Energy-efficient wireless transmission method for cellular communication systems based on smart reflective surfaces

Abstract

The present invention relates to the field of wireless communication technology and provides a high-energy-efficiency wireless transmission method for a cellular communication system based on a smart reflector, comprising: constructing a downlink transmission system for a smart reflector-assisted multi-user NOMA (non-contact multi-user automated multi-network architecture); constructing an energy efficiency optimization model; fixing the smart reflector phase shift, solving for maximum system energy efficiency based on the user's signal-to-interference-and-noise ratio constraint and the base station's maximum transmit power constraint, and obtaining an optimal base station transmit beam; fixing the base station transmit beam, with the system's total power consumption constant, solving for maximum system sum rate based on the user's signal-to-interference-and-noise ratio constraint, the smart reflector phase shift constraint, and the SIC decoding order constraint, and obtaining an optimal smart reflector phase shift; and alternately solving for the optimal base station transmit beam and the optimal smart reflector phase shift to obtain the maximum system energy efficiency. The present invention converts the original non-convex optimization problem into a convex optimization problem, reducing the solution complexity, meeting growing user needs, and providing a better communication experience.

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 for a cellular communication system based on an intelligent reflecting surface. Background Art

[0002] In the context of smart reflector-assisted multi-user non-orthogonal multiple access (NOMA) technology, current research focuses on maximizing system sum and rate while minimizing base station transmit power. However, relatively few studies consider both maximizing sum and rate and minimizing power consumption simultaneously. With the growing demand for wireless devices and users, energy consumption is also increasing. Future wireless networks urgently need to design more energy-efficient resource allocation solutions.

[0003] Existing technologies focus on optimizing the phase shift of smart reflectors to improve system performance and data rates, but only consider single-antenna transmitters. Using multiple antennas to transmit signals allows for the transmission of multiple data streams over the same time and frequency. Compared to single-input single-output (SISO), MISO offers higher system capacity and information transmission rates, further improving the system's spectral and energy efficiency. The receiver considers only two users, resulting in a relatively simple and ideal model. The impact of smart reflectors on the decoding order of Successive Interference Cancellation (SIC) is not considered. Some studies have considered a smart reflector-assisted NOMA system with only two users: a strong user close to the base station and a weak user farther away. The limited number of users considered and the relatively simple and ideal model significantly limit the application of smart reflectors in NOMA systems. Furthermore, these models do not consider the more general case of direct links between the base station and users, but only consider links assisted by smart reflective surfaces (IRS). In the presence of direct links, the performance of both the direct link and the IRS-assisted link must be optimized simultaneously. 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 a strong / weak relationship between user channels and have not considered the SIC decoding order in the optimization problem. In wireless communication systems, especially in complex channel environments with smart reflector-assisted NOMA, determining the SIC decoding order is particularly important. Because smart reflectors can influence the signal transmission path by adjusting their phase shift, the channel combining gain becomes highly dynamic and complex. When multiple users share the same spectrum resources, the channel gain between different users may vary significantly, but it may also be minimal due to the randomness of user distribution. In this case, if the decoding order is not appropriate, one user's signal may be interpreted as interference from another user, seriously affecting the overall system performance. 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 area of ​​the smart reflective surface, a more general scenario is taken into account. The base station can simultaneously provide services to multiple users through a direct transmission link and a link assisted by the 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:

[0007] S1: Build a smart reflector-assisted multi-user NOMA downlink transmission system;

[0008] S2: Construct an energy efficiency optimization model based on the downlink transmission system of multi-user NOMA assisted by smart reflective surfaces;

[0009] 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;

[0010] S4: Fixed intelligent reflector phase shift, based on the user's signal-to-interference-and-noise ratio constraints and the base station's maximum transmit power constraints, solves for maximum system energy efficiency and obtains the optimal base station transmit beam.

[0011] S5: The base station transmits a fixed beam, with the total system power consumption set to a constant. Based on the user's signal-to-interference-and-noise ratio constraints, the smart reflector phase shift constraints, and the SIC decoding order constraints, the maximum system sum rate is calculated to obtain the optimal smart reflector phase shift.

[0012] S6: Use the alternating iterative algorithm to alternately solve the optimal base station transmit beam and the optimal smart reflector phase shift until the energy efficiency converges or the maximum number of iterations is reached.

[0013] Furthermore, in step S1, the downlink transmission system of the smart 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 at the same frequency at the same time.

[0014] Furthermore, a system energy efficiency optimization model is constructed based on the superimposed signals transmitted by the base station and the layer-by-layer signal decoding through the SIC technology. The system energy efficiency is the ratio of the system total rate to the total system power consumption. The total system power consumption includes the base station's transmit power and the total circuit power. The constraints include the user signal-to-interference-and-noise ratio constraint, the base station's transmit power constraint, the IRS phase shift angle constraint, and the SIC decoding order constraint.

[0015] Furthermore, the calculation expression of the system energy efficiency is:

[0016]

[0017] 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.

[0018] Furthermore, considering the service quality requirements of each user, the base station transmit power constraint, the SIC decoding order constraint and the IRS phase shift angle constraint, the optimization problem of maximizing the system energy efficiency is:

[0019]

[0020] in, For the The rate at which the signal can be decoded at each user is For IRS The phase shift angle of each 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;

[0021] Ensure the service quality requirements of each user. The user's service quality requirements are constrained as follows:

[0022]

[0023] in, For the The signal-to-interference-noise ratio threshold of each user, From base station to The channel gain of each user, From IRS to The channel gain of each user, 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;

[0024] Limit the base station transmit power to no more than the maximum transmit power. The base station transmit power constraint is:

[0025]

[0026] in, is the maximum transmit power of the base station;

[0027] Constrain the value range of the IRS phase shift angle. The IRS phase shift angle constraint is:

[0028]

[0029] in, is the number of passive reflective elements,

[0030] To ensure the SIC decoding order, the SIC decoding order constraints are:

[0031]

[0032] in, From base station to The channel gain of each user.

[0033] Furthermore, according to the system energy efficiency optimization model, the optimization problem is non-convex.

[0034] Furthermore, by fixing the phase shift of the smart reflector, the optimization problem is simplified to maximizing the system energy efficiency under the constraints of each user's signal-to-interference-and-noise ratio and the base station's maximum transmit power. Solving this optimization problem yields the optimal base station transmit beam.

[0035] S41: The calculation expression of the signal-to-interference-and-noise ratio constraint of each user is constructed as follows:

[0036]

[0037] in, is the signal coefficient, , From base station to The channel gain of each user, From IRS to The channel gain of each user, 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-and-noise ratio threshold of each user;

[0038] S42: Using the second-order cone-convex optimization method, the non-convex constraint is converted into a second-order cone-convex constraint by rotating the transmit beam in phase by a certain angle;

[0039] S43: Using the Dinkelbach algorithm, the objective function is converted into a parameterized reduction function through non-negative variables:

[0040]

[0041] in, is the first non-negative variable, For the The beam vector of each user, For the The rate at which the signal can be decoded at each user is is the power amplifier efficiency at the base station is the total circuit power, is the maximum value function;

[0042] S44: The parameterized subtraction function is converted into a convex function through relaxation variables, and the convex function is solved by the convex optimization solver to obtain the optimal base station transmission beam.

[0043] 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 sum rate maximization problem under the conditions of each user's signal-to-interference-and-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.

[0044] Furthermore, step S5 includes:

[0045] S51: Improving the signal-to-interference-and-noise ratio of each user by maximizing the signal-to-interference-and-noise ratio difference;

[0046] S52: The optimization problem is simplified to:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053] 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 the noise power, For the Phase shift constraints of the reflective elements, 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;

[0054] S53: Use the convex optimization solver to solve and obtain the optimal IRS phase shift.

[0055] 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 base station transmit beam, fixes the base station transmit beam, uses the semi-definite relaxation technique to solve the optimization problem, obtains the optimal IRS phase shift, and alternately solves the optimal base station transmit beam and the optimal IRS phase shift until the energy efficiency converges.

[0056] The above one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects:

[0057] The present invention provides a high-energy-efficiency wireless transmission method for a cellular communication system based on a smart reflective surface. The resource allocation scheme of the present invention takes into account the SIC decoding order constraint to avoid the impact of dynamic changes of the smart reflective surface on the SIC decoding order when user channel conditions are similar. The present invention converts the original non-convex optimization problem into a convex optimization problem, which can be solved using classic convex optimization solution tools, reducing the solution complexity.

[0058] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0060] 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.

[0061] 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.

[0062] Figure 3 The present invention provides a schematic diagram of the relationship between the energy efficiency of a high-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.

[0063] Figure 4The present invention provides a schematic diagram of the relationship between energy efficiency and base station circuit power consumption of a high-efficiency wireless transmission method for a cellular communication system based on an intelligent reflective surface.

[0064] Figure 5 The present invention provides a schematic diagram of the relationship between the energy efficiency of a high-efficiency wireless transmission method for a cellular communication system based on an intelligent reflective surface and the maximum transmission power of a base station.

[0065] 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

[0066] To make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below. Obviously, the embodiments described are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0067] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean 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 can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0068] The following combination Figures 1 to 6 The present invention describes a high energy-efficient wireless transmission method for a cellular communication system based on a smart reflective surface.

[0069] like Figure 1 As shown, a high-energy-efficiency wireless transmission method for a cellular communication system based on a smart reflective surface includes:

[0070] S1: Based on the smart reflector-assisted NOMA system model, a multi-smart reflector-assisted multi-user NOMA downlink transmission system is constructed;

[0071] like Figure 2As shown in the figure, the downlink transmission system of the smart 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 area of ​​the smart 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 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.

[0072] The channel gain from BS to IRS is , , for The channel gain matrix of order is, and the channel gain from IRS to the kth user is , for The channel gain matrix of order is, and the channel gain from BS to the kth user is , for The channel gain matrix of order is, and the IRS diagonal reflection matrix is , for The diagonal elements are the reflection coefficients and phase shifts of the reflective elements in the IRS. The IRS reflects the signal to the maximum extent, so the reflection coefficient of the N reflective elements is 1, and the phase shift angle ranges from 0 to 360 degrees.

[0073] 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:

[0074]

[0075] in, For users The received signal at For BS to The channel gain of each user, From IRS to The channel gain of each user, 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 , For users The beamforming vector, For users The additive white Gaussian noise received at is the noise power;

[0076] To keep the decoding order unchanged, assume The equivalent channel gains should be sorted in descending order, that is:

[0077]

[0078] in, For BS to The channel gain of each user,

[0079] user The signal to interference plus noise ratio (SINR) that can be achieved by decoding the signal itself is:

[0080]

[0081] 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;

[0082] In the user The corresponding achievable rate for decoding its own signal at is:

[0083]

[0084] in, For the The rate achievable by decoding the signal at each user.

[0085] S2: Construct an energy efficiency optimization model based on the downlink transmission system of multi-user NOMA assisted by multiple intelligent reflectors;

[0086] Based on the superimposed signals transmitted by the base station and the layer-by-layer decoding of the signals using SIC technology, a system energy efficiency optimization model is constructed. The system energy efficiency is the ratio of the total system rate to the total system power consumption. The total system power consumption includes the base station transmit power and the total circuit power. The constraints include the user signal-to-interference-and-noise ratio constraint, the base station transmit power constraint, the IRS phase shift angle constraint, and the SIC decoding order constraint.

[0087] The calculation expression of system energy efficiency is:

[0088]

[0089] 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.

[0090] Considering the service quality requirements of each user, the base station transmit power constraint, the SIC decoding order constraint and the IRS phase shift angle constraint, the optimization problem of maximizing the system energy efficiency is:

[0091]

[0092] in, For the The rate at which the signal can be decoded at each user is For IRS The phase shift angle of each 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;

[0093] Ensure the service quality requirements of each user. The user's service quality requirements are constrained as follows:

[0094]

[0095] in, For the The signal-to-interference-noise ratio threshold of each user, From base station to The channel gain of each user, From IRS to The channel gain of each user, 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;

[0096] Limit the base station transmit power to no more than the maximum transmit power. The base station transmit power constraint is:

[0097]

[0098] in, is the maximum transmit power of the base station;

[0099] Constrain the value range of the IRS phase shift angle. The IRS phase shift angle constraint is:

[0100]

[0101] in, is the number of passive reflective elements,

[0102] To ensure the SIC decoding order, the SIC decoding order constraints are:

[0103]

[0104] in, From base station to Channel gain for each user;

[0105] According to the system energy efficiency optimization model, the optimization problem is non-convex.

[0106] 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;

[0107] In order to solve the non-convex optimization problem with mutually coupled optimization variables, the original problem is decomposed into two sub-problems. An iterative algorithm based on the Dinkelbach technique is used to optimize the base station transmit beam, and an IRS phase shift is optimized based on the signal-to-interference-noise ratio difference algorithm, which is used alternately until convergence.

[0108] S4: Fixed intelligent reflector phase shift, based on the user's signal-to-interference-and-noise ratio constraints and the base station's maximum transmit power constraints, solves for maximum system energy efficiency and obtains the optimal base station transmit beam.

[0109] By fixing the phase shift of the intelligent reflector, the optimization problem is simplified to maximizing the system energy efficiency under the constraints of each user's signal-to-interference-and-noise ratio and the base station's maximum transmit power. Solving this optimization problem yields the optimal base station transmit beam.

[0110] S41: The calculation expression of the signal-to-interference-and-noise ratio constraint of each user is constructed as follows:

[0111]

[0112] 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-and-noise ratio threshold of each user;

[0113] S42: Using the second-order cone-convex optimization method, the non-convex constraint is converted into a second-order cone-convex constraint by rotating the transmit beam in phase by a certain angle;

[0114] Guarantee the service quality requirements of each user:

[0115]

[0116] This constraint is a non-convex constraint. It is converted into a convex constraint by the second-order cone programming method. Introduce 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:

[0117]

[0118]

[0119] in, for The real part of for The imaginary part of

[0120] S43: Using the Dinkelbach algorithm, the objective function is converted into a parameterized reduction function through non-negative variables:

[0121] The system energy efficiency optimization problem is simplified to the base station transmission beam optimization problem, and the objective function is:

[0122]

[0123] The objective function has the characteristics of non-convex fractional programming and is difficult to solve directly. Using the Dinkelbach algorithm, by introducing the first non-negative variable To solve this problem, convert the objective function into a parameterized reduction function:

[0124]

[0125] in, Part of it is a convex function, using The first non-negative variable of the 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.

[0126] S44: Convert the parameterized subtraction function into a convex function through slack variables, and use the convex optimization solver to obtain the optimal base station transmit beam;

[0127] The parameterized subtraction function is converted into a convex function through slack variables, and the calculation expression is:

[0128]

[0129] 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 get:

[0130]

[0131]

[0132] The non-convex constraint can be equivalently rewritten as:

[0133]

[0134] Further introduce the fourth slack variable , then:

[0135]

[0136]

[0137] Will is equivalent to:

[0138]

[0139] 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:

[0140]

[0141] in, The Dinkelbach iterative algorithm is Variables after iterations The value of The Dinkelbach iterative algorithm is Variables after iterations The value of

[0142] is a convex set, which can be rewritten into a second-order cone form as follows:

[0143]

[0144] Given from The optimized value at the beginning of the iteration At the iteration, the original optimization problem is transformed into:

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153] 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 item,

[0154] At each iteration, the problem given by the values ​​of the optimization variables from the previous iteration is solved using the CVX convex optimization solver until convergence.

[0155] S5: The base station transmits a fixed beam, with the total system power consumption set to a constant. Based on the user's signal-to-interference-and-noise ratio constraints, the smart reflector phase shift constraints, and the SIC decoding order constraints, the maximum system sum rate is calculated to obtain the optimal smart reflector phase shift.

[0156] When the optimal base station transmit beam is obtained, the total power consumption of the system is constant. The optimization problem is simplified to maximizing the system sum rate under the constraints of each user's signal-to-interference-and-noise ratio, the smart reflector phase shift, and the SIC decoding order. Based on a 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 enhance the system's energy efficiency.

[0157] After obtaining the optimized BS transmit beam, the total power consumption of the system becomes a constant. The energy efficiency maximization problem is simplified to the system sum rate maximization problem. The IRS phase shift optimization problem is then:

[0158]

[0159] Define the first auxiliary variable , the range constraint of the IRS phase shift angle is equivalent to the unit mode constraint: ,

[0160] Obtained by substitution , define User second auxiliary variable ,in, for The second auxiliary variable matrix of order, for The expansion matrix of

[0161] Then we have:

[0162]

[0163] in, for The conjugate transpose of

[0164] The optimization problem is equivalent to:

[0165]

[0166]

[0167]

[0168]

[0169] in, No. User second auxiliary variable;

[0170] S51: Improving the signal-to-interference-and-noise ratio of each user by maximizing the signal-to-interference-and-noise ratio difference;

[0171] A method based on maximizing the difference between signal-to-interference-and-noise ratios (SINRs) that forcibly improves each user's SINR while satisfying their SINR constraints. This allows users to achieve higher information transmission rates, resulting in higher energy efficiency and improved system performance.

[0172] By the second non-negative variable Convert the optimization problem to:

[0173]

[0174]

[0175]

[0176]

[0177]

[0178] in, For the A first auxiliary variable of the reflective element;

[0179] Introducing new optimization variables , need to meet and , since the rank-one constraint is non-convex;

[0180] S52: The optimization problem is simplified to:

[0181]

[0182]

[0183]

[0184] +1

[0185]

[0186]

[0187] in, For the Phase shift constraints of the reflective elements, for The conjugate transpose of for The conjugate transpose of

[0188] The constraints of the optimization problem are found to be convex sets. The problem is solved using the classic convex optimization solver CVX, and the optimal phase shift of the smart reflector is obtained.

[0189] S53: Use the convex optimization solver to solve and obtain the optimal IRS phase shift.

[0190] S6: Use the alternating iterative algorithm to alternately solve the optimal base station transmit beam and the optimal smart reflector phase shift until the energy efficiency converges or the maximum number of iterations is reached.

[0191] The alternating iterative algorithm fixes the IRS phase shift, uses the Dinkelbach technique and the continuous convex approximation technique to solve the optimization problem, and obtains the optimal base station transmit beam. The base station transmit beam is fixed, and the semi-positive definite relaxation technique is used to solve the optimization problem to obtain the optimal IRS phase shift. The optimal base station transmit beam and the optimal IRS phase shift are alternately solved until the energy efficiency converges.

[0192] This paper proposes a resource allocation scheme for 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. A joint optimization scheme is proposed to maximize system energy efficiency by optimizing the base station transmit beam and the IRS phase shift, subject to each user's signal-to-interference-and-noise ratio (SINR) constraint, base station 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 optimization variables, an alternating iterative algorithm is proposed to address this issue. This algorithm decouples 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 using the Dinkelbach algorithm to transform it into a parameterized subtraction function. Second-order cone programming is used to convert the non-convex constraints into convex constraints, and the optimal base station transmit beam is solved. Using the optimized base station transmit beam, the optimization problem is simplified to maximizing the system sum rate. An algorithm based on SINR 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, achieving maximum system energy efficiency. It can be seen from simulation verification that the present invention is conducive to improving the energy efficiency of the system.

[0193] like Figure 3As shown in FIG, the relationship between energy efficiency and 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.

[0194] 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.

[0195] like Figure 5 As shown in FIG, 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 advantages in improving system energy efficiency.

[0196] 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 high energy efficiency under higher signal to interference and noise ratio requirements, so the energy efficiency decreases slightly.

[0197] 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 SIC technology, effectively improving the spectrum utilization efficiency and solving the current problem of resource shortage.

[0198] The present invention deploys smart reflective surfaces to assist in communication, utilizing the reflective properties of smart reflective surfaces to enhance signal transmission and improve the sum rate of the system. Smart reflective surfaces are introduced into the NOMA system to control the propagation environment of the reflection channel, so that the channel conditions between NOMA users differ, overcoming NOMA's additional requirements for the channel, and further improving the spectrum efficiency of the communication system. The communication system of the present invention includes both direct transmission links and smart reflective surface auxiliary links, bringing significant diversity gain to the system. In addition, because smart reflective surfaces have the advantages of low profile, lightweight, and maintaining geometric shape, they are easy to install on buildings and convenient to deploy and replace. Therefore, this resource allocation scheme has strong applicability in user-intensive communication scenarios.

[0199] The resource allocation scheme proposed in the present invention takes into account the SIC decoding order constraint, avoiding the impact of dynamic changes in the smart reflective surface on the SIC decoding order when 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 using the classic convex optimization solver CVX, reducing the solution complexity.

[0200] 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 various 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: Build a smart reflector-assisted multi-user NOMA downlink transmission system; S2: Construct an energy efficiency optimization model based on the downlink transmission system of multi-user NOMA assisted by smart reflective surfaces; 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: Fixed intelligent reflector phase shift, based on the user's signal-to-interference-and-noise ratio constraints and the base station's maximum transmit power constraints, solves for maximum system energy efficiency and obtains the optimal base station transmit beam. S5: The base station transmits a fixed beam, with the total system power consumption set to a constant. Based on the user's signal-to-interference-and-noise ratio constraints, the smart reflector phase shift constraints, and the SIC decoding order constraints, the maximum system sum rate is calculated 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 reflector 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 smart 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 to the multiple users at the same frequency at the same time.

3. The high energy efficiency wireless transmission method for 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 signal decoding using SIC technology, a system energy efficiency optimization model is constructed. The system energy efficiency is the ratio of the system's total rate to the total system power consumption. The total system power consumption includes the base station's transmit power and the total circuit power. The constraints include the user's signal-to-interference-and-noise ratio constraint, the base station's transmit power constraint, the IRS phase shift angle constraint, and the SIC decoding order constraint.

4. The high energy efficiency wireless transmission method for 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 for 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 transmit power constraint, the SIC decoding order constraint and the IRS phase shift angle constraint, the optimization problem of maximizing the system energy efficiency is: in, For the The rate at which the signal can be decoded at each user is For IRS The phase shift angle of each 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, From base station to The channel gain of each user, From IRS to The channel gain of each user, 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 no more than 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 of passive reflective elements, To ensure the SIC decoding order, the SIC decoding order constraints are: 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 for 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 reflector, the optimization problem is simplified to maximizing the system energy efficiency under the constraints of each user's signal-to-interference-and-noise ratio and the base station's maximum transmit power. Solving this optimization problem yields the optimal base station transmit beam. S41: The calculation expression of the signal-to-interference-and-noise ratio constraint of each user is constructed as follows: in, is the signal coefficient, , From base station to The channel gain of each user, From IRS to The channel gain of each user, 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-and-noise ratio threshold of each user; S42: Using the second-order cone-convex optimization method, the non-convex constraint is converted into a second-order cone-convex constraint by rotating the transmit beam in phase by a certain angle; 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 signal can be decoded 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 subtraction function is converted into a convex function through relaxation variables, and the convex function is solved by the convex optimization solver to obtain the optimal base station transmission 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. The optimization problem is simplified to the system sum rate maximization problem under the constraints of each user's signal-to-interference-and-noise ratio, the smart reflector phase shift, and the SIC decoding order. 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 by maximizing the signal-to-interference-and-noise ratio difference; S52: The optimization problem is simplified 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 the noise power, For the Phase shift constraints of the reflective elements, 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 fixes the IRS phase shift, uses the Dinkelbach technique and the continuous convex approximation technique to solve the optimization problem, and obtains the optimal base station transmit beam. The base station transmit beam is fixed, and the semi-positive definite relaxation technique is used to solve the optimization problem to obtain the optimal IRS phase shift. The optimal base station transmit beam and the optimal IRS phase shift are alternately solved until the energy efficiency converges.

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