面向用户体验的移动互联网资源管理研究
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摘要
在移动互联网时代,无线通信技术的发展动力由技术驱动向用户驱动发生转变。终端用户体验质量(QoE,Quality of Experience)的好坏是决定移动互联网业务能否成功的关键因素。作为移动互联网业务的核心功能载体,无线通信网络性能的优劣直接影响终端用户的体验感受。因此,如何利用有限的网络资源为用户提供良好的业务体验,成为当下亟待解决的重要问题之一。本文研究面向用户体验的移动互联网资源配置策略。基于对通信业务的QoE建模与指标体系的研究和分析,本文围绕四个不同的网络场景——单小区蜂窝网、多小区蜂窝网、单接入异构网和多接入异构网展开研究,提出了四种提高用户体验的资源分配策略。本文的主要研究内容和贡献如下：
(1)针对面向QoE的单小区多用户资源分配问题,本文提出了一种基于MOS(Mean Opinion Score)的跨层资源优化策略。通过对话音、文件传输和视频业务基于速率的MOS建模,本文建立了以MOS最大化为目标的优化问题。由于待求问题是非凸的,本文首先将目标函数转换成无界形式,得到一个凸优化子问题,并利用拉格朗日对偶分解理论对其求解,得到一个联合子载波选择和自适应功率分配的子问题优化算法。然后根据可调的满意度阈值对资源进行重新调整,最终获得原问题的近似最优解。仿真实验证明,相比于吞吐量最大算法,本文所提算法不仅能够提高系统总的用户体验,而且能够提高用户之间的公平性大小。
(2)针对面向QoE的多小区多用户资源分配问题,本文提出了一个联合资源预留的多小区多用户OFDMA(Orthogonal Frequency Division Multiplexing Access)系统资源分配框架,将移动资源预留信息引入到多小区非合作功率分配中,极大地降低了正在进行的业务的切换中断概率,提高了系统中用户的平均MOS值。首先,移动资源预留机制根据长时统计的业务模型和用户移动性模型,分别预测和估计新业务及小区内有业务发生的用户的资源预留数目。然后,各小区通过小区间信令交互,将自身的资源预留信息告知相邻小区,并进行错位预留。接着,基于预留之后的可用资源,本文又将多小区OFDMA系统中的动态资源分配问题建模成一个非合作博弈过程,并对功率进行基于信道增益或SINR(Signal to Interference plus Noise Ratio)的差异化定价。通过证明目标问题纳什均衡解的存在性和唯一性,本文给出了基于各小区内独立的用户调度和功率分配的分布式资源分配算法。
(3)针对面向QoE的单接入异构网络选择问题,本文提出了一种基于匹配博弈的异构网络新型选择机制。不同于现有具有较低帕累托效率的网络选择方法,本文引入了匹配博弈这一新型模型,将异构网络选择问题建模成一个双边匹配问题。首先,根据网络性能参数和用户偏好,本文建立和量化了用户体验和网络收益矩阵。然后,基于不同的网络容量,本文又将该博弈进一步细分为一对一博弈和多对一博弈,并分别给出了符合帕累托最优、唯一性且稳定的用户占优和网络占优的匹配算法。仿真实验证明了这两种算法的有效性,即在用户体验和网络收益之间取得了平衡,使系统的整体性能得到一定的提升。
(4)针对面向QoE的多接入异构网络资源分配问题,本文提出了一个用户体验最优的资源分配算法。未来无线网络是一个面向融合的无线生态系统,多接入方式将成为一种有效的数据传输手段。不同于仅考虑吞吐量等QoS(Quality of Service)参数的已有研究,本文首先对实时和非实时业务进行基于业务效用及资源使用成本的用户体验建模,并以此为目标函数给出了最大化用户体验的优化问题。由于该问题是一个典型的凸优化问题,本文给出了一个基于主对偶内点法的迭代算法。为进一步降低计算复杂度和提高收敛速度,本文又提出了一种基于多级惩罚函数的粒子群算法,将原问题转换成一个无约束的优化问题,并通过适应度函数的不断约束,使粒子在优化空间内迅速逼近最优解。
In mobile Internet, the development motivation of wireless communication has changed from technology-driven to user-driven. Quality of Experience (QoE) of end-user becomes a key factor that impacts on the diffusion of mobile Internet services. As the core bearer of mobile Internet services, the performance of wireless communication networks directly affects the end-users'QoE. Therefore, how to provide a plenty good QoE based on the limited network resources has become a challenge.
In this dissertation, the strategies of QoE-oriented radio resource allocation in mobile Internet are investigated. Based on the analysis of the QoE modeling and quality index system of wireless services, this dissertation proposes four resource allocation mechanisms to improve end-user's QoE in four different but evolving network scenarios:single-cell, multi-cell, single access-based heterogeneous wireless network (HWN), and further multi-radio access-based HWN. The main contributions of this dissertation are listed as follows:
(1) To solve the QoE-oriented multi-user resource allocation problem in single cell OFDMA (Orthogonal Frequency Division Multiplexing Access) system, this dissertation proposes a MOS (Mean Opinion Score)-driven cross-layer optimization framework. Firstly, based on the data rate-to-MOS modeling of voice, file transferring and streaming services, this dissertation formulates a MOS-maximized optimization problem. Since the problem is non-convex, this dissertation transforms it to be an unbounded subproblem, which can be solved by Lagrange dual decomposition method. An iterative algorithm is then proposed for the final solution, which combines a joint subcarrier assignment and adaptive power allocation algorithm, resource adjustment procedure and further tunable minimal satisfactory threshold control. Simulation results show that, comparing to the classical throughput-optimized algorithm, this proposal can not only improve the overall perceived quality from the perspective of users, but also the fairness between users.
(2) To tackle the QoE-oriented multi-user resource allocation problem in multi-cell OFDMA system, this dissertation proposes a joint resource reservation and resource allocation framework, which introduces the information of mobile resource reservation into the non-cooperative distributed power allocation, greatly reducing the call dropping probability of the ongoing services and meanwhile improving the system's average MOS value. Firstly, according to the statistical service models and user mobility model, the mobile resource reservation mechanism forecasts and estimates the resource blocks to be reserved for new coming traffic and handover. Then, each cell informs its own reservation information to the neighboring cells through the inter-cell signaling interaction, and executes intersectant resource reservation. Based on the available resources after the former reservation, the dynamic resource allocation problem in multi-cell OFDMA system is further modeled as a non-cooperative game with differential pricing on power owing to the channel gain or SINR (Signal to Interference plus Noise Ratio). By proving the existence and uniqueness of Nash equilibrium of the objective, this dissertation presents a distributed resource allocation algorithm with independent user scheduling in a single cell and non-cooperative power allocation.
(3) To tackle the QoE-oriented network selection problem in single access-based HWN, this dissertation presents a matching game-based network selection mechanism. Unlike the existing methods with lower Pareto efficiency, this dissertation introduces a novel matching game model, which models the heterogeneous network selection problem as a two-sided matching problem. First of all, according to the network performance parameters and user preferences, this dissertation establishes and quantifies the user experience and network payoff matrix. Then, based on different network capacity, the former game can be decomposed into one-on-one game and many-to-one game. The corresponding user dominant and network dominant matching algorithms are further proposed, which are proved as Pareto optimal, unique and stable. Simulation results show the superiority of these algorithms, which could strike a balance between the user experience and network revenue, and meanwhile improves the system's overall performance.
(4) To solve the resource allocation problem in multi-radio access HWN, this dissertation proposed a QoE-optimized resource allocation algorithm. Future wireless network is a fusion-oriented ecosystem, where multi-radio access is an efficient data transmission method.Other than the current studies that only concern several QoS (Quality of Service) parameters, such as throughput, this proposal introduces the QoE modeling based on utility functions of real-time and non-real-time services as well as resource usage costs, and then builds an optimization problem with an objective of maximizing the user perceived QoE. Since it's a typical convex optimization problem, this dissertation firstly carries out a primal-dual interior point method-based iterative algorithm. To reduce the complexity of the former algorithm and improve the rate of convergence, this dissertation further proposed a multi-level penalty function-based particle swarm optimization, where the original problem is gradually converted into an unconstrained optimization problem using penalty transformation. Based on the fitness function, the particles could rapidly approach the optimal solution in the optimization space.
(1)针对面向QoE的单小区多用户资源分配问题,本文提出了一种基于MOS(Mean Opinion Score)的跨层资源优化策略。通过对话音、文件传输和视频业务基于速率的MOS建模,本文建立了以MOS最大化为目标的优化问题。由于待求问题是非凸的,本文首先将目标函数转换成无界形式,得到一个凸优化子问题,并利用拉格朗日对偶分解理论对其求解,得到一个联合子载波选择和自适应功率分配的子问题优化算法。然后根据可调的满意度阈值对资源进行重新调整,最终获得原问题的近似最优解。仿真实验证明,相比于吞吐量最大算法,本文所提算法不仅能够提高系统总的用户体验,而且能够提高用户之间的公平性大小。
(2)针对面向QoE的多小区多用户资源分配问题,本文提出了一个联合资源预留的多小区多用户OFDMA(Orthogonal Frequency Division Multiplexing Access)系统资源分配框架,将移动资源预留信息引入到多小区非合作功率分配中,极大地降低了正在进行的业务的切换中断概率,提高了系统中用户的平均MOS值。首先,移动资源预留机制根据长时统计的业务模型和用户移动性模型,分别预测和估计新业务及小区内有业务发生的用户的资源预留数目。然后,各小区通过小区间信令交互,将自身的资源预留信息告知相邻小区,并进行错位预留。接着,基于预留之后的可用资源,本文又将多小区OFDMA系统中的动态资源分配问题建模成一个非合作博弈过程,并对功率进行基于信道增益或SINR(Signal to Interference plus Noise Ratio)的差异化定价。通过证明目标问题纳什均衡解的存在性和唯一性,本文给出了基于各小区内独立的用户调度和功率分配的分布式资源分配算法。
(3)针对面向QoE的单接入异构网络选择问题,本文提出了一种基于匹配博弈的异构网络新型选择机制。不同于现有具有较低帕累托效率的网络选择方法,本文引入了匹配博弈这一新型模型,将异构网络选择问题建模成一个双边匹配问题。首先,根据网络性能参数和用户偏好,本文建立和量化了用户体验和网络收益矩阵。然后,基于不同的网络容量,本文又将该博弈进一步细分为一对一博弈和多对一博弈,并分别给出了符合帕累托最优、唯一性且稳定的用户占优和网络占优的匹配算法。仿真实验证明了这两种算法的有效性,即在用户体验和网络收益之间取得了平衡,使系统的整体性能得到一定的提升。
(4)针对面向QoE的多接入异构网络资源分配问题,本文提出了一个用户体验最优的资源分配算法。未来无线网络是一个面向融合的无线生态系统,多接入方式将成为一种有效的数据传输手段。不同于仅考虑吞吐量等QoS(Quality of Service)参数的已有研究,本文首先对实时和非实时业务进行基于业务效用及资源使用成本的用户体验建模,并以此为目标函数给出了最大化用户体验的优化问题。由于该问题是一个典型的凸优化问题,本文给出了一个基于主对偶内点法的迭代算法。为进一步降低计算复杂度和提高收敛速度,本文又提出了一种基于多级惩罚函数的粒子群算法,将原问题转换成一个无约束的优化问题,并通过适应度函数的不断约束,使粒子在优化空间内迅速逼近最优解。
In mobile Internet, the development motivation of wireless communication has changed from technology-driven to user-driven. Quality of Experience (QoE) of end-user becomes a key factor that impacts on the diffusion of mobile Internet services. As the core bearer of mobile Internet services, the performance of wireless communication networks directly affects the end-users'QoE. Therefore, how to provide a plenty good QoE based on the limited network resources has become a challenge.
In this dissertation, the strategies of QoE-oriented radio resource allocation in mobile Internet are investigated. Based on the analysis of the QoE modeling and quality index system of wireless services, this dissertation proposes four resource allocation mechanisms to improve end-user's QoE in four different but evolving network scenarios:single-cell, multi-cell, single access-based heterogeneous wireless network (HWN), and further multi-radio access-based HWN. The main contributions of this dissertation are listed as follows:
(1) To solve the QoE-oriented multi-user resource allocation problem in single cell OFDMA (Orthogonal Frequency Division Multiplexing Access) system, this dissertation proposes a MOS (Mean Opinion Score)-driven cross-layer optimization framework. Firstly, based on the data rate-to-MOS modeling of voice, file transferring and streaming services, this dissertation formulates a MOS-maximized optimization problem. Since the problem is non-convex, this dissertation transforms it to be an unbounded subproblem, which can be solved by Lagrange dual decomposition method. An iterative algorithm is then proposed for the final solution, which combines a joint subcarrier assignment and adaptive power allocation algorithm, resource adjustment procedure and further tunable minimal satisfactory threshold control. Simulation results show that, comparing to the classical throughput-optimized algorithm, this proposal can not only improve the overall perceived quality from the perspective of users, but also the fairness between users.
(2) To tackle the QoE-oriented multi-user resource allocation problem in multi-cell OFDMA system, this dissertation proposes a joint resource reservation and resource allocation framework, which introduces the information of mobile resource reservation into the non-cooperative distributed power allocation, greatly reducing the call dropping probability of the ongoing services and meanwhile improving the system's average MOS value. Firstly, according to the statistical service models and user mobility model, the mobile resource reservation mechanism forecasts and estimates the resource blocks to be reserved for new coming traffic and handover. Then, each cell informs its own reservation information to the neighboring cells through the inter-cell signaling interaction, and executes intersectant resource reservation. Based on the available resources after the former reservation, the dynamic resource allocation problem in multi-cell OFDMA system is further modeled as a non-cooperative game with differential pricing on power owing to the channel gain or SINR (Signal to Interference plus Noise Ratio). By proving the existence and uniqueness of Nash equilibrium of the objective, this dissertation presents a distributed resource allocation algorithm with independent user scheduling in a single cell and non-cooperative power allocation.
(3) To tackle the QoE-oriented network selection problem in single access-based HWN, this dissertation presents a matching game-based network selection mechanism. Unlike the existing methods with lower Pareto efficiency, this dissertation introduces a novel matching game model, which models the heterogeneous network selection problem as a two-sided matching problem. First of all, according to the network performance parameters and user preferences, this dissertation establishes and quantifies the user experience and network payoff matrix. Then, based on different network capacity, the former game can be decomposed into one-on-one game and many-to-one game. The corresponding user dominant and network dominant matching algorithms are further proposed, which are proved as Pareto optimal, unique and stable. Simulation results show the superiority of these algorithms, which could strike a balance between the user experience and network revenue, and meanwhile improves the system's overall performance.
(4) To solve the resource allocation problem in multi-radio access HWN, this dissertation proposed a QoE-optimized resource allocation algorithm. Future wireless network is a fusion-oriented ecosystem, where multi-radio access is an efficient data transmission method.Other than the current studies that only concern several QoS (Quality of Service) parameters, such as throughput, this proposal introduces the QoE modeling based on utility functions of real-time and non-real-time services as well as resource usage costs, and then builds an optimization problem with an objective of maximizing the user perceived QoE. Since it's a typical convex optimization problem, this dissertation firstly carries out a primal-dual interior point method-based iterative algorithm. To reduce the complexity of the former algorithm and improve the rate of convergence, this dissertation further proposed a multi-level penalty function-based particle swarm optimization, where the original problem is gradually converted into an unconstrained optimization problem using penalty transformation. Based on the fitness function, the particles could rapidly approach the optimal solution in the optimization space.
引文
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