arXiv subjects

Xiangbo Meng

Publications and source records attributed to Xiangbo Meng.

3 recordsLinked to original sources

arXiv2021

A class of quadratic reflected BSDEs with singular coefficients

In this paper, we study the existence and uniqueness of the solution to a reflected backward stochastic differential equation (RBSDE) with the generator $g(t,y,z)=G_f^F(t,y,z)+f(y)|z|^2$, where $f(y)$ is a locally integrable function defined on an open interval $D$, and $G_f^F(t,y,z)$ is induced by $f$ and a Lipschitz continuous function $F$. Both the solution $Y_t$ and the obstacle $L_t$ of this RBSDE take values in $D$. As applications, we provide a probabilistic interpretation of an obstacle problem for a quadratic PDE with a singular term, whose solution takes values in $D$, and study an optimal stopping problem for the payoff of American options under general utilities.

math.PR↗

arXiv2021

Training-Based Equivalence Relations in Large-Scale Quantized Communication Systems

We show that a quantized large-scale system with unknown parameters and training signals can be analyzed by examining an equivalent system with known parameters by modifying the signal power and noise variance in a prescribed manner. Applications to training in wireless communications and signal processing are shown. In wireless communications, we show that the optimal number of training signals can be significantly smaller than the number of transmitting elements. Similar conclusions can be drawn when considering the symbol error rate in signal processing applications, as long as the number of receiving elements is large enough. We show that a linear analysis of training in a quantized system can be accurate when the thermal noise is high or the system is operating near its saturation rate.

cs.IT↗

arXiv2021

A Training-Based Mutual Information Lower Bound for Large-Scale Systems

We provide a mutual information lower bound that can be used to analyze the effect of training in models with unknown parameters. For large-scale systems, we show that this bound can be calculated using the difference between two derivatives of a conditional entropy function. The bound does not require explicit estimation of the unknown parameters. We provide a step-by-step process for computing the bound, and provide an example application. A comparison with known classical mutual information bounds is provided.

cs.IT↗