scholarly journals Estimation of flood peaks from channel characteristics in Ohio

1985 ◽  
Keyword(s):  
Channel Characteristics ◽  
Flood Peaks

scholarly journals Analysis of Non-Stationarity for 5.9 GHz Channel in Multiple Vehicle-to-Vehicle Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3626
Author(s):  
Fang Li ◽  
Wei Chen ◽  
Yishui Shui
Keyword(s):  
Transmission Rate ◽  
Radio Channel ◽  
Vertical Direction ◽  
Statistical Characteristics ◽  
Small Scale ◽  
Delay Spread ◽  
Important Indicator ◽  
Power Delay Profile ◽  
Channel Characteristics ◽  
Vehicle To Vehicle

The vehicle-to-vehicle (V2V) radio channel is non-stationary due to the rapid movement of vehicles. However, the stationarity of the V2V channels is an important indicator of the V2V channel characteristics. Therefore, we analyzed the non-stationarity of V2V radio channels using the local region of stationarity (LRS). We selected seven scenarios, including three directions of travel, i.e., in the same, vertical, and opposite directions, and different speeds and environments in a similar driving direction. The power delay profile (PDP) and LRS were estimated from the measured channel impulse responses. The results show that the most important influences on the stationary times are the direction and the speed of the vehicles. The average stationary times for driving in the same direction range from 0.3207 to 1.9419 s, the average stationary times for driving in the vertical direction are 0.0359–0.1348 s, and those for driving in the opposite direction are 0.0041–0.0103 s. These results are meaningful for the analysis of the statistical characteristics of the V2V channel, such as the delay spread and Doppler spread. Small-scale fading based on the stationary times affects the quality of signals transmitted in the V2V channel, including the information transmission rate and the information error code rate.

scholarly journals Array Gain of a Linear Array in an Ocean Waveguide

2021 ◽  
Vol 11 (11) ◽  
pp. 5046
Author(s):  
Zong-Wei Liu ◽  
Chun-Mei Yang ◽  
Ying Jiang ◽  
Lei Xie ◽  
Jin-Yan Du ◽  
...  
Keyword(s):  
Transmission Loss ◽  
Spatial Coherence ◽  
Correlation Coefficients ◽  
Channel Correlation ◽  
Acoustic Channel ◽  
Physical Mechanisms ◽  
Channel Characteristics ◽  
Special Case ◽  
Array Gain ◽  
Insight Into

Array gain is investigated based on the acoustic channel characteristics manifested by the fluctuant transmission loss and decrease in the acoustic channel spatial coherence. An analytical expression is derived as the summation of the products of the acoustic channel correlation coefficients and root-mean-square pressures. The formula provides insight into the physical mechanisms of the gain degradation in the ocean waveguide. Furthermore, this formula provides a new method to study array gain in the ocean waveguide from underwater acoustic field. The obtained expression is a more general formula that is applicable to shallow water, deep sea, and continental slope, with the traditional methods as a special case. Numerical results show that the array gain calculated by previous formulas are generally overestimated, caused by ignoring the effect of transmission loss fluctuation.

scholarly journals Optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Haiyan Ye ◽  
Jiabao Jiang
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Spectrum Utilization ◽  
Channel Characteristics ◽  
Optimal Linear ◽  
Static Division ◽  
Cooperative Nodes

AbstractThe lack of spectrum resources restricts the development of wireless communication applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability.

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