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Design of AR-based general computer technology experiment demonstration platform

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Published/Copyright: May 27, 2025
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Nonlinear Engineering
From the journal Nonlinear Engineering Volume 14 Issue 1

Abstract

In order to improve the experimental demonstration effect of computer technology, this article combines virtual reality technology and intelligent reflective surface-assisted perception and communication technology to build a general computer experiment demonstration platform. Based on sparse code division multiple access, this article proposes an intelligent reflector-assisted joint multi-user information reliable access and environmental reflector sensing scheme. Moreover, this article extracts the information about the environmental reflector in the received signal by means of computational imaging. In addition, this article iteratively optimizes the performance of multi-user information access and environment awareness through the relationship between the information of the environmental reflector and the received signal. The experimental research shows that the augmented reality-based general computer technology experimental demonstration platform proposed in this article has good effects and can effectively promote the computer technology experimental demonstration effect.

Keywords: AR technology; general computer technology; experiment; demonstration platform

1 Introduction

With the rapid development of computer networks and distance education, network virtual laboratory is gradually shown in front of us. At present, many virtual technologies have been used in education systems abroad [1]. By applying information technologies such as computer technology and communication in teaching, it provides solutions for the experimental courses of distance education and builds a virtual laboratory environment in distance teaching. It has become a hotspot of current research to make the previous experiments using real experimental equipment develop toward an intelligent virtual experimental system.

In the virtual experiment platform, “components are software,” the design of the virtual laboratory mainly includes two aspects: the establishment of the component model library and the construction of the virtual experiment platform that can realize the human–computer interaction environment. The object-oriented method is applied to the modeling of the virtual device library, and the simulation model is constructed according to the objects that make up the system and their interaction relationships, so that the model has good independence, scalability, maintainability, and reusability [2]. Components can be divided into three types [3]: prototype atomic components, non-prototype atomic components, and molecular components. Prototype atomic components are provided by the original creator of the experiment (also a programmer), and non-prototype atomic components are dynamically loaded by the experiment creator (such as an experimenter and an experiment instructor) according to the authority of the corresponding component of a certain device product system, and through reflection technology and XML technology. Tteachers or experimenters set the corresponding properties of Beans according to the experimental requirements and generate new components with specific functions through simple configuration and construction, and no longer need to write a component program for each component to achieve; molecular components refer to the mixed packaging of multiple atomic components and molecular components [4].

Some researchers carry out virtual experimental operations on computer terminals to imitate actual experimental operations; the virtual experiment is to use mouse click and drag to assemble various virtual instruments on the microcomputer into a complete experimental system according to the experimental requirements and process and complete it on this system at the same time. The whole experiment includes the addition of raw materials, the change of experimental conditions, the data acquisition, and the simulation and analysis of the experimental results [5]. It highlights the new concept of “software as an instrument.” The virtual laboratory is the carrier of the virtual experiment and the running environment of the virtual experiment. Through the computer network system, researchers or students can use instruments and equipment, share data and computing resources, obtain other information, conduct seminars with peers, or receive remote guidance from teachers without being limited by time and space [6].

Software sharing network virtual laboratory: Its characteristic is that the virtual laboratory simulation software platform of the shared server accepts the experimental request sent by the client, analyzes and processes the experimental parameters, and finally returns the result to the client after calculation and simulation [7].

Network virtual laboratory for data sharing: Its characteristic is that the experimental data already exists in the remote database, and the server receives the client’s request to retrieve the experimental data in the database and return it to the client [8]. The client cannot control it remotely while the experiment is in progress. The study by Haryana et al. [9] is an implementation of this kind of virtual laboratory.

With the continuous development of computer technology, large-scale integrated circuits, and other technologies, the instrument system is closely integrated with computer software technology, which makes the concept of traditional instruments breakthrough, and virtual instruments appear [10]. With the development of virtual instruments for nearly three decades, it has been recognized by industry, academia, and research laboratories, and its application in all walks of life is also increasing. In education and teaching, especially in the teaching of professional courses in colleges and universities, it has an absolute advantage. Higher education, especially higher vocational education, attaches great importance to cultivating students’ professional skills [11]. In order to master these professional skills firmly and quickly, experimental teaching is an ideal way. As we all know, experiment is an indispensable link in teaching activities, and experimental teaching can make boring theoretical knowledge more intuitive and easy to accept. In the process of experiment, students’ practical ability, problem-solving ability, and innovation ability are also well cultivated. In fact, many disciplines are based on experimental courses, such as physics, chemistry, mechanics, computers, electronics, signal detection courses, and so on. However, with the continuous development of science and technology and the continuous popularization of higher education, higher vocational colleges are also facing some new problems in experimental teaching. This has a negative impact on the development of education [12].

The development of a laboratory based on virtual instrument technology can effectively reform the traditional experimental mode and is more conducive to updating the modern experimental teaching concept. In addition, the content and methods of the existing experimental teaching are outdated, the technical force is weak in management, the funds are scattered, the equipment is outdated, and there are still problems with repeated construction. Laboratory utilization is low, and students’ practice time is limited [13]. Virtual experiments can solve these difficult problems very well. It adopts the simulation method and comprehensively uses human–computer interaction, multimedia integration, network, virtual reality, and other technologies to give full play to the integration, visibility, and interactivity of modern information technology, breaking the traditional experimental teaching in time and space limitations, expanding the depth and breadth of experimental content [14].

The design of the virtual experiment is based on the computer, so during the virtual experiment, repeated work can be efficiently carried out, and various possible results can be obtained. Moreover, it can simulate experiments that are impossible in practice and can also demonstrate error conditions through which the staff understands the serious consequences of these problems. Moreover, the virtual experiment can facilitate network communication, so it can also help the exchange of technical information between schools and between schools and society [15]. Since the virtual laboratory is built on the computer platform with virtual instrument software as the core, it can be used to design soft instruments that have the same or even more functions as the traditional instruments in the previous laboratory and can be easily developed. It can also solve the problems of many traditional laboratory experimental equipment, repeated purchase of instruments, low utilization rate, and high damage rate of instruments. It can provide convenient, concise and efficient experimental instruments for experiments, which can greatly save the capital investment of experimental equipment. This kind of virtual laboratory has the characteristics of flexibility, low cost, and high efficiency and will play a great role in teaching and scientific research in higher vocational colleges [16]. Constructing a virtual experimental teaching environment that integrates experimental teaching, operation, guidance, and submission of results will help to achieve teaching reform, improve the experimental environment, increase the utilization rate of teaching resources, realize resource sharing, save the cost of updating experimental equipment, and improve teaching quality. The signal detection course is a very important professional course, and its practical application range is very wide [17].

Virtual instrument technology is an instrument measurement technology developed based on computer technology and is the product of the combination of computer technology and instrument technology. There are several ways to integrate general-purpose PCs with various instruments [18]: one is to install PCs into instruments, such as smart instruments with a high degree of automation. The volume is getting smaller and smaller, and the performance of this type of instrumentation in all aspects is also continuously improved and gradually there is an instrument with an embedded system; the second is to install the instrumentation system in the PC and use the software and hardware of the PC, to complete various required functions. This is actually the case with virtual instruments. The virtual instrument technology relies on software to achieve different detection tasks on the basis of hardware with superior performance. Users can create a visual interface of man–machine dialogue through virtual instrument software to control the instrument and analyze, store, and display data. Such virtual instrument systems can replace traditional instruments.

Intelligent reflective surface (IRS), also known as reconfigurable intelligent surface (RIS), has the ability to customize and control the propagation of reflected signals and has therefore been proposed as a candidate technology for the next generation of wireless networks. More specifically, by adjusting the phase and amplitude characteristics of the IRS element plane, the required reflected signals can be controlled and enhanced, and unwanted interference can be reduced to achieve the so-called “smart radio environment,” which can significantly improve the coverage range and spectral efficiency of existing wireless networks [19]. Compared with traditional active relay technology, IRS does not require the use of separate RF links or other high power consumption and high price components. IRS consumes very low power and is much cheaper in price. IRS can be seamlessly integrated into existing wireless communication networks to achieve various goals, including reducing inter-user interference, lowering transmission power consumption, reflecting and propagating signals to avoid communication congestion, and promoting advanced modulation techniques. These advantages of the IRS have led many people to start researching IRS-assisted communication networks. Not only in communication networks, the IRS also has many advantages in perceptual imaging. Each microvan of IRS can independently control the incident signal and alter the propagation environment in space. By continuously adjusting the reflection characteristics of the IRS, the receiver can obtain different echo signals, thereby obtaining more information in the environment [20].

Zhao et al. [21] proposed a robust downlink transmission scheme for multiple IRSs assisted corporate switched telecommunications network. The satellite network adopts a multigroup multicast transmission scheme to serve many earth stations, while the terrestrial network exploits space division multiple access and multi-IRS-enhanced non-orthogonal multiple access technology to communicate with many terrestrial users. Zhao et al. [22] proposed a dual-layer layer division multiplexing (LDM) enhanced scheme for computation and communication co-design (LDM-3 C) to facilitate simultaneous wireless data aggregation and uplink data transmission in unmanned aerial vehicle networks. Specifically, according to distinguishing signal strength, the communication signals are assigned to the upper layer for uplink data transmission, while the computation signals are mapped onto the lower layer, and the over-the-air computation technique is introduced to realize fast wireless data aggregation.

This article combines virtual reality technology and IRS-assisted perception and communication technology to build a general computer test demonstration platform to improve the effect of virtual computer demonstration teaching.

2 Algorithm model of virtual computer demonstration platform

Millimeter wave imaging technology has made significant progress. However, there are still drawbacks such as too many antenna elements, bulky equipment, and long imaging time. This leads to defects in the application conditions of the computer technology experimental demonstration platform.

In practical applications, millimeter wave signals suffer significant attenuation due to their extremely high frequency. Therefore, millimeter waves are almost unusable for long-distance applications. However, this also reduces signal interference and allows millimeter wave transceivers to be deployed very densely in one area without interfering with each other’s operation. These features not only generate high communication rates but also provide a great opportunity to accurately detect the surrounding environment, easily penetrating simple obstacles such as plastics and fabrics. To overcome these challenges, this article proposes a novel millimeter-wave imaging scheme based on IRS. IRS is a promising new technology recently proposed, which can control its own reflection characteristics through software and be used to reconfigure wireless propagation environments. Its application in computer technology experimental demonstration platforms can effectively enhance signal strength and improve imaging performance. This technology can be applied to Sparse Code Multiple Access (SCMA) encoder technology, and after successful application, VR display of computer technology experiments can be achieved.

2.1 Sparse code division multiple access

The process of SCMA coding is the process of mapping the binary bit stream to the complex number domain. The SCMA encoder can also be expressed as follows: f = Vg. Here, V is a binary mapping matrix, which can map N-dimensional constellation points to R-dimensional SCMA codewords. The mapping matrix of each user is different, and the mapping matrix of each user contains M–N all-zero rows. Each user’s mapping matrix is different, and each user’s mapping matrix contains M–N all-zero rows. Because codewords are sparse, not all user codewords will collide in resource block r, and whether user u occupies resource block r is determined by the corresponding mapping matrix V r . Figure 1 is an example of an uplink SCMA system, in which six users transmit on four resource blocks, namely U = 6, R = 4. Each user has its own codebook, which determines the resource blocks occupied by the user. As shown in the figure, user 1 transmits on resource blocks 1 and 2, while user 2 transmits on resource blocks 3 and 4.

Figure 1 Schematic diagram of sparse code division multiple access (take six users and four channels as an example).
Figure 1

Schematic diagram of sparse code division multiple access (take six users and four channels as an example).

2.2 System model

Based on SCMA, an intelligent reflector-assisted joint multi-user information reliable access and environmental reflector perception scheme is proposed. SCMA is a code domain non-orthogonal multiple access (code NOMA) method. IRS is a promising technology that surpasses 5G wireless networks, enhancing the transmission environment of wireless communication solutions. IRS uses low-cost passive reflective elements to adjust the phase of the incident signal to enhance the signal quality at the receiver end. In uplink communication, both the sparsity of structured signals based on SCMA and the sparsity of reflectors in unstructured environments should be considered simultaneously. For multi-user information access, SCMA signals can be separated at the receiving end through message-passing algorithms due to sparse codebooks. Moreover, through the separated signals, the information about the environmental reflector in the received signal is extracted by calculating the imaging method. By iteratively optimizing the performance of multi-user information access and environmental perception through the relationship between environmental reflector information and received signals. As shown in Figure 2, there is a millimeter-wave access point (AccessPoint, AP) and several users in the wireless environment. In upstream communication, multiple users send signals to the access point at the same time, and our purpose is twofold. The first is to complete the reliable access of multi-user information, that is, to decode the transmitted signals of all users. The second is to complete the perception and imaging of the wireless environment, that is, to obtain all reflector information in the wireless environment. To accomplish these two purposes, we assume that this system uses the SCMA scheme for data transmission; the signals of these users are distributed on R orthogonal resource blocks (ORE).

Figure 2 SCMA-IRS system model.
Figure 2

SCMA-IRS system model.

We divide it into pixel blocks of size l r , w r , and h r , the number of pixels is N = L r l r × W r w r × H r h r . The interior of each pixel may be empty, or there may be a reflective surface. If it is assumed that the reflection coefficient of the reflective surface is independent of the angle, we use a reflection coefficient x n to represent the reflective surface characteristics inside the n-th pixel. If the interior of the pixel is empty, then we obtain x n = 0. Therefore, the environmental information of the whole room can be represented by the following variables:

(1) x = [ x 1 , x 2 , … , x N ] T ,

If it is assumed that the IRS consists of K microfacets, each microfacet of the IRS reflects the incident signal with a set phase offset and amplitude reflection coefficient. We represent the reflection properties of the IRS by the following diagonal matrix:

(2) Θ = d i a g ( Θ 1 , … , Θ K ) ∈ C K × K ,

Among them, Θ k = ρ k e j θ k represents the reflection characteristic of the k-th microfacet of the IRS, ρ k ∈ [ 0 , 1 ] and θ k ∈ [ 0 , 2 π ] represent the amplitude reflection coefficient of this microfacet and the phase shift caused by the reflection, respectively, and the diag (·) function is used to construct the diagonal matrix.

If it is assumed that all signals arriving at the access point need to be reflected by the smart reflective surface, the signals reach the access point through two paths. The first is the path that directly reaches the IRS without being reflected by the reflector, which is called the Light Of Sight (LOS). The second is the path reflected by the reflector, which is called Non-Light Of Sight (NLOS). We denote the NLOS path channel on the r-th ORE from the u-th user to the n-th pixel block to the k-th microfacet of the IRS to the access point by h u , n , k , r , and

(3) h u , n , k , r = x n ρ r , k e j θ r , k α u , n , k , r e i φ u , n , k , r ,

Among them, x n represents the reflection coefficient of the internal reflective surface of the n-th pixel in the room. If this pixel is empty, then h u , n , k , r = 0 . ρ r , k and θ r , k represent the amplitude reflection coefficient and phase offset of the k-th microfacet of IRS on the r-th ORE, respectively, α u , n , k , r represents the Rayleigh fading channel coefficient of this channel, and φ u , n , k , r represents the phase shift caused by this channel propagation. h u , k , r L O S denotes the channel on the r-th ORE from user u to the k-th microfacet of the IRS to the access point, and

(4) h u , k , r L O S = ρ r , k e j θ r , k α u , k , r L O S e i φ u , k , r L O S ,

Among them, α u , k , r L O S represents the Rayleigh fading channel coefficient of this channel, and φ u , k , r L O S represents the phase shift caused by this channel propagation.

Therefore, the received signal received by the access point on the r-th ORE is

(5) y r = ∑ u ∈ Λ r ∑ n = 1 N ∑ k = 1 K h u , n , k , r c u , m , r + ∑ u ∈ Λ r ∑ k = 1 K h u , k , r L O S c u , m , r + w r ,

Among them, c u , m , r is the element of the codeword c u , m at the corresponding position of the r-th ORE, Λ r represents a set of user indices transmitted on the r-th ORE, u ∈ Λ r is the set of users, and w r is the additive white Gaussian noise of the r-th ORE. Substituting (3) and (4) into (5), the following formula is obtained:

(6) y r = ∑ u ∈ Λ r ∑ k = 1 K ρ r , k α ˜ u , k , r L O S e i φ ˜ u , k , r L O S + ∑ n = 1 N x n α ˜ u , n , k , r e i φ ˜ u , n , k , r c u , m , r + w r ,

Among them, α ˜ u , n , k , r = ρ r , k α u , n , k , r , φ ˜ u , n , k , r = θ r , k + φ u , n , k , r , α ˜ u , k , r L O S = ρ r , k + α u , k , r L O S , φ ˜ u , k , r L O S = θ r , k + φ u , k , r L O S is the respective total channel parameters, then (6) can be written as

(7) y r = ∑ u ∈ Λ r H r , u c u , m , r + w r ,

Among them, H r , u = ∑ k = 1 K ρ r , k α ˜ u , k , r L O S e i φ ˜ u , k , r L O S + ∑ n = 1 N x n α ˜ u , n , k , r e i φ ˜ u , n , k , r is the total channel of user u on the r-th ORE. Notably, since the OREs of SCMA can be orthogonal time slots, it is feasible to assume that each ORE has adjustable reflection parameters. The received signal of the LOS path part is the main part that affects the instantaneous signal-to-noise ratio (SNR) of the received signal. Therefore, in order to improve the instantaneous value of the r-th ORE, θ r , k can be set as

(8) θ r , k = a r g m i n θ r , k ∈ [ − π , π ] ∑ u ∈ Λ r ∣ θ r , k + φ u , k , r L O S ∣ 2 .

The optimal solution for phase θ r , k is the geometric median of all user phases that share this ORE, namely θ r , k = m e d ( φ u , k , r L O S ) , u ∈ Λ r , where med(·) represents the geometric median. Finally, for this SCMA-RIS scheme, the total received signal across all OREs is

(9) y = ∑ u = 1 U ( d i a g ( H u ) c u , m ) + w .

Among them, y = [ y 1 , … , y R ] T , w = [ w 1 , … , w R ] T , H u = [ H 1 , u , … , H R , u ] T , where d i a g ( H u ) ∈ ℂ R × R represents a diagonal matrix with H u as the diagonal element, and its r-th diagonal element is H r , u .

2.3 Multi-user information access and environmental awareness

The first goal we want to achieve is to complete the reliable access of multi-user information, that is, to detect the uplink signals of all users. There are U users, and each user has M kinds of codewords. By performing a full search on all (M)U user codeword combinations, a maximum likelihood decoder can provide the theoretical best bit error rate performance. The transmission codewords:

(10) C ^ ML = a r g m i n j ∈ M U y − ∑ u = 1 U ( d i a g ( H u ) c u , m ( j ) ) 2

Among them, C ^ ML ∈ ℂ R × U = [ c ^ 1 , m ( j ) , … , c ^ U , m ( j ) ] , m ( j ) , m ∈ { 1 , … , M } is the value of the j-th combination in the (M) U codeword combinations.

The SCMA-IRS-MPA decoder can be represented using a factor graph, as shown in Figure 3. Among them, function nodes (functionnodes, FNs) represent ORE, and variable nodes (variablenodes, VNs) represent users. Through message passing between nodes, the MPA decoder iteratively updates the message probabilities between FNs and VNSs.

Figure 3 SCMA factor graph (take six users and four OREs as an example).
Figure 3

SCMA factor graph (take six users and four OREs as an example).

Next, we analyze the computational complexity of the algorithm. We represent the decoding computational complexity of the MPA decoder in terms of the number of addition and multiplication operations required to decode the user codeword. Therefore, the number of additions and multiplications required by the MPA detector are Rd f ( M d f ( 4 d f + K i t + 1 ) + N − K i t ) + 1 and R d f ( M d f ( 4 d f + K i t d f + 3 ) + N + M K i t ( d v − 1 ) ) + U M ( d v − 1 ) , respectively.

In the process of multi-user signal detection, we obtain the codeword estimates C ˆ = { c ^ 1 , m , … , c ^ U , m } for all users. On the r-th ORE, the user’s codeword is C ^ r = { c ^ 1 , m , r , … , c ^ U , m , r } . According to the system model, the received signal on the r-th ORE is y r = ∑ u ∈ Λ r ∑ n = 1 N ∑ k = 1 K h u , n , k , r c u , m , r + ∑ u ∈ Λ r ∑ k = 1 K h u , k , r L O S c u , m , r + w r . If it is assumed that the signal part ∑ u ∈ Λ r ∑ k = 1 K h u , k , r L O S c u , m , r of the line-of-sight path channel is known, the received signal in the noise-free case can be written as

(11) y ˜ r = ∑ n = 1 N ∑ u ∈ Λ r ∑ k = 1 K α ˜ u , n , k , r e i φ ˜ u , n , k , r c u , m , r x n

Among them, y ˜ r is the reflected signal after removing the signal on the line-of-sight path. The above formula can be written in matrix form as

(12) y ˜ r ∑ u ∈ Λ r ∑ k = 1 K a ˜ u , 1 , k , r e i φ ˜ u , 1 , k , r c u , m , r , .. . , ∑ u ∈ Λ r ∑ k = 1 K a ˜ u , N , k , r e i φ ˜ u , N , k , r c u , m , r X 1 ⋮ X N

This is an equation about the target x = [ x 1 , x 2 , … , x N ] T . According to the theory of linear algebra, for N different variables x n , n = 1 , … , N , as long as there are N different equations, x can be solved. Obviously, in a single transmission, we can hardly get enough different equations. According to the system model, at most R ≪ N equations can be obtained at a time, and many equations are repeated due to channel correlation.

As the communication process continues, different users send different codeword combinations at different locations, which can generate many different equations. As long as the receiver records all these equations, as the number of obtained equations gets closer and closer to N, it can obtain a better reconstruction effect and obtain x.

The performance of the proposed joint multi-user communication and environment awareness method is gradually improved with the progress of the communication process and the continuous iteration of multi-user communication and environment awareness. As shown in Figure 4, at the beginning, the access point is blind to the wireless environment and has no information about the wireless environment.

Figure 4 Flow chart of an iterative method for joint communication and environment awareness.
Figure 4

Flow chart of an iterative method for joint communication and environment awareness.

The SCMA-IRSMPA decoder may not perform well due to the lack of channel information. However, due to the sparsity of the SCMA codebook, the receiver can still decode certain user signals based on the differences between different codewords themselves. By decoding the user’s transmitted signal, a portion of environmental information can be obtained, which includes channel information. Then pass this channel information to the SCMA-IRS-MPA decoder for user decoding again, obtaining more accurate user codewords, and repeat the previous iteration process through the user codewords. Continuously iterate until the performance reaches the set threshold or the number of iterations reaches the set number, completing the self-loop within a single ORE. After completing the self-loop, the obtained user code-word information, the received signal at the intervention point, and the obtained environmental information will be transmitted to the adjacent ORE. Adjacent ORE can use this information as a starting point to complete their own inner loop and then pass the obtained results to adjacent ORE. After completing its own inner loop, each ORE can wait for more accurate results from adjacent ORE, such as obtaining more information in subsequent time slots and then re decode the user code to obtain more accurate user codewords. This completes the loop between ORE. By continuously iterating between internal loops and ORE loops, as the communication process continues, user information decoding and environmental perception will become better and better.

3 Design of augmented reality (AR)-based general computer technology experiment demonstration platform

The function items of the remote virtual laboratory system are divided into five relatively independent function modules, namely, experiment customization module, experiment simulation operation module, experiment result visualization module, experiment equipment component module, and component registration module. There is high cohesion and low coupling between modules. Figure 5 shows the functional block diagram of the system.

Figure 5 System overall design module diagram.
Figure 5

System overall design module diagram.

The design process and design content of each digital signal processor system will vary greatly according to specific requirements. Complex DSP designs can include front-end analog circuit interfaces and digital interfaces to other digital devices and may require algorithm simulation and analysis before design, while simple DSP designs only include digital information processing. The design process of the digital signal processor system is shown in Figure 6.

Figure 6 Design flow of digital signal processor system.
Figure 6

Design flow of digital signal processor system.

The performance effect of the proposed joint multi-user information access and environment awareness scheme based on the uplink SCMA-IRS system is verified by simulation analysis. We assume the room is 3 m long, wide, and high. The operating frequency band of the system is f c = 60 GHz, and the system bandwidth is B = 1 GHz. There is a mmWave access point inside the room, at the midpoint of an edge from the top of the room. It is assumed that the distribution of reflective surfaces inside the room is randomly generated, as shown in Figure 7. Figure 8 shows the results of environment perception under different SNRs. The number of users is set to 6, which is randomly generated in the environment; the smart reflective surface is composed of 10 × 10 microfacets; and the transmission time is 50 time slots. It can be seen that with the improvement of the SNR, the effect of environmental perception also becomes better when other conditions are the same.

Figure 7 The distribution of reflecting surfaces inside the room.
Figure 7

The distribution of reflecting surfaces inside the room.

Figure 8 Environment perception results with different SNRs.
Figure 8

Environment perception results with different SNRs.

To measure the performance of the proposed SCMA-IRS-based joint multi-user information access and environment awareness scheme, we use two metrics to represent the performance of environment awareness and the performance of multi-user information access, respectively. Among them, the mean square error (MSE) measures the performance of environmental perception. The smaller the MSE, the more accurate the perception of the environment, and vice versa. The symbol error probability (SER) is used to measure the performance of multi-user information access. The smaller the SER, the more accurate the decoding of user information.

Figure 9 shows the performance of the proposed joint communication and sensing scheme as the number of transmitted time slots increases at SNRs of 10, 15, and 20 dB. Figure 9(a) is the change in MSE, and Figure 9(b) is the change in SER. The size of the smart reflective surface is 10 × 10, the number of users is 6, and the location is randomly generated. It can be seen that as the number of time slots increases, that is, as the communication process continues, both the performance of environment perception (MSE) and the performance of multi-user information access (SER) continue to improve. The reason is that with the progress of the communication process, the mutual iteration between environment perception and multi-purpose information decoding makes the obtained channel of access become more and more accurate so that better effect is obtained.

Figure 9 Performance changes as the communication process continues: (a) MSE and (b) SER.
Figure 9

Performance changes as the communication process continues: (a) MSE and (b) SER.

Figure 10 shows the performance change of the proposed joint communication and perception scheme as the number of users changes. Figure 10(a) is the change in MSE, and Figure 10(b) is the change in SER. The SNR is 30 dB, the duration is 50 time slots, and the user location is randomly generated. It can be seen that as the number of users increases, the performance of environment perception (MSE) will continue to get better. The reason is that the increase in the number of users can bring more observation points to environmental perception, and the environmental information in the received signals formed by different user positions can be combined to obtain more accurate environmental information. In the iterative process of perception and communication, better perception will bring more accurate channel information, which will lead to better communication performance.

Figure 10 Relationship between the number of users and the proposed joint communication and perception scheme: (a) MSE and (b) SER.
Figure 10

Relationship between the number of users and the proposed joint communication and perception scheme: (a) MSE and (b) SER.

To demonstrate the rationality of the joint multi-user information access and environmental perception scheme introduced by SCMA-IRS in this article’s model, a comparative experiment was conducted between the model with SCMA-IRS and existing models, with SER set at 15 dB. The most widely used hybrid analog digital beamforming (HAD-BF) scheme was selected for comparison, and the MSE and SER of the two were calculated with a duration of 100 s, resulting in Figure 11.

Figure 11 Performance comparison results of different models: (a) MSE and (b) SER.
Figure 11

Performance comparison results of different models: (a) MSE and (b) SER.

In Figure 11, compared with HAD-BF, SCMA-IRS has certain advantages in the comparison of MSE and SER parameters. It is verified that introducing SCMA-IRS is a reasonable improvement on existing models.

On this basis, the effect of the AR-based general computer technology experimental demonstration platform proposed in this article is verified, and its practical effects are counted, and the practical results shown in Table 1 are obtained.

Table 1

Practical effect of AR-based general computer technology experimental demonstration platform

Number Teaching practice Number Teaching practice Number Teaching practice
1 86.99 21 86.62 41 81.33
2 81.34 22 81.98 42 80.18
3 83.29 23 79.69 43 79.95
4 83.75 24 80.99 44 81.63
5 80.80 25 79.09 45 85.40
6 82.83 26 81.22 46 82.41
7 81.20 27 83.20 47 79.85
8 79.24 28 83.74 48 83.88
9 83.41 29 83.29 49 82.23
10 85.82 30 80.12 50 79.10
11 83.19 31 84.62 51 80.37
12 83.95 32 84.40 52 82.76
13 82.91 33 80.95 53 79.35
14 83.05 34 83.60 54 81.66
15 79.86 35 79.98 55 79.02
16 85.07 36 79.04 56 84.04
17 84.00 37 86.69 57 86.94
18 83.15 38 81.19 58 81.70
19 83.37 39 82.97 59 84.12
20 82.41 40 79.53 60 84.72

It can be seen from the above research that the AR-based general computer technology experimental demonstration platform proposed in this article has a good effect and can effectively promote the computer technology experimental demonstration effect.

For some theoretical basic courses, due to the fact that the course content is mostly about understanding and analyzing basic theories and algorithm formulas, the experimental projects are mainly focused on verification. When specific hardware (such as course specific experimental boxes) is used for experimental operations, they often cannot be exactly the same as the theoretical results and phenomena described in the book. The target audience for professional basic courses is undergraduate students in their early years. Most students have weak independent hands-on innovation abilities, which can lead to confusion and misunderstanding during the experimental process, which is not conducive to their better grasp of the course content. In addition, for the analysis and processing of some ideal situations in textbooks, hardware experimental boxes also have significant limitations. For example, the most commonly used unit impulse signal in signal analysis cannot be perfectly implemented in experimental boxes. To solve these problems, simulation experimental software needs to be introduced in experimental teaching. In the experiments of basic theoretical courses, simulation software experiments can keep the experimental results highly consistent with what is taught in the textbook and have strong flexibility. Changes in experimental projects can be simplified to only modify a few parameters. The requirements for simulation software experiments on the platform are only a microcomputer and related software. Currently, university computer rooms are relatively abundant, and student experimental venues are no longer limited to designated professional laboratories. This is also conducive to integrating experimental resources and sharing, maximizing laboratory functions, and effectively improving the efficiency of experimental teaching.

4 Conclusion

The popularization of the Internet has provided the possibility for distance education to better understand the concepts learned for students, so it is necessary to provide a learning platform that closely integrates theory and experiment. Traditional experiments are inseparable from experimental equipment, and experiments often require preliminary preparation and regular maintenance of experimental equipment. Moreover, the experimental conditions are limited, which leads to low experimental efficiency, which in turn affects the teaching effect. In addition, the results of the experiments cannot be analyzed in time, and the experimental methods cannot be improved according to the results of the experimental analysis, including the monitoring of the experimental process. This article combines virtual reality technology and IRS-assisted perception and communication technology to build a general computer test demonstration platform. The research shows that the AR-based general computer technology experimental demonstration platform proposed in this article has good effects and can effectively promote the computer technology experimental demonstration effect.

The decoding algorithm for SCMA-IRS multi-user information access is based on message passing algorithm, which has a high computational complexity. How to design a more reasonable decoder. Perception and distributed computing can be combined together. Currently, the perception process is carried out in one terminal, while in communication networks such as the Internet of Things, there are many computing devices, and each device can obtain different information. In the future, it is necessary to further improve the performance of this model by combining distributed IoT technology.

  1. Funding information: This work was funded by Key Natural Science Project of Huainan Normal University: “Research on the Design and Experience of Computerized and Non Computerized Children’s Programming Games” (2023XJZD017).

  2. Author contribution: Fang Xu: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, software, supervision, validation, visualization, writing-original draft, writing-review & editing. Author has accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Conflict of interest: Author states no conflict of interest.

  4. Data availability statement: All data generated or analyzed during this study are included in this published article.

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Received: 2024-08-13
Revised: 2025-01-16
Accepted: 2025-02-16
Published Online: 2025-05-27

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/nleng-2025-0091
Keywords for this article
AR technology; general computer technology; experiment; demonstration platform
Creative Commons
BY 4.0

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