scispace - formally typeset
Y

Yifan Wang

Researcher at University of Tennessee

Publications -  10
Citations -  83

Yifan Wang is an academic researcher from University of Tennessee. The author has contributed to research in topics: Communications system & Cognitive radio. The author has an hindex of 3, co-authored 8 publications receiving 73 citations. Previous affiliations of Yifan Wang include University of Electronic Science and Technology of China.

Papers
More filters
Journal ArticleDOI

Physical layer assist authentication technique for smart meter system

TL;DR: The proposed novel message authentication schemes for the smart meter system yield the lower time delay for authenticating each message, which can satisfy the requirement of the real-time control over the smart grid.
Journal ArticleDOI

Belief Propagation and Quickest Detection-Based Cooperative Spectrum Sensing in Heterogeneous and Dynamic Environments

TL;DR: A framework that integrates quickest detection and belief propagation is applied to the cooperative spectrum sensing, where the primary user activities are heterogeneous in the space and dynamic in the time.
Proceedings ArticleDOI

Belief Propagation Based Spectrum Sensing Subject to Dynamic Primary User Activities: Phantom of Quickest Detection

TL;DR: A framework integrating quickest detection with belief propagation is applied to the scenario of temporally and spatially varying primary user activities and shows that the proposed scheme achieves better detection error rate and detection delay tradeoff.
Book ChapterDOI

The Performance Analysis of LT Codes

TL;DR: Analyzing and comparison results of the short and long of LT code performance under the different channel probability show that in the case of large delete the probability, the original data packets can be recovered with high probability as long as the decoder receives a sufficient number of packets.
Proceedings ArticleDOI

Universal Quickest Spectrum Sensing

TL;DR: This paper proposes a universal quickest change detection scheme based on density ratio estimation for spectrum sensing by detecting the sudden change of spectrum by detecting neither the pre- change nor post-change distribution (even the distribution forms) is known to SUs, thus achieving robustness to complex spectrum environment.