4 research outputs found
Model Selection in High-Dimensional Block-Sparse Linear Regression
Model selection is an indispensable part of data analysis dealing very
frequently with fitting and prediction purposes. In this paper, we tackle the
problem of model selection in a general linear regression where the parameter
matrix possesses a block-sparse structure, i.e., the non-zero entries occur in
clusters or blocks and the number of such non-zero blocks is very small
compared to the parameter dimension. Furthermore, a high-dimensional setting is
considered where the parameter dimension is quite large compared to the number
of available measurements. To perform model selection in this setting, we
present an information criterion that is a generalization of the Extended
Bayesian Information Criterion-Robust (EBIC-R) and it takes into account both
the block structure and the high-dimensionality scenario. The analytical steps
for deriving the EBIC-R for this setting are provided. Simulation results show
that the proposed method performs considerably better than the existing
state-of-the-art methods and achieves empirical consistency at large sample
sizes and/or at high-SNR.Comment: 5 pages, 2 figure
Evidence Theory based Cooperative Energy Detection under Noise Uncertainty
Noise power uncertainty is a major issue in energy-based spectrum sensors. Any uncertainty in the noise power leads to significant reduction in the detection performance of the energy detector and also results in a performance limitation in the form of SNR walls. In this paper, we propose an evidence theory (also called Dempster-Shafer theory (DST)) based cooperative energy detection (CED) for spectrum sensing. The noise variance is modeled as a random variable with a known distribution. The analyzed system model is similar to a distributed parallel detection network where each secondary user (SU) evaluates the energy from its received signal samples and sends it to a fusion center (FC), which makes the final decision. However, in the proposed DST-based method, the SUs sends computed belief-values instead of actual energy value to the FC. The uncertainty in the noise variance is accounted for by discounting the belief values based on the amount of uncertainty associated with each SU. Finally, the discounted belief values are combined using Dempster rule to reach at a global decision. Simulation results indicate that the proposed DST scheme significantly improves the detection probability under low average signal-to-noise ratio(ASNR) compared to the traditional sum fusion rule in the presence of noise uncertainty.Peer reviewe