Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust.

Unmanned aerial vehicles (UAVs) have been widely used in both military and civilian applications, where a stable communication link is vital for safe flight control and robust data transmission. To develop a reliable UAV communication system, it is necessary to deeply understand the UAV channel characteristics and establish accurate channel models. Channel sounding is the most effective way to obtain realistic channel characteristics and validate the theoretical channel model. However, the studies on UAV channel sounding are still insufficient in terms of system design and data processing due to the complexity of developing a UAV channel sounder. Different from the terrestrial channel sounders, the implementation of a UAV channel sounder is tortured by the limited battery life and payload capacity of the UAV platform. The sounding scheme and data post-processing also need to be specially designed for highly dynamic UAV channels. So far, most existing survey studies on UAV channels focus on modeling methodology and model presentation. To fill this gap, this paper provides a comprehensive survey on the design of the UAV channel sounder, in terms of the hardware scheme, sounding signal, time synchronization, calibration, and data post-processing. Current issues and potential research topics behind existing sounding technologies and measurement campaigns are analyzed. Moreover, future challenges and open issues are also discussed.

With the rapid development and broad applications of unmanned aerial vehicle (UAV) based wireless stations in the sky, fundamental understanding and characterization of the realistic air-to-ground (A2G) communication link properties are crucial. In this paper, a UAV-aided channel sounder with a real-time processing hardware system is developed for highly dynamic and non-stationary A2G channel measurements. In the hardware system, a global positioning system (GPS) -based triggering signal is designed, the equivalent antenna pattern affected by UAV airframe is considered, and an appropriate sounding signal is selected, in order to improve the accuracy of measured channel impulse response (CIR). Moreover, real-time hardware processing algorithms for raw channel data, i.e., CIR extraction, system response elimination (SRE), power loss recovery (PLR), and adaptive multi-path component (MPC) recognition are developed and implemented on a single field-programmable gate array (FPGA) chip. In this way, the required storage size of channel data and the processing time for one slice of CIR is greatly decreased, which can meet the requirement of non-stationary A2G channel measurement with a high sampling rate and long-time measurement. A commercial channel emulator is used to reproduce controllable channels and verify the performance of developed channel sounder. Finally, the developed channel sounder is applied to carry out A2G measurement campaigns at 3.5 GHz in a campus scenario. The channel characteristics, i.e., path loss (PL), K-factor, and path angle are analyzed. The measured channel characteristics are consistent with existing measurements under a similar scenario. The estimated path angles are also validated by the theoretical results. Thus, the channel sounder can be used to capture the non-stationary A2G channel characteristics for the system design and algorithm optimization of A2G communications.

In this paper, a 3D geometry-based double-spherical terahertz (THz) channel model is proposed for ultra massive multiple-input multiple-output (MIMO) communications. To reflect real THz ultra massive MIMO communications, the characteristic of nano-material array antenna and the high loss of THz bands are considered in channel modeling. Based on the proposed geometry-based stochastic model (GBSM), power delay profile (PDP), space-time-frequency correlation function, and the Doppler power spectral density are derived and analyzed. Furthermore, the proposed model is validated by comparing the simulated results with the measurement reference. Influences of channel parameters, namely, antenna element spacing, carrier frequency, number and location of clusters, on the correlation functions are investigated. The results indicate that antenna element spacing and number of clusters have significant impacts on channel correlation, and the decreasing of correlation function becomes slower when antenna element spacing decreases. Moreover, location of clusters also affects the drop rate of correlation function. The influences of the number of clusters and concentration of intra-cluster multipath components (MPCs) on the Doppler power spectral density are analyzed. It is found that a larger number of clusters and a more concentrated distribution of intra-cluster MPCs increase the degree of Doppler spectrum variation. The proposed channel model and the corresponding statistical properties are insightful for designing and realizing THz ultra massive MIMO systems for 6G and beyond.

In recent years wireless propagation channel research has paid considerable attention to dense multipath, which is an indispensable part of propagation channels and may significantly contribute to the received power in variety of environments and frequency bands. Mathematical representation of dense multipath is different from that of specular components (SCs) due to its distinct propagation mechanism and impact on system performance. Therefore, accurate understanding and modeling of dense multipath together with SCs are important for parametric channel estimation and for reliable simulation in wireless applications. This paper first presents a systematic survey of studies of dense multipath in terms of different representations, channel modeling approaches and estimation methods. Thereafter, a comprehensive review of the characteristics and impact of dense multipath on performance of communication, localization and sensing systems is provided. Finally, open research topics are discussed.

The development of sixth-generation (6G) wireless communication systems brings significant challenges in channel modeling. Conducting channel measurements for 6G communications is highly expensive and cannot cover all scenarios and frequency bands. Moreover, existing conventional channel models fail to accurately predict channel characteristics in unknown frequency band. As a result, predictive channel modeling has emerged as a promising solution for addressing these challenges in 6G channel modeling. In this study, we propose a frequency domain predictive channel model that combines an autoencoder with a coupling Convolution Gated Recurrent Unit (Conv-GRU) cells. The proposed model aims to predict channel characteristics in unknown frequency bands. The proposed predictive channel model is validated by using data collected from multiple frequency bands channel measurements. To evaluate its performance, several commonly used prediction networks, i.e., a general LSTM network, a GRU-based predictive network, and a Conv-LSTM-based predictive network, are conducted as benchmarks for comparison. Based on evaluation results, our proposed predictive channel model achieves the highest level of accuracy in predicting channels. Additionally, we provide a performance bound for extrapolation predictability using a Ray Tracing simulator.