No abstract

Using a mobile dataset from Orange Senegal small cell 4G network, we study the effect of variables such as data traffic at the downlink level, data traffic at the uplink level, total data traffic, maximum number of active users, signaling protocol, uplink user rate, downlink user rate, physical resource block rate for downlink, block rate physical resources for uplink, load data logging, channel quality indicator, downlink radio delay average, over the perceived rate at the downlink. We are looking to find the variable that most affects the perceived flow. We do this by using machine learning to find the variable that closely explains the variation in perceived flow and helps predict flow with greater accuracy. We observe that correlation analysis is unable to find a hidden relationship between throughput and other variables. With models such as linear regression, decision tree, random forest, multi-layered perceptron and deep neural network, the channel quality indicator (CQI_Avg) turns out to be the variable that more closely explains the variation in perceived flow and more accurately contributes to the prediction compared to others variables.

No abstract

The massive growth of wireless traffic goes hand in hand with the deployment of advanced radio interfaces as well as network densification. This growth has a direct impact on the radio access architecture, which today is moving from centralized to distributed deployments through the use of a large number of access points (APs). This paper verifies the feasibility of deploying multiple APs in series on a single line in a ring topology in a cell-less network. On the one hand, this technique will further improve the communication capacity and flexibility of a Radio-over-Fiber (RoF) based mobile communication system and will reduce its construction cost. And on the other hand, this deployment topology is a solution to achieve a massive cell-free Multiple-Input Multiple-Output (MIMO) architecture and a cost-effective fronthaul solution. First, a passive optical add/drop multiplexer (OADM) is used to extract and add downlink and uplink signals from the remote access points of one kilometer. Then, a deployment model is developed with version 17 Optisystem software. The results obtained showed that the quadrature amplitude modulation (QAM) does not adapt to this multi-carrier transmission to deploy several AP in series on a single line. Thus, the performance degradation increases when the number of APs integrated on the line increases.