriving the service curve of weibull fading channel. With the closed-form stochastic service curve, the QoS performance like stochastic delay and backlog bound can be derived. Simulation results verify that the delay and backlog are inversely proportional to their bounds. Moreover, through numerical experiments, we show the method is not only capable of deriving stochastic performance bounds, but also can provide guidelines for designing transmission strategies on FPGA platform.

The green communication draws a lot of attention in recent years. Meanwhile, as a promising technology, cognitive radio will constitute an important part of the future communication networks. Energy consumption of cognitive radio network is worthy studying, which is analyzed in this paper applying stochastic network calculus. A multi-channel cognitive radio network model is introduced. Stochastic network calculus is then used in the analysis, which is a newly developed theory dealing with queuing system found in computer networks. Guaranteed service for secondary users is firstly derived, followed by stochastic service curve derivation. Then, minimum transmission rate is conducted which is an important variable in energy analysis. After that, relationship between energy consumption and transmission rate is presented. As part of the derivation, delay bound and transmission power are discussed. Finally, numerical results of transmission rate, transmission power and energy consumption are shown for different constraint, where the influence of channel quantity is further discussed.

M2M (machine-to-machine) is required and spreads rapidly in recent years. Wireless multi-hop communication is one of the communication styles to construct M2M. However, the performance estimation of such wireless multi-hop communication is not so easy because the bit error ratio and symbol rate adaptation heavily depend on various environmental conditions. Stochastic network calculus (SNC) is a theory for the performance estimation of network. However, current SNC lacks ways to take wireless random errors, branching and joining of traffic flows into calculation, these are necessary to estimate the performance of sink-tree wireless-sensor network. This paper proposed a performance estimation method for sink-tree wireless multi-hop network based on SNC. The proposed method has three attractive points. Firstly, this introduces parameters of hop number and node number considering wireless random error due to packet collision. Secondly, this treats actual sink-tree network model by considering joining of traffic. Thirdly, this separates the service curve and bounding function calculation to avoid double counting. To evaluate the accuracy of the result of the proposed method, conventional estimation methods and simulation evaluation were conducted on a typical wireless multi-hop sensor data collection network. The results validate that the proposed method achieves much higher-accuracy than conventional methods.

Aquaculture is gradually moving toward Open Ocean Fish Farming (OOFF) in recent times because of the huge environmental impacts caused by onshore fish farming. Although Offshore fish farming has a lot of benefits over onshore fish farming it has its threats as well. Harmful Algae Blooms (HAB) are one such threat to Offshore fish farming. HAB is the formation of algae in groups that are toxic. The HAB can significantly affect the health of the fish that are being cultured. To prevent and act on HAB, monitoring of different environmental parameters is done in the field that is of concern. Traditionally for these kinds of monitoring human drivers are employed. But involving human drivers for monitoring in offshore conditions is cost expensive and difficult. Technology-enabled autonomous monitoring is needed in an offshore environment. Here comes underwater Wireless Sensor Networks (UWSN) which can be used for inspection of underwater fish farms for the detection of HAB. In UWSN loss is one of the main parameters that affect the Quality of Service. So this research focuses on the development of a Stochastic Network Calculus (SNC) based mathematical model of the network layer design for underwater communication that can be used in OOFF. In this work, we introduced the loss factor parameter into SNC and derived the loss bound for underwater communication, and the same is evaluated using discrete event simulation.