Real-Time Geometry-Based Wireless Channel Emulation

Hofer, Markus; Xu, Zhinan; Vlastaras, Dimitrios; Schrenk, Bernhard; Löschenbrand, David; Tufvesson, Fredrik; Zemen, Thomas

doi:10.1109/tvt.2018.2888914

Cited by 48 publications

(37 citation statements)

References 45 publications

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“…The measured and simulated frequency responses enable us to obtain high resolution time-variant PERs by using a HiL setup with our AIT channel emulator [31]. We used the obtained PERs to show the consistency of the model quality at the link-level for different modulation parameters.…”

Section: Discussionmentioning

confidence: 99%

“…As an additional step to verify the channel model we use a hardware-in-the-loop (HiL) methodology that allows us to obtain high resolution time-variant PERs from measured frequency responses [30]. We use the measured and the simulated frequency responses as input to the AIT real-time wireless channel emulator [31] and then compare the PER of wireless links emulated between two off-the-shelf IEEE 802.11p [32] modems. The diagram of the HiL setup is depicted in Fig.…”

Section: Packet Error Rate Evaluationmentioning

confidence: 99%

“…For our link-level emulation, we use off-the-shelf Cohda Wireless MK5 modems [33] as transceivers. The emulator [31] is based on the basis expansion model approach and consists of a propagation module, implemented on a host PC, and a convolution module implemented on the FPGA of a USRP SDR, as shown in Fig. 15.…”

Section: A Link-level Emulation Setupmentioning

confidence: 99%

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Multi-Node Vehicular Wireless Channels: Measurements, Large Vehicle Modeling, and Hardware-in-the-Loop Evaluation

et al. 2021

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Understanding multi-node vehicular wireless communication channels is crucial for future time-sensitive safety applications for human-piloted as well as partly autonomous vehicles on roads, railways and in the air. These highly dynamic wireless communication channels are characterized by rapidly changing channel statistics. In this paper we present the first fully mobile multi-node vehicular wireless channel sounding system, which is capable of simultaneously capturing multiple channel frequency responses, ensuring that measurement conditions are identical for all observed links. We use it to analyze road scenarios with multiple vehicles and a large obstructing double-decker bus. The empirical measurement data is used to parametrize a model for the large obstructing vehicle within a geometry-based stochastic channel model. We compare the time-variant channel statistics obtained from our channel model with the ones from the measurement campaign. By means of a channel emulator we obtain the packet error rates of commercial modems for the measured and the simulated wireless communication channels and compare them, in order to validate the model at the link level. We find that the path loss, the root mean square (RMS) delay spread, and the RMS Doppler spread deviate by less than 3.6 dB, 78 ns, and 52 Hz, respectively, for 80% of the total simulation duration. The PER obtained from measured data is within the maximum and minimum bounds of our model for 86% of the simulation duration.

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Section: Discussionmentioning

confidence: 99%

Section: Packet Error Rate Evaluationmentioning

confidence: 99%

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Multi-Node Vehicular Wireless Channels: Measurements, Large Vehicle Modeling, and Hardware-in-the-Loop Evaluation

et al. 2021

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“…In the non-stationary IMT-A MIMO channel model, the B-D process is used to calculate the instantaneous number of clusters. With the parameters in Table 3, each cluster remains from a time sample to the following one with the probability P remain = 0.9986, which can be calculated by (34). Then, according to (41), the mean number of newly generated clusters L = 0.029 in the selected scenario.…”

Section: ) Complexity Reduction Analysismentioning

confidence: 99%

“…A compact channel emulation scheme with low complexity on a FPGA platform was developed and validated in [32]. In [33] and [34], real time channel emulation methods and signal processing algorithms were introduced for non-stationary vehicular scenarios based on geometry-based stochastic channel models. The non-stationary fading process was partitioned into a sequence of local stationarity regions and a subspace projection of the propagation path parameters was adopted to compress the time-variant channel impulse response.…”

Section: Introductionmentioning

confidence: 99%

Accuracy-Complexity Tradeoff Analysis and Complexity Reduction Methods for Non-Stationary IMT-A MIMO Channel Models

et al. 2019

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High-mobility wireless communication systems have attracted growing interests in recent years. For the deployment of these systems, one fundamental work is to build accurate and efficient channel models. In high-mobility scenarios, it has been shown that the standardized channel models, e.g., IMT-Advanced (IMT-A) multiple-input multiple-output (MIMO) channel model, provide noticeable longer stationary intervals than measured results and the wide-sense stationary (WSS) assumption may be violated. Thus, the non-stationarity should be introduced to the IMT-A MIMO channel model to mimic the channel characteristics more accurately without losing too much efficiency. In this paper, we analyze and compare the computational complexity of the original WSS and non-stationary IMT-A MIMO channel models. Both the number of real operations and simulation time are used as complexity metrics. Since introducing the non-stationarity to the IMT-A MIMO channel model causes extra computational complexity, some computation reduction methods are proposed to simplify the non-stationary IMT-A MIMO channel model while retaining an acceptable accuracy. Statistical properties including the temporal autocorrelation function, spatial cross-correlation function, and stationary interval are chosen as the accuracy metrics for verifications. It is shown that the tradeoff between the computational complexity and modeling accuracy can be achieved by using these proposed complexity reduction methods. P n Power of the n-th cluster. S Number of transmitter (Tx) antenna elements. s ASA Log-normal distributed random variable (RV) of angle spread of arrival (ASA). s ASD Log-normal distributed RV of angle spread of departure (ASD). s DS Log-normal distributed RV of delay spread (DS). s SF Log-normal distributed RV of shadow fading (SF). s K Log-normal distributed RV of Rician K-factor. T The number of time samples. U Number of receiver (Rx) antenna elements. v, θ v Speed and mobile direction of MS, respectively. v c , θ c Speed and mobile direction of mobile scatterer, respectively. φ n,m Angle of departure (AoD) related to the m-th (m = 1, • • • , M) ray within the n-th cluster. m,n Random initial phases related to the m-th ray within the n-th cluster. υ n,m Doppler frequency component related to the m-th ray within the n-th cluster. ϕ n,m Angle of arrival (AoA) related to the m-th ray within the n-th cluster. AMMAR GHAZAL received the B.Sc. degree in electronics and telecommunication engineer

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Real-time ray-based channel generation and emulation for UAV communications

Yang

Chen

et al. 2022

Chinese Journal of Aeronautics

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Real-Time Geometry-Based Wireless Channel Emulation

Cited by 48 publications

References 45 publications

Multi-Node Vehicular Wireless Channels: Measurements, Large Vehicle Modeling, and Hardware-in-the-Loop Evaluation

Multi-Node Vehicular Wireless Channels: Measurements, Large Vehicle Modeling, and Hardware-in-the-Loop Evaluation

Accuracy-Complexity Tradeoff Analysis and Complexity Reduction Methods for Non-Stationary IMT-A MIMO Channel Models

Real-time ray-based channel generation and emulation for UAV communications

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