A Trace Data-Based Approach for an Accurate Estimation of Precise Utilization Maps in LTE

Abstract

For network planning and optimization purposes, mobile operators make use of Key Performance Indicators (KPIs), computed from Performance Measurements (PMs), to determine whether network performance needs to be improved. In current networks, PMs, and therefore KPIs, suffer from lack of precision due to an insufficient temporal and/or spatial granularity. In this work, an automatic method, based on data traces, is proposed to improve the accuracy of radio network utilization measurements collected in a Long-Term Evolution (LTE) network. The method’s output is an accurate estimate of the spatial and temporal distribution for the cell utilization ratio that can be extended to other indicators. The method can be used to improve automatic network planning and optimization algorithms in a centralized Self-Organizing Network (SON) entity, since potential issues can be more precisely detected and located inside a cell thanks to temporal and spatial precision. The proposed method is tested with real connection traces gathered in a large geographical area of a live LTE network and considers overload problems due to trace file size limitations, which is a key consideration when analysing a large network. Results show how these distributions provide a very detailed information of network utilization, compared to cell based statistics.

A Context-Aware Data-Driven Algorithm for Small Cell Site Selection in Cellular Networks

“…This data is delivered as binary files that must be decoded to extract performance measurements at a connection level. The reader is referred to [50] for details on trace processing. Table 2 summarizes the data fields needed by the algorithm.…”

Encrypted Traffic Classification Based on Unsupervised Learning in Cellular Radio Access Networks

“…Their ability to generate new indicators different from counters provided by vendors is extremely valuable for operators [24]. For instance, traces can be used in network planning to derive spectral efficiency curves required in cellular planning and simulation [25] or the spatiotemporal distribution of radio resources in a live network [26]. Likewise, traces can be used in the operational stage to tune network parameters (e.g., link adaptation offset [27], antenna tilt angle [28] or inter-system handover margin [29]) or find the root cause of problems (e.g., dropped connections [30]).…”