Estimating 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.
Software 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.
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