Validation of a Real-Time Geometry-Based Stochastic Channel Model for Vehicular Scenarios

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

doi:10.1109/vtcspring.2018.8417476

Cited by 4 publications

(6 citation statements)

References 25 publications

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“…Geometry‐based channel emulation employs tools such as Ray Tracing [7, 8] or Geometry‐based Stochastic Channel Models (GSCMs) [9, 10] to generate a vehicular channel representation from geometric data. Although in this approach, model assumptions must be made to achieve the emulation, the benefit of this approach is that the channel streamed to the emulator can be updated according to the time‐varying location of the vehicle, which provides reactive channel emulations that support virtual drive tests.…”

Section: Introductionmentioning

confidence: 99%

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Design and implementation of a wideband channel emulation platform for 5G mmWave vehicular communication

2020

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Section: Introductionmentioning

confidence: 99%

“…Measurement playback‐based channel emulation is reported in [11]. [9, 10] have studied geometric emulation. Results have been shown for model‐based emulation in [12].…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of a wideband channel emulation platform for 5G mmWave vehicular communication

2020

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“…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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“…Geometry-based channel emulation generates a vehicular channel from geometric data, employing tools such as Geometry-based Stochastic Channel Model (GSCMs) [ 18 , 19 ]. Alternatively, raytracing could be applied [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

“…Channel emulation on the other hand has only been conducted on sub-

GHz channels. Results have been shown for measurement playback [ 17 ], geometric emulation [ 18 , 19 ], as well as model based emulation [ 32 ]. Furthermore, Field-Programmable Gate Array (FPGA) implementations are presented in [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Millimeter Wave Vehicular Channel Emulation: A Framework for Balancing Complexity and Accuracy

Blazek

Zöchmann

Mecklenbräuker

2018

Sensors

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We propose a general framework for the specification of a sparse representation of millimeter wave vehicular propagation channels and apply this to both synthetic data and real-world observations from channel sounding experiments. The proposed framework is based on the c-LASSO (complex Least Absolute Shrinkage and Selection Operator) which minimizes the mean squared error of the sparse representation for a given number of degrees of freedom. By choosing the number of degrees of freedom, we balance the numerical complexity of the representation in the channel emulation against its accuracy in terms of the mean squared error. A key ingredient is the choice of basis of the representation and we discuss two options: the Fourier basis and its projection onto a given subband. The results indicate that the subband-projected Fourier basis is a low-complexity choice with high fidelity for representing clustered channel impulse responses. Finally, a sequential estimator is formulated which enforces a consistent temporal evolution of the geometry of the interacting objects in the propagation environment. We demonstrate the performance of our approach using both synthetic data and measured 60 GHz vehicular channel traces.

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Validation of a Real-Time Geometry-Based Stochastic Channel Model for Vehicular Scenarios

Cited by 4 publications

References 25 publications

Design and implementation of a wideband channel emulation platform for 5G mmWave vehicular communication

Design and implementation of a wideband channel emulation platform for 5G mmWave vehicular communication

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

Millimeter Wave Vehicular Channel Emulation: A Framework for Balancing Complexity and Accuracy

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