Channel Prediction Assisted By Radio Propagation Environments Information

Zhang, Yan; Liu, Shanjian; Rui, Yanhua; Zhou, Shidong; Wang, Jing

doi:10.1109/iccsc.2008.160

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…In the sequel, we present how to update real-time primal solution such that the computation and signaling delay δT f can be substantially decreased. In practice, the BS is able to acquire the channel gain {g s,r n (ℓ), g s,d n (ℓ), g r,d n (ℓ)} N n=1 through channel prediction before Frame ℓ starts, if the wireless channel varies slowly across the frames [36]. Given the channel gain {g s,r n (ℓ), g s,d n (ℓ), g r,d n (ℓ)}, the BS can compute the ratios P (1) s,n (ω ℓ ) θ(1) m (ω ℓ ) , P (2) s,n (ω ℓ ) θ( 2 Once the source and relay nodes obtain the sensing outcomes x m (ℓ) and y m (ℓ), they can compute θ(1) m (ω ℓ ) and θ(2) m (ω ℓ ) according to the closed-form solutions (30), (32), (50), and (51).…”

Section: B Solving Problem (47) In Real-timementioning

confidence: 99%

Optimal Real-Time Spectrum Sharing Between Cooperative Relay and Ad hoc Networks

Sun

Zhong

Chang

et al. 2012

IEEE Trans. Signal Process.

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Abstract-Optimization based spectrum sharing strategies have been widely studied. However, these strategies usually require a great amount of real-time computation and significant signaling delay, and thus are hard to be fulfilled in practical scenarios. This paper investigates optimal real-time spectrum sharing between a cooperative relay network (CRN) and a nearby ad-hoc network. Specifically, we optimize the spectrum access and resource allocation strategies of the CRN so that the average traffic collision time between the two networks can be minimized while maintaining a required throughput for the CRN. The development is first for a frame-level setting, and then is extended to an ergodic setting. For the latter setting, we propose an appealing optimal real-time spectrum sharing strategy via Lagrangian dual optimization. The proposed method only involves a small amount of real-time computation and negligible control delay, and thus is suitable for practical implementations. Simulation results are presented to demonstrate the efficiency of the proposed strategies.

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Section: B Solving Problem (47) In Real-timementioning

confidence: 99%

Optimal Real-Time Spectrum Sharing Between Cooperative Relay and Ad hoc Networks

Sun

Zhong

Chang

et al. 2012

IEEE Trans. Signal Process.

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“…Then the static environment parameter of this position can be looked up in the BS. By combining the DoAs and velocity, the Doppler frequencies can be calculated by (2).…”

Section: B Parametric Prediction Methodsmentioning

confidence: 99%

“…In [2], a new method is proposed to make accurate prediction of flat fading channel based on prior information of DOA. In this paper, we extend it to a new channel predictor relying on environment information (CPREI) for broadband multipath environment.…”

Section: Introductionmentioning

confidence: 99%

Wideband wireless channel predictor relying on environment information

Zhang

Liu

Rui

et al. 2008

2008 8th International Symposium on Antennas, Propagation and EM Theory

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With the development of wireless communication system, long-range precise channel prediction is considered to be more important. Traditional predictors require a lot of pilots and their performances degrade very fast after the correlation time, especially in high speed mobile environment. A new parametric channel predictor, CPREI, which is based on the auxiliary environment information, is presented in this paper. This new method can reduce the number of pilots and improve the prediction length. From the evaluation with real measured data, this predictor outperforms the former Auto Regression (AR) predictor.

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“…When the MS moves a short distance, the static scatterers change slowly compared with the receivers motion. Previous research revealed that, because the BS and main scatterers (e.g., outdoor buildings and indoor walls) are static, some variables in the multi-path channel model mainly affected by static scatterers can be considered static [22,23] . Thus, the number of static scatterers and the DOAs of rays from these scatterers, which are referred to as static parameters, stay relatively invariable.…”

Section: Tomographic Channel Modelmentioning

confidence: 99%

Wireless channel feature extraction via GMM and CNN in the tomographic channel model

Zhou

et al. 2017

J. Commun. Inf. Netw.

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Wireless channel modeling has always been one of the most fundamental highlights of the wireless communication research. The performance of new advanced models and technologies heavily depends on the accuracy of the wireless CSI (Channel State Information). This study examined the randomness of the wireless channel parameters based on the characteristics of the radio propagation environment. The diversity of the statistical properties of wireless channel parameters inspired us to introduce the concept of the tomographic channel model. With this model, the static part of the CSI can be extracted from the huge amount of existing CSI data of previous measurements, which can be defined as the wireless channel feature. In the proposed scheme for obtaining CSI with the tomographic channel model, the GMM (Gaussian Mixture Model) is applied to acquire the distribution of the wireless channel parameters, and the CNN (Convolutional Neural Network) is applied to automatically distinguish different wireless channels. The wireless channel feature information can be stored offline to guide the design of pilot symbols and save pilot resources. The numerical results based on actual measurements demonstrated the clear diversity of the statistical properties of wireless channel parameters and that the proposed scheme can extract the wireless channel feature automatically with fewer pilot resources. Thus, computing and storage resources can be exchanged for the finite and precious spectrum resource.Keywords: wireless channel modeling, tomographic channel model, Gaussian mixture model, convolutional neural network

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Channel Prediction Assisted By Radio Propagation Environments Information

Cited by 6 publications

References 9 publications

Optimal Real-Time Spectrum Sharing Between Cooperative Relay and Ad hoc Networks

Optimal Real-Time Spectrum Sharing Between Cooperative Relay and Ad hoc Networks

Wideband wireless channel predictor relying on environment information

Wireless channel feature extraction via GMM and CNN in the tomographic channel model

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