单变量
符号
衰退
频道(广播)
算法
矩母函数
伽马分布
数学
功能(生物学)
计算机科学
概率密度函数
离散数学
多元统计
统计
电信
算术
进化生物学
生物
作者
Vinay Kumar Chapala,S. M. Zafaruddin
出处
期刊:IEEE transactions on intelligent vehicles
[Institute of Electrical and Electronics Engineers]
日期:2023-06-23
卷期号:8 (10): 4445-4459
被引量:9
标识
DOI:10.1109/tiv.2023.3288161
摘要
The current research offers only approximations for evaluating the performance of wireless systems that use reconfigurable intelligent surfaces (RIS) over generalized fading channels with phase error caused by imperfect phase compensation at the RIS. This article presents an exact analysis of RIS-assisted vehicular communication considering uniformly distributed phase error and generalized fading channels with a coherent combining of received signals reflected by RIS elements and direct transmissions from the source terminal. We use a generalized- $K$ shadowed distribution for the direct link and asymmetrical channels for the RIS-assisted transmission with $\kappa$ - $\mu$ distribution for the first link and double generalized Gamma (dGG) distribution for the second link, combined with a statistical random waypoint (RWP) model for the moving vehicle. We employ a novel approach to represent the probability density function (PDF) and cumulative distribution function (CDF) of the resultant channel in terms of a single univariate Fox-H function and use the multivariate Fox-H approach to develop an exact statistical analysis of the end-to-end signal-to-noise ratio (SNR) for the RIS-assisted system. We also use the inequality between the arithmetic and geometric means to simplify the statistical results of the considered system in terms of the univariate Fox-H function. Our analysis also provides exact, upper bound, and asymptotic expressions of the outage probability and average bit-error-rate (BER) performance using the derived density and distribution functions. We conduct computer simulations in various practically relevant scenarios to assert that mitigating phase errors is achievable by augmenting the number of elements in the RIS module and employing a higher quantization level.
科研通智能强力驱动
Strongly Powered by AbleSci AI