论文标题
通过LTE-A网络对VoIP的实验评估和表征
An experimental evaluation and characterization of VoIP over an LTE-A network
论文作者
论文摘要
移动电信正汇合到All-IP解决方案。长期演化(LTE)技术的情况是,没有电路开关载体以支持语音流量,需要专用的VoIP基础架构,这通常依赖于IP Multimedia子系统体系结构。大多数电信运营商实施LTE-A,这是LTE的高级版本,通常以4G+销售,可达到300 Mbps的数据速率峰值。然而,尽管这种新颖的技术促进了对高级多媒体内容和服务的访问,但电信运营商仍将VoIP市场视为其业务的主要收入。在这项工作中,作者通过在真正的LTE-A环境中进行实验试验对VoIP流量进行了详细的绩效评估。实验活动包括两个阶段。首先,我们基于包含超过750,000个数据录音数据包的数据集对固定终端和移动终端之间的VoIP调用表征。我们分析了服务质量指标,例如往返时间(RTT)和抖动,以捕获通常在现实世界中出现的不受控制因素的影响。在第二阶段,我们进一步考虑了跨各种编解码器的VoIP流,研究质量和带宽消耗之间的权衡。此外,我们提出了抖动和RTT的统计表征(代表最关键的参数),确定最佳近似分布,即广义极值(GEV)。通过最大似然标准的参数估计使我们分别揭示了抖动和RTT的短尾和长尾行为。
Mobile telecommunications are converging towards all-IP solutions. This is the case of the Long Term Evolution (LTE) technology that, having no circuit-switched bearer to support voice traffic, needs a dedicated VoIP infrastructure, which often relies on the IP Multimedia Subsystem architecture. Most telecom operators implement LTE-A, an advanced version of LTE often marketed as 4G+, which achieves data rate peaks of 300 Mbps. Yet, although such novel technology boosts the access to advanced multimedia contents and services, telco operators continue to consider the VoIP market as the major revenue for their business. In this work, the authors propose a detailed performance assessment of VoIP traffic by carrying out experimental trials across a real LTE-A environment. The experimental campaign consists of two stages. First, we characterize VoIP calls between fixed and mobile terminals, based on a dataset that includes more than 750,000 data-voice packets. We analyze quality-of-service metrics such as round-trip time (RTT) and jitter, to capture the influence of uncontrolled factors that typically appear in real-world settings. In the second stage, we further consider VoIP flows across a range of codecs, looking at the trade-offs between quality and bandwidth consumption. Moreover, we propose a statistical characterization of jitter and RTT (representing the most critical parameters), identifying the optimal approximating distribution, namely the Generalized Extreme Value (GEV). The estimation of parameters through the Maximum Likelihood criterion, leads us to reveal both the short- and long-tail behaviour for jitter and RTT, respectively.