Large-scale Channel Parameters Research Articles

Multi-Scenario Millimeter Wave Channel Measurements and Characteristic Analysis in Smart Warehouse at 28 GHz

Smart warehouses are revolutionizing traditional logistics operations by incorporating advanced technologies such as Internet of Things, robotics, and artificial intelligence. In these complex and dynamic environments, control and operation instructions need to be transmitted through wireless networks. Therefore, wireless communication plays a crucial role in enabling efficient and reliable operations. Meanwhile, channel measurements and modeling in smart warehouse scenarios are essential for understanding and optimizing wireless communication performance. By accurately characterizing radio channels, communication systems can be better designed and deployed to meet unique challenges in smart warehouse scenarios. In this paper, we present an overview of smart warehouse scenarios and explore channel characteristics in smart warehouse scenarios. We conducted a measurement campaign for millimeter wave radio channels in smart warehouse scenarios. A vector network analyzer-based channel sounder was exploited to record channel characteristics at 28 GHz. Based on the measurements, large-scale channel parameters, including path loss, root-mean-square (RMS) delay spread, and Rician K factor were investigated. The unique channel characteristics in smart warehouse scenarios were explored.

Measurements and Modeling of Large-Scale Channel Characteristics in Subway Tunnels at 1.8 and 5.8 GHz

Radio propagation characteristics in subway tunnels are significant for designing reliable wireless communications. To obtain large-scale channel characteristics, extensive propagation measurements are conducted in a straight subway tunnel at 1.8 and 5.8 GHz, respectively. Based on the measurements, some typical large-scale channel parameters are extracted and modeled, such as path loss, shadow fading, and channel correlation. The path loss exponents are found to be 2.83 and 2.55 at 1.8 and 5.8 GHz, respectively, with shadow fading standard deviation of 1.58 and 2.58 dB. Shadow fading decorrelation distance is 8.71 m for 1.8 GHz and 11.65 m for 5.8 GHz, respectively. Moreover, cross-correlation coefficient of shadow fading in two dual-link scenarios is around 0.1, which shows that there is fairly low cross-correlation in a subway tunnel. The results in this letter are useful for link budget, handover scheme design, and network planning of subway communication system.

Learning-Based Resource Allocation for Ultra-Reliable V2X Networks With Partial CSI

In this paper, we study the resource allocation in high mobility vehicle-to-everything (V2X) networks with only slowly varying large-scale channel parameters. For satisfying the diversity requirements of different types of links, i.e., low delay for vehicle-to-infrastructure (V2I) connections and ultra-reliability for vehicle-to-vehicle (V2V) connections, we formulate a joint power, spectrum and vehicle local computing ratio allocation problem to minimize the delay of V2I links whilst satisfying the V2V reliability constraint. For solving the formulated problem, a Feasible Region Transformation Method is firstly developed to convert the probabilistic V2V reliability requirement into a computable constraint. In addition, a Robust Signal to Interference Plus Noise Ratio (SINR) Modified Method is proposed to give the computable expression for the V2I throughput. Then, a parallel Deep Neural Network (DNN) framework is designed for the resource allocation in V2X networks, where one is the transmit power control unit and the other is the local computing ratio allocation unit. After that, a Feedback-oriented Learning Method is proposed to train the parallel DNN-based resource allocation framework, in which the output of DNN is used as feedback to dynamically revise the training loss function along with the training process. Afterwards, the Hungarian method is employed to obtain the optimal spectrum matching. Finally, we conduct the simulations to show that the proposed learning-based algorithm has better performance compared with other general algorithms.

Hybrid millimetre‐wave channel simulation approach based on long short‐term memory networks

In order to overcome the disadvantages of traditional channel simulation approaches and achieve more accurate millimetre-wave (mmWave) channel simulation under the condition of limited measured data, a novel Long Short-term Memory Networks (LSTM) based hybrid mmWave channel simulation approach is proposed in this work. The proposed hybrid approach takes advantages of LSTM-based time-varying model of path loss and large-scale channel parameters, statistical model of intra-cluster multipath parameters and ray tracing, which are able to accurately simulate path loss, large-scale channel parameters, multipath parameters and channel impulse responses, respectively. Based on the mmWave channel data measured in the waiting hall of Qingdaobei Railway Station, it is verified that the simulation results of our proposed hybrid approach are in good agreement with the measured data and better than that of existing channel simulation approaches. The hybrid channel simulation approach proposed in this work has important application value for channel modelling and simulation in the case of small amount of data.

Semi-Deterministic Dynamic Millimeter-Wave Channel Modeling Based on an Optimal Neural Network Approach

Billions of mobile terminals will be deployed in various Internet of Things (IoT), in which millimeter-wave (mmWave) technology will be gradually applied. Accurate modeling and simulation of wireless channel is the base for efficient design and performance evaluation. This becomes more important for industrial scenarios, which might be highly dynamic and potentially different from well-investigated cellular deployment scenarios. In this work, a novel semi-deterministic mmWave dynamic channel modeling approach based on optimal neural network (ONN) principle is proposed. The ONNs are radial basis function (RBF) neural networks (NNs) trained with optimal variance parameters and are applied to predict large-scale channel parameters (LSCPs) [e.g., path loss (PL), delay spread (DS), and angle spread (AS)]. Based on the ONNs’ predicted large-scale parameters and simplified propagation environment including the layout of transmitter (TX), receiver (RX), and major scatterers, the proposed channel modeling approach can generate accurate dynamic channel parameters. The proposed approach is validated by the channel data measured at a high-voltage substation. Large-scale parameters, multipath component (MPC) distributions, and power delay profiles (PDPs) are validated. The proposed approach is demonstrated to be an accurate, fast, and robust channel modeling method, which can be used for both link-level and system-level channel simulation for future design and optimization of industrial IoT.

An ANN‐based channel modeling in 5G millimeter wave for a high‐voltage substation

In this work, an artificial neural network (ANN) based time-varying channel modeling framework is proposed, including a playback model and a prediction model. The purpose of the ANN-based modeling framework is to playback 5G measured radio channels at certain measurement positions, and further predict large scale channel parameters (LSCPs) at unmeasured positions with limited amount of measurement data. 28 GHz channel measurements were also conducted at a high-voltage substation for the first time worldwide to meet with 5G radio system deployment for China Energy Internet. Meanwhile, the performance of the playback channels is evaluated by comparison with the measurements and traditional geometry based stochastic modeling (GBSM) simulated channels. An optimized radial basis function (ORBF) ANN is applied in the prediction model, and the predicted LSCPs are compared with the other approaches, which shows that the ORBF has the best performance. This work offers a solution to predict radio channels and parameters in case of big measured or simulated channel datasets.

RBF neural network channel modeling of millimeter wave based on adaptive particle swarm optimization algorithm

In this paper, the method of radial basis function (RBF) in machine learning is applied to the modeling of millimeter-wave channel based on millimeter wave indoor wireless channel measurement data. A RBF neural network channel parameter prediction model based on adaptive particle swarm optimization (APSO) is established, and the prediction results of the traditional RBF algorithm are compared. Specifically, the RBF model optimized based on APSO is used to learn and predict the characteristics of large-scale channel parameters (LSCP), such as path loss and delay spread. The results show that the predicted channel parameters of the APSO-RBF model is consistent well with the actual measured value. The learning performance and prediction effect of this algorithm are better than the traditional RBF algorithm, that is, the RBF algorithm has a smaller root-mean-square error (RMSE), and the predicted curve has a larger fitting degree with the original measured curve. In addition, the APSO-RBF model has good adaptability to the change of channel parameters in the case of large data fluctuation, and can achieve good prediction effect for 5G millimeter wave channel parameters.

Research on the method of spatial consistency simulation for millimeter wave time-varying channels

In order to satisfy the demand of modeling and simulation of 5G millimeter wave mobile channels, this paper investigates the method of spatial consistency simulation for millimeter wave time-varying channels. Based on the general 5G millimeter wave channel model, the simulation and analysis are carried out, and the terminal moving track is segmented to determine the correlation of large-scale channel parameters between continuous simulation segments. At the same time, the Markov chain is introduced to realize the dynamic evolution of the number of multipath clusters between different segments. For the small-scale channel parameters in the segment, the state of the channel is updated continuously based on the geometric distribution structure of multipath. Finally, the accuracy of spatial consistency simulation is evaluated by using the autocorrelation coefficient of the number of multipath clusters and the delay-power-angle spectrum. The results show that the proposed simulation method realizes the smooth evolution of the parameters between continuous statistical segments of channel, thus it can accurately characterize the spatial consistency of 5G millimeter wave channels. The related work of this paper realizes the accurate simulation of millimeter wave time-varying channels.

Deterministic and Empirical Approach for Millimeter-Wave Complex Outdoor Smart Parking Solution Deployments.

The characterization of different vegetation/vehicle densities and their corresponding effects on large-scale channel parameters such as path loss can provide important information during the deployment of wireless communications systems under outdoor conditions. In this work, a deterministic analysis based on ray-launching (RL) simulation and empirical measurements for vehicle-to-infrastructure (V2I) communications for outdoor parking environments and smart parking solutions is presented. The study was carried out at a frequency of 28 GHz using directional antennas, with the transmitter raised above ground level under realistic use case conditions. Different radio channel impairments were weighed in, considering the progressive effect of first, the density of an incremental obstructed barrier of trees, and the effect of different parked vehicle densities within the parking lot. On the basis of these scenarios, large-scale parameters and temporal dispersion characteristics were obtained, and the effect of vegetation/vehicle density changes was assessed. The characterization of propagation impairments that different vegetation/vehicle densities can impose onto the wireless radio channel in the millimeter frequency range was performed. Finally, the results obtained in this research can aid communication deployment in outdoor parking conditions.

Playback of 5G and Beyond Measured MIMO Channels by an ANN-Based Modeling and Simulation Framework

In this work, firstly we propose an artificial neural network (ANN) based channel modeling and simulation framework to playback a measurement channel to overcome the shortcomings of traditional geometry based stochastic modelling (GBSM) and simulation approach which is unable to predict a time or position-varying channel to match with real environment. Secondly, we implement the framework based on channel measurements performed at 28 GHz in a large waiting hall at Qingdao high-speed railway station, China. Thirdly, we validate the proposed framework by comparisons of the large scale channel parameters (LSCPs) and small scale channel parameters (SSCPs) extracted from the measured, ANN and GBSM simulation channels. The results show that the ANN-based framework can playback the measured channels accurately, while GBSM-based simulated channels have large deviations. This work offers a solution to playback the measured channels accurately to be used in 5G and beyond radio system research and engineering applications, while it’s also able to be applied in future channel predictions in case of large amount of measured data available.

Application of Radio Environment Map Reconstruction Techniques to Platoon-based Cellular V2X Communications.

Vehicle platoons involve groups of vehicles travelling together at a constant inter-vehicle distance, with different common benefits such as increasing road efficiency and fuel saving. Vehicle platooning requires highly reliable wireless communications to keep the group structure and carry out coordinated maneuvers in a safe manner. Focusing on infrastructure-assisted cellular vehicle to anything (V2X) communications, the amount of control information to be exchanged between each platoon vehicle and the base station is a critical factor affecting the communication latency. This paper exploits the particular structure and characteristics of platooning to decrease the control information exchange necessary for the channel acquisition stage. More precisely, a scheme based on radio environment map (REM) reconstruction is proposed, where geo-localized received power values are available at only a subset of platoon vehicles, while large-scale channel parameters estimates for the rest of platoon members are provided through the application of spatial Ordinary Kriging (OK) interpolation. Distinctive features of the vehicle platooning use case are explored, such as the optimal patterns of vehicles within the platoon with available REM values for improving the quality of the reconstruction, the need for an accurate semivariogram modeling in OK, or the communication cost when establishing a centralized or a distributed architecture for achieving REM reconstruction. The evaluation results show that OK is able to reconstruct the REM in the platoon with acceptable mean squared estimation error, while reducing the control information for REM acquisition in up to 64% in the best-case scenario.

Channel sounding, modelling, and characterisation in a large waiting hall of a high‐speed railway station at 28 GHz

In this study, based on channel measurements for a fifth generation (5G) hotspot of a large waiting hall of a high-speed railway station at 28 GHz, the channel modelling, simulation, and validation are performed. To investigate clustering sparse features at a millimetre-wave band, an adaptive kernel-power-density algorithm is used to identify clusters. Moreover, by using a quasi-deterministic radio channel generator (QuaDRiGa) simulation platform, the large-scale channel parameters such as delay spread, azimuth spread of arrival, and time-evolution characteristics are simulated and validated. The QuaDRiGa simulation results agree well with measurement data, which verifies the applicability of QuaDRiGa in a millimetre-wave band. The results could be very useful in millimetre-wave over-the-air testing and system performance evaluation in the 5G hotspot scenario.

Wideband Millimeter-Wave Channel Characterization in an Open Office at 26 GHz

Based on the newest frequency allocation for the fifth generation (5G) radio systems at 26 GHz millimeter wave band by the World Radio Communications Conference, this paper investigates the wideband channel properties by measurements carried out in the LOS and NLOS environments at 26 GHz with 1 GHz bandwidth in an open office at KeySight Beijing, China, which is a representative of an indoor hotspot scenario. In the time domain measurements, an omni-directional biconical horn is used at the transmitter, while at the receiver a 24.3 dBi horn is applied and rotated with 5° angular step in the whole azimuth plane, and from −20° to 30° in the elevation plane with 10° angular step. In the work, two kinds of path-loss models are developed, namely directional and omni-directional models by using close-in and float intercept methods. The directional path-loss model is useful for adopting beamforming techniques. The large scale channel parameters such as the shadow fading, root mean square (RMS) delay spread, RMS angular spread in the azimuth and elevation planes, Ricean K-factor, number of clusters and their correlations are investigated for the fifth generation (5G) link and system level simulations. A new method for extracting number of clusters is proposed to find the peak power within a sliding window. The power angular profiles are employed at the measurement locations for propagation mechanisms studies. We believe that the newest results in this work are useful in the simulations and planning for future 5G radio systems at 26 GHz.

Channel Measurements, Modeling, Simulation and Validation at 32 GHz in Outdoor Microcells for 5G Radio Systems

In this paper, based on outdoor microcellular channel measurements at 32 GHz for 5G radio systems, a comprehensive channel modeling, simulation, and validation are performed. The directional-scan-sounding measurements using a horn antenna rotated with an angular step at the receiver are carried out, which constitutes a virtual array to form a single-input multiple-output radio channel. The directional- and omni-directional path-loss models are developed by using close-in and floating-intercept methods. Non-parametric and parametric methods are applied to extract large-scale channel parameters (LSPs). The non-parametric method is based on the definition of a channel parameter, whereas the parametric method is derived by the space-alternating generalized expectation–maximization (SAGE) algorithm, which can de-embed an antenna pattern. It is found that the LSPs in the angular domain are significantly different by using the two methods; however, the LSPs in the delay domain almost stay the same. By comparing the LSPs with the parameter table at 32 GHz with 3GPP standard, it is found that 3GPP LSPs should be corrected at the International Telecommunications Union-assigned millimeter wave (mmWave) frequencies for 5G. In this paper, the channel simulation is implemented by using the quasi-deterministic radio channel generator (QuaDRiGa) platform recommended by 3GPP. By comparing the LSPs with the simulated and measured results, it is found that QuaDRiGa is a good platform at the mmWave band, even if it is originally developed for channel simulation below 6 GHz. The results of this paper are important and useful in the simulations and design of future 5G radio systems at 32 GHz.

Measurements and analyses of 28 GHz indoor channel propagation based on a synchronized channel sounder using directional antennas

This paper presents measurements and analyses of channel propagation to investigate the channel characteristics of the millimeter wave for the indoor mobile communication system. The candidate for the promising carrier frequency is 28 GHz. We conducted the measurements considering conditions for the line of sight and the obstructed line of sight. The measurement points are located in a typical office room that is one of the main scenarios for the channel model. For channel propagation analysis, a novel channel sounder with a time synchronization scheme is introduced to combine channel measurements from different directions using an automatic scanning measurement system. With this system, omni-like power delay profiles of 28 GHz communication was successfully obtained. Furthermore, we analyze the spatial-temporal channel characteristics of an indoor environment such as path loss, delay and angular distributions, delay and angular spreads, and cross-correlation between the large-scale channel parameters.

A Comparison of Outdoor-to-Indoor Wideband Propagation at S-Band and C-Band for Ranging

Indoor positioning using mobile radio signals at different frequency bands has gained remarkable attention. Thus, evaluating the influence of the radio propagation channel at different carrier frequencies with respect to the ranging performance is of interest. This paper presents a measurement-based comparison of the outdoor-to-indoor wideband propagation channel at S-band and C-band. We evaluate channel parameters that have an impact on the ranging and, in turn, on the positioning performance. Our comparison is threefold and is based on large-scale channel parameters, ranging performance, and spatial characteristics of the channel. In the first set of comparisons, we consider received power, power delay profile (PDP), delay spread, mean delay, and a number of multipath components (MPCs). The second set of comparisons considers ranging performance: Both the correlator-based and recursive-Bayesian-filter-based ranging estimators are evaluated using the measurement data. In the third set of comparisons, spatial characteristics of the propagation channel at S-band and C-band are compared and analyzed for range estimates to provide an insight for positioning applications.

Measurements and Analysis of Propagation Channels in High-Speed Railway Viaducts

This paper reports (i) a set of measurements of the wireless propagation channel at 930 MHz, conducted along the "Zhengzhou-Xian" high-speed railway of China in various railway viaduct scenarios, and (ii) an analysis and modeling of the small-scale and large-scale channel parameters based on those measurements. The environment can be categorized into four cases, covering viaducts with different heights and in different suburban environments. Small values of fade depth, level crossing rates, and average fade duration are observed. Akaike's Information Criterion (AIC)-based evaluation indicates that the Ricean distribution is the best to describe small-scale amplitude fading. An analysis of the envelope autocovariance function shows that the coherence distance is less than 10 cm. The Ricean K-factor is modeled as a piecewise-linear function of distance. Moreover, a breakpoint path loss model is developed and shadow fading is investigated using the same break point as for the distance-dependent K-factor model. The Suzuki distribution is found to offer a good fit for the composite multipath/shadowing channels. We find that the viaduct height H, together with the number of surrounding scatterers, significantly affects the small- and large-scale channel parameters. These results are applicable to both normal-speed and high-speed railways, and will be useful in the modeling of railway viaduct channels and the design of railway wireless communication systems.