On the Clustering of Radio Channel Impulse Responses Using Sparsity-Based Methods

Radio channel modeling has been an important research topic, as the analysis and evaluation of any wireless communication system requires a reliable model of the channel impulse response (CIR). The classical work by Saleh and Valenzuela and many recent measurements show that multipath component (MPC) arrivals in CIRs appear at the receiver in clusters. To parameterize the CIR model, the first step is to identify clusters in CIRs, and a clustering algorithm is thus needed. However, the main weakness of the existing clustering algorithms is that the specific model for the cluster shape is not fully taken into account in the clustering algorithm, which leads to erroneous clustering and reduced performance. In this paper, we propose a novel CIR clustering algorithm using a sparsity-based method, which exploits the feature of the Saleh-Valenzuela (SV) model that the power of the MPCs is exponentially decreasing with increasing delay. We first use a sparsity-based optimization to recover CIRs, which can be well solved using reweighted ℓ1 minimization. Then, a heuristic approach is provided to identify clusters in the recovered CIRs, which leads to improved clustering accuracy in comparison to identifying clusters directly in the raw CIRs. Finally, a clustering enhancement approach, which employs the goodness-of-fit (GoS) test to evaluate clustering accuracy, is used to further improve the performance. The proposed algorithm incorporates the anticipated behaviors of clusters into the clustering framework and enables applications with no prior knowledge of the clusters, such as number and initial locations of clusters. Measurements validate the proposed algorithm, and comparisons with other algorithms show that the proposed algorithm has the best performance and a fairly low computational complexity.