Radio channel modeling has been an important research topic, since the performance of any communication system depends on channel characteristics. So far, most of existing clustering algorithms are conducted based on the multipath components (MPCs) extracted by using high-resolution parameter estimation approach, e.g., SAGE or MUSIC, etc. However, most of the estimation approaches require prior information to extract MPCs. Moreover, the high-resolution estimation approach usually results in relatively high complexity and thus the clusters can only be identified by using off-line approach after the measurements. Therefore, a power angle spectrum based clustering and tracking algorithm (PASCT) is proposed in this paper. Firstly, power angle spectrum (PAS) is extracted from measurement data by using Bartlett beamformer. For each PAS, potential targets are selected from background and separated into clusters by using image processing approaches. The recognized clusters are characterized into three attributes: i) size, ii) position, and iii) shape feature, where histogram of oriented is developed to describe the shape feature of the clusters. Moreover, a minimizing cost tracking approach based on Kuhn-Munkres method is proposed to accurately identify the clusters in non-stationary channels. The proposed PASCT algorithm is validated based on both simulations and measurements. It is found that the dominating clusters in both line-of-sight and non-line-of-sight environments can be well recognized and tracked with the proposed algorithm. By using the proposed algorithm, the dynamic changes of the clusters in real-time channel measurements, e.g., number, birthdeath process, and size of the clusters, can be well observed. Through the experiments, the proposed can achieve fairly good accuracy on the cluster identification with lower complexity compared to the conventional solution.

For the millimeter wave (mm-Wave) and terahertz (THz) indoor wireless communication system, the reflection channels need to be characterized and modeled. In this paper, the reflection measurements of the parallel polarized wave are carried out under multiple incident angles and five kinds of materials in the D-band (110–170 GHz). A modified reflection model with two parameters estimated by the minimum mean square error (MMSE) criterion is proposed. The results show that the measurements are in good agreement with the proposed model. Furthermore, a set of measured properties is demonstrated and it can be concluded that both the reflection coefficients and relative permittivity gradually decrease, whereas the surface roughness increases slightly with the increasing frequency, indicating a weak frequency dependence. Interestingly, the concrete board with high surface roughness, which means more power loss in a specular direction, has the lowest reflection loss at a certain frequency and incident angle. It implies that the reflection characteristics of indoor building materials are determined not only by surface roughness, but also by many other factors, such as relative permittivity, frequency, and incident angle. Our work suggests that the reflection measurements of indoor D-band wireless links have a prospective application for future indoor wireless communication systems.

The fifth-generation (5G) mobile communications system will adopt the millimeter wave (mmWave) band for outdoor-to-indoor (O2I) coverage to achieve ultrahigh data rate. However, it is a challenging task because of the large path loss and almost total blocking by building walls. In this work, we performed extensive measurements on the O2I propagation at 3.5, 4.9, and 28 GHz simultaneously by using a multiband channel sounder. We captured the path loss distribution and angular power arrival profiles. We also measured the penetration loss at 28 GHz through different kinds of glass windows. The widely adopted ordinary glass windows introduce the penetration loss of 3 to 12 dB that is acceptable and makes mmWave O2I coverage feasible. But the low-emissivity (low-E) windows that will be more popular in the future introduce 10 dB higher loss. The measurement results in this work can help analyse and anticipate the O2I coverage by mmWave, which is important for the design and deployment of the 5G network.

In order to study the feasibility of co-time co-frequency full-duplex (CCFD) relay, it is crucial to characterize the self-interference (SI) in practical deployment scenarios. However, modeling the SI channels is still an open issue. In this paper, a measurement campaign on the SI channels for relay communications in a dense urban scenario is presented. The transmitting and receiving antennas were installed next to each other and raised up to different heights. At each height the transmitting antenna was rotated to change the angle between their orientations. The channels were sounded at 2.6 GHz with the bandwidth of 32 MHz. The loop-back propagation and direct coupling were measured. Multiple clusters were observed in the power delay profiles (PDPs) which were reflected by the nearby buildings. The delay spreads of the loop-back channels had a decreasing tendency with the increase of the angular gap between the antenna orientations, because the second cluster was attenuated more severely than the first one. The loop-back power and the coupling power both decreased too, and the ratio between them remained consistent. The antenna isolation by angular gap is also evaluated. The measurement results in this work illustrate the behavior of loop-back propagation in an dense urban area and can help to design and evaluate the SI cancellation algorithms for the in-band full-duplex relay.

In this paper, a method of choosing direction estimation range is proposed for high-resolution channel parameter estimation by using the Space-Alternating Generalized Expectation-maximization (SAGE) algorithm. The proposed method applies in cases where directional multi-antenna arrays are used for channel measurements. In the method, the direction estimation range is determined by specifying appropriate values for the dynamic range of array gain (DRAG) of directional arrays. A simple rationale is provided for calculating the lower-bound of DRAG. Both simulation and measurement results demonstrate that when the DRAG is properly selected, the estimation errors retains at an acceptable level and the spurious paths, i.e. the estimated path without its counterpart in reality, appear with low possibilities.

In this chapter, the authors address the wireless channel prediction using state-ofthe-art machine-learning techniques, which is important for wireless communication network planning and operation. Instead of the classic model-based methods, the authors provide a survey of recent advances in learning-based channel prediction algorithms. Some open problems in this field are then proposed.