Joint Power Splitting and Secure Beamforming Design in the Wireless-powered Untrusted Relay Networks

In this work, we maximize the secrecy rate of the wireless-powered untrusted relay network by jointly designing power splitting (PS) ratio and relay beamforming with the proposed global optimal algorithm (GOA) and local optimal algorithm (LOA). Different from the literature, artificial noise (AN) sent by the destination not only degrades the channel condition of the eavesdropper to improve the secrecy rate, but also becomes a new source of energy powering the untrusted relay based on PS. Hence, it is of high economic benefits and efficiency to take advantage of AN compared with the literature. Simulation results show that LOA can achieve satisfactory secrecy rate performance compared with that of GOA, but with less computation time.Comment: Submitted to GlobeCom201

Exploiting Trust Degree for Multiple-Antenna User Cooperation

For a user cooperation system with multiple antennas, we consider a trust degree based cooperation techniques to explore the influence of the trustworthiness between users on the communication systems. For the system with two communication pairs, when one communication pair achieves its quality of service (QoS) requirement, they can help the transmission of the other communication pair according to the trust degree, which quantifies the trustworthiness between users in the cooperation. For given trust degree, we investigate the user cooperation strategies, which include the power allocation and precoder design for various antenna configurations. For SISO and MISO cases, we provide the optimal power allocation and beamformer design that maximize the expected achievable rates while guaranteeing the QoS requirement. For a SIMO case, we resort to semidefinite relaxation (SDR) technique and block coordinate update (BCU) method to solve the corresponding problem, and guarantee the rank-one solutions at each step. For a MIMO case, as MIMO is the generalization of MISO and SIMO, the similarities among their problem structures inspire us to combine the methods from MISO and SIMO together to efficiently tackle MIMO case. Simulation results show that the trust degree information has a great effect on the performance of the user cooperation in terms of the expected achievable rate, and the proposed user cooperation strategies achieve high achievable rates for given trust degree.Comment: 15 pages,9 figures, to appear in IEEE Transactions on Wireless communication

Edge intelligence empowered metaverse: architecture, technologies, and open issues

Recently, the metaverse has emerged as a focal point of widespread interest, capturing attention across various domains. However, the construction of a pluralistic, realistic, and shared digital world is still in its infancy. Due to the ultra-strict requirements in security, intelligence, and real-time, it is urgent to solve the technical challenges existed in building metaverse ecosystems, such as ensuring the provision of seamless communication and reliable computing services in the face of a dynamic and time-varying complex network environment. In terms of digital infrastructure, edge computing (EC), as a distributed computing paradigm, has the potential to guarantee computing power, bandwidth, and storage. Meanwhile, artificial intelligence (AI) is regarded as a powerful tool to provide technical support for automated and efficient decision-making for metaverse devices. In this context, this paper focuses on integrating EC and AI to facilitate the development of the metaverse, namely, the edge intelligence-empowered metaverse. Specifically, we first outline the metaverse architecture and driving technologies and discuss EC as a key component of the digital infrastructure for metaverse realization. Then, we elaborate on two mainstream classifications of edge intelligence in metaverse scenarios, including AI for edge and AI on edge. Finally, we identify some open issues

Knockdown of ZNF268, which Is Transcriptionally Downregulated by GATA-1, Promotes Proliferation of K562 Cells

The human ZNF268 gene encodes a typical KRAB-C2H2 zinc finger protein that may participate in hematopoiesis and leukemogenesis. A recent microarray study revealed that ZNF268 expression continuously decreases during erythropoiesis. However, the molecular mechanisms underlying regulation of ZNF268 during hematopoiesis are not well understood. Here we found that GATA-1, a master regulator of erythropoiesis, repressed the promoter activity and transcription of ZNF268. Electrophoretic mobility shift assays and chromatin immunoprecipitation assays showed that GATA-1 directly bound to a GATA binding site in the ZNF268 promoter in vitro and in vivo. Knockdown of ZNF268 in K562 erythroleukemia cells with specific siRNA accelerated cellular proliferation, suppressed apoptosis, and reduced expression of erythroid-specific developmental markers. It also promoted growth of subcutaneous K562-derived tumors in nude mice. These results suggest that ZNF268 is a crucial downstream target and effector of GATA-1. They also suggest the downregulation of ZNF268 by GATA-1 is important in promoting the growth and suppressing the differentiation of K562 erythroleukemia cells

Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning

The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration; therefore, traditional manufacturing and value creation will undergo revolutionary changes. Artificial intelligence will drive the next industrial technology revolution, and knowledge graphs comprise the main foundation of this revolution. The intellectualization of industrial information is an important part of industry 4.0, and we can efficiently integrate multisource heterogeneous industrial data and realize the intellectualization of information through the powerful semantic association of knowledge graphs. Knowledge graphs have been increasingly applied in the fields of deep learning, social network, intelligent control and other artificial intelligence areas. The objective of this present study is to combine traditional NLP (natural language processing) and deep learning methods to automatically extract triples from large unstructured Chinese text and construct an industrial knowledge graph in the automobile field