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A simple and effective body-centric localization algorithm has been proposed in this paper using UWB wearable technology. The algorithm has been validated through measurement campaigns with a human subject volunteer in an indoor environment. The algorithm takes into account statistical channel parameter analysis, machine learning (ML) algorithms and time-of-arrival (TOA) based range estimation/data fusion techniques. Two channel parameters namely path loss magnitude and rms delay spread are proposed as classification features to be applied to the ML algorithm to accurately classify the off-body channel links into LOS, PNLOS and NLOS scenarios. Multi-class support vector machine (MC-SVM) classifier along with SMOTE algorithm to take into account class imbalance is applied with the channel classification accuracy of 98.63 %. Threshold based range estimation algorithms are applied in order to mitigate NLOS scenarios caused mainly due to the presence of the human subject. Results report human localization accuracy in 0.5-3 cm range using TDOA data fusion technique for target estimation. Further validation is presented considering wide range of Tx-Rx distance, presence of another obstruction between the Tx-Rx links and performance in different environment which shows the suitability of the proposed methodology.

Machine learning (ML) is playing an increasingly important role in processing large amounts of data generated by communication networks, since it can efficiently solve the problems of non-linearity and unstructured data. Recently, ML has been widely used in the processing of wireless channel data, as the noisy channel in real propagation environment is usually non-linear and unstructured. In this paper, a denoising method based on ML is presented. Two ML algorithms are used to classify and remove noise in channel impulse responses. Then, the results of the traditional noise threshold denoising are compared with ML denoising, and it is found that the denoising classifier using the bidirectional recurrent neural network has the better denoising performance. Finally, some channel parameters such as RMS delay spread are estimated based on measured channel data using different denoising methods. The results are evaluated and compared to explore the impact of denoising method on the extracted channel parameters.

Multimedia Broadcast Multicast Service (MBMS)-dedicated cell is a Long Term Evolution (LTE) based broadcast transmission mode with both high transmission efficiency and good compatibility with LTE unicast systems. However, since the physical design of Cell Acquisition Subframe (CAS), which is the control part of an MBMS-dedicated cell transmission period, does not take into account the long delay spread channels in Medium Tower Medium Power (MTMP) and High Tower High Power (HTHP) cells, receivers would probably suffer from multipath aliasing errors in practical transmission. This paper analyzes the impact of aliasing errors on channel estimation (CHE) accuracy and proposes an Aliasing-Elimination CHE algorithm for CAS reception. In the proposed algorithm, the pre-decoded Physical Broadcast Channel (PBCH) is reconstructed and utilized to generate a complete multipath distribution within the entire OFDM symbol length. By using this full-symbol length multipath distribution, the proposed algorithm eliminates aliasing errors effectively. Simulation results and complexity analysis show that the proposed CHE algorithm achieves a significant performance improvement compared to conventional CHE algorithms in long delay spread channels, with acceptable increment in complexity.

In this letter, the channel characteristics of tunnels with different curvatures at different frequencies for vehicle-to-vehicle (V2V) communications are investigated. The relationship between the mean value of path loss, delay spread (DS), frequency and curvature are analyzed. A high order polynomial is proposed to describe the path loss exponent (PLE) and a first-order polynomial is used to fit the root-mean-square (RMS) DS. Finally, the neural network is used to build a model to predict the PLE and RMS-DS and to verify the proposed model.

The millimeter-wave (mmWave) and massive multiple-input multiple-output (MIMO) wireless communication technologies provide vital means to resolve many technical challenges of the fifth-generation (5G) or beyond 5G (B5G) network. Analyzing the measured datasets extracted from the channel measurements can provide insight into the characteristics of radio channels in different scenarios. Therefore, mmWave massive MIMO channel measurements, simulation, and modeling are carried out in the high-speed railway waiting hall environments at 28 GHz. The multipath components (MPCs) parameters are estimated for line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios based on the space-alternating generalized expectation-maximization (SAGE) algorithm. Delay spread (DS), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) are analyzed. And they are processed by using the K-mean algorithm. In addition, propagation characteristics are simulated based on the improved ray tracing method of shooting and bouncing ray tracing/image (SBR/IM). The correctness of the improved ray tracing method is verified by comparing the measured results with the simulated results. The large-scale path loss (PL) is characterized based on close-in (CI) free-space reference distance model and the floating-intercept (FI) path loss model. Furthermore, statistical distributions for root-mean-square delay spread (RMS DS) are investigated. The Gaussian distribution best fits the measured data of RMS delay spread. Finally, multipath clustering is identified using the multipath component distance (MCD). The analysis of these results from mmWave massive MIMO channel measurements and simulation may be instructive for the deployment of the 5G or B5G wireless communications systems at 28 GHz.

To achieve space-air-ground-sea integrated communication networks for future sixth generation (6G) communications, unmanned aerial vehicle (UAV) communications applying to maritime scenarios serving as mobile base stations have recently attracted more attentions. The UAV-to-ship channel modeling is the fundamental for the system design, testing, and performance evaluation of UAV communication systems in maritime scenarios. In this paper, a novel non-stationary multi-mobility UAV-to-ship channel model is proposed, consisting of three kinds of components, i.e., the line-of-sight (LoS) component, the single-bounce (SB) components resulting from the fluctuation of sea water, and multi-bounce (MB) components introduced by the waveguide effect over the sea surface. In the proposed model, the UAV as the transmitter (Tx), the ship as the receiver (Rx), and the clusters between the Tx and Rx, can be seen as moving with arbitrary velocities and arbitrary directions. Then, some typical statistical properties of the proposed UAV-to-ship channel model, including the temporal autocorrelation function (ACF), spatial cross-correlation function (CCF), Doppler power spectrum density (PSD), delay PSD, angular PSD, stationary interval, and root mean square (RMS) delay spread, are derived and investigated. Finally, by comparing with the available measurement data, the accuracy of proposed channel model is validated.

The tripolarized MIMO system can provide one more degree of freedom and have a more compacted size over a dual-polarized MIMO system, which is attractive for high-capacity wireless communication systems. In this paper, we analyze and model channel properties for tripolarized MIMO systems based on experimental channel measurements in typical indoor and outdoor scenarios. Firstly, channel measurement campaigns in the laboratory and the Urban Micro (UMi) scenarios on sub-6 GHz bands are presented. Then, based on measured data, path loss, delay spread (DS), and cross-polarization discrimination (XPD) for 9 polarization combinations are analyzed and modeled in a statistical way. Statistical results of these channel properties are also given. It is observed that channel properties of both large-scale fading and small-scale fading depend strongly on the polarization direction. Furthermore, we evaluate the performance of tripolarized MIMO systems by analyzing the Demmel condition number and channel capacity gain (CG). For both the indoor and the outdoor scenarios, it is found that colocated tripolarized antenna can bring a nearly threefold CG with respect to the unipolarized one. These results can give good insights into the design and evaluation of tripolarized MIMO systems.

Human communication is a new method that uses human body biological tissue as the transmission medium for short-distance communication. Because static human communication characteristics have been studied more, this paper proposed and verified a dynamic human body communication channel model in the Human Body Communication band, and the dynamic channel characteristics of 35 kinds of human body continuous postures were analyzed. The results of using five typical communication links on the dynamic human body model show that the average power delay is following a second order exponential decay. The Akaike information criterion was used to determine the cumulative probability density function distribution of the first path delay, interval delay and delay spread. The channel characteristics of two-person standing side-by-side communication were studied to extend the application of human communication technology, and the two human models touch each other through their arms. The forward and backward motion of the noncontact arm along the human body surface has different effects on the path loss of the communication link with the chest as the transmitter and the arm as the transmitter. To evaluate the safety of human body communication technology on the human body, the frequency domain finite differential analysis method was used to analyze the distribution of the electromagnetic field. The absorption rate is the quantification of the electromagnetic energy absorbed by the human body, and the absorptivity of 35 postures demonstrated that human communication technology is not harmful to the human body.

Multiple Input Multiple Output (MIMO) and massive MIMO technologies play a significant role in mitigating five generation (5G) channel propagation impairments. These impairments increase as frequency increases, and they become worse at millimeter-waves (mmWaves). They include difficulties of material penetration, Line-of-Sight (LoS) inflexibility, small cell coverage, weather circumstances, etc. This paper simulates the 5G channel at the E-band frequency using the Monte Carlo approach-based NYUSIM tool. The urban microcell (UMi) is the communication environment of this simulation. Both MIMO and massive MIMO use uniformly spaced rectangular antenna arrays (URA). This study investigates the effects of MIMO and massive MIMO on LOS and Non-LOS (NLOS) environments. The simulations considered directional and omnidirectional antennas, the Power Delay Profile (PDP), Root Mean Square (RMS) delay spread, and small-scale PDP for both LOS and NLOS environments. As expected, the wide variety of the results showed that the massive MIMO antenna outperforms the MIMO antenna, especially in terms of the signal power received at the end-user and for longer path lengths.

In this article, we propose a novel waveform with low cyclic prefix (CP) overhead for narrow-band Internet-of-things (NB-IoT) systems. Compared with the classical orthogonal frequency-division multiplexing (OFDM), the proposed waveform employs the concept of symbol repetition on multiple successive OFDM symbols (SR-OFDMs) and only one CP is occupied by multiple SR-OFDM symbols; hence, the CP overhead is reduced significantly, improving the spectral and energy efficiencies in NB-IoT systems. It is proved that the proposed SR-OFDM can effectively fight against the multipath fading channels by a simple single-tap equalization, avoiding the mutual interference among symbols. In addition, it is shown that the proposed SR-OFDM and classical OFDM have the same size of the inverse discrete Fourier transform (IDFT) at the transmitter, which indicates the same subcarrier spacing. Especially, it is demonstrated that the signals of multiple symbols are separated at the receiver, and the demodulation of each symbol can be performed independently, which is helpful to reduce the complexity of symbol demodulation. To evaluate the proposed SR-OFDM, simulations have been done by considering the Stanford university interim (SUI) channels, which have large channel delay spread and exhibit high frequency selectivity.

IEEE 802.11n technology comes with many advantages such as high data rate and reliability. In 2,4 GHz and 20 MHz bandwidth, it gives data rate up to 300 Mbps. In order to improve reliability without complexity, it uses STBC (Space Time Block Coding) using Alamouti algorithm. This algorithm can be implemented with many STBC scheme, i.e., 2x1, 2x2, 3x1, 3x2, 3x3, 4x1, 4x2, 4x3 and 4x4 using BPSK, QPSK, 16-QAM and 64-QAM modulation. Impulse respons of (Laboratorium Telekomunikasi Radio dan Gelombang Mikro) LTRGM indoor channel can be modeled by using ray tracing method. This simulation also investigates the effect of MIMO channel corelation caused by channel fading and physical antenna factor. The results, LTRGM indoor channel give delay spread and rms delay of 71.94 ns and 1.5 ns respectively. The ergodic channel capacity declines steadily as the channel correlation becomes worse. This problem also cause a decrease in SNR performance to achieve SER of 10-3 with various modulation. However, this does not have significant influence on WLAN system that uses STBC with four both transmit and receive antenna

In order to provide reliable and efficient connections between unmanned aerial vehicles (UAVs) and ground stations (GSs), realistic UAV-to-ground channel models are indispensable. In this article, we propose a novel 3-D nonstationary geometry-based stochastic model (GBSM) for UAV-to-ground multiple-input–multiple-output (MIMO) channels. Distinctive UAV-to-ground channel characteristics, such as time-domain nonstationarity, distinctions between different altitudes, spatial consistency, and 3-D arbitrary UAV movement trajectories, are taken into account. By adjusting parameter settings, the proposed channel model framework is sufficiently general to support multiple frequency bands and multiple scenarios, including millimeter wave (mmWave) and massive MIMO configurations. Statistical properties, including power delay profile (PDP), stationary interval, space–time correlation function (STCF), and root-mean-square (RMS) delay spread are derived and analyzed for different frequencies and scenarios. The accuracy of the proposed model is validated by comparing its statistical properties with corresponding available channel measurements. The proposed channel model will provide a fundamental support for the design, performance evaluation, and optimization of future UAV integrated sixth-generation (6G) wireless networks.

Industrial Internet of Things (IIoT) is an emerging area that fifth-generation (5G) mobile communication system penetrates industrial manufacturing applications. The indoor factory has larger space and there are a lot of metal machine tools distributed in it, which makes its radio propagation characteristics and corresponding channel models significantly different from those of the indoor office and indoor hotspot. To support the design and the evaluation of the IIoT techniques, the 3rd Generation Partnership Project (3GPP) released the first 5G IIoT standard model in October 2019. In this article, we give a detailed explanation of this IIoT model and compare it with other indoor models. First, we introduce the standardization of 3GPP IIoT channel model and its motivation. Second, four IIoT subscenarios, which are classified according to the clutter density and antenna height, are described. Third, the potential frequency bands of 5G IIoT are summarized. Fourth, the models of channel parameters, including the path loss and the line-of-sight (LOS) probability, the root mean-square (RMS) delay spread, and the angular spread, are given. Among them, the models of path loss and LOS probability take the antenna height and clutter density into consideration. The model of RMS delay spread changes from frequency-dependent to volume-dependent in order to catch the size variation of factories. Finally, two newly added key channel characteristics, dual mobility and absolute time of arrival, which help to describe the robot movement and positioning, are also presented.

Ultraviolet (UV) wireless communication has recently received much attention only for outdoor applications. Due to inherent nature of UV transmission, we believe that UV can also be considered for efficient and low-cost indoor communications. In this article, we present a novel UV indoor communication with an in-depth description of design implementation, channel modeling, and performance analysis. We consider a discrete Poisson channel with transmit power constraints. While focusing mainly on the development of a UV-based indoor communication, an indoor UV channel model is first proposed. Based on this, physical characteristics of the UV indoor channel using on–off keying modulation are analyzed with direct detection in terms of bit error rate, channel dc gain, and delay spread. As the proposed UV indoor communication exhibits some limitations, we provide a brief discussion on design implementation and challenges. From the performance analysis, it is found that the proposed UV indoor communication can provide ubiquitous link connectivity in indoor applications, while keeping network structure and management simple.

As a vital vertical industry empowered by the sixth-generation mobile communication system (6G), the vision of “smart rail mobility” requires a seamless wireless connectivity with the ultra high-data rate on the order of 100 Gbps or even more. This forms a strong driving force for exploiting the terahertz (THz) band where the available spectrum is massively abundant. In this paper, the smart rail mobility channel is characterized through ultra-wideband (UWB) channel sounding and ray tracing (RT) at 300 GHz band with an 8 GHz bandwidth in the five scenarios – Train-to-infrastructure (T2I), Inside station, Train-to-train (T2T), Infrastructure-to-infrastructure (I2I), and Intra-wagon. Corresponding channel characteristics such as path loss, shadow fading, Rician $K$ -factor, root-mean-square delay spread, azimuth/elevation angular spread of arrival/departure, and cross-polarization ratio are extracted and analyzed. Based on realistic channel information, the performance of THz communication is investigated in terms of channel capacity for all above five scenarios with various antenna patterns and weather conditions (except Intra-wagon) – sunny, rainy, and snowy. The results provide valuable insights into the system design and evaluation for THz communication enabled Smart Rail Mobility.