Channel modeling is fundamental to design wireless communication systems. A common practice is to conduct tremendous amount of channel measurement data and then to derive appropriate channel models ...using statistical methods. For highly mobile communications, channel estimation on top of the channel modeling enables high bandwidth physical layer transmission in state-of-the-art mobile communications. For the coming 5G and diverse Internet of Things, many challenging application scenarios emerge and more efficient methodology for channel modeling and channel estimation is very much needed. In the mean time, machine learning has been successfully demonstrated efficient handling big data. In this paper, applying machine learning to assist channel modeling and channel estimation has been introduced with evidence of literature survey.
Traditional deterministic channel modeling is accurate in prediction, but due to its complexity, improving computational efficiency remains a challenge. In an alternative approach, we investigated a ...multilayer artificial neural network (ANN) to predict large‐scale and small‐scale channel characteristics in metro tunnels. Simulated high‐precision training datasets were obtained by combining measurement campaign with a ray tracing (RT) method in a metro tunnel. Performance on the training data was used to determine the number of hidden layers and neurons of the multilayer ANN. The proposed multilayer ANN performed efficiently (10 s for training; 0.19 ms for prediction), and accurately, with better approximation of the RT data than the single‐layer ANN. The root mean square errors (RMSE) of path loss (2.82 dB), root mean square delay spread (0.61 ns), azimuth angle spread (3.06°), and elevation angle spread (1.22°) were impressive. These results demonstrate the superior computing efficiency and model complexity of ANNs.
This paper investigates the utilization of triple polarization (TP) for multi-user (MU) wireless communication systems with holographic multiple-input multi-output surfaces (HMIMOSs), targeting ...capacity boosting and diversity exploitation without enlarging the antenna array sizes of the transceivers. We specifically consider that both the transmitter and receiver are equipped with an HMIMOS consisting of compact sub-wavelength TP patch antennas and operating in the near-field (NF) regime. To characterize TP MU-HMIMOS systems, a TP NF channel model is constructed using the dyadic Green's function, whose characteristics are leveraged to design two precoding schemes for mitigating the cross-polarization and inter-user interference contributions. Specifically, a user-cluster-based precoding scheme that assigns different users to one of three polarizations, at the expense of system's diversity, is presented together with a two-layer precoding technique that removes interference using a Gaussian elimination method. A theoretical correlation analysis for HMIMOS-based systems operating in the NF region is also derived, revealing that both the spacing of transmit patch antennas and user distance impact transmit correlation factors. Our numerical results showcase that the users located far from the transmit HMIMOS experience higher correlation than those closer in the NF region, resulting in a lower channel capacity. In terms of channel capacity, it is demonstrated that the proposed TP HMIMOS-based systems almost achieve 1.25 and 3 times larger gain compared to their dual-polarized version and conventional HMIMOS systems, respectively. It is also shown that the the proposed two-layer precoding scheme combined with two-layer power allocation realizes the highest spectral efficiency, among compared schemes, without sacrificing diversity.
The demands for higher throughput, data rate, low latency, and capacity in 5G communication systems prompt the use of millimeter-wave frequencies that range from 3–300 GHz with spatial multiplexing ...and beamforming. To get the maximum benefit from this technology, it’s important to study all the challenges of using mm-wave for 5G and beyond. One of the most important impacts is weather conditions such as humidity, temperature, dust, and sand storms. This study investigates the parameters of the channel model and its statistical behavior with the effect of dust and sand storms. The latter effects can be considered the main challenges these days, especially in middle-eastern countries. A 128 x 128 massive MIMO with URA (uniformly spaced rectangular antenna arrays) uniformly spaced has been considered in the simulation assessment with mm-wave channels operating at 28 GHz and 73GHz are examined by using NYUSIM (New York University Wireless Simulator) software. The simulation results show that the dust increases the attenuation and the path loss when working at higher frequencies compared to the clear weather conditions. Moreover, their effect can be reduced by adapting the transmitted power.
The application of unmanned aerial vehicles (UAVs) has recently attracted considerable interest in various areas. A three‐dimensional multiple‐input multiple‐output concentric two‐hemisphere model is ...proposed to characterize the scattering environment around a vehicle in an urban UAV‐to‐vehicle communication scenario. Multipath components of the model consisted of line‐of‐sight and single‐bounced components. This study focused on the key parameters that determine the scatterer distribution. A time‐variant process was used to analyze the nonstationarity of the proposed model. Vital statistical properties, such as the space–time–frequency correlation function, Doppler power spectral density, level‐crossing rate, average fade duration, and channel capacity, were derived and analyzed. The results indicated that with an increase in the maximum scatter radius, the time correlation and level‐crossing rate decreased, the frequency correlation function had a faster downward trend, and average fade duration increased. In addition, with the increase of concentration parameter, the time correlation, space correlation, and level‐crossing rate increased, average fade duration decreased, and Doppler power spectral density became flatter. The proposed model was compared with current geometry‐based stochastic models (GBSMs) and showed good consistency. In addition, we verified the nonstationarity in the temporal and spatial domains of the proposed model. These conclusions can be used as references in the design of more reasonable communication systems.
Due to low cost and high operability, unmanned aerial vehicle(UAV) becomes a key component in future intelligent transportation system(ITS) for various use cases. Air-to-ground(A2G) communication is ...a fundamental technology for integration of UAV in the ITS, where accurate analysis and modeling of UAV A2G wireless channel are of great importance for communication algorithm development. In this paper, we present a comprehensive analysis and modeling of low altitude UAV A2G channel at 2.4 and 5.9 GHz based on multi-link channel measurement campaign. Based on the measured data, the value of path loss as a function of the elevation angle and distance is proposed. Further, the shadow fading(SF) is analyzed for different frequency bands and the spatial correlation is evaluated. Particularly, it is found that the influence of UAV altitude on shadowing is significant and should be considered when developing A2G communication system.
In the most recent years, wireless communication networks have been facing a rapidly increasing demand for mobile traffic along with the evolvement of applications that require data rates of several ...10s of Gbit/s. In order to enable the transmission of such high data rates, two approaches are possible in principle. The first one is aiming at systems operating with moderate bandwidths at 60 GHz, for example, where 7 GHz spectrum is dedicated to mobile services worldwide. However, in order to reach the targeted date rates, systems with high spectral efficiencies beyond 10 bit/s/Hz have to be developed, which will be very challenging. A second approach adopts moderate spectral efficiencies and requires ultra high bandwidths beyond 20 GHz. Such an amount of unregulated spectrum can be identified only in the THz frequency range, i.e. beyond 300 GHz. Systems operated at those frequencies are referred to as THz communication systems. The technology enabling small integrated transceivers with highly directive, steerable antennas becomes the key challenges at THz frequencies in face of the very high path losses. This paper gives an overview over THz communications, summarizing current research projects, spectrum regulations and ongoing standardization activities.
With the rapid development of mobile broadband communications such as the "fifth generation (5G)" systems, accurate and efficient simulation-based channel modeling becomes more essential for ...propagation channel characterization in the complicated environments. In the previous works, the so-called "propagation-graph-based channel modeling" method has been introduced and effectively applied to the simulation of channel characteristics. Compared with the conventional ray-tracing-based approaches, the graph-based modeling may significantly reduce the computational complexity. In this contribution, without increasing computational complexity, the graph-based channel modeling method is proposed for channel characterization in the dense urban environments with the following improvements, i.e. realistic geometry-assisted scattering point placement, scattering coefficient definition, and line-of-sight (LoS) path presence probability settings. By comparing the simulated channel characteristics with those obtained in the real measurements in two urban scenarios, the proposed improvements are demonstrated in predicting channel characteristics more accurately.
Due to the influence of sparse scattering, wave movement and variable climate, maritime wireless channel modeling is particularly difficult. In this letter, a maritime channel measurement scheme is ...designed and implemented. A clustering algorithm-based channel modeling framework is further proposed to process the measurement data and build a maritime propagation model. Specifically, the clustering algorithm is introduced in the framework to adaptively discover data characteristics in complex environments, which avoids error accumulation. Numerical results illustrate that the proposed model outperforms existing empirical path loss models in describing the signal propagation at sea, which provides good guidance for designing and deploying of maritime communication systems.
Channel modeling is critical for the design and performance evaluation of visible light communication (VLC). Although a considerable amount of research has focused on indoor VLC systems using ...single-element photodiodes, there remains a need for channel modeling of VLC systems for outdoor mobile environments. In this paper, we describe and provide results for modeling image sensor based VLC for automotive applications. In particular, we examine the channel model for mobile movements in the image plane as well as channel decay according to the distance between the transmitter and the receiver. Optical flow measurements were conducted for three VLC situations for automotive use: infrastructure to vehicle VLC (I2V-VLC); vehicle to infrastructure VLC (V2I-VLC); and vehicle to vehicle VLC (V2V-VLC). We describe vehicle motion by optical flow with subpixel accuracy using phase-only correlation (POC) analysis and show that a single-pinhole camera model successfully describes these three VLC cases. In addition, the luminance of the central pixel from the projected LED area versus the distance between the LED and the camera was measured. Our key findings are twofold. First, a single-pinhole camera model can be applied to vehicle motion modeling of a I2V-VLC, V2I-VLC, and V2V-VLC. Second, the DC gain at a pixel remains constant as long as the projected image of the transmitter LED occupies several pixels. In other words, if we choose a pixel with highest luminance among the projected image of transmitter LED, the value remains constant, and the signal-to-noise ratio does not change according to the distance.
