820 research outputs found
A Data-Aided Channel Estimation Scheme for Decoupled Systems in Heterogeneous Networks
Uplink/downlink (UL/DL) decoupling promises more flexible cell association
and higher throughput in heterogeneous networks (HetNets), however, it hampers
the acquisition of DL channel state information (CSI) in time-division-duplex
(TDD) systems due to different base stations (BSs) connected in UL/DL. In this
paper, we propose a novel data-aided (DA) channel estimation scheme to address
this problem by utilizing decoded UL data to exploit CSI from received UL data
signal in decoupled HetNets where a massive multiple-input multiple-output BS
and dense small cell BSs are deployed. We analytically estimate BER performance
of UL decoded data, which are used to derive an approximated normalized mean
square error (NMSE) expression of the DA minimum mean square error (MMSE)
estimator. Compared with the conventional least square (LS) and MMSE, it is
shown that NMSE performances of all estimators are determined by their
signal-to-noise ratio (SNR)-like terms and there is an increment consisting of
UL data power, UL data length and BER values in the SNR-like term of DA method,
which suggests DA method outperforms the conventional ones in any scenarios.
Higher UL data power, longer UL data length and better BER performance lead to
more accurate estimated channels with DA method. Numerical results verify that
the analytical BER and NMSE results are close to the simulated ones and a
remarkable gain in both NMSE and DL rate can be achieved by DA method in
multiple scenarios with different modulations
Influence of coastal groundwater salinization on the distribution and risks of heavy metals
Coastal groundwater quality significantly affects the regional sustainable development, due to the water resource shortage in coastal zone. Current studies on coastal groundwater have mainly focused on saline water intrusion and over-extraction. Information on the heavy metal pollution of coastal groundwater with salinization trend is limited. This study investigated heavy metals in groundwater from a typical coastal region with intensive anthropogenic activities and saline water intrusion. The southwestern coastal plains of the study area showed significant groundwater salinization trend. Heavy metals in the coastal groundwater mainly originated from anthropogenic activities and groundwater salinization according to principal component analysis. Relative high concentrations of As, Co, Cr, Cu, Fe, Mn, and Ni occurred in the southwestern coast of the study area with high TDS level, indicating that the concentrations of these heavy metals in shallow aquifer of the study area might be influenced by the groundwater salinization. Average concentrations of heavy metals in groundwater of the study area ranged from 0.03 (Cd) to 686.92 (Fe) mu g/L. Fe was the dominant heavy metal in groundwater with the maximal concentration of 2333.76 mu g/L and exceeding-standard rate reaching 98.23%. Approximately 13.27% of sampling sites showed moderate or higher heavy metal pollution of groundwater based on heavy metal pollution index. Heavy metals in groundwater exerted low ecological risks. Elements Fe, Ni, and As were the main contributors for ecological risks. Cancer risks of heavy metals for both adults and children were high at all sampling sites. Non-cancer risks for adults and children were unacceptable at 4.42% and 17.70% of sampling sites, respectively. The salinization of the coastal groundwater could also lead to the increase in the ecological and health risks of heavy metals in coastal groundwater. These findings provide initial and important information on heavy metals pollution in coastal aquifer with saline water intrusion. (C) 2018 Elsevier B.V. All rights reserved
HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding
The dissection of hyperspectral images into intrinsic components through
hyperspectral intrinsic image decomposition (HIID) enhances the
interpretability of hyperspectral data, providing a foundation for more
accurate classification outcomes. However, the classification performance of
HIID is constrained by the model's representational ability. To address this
limitation, this study rethinks hyperspectral intrinsic image decomposition for
classification tasks by introducing deep feature embedding. The proposed
framework, HyperDID, incorporates the Environmental Feature Module (EFM) and
Categorical Feature Module (CFM) to extract intrinsic features. Additionally, a
Feature Discrimination Module (FDM) is introduced to separate
environment-related and category-related features. Experimental results across
three commonly used datasets validate the effectiveness of HyperDID in
improving hyperspectral image classification performance. This novel approach
holds promise for advancing the capabilities of hyperspectral image analysis by
leveraging deep feature embedding principles. The implementation of the
proposed method could be accessed soon at https://github.com/shendu-sw/HyperDID
for the sake of reproducibility.Comment: Submitted to IEEE TGR
Sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit
In this paper, we use the block orthogonal matching pursuit (BOMP) algorithm to recover block sparse signals x from measurements y = Ax + v, where v is an ℓ2-bounded noise vector (i.e., kvk2 ≤ ǫ for some constant ǫ). We investigate some sufficient conditions based on the block restricted isometry property (block-RIP) for exact (when v = 0) and stable (when v , 0) recovery of block sparse signals x. First, on the one hand, we show that if A satisfies the block-RIP with δK+1 1 and √2/2 ≤ δ < 1, the recovery of x may fail in K iterations for a sensingmatrix A which satisfies the block-RIP with δK+1 = δ. Finally, we study some sufficient conditions for partial recovery of block sparse signals. Specifically, if A satisfies the block-RIP with δK+1 < √2/2, then BOMP is guaranteed to recover some blocks of x if these blocks satisfy a sufficient condition. We further show that this condition is also sharp
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