Fair and optimal resource allocation for LTE multicast (eMBMS): Group partitioning and dynamics

Jiasi Chen, M. Chiang, Jeffrey Erman, Guangzhi Li, K. Ramakrishnan, R. Sinha
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引用次数: 95

Abstract

With recent standardization and deployment of LTE eMBMS, cellular multicast is gaining traction as a method of efficiently using wireless spectrum to deliver large amounts of multimedia data to multiple cell sites. Cellular operators still seek methods of performing optimal resource allocation in eMBMS based on a complete understanding of the complex interactions among a number of mechanisms: the multicast coding scheme, the resources allocated to unicast users and their scheduling at the base stations, the resources allocated to a multicast group to satisfy the user experience of its members, and the number of groups and their membership, all of which we consider in this work. We determine the optimal allocation of wireless resources for users to maximize proportional fair utility. To handle the heterogeneity of user channel conditions, we efficiently and optimally partition multicast users into groups so that users with good signal strength do not suffer by being grouped together with users of poor signal strength. Numerical simulations are performed to compare our scheme to practical heuristics and state-of-the-art schemes. We demonstrate the tradeoff between improving unicast user rates and improving spectrum efficiency through multicast. Finally, we analyze the interaction between the globally fair solution and individual user's desire to maximize its rate. We show that even if the user deviates from the global solution in a number of scenarios, we can bound the number of selfish users that will choose to deviate.
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LTE组播(eMBMS)的公平和最优资源分配:组划分和动态
随着最近LTE eMBMS的标准化和部署,蜂窝多播作为一种有效地利用无线频谱向多个蜂窝站点传输大量多媒体数据的方法正在获得关注。蜂窝运营商仍在寻求在eMBMS中执行最佳资源分配的方法,这些方法基于对多个机制之间复杂交互的完全理解:组播编码方案、分配给单播用户的资源及其在基站的调度、分配给组播组以满足其成员的用户体验的资源、组的数量及其成员,所有这些都是我们在本工作中考虑的。我们确定了用户无线资源的最佳分配,以最大化比例公平效用。为了处理用户信道条件的异构性,我们对组播用户进行了高效、优化的分组,使信号强度好的组播用户不会与信号强度较差的组播用户分组。进行了数值模拟,将我们的方案与实际启发式和最先进的方案进行比较。我们演示了通过组播提高单播用户速率和提高频谱效率之间的权衡。最后,我们分析了全局公平解与个体用户最大化其比率的愿望之间的交互作用。我们表明,即使用户在许多场景中偏离了全局解决方案,我们也可以限制将选择偏离的自私用户的数量。
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