Performance Analysis of V2V Radio Channel under Typical Urban Intersection Scenario

Shu, Xuanhao; Li, Changzhen; Chen, Wei; Yu, Jiguo; Yang, Kun

doi:10.1109/iccs.2018.8689245

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Dengan kondisi kendaraan yang saling bergerak, Doppler shift akan mempengaruhi komunikasi V2V. Adanya Doppler shift pada sistem komunikasi V2V menyebabkan terjadinya penurunan Signal to Noise Ratio (SNR) yang diterima oleh receiver dan peningkatan nilai Bit Error Rate (BER) [3]. Dengan tingginya nilai BER maka tingkat kesalahan informasi yang diterima oleh receiver juga tinggi.…”

Section: Pendahuluanunclassified

Penentuan Batas Gain Kanal Modulasi Adaptif V2V dengan Doppler Shift yang Bervariasi Menggunakan Machine Learning

Kurniawati,

Novfitri,

Tyas

2023

FaktorExacta

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Doppler shift is a phenomenon that occurs when the vehicle is moving. The effect of Doppler shift is a degradation in performance of Vehicle to Vehicle (V2V) communication. Adaptive modulation is a technique to improve the performance. It is done by adjusting the modulation scheme used according to noise conditions while keeping the Bit Error Rate (BER) value not exceeding 10-3. In this research, three Doppler shift values are used. The shift is derived from speed limit determined by The Government of Indonesia. Then machine learning algorithm is used to predict channel gain threshold that can optimize the use of Signal to Noise Ratio (SNR) with a BER limit of 10-3. From the prediction results, it is found that by implementing the predicted channel gain threshold, the SNR required by adaptive modulation has the lowest value compared to non-adaptive modulation schemes. The lower the required SNR value, the communication is more resistant to noise interference.

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Section: Pendahuluanunclassified

Penentuan Batas Gain Kanal Modulasi Adaptif V2V dengan Doppler Shift yang Bervariasi Menggunakan Machine Learning

Kurniawati,

Novfitri,

Tyas

2023

FaktorExacta

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“…7. The authors of [29] show that the side of a bus passing parallel to the measuring nodes, behaves as a moving reflecting This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”

Section: B Large Vehicle Modelmentioning

confidence: 99%

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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Deep Learning Based Predictive Power Allocation for V2X Communication

Sang

Zhou

et al. 2021

IEEE Access

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Recently, deep learning (DL) based automatic modulation classification (AMC) has received much attention. Various network structures with higher complexity are utilized to boost the performance of classification model. We divide the issue of AMC into two objectives and propose a disentangled approach with a signal processing module. Unlike popular end-to-end training strategy, we first consider a simple model with much fewer trainable parameters to learn accurate modulation features for classification. Then a U-net based signal processing module using a specially designed function is introduced to transfer the knowledge stored in classification module. We compare the performance of the proposed method with several baseline models on two well known datasets. Experimental results demonstrate that the proposed method gives superior performance with lower computational complexity compared with other methods. Furthermore, we also verify the feasibility and huge potential of the knowledge transferring in the field of wireless communications.INDEX TERMS Automatic modulation classification, deep learning, knowledge transfer.

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Performance Analysis of V2V Radio Channel under Typical Urban Intersection Scenario

Cited by 6 publications

References 11 publications

Penentuan Batas Gain Kanal Modulasi Adaptif V2V dengan Doppler Shift yang Bervariasi Menggunakan Machine Learning

Penentuan Batas Gain Kanal Modulasi Adaptif V2V dengan Doppler Shift yang Bervariasi Menggunakan Machine Learning

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

Deep Learning Based Predictive Power Allocation for V2X Communication

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