在无线环境中为时空异步用户请求实现多播传输
Enabling Multicast Transmission for Spatio-Temporally Asynchronous User Requests in Wireless Environments
AI总结：
本文提出基于建立时间的合并多播（SMMC）机制，通过优化建立时间与单播/多播速率并利用后验信息，在时空异步请求下最小化无线网络文件交付时间，性能优于传统单播。
摘要：
无线设备数量和数据流量的激增要求更高效的传输方法。多播作为满足日益增长的高效数据传输需求的一种手段，一直受到持续关注。然而，利用多播无线网络处理时空异步的数据请求面临诸多挑战。在此背景下，本文提出了一种新的多播机制，称为基于建立时间的合并多播（SMMC），通过考虑无线信道固有的不确定性，最小化无线网络中请求文件的交付时间。所提机制包含两个阶段。第一阶段收集来自经历不同信道条件的用户对某一文件的异步请求。在此阶段，请求文件的数据包在指定的建立时间内以单播模式逐个传输。随后，第二阶段启动多播传输，以多播模式依次处理文件的剩余数据包。在所提机制中，我们联合考虑无线信道的统计特性、用户位置和文件流行度，优化建立时间以及单播和多播模式的传输速率，以最小化期望文件交付时间。此外，我们还利用多播组大小的后验信息，深入研究了精细调优的SMMC，并进一步提升了性能。性能评估表明，所提出的SMMC优于传统单播方法，尤其是在高需求数据场景下。
英文摘要：
The surge in wireless devices and data traffic volume necessitates more efficient transmission methods. Multicasting has garnered consistent attention as a means to fulfill the increasing demand for more efficient data transmission methods. Nevertheless, leveraging multicast wireless networks for spatio-temporally asynchronous data requests poses challenges. In this context, this paper introduces a new multicast mechanism called \emph{set-up based merged multicast (SMMC)} to minimize the delivery time of the requested file in wireless networks by considering the uncertainties inherent in wireless channels. The proposed mechanism comprises two phases. The first phase involves gathering asynchronous requests for a file from users experiencing diverse channel conditions. During this phase, packets of the requested file are transmitted individually in unicast mode within a specified set-up time. Following this, the second phase initiates multicast transmission, which sequentially handles the remaining packets of the file in multicast mode. In the proposed mechanism, we optimize the set-up time and transmission rates of both unicast and multicast modes to minimize the expected file delivery time by jointly taking into account the statistical characteristics of wireless channels, users' locations, and file popularity. Additionally, we also delve into a \emph{fine-tuned SMMC} by utilizing posterior information on the multicast group size and further improve the performance. Our performance evaluations reveal that the proposed SMMC outperforms conventional unicast methods, especially with high-demand data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
