Clustering characterizes the grouping of multipath components (MPCs) in radio channels. Accurate clustering is a prerequisite for cluster-based channel characterization and sensing in the beyond fifth-generation (B5G) and sixth-generation (6G) communication. However, existing clustering algorithms commonly depend on thresholds and initializations, and are not fully consistent with the characteristics of MPC distributions in the radio channel. Additionally, clustering based on power spectrum has not been thoroughly researched. In this paper, we propose a unified clustering method named power-weighted nearest-neighbor robust mean-shift (MP-NN-RMS) algorithm, which is a kernel density estimation (KDE)-based method. The K-nearest neighbor (KNN) kernel is utilized to adapt to the changes in local density. Two variants of this clustering method for the power spectrum and MPCs are provided. Both simulation and measurement-based verifications demonstrate the effectiveness of the proposed algorithms. Compared with traditional clustering methods, the proposed algorithm can achieve more accurate and robust clustering results without requiring prior information of predefined parameters or models. Moreover, mathematical proof on the convergence guarantees the rationality of the proposed algorithm. This advancement is beneficial for the development of future wireless communication systems.

In this paper, the novel extended space-alternating generalized expectation-maximization (SAGE) algorithm, providing joint propagation channel multi-path component (MPC) estimation and scatterer localization underlying spherical-wavefront multipath model, is proposed. Two geometry-based models are aided for estimating the first and last hop scatterers under different bouncing orders. The performance of the proposed algorithm, as called geometry-aided SAGE (GA-SAGE), is illustrated by means of the Cramér-Rao lower bound derived for parameter estimates and the root-mean-square-estimation-errors (RMSEEs) obtained through and Monte-Carlo simulations, which shows the applicability both near- and far-field estimation. Finally, the GA-SAGE is applied to processing the experimental data obtained from measurements in an indoor office environment by using a single-input multiple-output (SIMO) configuration. The obtained results show that the method proposed outperforms the traditional SAGE algorithm in terms of MPCs estimation accuracy, convergence rate, and the extra capability of localizing scatterers involved in different bouncing order propagation paths. It is considered useful in environment sensing alike applications and makes the GA-SAGE an efficient and effective tool for the development of geometry-based stochastic channel models capable of reproducing channel realizations of the so-called spatial consistency or spatial non-stationarity, which are the basis for the design of transmission technologies using extremely-large antenna array (ELAA) for 5G and beyond.

With the rapid development of the sixth generation (6G) communication systems, the extremely large antenna arrays (ELAAs) have attracted substantial attention in both academia and industry. As the array size increases, the so-called “near-field” effect, i.e. channel parameters varying with respect to the antenna’s position becomes evident. Conventional channel models fail to describe such effects as the parameters of multi-path components (MPCs) specified in these models are fixed regardless of array configuration. Due to the mismatch between the constant MPC assumption and the actual variant behavior observed, the parameterization of the models aiming to describe the spatial non-stationary channels cannot be conducted effectively. In order to mitigate the model mismatch and reproduce the channels with spatial non-stationarity specifically for ELAA applications, in this work, a novel measurement-based stochastic channel modeling method is proposed and applied to establishing Scatterer-based Spatial Channel Model (SSCM) that can be used to unify the far- and near-field models using spherical wave and generate the spatial non-stationary channels for ELAAs by integrating the traditional spatial channel model (SCM) with “clusters-of-scatterers (CoS)”. To establish the models based on measurements, the locations of first- and last-hop scatterers existing along propagation paths in a channel are estimated based on the spherical-wave assumption. A novel clustering algorithm is used to group the MPCs taking into account those scatterers’ locations. With a channel measurement campaign conducted using a$32\!\!\times \!\!32$Rx planar antenna array at carrier frequency of 10 GHz, exemplary SSCMs are established. The SSCM is capable of reproducing the parts of the environment influencing wave propagation, and therefore can be used for the research of integrated sensing and communication (ISAC) applications.