2,821 research outputs found
Multi-Antenna Assisted Virtual Full-Duplex Relaying with Reliability-Aware Iterative Decoding
In this paper, a multi-antenna assisted virtual full-duplex (FD) relaying
with reliability-aware iterative decoding at destination node is proposed to
improve system spectral efficiency and reliability. This scheme enables two
half-duplex relay nodes, mimicked as FD relaying, to alternatively serve as
transmitter and receiver to relay their decoded data signals regardless the
decoding errors, meanwhile, cancel the inter-relay interference with
QR-decomposition. Then, by deploying the reliability-aware iterative
detection/decoding process, destination node can efficiently mitigate
inter-frame interference and error propagation effect at the same time.
Simulation results show that, without extra cost of time delay and signalling
overhead, our proposed scheme outperforms the conventional selective
decode-and-forward (S-DF) relaying schemes, such as cyclic redundancy check
based S-DF relaying and threshold based S-DF relaying, by up to 8 dB in terms
of bit-error-rate.Comment: 6 pages, 4 figures, conference paper has been submitte
Symbol-Level Selective Full-Duplex Relaying with Power and Location Optimization
In this paper, a symbol-level selective transmission for full-duplex (FD)
relaying networks is proposed to mitigate error propagation effects and improve
system spectral efficiency. The idea is to allow the FD relay node to predict
the correctly decoded symbols of each frame, based on the generalized square
deviation method, and discard the erroneously decoded symbols, resulting in
fewer errors being forwarded to the destination node. Using the capability for
simultaneous transmission and reception at the FD relay node, our proposed
strategy can improve the transmission efficiency without extra cost of
signalling overhead. In addition, targeting on the derived expression for
outage probability, we compare it with half-duplex (HD) relaying case, and
provide the transmission power and relay location optimization strategy to
further enhance system performance. The results show that our proposed scheme
outperforms the classic relaying protocols, such as cyclic redundancy check
based selective decode-and-forward (S-DF) relaying and threshold based S-DF
relaying in terms of outage probability and bit-error-rate. Moreover, the
performances with optimal power allocation is better than that with equal power
allocation, especially when the FD relay node encounters strong
self-interference and/or it is close to the destination node.Comment: 34 pages (single-column), 14 figures, 2 tables, accepted pape
Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering
Random dimensionality reduction is a versatile tool for speeding up
algorithms for high-dimensional problems. We study its application to two
clustering problems: the facility location problem, and the single-linkage
hierarchical clustering problem, which is equivalent to computing the minimum
spanning tree. We show that if we project the input pointset onto a random
-dimensional subspace (where is the doubling dimension of
), then the optimum facility location cost in the projected space
approximates the original cost up to a constant factor. We show an analogous
statement for minimum spanning tree, but with the dimension having an extra
term and the approximation factor being arbitrarily close to .
Furthermore, we extend these results to approximating solutions instead of just
their costs. Lastly, we provide experimental results to validate the quality of
solutions and the speedup due to the dimensionality reduction. Unlike several
previous papers studying this approach in the context of -means and
-medians, our dimension bound does not depend on the number of clusters but
only on the intrinsic dimensionality of .Comment: 25 pages. Published as a conference paper in ICML 202
Estimation of Higher-order Regression via. Sparse Representation Model for Single Image Super-resolution Algorithm
Super-resolution algorithms generate high-resolution (HR) imagery from single or multiple low-resolution (LR) degraded images. In this paper, an efficient single image super-resolution (SR) algorithm using higher-order regression is proposed. Image patches extracted from HR image will have self-similar example patches near its corresponding location in the LR image. A higherorder regression function is learned using these self-similar example patches via. sparse representation model. The regression function is based on local approximations and henceforth estimated from the localized image patches. Taylor series is used as local approximation of the regression function and hence the zeroth order regression co-efficient will yield the local estimate of the regression function and the higher-order regression co-efficient will provide the local estimate of the higher-order derivative of the regression function. The learned higher-order regression mapping function is applied to LR image patches to approximate its corresponding HR version. The proposed super-resolution approach is evaluated with standard test images and is compared against state-of-the-art SR algorithms. It is observed that the proposed technique preserves sharp high-frequency (HF) details and reconstructs visually appealing HR images without introducing andy artifacts
An Example-Based Super-Resolution Algorithm for Selfie Images
A selfie is typically a self-portrait captured using the front camera of a smartphone. Most state-of-the-art smartphones are equipped with a high-resolution (HR) rear camera and a low-resolution (LR) front camera. As selfies are captured by front camera with limited pixel resolution, the fine details in it are explicitly missed. This paper aims to improve the resolution of selfies by exploiting the fine details in HR images captured by rear camera using an example-based super-resolution (SR) algorithm. HR images captured by rear camera carry significant fine details and are used as an exemplar to train an optimal matrix-value regression (MVR) operator. The MVR operator serves as an image-pair priori which learns the correspondence between the LR-HR patch-pairs and is effectively used to super-resolve LR selfie images. The proposed MVR algorithm avoids vectorization of image patch-pairs and preserves image-level information during both learning and recovering process. The proposed algorithm is evaluated for its efficiency and effectiveness both qualitatively and quantitatively with other state-of-the-art SR algorithms. The results validate that the proposed algorithm is efficient as it requires less than 3 seconds to super-resolve LR selfie and is effective as it preserves sharp details without introducing any counterfeit fine details
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