An auto-polarization fluid antenna (APFA) is developed for indoor wireless channel sounding and employed to reveal a novel “fluid polarization effect” (FPE) in wireless communications. Unlike conventional fluid antennas (FAs) that are steering their beams/nulls with the aid of external mechanical/electronic actuators, the APFA only relies on the natural swinging of human arms to yield a self-driven polarization switching ability. Compared with the conventional fixed circularly polarized antennas, the wrist-worn, self-driven APFA in indoor wireless channel sounding systems effectively reduces multipath clusters (MPCs), attains smaller path loss exponent (PLE), and consequently yields the FPE. Compared to the fixed circularly polarized case with PLE= 1.62, the measured PLE is reduced by 14% to 1.38, and the system packet error rate (PER) is improved by 76%. It realizes robust anti-multipath fading performance owing to the much-improved FPE. The fluid effect in polarization domain is anticipated to remarkably enhance the anti-multipath fading performance of wearable wireless communication systems. It opens a new horizon to develop self-driven, cost-effective fluid antenna systems (FASs) for universal applications.

Investigations on surface waves (SW) launchers wireless coupled to a long, single conductor in arbitrary azimuth angles rotation are carried out to inspire a novel SW wireless channel model in Industrial Internet-of-Things (IIoT) environment. A path loss (PL) model with two degrees in freedom, i.e., the coupled azimuth angle α and the transceiver separationd, is modeled at first. It is revealed that the angular factor governs the excitation degree of the SW propagation mode. Then, a dual-factor root mean square (RMS) delay extension model is developed. Next, the fast-fading distribution is modeled as a nonlinear combination of harmonic functions of the coupled azimuth angle α. It is validated that the PL of the SW propagation mode is reduced by 16~28dB, with the channel impulse response (CIR) principal path level increased by 18dB and the first multi-path suppressed by 7.5dB compared to the free-space propagation mode. Finally, calculations on channel capacity (CC) are performed to demonstrate a CC enhancement of 7~17Gbps. The advanced separation-angle joint channel model is expected to provide useful guidelines in future SW communication nodes deployments in IIoT scenarios.

The Industrial Internet of Things (IIoT) is one of the typical application scenarios in the beyond fifth generation (B5G) wireless communication systems. Due to numerous metal obstacles and machines, the industrial channel, especially at the millimeter-wave (mmWave) bands, exhibits complex characteristics that have not been considered in existing literature. This article proposes an innovative 3-D nonstationary geometry-based stochastic model (GBSM) for IIoT scenarios at mmWave bands. In the proposed model, device reflections (DRs) caused by massive metal machines are modeled based on geometrical optics. Furthermore, the generalized extreme value (GEV) distribution and generalized Pareto (GP) distribution are used to parameterize the number of clusters and rays within a cluster, respectively. Further, the Doppler shift is modeled and analyzed using the Gaussian distribution. Some channel statistical characteristics are captured by the proposed model, such as the power delay profile, root-mean-square delay spread, root-mean-square angle spread, intercluster delay, and space–time–frequency correlation function. Then, these channel statistical characteristics are well fitted to the ray-tracing simulations and the channel measurements. The excellent fitting results demonstrate the high accuracy of the proposed model, which is crucial for future IIoT communication system design. What is more, this article shows the antenna height and propagation scenarios can significantly affect the DR ratio, which should adapt to various IIoT communication scenarios.

Studies on novel sub-6GHz indoor surface wave communication channel models are presented. With the aid of a surface wave launcher that based on planar magnetic dipole antennas, a wireless coupled, indoor surface wave channel measurement apparatus is constructed to validate the channel characteristics, i.e., path loss, channel impulse response, and channel capacity. As validated in the 2.5-4.5GHz band, the path loss can be reduced by 30dB compared to the free-space propagation case, owing to the sufficient excitation and propagation of the surface wave mode. In addition, scattering paths can be clearly identified with mitigated multi-path effect. Unlike traditional coaxial surface wave launchers that cascading with the transmission line in a wired fashion, the surface wave launcher is coupled to a long, single conductor line with length longer than 30 wavelengths that emulating the long, bulky conductors in Industrial Internet-of-Things (IIoT) in a wireless manner. Therefore, the wireless IIoT nodes can be flexibly networked and deployed as desired in a low-loss fashion with less multi-path effect. The conceptual propagation scheme and channel modelling approach is expected to benefit the design and deployment of IIoTs.

Novel empirical path-loss and root mean square delay spread (RDS) models for smart office scenarios are proposed. The effects of person density on the path loss and RDS are investigated based on the extensive measurements at 2.3–2.5 GHz. First, both of the measured path loss and RDS data are modeled as the dual log-distance functions. It is caused by the regular structure and furniture in the office environment. Second, in the proposed path-loss model, the path-loss exponents and the additional attenuation factor are modeled as quadratic functions of the person density. Meanwhile, the RDS is found to be uncorrelated with the person density. These phenomena reveal that the persons in the environments can be regarded as absorbers rather than scatters. Then, the accuracy of the proposed models is validated by the measured data and compared with two traditional models. Finally, the effect of the persons’ movements on the path loss and RDS is investigated, and the proposed models are extended to millimeter wave bands by a ray tracing technology. The proposed models and results can provide necessary information for link budget and algorithm design for the Internet of Things smart office scenarios.

Geometric stochastic method and deterministic ray tracing method are two common methods of modelling massive multiple‐input multiple‐output (MIMO) channels. The former has high computational efficiency but a large number of input parameters need to be extracted from measurements for different environments. Conversely, the latter is more suitable for a specific environment but consumes a lot of computing costs. In this study, a novel three‐dimensional (3D) deterministic ray tracing method for massive MIMO channel modelling is proposed. The computational efficiency can be improved in two aspects compared with conventional deterministic ray tracing methods. Firstly, substantial intersection tests used in determining the propagation paths of rays are replaced with the adjacency relationships between tetrahedrons. Secondly, the process of ray tracing is independent of the location of a receiving antenna and therefore repeated ray tracing process is unnecessary for different elements of receiving antenna array. The proposed method is also used as a substitute for measurements to extract input parameters for geometric stochastic channel models. The accuracy of the proposed method in massive MIMO channel modelling and parameters extraction is verified by comparing the results with measurements and other existing channel models.

Massive robust communication demands among machines and humans are required in ubiquitous Internet-of-Things (IoT) applications. To design the appropriate communication system, the knowledge of the propagation characteristics for various IoTs scenarios is necessary. In this article, a measurement-based neural-network-based root-mean-square (RMS) delay spread model for ubiquitous indoor IoTs scenarios is presented. The proposed model is a two-layer feedforward neural network plus a random variable, characterizing the average RMS delay spread and uncertain shadowing effect, respectively. The neural network consists of five inputs, including transmitting/receiving antennas (Tx/Rx) separation, frequency, antenna height, environment, and line-of-sight/non-line-of-sight (LOS/NLOS) propagation condition, seven hidden layer neurons, and one output layer neuron. Compared with different configurations of the neural network, the hyperbolic tangent sigmoid functions and the Levenberg-Marquardt backpropagation algorithm are selected as neurons' activation functions and training method, respectively. Additionally, the random variable is found to follow the normal distribution using the maximum-likelihood estimation. Finally, the novel model is experimentally validated to be accurate, general, and extensible compared with the conventional normally distributed RMS delay spread model. This model is well applicable to the design and planning of the ubiquitous communication links for future IoTs scenarios.

Massive MIMO can dramatically improve capacity and spectral efficiency. However, it is not very clear whether it can significantly improve the signal blockage problem that exists in single antenna systems. In this paper, we investigate the impact of the human body on indoor massive MIMO channels, using practically measured channel data for a 32x8 massive MIMO system in a complex office environment. We introduce a parameter of Power Imbalance (PI) indices to estimate the wide-sense none-stationarity in multiple domains and another parameter of Channel Popularity Indices (CPI) to predict the popularity of MIMO channel. We find that in most cases, the presence of the human body still has a non- negligible negative impact. It decreases the ergodic capacity by about 8% and increases the path loss exponent by 1. In average, the ergodic capacity for NLOS channels are 15% higher than that for LOS.

Time-domain wireless channels are generally modeled by Tapped Delay Line (TDL) model and its variants. These models are not effective for channel representation and estimation when the number of multipath taps is large. Compressive sensing (CS) provides a powerful tool for sparse channel modeling and estimation. Most of the research has been focusing on sparse channel estimation, while sparse channel modeling (SCM) is rarely considered for centimetre-wave channels. In this paper, we investigate statistical sparse channel modeling, using both measured and simulated channels over a frequency range of 6 to 8.5 GHz. We first introduce the triple equilibrium principle to explore the trade-off between sparsity, modeling accuracy, and algorithm complexity in SCM, and provide a methodology for characterizing the sparsity of time-domain channels using single-measurement-vector compressive sensing algorithms. Using mainly the selected wavelet dictionary and various CS reconstruction (aka recovery) algorithms, we then present comprehensive statistical sparse channel models, including channel sparsity, magnitude decaying profile, sparse coefficient distribution and atomic index distribution. Connections between the parameters of conventional TDL and sparse channel models are mathematically established. We also propose three methods for generating simulated channels from the developed sparse channel models, which validates their effectiveness.

Channel sparsity is well exploited for channel estimation, but there is very limited work on sparse channel modelling, which studies and characterizes the statistical properties of sparse channel coefficients. In this paper, we study sparse channel modelling using real measured channel data in off-body signal propagation. We propose multi-measurement vector based compressive sensing algorithms for extracting sparse channel coefficients, study the statistical properties of these extracted coefficients, and develop an algorithm for generating simulated channels using the statistical sparse model. The proposed method can be directly applied to other channel measurements, and is very useful for channel simulation and developing advanced sparse channel estimation schemes.

In this paper, we present the link-level simulation of a time division duplex (TDD)-based massive multiple-input multiple-output (MIMO) system with multi-antenna user equipments (UEs). The system model of the proposed massive MIMO system is first given, based on which the general settings of our link-level simulation are presented, including the essential parameters, the LTE-like frame structure, and the discontinuous pilot allocation for all UEs. Block diagrams are then illustrated, along with the link-level data transmission procedures of both the uplink and the downlink. Thereinto, the low-complexity but well-performance channel estimation algorithm and the MIMO detector for the uplink are introduced, and two block diagonalization (BD) precoding schemes for the downlink are also illustrated. Finally, numerical results in terms of the bit error rate (BER) and the throughput are investigated and analyzed. Running time for these two proposed precoding schemes is also given for comparison of computational complexity.

An empirical stochastic discrete tapped delay line (DTDL) power delay profile (PDP) model is presented. It is used for characterising the multipath effects under indoor stair environment. In this model, the amplitude at each DTDL tap and stair step follows the Nakagami distribution. Its scale parameters are lognormally distributed, and its shape parameters are distance and propagation delay dependent. Then, the procedure for simulating the PDPs is given. In addition, the average PDP, root mean square delay spread and capacity extracted using the measured and simulated channels are compared to validate the accuracy of the proposed model. Finally, a measurement‐based channel simulator is developed by implementing an orthogonal frequency division multiplexing communication procedure on the simulated channel. These works can provide important information about the designs of the physical layer algorithms in small cells scenarios.

A root mean square (RMS) delay spread model in indoor femtocell scenarios is proposed. The proposed model is based on extensive channel sounding of indoor stair, corridor and office environments. In this model, the RMS delay spread is described as a linear function of the path loss, and a normal stochastic variable is introduced and utilised to characterise the deviation of the measured RMS delay spread from the linear function. The proposed model can be used to simulate the RMS delay spread directly from extracted model parameters and the separation of the transmitting and receiving antennas. The closed‐form formulas for fast calculating the mean value and variance of the RMS delay spread using some deterministic values, including the femtocell coverage distance and the parameters of the proposed model, are derived. The validity of the proposed model is verified by comparing the cumulative distribution functions and the statistical values of the measured and simulated RMS delay spread.

The influence of the person density on the indoor MIMO channel models is experimentally investigated. The path loss is modeled as a log-distance function adding additional attenuations related to both of the distance and person density. Then, the shadowing, the root mean square delay spread, and the channel capacity are described as normal distributed random variables. Their mean value and the standard deviation are depicted as the sum of the basic values (no person in the channel) and the person density correction factors. Finally, the eigenvalues of the channel matrix are found to be a Gamma random variable with the person density dependent shape and scale parameters.

A novel stochastic multiple‐input multiple‐output (MIMO) channel model based on the singular value decomposition of the channel matrix is proposed in this study. Under the framework of the proposed model, each of the right singular vectors can be modelled as the product of a stochastic scalar and a non‐random vector, as is each of the left singular vectors. The non‐random vectors, defined as the eigenmodes of the transmitter and receiver, respectively, can be easily extracted from the measurements, so are the singular values of the channel matrix. The implications of the proposed model's parameters that provide further insight into the MIMO channel are interpreted and a way of exploiting the parameters is given. To validate the proposed model, MIMO channel measurement is carried out under different indoor environments and the channel capacity is analysed. It is shown that the proposed model provides a better fit to the measurement results than the other popular stochastic channel models. The proposed stochastic MIMO channel model can be used for the MIMO communication system design and evaluation.