In the era of sixth-generation (6G) wireless communications, integrated sensing and communications (ISAC) is recognized as a promising solution to upgrade the physical system by endowing wireless communications with sensing capability. Existing ISAC is mainly oriented to static scenarios with radio-frequency (RF) sensors being the primary participants, thus lacking a comprehensive environment feature characterization and facing a severe performance bottleneck in dynamic environments. To date, extensive surveys on ISAC have been conducted but are limited to summarizing RF-based radar sensing. Currently, some research efforts have been devoted to exploring multi-modal sensing-communication integration but still lack a comprehensive review. To fill the gap, we embark on an initial endeavor with the goal of establishing a unified framework of intelligent multi-modal sensing-communication integration by generalizing the concept of ISAC and providing a comprehensive review under this framework. Inspired by the human synesthesia, the so-termed Synesthesia of Machines (SoM) gives the clearest cognition of such an intelligent integration and details its paradigm for the first time. We commence by justifying the necessity and potential of the new paradigm. Subsequently, we offer a rigorous definition of SoM and zoom into the detailed paradigm, which is summarized as three operational modes realizing the integration. To facilitate SoM research, we overview the prerequisite of SoM research, that is, mixed multi-modal (MMM) datasets, and introduce our work. Built upon the MMM datasets, we introduce the mapping relationships between multi-modal sensing and communications, and discuss how channel modeling can be customized to support the exploration of such relationships. Afterward, aiming at giving a comprehensive survey on the current research status of multi-modal sensing-communication integration, we cover the technological review on SoM-enhance-based and SoM-concert-based applications in transceiver design and environment sensing. To corroborate the rationality and superiority of SoM, we also present simulation results related to dual-function waveform and predictive beamforming design tailored for dynamic scenarios. Finally, we propose some open issues and potential directions to inspire future research efforts on SoM.

As the fifth-generation (5G) wireless communication networks are at the stage of commercial deployment, beyond 5G (B5G)/sixth-generation (6G) wireless communication networks have also been under extensive research. In the B5G/6G era, the vision of the wireless communication network is the so-termed space-air-ground-sea integrated network (SAGSIN), which will focus on more various and dynamic communication scenarios, including vehicle-to-vehicle (V2V), high-speed train (HST), unmanned aerial vehicle (UAV), satellite, and maritime communications. Meanwhile, B5G/6G communication systems will also employ two potential technologies, i.e., millimeter wave (mmWave)-terahertz (THz) and ultra-massive multiple-input multiple-output (MIMO), and have a new development trend, i.e., integrated sensing and communication (ISAC) systems. For the successful design of B5G/6G communication systems, accurate and easy-to-use channel models, which can fully mimic the underlying characteristics and features of B5G/6G channels, are indispensable. However, more diverse communication scenarios, higher frequency band, larger-scale antenna array, and the emergence of ISAC systems in B5G/6G will bring two significant points of concern for wireless channels, i.e., channel non-stationarity and channel consistency. Channel non-stationarity is a typical channel characteristic, whereas channel consistency is an inherent channel physical feature. To capture those, extensive works have been carried out, but have not yet been adequately summarized, compared, and analyzed. This paper first provides the definitions of channel non-stationarity and channel consistency from mathematical and physical perspectives, and then discusses the necessity of capturing them for various wireless applications. Recent advances in the topic of capturing channel non-stationarity and channel consistency are further elaborated and investigated. Additionally, simulation results concerning them are provided and analyzed. Finally, promising and meaningful future research directions for this topic are outlined.

This paper proposes a non-stationary three-dimensional (3D) irregular-shaped geometry-based stochastic model (IS-GBSM) for fifth generation (5G) and beyond massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) unmanned aerial vehicle (UAV) channels. This is the first sixth generation (6G) massive MIMO mmWave UAV IS-GBSM that can model the UAV channel space-time non-stationarity, and can describe the impact of some unique UAV-related parameters, e.g., the UAV’s moving direction, height, and speed, on channel statistical properties. To better represent the space-time non-stationarity in UAV scenarios, a novel UAV-related space-time cluster evolution algorithm is developed. The developed algorithm considers the characteristics of UAV communications on the modeling of space-time non-stationarity. Based on the proposed model, some channel statistical properties are derived and thoroughly investigated, including the space-time-frequency correlation function, Doppler power spectral density, envelope level crossing rate, and average fade duration. Some numerical results and interesting observations are given, and the impact of UAV-related parameters on channel statistical properties is explored, which can provide assistance for the design of 6G massive MIMO mmWave UAV communication systems. Finally, the applicability of the proposed model is verified by the close agreement between simulation results and measurement.

In this paper, we propose a three-dimensional (3D) cluster-based model for the fifth generation (5G)/beyond 5G (B5G) vehicle-to-vehicle (V2V) massive multiple-input multiple-output (MIMO) wideband channels. It is the first cluster-based irregular shaped geometry-based stochastic model (IS-GBSM) to distinguish the dynamic clusters and static clusters in vehicular massive MIMO communication scenarios. This model not only considers the high time-variance, the time non-stationarity, and the vehicular traffic density (VTD) of V2V channels, but also models the massive MIMO channel characteristics, such as spherical wavefronts by 3D vector calculation and space non-stationarity. Meanwhile, this model for the first time integrates the VTD into birth-death process to model the channel characteristics of V2V and massive MIMO jointly and deeply, where a novel VTD-combined time-array cluster evolution algorithm for 5G/B5G V2V massive MIMO channel model is developed. Based on the proposed model, we derive some expressions of channel statistical properties, including time-space-frequency correlation function (STF-CF) and Doppler power spectral density (DPSD). The influence of several parameters, e.g., VTDs, vehicle moving directions, and antenna spacings, on the channel characteristics are sufficiently explored, which can provide assistance for the design of vehicular massive MIMO communication systems. Finally, the utility of the proposed model is verified by the close agreement between simulation results and measurement data.

This paper proposes a novel general sixth-generation (6G) irregular-shaped geometry-based stochastic model (IS-GBSM) for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless communication systems. The proposed IS-GBSM can model spherical wavefront propagation, high delay resolution, and three-dimensional (3D) space-time-frequency non-stationarity of channels with time-space consistency, and thus can be applied to integrated communication and sensing systems. To capture the 3D non-stationarity with channel consistency, a new hybrid method combining geometric and parametric methods is developed. Based on the geometric method, i.e., visibility region (VR), the smooth and consistent appearance and disappearance of clusters during array-time evolution are modeled. With the help of the parametric method, a frequency-dependent path gain is introduced, and a visible factor based on uniform continuity theorem and bounded variation function is further proposed to capture the soft cluster power handover during cluster array-time evolution. Key channel statistics of the proposed IS-GBSM are derived and investigated. Simulation results demonstrate that the space-time-frequency non-stationarity is modeled and time-space consistency is captured. Finally, the utility of the proposed IS-GBSM is verified by the excellent agreement between simulation results and measurement.

This paper proposes a novel three-dimensional (3D) non-stationary irregular-shaped geometry-based stochastic model (IS-GBSM) for beyond fifth-generation (B5G) and sixth-generation (6G) vehicle-to-vehicle (V2V) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) channels. By distinguishing dynamic clusters and static clusters, the proposed IS-GBSM for the first time explores the impact of vehicular traffic density (VTD) on channel statistics in B5G/6G V2V mmWave massive MIMO scenarios. Furthermore, a novel method is developed to model the channel space-time-frequency non-stationarity. In the developed method, dynamic/static correlated clusters are first generated by an improved $K$ -Means clustering algorithm. Then, by employing a birth-death process based on correlated clusters, the consistency in birth and death between dynamic/static correlated clusters during time-array evolution is modeled. Key channel statistical properties, e.g., space-time-frequency correlation function (STF-CF) and Doppler power spectral density (DPSD), are derived. Simulation results demonstrate that the space-time-frequency non-stationarity is captured and the influence of VTDs on channel statistics is explored. Finally, the close agreement is achieved between simulation results and measurements, verifying the utility of proposed IS-GBSM.