Stochastic differential equations for performance analysis of wireless communication systems
CoRR(2024)
摘要
This paper addresses the difficulty of characterizing the time-varying nature
of fading channels. The current time-invariant models often fall short of
capturing and tracking these dynamic characteristics. To overcome this
limitation, we explore using of stochastic differential equations (SDEs) and
Markovian projection to model signal envelope variations, considering scenarios
involving Rayleigh, Rice, and Hoyt distributions. Furthermore, it is of
practical interest to study the performance of channels modeled by SDEs. In
this work, we investigate the fade duration metric, representing the time
during which the signal remains below a specified threshold within a fixed time
interval. We estimate the complementary cumulative distribution function (CCDF)
of the fade duration using Monte Carlo simulations, and analyze the influence
of system parameters on its behavior. Finally, we leverage importance sampling,
a known variance-reduction technique, to estimate the tail of the CCDF
efficiently.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要