Multi-objective robust resource allocation for secure communication in full-duplex MIMO systems

Abstract In this paper, we study robust resource allocation for the multi-user full-duplex (FD) multiple-input multiple-output (MIMO) communication systems. Particularly, we aim at minimizing uplink (UL) transmit power and downlink (DL) transmit power simultaneously while guaranteeing the quality of service (QoS) requirements regarding secure UL and DL communication, under the consideration of the imperfect channel state information (CSI) of the wiretap channels and the inter-user interference channels. In view of the conflicting of two objectives, we propose a multi-objective optimization (MOO) framework to achieve the trade-off between them. The formulated MOO problem is non-convex and intractable. By employing the weighted Tchebycheff, the Taylor series expansion, and the S-procedure approaches, we convert the MOO problem into the convex one and propose an iterative algorithm to solve it optimally. Simulation results not only demonstrate an interesting trade-off between the considered conflicting objectives but also show the efficiency of our proposed robust resource allocation designs

The group of commutativity preserving maps on strictly upper triangular matrices

summary:Let $\mathcal {N}=N_n(R)$ be the algebra of all $n\times n$ strictly upper triangular matrices over a unital commutative ring $R$. A map $\varphi$ on $\mathcal {N}$ is called preserving commutativity in both directions if $xy=yx\Leftrightarrow \varphi (x)\varphi (y)=\varphi (y)\varphi (x)$. In this paper, we prove that each invertible linear map on $\mathcal {N}$ preserving commutativity in both directions is exactly a quasi-automorphism of $\mathcal {N}$, and a quasi-automorphism of $\mathcal {N}$ can be decomposed into the product of several standard maps, which extains the main result of Y. Cao, Z. Chen and C. Huang (2002) from fields to rings

High-Resolution Representations Network for Single Image Dehazing

Deep learning-based image dehazing methods have made great progress, but there are still many problems such as inaccurate model parameter estimation and preserving spatial information in the U-Net-based architecture. To address these problems, we propose an image dehazing network based on the high-resolution network, called DeHRNet. The high-resolution network originally used for human pose estimation. In this paper, we make a simple yet effective modification to the network and apply it to image dehazing. We add a new stage to the original network to make it better for image dehazing. The newly added stage collects the feature map representations of all branches of the network by up-sampling to enhance the high-resolution representations instead of only taking the feature maps of the high-resolution branches, which makes the restored clean images more natural. The final experimental results show that DeHRNet achieves superior performance over existing dehazing methods in synthesized and natural hazy images