Measurement‐based Statistical Channel Modeling
Xuefeng Yin, Xiang Cheng · 2016
This chapter introduces the general measurement-based statistical channel modeling procedure. It elaborates the clustering algorithms applied for grouping specular paths. The chapter describes the approaches applicable for separating the observations into multiple segments with different stationarity. It discusses some preliminary thoughts and modeling results for the relay channel and cooperative multi-point (CoMP) channel models. The channel parameter estimation block also consists of three steps: generic model selection, algorithm selection, and parameter extraction. Stochastic cluster-based channel modeling relies on decomposing a channel into multiple clusters of multipath components. In order to extract the statistics of channel characteristics, multiple observations of wide-sense-stationary (WSS) channels are needed. The Kolmogorov-Smirnov test based method was used to check whether the distributions of the paths estimated from individual bursts of data are consistent. The sensitivity of the distribution of small-scale fading (SSF) cross-correlation coefficients with respect to the proposed variables is assessed.