Lihui Lei | Searcharxiv

arXiv2026

HyperCEUNet: Parameter-Aware Hypernetwork-Driven UNet for Channel Estimation

Deep learning-based channel estimation has been recognized as a promising technique for sixth-generation wireless systems. However, most existing approaches rely solely on least-squares estimates obtained from demodulation reference signals, which fail to explicitly exploit channel time-frequency correlation parameters. Inspired by the independent channel parameter estimation enabled by semi-static reference signals in modern wireless systems, this letter presents a parameter-aware deep learning-based channel estimation framework termed HyperCEUNet. Specifically, the proposed hypernetwork generates an adaptive front-end convolutional layer based on estimated channel parameters, serving as a pre-filtering stage before the UNet-based estimator. In addition, the Wiener-filtered channel estimates are adopted to provide a correlation-aware initialization for data resources. Simulation results demonstrate that our proposed HyperCEUNet effectively improves channel estimation accuracy compared with its conventional counterparts.

eess.SP↗

arXiv2016

Model-Checking of Linear-Time Properties in Multi-Valued Systems

In this paper, we study model-checking of linear-time properties in multi-valued systems. Safety property, invariant property, liveness property, persistence and dual-persistence properties in multi-valued logic systems are introduced. Some algorithms related to the above multi-valued linear-time properties are discussed. The verification of multi-valued regular safety properties and multi-valued $ω$-regular properties using lattice-valued automata are thoroughly studied. Since the law of non-contradiction (i.e., $a\wedge \neg a=0$) and the law of excluded-middle (i.e., $a\vee \neg a=1$) do not hold in multi-valued logic, the linear-time properties introduced in this paper have the new forms compared to those in classical logic. Compared to those classical model checking methods, our methods to multi-valued model checking are more directly accordingly. A new form of multi-valued model checking with membership degree is also introduced. In particular, we show that multi-valued model-checking can be reduced to the classical model checking. The related verification algorithms are also presented. Some illustrative examples and case study are also provided.

cs.LO↗