Joint Relay Selection and Power Allocation for the Physical Layer Security of Two-Way Cooperative Relaying Networks

In this paper, we investigate the physical layer security of cooperative two-way relay transmission systems using the amplify-and-forward (AF) protocol in the presence of an eavesdropper. A joint relay selection (RS) and power allocation (PA) scheme is proposed to protect the source-destination transmission against the eavesdropper. However, due to the high computational complexity, it is difficult to obtain the optimal solution for the system secrecy rate. Fortunately, an approximate optimal solution by using the particle swarm optimization (PSO) algorithm is derived. In the simulations, we use random relay selection with optimal power allocation (RRS-OPA) and equal power allocation with optimal relay selection (EPA-ORS) as benchmark schemes to verify the effectiveness of the proposed method. The simulation results show that the proposed method outperforms both RRS-OPA and EPA-ORS and significantly improves the system performance with low complexity

Analysis and Design of Systematic Rateless Codes in FH/BFSK System with Interference

The asymptotic analysis of systematic rateless codes in frequency hopping (FH) systems with interference is first provided using discretized density evolution (DDE) and compared with the traditional fixed-rate scheme. A simplified analysis with Gaussian assumption of initial message is proposed in the worst case of interference, which has much lower complexity and provides a very close result to DDE. Based on this simplified analysis, the linear programming is employed to design rateless codes and the simulation results on partial-band interference channels show that the optimized codes have more powerful antijamming performance than the codes originally designed for conventional systems

The effects of phase changes on the bonding property of geopolymer to hydrated cement

Using geopolymers as bonding materials could be an alternative solution to the concrete repair. This study reports the effects of phase changes on the bonding property of geopolymer to hydrated cement. A geopolymer binder was prepared by activation of heated kaolin with sodium silicate solution, bonded with 28 days aged cement paste and cured under different conditions. The experimental results demonstrated that under the 20C air curing conditions, the bond strength achieved 1.3 MPa at 7 days and increased to 1.5 MPa at 28 days. While under the 80C steam and water curing conditions, the bond strength decreased by 31% and 37% respectively. The XRD, FTIR and SEM analysis of the geopolymers and hydrated cement pastes show that the strength loss is due to two factors: (1) the increased porosity in cement paste due to the water loss and/or crystallization of C-S-Hs; and (2) the mineralogical change (crystallization) in geopolymer binder which becomes more ordered structures. The increased porosity and the crystallization either in cement or geopolymer generate local stress and weak regions at the interface. This study suggests that the stability of metakaolin-based geopolymers should be taken into consideration when they are used as bonding or coating materials for concrete repair, particularly at the conditions with elevated temperatures

An Efficient Design of Systematic Rateless Codes

The asymptotic performance of systematic rateless codes is first analysed using Gaussian approximation (GA) based on mutual information, which provides more accurate decoding thresholds than the method based on message mean. A modified linear program (LP) algorithm is proposed, where two key parameters are precomputed to efficiently search degree distributions with low overhead and appropriate average degree. The asymptotic analysis and simulation results show that the optimized code outperforms the codes obtained by conventional LP. Furthermore, the effects of outer code on overall code rate and decoding complexity are discussed

Bi-Kernel Graph Neural Network with Adaptive Propagation Mechanism for Hyperspectral Image Classification

Graph neural networks (GNNs) have been widely applied for hyperspectral image (HSI) classification, due to their impressive representation ability. It is well-known that typical GNNs and their variants work under the assumption of homophily, while most existing GNN-based HSI classification methods neglect the heterophily that is widely present in the constructed graph structure. To deal with this problem, a homophily-guided Bi-Kernel Graph Neural Network (BKGNN) is developed for HSI classification. In the proposed BKGNN, we estimate the homophily between node pairs according to a learnable homophily degree matrix, which is then applied to change the propagation mechanism by adaptively selecting two different kernels to capture homophily and heterophily information. Meanwhile, the learning process of the homophily degree matrix and the bi-kernel feature propagation process are trained jointly to enhance each other in an end-to-end fashion. Extensive experiments on three public data sets demonstrate the effectiveness of the proposed method