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The Internet of things (IoT) era needs to realize the wide coverage and connections for the IoT nodes.However,the IoT communication technology cannot collect data timely in the remote area.UAV has been widely used in the IoT wireless sensor network for the data collection due to its flexibility and mobility.The trajectory design of the UAV assisted sensor network data acquisition was discussed in the proposed scheme,as well as the UAV charging demand in the data collection process was met.Specifically,based on the hierarchical reinforcement learning with the temporal abstraction,a novel option-DQN (option-deep Q-learning) algorithm targeted for the discrete action was proposed to improve the performance of the data collection and trajectory design,and control the UAV to recharge in time to ensure its normal flight.The simulation results show that the training rewards and speed of the proposed method are much better than the conventional DQN (deep Q-learning) algorithm.Besides,the proposed algorithm can guarantee the sufficient power supply of UAV by controlling it to recharge timely.

At the era of the Internet of things,networking mode that connects everything would bring tremendous increase in the data volume and challenge the traditional routing protocols.The limitations of the existing routing protocols was analyzed when facing the data explosion and then the routing selection problem was re-modeled as a Markov decision process.On this basis,the deep reinforcement learning technique was utilized to choose the next-hop router for data transmission task in order to shorten the transmission path length while network congestion was avoided.The simulation results demonstrate that the congestion probability can be reduced significantly and the network throughput can be enhanced by the proposed strategy.

Intelligent communication is considered to be one of themainstream directions in the development of wireless communications after 5G.Its basic idea is to introduce artificial intelligence into all aspects of wireless communication systems,realizing the significant integration of wireless communication and artificial intelligence technology.At present,the research in this field is advancing to the physi cal layer.The combination of wireless transmission and deep learning is also in the preliminary exploration stage.Key technologies of wireless physi cal layer based on deep learning were introduced in detail from the aspects of channel estimation,signal detection,feedback and reconstruction of channel state information,channel decoding,end-to-end wireless communication systems,the latest research progress of international academic circles in recent years was presented.On this basis,the development trend in the future was preliminarily forecasted.

Compared to the 4th generation (4G) mobile communication technology,the 5th generation (5G) mobile communication technology will provide tens of times of peak rate increase,reaching up to several thousands megabit per second.The coming wireless communication systems face the problems of expensive hardware cost and high power consumption,which is becoming a bottleneck of high-rate communication systems.To cope with these problems,redesigning communication systems with the receiver using finite precision analog-to-digital converters (ADC) is one possible approach.The analysis of recent information theory for the AWGN channel shows that the performance loss due to 2~3 bit ADC acceptable.The application of low resolution quantization in wireless communication systems was focused on,an overview of the channel capacity and receiver design under low-precision ADC was provided.

Within just a few decades, mobile communications have undergone a rapid growth from its initial concept to the worldwide prosperity, and essentially changed people's daily life as well as the way of working and thinking, thereby becoming the most important connection between individuals and information networks. Although almost all the spectrum resources have been devoted to mobile communications, the demand of higher capacity over a unit area is still increasing, due to the trend that the density of mobile users is increasing and the amount of traffics grow explosively. No doubt such an ever-increasing demand cannot be met by simply relying on the improvement of either physical layer transmission techniques or planar cellular infrastructures. As an emerging network architecture aiming to future metropolitan patterns, 3-deminsional dense coverage is expected to provide a novel spectrum reuse methodology, significantly improving the spectrum efficiency of mobile communication systems through spectrum reuse in the third dimension beyond the planar dimensions. Some fundamental problems of 3-deminsional dense coverage oriented mobile communications systems were investigated, the key challenges was pointed out, and possible solutions for 3-deminsional networks from both the physical and logical perspectives were provided.

A water calorimeter based on thermistor is developed in this paper, and for the correction of chemical thermal defects in the absolute measurement of absorbed dose to water from proton beam, the design of gas saturated ultrapure water system is completed. By filling the ultrapure water inside the calorimeter core with high purity gas to form saturation condition, the effect of radiation chemical reaction is minimized. And the irradiation reaction of O2 in the ultrapure water is fully occurred through pre-irradiation experiment after the encapsulation of the calorimeter core, so that the water environment inside reaches a stable state without heat loss,which successfully developed a stable zero heat loss water calorimeter and the influence of heat loss on the absolute measurement of absorbed dose to water is minimized, which provides an important basis for the subsequent absolute measurement of absorbed dose to water.

Background and purpose: Low-density lipoprotein receptor-related protein 8 (LRP8) plays an important role in the development of liver cancer, lung cancer, breast cancer, colorectal cancer and other tumors. This study aimed to explore the expression and prognostic role of LRP8 in gastric cancer, and evaluate the possibility of a new target for gastric cancer biotherapy. Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were used to analyze the expression of LRP8 in gastric cancer tissues. The co-expressed gene with LRP8 in gastric cancer was analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. RNA interference (RNAi) was used for silencing the expression of LRP8 in AGS cell line, and functional assays were performed to evaluate the effects of LRP8 knockdown on the proliferation ability, invasiveness and the Wnt signaling pathway of AGS cell line in vitro. The relationship between LRP8 expression and prognosis of patients with gastric cancer was assessed by using Kaplan-Meier plotter database. To validate the 240 gastric cancer patients from TCGA database results, retrospective immunohistochemical analysis was performed to classify the relationship between the expression of LRP8 and clinical pathological features of 25 gastric cancer patients who underwent total or subtotal gastrectomy from August 2018 to July 2019 in the Fifth Affiliated Hospital of Zhengzhou University. Results: Data from the TCGA database showed LRP8 was overexpressed in gastric cancer tissues compared with normal gastric mucosa (P