An in-depth study of LTE: effect of network protocol and application behavior on performance
By Junxian Huang, Feng Qian, Y. Guo, Yuanyuan Zhou, Qiang Xu, Z. Morley Mao, Subhabrata Sen, Oliver Spatscheck. 2013
414 citations recorded by the source corpus.
With lower latency and higher bandwidth than its predecessor 3G networks, the latest cellular technology 4G LTE has been attracting many new users. However, the interactions among applications, network transport protocol, and the radio layer still remain unexplored. In this work, we conduct an in-depth study of these interactions and their impact on performance, using a combination of active and passive measurements. We observed that LTE has significantly shorter state promotion delays and lower RTTs than those of 3G networks. We discovered various inefficiencies in TCP over LTE such as undesired slow start. We further developed a novel and lightweight passive bandwidth estimation technique for LTE networks. Using this tool, we discovered that many TCP connections significantly under-utilize the available bandwidth. On average, the actually used bandwidth is less than 50% of the available bandwidth. This causes data downloads to be longer, and incur additional energy overhead. We found that the under-utilization can be caused by both application behavior and TCP parameter setting. We found that 52.6% of all downlink TCP flows have been throttled by limited TCP receive window, and that data transfer patterns for some popular applications are both energy and network unfriendly. All these findings highlight the need to develop transport protocol mechanisms and applications that are more LTE-friendly.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering
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In Computer Networks and Communications
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In Electrical and Electronic Engineering
Citations in the corpus
- Federated Multi-Task Learning (2017, methodology): “Communication can also exacerbate poor performance, as it can be slower than computation by many orders of magnitude in typical cellular or wireless networks [49, 19, 46, 8, 36].”
- Federated Learning: Challenges, Methods, and Future Directions (2019, neutral): “ed networks are potentially comprised of a massive number of devices, e.g., millions of smart phones, and communication in the network can be slower than local computation by many orders of magnitude [50, 115]. In order to fit a model to data generated by the devices in the federated network, it is t”