A beamforming optimization method and device for an IRS-aided MISO communication system and an electronic device

Abstract

The present application relates to the technical field of wireless communication, in particular to a beamforming optimization method, device and electronic equipment of an IRS assisted MISO communication system; comprising establishing an IRS assisted MISO communication system; the system comprises an AP, an IRS and a user; with the data s sent by the AP to the user as the target, a problem P of jointly optimizing the AP active beamforming and the IRS passive beamforming is constructed; the problem P is decomposed into a beamforming vector optimization problem P1 and an IRS phase shift optimization problem P2; given the IRS phase shift, the beamforming vector optimization problem P1 is solved; according to the optimal beamforming vector obtained, the IRS phase shift optimization problem P2 is solved, and finally the original data s sent by the AP to the user is obtained.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a beamforming optimization method, apparatus, and electronic device for an IRS-assisted MISO communication system. Background Technology

[0002] In recent years, Intelligent Reflecting Surfaces (IRS), as a novel concept, have attracted considerable attention from scholars. An IRS consists of numerous low-cost, virtually passive reflective elements, equipped with a controller. This controller can adjust parameters such as capacitance, inductance, and resistance of each element in real time, thereby achieving real-time control of the IRS reflection coefficient. By effectively designing the phase shift of the reflective elements, the IRS can achieve directional reflection of incident electromagnetic waves, intelligently reconfiguring the wireless propagation environment. Compared with traditional communication technologies, it overcomes the long-standing limitation of uncontrollable electromagnetic environments. The emergence of IRS will significantly reduce electromagnetic wave propagation energy consumption, expand communication coverage, resist interference noise, and significantly improve spectrum utilization, thus gaining widespread attention.

[0003] Beamforming and phase rotation techniques in IRS are crucial for IRS because beamforming enhances the signal in the target direction while suppressing noise and interference from other directions. Therefore, its quality directly affects the gain and interference suppression performance of the array antenna in various directions. However, existing beamforming technologies also have some problems, such as high complexity and energy consumption, low spectral efficiency, and limited flexibility, failing to meet future needs and improve communication signals in corners blocked by tall buildings and obstacles, thus hindering communication quality improvement. Therefore, effectively designing a joint beamforming algorithm for IRS-assisted communication is essential. However, current research on IRS-assisted communication systems still faces challenges such as high algorithm complexity, difficulty in implementation, and high cost. This patent proposes a low-complexity algorithm for joint beamforming in IRS-assisted MISO communication systems, which minimizes complexity, improves spectral and energy efficiency, saves energy, and reduces deployment costs at both the transmitter and receiver. Summary of the Invention

[0004] The purpose of this invention is to provide a beamforming optimization method, apparatus, and electronic device for an IRS-assisted MISO communication system, to obtain the optimal transmit beamforming vector and reduce algorithm complexity.

[0005] In a first aspect, the present invention provides a beamforming optimization method for an IRS-assisted MISO communication system, comprising the following steps:

[0006] S1. Establish an IRS-assisted MISO communication system; the system includes an access point (AP), an IRS, and users;

[0007] S2. With the goal of restoring the data s sent by the AP to the user, construct a problem P that jointly optimizes the AP's active beamforming and the IRS's passive beamforming;

[0008] S3. Decompose problem P into beamforming vector optimization problem P1 and IRS phase shift optimization problem P2;

[0009] S4. Given the IRS phase shift, solve the beamforming vector optimization problem P1 to obtain the optimal beamforming vector;

[0010] S5. Based on the optimal beamforming vector obtained in step S4, solve the IRS phase shift optimization problem P2 to finally obtain the original data s sent by the AP to the user.

[0011] Furthermore, in an IRS-assisted MISO communication system, the AP is equipped with M antennas, the user is equipped with a single antenna, and the IRS has N reflecting elements, with the phase of each reflecting element being controllable; the reflection coefficient matrix of the IRS is a diagonal matrix. , Let n be the phase shift of the nth reflection unit of the IRS; define the channel gain matrix between the AP and the user as follows: The channel gain matrix between the IRS and the user is The channel gain matrix between AP and IRS is ; Let represent a complex matrix of dimension N×M.

[0012] Furthermore, the AP's transmit signal is defined as:

[0013]

[0014] Where s represents the data sent by the AP to the user, and the AP uses linear transmission precoding as the beamforming vector, denoted as s. ;

[0015] The user's received signal is:

[0016]

[0017] in, This indicates that the expression follows a pattern with a mean of 0 and a variance of . Gaussian noise.

[0018] Furthermore, the problem P of jointly optimizing AP active beamforming and IRS passive beamforming constructed in step S2 is expressed as:

[0019]

[0020] in, This is the channel gain matrix between the AP and the user. This is the channel gain matrix between the IRS and the user. This is the channel gain matrix between the AP and the IRS. Represents the beamforming vector for linear transmit precoding, a diagonal matrix. Here is the reflection coefficient matrix of the IRS. Let be the phase shift of the nth reflecting unit of the IRS.

[0021] Furthermore, the fixed IRS phase shift matrix Solving for the beamforming vector w transforms problem P into a beamforming vector optimization problem P1, expressed as:

[0022]

[0023] in, Given that H is a known quantity, and since the rank of H is 1, w can be written as a general solution with M-1 parameters:

[0024]

[0025] in, For the equation The general solution, For the equation A particular solution; solve for the corresponding This minimizes the power of w.

[0026] Furthermore, by determining the beamforming vector to optimize the IRS phase shift matrix, problem P is transformed into an IRS phase shift optimization problem P2, expressed as:

[0027]

[0028] Where w is a known quantity, use Substitution yields a system of easily solvable nonhomogeneous linear equations, expressed as:

[0029]

[0030] in, For the equation The general solution, For the equation The particular solution is obtained by solving for it. , so that:

[0031]

[0032] Where N is the number of reflective units in the IRS.

[0033] In a second aspect, based on the method provided in the first aspect, the present invention proposes a beamforming optimization device for an IRS-assisted MISO communication system, comprising:

[0034] The channel estimation module is used to construct an IRS-assisted MISO communication system and to construct a problem P that jointly optimizes AP active beamforming and IRS passive beamforming.

[0035] The problem transformation module is used to decompose problem P into beamforming vector optimization problem P1 and IRS phase shift optimization problem P2 through alternating optimization.

[0036] AP transmitter beamforming module is used to fix the IRS phase shift matrix. Solve the beamforming vector optimization problem P1 to obtain the optimal beamforming vector. ;

[0037] The IRS phase rotation factor calculation module is used to calculate the optimal beamforming vector obtained by the AP transmitter beamforming module. Solve the IRS phase shift optimization problem P2 to finally restore the data s sent by the AP to the user.

[0038] In a third aspect, the present invention provides an electronic device, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0039] The memory is used to store computer programs;

[0040] The processor is used to execute the program stored in the memory to implement the steps of a beamforming optimization method for an IRS-assisted MISO communication system.

[0041] The beneficial effects of this invention are:

[0042] Current research on IRS-assisted communication systems still faces challenges such as high algorithm complexity, difficulty in implementation, and high cost. To address these issues, this invention proposes a joint beamforming optimization method for IRS-assisted MISO communication systems. This method minimizes algorithm complexity, allows users to directly receive the transmitted signal from the AP, saves energy, and reduces deployment costs at both the transmitter and receiver. The total computational complexity of the proposed optimization algorithm is [insert value here]. The algorithm's complexity grows exponentially. Other classic algorithms, such as semidefinite relaxation and alternating optimization, have a minimum complexity of O(n log n). This demonstrates the superiority of the algorithm proposed in this invention. Compared to traditional algorithms, the algorithm proposed in this invention exhibits lower transmission power consumption and better bit error rate and channel capacity performance: transmission power consumption is reduced by at least 7 dBm; when the bit error rate reaches 10... -6 At that time, the signal-to-noise ratio decreased by about 0.2dB; the channel capacity increased by at least 2.2bit / s. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating a beamforming optimization method for an IRS-assisted MISO communication system according to an embodiment of the present invention.

[0044] Figure 2 This is an IRS-assisted MISO communication system model according to an embodiment of the present invention;

[0045] Figure 3 This is a structural diagram of the algorithm device according to an embodiment of the present invention;

[0046] Figure 4 This is a structural diagram of an electronic device according to an embodiment of the present invention;

[0047] Figure 5 The relationship between the number of reflection units (IRS) N and the channel capacity C;

[0048] Figure 6 The relationship between the number of transmitting antennas M and the channel capacity C;

[0049] Figure 7 The relationship between the number N of the reflective units IRS and the transmit power P at the AP end;

[0050] Figure 8 The relationship between the number of transmitting antennas M and the transmit power P at the AP;

[0051] Figure 9 This represents the relationship between the signal-to-noise ratio (Eb / N0) and the error rate (BER). Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] This invention provides a beamforming optimization method for an IRS-assisted MISO communication system, such as... Figure 1 As shown, it includes the following steps:

[0054] S1. Establish an IRS-assisted MISO communication system; the system includes an AP, an IRS, and users.

[0055] Specifically, in an IRS-assisted MISO communication system, the AP is equipped with M antennas, the user has a single antenna, and the IRS has N reflecting elements, the phase of which is controllable. Assuming the direct link from the AP to the user is unobstructed, and the AP and user can also communicate via the IRS, each user can receive superimposed signals from the AP-User direct link and the AP-IRS-User reflected link.

[0056] Assuming there is linear transmit precoding on the AP, define Each user is assigned a dedicated beamforming vector; the channel gain matrix between the AP and the user is defined as follows. The channel gain matrix between the IRS and the user is The channel gain matrix between AP and IRS is ; Let represent a complex matrix of dimension N×M. The reflection coefficient matrix of an IRS is defined as a diagonal matrix. , Let be the phase shift of the nth reflecting unit of the IRS.

[0057] Define the AP's transmit signal as:

[0058]

[0059] Where s represents the data sent by the AP to the user;

[0060] If Rayleigh fading is considered for all channels, the signals received by the user on the AP-IRS-User reflection link and the AP-User direct link can be expressed as:

[0061]

[0062] in, This indicates that the expression follows a pattern with a mean of 0 and a variance of . Gaussian noise.

[0063] S2. With the goal of restoring the data s sent by the AP to the user, construct a problem P that jointly optimizes the AP active beamforming and the IRS passive beamforming.

[0064] Specifically, to minimize algorithm complexity, conserve energy, and reduce deployment costs, considering the limitations of AP transmit power and IRS reflection phase shift, a problem P is constructed by jointly optimizing AP beamforming and IRS phase shift constraints to minimize the transmit beamforming vector.

[0065] .

[0066] S3. Decompose problem P into beamforming vector optimization problem P1 and IRS phase shift optimization problem P2.

[0067] S4. Given the IRS phase shift, solve the beamforming vector optimization problem P1.

[0068] Specifically, assuming the IRS phase shift is known, the beamforming vector is optimized using the idea of ​​solving a system of non-homogeneous linear equations; the problem P is transformed into a beamforming vector optimization problem P1, expressed as:

[0069]

[0070] Among them, at this time Given that H is a known quantity, and since the rank of H is 1, w can be written as a general solution with M-1 parameters:

[0071]

[0072] in, For the equation The general solution, For the equation A particular solution; solve for the corresponding This minimizes the power of w.

[0073] Therefore, the beamforming vector optimization problem P1 constructed in this invention can also be expressed as:

[0074]

[0075] The above equation first uses the method of solving a non-homogeneous linear system of equations to find the general solution and particular solution of w. Since (P1) is a convex problem, it can be optimized and solved using convex optimization tools: Assume First, calculate w with the variable, then use the preconditions. Find the minimum value of w and the variable. .

[0076] S5. Based on the optimal beamforming vector obtained in step S4, solve the IRS phase shift optimization problem P2 to finally obtain the original data s sent by the AP to the user.

[0077] Specifically, based on the optimal beamforming vector obtained in step S4, the IRS phase shift is optimized using the idea of ​​solving a system of non-homogeneous linear equations; problem P is transformed into an IRS phase shift optimization problem P2, expressed as:

[0078]

[0079] Where w is a known quantity, use Substitution yields a system of easily solvable nonhomogeneous linear equations, expressed as:

[0080]

[0081] in, This is the general solution of the equation. To find a particular solution to the equation, a suitable... , so that:

[0082]

[0083] Where N is the number of reflective units in the IRS; and use and By making the substitution, we can obtain:

[0084]

[0085] The solution process is the same as above; first, calculate... Find the general solution and particular solution, and then use the idea of ​​solving non-homogeneous linear equations to solve... Let the variables be solved to make the above equation true.

[0086] Preferably, the IRS-assisted MISO communication system established in a simulation example is as follows: Figure 2 As shown, the system includes one access point (AP), one IRS, and one user. All parameters in the IRS-assisted MISO communication system are initialized, and Table 1 lists the parameter values ​​for system initialization.

[0087] Table 1 System Initialization Parameters

[0088]

[0089] The simulation scenario sets the AP's horizontal position to (0m, 0m) and the user's horizontal position to (50m, 0m). The number of IRS reflector units N and the number of AP transmit antennas M are set according to the simulation requirements. Considering small-scale fading, the channel is a Rayleigh fading channel with path loss. The path loss model is set as follows:

[0090]

[0091] Where C0 is the path loss when the reference distance D0 = 1m, d is the link distance, and α is the path loss factor.

[0092] To verify the performance of the algorithm of this invention, it is compared with several other algorithms in the simulation results. Comparison Algorithm 1: The solution obtained by applying semi-definite relaxation (SDR) and Gaussian randomization; Comparison Algorithm 2: Let... Comparison Algorithm 3: Minimum transmit power based on the optimal solution of the semi-definite program (SDP) problem; Comparison Algorithm 4: Randomly set the elements in θ to [0, 2π], and then perform MRT at the AP according to the cascaded channel; Comparison Algorithm 5: Suboptimal substitution algorithm based on alternating optimization.

[0093] Figure 5 The simulation results illustrate the relationship between the number of reflective elements (IRS) N and the channel capacity C. With the AP's transmit antennas set to M=4, it can be seen that the channel capacity increases with the increase in the number of IRS. Therefore, it can be concluded that having IRS improves system performance because IRS essentially performs passive beamforming by adjusting the reflection coefficient, thereby concentrating the dispersed energy in the channel. As the number of IRS reflective elements increases, the area of ​​signal energy received by the IRS in the channel also increases. Furthermore, a larger reflective element array is more favorable for beamforming, thus increasing signal power. Since channel capacity is positively correlated with signal power and signal-to-noise ratio (SNR), the signal capacity also increases accordingly.

[0094] Figure 6 The simulation results in the figure represent the relationship between the number of transmit antennas M and the channel capacity C. The number of IRS is set to N=50. Figure 6 As the number of transmitting antennas increases, the transmitting power at the transmitting end also increases, and the channel capacity of the system also increases. This is because the IRS uses passive beamforming to refocus the signal energy scattered freely in the channel and directs the beam generated by the reflection coefficient towards the user. As the number of transmitting antennas and the transmitting power increase, the signal energy that can be concentrated and used in the channel gradually increases, so the channel capacity increases with the increase in the number of transmitting antennas.

[0095] Figure 7The simulation results show the relationship between the number of reflective elements (IRS) N and the transmit power P at the AP. Similarly, with the AP having M=4 transmit antennas, it can be seen that the AP transmit power decreases as the number of reflective elements increases. This proves the effectiveness of the proposed solution. The reason is that as the number of IRS increases, the energy from passive beamforming via the IRS becomes larger, reaching the standard required for user reception, thus reducing the required AP transmit power. Therefore, due to the relationship between transmit power and beamforming vector... The relationship is positively correlated, therefore The power will also decrease.

[0096] Figure 8 The simulation results show the relationship between the number of transmitting antennas M and the transmit power P at the AP. Similarly, with the number of reflector elements N=50, as shown in the figure, the results are also the same: the transmit power decreases as the number of transmitting antennas increases. This is because an IRS is added to the system. The IRS can concentrate the energy in the channel; therefore, by optimizing the passive beamforming of the IRS and the active beamforming at the AP, the beamforming vector can be optimized. This reduction in power consumption leads to a decrease in transmit power at the AP. This demonstrates that the IRS designed with beamforming improves system performance.

[0097] Figure 9 The simulation results illustrate the relationship between the signal-to-noise ratio (SNR) Eb / N0 and the error rate (BER). The parameters were set as follows: number of reflection units N=30, number of transmit antennas M=4, OFDM system parameters set as number of subcarriers Nc=1, 100 independent data symbols per simulation (each symbol containing 1 bit), cyclic prefix length of 0, baseband modulation scheme of BPSK, and AWGN channel. It is evident that the error rate decreases as the SNR increases. This demonstrates that the algorithm proposed in this invention exhibits superior performance compared to other algorithms, achieving increased channel capacity and reduced transmit power while maintaining low computational complexity and effectively suppressing the bit error rate.

[0098] Simulation results show that, compared with traditional algorithms, the algorithm proposed in this invention has lower transmission power consumption and better bit error rate and channel capacity performance: transmission power consumption is reduced by at least 7 dBm; when the bit error rate reaches 10... -6 At that time, the signal-to-noise ratio decreased by about 0.2dB; the channel capacity increased by at least 2.2bit / s.

[0099] Corresponding to the above method embodiments, this invention provides a beamforming optimization device for an IRS-assisted MISO communication system, such as... Figure 3 As shown, it includes:

[0100] The channel estimation module 301 is used to construct an IRS-assisted MISO communication system and consider the imperfect CSI situation. Under the constraints of base station transmit power and IRS reflection phase shift, it constructs a problem P that jointly optimizes AP active beamforming and IRS passive beamforming.

[0101] Problem transformation module 302 is used to decompose problem P into beamforming vector optimization problem P1 and IRS phase shift optimization problem P2 through alternating optimization.

[0102] AP transmitter beamforming module 303 is used to fix the IRS phase shift matrix. Solve the beamforming vector optimization problem P1 to obtain the optimal beamforming vector. ;

[0103] The IRS phase rotation factor calculation module 304 is used to calculate the optimal beamforming vector obtained by the AP transmitter beamforming module. Solve the IRS phase shift optimization problem P2 to finally restore the data s sent by the AP to the user.

[0104] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0105] This invention also provides an electronic device, see [link to relevant documentation]. Figure 4 , Figure 4 The electronic device according to an embodiment of the present invention includes: a processor 401, a communication interface 402, a memory 403, and a communication bus 404, wherein the processor 401, the communication interface 402, and the memory 403 communicate with each other through the communication bus 404;

[0106] Memory 403 is used to store computer programs;

[0107] When the processor 401 executes the program stored in the memory 403, it implements the steps of the beamforming optimization method of the above-mentioned IRS-assisted MISO communication system.

[0108] It should be noted that the communication bus 404 mentioned in the above electronic device can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. This communication bus 404 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0109] Communication interface 402 is used for communication between the above-mentioned electronic device and other devices.

[0110] The memory 403 may include RAM (Random Access Memory) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0111] The processor 401 mentioned above can be a general-purpose processor, including: CPU (Central Processing Unit), NP (Network Processor), etc.; it can also be DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0112] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "setting," "connection," "fixing," "rotation," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Unless otherwise explicitly limited, those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0113] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A beamforming optimization method for an IRS-assisted MISO communication system, characterized in that, Includes the following steps: S1. Establish an IRS-assisted MISO communication system; the system includes an access point (AP), an IRS, and users; S2. With the goal of restoring the data s sent by the AP to the user, construct a problem P that jointly optimizes the AP's active beamforming and the IRS's passive beamforming; S3. Decompose problem P into beamforming vector optimization problem P1 and IRS phase shift optimization problem P2; S4. Given the IRS phase shift, solve the beamforming vector optimization problem P1 to obtain the optimal beamforming vector; S5. Based on the optimal beamforming vector obtained in step S4, solve the IRS phase shift optimization problem P2 to obtain the optimal IRS phase shift matrix; finally, apply the optimal beamforming vector and the optimal phase shift matrix to the link beamforming process to achieve reliable transmission of the original data s by the AP. The problem P of jointly optimizing AP active beamforming and IRS passive beamforming constructed in step S2 is represented as: in, This is the channel gain matrix between the AP and the user. This is the channel gain matrix between the IRS and the user. This is the channel gain matrix between the AP and the IRS. Represents the beamforming vector for linear transmit precoding, a diagonal matrix. Here is the reflection coefficient matrix of the IRS. The phase shift of the nth reflecting unit in the IRS; Fixed IRS phase shift matrix Solving for the beamforming vector w transforms problem P into a beamforming vector optimization problem P1, expressed as: in, Given that H is a known quantity, and since the rank of H is 1, w can be written as a general solution with M-1 parameters: in, For the equation The general solution, For the equation Special solution; For variables, first calculate the variables. w, and then use the preconditions. Find the optimal w that minimizes the beamforming vector power; Determine the beamforming vector to optimize the IRS phase shift matrix, transforming problem P into an IRS phase shift optimization problem P2, expressed as: Where w is a known quantity, use Substitution yields a system of easily solvable nonhomogeneous linear equations, expressed as: in, For the equation The general solution, For the equation The particular solution is obtained by solving for it. , so that: Where N is the number of reflective units in the IRS; Thus, the final vector solution is obtained. Each element is Thus, the optimal phase shift of each reflection unit in the IRS is obtained, which together constitute the optimal IRS phase shift matrix.

2. The beamforming optimization method for an IRS-assisted MISO communication system according to claim 1, characterized in that, In an IRS-assisted MISO communication system, the access point (AP) is equipped with M antennas, the user has a single antenna, and the IRS has N reflecting elements, each with a controllable phase. The reflection coefficient matrix of the IRS is a diagonal matrix. , The phase shift of the nth reflecting unit in the IRS; Define the channel gain matrix between the AP and the user as follows: The channel gain matrix between the IRS and the user is The channel gain matrix between AP and IRS is ; Let represent a complex matrix of dimension N×M.

3. The beamforming optimization method for an IRS-assisted MISO communication system according to claim 2, characterized in that, Define the AP's transmit signal as: Where s represents the data sent by the AP to the user, and the AP uses linear transmission precoding as the beamforming vector, denoted as... ; The user's received signal is: in, This indicates that the expression follows a pattern with a mean of 0 and a variance of . Gaussian noise.

4. A beamforming optimization device for an IRS-assisted MISO communication system implementing the method of any one of claims 1-3, characterized in that, include: The channel estimation module is used to construct an IRS-assisted MISO communication system and to construct a problem P that jointly optimizes AP active beamforming and IRS passive beamforming. The problem transformation module is used to decompose problem P into beamforming vector optimization problem P1 and IRS phase shift optimization problem P2 through alternating optimization. AP transmitter beamforming module is used to fix the IRS phase shift matrix. Solve the beamforming vector optimization problem P1 to obtain the optimal beamforming vector. ; The IRS phase rotation factor calculation module is used to calculate the optimal beamforming vector obtained by the AP transmitter beamforming module. Solve the IRS phase shift optimization problem P2 to finally restore the data s sent by the AP to the user.

5. An electronic device, characterized in that, include: The processor, communication interface, memory, and communication bus are connected, with the processor, communication interface, and memory communicating with each other via the communication bus. The memory is used to store computer programs; The processor is used to execute the program stored in the memory to implement the steps of the beamforming optimization method for an IRS-assisted MISO communication system as described in any one of claims 1 to 3.

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