Prediction of Farmers’ Income in Hebei Province Based on the Fractional Grey Model (1,1)

The problem of increasing farmers’ income has been a hot issue of social concern. Understanding the farmers’ income and the overall development trend is conducive to the rational layout of the social economy and promotes the steady development of a well-off society. Based on this, the paper selects the per capita disposable income (PCDI) of farmers’ households from 2012 to 2019 in 11 cities of Hebei Province as the research object and applies the fractional grey model (FGM (1,1)) to predict farmers’ income from 2020 to 2024. The results show that the farmers’ income will increase in the next five years. However, the growth rate of farmers’ income is slow in the areas with a large base of farmers’ income and strong in the areas with a small base of farmers’ income. The area with the highest trend in the growth rate of farmers’ income is Zhangjiakou and the lowest place is Handan. Only Shijiazhuang’s income growth rate is on the rise. Finally, suggestions are given to promote farmers’ income in Hebei Province.

Duplication elimination in cache-uplink transmission over B5G small cell network

In this era of the digital world, data play a central role and are continuously challenging spectrum efficiency. With the introduction of enriched multimedia user-generated content, the challenges are even more aggravated. In this vein, uplink caching is considered as one of the promising solutions to effectively cater the user’s demands. One of the main challenges for uplink caching is duplication elimination. In this paper, a cache enabled uplink transmission with a duplication elimination scheme is proposed. The proposed scheme matches the mobile’s data to be uploaded with the cached contents both at mobile station (MS) and small base station (SBS). In contrast to existing techniques, the proposed scheme broadcasts the cached contents at an SBS to all the MSs under its footprint. This provides MS an opportunity to exploit the list of cached contents before uploading its data. A MS only uploads its data if it is not already cached at an SBS. This significantly reduces duplication before the real transmission takes place. Furthermore, the proposed technique reduces energy consumption in addition to improving spectral efficiency and network throughput. Besides, a higher caching hit ratio and lower caching miss ratio are also observed as compared to other schemes. The simulation results reveal that the proposed scheme saves 97% energy for SBS, whereas 96–100% energy is saved for MS on average.

Intelligent Resource Allocation Method for Wireless Communication Networks Based on Deep Learning Techniques

In this paper, a deep learning approach is used to conduct an in-depth study and analysis of intelligent resource allocation in wireless communication networks. Firstly, the concepts related to CSCN architecture are discussed and the throughput of small base stations (SBS) in CSCN architecture is analyzed; then, the long short-term memory network (LSTM) model is used to predict the mobile location of users, and the transmission conditions of users are scored based on two conditions, namely, the mobile location of users and whether the small base stations to which users are connected have their desired cache states, and the small base stations select the transmission. The small base station selects several users with optimal transmission conditions based on the scores; then, the concept of game theory is introduced to model the problem of maximizing network throughput as a multi-intelligent noncooperative game problem; finally, a deep augmented learning-based wireless resource allocation algorithm is proposed to enable the small base station to learn autonomously and select channel resources based on the network environment to maximize the network throughput. Simulation results show that the algorithm proposed in this paper leads to a significant improvement in network throughput compared to the traditional random-access algorithm and the algorithm proposed in the literature. In this paper, we apply it to the fine-grained resource control problem of user traffic allocation and find that the resource control technique based on the AC framework can obtain a performance very close to the local optimal solution of a matching-based proportional fair user dual connection algorithm with polynomial-level computational complexity. The resource allocation and task unloading decision policy optimization is implemented, and at the end of the training process, each intelligent body independently performs resource allocation and task unloading according to the current system state and policy. Finally, the simulation results show that the algorithm can effectively improve the quality of user experience and reduce latency and energy consumption.

On fibrations approaching the Arakelov equality

The sum of Lyapunov exponents $$L_f$$ L f of a semi-stable fibration is the ratio of the degree of the Hodge bundle by the Euler characteristic of the base. This ratio is bounded from above by the Arakelov inequality. Sheng-Li Tan showed that for fiber genus $$g\ge 2$$ g ≥ 2 the Arakelov equality is never attained. We investigate whether there are sequences of fibrations approaching asymptotically the Arakelov bound. The answer turns out to be no, if the fibration is smooth, or non-hyperelliptic, or has a small base genus. Moreover, we construct examples of semi-stable fibrations showing that Teichmüller curves are not attaining the maximal possible value of $$L_f$$ L f .

A Novel Cooperative Cache Policy for Wireless Networks

Mobile edge caching is an emerging approach to manage high mobile data traffic in fifth-generation wireless networks that reduces content access latency and offloading data traffic of backhaul links. This paper proposes a novel cooperative caching policy based on long short-term memory (LSTM) neural networks considering the characteristics between the features of the heterogeneous layers and the user moving speed. Specifically, LSTM is applied to predict content popularity. Size-weighted content popularity is utilised to balance the impact of the predicted content popularity and content size. We also consider the moving speeds of mobile users and introduce a two-level caching architecture consisting of several small base stations (SBSs) and macro base stations (MBSs). To avoid content requests of fast-moving users affecting the content popularity distribution of the SBS since fast-moving users frequently handover among SBSs, fast-moving users are served by MBSs no matter which SBS they are in. SBSs serve low-speed users, and SBSs in the same cluster can communicate with one another. The simulation results show that compared to common cache methods, for example, the least frequently used and least recently used methods, our proposed policy is at least 8.9% lower and 6.8% higher in terms of the average content access latency and offloading ratio, respectively.

Soft Mobility: Transparent Handover with Zero Handover Failure in User-Centric Networks

User-centric network (UCN) is regarded as a promising candidate to approach the challenges of more radio link failures (RLFs) due to the ultradense deployment of small base stations (SBSs) and meet the requirements of ultrahigh throughput, ultrahigh reliability, and ultralow latency for the 6G system. In this paper, soft mobility is proposed for UCN with the split of control and user plane (C/U-plane) and shared physical cell identifier (PCI) to achieve the goal of zero handover failure (HOF) probability, where transparent handover (HO) within a cell is realized with user configuration duplication and measurement enhancement. Specifically, the cell is composed of several SBSs around the user, where one anchor SBS is selected for controlling, and others act as slave SBSs for transmission with duplicated UE configuration from the anchor SBS. Based on the proposed architecture, the user measures downlink channel quality for cells and SBSs distinguishingly, via SS/PBCH Block (SSB) and channel-state information-reference signal (CSI-RS), respectively, and then makes the HO decision. Results show that soft mobility can reduce the number of HOF by about 50% over the current system, and the HOF probability is lower than 1% for TTT = 40 ms and offset = − 1 dB.

Location-Based Resource Allocation in Ultra-Dense Network with Clustering

With the rapid deployment of present-day mobile communication systems, user traffic requirements have increased tremendously. An ultra-dense network is a configuration in which the density of small base stations is greater than or equal to that of the user equipment. Ultra-dense networks are considered as the key technology for 5th generation networks as they can improve the link quality and increase the system capacity. However, in an ultra-dense network, small base stations are densely positioned, so one user equipment may receive signals from two or more small base stations. This may cause a severe inter-cell interference problem. In this study, we considered a coordinated multi-point scenario, a cooperative technology between base stations to alleviate the interference. In addition, to suppress the occurrence of severe interference at the cell edges, link formation was carried out by considering the degree of cell load for each cluster. After the formation of links between all the base stations and user equipment, a subcarrier allocation procedure was performed. The subcarrier allocation method used in this study was based on the location of base stations with clustering to improve the data rate and reduce the interference between the clusters. Power allocation was based on the channel gain between the base station and user equipment. Simulation results showed that the proposed scheme delivered a higher sum rate than the other resource allocation methods reported previously for various types of user equipment.