无线网络中的流量预测与MAC算法研究
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摘要
无线网络以其不受时间和空间束缚的特点，帮助人们实现无处不在的服务和应用，在国民经济中发挥着越来越重要的作用。由于无线信道具有时变和不可靠等物理特性，无线网络数据链路层中的媒体接入控制（MAC）算法对无线网络的吞吐量、容量和时延等性能都具有很大的影响。为此，本文针对单跳无线网络中流量预测和MAC算法、多跳无线网络MAC算法以及认知多跳无线网络中的MAC机制展开了研究。
与网络业务的流量特性结合以提升无线网络数据链路层性能是当前MAC算法研究中的难点。由于无线网络流量的波动性与自相似特性给流量预报技术提出了很大的挑战，本文在第二章中对无线网络流量预测算法进行了研究，并提出了一种基于FARIMA-GARCH模型的预测算法。该算法首先利用分段双向CUSUM检测算法对流量序列的均值进行有效检测，并在此基础上将序列进行零均值化；然后采用限定搜索法对分数差分阶数进行精确估计；在获得必要的模型参数后，利用GARCH模型对新息序列进行建模，以跟踪流量序列波动性的变化；最后对模型预测的结果进行均值补偿。仿真结果表明，该算法能与传统的FARIMA预测算法保持相同的时间复杂度，并能提供比后者更好的预测性能。
第三章则进一步将第二章提出的预测算法应用到单跳无线网络MAC算法设计中。通过将预测结果结合冲突分解算法对信道进行合理调度，我们提出了两种新的冲突分解算法。首先，我们针对分组的到达时间间隔建立流量模型，提出了一种基于流量预测的混合冲突分解算法，仿真结果表明其性能比已有的FCFS算法有所提高；然后，由于小时间尺度上流量特性太过复杂，于是针对聚合的业务流，我们又提出了一种基于流量预测的两段式树形冲突分解算法。仿真结果表明，相对于现有的二叉树形算法，该算法能有效地提高系统吞吐率和降低平均时延及平均分解周期，从而改善系统的整体性能。
针对传统的FPRP算法在多跳无线网络场景下调度单播业务效率不高的问题，第四章提出了一种基于FPRP改进的MAC算法。该算法通过增加一轮预约过程和对业务的区分服务来提高节点的空间复用率。仿真结果表明其性能比FPRP算法有所提升。同时，针对物理层具有多包接收能力的多跳无线网络，我们又提出了一种结合多包接收的预约调度MAC算法。该算法将多包接收和FPRP的多轮预约调度相结合，通过邻居节点之间的控制信息交互，充分利用节点多包接收能力。通过理论推导，我们得出了该算法在理想条件下的节点吞吐率估计式。此外，对该算法的仿真结果也验证了其有效性。
第五章研究了认知多跳无线网络中网络容量的求解与网络吞吐量的优化问题。我们首先推导了混叠（Underlay）模式下认知多跳无线网络容量上界的闭合表达式，并指出该上界只与用户空间分布特性相关；然后提出了一种新的基于遗传算法的跨层优化MAC算法，通过联合优化邻居选择与功率分配实现网络吞吐量的最大化。仿真结果表明，该算法所获得的吞吐量能够较好地逼近网络容量上界。
最后，第六章对本文所做的研究工作进行了总结，并对未来的研究方向作出了展望。
Wireless networks, which liberate people from conventional constraints of timeand space, provide ubiquitous services and applications, and play a fairly significantrole in national economy. Because of the time variation and unreliability of wirelesschannels, medium access control (MAC) in data link layer has a significant influenceupon the throughput, capacity and delay of wireless networks. This dissertation studiesthe traffic prediction and MAC algorithm in single-hop wireless networks, MACalgorithm in multi-hop wireless networks and MAC mechanism in cognitive radio adhoc networks.
In the current study of MAC algorithms, the incorporation of the characteristics ofnetwork traffic for improving the performance of wireless networks is a fairly difficultissue because the volatility and self-similarity features of network traffic in wirelessnetworks pose great difficulty to network traffic prediction. Chapter2of thisdissertation hence is devoted to the study of the traffic prediction algorithm in wirelessnetworks. We propose a novel network traffic prediction scheme based on theFARIMA-GARCH model. A new method is presented to obtain a zero-mean trafficseries by a piecewise two-way CUSUM detection algorithm. Then the fractiondifference order is evaluated with good precision by the proposed bounded searchmethod. After obtaining the necessary model parameters, the innovation series aremodeled by GARCH to track the volatility of the network traffic. Finally, the meanprediction resulted from the model is compensated. The proposed prediction methodkeeps the same time complexity as the FARIMA model prediction method, and thesimulation results show that the prediction performance is better than the FARIMAprediction method.
In chapter3, the prediction algorithm in chapter2is applied to MAC design insingle-hop wireless networks, which combines the collision resolution algorithm (CRA)with the prediction results to obtain optimal allocation of wireless channels, and wepropose two CRAs. First, by modeling the packet inter-arrival time, we propose aprediction-based hybrid CRA, of which the performance, as shown by simulation results is better than the FCFS algorithm. Then, because of the complexity in small scale traffic,we propose a prediction-based two-stage tree CRA by modeling the aggregate trafficflow. The simulation results show that the two-stage tree CRA performs better than thebinary-tree splitting algorithm in terms of network throughput, average delay andcollision resolution period.
Because of the low allocation efficiency of FPRP for unicast traffic, Chapter4proposes an enhanced MAC based on FPRP for multi-hop wireless networks, whichimproves spatial reuse ratio by the introduction of one more reservation round anddifferentiated services. Simulations show that the enhanced MAC has betterperformance than FPRP. Then, with the ability of receiving multiple packets by thephysical layer, we propose a new reservation MAC for multi-hop wireless networks,which utilizes multi-way handshakes for exchanging control information and getting theinformation of node’s ability on multi-packet reception, and derive the throughputestimation formula under ideal conditions. Numerical simulations show that ourapproach is effective.
Chapter5studies the network capacity and optimization of network throughput incognitive radio ad hoc networks (CRAHN). First, we derive the closed-form expressionof the upper bound of network capacity for CRAHNs under underlay spectrum accessmodel, which shows that this upper bound is only determined by the space distributionof nodes. Then we present a novel cross-layer optimization algorithm for maximizingthe network throughput, which adopts genetic algorithm (GA) to achieve the optimalneighbor selection and power allocation. The simulation results show that the obtainednetwork throughput achieves a performance closely approximate to the upper bound ofnetwork capacity.
Finally, chapter6summarizes this dissertation and presents the future researchdirections.
与网络业务的流量特性结合以提升无线网络数据链路层性能是当前MAC算法研究中的难点。由于无线网络流量的波动性与自相似特性给流量预报技术提出了很大的挑战，本文在第二章中对无线网络流量预测算法进行了研究，并提出了一种基于FARIMA-GARCH模型的预测算法。该算法首先利用分段双向CUSUM检测算法对流量序列的均值进行有效检测，并在此基础上将序列进行零均值化；然后采用限定搜索法对分数差分阶数进行精确估计；在获得必要的模型参数后，利用GARCH模型对新息序列进行建模，以跟踪流量序列波动性的变化；最后对模型预测的结果进行均值补偿。仿真结果表明，该算法能与传统的FARIMA预测算法保持相同的时间复杂度，并能提供比后者更好的预测性能。
第三章则进一步将第二章提出的预测算法应用到单跳无线网络MAC算法设计中。通过将预测结果结合冲突分解算法对信道进行合理调度，我们提出了两种新的冲突分解算法。首先，我们针对分组的到达时间间隔建立流量模型，提出了一种基于流量预测的混合冲突分解算法，仿真结果表明其性能比已有的FCFS算法有所提高；然后，由于小时间尺度上流量特性太过复杂，于是针对聚合的业务流，我们又提出了一种基于流量预测的两段式树形冲突分解算法。仿真结果表明，相对于现有的二叉树形算法，该算法能有效地提高系统吞吐率和降低平均时延及平均分解周期，从而改善系统的整体性能。
针对传统的FPRP算法在多跳无线网络场景下调度单播业务效率不高的问题，第四章提出了一种基于FPRP改进的MAC算法。该算法通过增加一轮预约过程和对业务的区分服务来提高节点的空间复用率。仿真结果表明其性能比FPRP算法有所提升。同时，针对物理层具有多包接收能力的多跳无线网络，我们又提出了一种结合多包接收的预约调度MAC算法。该算法将多包接收和FPRP的多轮预约调度相结合，通过邻居节点之间的控制信息交互，充分利用节点多包接收能力。通过理论推导，我们得出了该算法在理想条件下的节点吞吐率估计式。此外，对该算法的仿真结果也验证了其有效性。
第五章研究了认知多跳无线网络中网络容量的求解与网络吞吐量的优化问题。我们首先推导了混叠（Underlay）模式下认知多跳无线网络容量上界的闭合表达式，并指出该上界只与用户空间分布特性相关；然后提出了一种新的基于遗传算法的跨层优化MAC算法，通过联合优化邻居选择与功率分配实现网络吞吐量的最大化。仿真结果表明，该算法所获得的吞吐量能够较好地逼近网络容量上界。
最后，第六章对本文所做的研究工作进行了总结，并对未来的研究方向作出了展望。
Wireless networks, which liberate people from conventional constraints of timeand space, provide ubiquitous services and applications, and play a fairly significantrole in national economy. Because of the time variation and unreliability of wirelesschannels, medium access control (MAC) in data link layer has a significant influenceupon the throughput, capacity and delay of wireless networks. This dissertation studiesthe traffic prediction and MAC algorithm in single-hop wireless networks, MACalgorithm in multi-hop wireless networks and MAC mechanism in cognitive radio adhoc networks.
In the current study of MAC algorithms, the incorporation of the characteristics ofnetwork traffic for improving the performance of wireless networks is a fairly difficultissue because the volatility and self-similarity features of network traffic in wirelessnetworks pose great difficulty to network traffic prediction. Chapter2of thisdissertation hence is devoted to the study of the traffic prediction algorithm in wirelessnetworks. We propose a novel network traffic prediction scheme based on theFARIMA-GARCH model. A new method is presented to obtain a zero-mean trafficseries by a piecewise two-way CUSUM detection algorithm. Then the fractiondifference order is evaluated with good precision by the proposed bounded searchmethod. After obtaining the necessary model parameters, the innovation series aremodeled by GARCH to track the volatility of the network traffic. Finally, the meanprediction resulted from the model is compensated. The proposed prediction methodkeeps the same time complexity as the FARIMA model prediction method, and thesimulation results show that the prediction performance is better than the FARIMAprediction method.
In chapter3, the prediction algorithm in chapter2is applied to MAC design insingle-hop wireless networks, which combines the collision resolution algorithm (CRA)with the prediction results to obtain optimal allocation of wireless channels, and wepropose two CRAs. First, by modeling the packet inter-arrival time, we propose aprediction-based hybrid CRA, of which the performance, as shown by simulation results is better than the FCFS algorithm. Then, because of the complexity in small scale traffic,we propose a prediction-based two-stage tree CRA by modeling the aggregate trafficflow. The simulation results show that the two-stage tree CRA performs better than thebinary-tree splitting algorithm in terms of network throughput, average delay andcollision resolution period.
Because of the low allocation efficiency of FPRP for unicast traffic, Chapter4proposes an enhanced MAC based on FPRP for multi-hop wireless networks, whichimproves spatial reuse ratio by the introduction of one more reservation round anddifferentiated services. Simulations show that the enhanced MAC has betterperformance than FPRP. Then, with the ability of receiving multiple packets by thephysical layer, we propose a new reservation MAC for multi-hop wireless networks,which utilizes multi-way handshakes for exchanging control information and getting theinformation of node’s ability on multi-packet reception, and derive the throughputestimation formula under ideal conditions. Numerical simulations show that ourapproach is effective.
Chapter5studies the network capacity and optimization of network throughput incognitive radio ad hoc networks (CRAHN). First, we derive the closed-form expressionof the upper bound of network capacity for CRAHNs under underlay spectrum accessmodel, which shows that this upper bound is only determined by the space distributionof nodes. Then we present a novel cross-layer optimization algorithm for maximizingthe network throughput, which adopts genetic algorithm (GA) to achieve the optimalneighbor selection and power allocation. The simulation results show that the obtainednetwork throughput achieves a performance closely approximate to the upper bound ofnetwork capacity.
Finally, chapter6summarizes this dissertation and presents the future researchdirections.
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