Wireless transmission method and system assisted by intelligent reflective surface

Abstract

The present application provides a wireless transmission method and system assisted by an intelligent reflecting surface. Under any determined base station, intelligent reflecting surface and user distribution conditions, all users are sorted according to channel gain to determine the preset OMA / NOMA user pairs. An intelligent reflecting surface based on element grouping is used to assist the user's communication, and an optimization goal based on system and rate maximization is established; the goal is decomposed into sub-problems of intelligent reflecting surface element grouping, intelligent reflecting surface sub-surface matching with user groups, intelligent reflecting surface phase optimization, user pairing and user mixed access mode. According to the preset user pairs, the reflecting elements of the intelligent reflecting surface are grouped; based on the channel state information of the grouped intelligent reflecting surface elements IRS to the user link, the pairing relationship between each sub-surface and the preset user pair is determined. The optimal phase angle is determined; the users are layered, and multiple layers of users are alternately coupled and paired between layers; and the minimum signal-to-noise ratio difference condition for NOMA access is determined.

Description

Technical Field

[0001] The present application relates to the field of wireless transmission technology, and in particular to a wireless transmission method and system assisted by an intelligent reflective surface. Background Art

[0002] In recent years, research on non-orthogonal multiple access (NOMA) technology has made significant progress, becoming a hot topic in the communications field. NOMA has been extensively studied in next-generation communications and is considered a key technology for next-generation wireless communication systems. It is believed to effectively address the challenges of growing mobile data demand and limited spectrum resources, improving system spectral efficiency and capacity.

[0003] In related technologies, the NOMA wireless transmission method has problems such as low system and rate. Summary of the Invention

[0004] Based on the above objectives, the present application provides a wireless transmission method assisted by an intelligent reflective surface, comprising:

[0005] According to the location of the base station, the location of the smart reflective surface, the distribution conditions of multiple users, and the initialized channel state information of the multiple users, the channel state information of the users is sorted, the initial signal-to-noise ratio of each user is calculated, and a preset OMA / NOMA hybrid access scheme is obtained; an optimization goal based on system and rate maximization is established; wherein the smart reflective surface is a smart reflective surface capable of implementing element-based grouping;

[0006] According to the initialized channel state information of the multiple users and the preset OMA / NOMA hybrid access scheme, using an element-grouped smart reflection surface to assist the communications of the multiple users respectively;

[0007] Determining an optimal phase angle for each element in an element-grouped smart reflective surface; wherein the element-grouped smart reflective surface has a plurality of sub-surfaces, and the plurality of sub-surfaces respectively assist a plurality of user groups in the preset OMA / NOMA hybrid access solution in a one-to-one manner;

[0008] Assisting the multiple user groups respectively based on the optimal phase angle of each sub-surface in the element-grouping-based smart reflective surface to obtain updated channel state information of the multiple users;

[0009] According to the updated channel state information, the multiple users are layered to obtain multiple layers of users, and the multiple layers of users are alternately coupled and paired between the layers;

[0010] According to the set standard that when strong and weak users adopt NOMA access, their respective rates and sum rates are greater than those of OMA access under the same channel conditions, the minimum signal-to-noise ratio difference condition for allowing NOMA access is determined; according to the order of users in each layer, it is judged in turn whether the users in the current layer and the users in the predetermined paired layer meet the NOMA access conditions; the predetermined paired user group that meets the NOMA access conditions is determined as the NOMA user group; the users in the current layer that do not meet the NOMA access conditions are determined as OMA users.

[0011] In some embodiments, the minimum signal-to-noise ratio difference condition for allowing NOMA access is determined based on the setting that when strong and weak users access using NOMA, their respective rates and sum rates are greater than those under the same channel conditions when accessing using OMA, and the minimum signal-to-noise ratio difference condition for allowing NOMA access includes:

[0012] Determine the upper and lower bounds of the power allocation factor for the user group allowed to adopt NOMA access;

[0013] Determine the upper bound of the incomplete interference cancellation coefficient and obtain the relationship between the power allocation factor and the incomplete interference cancellation coefficient;

[0014] According to the relationship between the power allocation factor and the incomplete interference cancellation coefficient and the NOMA pairing criterion, the minimum signal-to-noise ratio difference threshold is determined.

[0015] In some embodiments, the minimum signal-to-noise ratio difference threshold is obtained by the formula Calculate; where, Minimum signal-to-noise ratio difference threshold; γ s is the signal-to-noise ratio of the strong user s in the user group; h s is the channel gain of strong user s in the user group; γ w is the signal-to-noise ratio of weak user w in the user group; h w is the channel gain of the weak user w in the user group; P is the power sent by the base station, σ is the noise variable, σ 2 is the power spectral density of the noise, and I is the interference received from other base stations on the subchannel assigned to user u.

[0016] In some embodiments, the using of the element-grouping-based smart reflective surface to assist communications of the multiple users respectively includes:

[0017] Partitioning the reflective elements of the smart reflective surface using a Kronecker product method according to preset user pair types and quantities to form the plurality of sub-surfaces;

[0018] Based on the channel state information of the link from the smart reflecting surface to the user after the smart reflecting surface element is partitioned, the Hungarian algorithm is used to determine the pairing relationship between the multiple sub-surfaces and the multiple user groups in the preset OMA / NOMA hybrid access solution.

[0019] In some embodiments, the method further comprises determining the optimal phase angle of each element in the smart reflective surface based on element grouping by:

[0020] Initialize the phase angles of all elements so that the phase angle of each element is the initial value;

[0021] The phase matrix of the smart reflector based on element grouping is decomposed into the multiplication of n sub-matrices. The solution of the t-th iteration can be obtained by rotating the solution of N iterations.

[0022] in, Θ (t-1) ,Θ (t) are the optimal solutions to the problem in the (t-1)th and tth iterations respectively;

[0023] According to the optimization goal of maximizing the system and rate, the phase angle of each element is adjusted in turn according to the preset step size; wherein the value of each iteration satisfies θ n =2kπ / 2 D ,k∈{0,1,…,2 D -1};

[0024] For each element, calculate the impact of the element on the optimization target; after each iteration, update the overall reflection matrix.

[0025] Until the phase angles of all elements reach a state that meets the convergence conditions.

[0026] In some embodiments, the layering of the multiple users according to the updated channel state information to obtain multiple layers of users includes:

[0027] determining relative positions of multiple users and a base station based on the updated channel state information;

[0028] The multiple users are differentiated with equal radius according to the direction away from the base station to obtain multiple layers of users.

[0029] In some embodiments, determining whether a user in the current layer and a user in a predetermined paired layer meet the NOMA access condition includes:

[0030] Determine whether the user in the current layer and the first-order predetermined paired user in the predetermined paired layer meet the NOMA access conditions;

[0031] In response to determining that the user of the current layer and the predetermined paired users of the first order in the predetermined pairing layer do not meet the NOMA access conditions, it is judged whether the user of the current layer and the predetermined paired users of the second order in the predetermined pairing layer meet the NOMA access conditions; the predetermined paired users of the second order are set to at least one.

[0032] In some embodiments, the method further comprises: establishing a rate of each user after assistance by the intelligent reflective surface based on element grouping according to the user's signal-to-noise ratio in combination with the Shannon formula, and summing the rates to obtain a system sum rate in the assisted area;

[0033] The system and rate in the assisted area are taken as optimization targets, and the power allocation factor allocated to each user in the NOMA user group is determined.

[0034] In some embodiments, the method further includes: accessing the NOMA user group using NOMA; and accessing the OMA user using a reserved OMA frequency band.

[0035] The embodiment of the present application further provides a wireless transmission system assisted by an intelligent reflective surface, comprising an intelligent reflective surface, a base station, and multiple users; wherein the intelligent reflective surface is an intelligent reflective surface capable of being grouped based on elements;

[0036] The smart reflecting surface capable of element-based grouping is used to: sort the channel state information of the users according to the location of the base station, the location of the smart reflecting surface, the distribution conditions of multiple users, and the initialized channel state information of the multiple users, calculate the initial stage signal-to-noise ratio of each user, and obtain a preset OMA / NOMA hybrid access solution; establish an optimization goal based on system and rate maximization; wherein the smart reflecting surface is a smart reflecting surface capable of element-based grouping;

[0037] According to the initialized channel state information of the multiple users and the preset OMA / NOMA hybrid access scheme, using an element-grouped smart reflection surface to assist the communications of the multiple users respectively;

[0038] Determining an optimal phase angle for each element in an element-grouped smart reflective surface; wherein the element-grouped smart reflective surface has a plurality of sub-surfaces, and the plurality of sub-surfaces respectively assist a plurality of user groups in the preset OMA / NOMA hybrid access solution in a one-to-one manner;

[0039] Assisting the multiple user groups respectively based on the optimal phase angle of each sub-surface in the element-grouping-based smart reflective surface to obtain updated channel state information of the multiple users;

[0040] The base station is configured to: stratify the multiple users according to the updated channel state information to obtain multiple layers of users, and perform alternating coupling and pairing between the multiple layers of users;

[0041] According to the set standard that when strong and weak users adopt NOMA access, their respective rates and sum rates are greater than those of OMA access under the same channel conditions, the minimum signal-to-noise ratio difference condition for allowing NOMA access is determined; according to the order of users in each layer, it is judged in turn whether the users in the current layer and the users in the predetermined paired layer meet the NOMA access conditions; the predetermined paired user group that meets the NOMA access conditions is determined as the NOMA user group; the users in the current layer that do not meet the NOMA access conditions are determined as OMA users.

[0042] From the above description, it can be seen that the present application focuses on the user pairing scheme in the multi-user multiplexing scenario to maximize the system sum rate, while ensuring that the imperfect continuous interference cancellation existing in actual communications is taken into account. The purpose of the present application scheme is to improve the user's communication link quality by introducing an intelligent reflector while ensuring the basic communication quality of working users, and adjust the pairing strategy of NOMA users so that users with higher matching degrees can work on the same spectrum. At the same time, for users who are more suitable for OMA access, the OMA hybrid access method is used to further improve the system sum rate. In the multi-user multiplexing scenario with imperfect SIC, this patent application proposes an intelligent reflector-assisted NOMA user pairing scheme based on element grouping. The expression of the minimum signal-to-noise ratio difference criterion is derived and applied to the determination of paired users. Dynamic adjustment of the pairing combination based on user channel state information can effectively obtain the maximum pairing selection of the system sum rate and significantly improve the system spectrum utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in this application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are merely embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0044] Figure 1 Schematic diagram of a single smart reflector-assisted NOMA communication system;

[0045] Figure 2 Schematic diagram of the OIRS-NOMA system;

[0046] Figure 3 Schematic diagram of dual smart reflectors assisting downlink NOMA communication;

[0047] Figure 4 Schematic diagram of the IRS-assisted layered downlink NOMA user hybrid pairing model according to an embodiment of the present application;

[0048] Figure 5 A schematic flow chart of a wireless transmission method assisted by a smart reflective surface according to an embodiment of the present application;

[0049] Figure 6 A layered schematic diagram of an embodiment of the present application;

[0050] Figure 7 This is a schematic diagram showing the impact of the transmit signal-to-noise ratio on the system and rate according to an embodiment of the present application;

[0051] Figure 8 This is a schematic diagram showing the impact of the number of users on the system and rate according to an embodiment of the present application;

[0052] Figure 9a This is a schematic diagram of user pairing before the intelligent reflective surface assists based on element grouping according to an embodiment of the present application;

[0053] Figure 9b This is a schematic diagram of user pairing after assistance from an intelligent reflective surface based on element grouping according to an embodiment of the present application;

[0054] Figure 10 This is another flowchart of the wireless transmission method assisted by the smart reflective surface according to an embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the objectives, technical solutions and advantages of this application more clear, this application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0056] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the usual meanings understood by people with ordinary skills in the field to which this application belongs. The "first", "second" and similar words used in the embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0057] An intelligent reflecting surface (IRS) is a surface composed of numerous tiny, controllable units. By adjusting the surface's reflection properties for electromagnetic waves, it can precisely manipulate the signal propagation environment. Unlike traditional amplifying and forwarding relays, an IRS has no signal processing capabilities. Instead, it passively reflects signals, making it more energy-efficient.

[0058] Research on intelligent reflective surfaces (IRS) has made significant progress, becoming a hot topic in the field of communications technology. While still in the research and development phase, IRS technology has already garnered widespread interest in academia and industry. Studies have shown that IRSs can dynamically adapt to and optimize complex communication environments, demonstrating their potential in wireless communications, millimeter-wave communications, and indoor positioning.

[0059] Key technologies for smart reflectors include controlling reflection parameters, determining reflector placement, and setting the number of reflective elements. Researchers are currently conducting extensive research on fundamental principles, signal propagation models, and optimization algorithms, primarily focusing on addressing key issues such as reflector layout, power allocation, user identification, and interference management to maximize system performance. Smart reflector technology is also being integrated with other key technologies to a certain extent. For example, combining smart reflectors with Massive Multiple Input Multiple Output (MIMO) and beamforming can further improve system performance and coverage.

[0060] With the further development of new material technologies, research has proposed a smart reflective surface model based on element grouping. Specifically, it has the following features: 1. Spatial Segmentation and Multi-User Support: By independently adjusting each sub-surface, space can be divided into multiple areas, and personalized signal coverage and optimization can be provided to users in each area. This makes it possible to support multiple users in the same area, improving system capacity and efficiency. 2. Dynamic Signal Optimization: By dynamically adjusting the state of each sub-surface, the smart reflective surface can optimize signal transmission characteristics based on different communication needs and environmental changes. This includes adjusting the reflection angle, phase, amplitude, and other factors to maximize signal strength, quality, and coverage. 3. Interference Reduction: By independently controlling each sub-surface, the smart reflective surface can reduce multipath interference and other signal interference. It can eliminate interfering signals or direct them to areas where they will not interfere with the target user through phase and amplitude adjustment.

[0061] There are currently several studies on smart reflectors assisting NOMA communications. These include scenarios using a single smart reflector and multiple smart reflectors, with most of the research focused on improving system performance in terms of rate and spectrum efficiency.

[0062] In terms of improving the system and rate in single intelligent reflector-assisted NOMA communication, the system models currently studied are roughly similar (such as Figure 1 (As shown): One related technique studies a two-user downlink intelligent reflecting surface (IRS)-assisted network over a fading channel. A low-complexity phase shifter adjustment algorithm is proposed, and the optimal power allocation is obtained using Lagrangian dual decomposition with fixed phase shifters, thereby improving system sum and rate. Similarly, in an intelligent reflecting surface-assisted downlink NOMA system, two other related techniques establish a joint optimization problem involving channel allocation, decoding order of NOMA users, power allocation, and reflection coefficient. To address this problem, one of the related techniques first employs a many-to-one matching algorithm to solve the channel allocation problem, then proposes an algorithm that jointly optimizes power allocation and reflection coefficient to improve system sum and rate. The other technique first proposes a low-complexity decoding order optimization algorithm. Secondly, for a given decoding order, an alternating algorithm is proposed to solve the power allocation and reflection coefficient optimization problems. All of these intelligent reflecting surface-assisted schemes utilize full-surface assistance, meaning that all elements of the intelligent reflecting surface participate in reflection to assist users. Results show that these assistance schemes significantly improve signal quality and system performance, and initially achieve precise control of reflected signals. However, the full-surface assisted solution cannot overcome the subwavelength coupling problem in terms of hardware, and there is a problem of high channel overhead in channel estimation. The solution of element partitioning for the reflective surface brings additional flexibility and advantages to the application of smart reflective surfaces. For example, the reflection characteristics of each area can be optimized according to the specific needs of different users or signals; the directionality of the reflected signal can be enhanced, so that the signal is transmitted more concentratedly to a specific receiver location; and the complexity of channel estimation can be reduced while enhancing the system's adaptability to dynamic environments. In addition, there is a related technology, such as Figure 2 As shown in the figure, the joint power and number optimization problem of smart reflector units in the smart reflector-assisted NOMA network is studied, and an OIRS-NOMA optimization scheme based on element partitioning is proposed, which further divides the reflector elements into K sub-surfaces, thereby maximizing the system and rate.

[0063] In terms of improving the system and rate in terms of multi-intelligent reflector-assisted NOMA communication: A related technology addresses the problem of variable mobile user locations and the problem that a single IRS in the traditional IRS-assisted NOMA system cannot cover all mobile users. A new dual-IRS-assisted downlink NOMA network (such as Figure 3As shown). The base station sends superimposed signals to multiple users with variable positions through two IRS reflective elements. Each user selects one IRS from the two IRSs to enhance its own channel, and the sum rate is maximized by optimizing the phase shift matrix of the two IRSs. In another related technology, with the goal of improving the performance of cell-edge user equipment (UE), two IRSs are used to simultaneously assist two edge users. The performance parameters of the system are calculated, and closed mathematical expressions for the outage probability and system rate are given. In addition, the diversity order of the system is studied, and a power allocation optimization algorithm is proposed to improve system performance. Compared with the two solutions provided above, the third related technology considers scenarios with a larger number of smart reflective surfaces and specifies the users assisted by each smart reflective surface. First, the spatial direction of the user channel is used to generate orthogonal beams. Then, the IRS assists in aligning the effective channel vector of the cell-edge user with the predetermined spatial direction. It is worth noting that there is a one-to-one correspondence between the smart reflective surface and the assisted user, and it is assumed that only the assisted user can receive the signal from the reflective surface.

[0064] Non-Orthogonal Multiple Access (NOMA) is an innovative multiple access technology that has attracted widespread research interest in the field of wireless communications in recent years. NOMA improves spectrum efficiency and system speed by transmitting data to multiple users simultaneously on the same time and frequency resources. The key technologies in this technical field currently include: user pairing, power allocation, interference management, multi-user detection, etc. Among them, user pairing is a key strategy for optimizing system performance and channel utilization efficiency. It pairs users with better channel conditions with users with worse channel conditions, and realizes differentiated channel quality services through power allocation differences. By optimizing the user pairing scheme, the spectrum efficiency, capacity and flexibility of the NOMA system can be improved to a certain extent.

[0065] Regarding user pairing strategies in NOMA communications, most existing solutions study the impact of different user pairing strategies on system performance, such as sum rate and energy efficiency, under perfect SIC conditions. Regarding optimizing system rate, NOMA typically pairs users with large channel gain differences to achieve rates exceeding those achieved by OMA access. One related technique proposes the Channel State Sorted Pairing Algorithm (CSS-PA). This strategy divides all users in a cell into several equal sets, sorts users in each set based on channel state information, and then selects users with the largest channel gain difference within each set for pairing. This strategy significantly improves system sum rate compared to the OMA access algorithm while ensuring user fairness. Furthermore, another related technique proposes a minimum distance-based NOMA user pairing strategy (MD-NOMA) by setting a minimum pairing distance to distinguish between near and far users. This pairing strategy provides an analytical expression for the pairing distance threshold under a fixed power allocation. It solves the minimum distance threshold for each far user and selects a near user for pairing based on the matching status of other far users. Compared with traditional OMA and maximum-minimum pairing strategies, this strategy also significantly improves the number of paired users and system sum rate. Another related technique studies the impact of near-far pairing on performance when the channel gain difference between users is small. Based on this, two user pairing schemes, uniform channel gain difference (UCGD) and hybrid-UCGD, are proposed. Uniform channel gain difference pairing focuses on pairing by maintaining a relative UCGD between all users within a pair. In hybrid-UCGD pairing, for end users with large channel gain differences, traditional near-far pairing is used. When the channel gain difference between users begins to decrease, UCGD pairing is switched to. Regarding optimizing system outage probability, another related technique simultaneously focuses on the impact of user pairing strategies on system sum rate and outage probability, demonstrating that pairing users using channel gain differences can achieve lower outage probability performance. A user pairing algorithm based on fractional maximum difference is proposed and its superiority in outage probability performance is verified.

[0066] There are also a few studies on NOMA user pairing in imperfect SIC scenarios. For example, a related technology study shows that imperfect SIC affects the interruption of NOMA users and the system and rate performance, and theoretically proposes the necessity of studying user pairing strategies in NOMA systems with incomplete continuous interference cancellation. In another related technology, in an actual downlink NOMA system with incomplete continuous interference cancellation (SIC), the impact of imperfect SIC on NOMA rate performance is demonstrated, as well as a comparison with the OMA (Orthogonal Multiple Access) scheme under the same conditions. An adaptive user pairing (A-UP) algorithm is proposed to achieve better user rates by dynamically adjusting the user pairing combination and access method according to the set minimum signal-to-noise ratio difference threshold.

[0067] Clearly, existing research on smart reflector-assisted NOMA communication technologies leaves room for improvement. For example, the distributed deployment of multiple IRSs is under-considered, and the flexible passive nature of IRSs is underutilized. Furthermore, optimization algorithms lack research into the impact of user pairing schemes on system performance after the introduction of IRSs.

[0068] Moreover, the relevant technologies are mainly about the joint optimization of the power allocation of users assisted by the smart reflective surface and the phase shift of the smart reflective surface itself. In the proposed scheme, the optimization is based on a fixed user pairing combination, so only the relationship between the smart reflective surface and the power allocation factor is considered. However, through research, it can be seen that in the scenario where the smart reflective surface is introduced, the user's front and back channel state information will change, which will also affect the user pairing combination. Therefore, it is of great significance to study the user pairing problem in the NOMA scenario assisted by the smart reflective surface. Therefore, although the system implementation of the full reflection mode is simple, there is a large reflection power loss and direct channel interference in the corresponding training reflection mode, and it will also cause a large channel overhead and sub-wavelength coupling effect. The above shortcomings are problems that need to be solved urgently whether in a single IRS or in a multi-IRS assisted system.

[0069] In addition, under the conditions of dynamically changing user locations, the user set assisted by each IRS sub-surface in the element-grouping IRS system is also a point worthy of attention. By reasonably setting the correspondence between the two and jointly considering this factor with the optimization scheme of IRS phase shift and user pairing, it is possible to improve the system performance in terms of rate and interruption probability.

[0070] When studying NOMA pairing, most existing research on NOMA communication systems assumes a normal non-orthogonal multiple access network, assuming perfect continuous interference cancellation (CIC) throughout the system's communications. The impact of imperfect SIC on user pairing is not considered. However, in actual communications, imperfections such as channel interference and hardware defects exist. These imperfections must be considered in real-world environments and often lead to imperfect CIC in the system. In such situations, traditional near-far pairing algorithms based on maximizing the signal-to-noise ratio (SNR) difference cannot achieve ideal results. In particular, for intermediate users with small SNR differences, pairing and NOMA access can sometimes even underperform OMA access.

[0071] The few schemes that consider incomplete SIC, such as pairing schemes, primarily focus on studying the impact of different user pairing combinations on system rate under incomplete SIC conditions. However, none of these schemes compare the proposed NOMA scheme with OMA access. Their derivation suggests that under incomplete SIC conditions, there is a certain probability that the NOMA scheme's access rate will be lower than the OMA scheme, and in this case, pure NOMA access often fails to achieve ideal performance. In contrast to the aforementioned studies, related technologies have compared NOMA and OMA schemes and proposed adaptive user pairing schemes based on this. However, in the derivation of the access signal-to-noise ratio difference, the paper only considers that the system sum rate of the two users after access is greater than that of the OMA access scheme, without considering that each user in the NOMA pair must strictly achieve a NOMA rate greater than the OMA rate. This is unsound because the pairing and power allocation algorithms may allocate more power to strong users at the expense of weaker users' rates, resulting in a higher sum rate. This often leads to extremely poor communication quality for edge users, significantly increasing the probability of system outages, which clearly requires improvement. In addition, none of the above existing studies have considered introducing IRS technology to further improve the system speed and reduce transmission loss.

[0072] Furthermore, for users operating on the same spectrum, the total system capacity will vary with the optimization of the smart reflector phase, the user pairing method, and the number of users connected to the system. In summary, when accessing users using given communication resources, in order to achieve the largest possible system and rate, it is necessary to consider the design of a NOMA user solution suitable for the corresponding scenario.

[0073] Based on this, the embodiment of the present application provides a solution for the situation where there is imperfect continuous interference (SIC, Successive Interference Cancellation) cancellation in the IRS-assisted NOMA wireless communication system. First, by analyzing the impact of imperfect SIC on the rate performance of NOMA users, a strict access criterion is proposed for users using NOMA access; for distributed users, smart reflective surfaces are set up in the corresponding areas for assistance, and a smart reflective surface sub-surface optimization scheme based on element grouping is designed. A smart reflective surface phase optimization algorithm based on sequential rotation is proposed to improve the user and rate of the system; according to the access criteria and the user channel state information after assistance, the user is judged as a NOMA and OMA user; finally, the system and rate are further improved through the user layered pairing and hybrid access strategy to achieve the goal of high-density user access and capacity improvement in future mobile communications. It can solve the problems of low system and rate in the NOMA wireless transmission method to a certain extent.

[0074] like Figure 4 As shown, the efficient wireless transmission system provided by the embodiment of the present application may include: a base station (BS), a partitioned intelligent reflector IRS that can be moved, and multiple users. Among them, multiple users are located in the same cell. In a communication scenario where multiple users reuse the same spectrum resources, multiple users can be cellular users and perform downlink communication with a base station with multiple antennas through NOMA technology. In the process of using NOMA communication, there is imperfect continuous interference cancellation.

[0075] The partitioned intelligent reflective surface (IRS) can be composed of multiple sensors, a control unit, and numerous tiny, adjustable reflective element sub-surfaces. These sub-surfaces can be adaptively adjusted based on the environment and communication requirements to achieve more refined control and optimization. The control unit collects environmental information and adjusts the status and parameters of each sub-surface based on this information. The intelligent reflective surface assists users in the initial stage, establishing auxiliary relationships between corresponding sub-surfaces and user groups, and optimizing user channel conditions. The reflection factor is optimized through a sequential rotation method to maximize the system sum rate.

[0076] Among them, the base station can be used to first determine the NOMA / OMA user type in the pairing stage, then perform alternating pairing processing on regional users, and then perform user pairing access with the goal of maximizing the system and rate. After determining the user pairing combination, the power allocation factor is determined using the dichotomy method to allocate power to the user.

[0077] like Figure 5 As shown, the wireless transmission method assisted by the smart reflective surface provided in the embodiment of the present application may include:

[0078] S100: sorting the channel state information of the users based on the location of the base station, the location of the smart reflective surface, the distribution conditions of the multiple users, and the initialized channel state information of the multiple users, calculating the initial signal-to-noise ratio of each user, and obtaining a preset OMA / NOMA hybrid access solution; establishing an optimization goal based on system and rate maximization; wherein the smart reflective surface is a smart reflective surface capable of implementing element-based grouping;

[0079] S200, according to the initialized channel state information of the multiple users and the preset OMA / NOMA hybrid access scheme, using an element-based grouping smart reflecting surface to assist the communication of the multiple users respectively; determining the optimal phase angle of each element in the element-based grouping smart reflecting surface; wherein the element-based grouping smart reflecting surface has multiple sub-surfaces, and the multiple sub-surfaces respectively assist multiple user groups in the preset OMA / NOMA hybrid access scheme in a one-to-one correspondence;

[0080] S300, assisting the multiple user groups based on the optimal phase angle of each sub-surface in the element-grouping-based smart reflective surface, to obtain updated channel state information of the multiple users;

[0081] S400, stratifying the multiple users according to the updated channel state information to obtain multiple layers of users, and alternately coupling and pairing the multiple layers of users between layers;

[0082] S500, according to the set standard that when strong and weak users adopt NOMA access, their respective rates and the sum rate are greater than the access using OMA mode under the same channel conditions, determine the minimum signal-to-noise ratio difference condition that allows NOMA access; according to the order of users in each layer, judge in turn whether the users in the current layer and the users in the predetermined paired layer meet the NOMA access conditions; determine the predetermined paired user group that meets the NOMA access conditions as the NOMA user group; determine the users in the current layer that do not meet the NOMA access conditions as OMA users.

[0083] In the wireless transmission method assisted by an intelligent reflective surface provided in the embodiment of the present application, the scenario studied is that multiple users reuse the same spectrum resources within a circular cell with a radius of R. Due to the randomness of user distribution, there is a situation where the channel gain difference between users is very small. By introducing an intelligent reflective surface to assist some users and further optimizing the system's total rate based on a user-layered pairing algorithm, the system sum rate can be improved, avoiding the problem of users with similar channel gain differences being paired with each other according to the traditional near-far pairing algorithm, resulting in a continuous decline in interference cancellation (SIC) performance, and ultimately a reduction in the system sum rate.

[0084] Furthermore, in traditional communication systems, signals are subject to interference from channel attenuation and multipath effects during transmission, resulting in degraded signal quality and slower transmission rates. Smart reflective surfaces can adjust reflection parameters such as phase, amplitude, and direction in real time based on the environment and communication requirements to maximize received signal power and quality. Therefore, with the introduction of smart reflective surfaces, signals can be precisely focused, expanded, or redirected, enabling dynamic adjustment and optimization of communication links.

[0085] In step S100, the base station, smart reflecting surface, and user distribution conditions can be arbitrary. That is, they can be any base station, smart reflecting surface, and user distribution conditions at any given location. This embodiment of the application does not impose any limitations on the base station, smart reflecting surface, and user distribution conditions. This embodiment of the application is applicable to any base station, smart reflecting surface, and user distribution conditions at any given location.

[0086] In a cell, multiple users use NOMA technology to reuse the same space-time-frequency resources for signal transmission. Due to the randomness of user distribution, the channel gains of users are distributed similarly. When considering imperfect successive interference cancellation (SIC), when the channel gain difference of paired users is too close, it will affect the access rate of the user pair. Therefore, the present application uses an intelligent reflection surface to assist users in NOMA pairing, which can make the system and rate after access greater than the traditional NOMA and OMA access algorithms. Step S100 can be performed by an intelligent reflection surface. Typically, the intelligent reflection surface is an intelligent reflection surface based on element grouping.

[0087] In some embodiments, the optimization objective based on system and rate maximization can be

[0088]

[0089] Among them, (8a) represents the constant modulus constraint of the smart reflector based on element grouping, (8b) represents the imperfect factor constraint in the imperfect SIC, and (8c) represents the NOMA power allocation factor restriction. Λ k θ is the specific NOMA user pairing combination relationship. k is the angle of each reflective surface element in the element-based grouping) θ is the matrix of the intelligent reflection surface based on element grouping, M is the number of users in the system, M / 2 is the number of sub-surfaces, N k is the number of elements contained in the sub-surface; k is the index value of each sub-surface, is the phase representation of a specific sub-surface. When using the smart reflective surface based on element grouping for assistance, is the link from BS to IRS, BS is the base station, g ks is the channel gain from the smart reflector to the strong user, h ks is the channel coefficient after strong user assistance, s is the index of the strong user, w is the index of the weak user, g kw is the channel gain from the smart reflector to the weak user, h kw is the channel coefficient after weak user assistance.

[0090] In step S200, in some embodiments, assisting the communication of the multiple users respectively using an element-grouped smart reflection surface according to the initialized channel state information of the multiple users and the preset OMA / NOMA hybrid access scheme includes:

[0091] According to the initialized channel state information, the multiple users are sorted in descending order. Generally, the users can be sorted in descending order according to the channel gain h1 2 ≥h2 2 ≥…≥|h M 2 .

[0092] The communication of the multiple users is assisted according to the descending sorting result and the optimal phase angle of each element in the smart reflective surface based on element grouping.

[0093] In some embodiments, the using of the element-grouping-based smart reflective surface to assist communications of the multiple users may include:

[0094] According to the preset user pair type and quantity, the reflective elements of the smart reflective surface are partitioned using the Kronecker product method to form the multiple sub-surfaces. The Kronecker product method is an existing method, and this application does not involve improvements to the existing Kronecker product method.

[0095] Based on the channel state information of the link from the smart reflective surface to the user after the smart reflective surface element is partitioned, the Hungarian algorithm is used to determine the pairing relationship between the multiple sub-surfaces and the multiple user groups in the preset OMA / NOMA hybrid access scheme. The Hungarian algorithm is an existing method, and this application does not involve improvements to the existing Hungarian algorithm.

[0096] In this way, the user's signal-to-noise ratio can be improved, thereby increasing the user rate.

[0097] In some embodiments, the optimal phase angle of each element in the intelligent reflection surface based on element grouping can be obtained by a sequential rotation method. The specific sequential rotation method can be: the sequential rotation method is a commonly used numerical calculation method for solving the phase angle of elements in the intelligent reflection surface (IRS), which is used to achieve directional reflection of the incident signal. It adjusts the phase angle of each element by step-by-step iteration so that the synthetic beam direction of the target signal is as close as possible to the desired direction. A plurality of sub-surfaces constitute an entire plane (i.e., an intelligent reflection surface), and the sub-surface may include a plurality of elements, i.e., a plurality of elements constitute a sub-surface, and each element may have a plurality of different phase angles. The method also includes determining the optimal phase angle of each element by the following method:

[0098] Initialization: Initialize the phase angles of all elements to their initial values. Typically, the initial values ​​can be uniformly distributed random phases or a specific initial phase configuration.

[0099] Iterative Adjustment: Each element is iterated sequentially according to a pre-set iteration order. This can be understood as iterating each element sequentially according to a certain rule. Typically, the pre-set iteration order can include iterating over each element sequentially, or selecting the iteration order based on the element's distance from the desired direction. For each element, the element's contribution to the composite beam direction is calculated, and the element's phase angle is adjusted to align the composite beam direction with the desired direction. This adjustment process is typically implemented using an optimization algorithm, such as a sequential rotation method.

[0100] In some embodiments, the phase matrix of the smart reflective surface based on element grouping may be decomposed into the multiplication of n sub-matrices, and the solution of the t-th iteration may be obtained by rotating the solution of the N-th iteration.

[0101] in, Θ (t-1) ,Θ (t) are the optimal solutions to the problem in the (t-1)th and tth iterations respectively;

[0102] According to the optimization goal of maximizing the system and rate, the phase angle of each element is adjusted in turn according to the preset step size; wherein the value of each iteration satisfies θ n =2kπ / 2 D ,k∈{0,1,…,2 D -1};

[0103] For each element, calculate the impact of the element on the optimization target; after each iteration, update the overall reflection matrix.

[0104] Until the phase angles of all elements reach a state that meets the convergence conditions.

[0105] The phase angle of each element is adjusted in turn until the phase angles of all units meet the convergence condition. Once the iterative process converges, the optimal phase angle configuration for each element can be obtained. These phase angles can be directly applied to the smart reflector system to achieve directional reflection of the incident signal.

[0106] The choice of convergence conditions typically depends on the application scenario and performance requirements and can be adjusted as needed. In some embodiments, determining the convergence condition may include checking the angle between the synthesized beam direction and the desired direction during each iteration. If the angle is less than a pre-set threshold or a pre-set maximum number of iterations has been reached, the iteration is terminated. Thus, the convergence condition is satisfied.

[0107] In some embodiments, in step S300, a sequential rotation method is used to adjust the reflective element matrix parameters of each sub-surface in turn for the user to be assisted. The strong user signal after being assisted by the sub-surface of the smart reflective surface based on element grouping can be expressed as: The weak user signal can be expressed as The signal-to-noise ratio of the strong user after the sub-surface assistance of the intelligent reflective surface based on element grouping can be expressed as: The weak user signal can be expressed as Among them, h u is the link from BS to user; g w is the link from IRS to weak user w; g s is the link of the IRS to the strong user w. In addition, the decoding of the NOMA signal starts with the high-power signal directly decoded by the far UE, and the low-power signal of the far UE is treated as noise. On the other hand, the near user UE sequentially decodes and removes the high-power signal until its own signal is decoded.

[0108] In step S400, in some embodiments, as Figure 4 As shown, the multiple users are layered according to the updated channel state information to obtain multiple layers of users, which may include:

[0109] Based on the updated channel state information, the relative positions of multiple users and the base station are determined. Usually, the channels of strong and weak users are still represented by h according to the channel gain of the user relative to the BS. s , h w In some embodiments, each user may be re-channeled H1 2 ≥H2 2 ≥…≥H M 2 Among them, H MRepresents the new channel coefficient after the Mth user undergoes channel adjustment.

[0110] The multiple users are differentiated with equal radius according to the direction away from the base station to obtain multiple layers of users.

[0111] In some embodiments, multiple users in cells within a region may be differentiated by equal radius starting from the region closest to the base station and gradually moving outward (ie, in a direction away from the base station), so as to split the multiple users into multiple layers, for example, four layers.

[0112] Among them, the alternating coupling and pairing of multiple layers of users between layers can be understood as follows: Figure 6 As shown, the current layer (eg, the first layer or the third layer) is alternately paired with the nearest layer (eg, the second layer or the fourth layer) in the non-adjacent layers. The alternate coupling pairing here can be understood as alternate coupling predetermined pairing.

[0113] In step S500, in some embodiments, the minimum signal-to-noise ratio difference condition for allowing NOMA access may include:

[0114] Determine the upper and lower bounds of the power allocation factor for the user group allowed to adopt NOMA access;

[0115] Determine the upper bound of the incomplete interference cancellation coefficient and obtain the relationship between the power allocation factor and the incomplete interference cancellation coefficient;

[0116] According to the relationship between the power allocation factor and the incomplete interference cancellation coefficient and the NOMA pairing criterion, the minimum signal-to-noise ratio difference threshold is determined.

[0117] In some embodiments, in the OMA system, for a given set of user groups, the signal received by the user is set to: According to the signal model, the signal-to-noise ratio of OMA users can be obtained: Therefore, the signal-to-noise ratio of users in the user group can be expressed as: Where u is the user's index. u is the channel coefficient of user u. s is the signal received by the strong user s in the user group; y w is the signal received by weak user w in the user group; h s is the channel coefficient of the strong user s in the user group; h w is the channel coefficient of the weak user w in the user group; P is the power sent by the base station. Usually, the base station uses equal power transmission, that is, the power sent at different times is the same; σ is a constant, is the noise variable, σ 2is the power spectral density of the noise, and I is the interference received from other base stations on the subchannel allocated to user u. It should be understood that since the signals sent by other base stations are not required by user u, they are determined to be interference.

[0118] In the NOMA system, for a given set of user groups, the signal sent by the base station is expressed as: P represents the power sent by the base station, α s is the power allocation factor allocated to the strong user s in a given user pair in the NOMA system, α w is the power allocation factor allocated to the weak user w in a given user pair in the NOMA system, X s and X w They represent the transmission signals from the base station to the strong user s and the weak user w in the user pair respectively.

[0119] Thus, in a downlink communication system considering imperfect SIC, the signal-to-noise ratio of a strong user can be obtained based on the expression of the signal sent by the base station: The signal-to-noise ratio of the weak user is: Among them, h s and h w denote the channel coefficients of the strong and weak users, respectively, in a set of NOMA users. β∈[0,1] represents the imperfection in SIC due to implementation issues such as scaling and error propagation. β=0 means that the strong user is able to completely cancel the interference of the weak user, i.e., perfect SIC. and are the SINR values ​​of strong users and weak users, and when substituted into the signal-to-noise ratio of users in the user group of the OMA system, we can get:

[0120]

[0121] The condition for determining the power factor range is that the corresponding NOMA access and rate need to be higher than the corresponding OMA user group. In some embodiments, the upper bound of the power allocation factor can be The lower bound of the power allocation factor can be Among them, α s is the power allocation factor assigned to the strong user s in a given user pair (e.g., potential user group) in the NOMA system, γ w is the signal-to-noise ratio of the weak user w in a given user pair in the OMA system. s is the signal-to-noise ratio of the strong user s in a given user pair (e.g., potential user group) in the OMA system. β is the incomplete interference cancellation coefficient, which is usually a fixed value. Usually, there is a trade-off between strong and weak users in the selection of power allocation factors. sThe choice of needs to satisfy the requirement that the individual NOMA rates of strong users and weak users are higher than the OMA rates.

[0122] In some embodiments, the weak user rate in the NOMA scheme may be greater than the rate when the user accesses using the OMA scheme. The upper bound of the power allocation factor is obtained. Specifically, it can be simplified by for Arrange the corresponding parameters to one side.

[0123] In some embodiments, the strong user rate in the NOMA scheme may be greater than the rate when the user accesses using the OMA scheme. The lower bound of the power allocation factor is obtained. Specifically, it can be simplified by

[0124] for Arrange the corresponding parameters to one side.

[0125] In this way, by analyzing the boundaries of the power allocation factor, it is first assumed at this stage that the incomplete interference cancellation coefficient is 0. This can avoid the NOMA transmission providing more power to users with poor channel conditions during the transmission process, and allocating less power to users with good channel conditions. The power allocation mode essentially sacrifices the rate of some strong users and does not fully utilize the channel conditions of strong channel users.

[0126] The value range of the power allocation factor β is determined by the total rate and the size relationship (the NOMA total rate is greater than the OMA total rate). In some embodiments, the upper bound of the incomplete interference cancellation coefficient satisfies In this way, within the feasible range of the power allocation factor, a larger power can be allocated to the strong user, thereby making greater use of the more advantageous channel conditions of the strong user and further contributing to the improvement of the total rate.

[0127] In some embodiments, the upper bound of the incomplete interference cancellation coefficient can be obtained by the following method to solve the expression of the value range of the power allocation factor β:

[0128] Then the relationship between β and power allocation factor can be obtained as: Substituting the upper bound of the power allocation factor into the equation, we can obtain the upper bound of the incomplete interference cancellation coefficient.

[0129] The minimum signal-to-noise ratio difference threshold can be obtained by the formula Calculate; where, is the minimum signal-to-noise ratio difference threshold; γ s is the signal-to-noise ratio of strong user s in the user group; γ wis the signal-to-noise ratio of weak user w in the user group; P t is the power transmitted by the base station at time t, h s is the channel gain of strong user s in the user group; h w is the channel gain of weak user w in the user group.

[0130] Specifically, the criteria for determining whether the initial user is suitable for the NOMA pairing scheme can be as follows: 1) Split the total NOMA rate into the sum of the rates of strong users and weak users:

[0131] The total OMA rate is divided into the sum of the rates of strong users and weak users: 2) The total rate of paired NOMA user pairs is greater than the total rate of OMA user pairs: ASR NOMA >ASR OMA 3) The rate of NOMA weak users is greater than that of OMA weak users: 4) The rate of NOMA strong users is greater than that of OMA strong users: Apply a positive constraint to the molecule. We get Then combine Determine the conditions that the signal-to-noise ratio needs to meet when this condition is always true, that is, Finally, we can get the following solution: In this way, the formula for the lower limit of the signal-to-noise ratio difference that needs to be met (that is, the minimum signal-to-noise ratio difference threshold) is obtained: In this way, we can avoid s Therefore, after determining whether the initial user is suitable for the NOMA pairing scheme, suitable users are set to NOMA access and a NOMA access user group is obtained. Unsuitable users are set to OMA access and an OMA access user group is obtained. This allows the preset OMA / NOMA hybrid access scheme to be obtained.

[0132] The step of sequentially judging whether the users in the current layer and the users in the predetermined paired layer meet the NOMA access conditions may be performed based on a minimum signal-to-noise ratio difference threshold condition.

[0133] Determining whether users in the current layer and users in the predetermined paired layer meet the NOMA access condition may include:

[0134] Determine whether the user in the current layer and the first-order predetermined paired user in the predetermined paired layer meet the NOMA access condition. The NOMA access condition can be understood as the signal-to-noise ratio difference between the current user in the current layer and the first-order predetermined paired user in the predetermined paired layer is greater than the minimum signal-to-noise ratio difference threshold. There are multiple users in the current layer and the predetermined pairing layer, respectively. In each layer, the users are arranged in the order in step S300. The first-order predetermined pairing user can be understood as the user in the same order as the current user in the predetermined pairing layer. For example, for the first user in the first layer, the first-order predetermined pairing user is the first user in the third layer; for the second user in the first layer, the first-order predetermined pairing user is the second user in the third layer; ...; for the nth user in the first layer, the first-order predetermined pairing user is the nth user in the third layer. Typically, when the first user in the first layer does not meet the NOMA access conditions with the first user in the third layer, but meets the NOMA access conditions with the nth user in the third layer, the first-order predetermined pairing user of the second user in the first layer becomes the third user in the third layer.

[0135] In response to determining that the user of the current layer and the predetermined paired users of the first order in the predetermined pairing layer do not meet the NOMA access conditions, it is judged whether the user of the current layer and the predetermined paired users of the second order in the predetermined pairing layer meet the NOMA access conditions; the predetermined paired users of the second order are set to at least one.

[0136] It can be understood that, assuming that the first user in the first layer and the first user in the third layer do not meet the NOMA access conditions, then it is determined whether the first user in the first layer and the first second-order scheduled paired user (that is, the second user in the third layer) meet the NOMA access conditions. If the first user in the first layer and the first second-order scheduled paired user meet the NOMA access conditions, then it is determined that the first user in the first layer and the first second-order scheduled paired user paired with it are both NOMA users, and the first user in the third layer is determined to be paired with the second user in the first layer. If the first user in the first layer and the first second-order scheduled paired user do not meet the NOMA access conditions, then it is continued to determine whether the first user in the first layer and the second second-order scheduled paired user do not meet the NOMA access conditions. If the first user in the first layer and all second-order scheduled paired users do not meet the NOMA access conditions, then it is determined that the first user in the first layer is an OMA user. At this time, the first user in the first layer is deleted from the NOMA user candidate set and re-added to the OMA user candidate set.

[0137] In some embodiments, the method may further include: establishing a rate of each user after assistance by the intelligent reflective surface based on element grouping according to the user's signal-to-noise ratio in combination with the Shannon formula, and summing the rates to obtain a system sum rate in the assisted area;

[0138] The system and rate in the assisted area are taken as optimization targets, and the power allocation factor allocated to each user in the NOMA user group is determined.

[0139] In some embodiments, after being assisted by the smart reflective surface based on element grouping, the received signal at the user is as shown in the formula: Among them, the signal-to-noise ratio of the strong user is: The signal-to-noise ratio of the weak user is: Using Shannon's formula theorem, the corresponding user rate can be obtained: in, is the rate of NOMA strong users; is the rate of NOMA weak users.

[0140] Therefore, the optimization objective function can be determined as:

[0141]

[0142] After solving the objective optimization function, the value of the power allocation factor can be obtained.

[0143] In some embodiments, the obtained power allocation factor may be a fixed factor. In other embodiments, a dichotomy method may be used to optimize the power factor.

[0144] In some embodiments, the method may further include: accessing the NOMA user group using NOMA; and accessing the OMA user using a reserved OMA frequency band. This may be understood as follows: for a user identified as a NOMA user, accessing the NOMA user using NOMA according to the pairing result after performing alternating coupling pairing between layers; and for a user identified as an OMA user, accessing the user using the reserved OMA frequency band using the OMA solution.

[0145] In some embodiments, such as Figure 10 As shown, the method may further include: obtaining a system and rate after intelligent reflective surface assistance based on element grouping, user pairing strategy optimization, power factor optimization, and hybrid access mode optimization.

[0146] In some embodiments, the optimization problem

[0147]

[0148] Maximize the system sum rate. Where (8a) represents the constant modulus constraint of the smart reflector, (8b) represents the imperfect factor constraint in the imperfect SIC, and (8c) represents the NOMA power allocation factor constraint. k θ is the specific NOMA user pairing combination relationship. kis the angle of each reflective surface element in the sub-surface) θ is the matrix of the smart reflection surface, M is the number of users in the system, M / 2 is the number of sub-surfaces, and N k is the number of elements contained in the sub-surface; k is the index value of each sub-surface, is the phase representation of a specific sub-surface. When using a partitioned smart reflective surface for assistance, is the link from BS to IRS, BS is the base station, g ks is the channel gain from the smart reflector to the strong user, h ks is the channel coefficient after strong user assistance, s is the index of the strong user, w is the index of the weak user, g kw is the channel gain from the smart reflector to the weak user, h kw is the channel coefficient after weak user assistance.

[0149] In this way, the efficient wireless transmission system and rate of the embodiment of the present application can be understood, and then by comparing with the systems and rates of other solutions, the advantages of the method of the present application can be intuitively seen.

[0150] Verification Example Simulation Analysis of the Smart Reflector-Assisted NOMA User Pairing Scheme Based on User Stratification

[0151] In order to verify the performance improvement of this application solution, it is necessary to compare and analyze the proposed solution with other user-selected solutions. Therefore, this application selects the following solutions for comparative analysis:

[0152] M1 (C-NOMA): A user pairing algorithm based on user channel status ranking. This algorithm first sorts system users in ascending order of channel status. Users with good channel conditions are paired with users with poor channel conditions. The user with the best channel condition is paired with the user with the worst channel condition, the user with the second-best channel condition is paired with the user with the second-worst channel condition, and so on.

[0153] M2 (Adaptive-NOMA): Adaptive user pairing (A-UP) based on the minimum signal-to-noise ratio criterion. NOMA user pairing is performed on users that meet the signal-to-noise ratio requirements. Users that do not meet any other user signal-to-noise ratio requirements are designated as OMA users. Users that cannot access using NOMA after traversal are assigned OMA access.

[0154] M3 (Proposed-IRS-Assisted NOMA): represents the NOMA user hierarchical pairing solution based on element grouping and assisted by intelligent reflective surface proposed in this application.

[0155] System setup: MATLAB R2020b

[0156] Assume that all channel coefficients are independent and identically distributed complex Gaussian variables with zero mean and unit variance, users are randomly distributed according to the system model, large-scale fading is considered, the Rayleigh fading factor is set to 1, the system bandwidth is 1 MHz, and fixed power allocation is used in NOMA transmission, with a power allocation factor α s is 0.4, the base station coverage radius is 100 meters, and the noise power is 1dB.

[0157] The results are as follows Figure 7 , Figure 8 , Figure 9a and Figure 9b shown.

[0158] Depend on Figure 7 The simulation results show that as the base station transmit power increases, the system sum rate tends to gradually increase. As the transmit power gradually increases, the system sum rate of the solution proposed in this application increases much more than the compared solution. This result further illustrates the advantages of the proposed NOMA solution. For C-NOMA and Adaptive NOMA, under high signal-to-noise ratio conditions, the solution proposed in this application is completely superior to C-NOMA and Adaptive NOMA. When the transmit signal-to-noise ratio is 25dB, the gap reaches 1.75Mbps and 2.25Mbps respectively.

[0159] Depend on Figure 8 It can be seen that as the number of users gradually increases, the system and rate show a trend of gradually increasing. However, it can be seen that whether it is the intelligent reflective surface assisted NOMA user stratification pairing scheme proposed in this application or the comparative C-NOMA and Adaptive NOMA schemes, the rate of increase tends to be slow. In scenarios with a large number of users, the advantages of the scheme proposed in this application over the other schemes gradually become obvious. When the number of users is 20, the gap is 2.25Mbps compared with C-NOMA and Adaptive NOMA; when the number of users is 64, the gap is 4Mbps compared with C-NOMA and Adaptive NOMA. This is also in line with the analysis of this scheme, because as the number of users increases, there is always an upper limit on the total capacity of the system on the basis of keeping the total system resources fixed. The increase in the number of users only leads to an increase in the number of users with similar potential channel gain differences, and then the number of users to be assisted increases. After the channel adjustment, the number of so-called "high-quality channels" increases compared to when the number of system users is small, so the total capacity of the system will increase.

[0160] Depend on Figure 9a and Figure 9bIt can reflect the difference in user pairing in the system before and after the introduction of the intelligent reflection surface. The minimum signal-to-noise ratio difference derived by this solution is used as the access criterion to screen users before and after the introduction of the intelligent reflection surface in the system. The green part of the users indicates that they meet the signal-to-noise ratio conditions for NOMA user pairing proposed in this solution and are identified as NOMA users. The red part of the users indicates that they do not meet the signal-to-noise ratio conditions for NOMA user pairing proposed in this solution and are identified as OMA users. Figure 9a It can be seen that in the randomly generated user model, there are 8 users who do not meet the NOMA access conditions in the initialization phase. If these 8 users are forced to adopt NOMA access, the total rate of the system will be reduced to a certain extent. Figure 9b As can be seen in the figure, after the introduction of the intelligent reflective surface for assisted pairing, the number of users that do not meet the NOMA access conditions is reduced from 8 to 2. These two users use OMA access, while the remaining users can use NOMA access.

[0161] This application proposes a scheme for pairing NOMA users in a multi-user NOMA downlink network using smart reflective surfaces based on element grouping. First, a multi-user multiplexing communication resource is randomly generated in a downlink cellular scenario with imperfect SIC. To further maximize the total system capacity, a dynamic hybrid access scheme combining NOMA and OMA is proposed. First, the signal-to-noise ratio (SNR) conditions for NOMA access to outperform OMA access for two users are analyzed. Specifically, the sum of the NOMA rates of the paired users must be greater than the sum of the rates of the two users using OMA access, the rate of the strong user in the NOMA pairing must be greater than the rate of the user using OMA access, and the rate of the weak user in the NOMA pairing must be greater than the rate of the user using OMA access. Based on the analyzed SNR difference conditions, a minimum user SNR difference (MSD) is designed to allow NOMA pairing. Based on this criterion, smart reflective surface replication is performed to assist the designated user in channel reconstruction. The smart reflective surface is then partitioned into N independently controllable sub-surfaces to provide assistance to different users. Channel modeling and signal analysis are performed for randomly distributed users. By deriving the signal-to-noise ratio, an optimization problem is established to maximize the system and rate, using multiple sub-surface reflection coefficients, power allocation factors, and user pairing relationships as variables. Based on this optimization problem, an optimization algorithm is used to determine the optimal smart reflector auxiliary phase. Finally, multi-user access is achieved through user layered pairing and hybrid access.

[0162] According to the influencing factors considered, this application uses a two-dimensional matrix to record the corresponding MSD under the index of all possible user pairs in the system in real time for user type judgment. A matrix with multiple dimensions that is dynamically variable is used to record the reflection coefficients of the elements in each intelligent reflection surface in real time. After the phase optimization is completed, all users are finally judged and OMA and NOMA user sets are established respectively. Finally, a pairing method based on user stratification is used to assign all users to four layers. A new user candidate set is formed by arranging the sets in descending order of channel gain, and then the users are divided into four layers according to the layering idea. Cross-pairing is adopted between layers, and the first layer is paired with the third layer, and the second layer is paired with the fourth layer to form a pairing group for pairing. The following pairing scheme is adopted in each pairing group: the first high-gain channel user pair of each layer is paired with the first high-gain channel user pair of another layer, the second high-gain channel user pair of each layer is paired with the second high-gain channel user pair of another layer, and so on, until the end. For OMA users that do not meet the NOMA access conditions, reserved spectrum resources are used for OMA access. After simulation verification, it can be seen that the above scheme is conducive to achieving system and rate improvements.

[0163] In the solution proposed in this application, the necessity of considering the imperfect SIC situation in the communication system is first explained, which is more in line with the actual scenario of communication. Then, in the case of imperfect SIC, it is verified that when trying to maximize the total rate in the communication system, the use of NOMA access is not due to the OMA access scheme under all circumstances. In view of this, we derive the MSD conditions. Under these MSD conditions, the use of NOMA for users is beneficial to the entire system. In addition, the bounds on the power fractions divided between imperfect SIC for NOMA users are derived to ensure that NOMA is better than OMA, which contributes to further improving the system and rate; on the basis of meeting the above conditions, this application assumes that all working users reuse the spectrum, effectively saving resources, and at the same time, the signal-to-interference-noise ratio constraints of edge users ensure the basic QoS of user communications, providing new ideas for the access of massive users in the future;

[0164] The pairing scheme proposed in this application uses an intelligent reflective surface based on element grouping to assist communication. The intelligent reflective surface based on element grouping can optimize the signal propagation path so that the signal undergoes appropriate reflection during transmission, thereby enhancing the signal strength and quality, which helps to improve the overall signal transmission efficiency of the NOMA system. Secondly, this scheme innovatively uses intelligent reflective surfaces based on element grouping for assistance. Compared with traditional intelligent reflective surfaces, it can provide more advanced signal control capabilities, achieve more precise spatial segmentation and multi-user support, while reducing interference and improving system speed and efficiency. By independently adjusting each sub-surface, the model can adapt to different communication environments and needs, achieve dynamic signal optimization, and have greater flexibility and adjustability, thereby bringing more advantages to wireless communication systems.

[0165] By adjusting the intelligent reflective surface based on element grouping, NOMA user pairing can be optimized. The intelligent reflective surface based on element grouping can flexibly pair users according to the distance between users, signal quality and other related information to achieve the best resource utilization efficiency. This can improve the system speed and provide a better user experience. At the same time, since the intelligent reflective surface based on element grouping can be deployed on indoor walls or the outer surface of buildings, it is relatively easy to install and disassemble. Therefore, this pairing solution has strong applicability in user-intensive communication scenarios;

[0166] The proposed intelligent reflector-assisted scheme based on element grouping employs a user-based layered pairing and hybrid access approach, increasing the chances of achieving optimal performance for users with poor channel conditions. By designing different pairing and access schemes for users with different channel conditions, the system's spectrum resources are fully utilized, further improving system performance and speed compared to traditional pairing algorithms.

[0167] It is understandable that before using the technical solutions of each embodiment of the present disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0168] For example, in response to a user's active request, a prompt message is sent to the user to clearly inform the user that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the electronic device, application, server, storage medium, or other software or hardware that performs the operation of the disclosed technical solution based on the prompt message.

[0169] As an optional but non-limiting implementation, in response to a user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. Furthermore, the pop-up window may also contain a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0170] It is understandable that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0171] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied in a distributed scenario and performed by multiple devices working together. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the method.

[0172] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the attached embodiments of the present application, which also provide an efficient wireless transmission system, including a reflective surface, a base station, and the claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0173] Based on the same application concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a wireless transmission system assisted by an intelligent reflective surface.

[0174] refer to Figure 4 The wireless transmission system assisted by the intelligent reflective surface includes an intelligent reflective surface, a base station and multiple users. The intelligent reflective surface is an intelligent reflective surface that can be grouped based on elements;

[0175] The smart reflecting surface capable of element-based grouping is used to: sort the channel state information of the users according to the location of the base station, the location of the smart reflecting surface, the distribution conditions of multiple users, and the initialized channel state information of the multiple users, calculate the initial stage signal-to-noise ratio of each user, and obtain a preset OMA / NOMA hybrid access solution; establish an optimization goal based on system and rate maximization; wherein the smart reflecting surface is a smart reflecting surface capable of element-based grouping;

[0176] According to the initialized channel state information of the multiple users and the preset OMA / NOMA hybrid access scheme, an element-based grouping smart reflecting surface is used to assist the communication of the multiple users respectively; determining the optimal phase angle of each element in the element-based grouping smart reflecting surface; wherein the element-based grouping smart reflecting surface has multiple sub-surfaces, and the multiple sub-surfaces respectively assist the multiple user groups in the preset OMA / NOMA hybrid access scheme in a one-to-one correspondence;

[0177] Assisting the multiple user groups respectively based on the optimal phase angle of each sub-surface in the element-grouping-based smart reflective surface to obtain updated channel state information of the multiple users;

[0178] The base station is configured to: stratify the multiple users according to the updated channel state information to obtain multiple layers of users, and perform alternating coupling and pairing between the multiple layers of users;

[0179] According to the set standard that when strong and weak users adopt NOMA access, their respective rates and sum rates are greater than those of OMA access under the same channel conditions, the minimum signal-to-noise ratio difference condition for allowing NOMA access is determined; according to the order of users in each layer, it is judged in turn whether the users in the current layer and the users in the predetermined paired layer meet the NOMA access conditions; the predetermined paired user group that meets the NOMA access conditions is determined as the NOMA user group; the users in the current layer that do not meet the NOMA access conditions are determined as OMA users.

[0180] In some embodiments, the minimum signal-to-noise ratio difference condition for allowing NOMA access is determined based on the setting that when strong and weak users access using NOMA, their respective rates and sum rates are greater than those under the same channel conditions when accessing using OMA, and the minimum signal-to-noise ratio difference condition for allowing NOMA access includes:

[0181] Determine the upper and lower bounds of the power allocation factor for the user group allowed to adopt NOMA access;

[0182] Determine the upper bound of the incomplete interference cancellation coefficient and obtain the relationship between the power allocation factor and the incomplete interference cancellation coefficient;

[0183] According to the relationship between the power allocation factor and the incomplete interference cancellation coefficient and the NOMA pairing criterion, the minimum signal-to-noise ratio difference threshold is determined.

[0184] In some embodiments, the minimum signal-to-noise ratio difference threshold is obtained by the formula Calculate; where, Minimum signal-to-noise ratio difference threshold; γ s is the signal-to-noise ratio of the strong user s in the user group; h sis the channel gain of strong user s in the user group; γ w is the signal-to-noise ratio of weak user w in the user group; h w is the channel gain of the weak user w in the user group; P is the power sent by the base station, σ is the noise variable, σ 2 is the power spectral density of the noise, and I is the interference received from other base stations on the subchannel assigned to user u.

[0185] In some embodiments, the using of the element-grouping-based smart reflective surface to assist communications of the multiple users respectively includes:

[0186] Partitioning the reflective elements of the smart reflective surface using a Kronecker product method according to preset user pair types and quantities to form the plurality of sub-surfaces;

[0187] Based on the channel state information of the link from the smart reflecting surface to the user after the smart reflecting surface element is partitioned, the Hungarian algorithm is used to determine the pairing relationship between the multiple sub-surfaces and the multiple user groups in the preset OMA / NOMA hybrid access solution.

[0188] In some embodiments, the smart reflective surface is used to determine the optimal phase angle of each element in the smart reflective surface based on element grouping by the following method:

[0189] Initialize the phase angles of all elements so that the phase angle of each element is the initial value;

[0190] The phase matrix of the smart reflector based on element grouping is decomposed into the multiplication of n sub-matrices. The solution of the t-th iteration can be obtained by rotating the solution of N iterations.

[0191] in, Θ (t-1) ,Θ (t) are the optimal solutions to the problem in the (t-1)th and tth iterations respectively;

[0192] According to the optimization goal of maximizing the system and rate, the phase angle of each element is adjusted in turn according to the preset step size; wherein the value of each iteration satisfies θ n =2kπ / 2 D ,k∈{0,1,…,2 D -1};

[0193] For each element, calculate the impact of the element on the optimization target; after each iteration, update the overall reflection matrix.

[0194] Until the phase angles of all elements reach a state that meets the convergence conditions.

[0195] In some embodiments, the layering of the multiple users according to the updated channel state information to obtain multiple layers of users includes:

[0196] determining relative positions of multiple users and a base station based on the updated channel state information;

[0197] The multiple users are differentiated with equal radius according to the direction away from the base station to obtain multiple layers of users.

[0198] In some embodiments, determining whether a user in the current layer and a user in a predetermined paired layer meet the NOMA access condition includes:

[0199] Determine whether the user in the current layer and the first-order predetermined paired user in the predetermined paired layer meet the NOMA access conditions;

[0200] In response to determining that the user of the current layer and the predetermined paired users of the first order in the predetermined pairing layer do not meet the NOMA access conditions, it is judged whether the user of the current layer and the predetermined paired users of the second order in the predetermined pairing layer meet the NOMA access conditions; the predetermined paired users of the second order are set to at least one.

[0201] In some embodiments, the element-grouping-based smart reflective surface is further configured to: establish a rate of each user assisted by the element-grouping-based smart reflective surface based on the user's signal-to-noise ratio in combination with the Shannon formula, and sum the rates to obtain a system sum rate within the assisted area;

[0202] The system and rate in the assisted area are taken as optimization targets, and the power allocation factor allocated to each user in the NOMA user group is determined.

[0203] In some embodiments, the base station is further configured to: access the NOMA user group using NOMA; access the OMA user using a reserved OMA frequency band; and establish a rate for each user after element-grouped intelligent reflective surface assistance based on the user's signal-to-noise ratio and the Shannon formula.

[0204] The system of the above embodiment is used to implement the corresponding NOMA wireless method based on intelligent reflective surface assistance in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0205] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. Within the scope of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0206] In addition, for simplicity of description and discussion, and in order not to make the embodiment of the application difficult to understand, the known power supply / ground connection with integrated circuit (IC) chip and other components may or may not be shown in the accompanying drawings provided. In addition, the device can be shown in the form of a block diagram to avoid making the embodiment of the application difficult to understand, and this also takes into account the following fact, that is, the details of the embodiment of these block diagram devices are highly dependent on the platform to be implemented in the embodiment of the application (that is, these details should be fully within the scope of understanding of those skilled in the art). When specific details (for example, circuit) are set forth to describe exemplary embodiments of the application, it will be apparent to those skilled in the art that the embodiment of the application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0207] Although the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may utilize the embodiments discussed.

[0208] The embodiments of the present application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of this application.

Claims

1. A wireless transmission method assisted by an intelligent reflective surface, characterized in that: include: According to the location of the base station, the location of the smart reflective surface, the distribution conditions of multiple users, and the initialized channel state information of the multiple users, the channel state information of the users is sorted, the initial signal-to-noise ratio of each user is calculated, and a preset OMA / NOMA hybrid access scheme is obtained; an optimization goal based on system and rate maximization is established; wherein the smart reflective surface is a smart reflective surface capable of implementing element-based grouping; According to the initialized channel state information of the multiple users and the preset OMA / NOMA hybrid access scheme, an element-based grouping smart reflecting surface is used to assist the communication of the multiple users respectively; determining the optimal phase angle of each element in the element-based grouping smart reflecting surface; wherein the element-based grouping smart reflecting surface has multiple sub-surfaces, and the multiple sub-surfaces respectively assist the multiple user groups in the preset OMA / NOMA hybrid access scheme in a one-to-one correspondence; Assisting the multiple user groups respectively based on the optimal phase angle of each sub-surface in the element-grouping-based smart reflective surface to obtain updated channel state information of the multiple users; According to the updated channel state information, the multiple users are layered to obtain multiple layers of users, and the multiple layers of users are alternately coupled and paired between the layers; Based on the set standard that when strong and weak users use NOMA access, their respective rates and sum rates are greater than those under the same channel conditions when using OMA access, the minimum signal-to-noise ratio difference condition for allowing NOMA access is determined; based on the order of users in each layer, it is determined in turn whether the users in the current layer and the users in the predetermined paired layers meet the NOMA access conditions; the predetermined paired user group that meets the NOMA access conditions is determined as the NOMA user group; the users in the current layer that do not meet the NOMA access conditions are determined as OMA users; Among them, the minimum signal-to-noise ratio difference condition for allowing NOMA access is determined based on the setting that when strong and weak users adopt NOMA access, their respective rates and sum rates are greater than those of OMA access under the same channel conditions. The conditions include: Determine the upper and lower bounds of the power allocation factor for the user group allowed to adopt NOMA access; Determine the upper bound of the incomplete interference cancellation coefficient and obtain the relationship between the power allocation factor and the incomplete interference cancellation coefficient; According to the relationship between the power allocation factor and the incomplete interference cancellation coefficient and the NOMA pairing criterion, the minimum signal-to-noise ratio difference threshold is determined; The minimum signal-to-noise ratio difference threshold is obtained by Calculate; where, is the minimum signal-to-noise ratio difference threshold; γ s is the signal-to-noise ratio of the strong user s in the user group; h s is the channel gain of strong user s in the user group; γ w is the signal-to-noise ratio of weak user w in the user group; h w is the channel gain of weak user w in the user group; P is the power sent by the base station, σ is the noise variable, σ 2 is the power spectral density of the noise, I is the interference received from other base stations on the subchannel assigned to user u; α s is the power allocation factor allocated to the strong user s in a given user pair in the NOMA system; β∈(0,1].

2. The wireless transmission method assisted by a smart reflective surface according to claim 1, characterized in that: The adopting of the element-grouping-based intelligent reflective surface to assist the communications of the multiple users respectively includes: Partitioning the reflective elements of the smart reflective surface using a Kronecker product method according to preset user pair types and quantities to form the plurality of sub-surfaces; Based on the channel state information of the link from the smart reflecting surface to the user after the smart reflecting surface element is partitioned, the Hungarian algorithm is used to determine the pairing relationship between the multiple sub-surfaces and the multiple user groups in the preset OMA / NOMA hybrid access solution.

3. The smart reflective surface-assisted wireless transmission method according to claim 2, wherein: The method further includes determining the optimal phase angle of each element in the smart reflective surface based on element grouping by the following method: Initialize the phase angles of all elements so that the phase angle of each element is the initial value; The phase matrix of the smart reflector based on element grouping is decomposed into the multiplication of n sub-matrices. The solution of the t-th iteration can be obtained by rotating the solution of N iterations. in, Θ (t-1) ,Θ (t) are the optimal solutions to the problem in the (t-1)th and tth iterations respectively; According to the optimization goal of maximizing the system and rate, the phase angle of each element is adjusted in turn according to the preset step size; wherein the value of each iteration satisfies θ n =2kπ / 2 D ,k∈{0,1,…,2 D -1}; For each element, calculate the impact of the element on the optimization target; after each iteration, update the overall reflection matrix. Until the phase angles of all elements reach a state that meets the convergence conditions.

4. The smart reflective surface-assisted wireless transmission method according to claim 1, wherein: The step of stratifying the plurality of users according to the updated channel state information to obtain multiple layers of users includes: determining relative positions of multiple users and a base station based on the updated channel state information; The multiple users are differentiated with equal radius according to the direction away from the base station to obtain multiple layers of users.

5. The smart reflective surface-assisted wireless transmission method according to claim 1, wherein: Determining whether users in the current layer and users in the predetermined paired layer meet the NOMA access conditions includes: Determine whether the user in the current layer and the first-order predetermined paired user in the predetermined paired layer meet the NOMA access conditions; In response to determining that the user of the current layer and the predetermined paired users of the first order in the predetermined pairing layer do not meet the NOMA access conditions, it is judged whether the user of the current layer and the predetermined paired users of the second order in the predetermined pairing layer meet the NOMA access conditions; the predetermined paired users of the second order are set to at least one.

6. The smart reflective surface-assisted wireless transmission method according to claim 1, characterized in that: The method further includes: establishing a rate of each user after assistance by the intelligent reflective surface based on element grouping according to the user's signal-to-noise ratio in combination with the Shannon formula, and summing the rates to obtain a system sum rate in the assisted area; The system and rate in the assisted area are taken as optimization targets, and the power allocation factor allocated to each user in the NOMA user group is determined.

7. The smart reflective surface-assisted wireless transmission method according to claim 1, characterized in that: The method also includes: accessing the NOMA user group using NOMA; and accessing the OMA user using a reserved OMA frequency band.

8. A wireless transmission system assisted by an intelligent reflective surface, characterized in that: The invention comprises an intelligent reflecting surface, a base station and a plurality of users; wherein the intelligent reflecting surface is an intelligent reflecting surface capable of being grouped based on elements; The smart reflecting surface capable of element-based grouping is used to: sort the channel state information of the users according to the location of the base station, the location of the smart reflecting surface, the distribution conditions of multiple users, and the initialized channel state information of the multiple users, calculate the initial stage signal-to-noise ratio of each user, and obtain a preset OMA / NOMA hybrid access solution; establish an optimization goal based on system and rate maximization; wherein the smart reflecting surface is a smart reflecting surface capable of element-based grouping; According to the initialized channel state information of the multiple users and the preset OMA / NOMA hybrid access scheme, using an element-grouped smart reflection surface to assist the communications of the multiple users respectively; Determining an optimal phase angle for each element in an element-grouped smart reflective surface; wherein the element-grouped smart reflective surface has a plurality of sub-surfaces, and the plurality of sub-surfaces respectively assist a plurality of user groups in the preset OMA / NOMA hybrid access solution in a one-to-one manner; Assisting the multiple user groups respectively based on the optimal phase angle of each sub-surface in the element-grouping-based smart reflective surface to obtain updated channel state information of the multiple users; The base station is configured to: stratify the multiple users according to the updated channel state information to obtain multiple layers of users, and perform alternating coupling and pairing between the multiple layers of users; Based on the set standard that when strong and weak users use NOMA access, their respective rates and sum rates are greater than those under the same channel conditions when using OMA access, the minimum signal-to-noise ratio difference condition for allowing NOMA access is determined; based on the order of users in each layer, it is determined in turn whether the users in the current layer and the users in the predetermined paired layers meet the NOMA access conditions; the predetermined paired user group that meets the NOMA access conditions is determined as the NOMA user group; the users in the current layer that do not meet the NOMA access conditions are determined as OMA users; Among them, the minimum signal-to-noise ratio difference condition for allowing NOMA access is determined based on the setting that when strong and weak users adopt NOMA access, their respective rates and sum rates are greater than those of OMA access under the same channel conditions. The conditions include: Determine the upper and lower bounds of the power allocation factor for the user group allowed to adopt NOMA access; Determine the upper bound of the incomplete interference cancellation coefficient and obtain the relationship between the power allocation factor and the incomplete interference cancellation coefficient; According to the relationship between the power allocation factor and the incomplete interference cancellation coefficient and the NOMA pairing criterion, the minimum signal-to-noise ratio difference threshold is determined; The minimum signal-to-noise ratio difference threshold is obtained by Calculate; where, is the minimum signal-to-noise ratio difference threshold; γ s is the signal-to-noise ratio of the strong user s in the user group; h s is the channel gain of strong user s in the user group; γ w is the signal-to-noise ratio of weak user w in the user group; h w is the channel gain of weak user w in the user group; P is the power sent by the base station, σ is the noise variable, σ 2 is the power spectral density of the noise, I is the interference received from other base stations on the subchannel assigned to user u; α s is the power allocation factor allocated to the strong user s in a given user pair in the NOMA system; β∈(0,1].

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