Relay Selection for Bidirectional AF Relay Network with Outdated CSI

Most previous researches on bidirectional relay selection (RS) typically assume perfect channel state information (CSI). However, outdated CSI, caused by the the time-variation of channel, cannot be ignored in the practical system, and it will deteriorate the performance. In this paper, the effect of outdated CSI on the performance of bidirectional amplify-and-forward RS is investigated. The optimal single RS scheme in minimizing the symbol error rate (SER) is revised by incorporating the outdated channels. The analytical expressions of end-to-end signal to noise ratio (SNR) and symbol error rate (SER) are derived in a closed-form, along with the asymptotic SER expression in high SNR. All the analytical expressions are verified by the Monte-Carlo simulations. The analytical and the simulation results reveal that once CSI is outdated, the diversity order degrades to one from full diversity. Furthermore, a multiple RS scheme is proposed and verified that this scheme is a feasible solution to compensate the diversity loss caused by outdated CSI.Comment: accepted by IEEE Transactions on Vehicular Technolog

Truthful Mechanisms for Secure Communication in Wireless Cooperative System

To ensure security in data transmission is one of the most important issues for wireless relay networks, and physical layer security is an attractive alternative solution to address this issue. In this paper, we consider a cooperative network, consisting of one source node, one destination node, one eavesdropper node, and a number of relay nodes. Specifically, the source may select several relays to help forward the signal to the corresponding destination to achieve the best security performance. However, the relays may have the incentive not to report their true private channel information in order to get more chances to be selected and gain more payoff from the source. We propose a Vickey-Clark-Grove (VCG) based mechanism and an Arrow-d'Aspremont-Gerard-Varet (AGV) based mechanism into the investigated relay network to solve this cheating problem. In these two different mechanisms, we design different "transfer payment" functions to the payoff of each selected relay and prove that each relay gets its maximum (expected) payoff when it truthfully reveals its private channel information to the source. And then, an optimal secrecy rate of the network can be achieved. After discussing and comparing the VCG and AGV mechanisms, we prove that the AGV mechanism can achieve all of the basic qualifications (incentive compatibility, individual rationality and budget balance) for our system. Moreover, we discuss the optimal quantity of relays that the source node should select. Simulation results verify efficiency and fairness of the VCG and AGV mechanisms, and consolidate these conclusions.Comment: To appear in IEEE Transactions on Wireless Communication

Towards Adaptive Semantic Segmentation by Progressive Feature Refinement

As one of the fundamental tasks in computer vision, semantic segmentation plays an important role in real world applications. Although numerous deep learning models have made notable progress on several mainstream datasets with the rapid development of convolutional networks, they still encounter various challenges in practical scenarios. Unsupervised adaptive semantic segmentation aims to obtain a robust classifier trained with source domain data, which is able to maintain stable performance when deployed to a target domain with different data distribution. In this paper, we propose an innovative progressive feature refinement framework, along with domain adversarial learning to boost the transferability of segmentation networks. Specifically, we firstly align the multi-stage intermediate feature maps of source and target domain images, and then a domain classifier is adopted to discriminate the segmentation output. As a result, the segmentation models trained with source domain images can be transferred to a target domain without significant performance degradation. Experimental results verify the efficiency of our proposed method compared with state-of-the-art methods

Joint Relay and Jammer Selection for Secure Two-Way Relay Networks

In this paper, we investigate joint relay and jammer selection in two-way cooperative networks, consisting of two sources, a number of intermediate nodes, and one eavesdropper, with the constraints of physical layer security. Specifically, the proposed algorithms select two or three intermediate nodes to enhance security against the malicious eavesdropper. The first selected node operates in the conventional relay mode and assists the sources to deliver their data to the corresponding destinations using an amplify-and-forward protocol. The second and third nodes are used in different communication phases as jammers in order to create intentional interference upon the eavesdropper node. Firstly, we find that in a topology where the intermediate nodes are randomly and sparsely distributed, the proposed schemes with cooperative jamming outperform the conventional non-jamming schemes within a certain transmitted power regime. We also find that, in the scenario in which the intermediate nodes gather as a close cluster, the jamming schemes may be less effective than their non-jamming counterparts. Therefore, we introduce a hybrid scheme to switch between jamming and non-jamming modes. Simulation results validate our theoretical analysis and show that the hybrid switching scheme further improves the secrecy rate.Comment: 25 pages, 7 figures; IEEE Transactions on Information Forensics and Security, 201

2-(2,3-Difluoro­phen­yl)ethyl toluene-4-sulfonate

In the title compound, C15H14F2O3S, the dihedral angle between the aromatic rings is 6.19 (13)°. In the crystal, mol­ecules are linked by C—H⋯O hydrogen bonds, generating [110] chains

Value of Autonomous Last-mile Delivery: Evidence from Alibaba

This paper provides the first empirical evidence of consumer responses to autonomous last-mile delivery using Alibaba\u27s recent implementation in Chinese university campuses as a case study. The study leverages customer-level data from three universities over three years, employing a difference-in-differences (DID) approach combined with dynamic matching to estimate the impact of autonomous delivery adoption on order quantities. The results reveal a significant increase in the number of orders following autonomous delivery adoption with a 21% growth. The efficiency and flexibility of autonomous vehicles reduce consumers\u27 travel costs, driving long-term usage and increased sales. However, the value of autonomous delivery diminishes when a fee is charged. The study contributes to our understanding of the value of autonomous last-mile delivery and its potential advantages over traditional courier delivery