Using Hierarchical Data Mining to Characterize Performance of Wireless System Configurations

TL;DR

This paper introduces a statistical data mining framework to analyze and characterize the performance of wireless systems, specifically WCDMA with STTD and convolutional coding, across various configurations using simulation data.

Contribution

It develops a hierarchical data mining approach to identify regions of acceptable performance in wireless system configurations, providing explainable and statistically valid design insights.

Findings

Identified regions of configuration space with acceptable performance.

Demonstrated the effectiveness of data mining in wireless system analysis.

Provided a methodology for statistically valid performance assessment.

Abstract

This paper presents a statistical framework for assessing wireless systems performance using hierarchical data mining techniques. We consider WCDMA (wideband code division multiple access) systems with two-branch STTD (space time transmit diversity) and 1/2 rate convolutional coding (forward error correction codes). Monte Carlo simulation estimates the bit error probability (BEP) of the system across a wide range of signal-to-noise ratios (SNRs). A performance database of simulation runs is collected over a targeted space of system configurations. This database is then mined to obtain regions of the configuration space that exhibit acceptable average performance. The shape of the mined regions illustrates the joint influence of configuration parameters on system performance. The role of data mining in this application is to provide explainable and statistically valid design conclusions. The research issue is to define statistically meaningful aggregation of data in a manner that permits efficient and effective data mining algorithms. We achieve a good compromise between these goals and help establish the applicability of data mining for characterizing wireless systems performance.