2002/08/25 by Alex Verstak, Naren Ramakrishnan, Verstak, Alex +13
Computer Science · Engineering · #Advanced Wireless Communication Techniques #Advanced Wireless Network Optimization #Computational Engineering #FOS: Computer and information sciences #Finance #I.6.4 #Wireless Communication Networks Research #and Science (cs.CE) #cs.CE
paper · pdf · doi:10.48550/arxiv.cs/0208040
arxiv created 2002/08/25 · openalex publication_date 2002/08/25 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.