Radar simulation offers the potential to generate data cubes with effectiveness and accuracy. However, the radar simulator considering both dense multipaths of indoor environment and dynamic motions is rare. This paper develops a versatile channel simulator that can generate frequency modulation continuous wave (FMCW) waveform multiple inputs multiple outputs (MIMO) signals of indoor scenarios. In the proposed simulation framework, the open-source tool called Blender is utilized to model dynamic scenarios and render animations. Each frame of the animation is rendered into a picture with a defined number of pixels by the ray tracing (RT) engine embedded in Blender. Then, the distance, strength, angle of arrival (AoA), and motion speed of each pixel are calculated based on the traced rays. The sampled beat signal models of the commonly used orthogonal multiplexing MIMO modes are derived in terms of the Blender outputs. A virtual array generation method is utilized to improve the simulation time efficiency. To eliminate the velocity noise, a pixel filtering method is also introduced. For validation, the measurements of the time division multiplexing (TDM) FMCW MIMO sensor in both anechoic chamber and corridor scenario at the mmWave band are compared with the simulation using range, angle, Doppler, and micro-Doppler results. The comparisons show the merits and validation of the proposed simulation method.

Indoor sensing is challenging because of the multi-bounce effect, spherical wavefront, and spatial nonstationarity (SNS) of the near-field effect. This paper addresses radio-based environment sensing considering these issues. Specifically, graph theory (GT) is used to model the multi-bounce propagation of the near field. In this manner, indoor reflectors/scatterers are modeled as vertices in a propagation graph, the multi-bounce paths are modeled by the edges linking the vertices. Besides, the coupled multipath parameters in the near field, i.e., range and angles, are denoted directly by the coordinates of vertices. Then, the space-alternating generalized expectation-maximization (SAGE) algorithm is adapted to the proposed GT-based dictionary-aided multi-bounce SAGE (GM-SAGE), where the searching parameters including range and angle of departure/arrival (AoD/AoA) are transformed to the coordinates of vertices in the graph. To accelerate the two-bounce sensing, a recursive strategy is proposed. Furthermore, geometric information-based radio-sensing ambiguities are analyzed. The proposed algorithm is validated through a synthetic scattering channel and realistic ray tracing (RT) in a complex indoor office. The results demonstrate that the proposed GM-SAGE can deal with multi-bounce channels, where the one-bounce paths and near-field two-bounce paths are used to reconstruct the scatterers in the environment, the residual higher-bounce paths are identified and hence avoid ghost/mirror detection. Numerical simulations also show the influence of signal-noise ratio (SNR), grid size of vertices, aperture size, and near-far-field effects.

Propagation graph (PG) is a stochastic channel simulation method for scattering propagation. In this article, an embedded-PG (EPG) approach, an extension of conventional PG, is proposed to simulate reflection and scattering multi-path behaviors in wireless channels. In this method, multiple propagation paths are categorized into scattering-path, reflecting-path, and scattering-reflecting mixed paths among reflectors and scatterers. The matrix recursive formula of conventional PG modeling is used to calculate scattering-path, a recursive mathematical transformation is applied to adapt reflecting-path into the recursive formula, and an embedded graph method is used to decompose mixed-path into scattering effects and reflection effects. The proposed simulation approach is validated by comparison with conventional PG and measurement in 39 GHz millimeter-wave (mm-wave) time-variant corridor scenario. Power delay profiles (PDPs) and spatial consistency of multiple paths observed in concatenated-PDPs (CPDPs) obtained by EPG are more consistent with measurement than conventional PG, differences of mean delay and delay spread between simulations and measurement in typical snapshots are within 3 ns and 1.5 ns, respectively.

This paper considers the issue of acquiring channel state information (CSI) for multi-user orbital angular momentum (MU-OAM) wireless backhaul between the macro base station (MBS) and small base stations (SBSs) within broadcasting networks. Unlike prior works, we assume that each SBS transmits a pilot signal of length one on each multiplexed OAM mode and subcarrier, resulting in the coherent observations collected at the MBS. Then, we construct the data sets using the coherent observations, the components of which independently contain arbitrarily assumed positional information. The amplitude-phase multiple signal classification (AP-MUSIC) algorithm, a novel variant of the MUSIC, then conducts a two-dimensional (2-D) search on the amplitude and phase of the data component in both the OAM mode and frequency domains for estimating positions at each iteration. These estimates, together with the observations, are used to iteratively update the data sets, ultimately refining the distances and AoAs of all SBSs. The theoretical analysis and simulation results indicate that this solution not only yields the precise CSI for the MU-OAM system, but also markedly reduces the training overhead, compared to existing alternatives.

Time difference of arrival (TDoA) technology is widely utilized for source localization, which stimulates many studies on performance-evaluation approaches for TDoA localization systems. Some approaches using simulations are designed merely for a simple Line-of-Sight (LoS) scenario while some other ones using experiments show high cost and inefficiency. This paper proposes an integrated approach to evaluate a TDoA localization system in an area with a complicated environment. Radio propagation graph is applied through a simulation to obtain channel impulse responses (CIRs) between a source to be located and the TDoA sensors for the area. Realistic signals received by the sensors in baseband are emulated combining the source transmitted signal and the CIRs. A hardware unit takes charge of sending the radio emulated received signals to the system under test, which is consistent with real experimental measurements. Statistical analysis of the system is allowed based on localization errors obtained comparing the system’s estimates with the ground truth of the source location. Verified results for LoS and non-LoS scenarios with variable transmitted signal bandwidths and signal-to-noise ratios, as well as for three variations of the sensor locations in an automobile circuit, show the usability of the proposed experiment-free performance-evaluation approach.

Low Earth orbit (LEO) satellite constellations are rapidly becoming essential enablers of next-generation wireless systems, offering global broadband access, high-precision localization, and reliable sensing beyond terrestrial coverage. However, the inherent limitations of individual LEO satellites, including restricted power, limited antenna aperture, and constrained onboard processing, hinder their ability to meet the growing demands of 6G applications. To address these challenges, this article introduces the concept of distributed integrated sensing, localization, and communication (DISLAC) over LEO constellations, inspired by distributed multiple input multiple output architectures. By enabling inter-satellite cooperation through inter-satellite links, DISLAC jointly exploits communication, localization, and sensing functionalities, achieving synergistic gains in throughput, positioning accuracy, and sensing robustness through shared resources and cooperative design. We present illustrative case studies that quantify these benefits and analyze key system-level considerations, including synchronization, antenna reconfigurability, and inter-satellite link design. The article concludes by outlining open research directions to advance the practical deployment of DISLAC in future non-terrestrial networks.