High-energy-efficiency wireless transmission method of cellular communication system based on intelligent reflecting surface

Abstract

The invention relates to the technical field of wireless communication, and provides a high-energy-efficiency wireless transmission method of a cellular communication system based on an intelligent reflecting surface, which comprises the following steps: constructing a downlink transmission system of an intelligent reflecting surface assisted multi-user NOMA (Non-Orthogonal Multiple Access); constructing an energy efficiency optimization model; moving the fixed intelligent reflecting surface, solving the maximum system energy efficiency according to the signal to interference plus noise ratio constraint of the user and the maximum transmitting power constraint of the base station, and obtaining the optimal transmitting beam of the base station; the base station is fixed to transmit wave beams, the total power consumption of the system is a fixed value, the maximum system sum rate is solved according to the signal to interference plus noise ratio constraint, the intelligent reflector shift constraint and the SIC decoding sequence constraint of the user, and the optimal intelligent reflector shift is obtained; and alternately solving the optimal base station transmitting beam and the optimal intelligent reflecting surface shift to obtain the maximum energy efficiency of the system. According to the method, an original non-convex optimization problem is converted into a convex optimization problem, the solving complexity is reduced, the increasing user requirements are met, and better communication experience is provided.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a high-energy-efficiency wireless transmission method of a cellular communication system based on an intelligent reflective surface. Background Art

[0002] In the scenario of intelligent reflector-assisted multi-user non-orthogonal multiple access (NOMA) technology, existing research focuses on maximizing system sum and rate and minimizing base station transmission power, while relatively few studies consider maximizing sum and rate and minimizing power consumption at the same time. Faced with the growing demand for wireless devices and users, energy consumption is also increasing. In the future, wireless networks urgently need to design greener and more energy-saving resource allocation solutions.

[0003] The existing technologies are all about optimizing the phase shift of the intelligent reflector to improve the system and rate. Only a single-antenna transmitter is considered. The transmitter uses multiple antennas to transmit signals, which can realize the transmission of multiple data streams at the same time and frequency. Compared with single-input single-output, multiple-input single-output can provide higher system capacity and information transmission rate, further improving the spectrum efficiency and energy efficiency of the system. The receiving end only considers two users, and the model is relatively ideal and simple; the influence of the intelligent reflector on the decoding order of the successive interference cancellation technology (SIC) is not considered. In some studies, when there are only two users in the intelligent reflector-assisted NOMA system, a strong user close to the base station and a weak user far from the base station, the number of users considered by the system is limited, and the model is relatively ideal and simple, which greatly limits the application of intelligent reflectors in NOMA systems. In addition, the model does not consider the more general case of direct links between base stations and users, but only considers links assisted by intelligent reflectors (IRS). In the case of direct links, it is necessary to optimize the performance of direct links and IRS-assisted links at the same time. This involves joint optimization of base station beam optimization and IRS phase shift to find the optimal resource allocation solution.

[0004] Other studies have assumed the relationship between the strength of user channels and have not considered the SIC decoding order in the optimization problem. In wireless communication systems, especially in complex channel environments where smart reflectors are introduced to assist NOMA, the determination of the SIC decoding order is particularly important. Since smart reflectors can affect the path of signal transmission by adjusting their phase shifts, the channel combination gain becomes highly dynamic and complex. When multiple users share the same spectrum resources, the channel gains between different users may differ significantly, but there may also be very small differences in gain due to the randomness of user distribution. In this case, if the decoding order is not appropriate, the signal of one user may be regarded as interference from another user, which will seriously affect the overall performance of the system. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the related art. To this end, the present invention provides a high-energy-efficiency wireless transmission method for a cellular communication system based on a smart reflective surface. By randomly distributing users within the coverage of the smart reflective surface, a more general scenario is considered. The base station can provide services to multiple users simultaneously through a direct transmission link and a link assisted by a smart reflective surface. Compared with a system with only a smart reflective surface assisted link, the existence of a direct transmission link and an IRS assisted link can provide diversity gain. By transmitting the same information on different links, the robustness of the signal can be increased and the performance degradation caused by a single link failure can be reduced. The present invention provides an optimization solution to maximize the system energy efficiency by jointly optimizing the base station transmit beam and the IRS phase shift under the constraints of user service quality, base station transmit power, IRS phase shift angle, and SIC decoding order.

[0006] The present invention provides a high energy efficiency wireless transmission method for a cellular communication system based on a smart reflective surface, comprising: S1: Construct a downlink transmission system for multi-user NOMA assisted by a smart reflector; S2: Based on the downlink transmission system of multi-user NOMA assisted by smart reflective surface, an energy efficiency optimization model is constructed; S3: Based on the energy efficiency optimization model, the optimization problem is decoupled into base station transmit beam optimization and smart reflector phase shift optimization; S4: Fix the phase shift of the intelligent reflection surface, solve the maximum system energy efficiency according to the user's signal-to-interference-noise ratio constraint and the base station's maximum transmit power constraint, and obtain the optimal base station transmit beam; S5: The base station transmits a fixed beam, and the total power consumption of the system is a constant. According to the user's signal-to-interference-noise ratio constraint, the smart reflector phase shift constraint, and the SIC decoding order constraint, the maximum system sum rate is solved to obtain the optimal smart reflector phase shift; S6: Use the alternating iterative algorithm to alternately solve the optimal base station transmit beam and the optimal smart reflection surface phase shift until the energy efficiency converges or the maximum number of iterations is reached.

[0007] Furthermore, in step S1, the downlink transmission system of the intelligent reflecting surface assisted multi-user NOMA includes a base station, an intelligent reflecting surface and multiple users, and the multiple users are randomly distributed within the coverage range of the intelligent reflecting surface, and the base station provides services to the multiple users simultaneously at the same frequency.

[0008] Furthermore, a system energy efficiency optimization model is constructed based on the superimposed signals transmitted by the base station and the layer-by-layer decoding of the signals through the SIC technology. The system energy efficiency is the ratio of the system total rate to the system total power consumption. The system total power consumption includes the base station transmission power and the total circuit power. The constraints include the user signal-to-interference-and-noise ratio constraint, the base station transmission power constraint, the IRS phase shift angle constraint and the SIC decoding order constraint.

[0009] Furthermore, the calculation expression of the system energy efficiency is: in, is the system energy efficiency, For the The rate at which the decoded signal is achieved at each user, For the The beam vector of each user, is the total circuit power, is the power amplifier efficiency at the base station, and K is the number of users.

[0010] Furthermore, considering the service quality requirements of each user, the base station transmission power constraints, the SIC decoding order constraints and the IRS phase shift angle constraints, the optimization problem of maximizing the system energy efficiency is: in, For the The rate at which the decoded signal can be achieved at each user is For IRS The phase shift angle of the reflective element, For the The beam vector of each user, is the total circuit power, is the power amplifier efficiency at the base station, K is the number of users, is the maximum value function; Ensure the service quality requirements of each user. The user's service quality requirements are constrained as follows: in, For the The signal-to-interference-noise ratio threshold of each user is From base station to The channel gain of each user is From IRS to The channel gain of each user is is the IRS diagonal reflection matrix, is the channel gain from the base station to the IRS, is the noise power, For the user set, For the The beam vector of each user, For the The beam vector of each user; Limit the base station transmit power to not exceed the maximum transmit power. The base station transmit power constraint is: in, is the maximum transmit power of the base station; Constrain the value range of the IRS phase shift angle. The IRS phase shift angle constraint is: in, is the number set of passive reflective elements, To ensure the SIC decoding order, the SIC decoding order constraint is: in, From base station to The channel gain of each user.

[0011] Furthermore, according to the system energy efficiency optimization model, the optimization problem is non-convex.

[0012] Furthermore, by fixing the phase shift of the smart reflector, the optimization problem is simplified to the problem of maximizing the system energy efficiency under the constraints of each user's signal-to-interference-noise ratio and the base station's maximum transmit power. By solving this optimization problem, the optimal base station transmit beam is obtained. S41: The signal-to-interference-to-noise ratio constraint calculation expression for each user is constructed as follows: in, is the signal coefficient, , From base station to The channel gain of each user is From IRS to The channel gain of each user is is the IRS diagonal reflection matrix, is the channel gain from the base station to the IRS, For the The beam vector of each user, For the The beam vector of each user, is the noise power, For the The signal-to-interference-noise ratio threshold of each user; S42: The non-convex constraint is converted into a second-order cone convex constraint by rotating the transmit beam in phase by a certain angle using a second-order cone convex optimization method; S43: Using the Dinkelbach algorithm, the objective function is converted into a parameterized reduction function through non-negative variables: in, is the first non-negative variable, For the The beam vector of each user, For the The rate at which the decoded signal can be achieved at each user is is the power amplifier efficiency at the base station is the total circuit power, is the maximum value function; S44: The parameterized reduction function is converted into a convex function through relaxation variables, and the convex function is solved by a convex optimization solver to obtain the optimal base station transmit beam.

[0013] Furthermore, in step S5, when the optimal base station transmit beam is obtained, the total power consumption of the system is a constant, and the optimization problem is simplified to the system and rate maximization problem under the conditions of each user's signal-to-interference-noise ratio constraint, the smart reflector phase shift constraint and the SIC decoding order constraint. Based on the fixed base station transmit beam, the phase shift of the smart reflector is optimized.

[0014] Furthermore, step S5 includes: S51: improving the signal to interference and noise ratio of each user according to a method of maximizing the signal to interference and noise ratio difference; S52: The optimization problem is simplified by semi-definite relaxation technique to: in, is the second non-negative variable, To optimize the variables, For the User second auxiliary variable, For the The beam vector of each user, For the The beam vector of each user, for The conjugate transpose of For the The signal-to-interference-noise ratio threshold of each user is is the noise power, For the The phase shift constraint of the reflective element, is the constraint function, To find the trace function of a matrix, For the User second auxiliary variable, for The conjugate transpose of; K is the number of users, For the user set, is the number of passive reflective elements; S53: Use the convex optimization solver to solve and obtain the optimal IRS phase shift.

[0015] Furthermore, the alternating iterative algorithm fixes the IRS phase shift, uses the Dinkelbach technique and the continuous convex approximation technique to solve the optimization problem, obtains the optimal transmit beam of the base station, fixes the base station transmit beam, uses the semi-positive definite relaxation technique to solve the optimization problem, obtains the optimal IRS phase shift, and alternately solves the optimal transmit beam of the base station and the optimal IRS phase shift until the energy efficiency converges.

[0016] The above one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects: The present invention provides a high-energy-efficiency wireless transmission method for a cellular communication system based on an intelligent reflection surface. The resource allocation scheme of the present invention takes into account the SIC decoding order constraint to avoid the influence of the dynamic change of the intelligent reflection surface on the SIC decoding order when the user channel conditions are similar. The present invention converts the original non-convex optimization problem into a convex optimization problem, and the problem can be solved by a classic convex optimization solution tool, thereby reducing the solution complexity.

[0017] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a flow chart of a high energy-efficient wireless transmission method for a cellular communication system based on an intelligent reflective surface provided by the present invention.

[0020] Figure 2 The present invention provides a schematic diagram of a downlink transmission system of a smart reflective surface-assisted NOMA in which a high-energy-efficiency wireless transmission method of a cellular communication system based on a smart reflective surface is provided.

[0021] Figure 3 The present invention provides a schematic diagram of the relationship between the energy efficiency of a high-energy-efficiency wireless transmission method for a cellular communication system based on a smart reflective surface and the number of reflective elements of the smart reflective surface.

[0022] Figure 4 The present invention provides a schematic diagram of the relationship between the energy efficiency of a high-energy-efficiency wireless transmission method for a cellular communication system based on an intelligent reflective surface and the power consumption of a base station circuit.

[0023] Figure 5 The present invention provides a schematic diagram of the relationship between the energy efficiency of a high-energy-efficiency wireless transmission method for a cellular communication system based on an intelligent reflective surface and the maximum transmission power of a base station.

[0024] Figure 6 The present invention provides a schematic diagram of the relationship between energy efficiency and signal-to-interference-noise ratio threshold of a high-energy-efficiency wireless transmission method for a cellular communication system based on an intelligent reflective surface. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme in the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present invention. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0026] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0027] Combine the following Figures 1 to 6 The invention describes a high energy-efficient wireless transmission method for a cellular communication system based on a smart reflective surface.

[0028] like Figure 1 As shown, a high energy efficiency wireless transmission method for a cellular communication system based on a smart reflective surface comprises: S1: Based on the smart reflector-assisted NOMA system model, a multi-intelligent reflector-assisted multi-user NOMA downlink transmission system is constructed; like Figure 2 As shown in Figure 1, the downlink transmission system of the intelligent reflector-assisted multi-user NOMA consists of a base station (BS), an intelligent reflector (IRS) and K users. The K users are randomly distributed within the coverage of the intelligent reflector. The base station provides services to multiple users at the same frequency. The base station deploys M antennas, and each user deploys one antenna. The user set is , IRS is equipped with N passive reflective elements, and the number of passive reflective elements is ; Specifically, the base station provides services to K users simultaneously on the same frequency. In order to enhance the transmission channel gain of the NOMA system, the IRS is deployed on the surface of the building to assist the wireless communication from the base station to the users. It is assumed that the channel state information of all channels is completely known at the base station and all channels are quasi-static flat fading.

[0029] The channel gain from BS to IRS is , , for The channel gain matrix of order, the channel gain from IRS to the kth user is , for The channel gain matrix of order, the channel gain from BS to the kth user is , for The channel gain matrix of order, the IRS diagonal reflection matrix is , for It is a complex diagonal matrix of order, where the elements on the diagonal are the reflection coefficient and phase shift of the reflection elements in the IRS. The IRS reflects the signal to the maximum extent, so the reflection coefficient of the N reflection elements is 1, and the phase shift angle ranges from 0 to 360 degrees.

[0030] Due to the huge path loss, the signal reflected more than twice by the IRS is ignored, and the received signal at user k is: in, For users The received signal at For BS to The channel gain of each user is From IRS to The channel gain of each user is is the IRS diagonal reflection matrix, is the channel gain from BS to IRS, For user collection, is the superimposed signal transmitted by the base station, For the Each user transmits data to meet the unit energy normalization requirement. , For users The beamforming vector, For users The additive white Gaussian noise received at is the noise power; To keep the decoding order unchanged, assume The equivalent channel gains should be sorted in descending order, that is: in, For BS to The channel gain of each user is user The signal to interference plus noise ratio (SINR) that can be achieved by decoding the signal itself is: in, For users The signal-to-interference-noise ratio that can be achieved by decoding its own signal, For the The beam vector of each user, For the The beam vector of each user; In the user The corresponding achievable rate for decoding its own signal at is: in, For the The rate achievable by decoding the signal at each user.

[0031] S2: Based on the downlink transmission system of multi-user NOMA assisted by multiple intelligent reflectors, an energy efficiency optimization model is constructed; Based on the superimposed signals transmitted by the base station and the layer-by-layer decoding of the signals through the SIC technology, a system energy efficiency optimization model is constructed. The system energy efficiency is the ratio of the system total rate to the system total power consumption. The system total power consumption includes the base station transmission power and the total circuit power. The constraints include the user signal-to-interference-to-noise ratio constraint, the base station transmission power constraint, the IRS phase shift angle constraint, and the SIC decoding order constraint. The calculation expression of system energy efficiency is: in, is the system energy efficiency, is the total rate of the system, is the total power of the system, For the The rate at which the decoded signal can be achieved at each user, For the The beam vector of each user, is the total circuit power, is the power amplifier efficiency at the base station, K is the number of users, ,in, is the dynamic power consumption, is the static power consumption, and M is the number of antennas.

[0032] Considering the service quality requirements of each user, the base station transmission power constraints, the SIC decoding order constraints and the IRS phase shift angle constraints, the optimization problem of maximizing the system energy efficiency is: in, For the The rate at which the decoded signal can be achieved at each user is For IRS The phase shift angle of the reflective element, For the The beam vector of each user, is the total circuit power, is the power amplifier efficiency at the base station, K is the number of users; Ensure the service quality requirements of each user. The user's service quality requirements are constrained as follows: in, For the The signal-to-interference-noise ratio threshold of each user is From base station to The channel gain of each user is From IRS to The channel gain of each user is is the IRS diagonal reflection matrix, is the channel gain from the base station to the IRS, is the noise power, For the user set, For the The beam vector of each user, For the The beam vector of each user; Limit the base station transmit power to not exceed the maximum transmit power. The base station transmit power constraint is: in, is the maximum transmit power of the base station; Constrain the value range of the IRS phase shift angle. The IRS phase shift angle constraint is: in, is the number set of passive reflective elements, To ensure the SIC decoding order, the SIC decoding order constraint is: in, From base station to Channel gain for each user; According to the system energy efficiency optimization model, the optimization problem is non-convex.

[0033] S3: Based on the energy efficiency optimization model, the optimization problem is decoupled into base station transmit beam optimization and smart reflector phase shift optimization; In order to solve the non-convex optimization problem with mutually coupled optimization variables, the original problem is decomposed into two sub-problems. The iterative algorithm based on Dinkelbach technique is used to optimize the base station transmit beam and the signal-to-interference-noise ratio difference algorithm is used to optimize the IRS phase shift alternately until convergence.

[0034] S4: Fix the phase shift of the intelligent reflection surface, solve the maximum system energy efficiency according to the user's signal-to-interference-noise ratio constraint and the base station's maximum transmit power constraint, and obtain the optimal base station transmit beam; By fixing the phase shift of the intelligent reflection surface, the optimization problem is simplified to the problem of maximizing the system energy efficiency under the constraints of each user's signal-to-interference-noise ratio and the maximum transmission power of the base station. By solving this optimization problem, the optimal base station transmission beam is obtained. S41: The signal-to-interference-to-noise ratio constraint calculation expression for each user is constructed as follows: in, is the signal coefficient, , For the The beam vector of each user, For the The beam vector of each user, is the noise power, For the The signal-to-interference-noise ratio threshold of each user; S42: The non-convex constraint is converted into a second-order cone convex constraint by rotating the transmit beam in phase by a certain angle using a second-order cone convex optimization method; Ensure the service quality requirements of each user: This constraint is a non-convex constraint. It is converted into a convex constraint by using the second-order cone programming method. Introduce an arbitrary rotation on the phase to re-express becomes the real part, and the imaginary part becomes zero. Phase rotation is an operation in the complex domain. It does not change the amplitude of the signal, and therefore does not change the signal power or the achieved signal-to-interference-noise ratio. The quality of service requirement constraint for each user is equivalent to: in, for The real part of for The imaginary part of S43: Using the Dinkelbach algorithm, the objective function is converted into a parameterized reduction function through non-negative variables: The system energy efficiency optimization problem is simplified to the base station transmission beam optimization problem, and the objective function is: The objective function has a non-convex fractional programming characteristic and is difficult to solve directly. Using the Dinkelbach algorithm, by introducing the first non-negative variable To solve this problem, the objective function is converted into a parameterized reduction function: in, Part of it is a convex function, using The first non-negative variable of iteration Multiplying it ensures the convexity of this part, but The part is still a non-convex structure, and some relaxation is needed to convert it into a convex form. The first relaxation variable s is introduced. S44: converting the parameterized reduction function into a convex function through relaxation variables, and applying a convex optimization solver to the convex function to obtain an optimal base station transmit beam; The parameterized reduction function is converted into a convex function through slack variables, and the calculation expression is: Further tracking the convexity of the constraint, the left side of the inequality can be obtained by the second slack variable and the third slack variable To approximate, we obtain: The non-convex constraint can be equivalently rewritten as: Further introduce the fourth slack variable , then: Will is equivalent to: Use continuous convex approximation to convert non-convex constraints into convex approximation expressions. Using first-order Taylor approximation, the above formula can be written as: in, is the first step based on the Dinkelbach iterative algorithm The variable after iteration The value of is the first step based on the Dinkelbach iterative algorithm The variable after iteration The value of is a convex set, which can be rewritten into a second-order cone form: Given from The optimized value at the beginning of the iteration is At the iteration, the original optimization problem is transformed into: in, is the first constraint, is the second constraint, is the third constraint, is the fourth constraint, is the fifth constraint, 6 is the sixth constraint, 7 is the seventh constraint, At each iteration, the problem given by the values ​​of the optimization variables from the previous iteration is solved using a CVX convex optimization solver until convergence.

[0035] S5: The base station transmits a fixed beam, and the total power consumption of the system is a constant. According to the user's signal-to-interference-noise ratio constraint, the smart reflector phase shift constraint, and the SIC decoding order constraint, the maximum system sum rate is solved to obtain the optimal smart reflector phase shift; When the optimal base station transmit beam is obtained, the total power consumption of the system is a constant, and the optimization problem is simplified to the system sum rate maximization problem under the conditions of each user's signal-to-interference-noise ratio constraint, the smart reflector phase shift constraint and the SIC decoding order constraint. Based on the fixed base station transmit beam, the phase shift of the smart reflector is optimized to increase the information transmission rate of users sharing the same spectrum, improve the system sum rate, and improve the energy efficiency of the system.

[0036] After obtaining the optimized BS transmit beam, the total power consumption of the system becomes a constant, and the energy efficiency maximization problem is simplified to the system and rate maximization problem. Then the IRS phase shift optimization problem is: Define the first auxiliary variable , the range constraint of the IRS phase shift angle is equivalent to the unit mode constraint: , By substitution method, we get , define User second auxiliary variable ,in, for The second auxiliary variable matrix of order, for The expansion matrix of Then we have: in, for The conjugate transpose of The optimization problem is equivalent to: in, No. User second auxiliary variable; S51: improving the signal to interference and noise ratio of each user according to a method of maximizing the signal to interference and noise ratio difference; A method based on maximizing the difference between signal-to-interference-and-noise ratios, which forcibly improves the signal-to-interference-and-noise ratio of each user under the premise of satisfying the user's signal-to-interference-and-noise ratio constraints, so that the user can obtain a higher information transmission rate, thereby obtaining higher energy efficiency and improving system performance; By the second non-negative variable The optimization problem is transformed into: in, For the A first auxiliary variable of a reflective element; Introducing new optimization variables , need to meet and , since the rank-one constraint is non-convex; S52: The optimization problem is simplified by semi-definite relaxation technique to: +1 in, For the The phase shift constraint of the reflective element, for The conjugate transpose of for The conjugate transpose of ; The constraints of the optimization problem are convex sets. The problem is solved using the classic convex optimization solver CVX to obtain the optimal smart reflector phase shift. S53: Use the convex optimization solver to solve and obtain the optimal IRS phase shift.

[0037] S6: Use the alternating iterative algorithm to alternately solve the optimal base station transmit beam and the optimal smart reflection surface phase shift until the energy efficiency converges or the maximum number of iterations is reached.

[0038] The alternating iterative algorithm is to fix the IRS phase shift, use the Dinkelbach technique and the continuous convex approximation technique to solve the optimization problem, obtain the optimal base station transmission beam, fix the base station transmission beam, use the semi-positive definite relaxation technique to solve the optimization problem, obtain the optimal IRS phase shift, and alternately solve the optimal base station transmission beam and the optimal IRS phase shift until the energy efficiency converges.

[0039] The present invention proposes a resource allocation scheme based on a highly energy-efficient smart reflector-assisted NOMA system in a multi-user downlink NOMA network. First, multi-user multiplexing communication resources are randomly generated, and it is proposed to maximize the system energy efficiency by jointly optimizing the transmit beam at the BS and the phase shift at the IRS under the conditions of each user's signal-to-interference-to-noise ratio constraint, BS transmit power constraint, SIC decoding order constraint and IRS phase shift angle constraint. Due to the non-convexity of the optimization problem and the high coupling of the optimization variables, in order to solve this problem, an alternating iterative algorithm is proposed to decouple the original optimization problem into two sub-problems: base station transmit beam optimization and smart reflector phase shift optimization. First, the smart reflector phase shift is fixed, and the objective function is processed by the Dinkelbach algorithm to convert it into a parameterized reduction function. The non-convex constraints are converted into convex constraints using second-order cone programming, and the optimal base station transmit beam is solved. Then, using the optimized base station transmit beam, the optimization problem is simplified to maximize the system and rate. An algorithm based on the signal-to-interference-to-noise ratio difference is proposed to optimize the smart reflector phase shift. Finally, the base station transmit beam and the smart reflector phase shift are alternately optimized until convergence to obtain the maximum energy efficiency of the system. It can be seen from simulation verification that the present invention is conducive to improving the energy efficiency of the system.

[0040] like Figure 3As shown in FIG. 1 , the energy efficiency is related to the number of IRS reflective elements. The energy efficiency of the present invention, the random phase shift IRS-NOMA scheme, and the IRS-NOMA scheme without direct transmission link all increase with the increase of the number of reflective elements. The performance of the present invention is better than that of the random phase shift IRS-NOMA scheme, which reflects the importance of IRS phase shift optimization. The energy efficiency of the random phase shift IRS-NOMA scheme is greatly improved compared with the NOMA scheme without IRS, which illustrates the importance of deploying IRS in communication systems.

[0041] like Figure 4 As shown, the relationship between energy efficiency and circuit power consumption at the base station is shown. The energy efficiency of the present invention is better than that of the other three benchmark solutions.

[0042] like Figure 5 As shown in the figure, the energy efficiency variation curve relative to the maximum transmission power of the base station shows that the present invention performs better than the other three benchmark solutions under different transmission power conditions, verifying its significant advantage in improving system energy efficiency.

[0043] like Figure 6 As shown, the relationship between energy efficiency and user signal to interference and noise ratio threshold is shown. The present invention can still maintain a higher energy efficiency under a higher signal to interference and noise ratio requirement, so the energy efficiency decreases less.

[0044] The user of the present invention communicates with the multi-antenna base station through non-orthogonal multiple access technology. The base station transmits superimposed signals, and the user decodes the signals layer by layer through the SIC technology, which effectively improves the spectrum utilization efficiency and solves the current problem of resource shortage. The present invention deploys intelligent reflective surface assisted communication, and utilizes the reflective characteristics of intelligent reflective surface to enhance signal transmission and improve the sum rate of the system; the intelligent reflective surface is introduced into the NOMA system to control the propagation environment of the reflection channel, so that the channel conditions between NOMA users are different, overcoming the additional requirements of NOMA on the channel, thereby further improving the spectrum efficiency of the communication system. The communication system of the present invention includes both direct transmission links and intelligent reflective surface assisted links, which brings significant diversity gain to the system; in addition, since the intelligent reflective surface has the advantages of low profile, light weight and maintaining geometric shape, it is easy to install on buildings, convenient to deploy and replace, so the resource allocation scheme has strong applicability in user-intensive communication scenarios; The resource allocation scheme proposed in the present invention takes into account the SIC decoding order constraint, avoiding the influence of the dynamic change of the intelligent reflection surface on the SIC decoding order when the user channel conditions are similar; the resource allocation scheme of the present invention converts the original non-convex optimization problem into a convex optimization problem, which can be solved by the classic convex optimization solving tool CVX, reducing the complexity of the solution.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A high energy efficiency wireless transmission method for a cellular communication system based on a smart reflective surface, characterized in that: include: S1: Construct a downlink transmission system for multi-user NOMA assisted by a smart reflector; S2: Based on the downlink transmission system of multi-user NOMA assisted by smart reflective surface, an energy efficiency optimization model is constructed; S3: Based on the energy efficiency optimization model, the optimization problem is decoupled into base station transmit beam optimization and smart reflector phase shift optimization; S4: Fix the phase shift of the intelligent reflection surface, solve the maximum system energy efficiency according to the user's signal-to-interference-noise ratio constraint and the base station's maximum transmit power constraint, and obtain the optimal base station transmit beam; S5: The base station transmits a fixed beam, and the total power consumption of the system is a constant. According to the user's signal-to-interference-noise ratio constraint, the smart reflector phase shift constraint, and the SIC decoding order constraint, the maximum system sum rate is solved to obtain the optimal smart reflector phase shift; S6: Use the alternating iterative algorithm to alternately solve the optimal base station transmit beam and the optimal smart reflection surface phase shift until the energy efficiency converges or the maximum number of iterations is reached.

2. The high energy efficiency wireless transmission method of a cellular communication system based on a smart reflective surface according to claim 1, characterized in that: In step S1, the downlink transmission system of the intelligent reflecting surface assisted multi-user NOMA includes a base station, an intelligent reflecting surface and multiple users. The multiple users are randomly distributed within the coverage area of ​​the intelligent reflecting surface, and the base station provides services for the multiple users at the same frequency at the same time.

3. The high energy efficiency wireless transmission method of a cellular communication system based on a smart reflective surface according to claim 1, characterized in that: Based on the superimposed signals transmitted by the base station and the layer-by-layer decoding of the signals through the SIC technology, a system energy efficiency optimization model is constructed. The system energy efficiency is the ratio of the system total rate to the system total power consumption. The system total power consumption includes the base station transmission power and the total circuit power. The constraints include the user signal-to-interference-and-noise ratio constraint, the base station transmission power constraint, the IRS phase shift angle constraint and the SIC decoding order constraint.

4. The high energy efficiency wireless transmission method of a cellular communication system based on a smart reflective surface according to claim 3, characterized in that: The calculation expression of system energy efficiency is: in, is the system energy efficiency, For the The rate at which the decoded signal is achieved at each user, For the The beam vector of each user, is the total circuit power, is the power amplifier efficiency at the base station, and K is the number of users.

5. The high energy efficiency wireless transmission method of a cellular communication system based on a smart reflective surface according to claim 1, characterized in that: Considering the service quality requirements of each user, the base station transmission power constraints, the SIC decoding order constraints and the IRS phase shift angle constraints, the optimization problem of maximizing the system energy efficiency is: in, For the The rate at which the decoded signal can be achieved at each user is For IRS The phase shift angle of the reflective element, For the The beam vector of each user, is the total circuit power, is the power amplifier efficiency at the base station, K is the number of users, is the maximum value function; Ensure the service quality requirements of each user. The user's service quality requirements are constrained as follows: in, For the The signal-to-interference-noise ratio threshold of each user is From base station to The channel gain of each user is From IRS to The channel gain of each user is is the IRS diagonal reflection matrix, is the channel gain from the base station to the IRS, is the noise power, For the user set, For the The beam vector of each user, For the The beam vector of each user; Limit the base station transmit power to not exceed the maximum transmit power. The base station transmit power constraint is: in, is the maximum transmit power of the base station; Constrain the value range of the IRS phase shift angle. The IRS phase shift angle constraint is: in, is the number set of passive reflective elements, To ensure the SIC decoding order, the SIC decoding order constraint is: in, From base station to The channel gain of each user.

6. The high energy efficiency wireless transmission method of a cellular communication system based on a smart reflective surface according to claim 1, characterized in that: According to the system energy efficiency optimization model, the optimization problem is non-convex.

7. The high energy efficiency wireless transmission method of a cellular communication system based on a smart reflective surface according to claim 1, characterized in that: By fixing the phase shift of the intelligent reflection surface, the optimization problem is simplified to the problem of maximizing the system energy efficiency under the constraints of each user's signal-to-interference-noise ratio and the maximum transmission power of the base station. By solving this optimization problem, the optimal base station transmission beam is obtained. S41: The signal-to-interference-to-noise ratio constraint calculation expression for each user is constructed as follows: in, is the signal coefficient, , From base station to The channel gain of each user is From IRS to The channel gain of each user is is the IRS diagonal reflection matrix, is the channel gain from the base station to the IRS, For the The beam vector of each user, For the The beam vector of each user, is the noise power, For the The signal-to-interference-noise ratio threshold of each user; S42: The non-convex constraint is converted into a second-order cone convex constraint by rotating the transmit beam in phase by a certain angle using a second-order cone convex optimization method; S43: Using the Dinkelbach algorithm, the objective function is converted into a parameterized reduction function through non-negative variables: in, is the first non-negative variable, For the The beam vector of each user, For the The rate at which the decoded signal can be achieved at each user is is the power amplifier efficiency at the base station is the total circuit power, is the maximum value function; S44: The parameterized reduction function is converted into a convex function through relaxation variables, and the convex function is solved by a convex optimization solver to obtain the optimal base station transmit beam.

8. The high energy efficiency wireless transmission method of a cellular communication system based on a smart reflective surface according to claim 1, characterized in that: In step S5, when the optimal base station transmit beam is obtained, the total power consumption of the system is a constant, and the optimization problem is simplified to the system and rate maximization problem under the conditions of each user's signal-to-interference-noise ratio constraint, the smart reflector phase shift constraint and the SIC decoding order constraint. Based on the fixed base station transmit beam, the phase shift of the smart reflector is optimized.

9. The high energy efficiency wireless transmission method of a cellular communication system based on a smart reflective surface according to claim 1, characterized in that: Step S5 includes: S51: improving the signal to interference and noise ratio of each user according to a method of maximizing the signal to interference and noise ratio difference; S52: The optimization problem is simplified by semi-definite relaxation technique to: in, is the second non-negative variable, To optimize the variables, For the User second auxiliary variable, For the The beam vector of each user, For the The beam vector of each user, for The conjugate transpose of For the The signal-to-interference-noise ratio threshold of each user is is the noise power, For the The phase shift constraint of the reflective element, is the constraint function, To find the trace function of a matrix, For the User second auxiliary variable, for The conjugate transpose of , K is the number of users, For the user set, is the number of passive reflective elements; S53: Use the convex optimization solver to solve and obtain the optimal IRS phase shift.

10. The high energy efficiency wireless transmission method of a cellular communication system based on a smart reflective surface according to claim 1, characterized in that: The alternating iterative algorithm is to fix the IRS phase shift, use the Dinkelbach technique and the continuous convex approximation technique to solve the optimization problem, obtain the optimal base station transmission beam, fix the base station transmission beam, use the semi-positive definite relaxation technique to solve the optimization problem, obtain the optimal IRS phase shift, and alternately solve the optimal base station transmission beam and the optimal IRS phase shift until the energy efficiency converges.

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