Joint QoS-Aware Scheduling and Precoding for Massive MIMO Systems via Deep Reinforcement Learning

The rapid development of mobile networks proliferates the demands of high data rate, low latency, and high-reliability applications for the fifth-generation (5G) and beyond (B5G) mobile networks. Concurrently, the massive multiple-input-multiple-output (MIMO) technology is essential to realize the vision and requires coordination with resource management functions for high user experiences. Though conventional cross-layer adaptation algorithms have been developed to schedule and allocate network resources, the complexity of resulting rules is high with diverse quality of service (QoS) requirements and B5G features. In this work, we consider a joint user scheduling, antenna allocation, and precoding problem in a massive MIMO system. Instead of directly assigning resources, such as the number of antennas, the allocation process is transformed into a deep reinforcement learning (DRL) based dynamic algorithm selection problem for efficient Markov decision process (MDP) modeling and policy training. Specifically, the proposed utility function integrates QoS requirements and constraints toward a long-term system-wide objective that matches the MDP return. The componentized action structure with action embedding further incorporates the resource management process into the model. Simulations show 7.2% and 12.5% more satisfied users against static algorithm selection and related works under demanding scenarios

Improper Ferroelectric Polarisation in a Perovskite driven by Inter-site Charge Transfer and Ordering

It is of great interest to design and make materials in which ferroelectric polarisation is coupled to other order parameters such as lattice, magnetic and electronic instabilities. Such materials will be invaluable in next-generation data storage devices. Recently, remarkable progress has been made in understanding improper ferroelectric coupling mechanisms that arise from lattice and magnetic instabilities. However, although theoretically predicted, a compact lattice coupling between electronic and ferroelectric (polar) instabilities has yet to be realised. Here we report detailed crystallographic studies of a novel perovskite Hg$^{\textbf{A}}$Mn$^{\textbf{A'}}_{3}$Mn$^{\textbf{B}}_{4}$O$_{12}$ that is found to exhibit a polar ground state on account of such couplings that arise from charge and orbital ordering on both the A' and B-sites, which are themselves driven by a highly unusual Mn$^{A'}$-Mn$^B$ inter-site charge transfer. The inherent coupling of polar, charge, orbital and hence magnetic degrees of freedom, make this a system of great fundamental interest, and demonstrating ferroelectric switching in this and a host of recently reported hybrid improper ferroelectrics remains a substantial challenge.Comment: 9 pages, 7 figure

4,4′-[2,5-Bis(dodec­yloxy)-p-phenyl­ene]bis­(2-methyl­but-3-yn-2-ol)

In the title compound, C40H66O4, the C and O atoms of the propinyl and dodecoxyl substituents are nearly coplanar with the benzene ring, 1.735 (6), 8.804 (1), 8.786 (1) and 9.577 (3)°, respectively. In the crystal, mol­ecules are connected by inter­molecular O—H⋯O hydrogen bonds

Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer

Abstract Background Endometrial cancers (ECs) are the most common form of gynecologic malignancy. Recent studies have reported that ECs reveal distinct markers for molecular pathogenesis, which in turn is linked to the various histological types of ECs. To understand further the molecular events contributing to ECs and endometrial tumorigenesis in general, a more precise identification of cancer-associated molecules and signaling networks would be useful for the detection and monitoring of malignancy, improving clinical cancer therapy, and personalization of treatments. Results ECs-specific gene co-expression networks were constructed by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Important pathways and putative cancer hub genes contribution to tumorigenesis of ECs were identified. An elastic-net regularized classification model was built using the cancer hub gene signatures to predict the phenotypic characteristics of ECs. The 19 cancer hub gene signatures had high predictive power to distinguish among three key principal features of ECs: grade, type, and stage. Intriguingly, these hub gene networks seem to contribute to ECs progression and malignancy via cell-cycle regulation, antigen processing and the citric acid (TCA) cycle. Conclusions The results of this study provide a powerful biomarker discovery platform to better understand the progression of ECs and to uncover potential therapeutic targets in the treatment of ECs. This information might lead to improved monitoring of ECs and resulting improvement of treatment of ECs, the 4th most common of cancer in women.Peer Reviewe