Design of interactive enterprise financial system based on Multi-user scheduling and cellular network

doi:10.21203/rs.3.rs-2571561/v1

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Design of interactive enterprise financial system based on Multi-user scheduling and cellular network

https://doi.org/10.21203/rs.3.rs-2571561/v1

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published 24 Apr, 2023

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Enterprise financial system is an activity to establish common and reusable rules for actual or potential problems in order to achieve the best order of enterprise finance. The expansion of each theory is based on the collection of practical experience and the development of science and technology, which also brings demand and space for the research of interactive enterprise financial system. This paper uses heterogeneous cellular network to provide technical support for the construction of interactive financial system, proposes an algorithm to use small base stations in traditional macro cellular network, and plans the status of base stations according to various traffic patterns. Under the premise of ensuring the quality of service, the number of small base stations can be effectively reduced, and the status of base stations can dynamically adapt to the changes of spatial traffic. With the support of heterogeneous cellular network, interactive enterprise financial system can be built on the basis of more complete and comprehensive information. The final simulation results show that the method can effectively reduce the cost of providing heterogeneous cellular network while maintaining the quality of service, and improve the user and energy efficiency of interactive enterprise financial system. Heterogeneous cellular network provides a new insight for the design of interactive enterprise financial system, which helps enterprises improve the efficiency of processing financial transactions and maintain the financial security of enterprises.

Heterogeneous cellular network

artificial intelligence

Enterprise finance

System design

Through the creation, implementation, monitoring and evaluation of the enterprise financial system, the enterprise financial system organizes the elements and connections of the whole process of the enterprise financial system. According to the principle of system engineering, the transformation of input data into output becomes the process of creating value [1]. The increasingly complex financial activities of modern enterprises are systematized, standardized, scientific, programmed and simplified to create a normal and optimal order, so as to reduce the cost of various resources and achieve better results, which can continuously improve the quality and efficiency of financial activities [2]. The operation forecast of enterprise financial system the financial system of a company is to discover the objective law of financial activities on the basis of understanding the past and present situation of financial activities, and predict the future state and trend of the company on the basis of this conclusion [3]. A company's financial system and financial system prediction can't do without a company's business prediction, which is a logical synthesis of business prediction results based on the relationship between role and business activity response [4]. The prediction of enterprise financial system is the basis of other financial tasks, such as making financial decisions. The forecasting function of enterprise financial system is that the forecasters analyze, evaluate and output the operation state and results of enterprise financial system according to the known future state of the forecast object [5]. However, there are still various challenges in the construction of enterprise financial system in China, so this paper will propose a new idea of building enterprise financial system based on heterogeneous cellular network [6]. With the popularity of wireless intelligent terminals and the rapid development of mobile applications, people require that the existing infrastructure can not only significantly increase the network bandwidth, but also significantly reduce the network power consumption [7]. Using the small base station with low transmission power on the existing macro cellular network to build a heterogeneous cellular network can quickly and efficiently improve the coverage of enterprise financial data and enhance the capacity of the whole cellular network [8]. This paper focuses on the planning, deployment and resource management of heterogeneous cellular networks, as well as the deployment planning of small base stations and the relationship between base stations and users, realizes the resource allocation problem in the process of spectrum downlink transmission, and provides solutions and optimization of enterprise financial system modeling in the field of efficiency or energy [9]. With the change of traffic mode, we can refer to the determined best base station health status for base station health monitoring to meet the service needs of various financial systems of enterprises.

A user association algorithm based on multi-point transmission (users connected to multiple base stations) is proposed in this paper, which can balance the load of the cellular network and realize the load balancing of the whole heterogeneous cellular network [10]. Firstly, this paper models the user association problem of load balancing as the maximization of utility objectives, which is a NP hard mixed integer programming problem. In this paper, the one-to-one correspondence between base station and user is simplified to partial user allocation. That is, several base stations can be assigned to each user, which not only transforms the complex mixed integer programming problem into convex problem [11]. For optimization problems, the upper bound of the objective function can also be obtained. A high efficiency and low complexity iterative algorithm is proposed in this paper, which can ensure the optimal solution converging to the maximum step size. The algorithm is sensitive to the distribution of base station and users. Therefore, it must be executed many times to track the dynamic changes of the network, which is difficult to realize [12]. It is suggested that the cell coverage can be adjusted simply by adjusting the offset factor to achieve load balance, and the calculation method of bias factor is described. The experimental results show that the two methods can balance the load between the base stations, the multi-point algorithm can provide the best performance, and the offset based method can provide the best performance [13]. A resource allocation algorithm based on graph and interference coordination is proposed in this paper. By dynamically distributing sub channels and power, the interference of heterogeneous cellular networks can be effectively suppressed, thus improving the spectrum efficiency of the whole network. Firstly, the neighborhood relationship between cells is evaluated to divide the cells into cell clusters. Each cell cluster adopts independent interference coordination and resource allocation to reduce the complexity of the problem [14]. Secondly, the user cluster algorithm is used to divide the users in each cell cluster into custom clusters to minimize the interference in the user cluster. Finally, this paper uses a proportional and fair process to assign sub channels to the custom clusters in each cell cluster [15] It is suggested that the energy efficiency parameters be updated according to the optimal subchannel and power allocation results, and the parameter value should be used in the next iteration until the parameter value converges [16].

3.1. Formation and characteristics of heterogeneous cellular network

In 1947, Bell Laboratories introduced the concept of cellular wireless communication, and completed the feasibility demonstration of related technologies in 1977, which laid a solid foundation for human society to enter the era of personal mobile communication anytime and anywhere. In the next 30 years, with the emergence and development of a new generation of large-scale integrated circuit technology, cellular network has been constantly updated and developed rapidly, and has become one of the most important parts of human life. In 1980, a truly high-capacity cellular system was born. Although 1g system has achieved unprecedented success, it still has many shortcomings, such as low frequency, low privacy, easy to be interfered, unable to provide data services and automatic roaming services.

The emergence of the idea of multi-layer cellular network can be traced back to the mid-1990s, when the goal of researchers was to provide better quality of service for voice users. The use of heterogeneous cellular network is now regarded as the key technology to meet the future 5g demand, meet the rapid growth of data rate demand, improve weak signal coverage, and improve spectrum efficiency and energy efficiency. Figure 1 shows the specific process of heterogeneous cellular network, and its types and properties are shown in Table 1.

Table 1

Types and characteristics of low power nodes in heterogeneous cellular networks
Node type	Transmitting power	Coverage	Layout environment	Deployer	Installation cost
MBS	46dBm	1-10km	Outdoor layout	Operator	Expensive
RN	30dBm	300m	Outdoor layout, indoor enhancement	Operator	Moderate
PBS	23-30dBm	< 300m	Outdoor layout/indoor layout	Operator	Moderate
FBS	23dBm	< 50m	Indoor layout	Operator	Moderate

Unlike traditional macro cell-based networks, in addition to macro base stations with higher transmit power, HCN has other low-power nodes that can provide services to users, including pico base stations, femto base stations, and relay nodes. Different LPNs in HCN have their own characteristics, as shown in Table 1. It can be seen that, unlike other LPNs, FBS can be used independently by users to improve indoor signal coverage and improve business experience.

For a network composed of the same type of MBS or LPN, this can be referred to as the HCN layer. Therefore, Fig. 2 shows a multilayer HCN containing MBS, PBS, FBS and RN. Among them, the coverage of the macro cell is much larger than that of the pico base station and the femto base station, but due to the high cost of provision, their density will be much lower than the latter.

3.2. Heterogeneous cellular network algorithm

In order to simplify the analysis, it is assumed that there is only a pair of communication channels between the FU and the FBS in each femtocell in each time slot. In actual systems, this assumption can be realized through flexible scheduling. This article mainly discusses the problem of sub-channel allocation in the downlink transmission scenario (Note: Based on the above assumptions, the proposed mechanism is also applicable to the uplink transmission scenario), as shown in Fig. 3.

The increase of channel power is as formula (1):

$${g}_{n,m}^{k}=\left\{\begin{array}{c}-28-35{log}_{10}\left({d}_{n,m}\right)-\phi , n=m\\ -38.5-20{log}_{10}\left({d}_{n,m}\right)-{L}_{wall}-\phi ,n\ne m\end{array}\right.dB$$

The signal-to-noise ratio at the data receiving end can be expressed as formula (2):

$${\gamma }_{n}^{k}=\frac{{p}_{n}^{k}{g}_{n,n}^{k}}{{I}_{n}^{k}+{B}_{0}{N}_{0}}=\frac{{p}_{n}^{k}{g}_{n,n}^{k}}{\sum _{m=1,m\ne n }^{N}\sum _{l\in {S}_{m}\cap \left\{k\right\}}{p}_{m}^{l}{g}_{m,n}^{l}+{B}_{0}{n}_{0}}$$

Using Shannon’s formula, the femto base station bandwidth n (bandwidth for short) can be expressed as formula (3):

$${R}_{n}=\sum _{k\in {S}_{n}}{R}_{n}^{k}=\sum _{k\in {S}_{n}}{B}_{0}{log}_{2}\left(1+{\gamma }_{n}^{k}\right)$$

For each player n, its strategy space is composed of all available sub-channel sets, which can be expressed by formula (4):

$${S}_{n}=\left\{{S}_{n}\right|{S}_{n}\subset \left\{\text{1,2},\dots ,K\right\},\left|{S}_{n}\right|={K}_{n}\}$$

The utility function of player n can be expressed as formula (5):

$${U}_{n}\left({\left({S}_{n}\right)}_{n\in N}\right)=\sum _{k\in {S}_{n}}{B}_{0}{log}_{2}\left(1+\frac{{p}_{n}^{k}{g}_{n,n}^{k}}{{I}_{n}^{k}{(S}_{-n})+{B}_{0}{n}_{0}}\right),\forall n\in N$$

At this stage, for each player in the game, the strategy used and the optimal answer can be expressed as formula (6):

$$\underset{{S}_{n}}{{arg}}max{U}_{n}\left({S}_{1},{S}_{2}\right)=\underset{{S}_{n}}{{arg}}max{\gamma }_{n}^{{S}_{n}}\left({S}_{1},{S}_{2}\right)$$

Player n in the game should define a mixed strategy that he follows at time t, as in formula (7):

$${Q}_{n}=({q}_{1},{q}_{1},\dots ,{q}_{\left|{S}_{n}\right|})\in \varDelta \left({S}_{n}\right)$$

The probability that participant n chooses the i-th strategy in the strategy space is expressed as formula (8):

$${q}_{i}\ge 0,{\forall }_{i}\in \left\{\text{1,2},\dots ,\left|{S}_{n}\right|\right\},\sum _{i=0}^{\left|{S}_{n}\right|}{q}_{i}=1$$

The specific process of calculating the mixed strategy is as formula (9):

$${Q}_{n}\left(i\left({F}_{n}\right)\right)=\frac{1}{\left|{S}_{n}\right|},\forall {F}_{n}\in {S}_{n}$$

Among them, there is formula (10):

$${Q}_{n}\left(i\left({F}_{n}\right)\right)=\left\{\begin{array}{c}\frac{{\epsilon }^{w}}{\left|{S}_{n}\right|-1}, \forall {F}_{n}\in {S}_{n},{F}_{n}\ne {S}_{n}(t-1) \\ 1-{\epsilon }^{w} otherwise\end{array}\right.$$

The F_n parameter is used to regulate the player's utility in the game, as in formula (11):

$$\widehat{U}\left(t\right)={\left(\frac{{U}_{n}\left(t\right)}{{F}_{n}}\right)}^{\beta }ϵ\left(\text{0,1}\right),{\forall }_{t}$$

If there is no intra-layer interference, then the player n can obtain the maximum capacity, namely formula (12):

$${R}_{n,max}^{k}=B{log}_{2}\left(1+\frac{{p}_{n}^{k}{g}_{n,n}^{k}}{{B}_{0}{N}_{0}}\right)$$

For F_n, the following settings can be made, such as formula (13):

$${F}_{n}=\text{m}\text{a}\text{x}\left\{\sum _{l\in {S}_{n}}{R}_{n,max}^{l}|{S}_{n}\subseteq {S}_{n}\right\}$$

Then the strategy counter should be updated according to the current emotional state, as in formula (14):

$${C}_{n}\left(i\left({S}_{n}\left(t\right)\right)\right)={C}_{n}\left(i\left({S}_{n}\left(t\right)\right)\right)+1$$

When the algorithm jumps out of the loop, each player in the game completes the learning process and chooses the final strategy according to formula (15):

$${S}_{n}^{D}=\underset{{S}_{n}}{{arg}}max{C}_{n}\left(i\left({S}_{n}\right)\right),\forall {S}_{n}\in {C}_{n},\forall n\in N$$

Many strategies in the strategy room can maximize the standardized utility of the players, namely formula (16):

$${\left({S}_{n}^{O}\right)}_{n\in N}=\underset{{{(S}_{n})}_{n\in N}\in {{(C}_{n})}_{n\in N}}{arg}max\widehat{U}\left({{(S}_{n})}_{n\in N}\right)$$

3.3. Heterogeneous cellular network simulation scenarios and parameter settings

In addition, NST is uniformly distributed in a disk-shaped area centered on SR and a radius of SR. The simulation assumes that all STs have the same ER request. In the absence of specific instructions, the values of all simulation parameters will be as shown in Table 2. In addition, the simulation result corresponding to each data point is obtained by averaging 10,000 independent simulation results.

Table 2

Simulation parameters
Parameter	Value
Cognitive system radius	0.2 km
Noise power spectral density	5x10^− 16W/Hz
PT transmission power	0.2W
ST's transmission power limit	0.1W
Reference distance	0.1 km
Free space gain at distance d	-31.54dB
Path loss factor	3.71
Shadow fading variance	1dB

In this case, the locations of the transmitter and receiver are shown in Fig. 4, where PR is located at (0,0) kilometers, and PT and SR are located at 0.5 kilometers and 0.25 kilometers north of PR.

The increase of E means the slowdown of the algorithm's convergence speed (the increase of algorithm complexity), and the decrease of Br after the execution of the algorithm means the improvement of resource utilization. Therefore, by adjusting the size of the iterative step size of the price factor, the trade-off between algorithm complexity and resource usage is realized, as shown in Table 3.

Table 3

Algorithm expected number of iterations
	E	B_r(MHz)	E	B_r(MHz)	E	B_r(MHz)
5	57	0.1246	116	0.0684	476	0.0187
8	118	0.0586	246	0.0248	948	0.0057
10	161	0.0478	356	0.0298	1248	0.0087
16	196	0.0435	397	0.0247	1548	0.0089
20	225	0.0485	446	0.0258	1887	0.0054

Figure 5 shows that as the number of STs increases, the benefits realized in the cognitive system increase, mainly because the proposed algorithm can check the benefits of multiple people in the system from the perspective of EE.

4.1. Basic ideas and principles of interactive enterprise financial system

The distribution of financial management authority is the core of the financial system of an enterprise. The imbalance or mismatch of financial control will affect the effective allocation and use of the company's resources, increase the operating cost of the company and lead to the inefficient financial operation of the enterprise. Considering the transfer of financial management power of the above enterprises, this paper holds that the following ideas and principles should be followed to optimize the financial system of modern enterprises.

Management requirements determine management behavior: an effective mechanism for the allocation of financial management authority of an enterprise should fully reflect the management requirements of each configuration item, and provide a possible platform and channel for stakeholders to participate in financial management and implement management behavior.

In the field of enterprise financial management, the purpose of proper allocation of modern power is to meet the requirements of property rights protection by analyzing and balancing the financial information requirements of different stakeholders. In addition, the ownership rights and interests of all stakeholders of the company can not be directly reflected, but reflected in the financial income generated by the company, The adequacy and accuracy of the financial income ultimately depends on the objectivity and accuracy of the financial statements. Therefore, the reliability of financial information is also an important factor for all stakeholders to exercise ownership. However, no matter in the theoretical research of the distribution of financial management right or in the practice of enterprise financial management, this point has not been properly considered. This paper holds that when optimizing the distribution of financial management right in modern enterprises, we should not only consider the following contents: in addition to the ownership agreement, the company's financial management should not only consider the following contents, We should also consider the impact of various stakeholders on the generation and presentation of financial information. In the process of customization, a certain model or channel is adopted to ensure that the financial information of the enterprise is arranged symmetrically among the stakeholders, so as to ensure the objective accuracy of the financial information of the enterprise, and then realize the application function of financial management and realize the financial informatization of the enterprise.

4.2. Scale design of interactive enterprise financial organization mode

(1) The degree of decentralization of financial institutions

By studying 96 companies in Argentina, Sosa can use the degree of democratization of decision-making, the independence of branches and subsidiaries, and the independence of financial institutions to measure the degree of decentralization of enterprise financial institutions. The so-called degree of democratization of decision-making is related to whether the enterprise listens to the opinions of employees when making decisions. The so-called independence of branches and subsidiaries refers to whether the subsidiaries exert improper influence on the parent company in the process of operation. The independence of financial institutions refers to the situation that the daily operation of financial institutions is interfered by other departments. Based on the existing research, this paper proposes three questions on the degree of decentralization of financial institutions: e-commerce companies can appropriately delegate decision-making power, and employees can participate in the decision-making process. Subsidiaries, affiliated companies and business departments of e-commerce companies have more financial decision-making power. The daily work of the financial department of e-commerce enterprises can be completed independently without the intervention of the management and other departments, the similarities and differences between interactive finance and traditional financial management as shown in Table 4, and measurement scale of decentralization degree of financial organization as show in Table 5.

Table 4

The similarities and differences between interactive finance and traditional financial management
Index selection	Traditional financial management	Network Finance
Information Sharing	Pay more attention to financial indicators	The importance of non-financial indicators is increasing day by day
Treatment aging	Restricted by work outlets, often confined to the department	Network integrated processing, instant transmission, comprehensive sharing
Way of working	There is a certain hysteresis	Basically realize real-time processing
Work efficiency	Mainly manual preparation	Use computers on a large scale to automatically process data,
Index selection	Low efficiency, there is a certain distortion rate	Generate reports

Table 5

Measurement scale of decentralization degree of financial organization
Measurement dimension	Item
Degree of decentralization of financial organization	The e-commerce company can appropriately delegate decision-making power when making decisions, and allow employees to participate in the decision-making process extensively
	Subsidiaries, branches, and business units of the e-commerce company have greater financial decision-making power
	The daily work of the financial department of the e-commerce company can be completed independently without interference from the management and other departments

(2) Large scale design of accounting disclosure model for e-commerce enterprises.

Susso divides financial information into decision-making information and evaluation information. Decision making information refers to the information used by companies to make financial decisions and financial risk preference, while evaluation information mainly includes financial statements and audit reports. According to the different degree of formality, financial information can be divided into accurate reports and informal financial information, that is, the company's operating status. Due to the lack of literature, this paper puts forward three questions about the voluntary level of accounting disclosure: e-commerce companies want to disclose their financial risk preference publicly; e-commerce companies want to disclose their financial risk preference publicly; E-commerce companies voluntarily disclose their annual reports and audit reports.

4.3. Measures to optimize the allocation of corporate financial governance rights

In view of the problem of right distribution in the current financial management of modern enterprises, this paper thinks that it should be improved and optimized from two aspects, based on the above basic ideas and principles: one is to maximize the value of stakeholders in the financial management of modern enterprises. As a guide, we should establish a power distribution model based on the static configuration structure, so that all stakeholders can share the company's financial management authority, balance the configuration parameters of the enterprise's financial management authority, and enhance the interaction between the objects of financial management authority, so as to achieve a balanced state of mutual restriction and mutual restriction between the various configuration levels and objects. Second, by improving the internal management structure of the company and stabilizing the external market mechanism of the company, and improving the distribution of modern financial management rights, all parties can have equal opportunities to participate in the distribution of financial management rights in order to have their own property rights.

The objects of power distribution in modern enterprise financial management are all kinds of stakeholders whose production factors are invested in the enterprise, some of them are located in the company organization, others are located outside the company. Governance is different, and financial management functions are also different. Therefore, in order to improve and optimize the power distribution mode of enterprise financial management, we need to take a variety of measures inside and outside the company.

In view of the one-sided distribution of financial management power in many enterprises and the resulting serious imbalance, this paper proposes to strengthen the hierarchical and interactive distribution of financial management based on the organizational structure of the company. In this configuration mode, firstly, according to the management ability, management function and management level of each stakeholder, the specific financial management authority in the company is configured hierarchically, so that each configuration object can share the corresponding enterprise financial management authority. Secondly, certain governance channels and mechanisms realize the reciprocal interaction of financial management power between different levels of allocation and between allocation objects, which makes the financial management power of enterprises form the mutual restriction, interaction and mutual restriction among relevant stakeholders. On the one hand, the level configuration of authority in financial management field is fixed in the static structure of authority configuration in financial management field. On the other hand, in the interactive design of financial management in financial management field, the authority between configuration objects is called management, and the authority reflects the configuration structure of the system.

The interaction design of enterprise financial system is the specific implementation and implementation process of making strategic planning. It is a process that enables enterprises to make wise and efficient use of resources, give full play to their own advantages, and achieve strategic planning objectives. Enterprise planning is carried out according to the specific requirements of the strategic planning scheme, which is a complex system engineering, because the strategic business plan is only a bold framework document, which needs to be improved to implement, and the ambitious goals, milestones, priorities and countermeasures need to be transformed into specific action plans. A good implementation system of strategic planning is not only the premise of the success of enterprise strategic planning, but also can make up for some deficiencies in the formulation of strategic planning and reduce losses to a certain extent. In this paper, we will use heterogeneous cellular technology to design an interactive enterprise financial system. The main part of the construction includes the decision-making level of the head office, the Construction Department of the group company and the Construction Department of the member company. The organizational structure of an enterprise group should be the highest decision-making level of the financial group and financial policy level of the enterprise group headquarters, as well as the overall planning, guidelines, regulations and supervision of the financial activities of the enterprise group.

Funding

This work was supported by Research on the return mechanism of social capital investment based on PPP model of pension service (SK2019A0561) and Research on the economic transformation of resource-based cities in Anhui Province from the perspective of regional economic elasticity(SK2020A0426).

Conflict of interest

The authors declare that they have no conflict of interests

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Data Availability

Data will be made available on request.

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Journal Publication

published 24 Apr, 2023

Read the published version in Soft Computing →

Editorial decision: Major Revision
08 Mar, 2023
Reviewers agreed at journal
20 Feb, 2023
Editor assigned by journal
13 Feb, 2023
First submitted to journal
09 Feb, 2023

You are reading this latest preprint version

Design of interactive enterprise financial system based on Multi-user scheduling and cellular network

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Related Work

3. Heterogeneous Cellular Network

3.1. Formation and characteristics of heterogeneous cellular network

3.2. Heterogeneous cellular network algorithm

3.3. Heterogeneous cellular network simulation scenarios and parameter settings

4. Design And Evaluation Of Interactive Enterprise Financial System

4.1. Basic ideas and principles of interactive enterprise financial system

4.2. Scale design of interactive enterprise financial organization mode

4.3. Measures to optimize the allocation of corporate financial governance rights

5. Conclusion

Declarations

References

Status:

Journal Publication

Version 1