CQI reporting strategies for nonregenerative two-way relay networks

This paper considers data exchange between two terminals in a nonregenerative two-way relay network. We first propose two efficient channel quality indicator (CQI) reporting schemes based on XOR and superposition coding for single-relay networks. These schemes allow two terminals to simultaneously estimate the CQI of the distant link without incurring additional overhead. In addition, the transmission time for CQI feedback is reduced by half while the loss of performance is negligible. Upper and lower bounds of the mean square error (MSE) of the estimated CQI are derived to analyze various effects on the performance of the proposed schemes. We then extend our MSE analysis to multi-relay networks where a low-complexity relay selection scheme is proposed based on the derived bounds. Simulation results show that, in comparison with conventional methods, this suboptimal bound-based scheme achieves satisfac- tory performance while reducing the complexity at least three times in case of large number of relays

An exploration on the nexus between managers’ present bias and corporate investment

This study aims to explore the role of top manager’s present bias as a main driver of corporate investment. For this purpose, we embed an experiment in a firm-level panel survey with a sample of top managers from 623 textile and garment firms in Vietnam. The experiment enables us to elicit present bias for each individual manager. We find that firms led by managers with a greater level of present bias are more likely to have a lower investment. There also exists evidence that the effect of managers’ present bias on corporate investment is stronger for SMEs than for large firms

Network coding-based channel quality indicator reporting for two-way multi-relay networks

This paper considers channel quality indicator (CQI) reporting for data exchange in a two-way multi-relay network. We first propose an efficient CQI reporting scheme based on network coding, where two terminals are allowed to simultaneously estimate the CQI of the distant terminal-relay link without suffering from additional overhead. In addition, the transmission time for CQI feedback at the relays is reduced by half while the increase in complexity and the loss of performance are negligible. This results in a system throughput improvement of 16.7% with our proposed CQI reporting. Upper and lower bounds of the mean square error (MSE) of the estimated CQI are derived to study performance behaviour of our proposed scheme. It is found that the MSE of the estimated CQI increases proportionally with the square of the cardinality of CQI level sets although an increased number of CQI levels would eventually lead to a higher data rate transmission. On the basis of the derived bounds, a low-complexity relay selection (RS) scheme is then proposed. Simulation results show that, in comparison with optimal methods, our suboptimal bound-based RS scheme achieves satisfactory performance while reducing the complexity at least three times in case of large number of relays

Deep-NC: a secure image transmission using deep learning and network coding

Visual communications have played an important part in our daily life as a non-verbal way of conveying information using symbols, gestures and images. With the advances of technology, people can visually communicate with each other in a number of forms via digital communications. Recently Image Super-Resolution (ISR) with Deep Learning (DL) has been developed to reproduce the original image from its low-resolution version, which allows us to reduce the image size for saving transmission bandwidth. Although many benefits can be realised, the image transmission over wireless media experiences inevitable loss due to environment noise and inherent hardware issues. Moreover, data privacy is of vital importance, especially when the eavesdropper can easily overhear the communications over the air. To this end, this paper proposes a secure ISR protocol, namely Deep-NC, for the image communications based on the DL and Network Coding (NC). Specifically, two schemes, namely Per-Image Coding (PIC) and Per-Pixel Coding (PPC), are designed so as to protect the sharing of private image from the eavesdropper. Although the PPC scheme achieves a better performance than the PIC scheme for the entire image, it requires a higher computational complexity on every pixel of the image. In the proposed Deep-NC, the intended user can easily recover the original image achieving a much higher performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) than those at the eavesdropper. Simulation results show that an improvement of up to 32 dB in the PSNR can be obtained when the eavesdropper does not have any knowledge of the parameters and the reference image used in the mixing schemes. Furthermore, the original image can be downscaled to a much lower resolution for saving significantly the transmission bandwidth with negligible performance loss

An induction theorem and nonlinear regularity models

A general nonlinear regularity model for a set-valued mapping $F:X\times R_+\rightrightarrows Y$, where $X$ and $Y$ are metric spaces, is considered using special iteration procedures, going back to Banach, Schauder, Lusternik and Graves. Namely, we revise the induction theorem from Khanh, J. Math. Anal. Appl., 118 (1986) and employ it to obtain basic estimates for studying regularity/openness properties. We also show that it can serve as a substitution of the Ekeland variational principle when establishing other regularity criteria. Then, we apply the induction theorem and the mentioned estimates to establish criteria for both global and local versions of regularity/openness properties for our model and demonstrate how the definitions and criteria translate into the conventional setting of a set-valued mapping $F:X\rightrightarrows Y$.Comment: 28 page

Adaptive Image Denoising by Targeted Databases

We propose a data-dependent denoising procedure to restore noisy images. Different from existing denoising algorithms which search for patches from either the noisy image or a generic database, the new algorithm finds patches from a database that contains only relevant patches. We formulate the denoising problem as an optimal filter design problem and make two contributions. First, we determine the basis function of the denoising filter by solving a group sparsity minimization problem. The optimization formulation generalizes existing denoising algorithms and offers systematic analysis of the performance. Improvement methods are proposed to enhance the patch search process. Second, we determine the spectral coefficients of the denoising filter by considering a localized Bayesian prior. The localized prior leverages the similarity of the targeted database, alleviates the intensive Bayesian computation, and links the new method to the classical linear minimum mean squared error estimation. We demonstrate applications of the proposed method in a variety of scenarios, including text images, multiview images and face images. Experimental results show the superiority of the new algorithm over existing methods.Comment: 15 pages, 13 figures, 2 tables, journa

Efficient ARQ retransmission schemes for two-way relay networks.

In this paper, we investigate different practical automatic repeat request (ARQ) retransmission protocols for two-way wireless relay networks based on network coding (NC). The idea of NC is applied to increase the achievable throughput for the exchange of information between two terminals through one relay. Using NC, throughput efficiency is significantly improved due to the reduction of the number of retransmissions. Particularly, two improved NC-based ARQ schemes are designed based on go-back-N and selective-repeat (SR) protocols. The analysis of throughput efficiency is then carried out to find the best retransmission strategy for different scenarios. It is shown that the combination of improved NC-based SR ARQ scheme in the broadcast phase and the traditional SR ARQ scheme in the multiple access phase achieves the highest throughput efficiency compared to the other combinations of ARQ schemes. Finally, simulation results are provided to verify the theoretical analysis

Trust under the prospect theory and quasi-hyperbolic preferences: a field experiment in Vietnam

We conduct a field experiment in Vietnamese villages to explore the effect of the prospect theory and of quasi-hyperbolic time preferences parameters on trust and trustworthiness. We find that risk aversion, loss aversion and present bias do not influence trustors’ decisions, but a higher time discounting increases the amount sent in the South of Vietnam and probability weighting decreases it in the North. If time discounting and loss aversion do not influence trustworthiness, we show that more risk averse and less present biased trustees return a higher share of their wealth to the trustors. These results suggest that adopting another perspective than the expected utility theory and the exponential discounting approach of time preferences enables to uncover some channels by which risk and intertemporal time preferences influence trusting behavior in societies