Virtualization of Radio Access Network by Virtual Machine and Docker: Practice and Performance Analysis

International audienceSoftware defined networking (SDN) and network function virtualization (NFV) are the embraced technologies for the backhauling of future 5G networks. Virtual Machine (VM) and Docker container based deployments have received much attention. This paper presents the virtualization of a prototyped software defined radio access network (RAN) architecture by using VMs and Docker containers. In addition, it provides an analytical model for the generalized software defined RAN architecture with the practice of VM based and Docker container based implementations. Using measurements obtained from the two testbeds and the introduced queuing model, we compare their performances and analyze the two different architectures. Results verify the superiority of the Docker technology. Some observations from the behavior of the testbeds are concluded for a better understanding of the VM and Docker container based technologies for the future development of 5G SDN controller

Network-Based UE Mobility Estimation in Mobile Networks

International audienceThe coexistence of small cells and macro cells is a key feature of 4G and future networks. This heterogeneity, together with the increased mobility of user devices can generate a high handover frequency that could lead to unreasonably high call drop probability or poor user experience. By performing smart mobility management, the network can pro-actively adapt to the user and guarantee seamless and smooth cell transitions. In this work, we introduce an algorithm that takes as input sounding reference signal (SRS) measurements available at the base station (eNodeB in 4G systems) to estimate with a low computational requirement the mobility level of the user and with no modification at the user device/equipment (UE) side. The performance of the algorithm is showcased using realistic data and mobility traces. Results show that the classification of UE speed to three mobility classes can be achieved with accuracy of 87% for low mobility, 93% for medium mobility, and 94% for high mobility, respectively

Online Mobile User Speed Estimation: Performance and Tradeoff Considerations

International audienceThis paper presents an online algorithm for mobile user speed estimation in 3GPP Long Term Evolution (LTE)/LTE-Advanced (LTE-A) networks. The proposed method leverages on uplink (UL) sounding reference signal (SRS) power measurements performed at the base station, also known as eNodeB (eNB), and remains effective even under large sampling period. Extensive performance evaluation of the proposed algorithm is carried out using field traces from realistic environment. The on-line solution is proven highly efficient in terms of computational requirement, estimation delay, and accuracy. In particular, we show that the proposed algorithm can allow for the first speed estimation to be obtained after 10 seconds and with an average speed underestimation error of 14 kmph. After the first speed acquisition, subsequent speed estimations can be obtained much faster (e.g., each second) with limited implementation cost and still provide high accuracy

Network-based UE mobility estimation in mobile networks

International audienceThe co-existence of small cells and macro cells is a key feature of 4G and future networks. This heterogeneity, together with the increased mobility of user devices can generate a high handover frequency that could lead to unreasonably high call drop probability or poor user experience. By performing smart mobility management, the network can pro-actively adapt to the user and guarantee seamless and smooth cell transitions. In this work, we introduce an algorithm that takes as input sounding reference signal (SRS) measurements available at the base station (eNodeB in 4G systems) to estimate with a low computational requirement the mobility level of the user and with no modification at the user device/equipment (UE) side. The performance of the algorithm is showcased using realistic data and mobility traces. Results show that the classification of UE speed to three mobility classes can be achieved with accuracy of 87% for low mobility, 93% for medium mobility, and 94% for high mobility, respectively

Straight: stochastic geometry and user history based mobility estimation

International audience5G is envisioned to support scalable networks and improved user experience with virtually zero latency and ultra broad-band service. Supporting unlimited seamless mobility is one of the key issues and also for network resource utilization efficiency. In this paper, we focus on mobility management and user equipment (UE) speed class estimation, also known as mobility state estimation (MSE). We propose a method for estimating the UE mobility which is compliant with UE history information specifications by 3GPP (3rd Generation Partnership Project). We also exploit the impact of the environment on the UE trajectory and speed when determining UE mobility state. We evaluate the effectiveness of our algorithm using realistic mobility traces and network topology of the city of Cologne in Germany provided by the Kolntrace project [1]. Results show that the speed classification of UEs can be achieved with much higher accuracy compared to existing legacy 3GPP LTE MSE procedures

Mobility State Estimation in LTE

International audienceEstimating mobile user speed is a problematic issue which has significant impacts to radio resource management and also to the mobility management of Long Term Evolution (LTE) networks. This paper introduces two algorithms that can estimate the speed of mobile user equipments (UE), with low computational requirement, and without modification of neither current user equipment nor 3GPP standard protocol. The proposed methods rely on uplink (UL) sounding reference signal (SRS) power measurements performed at the eNodeB (eNB) and remain efficient with large sampling period (e.g., 40 ms or beyond). We evaluate the effectiveness of our algorithms using realistic LTE system data provided by the eNB Layer1 team of Alcatel-Lucent. Results show that the classification of UE’s speed required by LTE can be achieved with high accuracy. In addition, they have minimal impact to the central processing unit (CPU) and the memory of eNB modem. We see that they are very practical to today’s LTE networks and would allow a continuous and real-time UE speed estimation