Energy-modified Leverage Sampling for Radio Map Construction via Matrix Completion
원문 초록
This paper explores an energy-modified leverage sampling strategy for matrix completion in radio map construction. The main goal is to address potential identifiability issues in matrix completion with sparse observations by using a probabilistic sampling approach. Although conventional leverage sampling is commonly employed for designing sampling patterns, it often assigns high sampling probability to locations with low received signal strength (RSS) values, leading to a low sampling efficiency. Theoretical analysis demonstrates that the leverage score produces pseudo images of sources, and in the regions around the source locations, the leverage probability is asymptotically consistent with the RSS. Based on this finding, an energy-modified leverage probability-based sampling strategy is investigated for efficient sampling. Numerical demonstrations indicate that the proposed sampling strategy can decrease the normalized mean squared error (NMSE) of radio map construction by more than 10% for both matrix completion and interpolation-assisted matrix completion schemes, compared to conventional methods.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation
N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU function
Interference-Aware Edge Runtime Prediction with Conformal Matrix Completion
Robust Matrix Completion for Discrete Rating-Scale Data
Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data
토론
이 논문과 연결된 포럼 글이 아직 없습니다.