PRIS / ARIS assistance-based communication system and construction method thereof

Abstract

The invention discloses a method for constructing a communication system based on PRIS / ARIS assistance, and the method comprises the steps: constructing a wireless communication model based on a single base station, a single user and an RIS; wherein the RIS is a PRIS or an ARIS; acquiring a signal-to-noise ratio based on the wireless communication model and the constant total power; and obtaining the optimal position of the RIS based on the signal-to-noise ratio. Under the background of PRIS auxiliary communication, it is theoretically determined that the optimal deployment position should be symmetrical between the base station and the user. Under the condition of ARIS auxiliary communication, the determined deployment position of the ARIS is closely related to the amplification power and the number of antennas, and meanwhile, the transmitting power of a base station, the amplification power of the ARIS and amplification constraint conditions need to be considered. Especially, the amplification power can influence the signal transmission and processing capability, and the number of antennas is related to the signal processing efficiency.

Description

Technical Field

[0001] This application belongs to the field of wireless communication technology, and particularly relates to a communication system assisted by PRIS / ARIS and a construction method thereof. Background Art

[0002] Reconfigurable intelligent surface is an emerging technology that has received much attention in the field of wireless communication. It reshapes the radio propagation environment by intelligently adjusting the reflection amplitude and phase shift of a large number of reflecting elements, thereby improving communication quality. RIS is lightweight and aesthetically pleasing, and can be easily deployed on surrounding objects to enhance communication performance. In addition, it can achieve full-duplex communication with low power consumption. Compared with traditional technologies that are limited by environmental randomness and have limited effects in optimizing transmitters and receivers, RIS can significantly improve system performance by adaptively changing the phase shift of the reflecting elements. In terms of improving the spectral and energy efficiency of wireless networks, RIS consists of passive reflecting elements, does not require expensive radio frequency (RF) links, and consumes less power than traditional active relay devices. In addition, RIS can be flexibly deployed to expand the coverage area, increase the rate, and reduce interference.

[0003] Specifically, reconfigurable intelligent surface (RIS) can be divided into two categories: passive intelligent reflecting surface (PRIS) and active intelligent reflecting surface (ARIS). The characteristic of PRIS is that it only reflects passive load signals, operates in full-duplex mode, without amplification, noise processing, and self-interference, so it has higher spectral and energy efficiency. However, PRIS is limited by high path loss, which will affect its performance. ARIS is a solution to the dilemma of PRIS. It consists of active reflecting elements equipped with negative resistance elements such as tunnel diodes and negative impedance converters, and can amplify the incident signal. Different from traditional amplify-and-forward relays that require power-consuming RF links and orthogonal resources to send and receive signals, ARIS uses low-power reflective amplifiers to directly reflect signals in full-duplex mode. Due to the existence of amplification gain, it can achieve a higher downlink rate at the same location, but the hardware and energy consumption costs are slightly higher. There are significant differences between PRIS and ARIS in terms of hardware structure and power consumption characteristics. PRIS has a simple structure and only relies on passive reflecting elements to work, so its power consumption is extremely low; while ARIS is equipped with a phase-shifting circuit and a reflective amplifier to amplify signals, and the base station and amplifier will consume power, so its overall power consumption is much higher than that of PRIS. Although previous studies have conducted comparative analyses through numerical simulations under equivalent power consumption budgets, in actual wireless communication scenarios, power consumption budget is only one of many key factors. In addition, the deployment locations of PRIS and ARIS cannot be ignored. It can be reasonably speculated that deploying PRIS and ARIS at different locations will lead to significant differences in system performance, including changes in signal coverage, transmission rate, interference level, and other system metrics. Summary of the Invention

[0004] This application provides a communication system assisted by PRIS / ARIS and a construction method thereof to solve the above technical problems.

[0005] To solve the above technical problems, a technical solution adopted in this application is: a construction method of a communication system assisted by PRIS / ARIS, including:

[0006] Construct a wireless communication model based on a single base station, a single user, and RIS; where RIS is PRIS or ARIS;

[0007] Obtain the signal-to-noise ratio based on the wireless communication model and the constant total power;

[0008] Obtain the optimal position of RIS based on the signal-to-noise ratio.

[0009] Furthermore, in response to RIS being PRIS, the method for constructing the wireless communication model includes:

[0010]

[0011] where y p is the signal received by the user; P B is the supercritical transmission power at the base station; Θ P is the reflected signal of PRIS; is the direct channel from RIS to the user; h SR is the channel from the base station to RIS; x represents the signal transmitted from the base station, which follows a complex Gaussian distribution with a mean of 0 and a variance of 1, that is, x ∼ CN(0,1), and at the same time, represents the thermal noise at the receiving end, which follows a complex Gaussian distribution with a mean of 0 and a variance of σ 2 and n ∼ CN(0,σ 2 ).

[0012] Furthermore, the method for obtaining the signal-to-noise ratio is:

[0013] Obtain the signal-to-noise ratio based on formula (2); where formula (2) is:

[0014]

[0015] where the signal-to-noise ratio is γ P ; N P is the number of reflection units in PRIS; D is the horizontal straight-line distance between the base station and the user; H is the height at which RIS is deployed above the horizontal straight-line distance; the intersection point of the vertical line of RIS and this horizontal straight line is at a distance d1 from the base station.

[0016] Furthermore, the method for obtaining the optimal position of RIS is:

[0017] Based on formula (3), obtain the optimal position of the RIS; where formula (3) is:

[0018]

[0019] Simplify formula (7) to make the numerator zero, and obtain formula (4); where formula (4) is:

[0020]

[0021] Calculate formula (4) and solve for Determine the optimal position of the PRIS.

[0022] Furthermore, a method for constructing a wireless communication model in response to the RIS being an ARIS includes:

[0023] Based on formula (5), obtain the wireless communication model; where formula (5) is:

[0024]

[0025] where y a is the signal received by the user; the noise generated by the ARIS is represented by x represents the signal transmitted from the base station, which follows a complex Gaussian distribution with a mean of 0 and a variance of 1, i.e., x ∼ CN(0,1). At the same time, represents the thermal noise at the receiving end, which follows a complex Gaussian distribution with a mean of 0 and a variance of σ 2 , n ∼ CN(0, σ 2 ).

[0026] Furthermore, a method for obtaining the signal-to-noise ratio includes:

[0027] Based on formula (6), obtain the signal-to-noise ratio; where formula (6) is:

[0028]

[0029] where the signal-to-noise ratio is γ P , N a is the number of reflection units in the PRIS; P A is the amplification power of the ARIS, and P T is the transmission power of the base station.

[0030] Furthermore, a method for obtaining the optimal position of the RIS includes:

[0031] Based on formula (7), obtain the derivative of P A ; where formula (7) is:

[0032]

[0033] Calculate formula (7), and then set γ P to 0; solve for to determine the optimal position of ARIS.

[0034] Another technical solution adopted in this application is: a communication system model assisted by PRIS / ARIS, which is constructed by using the above construction method.

[0035] The beneficial effects of this application are as follows: In the context of PRIS-assisted communication, this application theoretically determines that the optimal deployment position should be symmetric between the base station and the user. In the case of ARIS-assisted communication, the deployment position of ARIS has been determined to be closely related to its amplification power and the number of antennas. At the same time, the transmit power of the base station, the amplification power of ARIS, and the amplification constraint conditions also need to be considered. In particular, the amplification power affects the signal transmission and processing capabilities, while the number of antennas is related to the signal processing efficiency. Both of these factors play important roles in determining the optimal deployment position. The results show that PRIS requires precise position deployment, while ARIS needs to balance between the amplification power and the number of antennas. Description of the Drawings

[0036] Figure 1 is a schematic flowchart of an embodiment of the construction method of the communication system assisted by PRIS / ARIS of this application;

[0037] Figure 2 is a schematic structural diagram of an embodiment of the communication system model assisted by PRIS / ARIS of this application;

[0038] Figure 3 is a curve graph of the approximation and best-case correlation analysis using ARIS in an embodiment of the construction method of the communication system assisted by PRIS / ARIS of this application;

[0039] Figure 4 is a curve graph of the capacity change that can be achieved at different deployment positions of RIS in an embodiment of the construction method of the communication system assisted by PRIS / ARIS of this application;

[0040] Figure 5 is a curve graph of the capacity that can be achieved by RIS based on position deployment under different power allocations in an embodiment of the construction method of the communication system assisted by PRIS / ARIS of this application. Detailed Embodiments

[0041] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0042] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways than those specifically described herein, and thus, the present invention is not limited to the limitations of the specific embodiments disclosed in the following specification.

[0043] Refer to Figure 1 , Figure 1 which is a schematic flowchart of an embodiment of a method for constructing a communication system assisted by PRIS / ARIS. The wireless communication system studied in this application (as Figure 2 shown) includes a single-antenna base station (BS) and a single-antenna user. In this system environment, due to the existence of complex terrain factors, the direct link between the base station and the user is severely blocked. In view of the above situation, PRIS or ARIS is deployed between the base station and the user to facilitate the communication process. Assume that the horizontal straight-line distance between the base station (BS) and the user is D, and the RIS is deployed at a height of H above this horizontal straight-line distance. In addition, the intersection point of the vertical line of the RIS and this horizontal straight-line is at a distance of d1 from the base station and at a distance of d2 from the user, and d1 + d1 = D. On this basis, assume that the distance between the base station and the RIS is d SR , and the distance between the RIS and the user is d RD , where

[0044] Embodiment 1

[0045] In this embodiment, the RIS in the wireless communication model is PRIS. In this case, we specify the supercritical transmission power at the base station as P B .

[0046] Step S11. Based on a single base station, a single user, and PRIS RIS, construct a wireless communication model.

[0047] Specifically, the supercritical transmission power at the base station is specified as P B , and the magnitude of this value will have various effects on the subsequent signal transmission process. The signal received by the user can be expressed as:

[0048]

[0049] where x represents the signal transmitted from the base station, which follows a complex Gaussian distribution with a mean of 0 and a variance of 1, that is, x ∼ CN(0,1). At the same time, represents the thermal noise at the receiving end, which follows a complex Gaussian distribution with a mean of 0 and a variance of σ 2 , n ∼ CN(0,σ 2 ).

[0050] Step S21. Based on the wireless communication model and the constant total power, obtain the signal-to-noise ratio.

[0051] Specifically, the total power consumption of the PRIS assistance system is expressed by the following formula:

[0052] Q = P B + P W ;

[0053] where P W represents the power consumed by the phase shifter switch and the control circuit in the RIS component. Subsequently, the signal-to-noise ratio (SNR) of the user-side PRIS assistance system can be expressed as

[0054]

[0055] where the signal-to-noise ratio is γ P ; Θ P is the radiated signal; N P is the number of reflection units in the PRIS; D is the horizontal straight-line distance between the base station and the user; H is the height at which the RIS is deployed above the horizontal straight-line distance; the intersection point of the vertical line of the RIS and the horizontal straight line is at a distance d1 from the base station.

[0056] It can be seen from the above equation that γ P will increase with the increase of , and the values of and h SR are related to d1 and d2. To maximize γ p , the optimal phase shift matrix Θ P is:

[0057]

[0058] Substituting formula (9) into formula (8), formula (10) can be derived. In formula (10), N P represents the number of PRIS elements.

[0059]

[0060] Next, to more intuitively show the relationship between the signal-to-noise ratio (SNR) and the variables d1 and d2, based on the relationship described above, formula (10) can be converted into the following form

[0061]

[0062] where the signal-to-noise ratio is γ P ; N P is the number of reflection units in the PRIS; D is the horizontal straight-line distance between the base station and the user; H is the height at which the RIS is deployed above the horizontal straight-line distance; the intersection point of the vertical line of the RIS and the horizontal straight line is at a distance d1 from the base station.

[0063] Step S31. Obtain the optimal position of the RIS based on the signal-to-noise ratio.

[0064] Specifically, to determine the best position for PRIS deployment to achieve the maximization of γ P take the partial derivative of γ P with respect to d1. By setting the obtained partial derivative to zero, constructing an equation and solving it, the specific value of d1 that can maximize γ P can be obtained.

[0065]

[0066] When γ P is maximized, the best position for PRIS deployment is finally determined. The derivation of γ P is as shown in the above formula. To find the value of d1 when the signal-to-noise ratio reaches the maximum, set formula (3) to zero. Given the structural characteristics of this equation, it can be clearly seen that its denominator is always non-zero. Therefore, only the numerator part needs to be concerned. By making the numerator zero, the simplified result can be obtained from the following

[0067]

[0068] By solving the above equation operation, the solution can be obtained as

[0069] In an actual communication application scenario, the base station usually needs to cover a wide area to serve many scattered users, which inevitably leads to a relatively large transmission distance D between the base station and the users. And PRIS plays a key relay role in the entire communication link. Its deployment height H is at a significantly lower order of magnitude compared to the large transmission distance D. Based on this, H can be reasonably omitted in mathematical approximation. After this simplification, the value range of d1 is significantly reduced to 0 or D. This result clearly shows that in the actual deployment scenario, to achieve a higher signal-to-noise ratio, PRIS should be preferentially deployed near the base station or near the users.

[0070] From the existing research results, it has been confirmed that the proximity of PRIS to the base station or user terminal can improve the signal-to-noise ratio in actual deployment; however, it is not enough to only know the value of this key indicator of the signal-to-noise ratio under optimal deployment. Considering the complexity and regularity of signal transmission in the communication system, it is reasonable to speculate that there may be some inherent symmetry in the signal-to-noise ratio. Conduct an in-depth analysis from the derived expression. When examining the structure of the signal-to-noise ratio expression and the relationship between variables, it is found that it may be symmetric about the line

[0071] Next, we prove that the signal-to-noise ratio is symmetric about Rewrite formula (2) into the following form:​

[0072]

[0073] To prove that f(d1) is symmetric with respect to it is necessary to verify that for any a,

[0074] First, calculate Substitute into f(d1) to obtain:

[0075]

[0076] Then, let to prove

[0077]

[0078] Obviously, Successfully prove that the function γ P is symmetric about

[0079] Example 2

[0080] The RIS in the wireless communication model of this example is an ARIS.

[0081] Step S12. Based on a single base station, a single user, and an ARIS, construct a wireless communication model.

[0082] Specifically, the signal received by the user can be expressed in the following form

[0083]

[0084] where y a is the signal received by the user, η is the amplification factor; the noise generated by the ARIS is represented by x represents the signal transmitted from the base station, which follows a complex Gaussian distribution with a mean of 0 and a variance of 1, i.e., x ∼ CN(0,1). At the same time, represents the thermal noise at the receiving end, which follows a complex Gaussian distribution with a mean of 0 and a variance of σ 2 and n ∼ CN(0,σ 2 ).

[0085] Step S22. Based on the wireless communication model and the constant total power, obtain the signal-to-noise ratio.

[0086] Specifically, the optimization problem of maximizing the signal-to-noise ratio can be formulated as:

[0087]

[0088] where P​A For the amplification power of ARIS, there is a relationship Q = P T + P A + P W , where P T is the transmission power of the base station; Θ A is the reflected signal of ARIS, and P W represents the hardware loss power. In addition, in order to align and maximize the designed ARIS phase in the cascaded channel, that is For ease of analysis, Equation (12) is rewritten as:

[0089]

[0090] In addition, the limiting conditions of the ARIS amplification coefficient are as follows:

[0091]

[0092] Substitute Equation (14) as the optimal case into Equation (13), where and D = d1 + d2. Formula (6) is obtained

[0093]

[0094] where the signal-to-noise ratio is γ P , N a is the number of reflection units in PRIS; P A is the amplification power of ARIS, and P T is the transmission power of the base station.

[0095] Step S32. Based on the signal-to-noise ratio, obtain the optimal position of RIS.

[0096] Specifically, the derivative with respect to P A is:

[0097]

[0098] where ξ = AP A + B(Q - P A - P W ). Subsequently, set the value of the remaining above equation to zero, thereby obtaining Equation (15).

[0099]

[0100] As can be seen from the above formula, the amplification power of ARIS depends on its actual deployment position. When ARIS is deployed at different positions, different amplification powers need to be allocated to it. As d1 approaches D, Approaching Approaching At this time, the numerator can be further simplified to To make the numerator as small as possible, on the one hand, the relationship between P W and Q can be adjusted to make the value of P W -Q smaller. On the other hand, for For example, if the noise variance σ 2 and Or optimizing D and H within the range allowed by the system design can also help reduce the numerator. Generally speaking, in actual deployment, not only the relationship between d1 and D should be considered to optimize the denominator, but also the parameters in the numerator should be adjusted from multiple perspectives to meet the requirement that the amplification power of ARIS is much smaller than the transmission power of the base station, so as to ensure a reasonable balance in terms of system performance and power distribution and lay a foundation for the stable and efficient operation of the system.

[0101] Next, in the actual deployment scenario, if D is much larger than H, formula (6) can be rewritten as:

[0102]

[0103] Since the amplification factor of ARIS needs to meet the condition of being greater than or equal to 1, that is, η≥1, substituting it into the constraint condition (14), the following formula can be obtained.

[0104]

[0105] It can be seen from formula (17) that d1 is a function of P T and P A . When the sum of P T and P A is a fixed value and P T increases, P A will decrease accordingly, which makes the value of d1 increase. In the actual deployment scenario, P T represents the transmission power of the base station, while P A represents the amplification power of ARIS. Obviously, during the actual deployment process, the transmission power of the base station is often much larger than the amplification power of ARIS.

[0106] In this embodiment, it is known that the deployment interval of ARIS is within the range of [d1, D], and the size of d1 is determined by formula (17). It can be easily seen that the size of d1 is not only related to P T and P A , but also closely related to P T , P A and N a . When N aWhen it increases, the fractional equation The numerator of corresponds to the denominator, and the increase rate is greater. Therefore, the value of the entire fractional equation increases, resulting in The value increases, thereby causing the value of d1 to increase. On the contrary, when N a decreases, The value decreases and the value of d1 decreases. Therefore, when considering the actual deployment of ARIS, N a is an important parameter, and its parameter value and its interaction with other relevant parameters should be comprehensively weighed to accurately determine the reasonable deployment interval of ARIS to ensure the performance and stability of the entire wireless communication system.

[0107] Next, consider a specific case. In formula (16), when or σ 2 (D - d1) 2 P T β is much larger than At this time, the term can be ignored. The following discussion is divided into two cases.

[0108] Case 1 assumes that Then At this time, it is necessary to determine Whether it holds. Through derivation, it can be known that it is necessary to satisfy

[0109] Case 2 assumes that To determine Whether it holds. After analysis, the conditions that need to be satisfied In the above two cases, as long as the conditions of one of the cases are satisfied, At this time, formula (14) can be rewritten as:

[0110]

[0111] The above formula performs a derivative operation on d1 and then sets its value to 0. After a series of operations, the solution is obtained Determine the optimal position of ARIS.. This means that when either of the above two conditions is satisfied, the ideal deployment position of ARIS should be on the straight line formed by the base station and the user, at a distance of from the base station.

[0112] Refer to Figure 3 , Figure 3 provides numerical results to demonstrate the effectiveness of the proposed PRIS and ARIS deployment schemes. Unless otherwise specified, we set Q = 30dBm, P W= 1 dBm, λ = 0.0125 m, β = (λ / 4π) 2 = 30 d, The number of reflection units of ARIS and PRIS is set to N a = N P = 1000.

[0113] Figure 3 Describes the characteristics of the system state in the ARIS-assisted wireless communication scenario mode when the distance D between the base station (BS) and the user is significantly greater than the height H. To make the analysis process simpler and more efficient, it is reasonable to set the value of H to 0 in this case. In terms of specific parameter settings, the straight-line distance between the base station and the user is precisely set to 100 meters, while the vertical distance H from ARIS to the straight line is determined to be 2 meters. From the graphical results, when ARIS is deployed at different positions, the achievable capacity values obtained based on the approximation of H≈0 are very close to the optimal capacity values.

[0114] Refer to Figure 4 , Figure 4 shows two graphs that are plotted under the condition that the total system power Q = 30 dBm. The left graph corresponds to d1 = 10 m, while the right graph corresponds to d1 = 90 m. Careful observation reveals that the change in the amplification power of ARIS will lead to a corresponding change in the achievable capacity of the system. An increase in the amplification power will expand the content volume within a given range, while a decrease in the amplification power will contract the content volume. These effects can be reversed. In addition, it should also be noted that even when the amplification power remains unchanged, when considering different values, especially different ARIS deployment positions, the achievable capacity of the system will also change significantly. These results indicate that the ARIS amplification power and deployment position are the main factors affecting the achievable capacity of the system. Therefore, in the system design and optimization process, these parameters must be comprehensively considered to improve communication performance and reliability, so as to meet the communication requirements of the system.

[0115] Refer to Figure 5 , Figure 5 shows the correlation between the PRIS / ARIS deployment position and the achievable capacity of the system. As Figure 5As shown, when the variable d1 dynamically changes within the range of 0 to 100, the achievable capacity of the system also changes accordingly. In the context of PRIS-assisted wireless communication, simulation results show that when PRIS is deployed at the base station or the user end, the system capacity is significantly improved compared to other locations. Moreover, the capacity values obtained at these two endpoints are almost the same, indicating an obvious symmetry. This result is consistent with the theoretical expectation and further confirms the specific pattern of the PRIS deployment location. For the ARIS-assisted radio communication system, the simulation diagram shows that when the amplification power of ARIS changes, the achievable capacity of the system also changes significantly. With the adjustment of the amplification power, the transmission characteristics and signal processing effects also change, thus affecting the overall achievable capacity of the system. The increase in the amplification power can effectively enhance the signal strength within a certain range, thereby improving the achievable capacity of the system. On the contrary, the decrease in the amplification power will lead to a corresponding reduction in the capacity. This indicates that the amplification power is a key parameter in the ARIS system and plays a crucial role in the communication performance of the system.

[0116] In the context of PRIS-assisted communication, this application theoretically determines that the optimal deployment location should be symmetric between the base station and the user. In the case of ARIS-assisted communication, it has been determined that the deployment location of ARIS is closely related to its amplification power and the number of antennas, while also considering the base station transmission power, ARIS amplification power, and amplification constraint conditions. In particular, the amplification power affects the signal transmission and processing capabilities, while the number of antennas is related to the signal processing efficiency. Both of these factors play important roles in determining the optimal deployment location. The results show that PRIS requires precise location deployment, while ARIS requires a balance between the amplification power and the number of antennas.

[0117] The above are only embodiments of this application, and do not limit the patent scope of this application. Any equivalent structural or equivalent process transformation made using the content of the specification and drawings of this application, or directly or indirectly applied in other related technical fields, shall be included in the patent protection scope of this application by the same token.

Claims

1. A method for constructing a PRIS / ARIS-assisted communication system, characterized in that: include: A wireless communication model is constructed based on a single base station, a single user and a RIS; wherein the RIS is PRIS or ARIS; Based on the wireless communication model and the constant total power, obtaining the signal-to-noise ratio; Based on the signal-to-noise ratio, an optimal position of the RIS is obtained.

2. The method according to claim 1, characterized in that: In response to the RIS being PRIS, the method for constructing the wireless communication model includes: Among them, y p is the signal received by the user; P B is the supercritical transmission power at the base station; Θ P is the reflected signal of PRIS; h is the direct channel from RIS to the user; SR is the channel from the base station to the RIS; x represents the signal transmitted from the base station, which obeys a complex Gaussian distribution with a mean of 0 and a variance of 1, that is, x~CN(0,1). At the same time, represents the thermal noise at the receiving end, which obeys a mean of 0 and a variance of σ 2 Complex Gaussian distribution, n~CN(0,σ 2 ).

3. The method according to claim 2, characterized in that The method for obtaining the signal-to-noise ratio is: Based on formula (2), the signal-to-noise ratio is obtained; wherein the formula (2) is: Among them, the signal-to-noise ratio is γ P ; N P is the number of reflection units in PRIS; D is the horizontal straight-line distance between the base station and the user; H is the height at which RIS is deployed above the horizontal straight-line distance; the intersection of the vertical line of RIS and the horizontal straight line is d1 from the base station.

4. The method according to claim 3, characterized in that The method for obtaining the optimal position of the RIS is: Based on formula (3), the optimal position of the RIS is obtained; wherein the formula (3) is: Simplify the formula (7) and make the numerator equal to zero to obtain the formula (4); wherein the formula (4) is: Calculate formula (4) and solve it to get The optimal position of the PRIS is determined.

5. The method according to claim 1, characterized in that In response to the RIS being ARIS, the method for constructing the wireless communication model includes: Based on formula (5), the wireless communication model is obtained; wherein the formula (5) is: Among them, y a is the signal received by the user; the noise generated by ARIS is x represents the signal transmitted from the base station, which obeys a complex Gaussian distribution with a mean of 0 and a variance of 1, that is, x~CN(0,1). At the same time, it represents the thermal noise at the receiving end, which obeys a mean of 0 and a variance of σ 2 Complex Gaussian distribution, n~CN(0,σ 2 ).

6. The method according to claim 5, characterized in that The method for obtaining the signal-to-noise ratio comprises: Based on formula (6), the signal-to-noise ratio is obtained; wherein the formula (6) is: Among them, the signal-to-noise ratio is γ P , N a is the number of reflection units in PRIS; P A is the amplification power of the ARIS, P T is the transmission power of the base station.

7. The method according to claim 6, characterized in that The method for obtaining the optimal position of the RIS comprises: Based on formula (7), the P A The derivative of ; wherein, the formula (7) is: Calculate formula (7), and then let the γ P The value is 0; the solution is The optimal position of the ARIS is determined.

8. A PRIS / ARIS-assisted communication system model, constructed using the construction method described in claims 1-7.

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