User rate enhancement method for an intelligent reflecting surface assisted fttr system

Abstract

The application discloses a user rate enhancement method of an intelligent reflecting surface assisted FTTR system, and belongs to the field of wireless communication.The method comprises the following steps: S1, determining the signal-to-interference-and-noise ratio of a target user according to the beamforming vectors of a target AP and each interference AP and the reflection phase matrix of an IRS, so as to represent the rate of the target user; and S2, maximizing the rate of the target user to obtain the optimal reflection phase matrix of the IRS.The method takes the rate of the target user as a target function, simultaneously considers the enhancement of the target user signal and the suppression of the interference signal by the intelligent reflecting surface, and adjusts the weight of the reflection unit of the IRS to enhance the user rate.The intelligent reflecting surface becomes a competitive communication resource, thereby greatly reducing the complexity of active and passive beam joint adjustment and greatly improving the achievable rate of the target user.The passive beam of the intelligent reflecting surface is quickly calculated through a Riemannian manifold gradient algorithm.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication, and more specifically, relates to a method for enhancing the user rate of an intelligent reflector-assisted FTTR system. Background Technology

[0002] FTTR (Fiber to the Room) brings gigabit broadband and gigabit Wi-Fi to every room, office, and even desktop by replacing traditional network cables with the latest fiber optic cables. This supports the digitalization, intelligentization, and informatization of homes, campuses, and businesses, achieving ultra-gigabit whole-house coverage through the new fiber optic + Wi-Fi 6 technology. In an FTTR system, each access point (AP) can fully utilize its allocated frequency band to improve user communication quality. However, due to the dense deployment of AP nodes, when multiple users are present, interference between multiple APs on the same frequency band can cause a sharp drop in user speeds at the edge of each AP's service area.

[0003] Therefore, reducing the impact of interfering access points on target users in order to improve the effectiveness of network services is an urgent problem to be solved. Summary of the Invention

[0004] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a method for enhancing the user rate of an intelligent reflective surface-assisted FTTR system, thereby solving the problem of how to ensure that users obtain rate improvement under conditions of strong interference.

[0005] To achieve the above objectives, according to a first aspect of the present invention, a method for enhancing the user rate of an intelligent reflector-assisted FTTR system is provided, comprising:

[0006] S1, based on the beamforming vectors of the target AP and each interfering AP, and the reflection phase matrix of the IRS, determine the signal-to-interference-plus-noise ratio (SIR) of the target user to characterize the rate of the target user; wherein, the target AP is the AP serving the target user;

[0007] S2, maximize the rate of the target user to obtain the optimal reflection phase matrix of the IRS.

[0008] According to a second aspect of the present invention, a user rate enhancement device for an intelligent reflector-assisted FTTR system is provided, comprising:

[0009] The first processing module is used to determine the signal-to-interference-plus-noise ratio (SIR) of the target user based on the beamforming vectors of the target AP and each interfering AP, and the reflection phase matrix of the IRS, so as to characterize the rate of the target user; wherein the target AP is the AP serving the target user.

[0010] The second processing module is used to maximize the rate of the target user in order to obtain the optimal reflection phase matrix of the IRS.

[0011] According to a third aspect of the present invention, a user rate enhancement system for an intelligent reflective surface-assisted FTTR system is provided, comprising: a computer-readable storage medium and a processor;

[0012] The computer-readable storage medium is used to store executable instructions;

[0013] The processor is configured to read executable instructions stored in the computer-readable storage medium and execute the method as described in the first aspect.

[0014] According to a fourth aspect of the invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to perform the method as described in the first aspect.

[0015] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0016] 1. The user rate enhancement method for an intelligent reflector-assisted FTTR system provided by this invention uses the unit frequency communication rate of the target user as the objective function and the phase of the IRS reflector unit as the optimization variable to obtain the optimal IRS passive beamforming. The solution process simultaneously considers the IRS's effect on both the target signal and interference signals, achieving efficient enhancement of the target user's communication quality.

[0017] 2. The user rate enhancement method for the intelligent reflector-assisted FTTR system provided by this invention uses the IRS as a channel resource that can be competitively used, thereby realizing targeted services for target users; at the same time, the passive beamforming of the IRS does not affect the active beamforming of the AP device, thereby reducing the time complexity of joint control of active and passive beams and realizing real-time implementation of IRS-assisted communication. Attached Figure Description

[0018] Figure 1 A flowchart of a user rate enhancement method for an intelligent reflective surface-assisted FTTR system provided in an embodiment of the present invention;

[0019] Figure 2 A schematic diagram of a model for an FTTR scene assisted by a smart reflective surface;

[0020] Figure 3 This is a parameter setting diagram for simulating the method provided in the embodiments of the present invention;

[0021] Figure 4A comparison of the target user communication rates between the interference suppression and rate enhancement method for indoor FTTR users based on intelligent reflective surfaces provided in this embodiment of the invention and the baseline scheme.

[0022] Figure 5 This diagram illustrates the changes in the target user's communication rate and the amount of interference in the communication scenario when using the interference suppression and rate enhancement method for indoor FTTR based on a smart reflective surface provided in this embodiment of the invention, compared to using a baseline scheme. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0024] Intelligent Reflecting Surface (IRS) technology, with its passive, low-cost, and high-energy-efficiency characteristics, has become one of the key technologies for next-generation communication networks and has been extensively studied. An IRS is a plane composed of a large number of low-cost passive reflective elements placed between the transmitter and receiver. Each reflective element can independently change the phase of the incident signal. By adjusting the phase shift settings on the reflective element, the wireless propagation environment can be altered, thereby improving the performance of the wireless communication system. Given its low cost, low power consumption, and ease of deployment, IRS is widely used in various communication systems and integrated with existing technologies to achieve higher-performance communication. Combining IRS with densely deployed APs in FTTR scenarios can simultaneously suppress interference and enhance the target signal, thereby increasing the communication rate of the target user while ensuring energy efficiency.

[0025] Based on this, to reduce the impact of interfering APs on target users and improve network service effectiveness, this invention proposes a user rate enhancement method for an intelligent reflector-assisted FTTR system. This method achieves rate enhancement with lower complexity by decoupling the AP's active and passive beams. Figure 1 As shown, it includes:

[0026] S1. Based on the beamforming vectors of the target AP and each interfering AP, and the reflection phase matrix of the IRS, determine the signal-to-interference-plus-noise ratio (SIR) of the target user to characterize the rate of the target user; wherein, the target AP is the AP serving the target user.

[0027] Specifically, the signal-to-interference-plus-noise ratio (SINR) at the target user is calculated based on the channel parameters related to the target user (the channel matrix between the IRS, the target AP, and each interfering AP and the target UE) and the active beamforming vectors of the target AP and the interfering AP, and the rate related to SINR is used as the objective function.

[0028] Among them, the channel conditions of the user are obtained through sensing and measurement as prior information, and the channel description matrix h, G, h is calculated. r The process of obtaining channel conditions and calculating the channel description matrix described above can be achieved using existing methods such as compressed sensing and machine learning.

[0029] S2, by maximizing the rate of the target user, the optimal reflection phase matrix of the IRS is obtained, that is, the optimal phase of each reflection unit of the IRS is obtained.

[0030] Specifically, by solving the vector derivative of the objective function, the optimal reflection unit phase for the target user served by the IRS is obtained, thereby enabling the passive beamforming of the IRS to be controlled by the active beamforming of each interfering AP based on channel information and the active beamforming of each interfering AP.

[0031] like Figure 2 The diagram shows a model of an FTTR scenario based on a smart reflector-assisted system provided in this embodiment of the invention. The system includes an IRS deployed on the ceiling of a room (or placed on an unobstructed wall) to assist in beamforming for the target AP. The interference suppression and rate enhancement method for indoor users based on a smart reflector provided in this embodiment of the invention is applicable to scenarios with multiple APs and UEs.

[0032] The target user is randomly distributed within the room. The APs serving the target user (i.e., the target AP) and the APs interfering with the target AP (i.e., the interfering AP) are both located at a relatively high height within the room to establish a line-of-sight path from the target AP to the UE. The UEs served by the interfering AP within the interference resource block are also randomly distributed within the room. That is, to reduce signal loss due to obstruction and improve the rate enhancement effect, preferably, the heights of the intelligent reflector, the target AP, the interfering AP, and the UE decrease sequentially.

[0033] The IRS controller and each AP are connected to the OLT (Optical Line Terminal). The OLT coordinates the use of the IRS, making it a channel resource that can be contested, thus enabling individual services to be provided to target users.

[0034] Since each AP and IRS controller is connected to the OLT and shares channel information, which can be obtained through compressed sensing and other methods, each AP serves one UE within its respective frequency band, based on OLT coordination.

[0035] like Figure 3 The diagram shows the parameter settings during scenario simulation, considering that each AP is equipped with N t There are 1 antenna, the UE has a single antenna, the IRS has M elements, and there are K interfering access points in the scenario. Assuming the target user is UE0, then... This represents the channel vector from the IRS to the UE. This represents the direct channel from the target AP to UE0. This represents the channel matrix from the AP to the IRS. This represents the beamforming vector of the target AP. Since it is assumed that the AP, UE, and IRS are all line-of-sight links, the channels among them all follow a Ricean distribution. Taking h as an example:

[0036]

[0037] in, The line-of-sight component represents a deterministic range. d represents the non-line-of-sight component. hi The subscript represents the distance of the channel, α represents the path loss exponent of the channel, and K represents the distance of the channel. hi The Rice factor value corresponding to the channel.

[0038] Specifically, each reflection unit on the IRS can independently adjust the phase shift of the incident signal. By changing the phase shift of each reflection unit in the IRS, the effective channel between the user and the AP can be further adjusted. The IRS reflection phase shift matrix is ​​represented as follows: The passive beamforming matrix representing the IRS, and let in, Let i represent the phase shift of the i-th reflecting unit on the IRS, where i = 1, ..., M.

[0039] The signal received by the UE from the AP can be represented as

[0040]

[0041] Where x represents the transmitted signal, and without loss of generality, n represents additive white Gaussian noise.

[0042] Considering the interference caused by interfering APs to this user, the rate can be represented by the signal-to-interference-plus-noise ratio (SINR), i.e., γ:

[0043]

[0044] Where k represents the number of the interfering AP in the scene, and there are a total of K interfering APs.

[0045] Therefore, the optimization problem under consideration can be expressed as:

[0046]

[0047] st

[0048]

[0049] Where log2(1+γ) is the target user's rate, h r The channel matrix from the IRS to the target user. Here is the reflection phase matrix of the IRS. Let G be the phase shift of the i-th reflecting unit of the IRS, i = 1, 2, ..., M, where M is the number of reflecting units in the IRS, G is the channel matrix from the target AP to the IRS, h is the channel matrix from the target AP to the target user, and f is the beamforming vector of the target AP. r,k Let G be the channel matrix of the k-th interfering AP after reflection by the IRS to the target user. k Let h be the channel matrix of the k-th interfering AP. k Let f be the direct channel matrix from the k-th interfering AP to the target user. k Let σ be the beamforming vector of the k-th interfering AP. 2 Let K represent noise, and K be the number of interfering APs.

[0050] Preferably, before step S1, the method further includes:

[0051] by Given the constraints, solve the objective function. The beamforming vector f of the target AP is obtained; where h is the channel matrix from the target AP to the target user.

[0052] Specifically, for step S2, since there is a line-of-sight link between the AP and the UE, it is easy to know that the AP can obtain the maximum received power when its beamforming vector is based on the AP-UE line-of-sight link channel state, that is, the optimal f must satisfy...

[0053]

[0054]

[0055] Where P is the maximum transmit power of each AP, and under this setting, f can be directly expressed as...

[0056]

[0057] Note that the above formula is simple in form, which can reduce the time complexity of real-time joint phase modulation and also solve the uncertainty of preemptive occupation when the IRS is used.

[0058] That is, the optimal active beam vector of the target AP is obtained based on the channel information from the target AP to the target UE.

[0059] To simplify the problem description and facilitate clearer subsequent explanations and algorithm implementation, let θ = [θ1,...,θ] M ] H and And further

[0060] The problem of maximizing the target user's rate can then be transformed into...

[0061]

[0062] st

[0063] Preferably, the above problems can be solved using the Riemannian gradient descent method or the branch and bound method.

[0064] When using the sampling Riemannian gradient descent method, specifically, the M unit modulus constraints can be used to construct a complex circular manifold space, i.e., an M-dimensional Riemannian subspace. By orthogonally projecting the Euclidean gradient onto the constructed M-dimensional manifold, the Riemannian gradient of the objective function f(θ) is obtained, thus determining the search direction at the current point. The final target point is obtained through iteration.

[0065] Therefore, it is necessary to first calculate the Euclidean gradient of the objective function f(θ), that is...

[0066]

[0067] Calculate the Euclidean gradient Then, the Riemann gradient can be obtained.

[0068]

[0069] in, Represents the Hadamard product, θ * Let θ denote the conjugate of θ, and Re{·} denote taking the real part.

[0070] In this algorithm, the search direction is determined based on the negative gradient direction of the objective function, and the additional direction change needs to be determined by updating the parameter η1 through the conjugate gradient. Furthermore, since each point on the manifold plane has its corresponding unique tangent plane, and the Riemann gradient is defined based on this tangent plane, the search direction from the previous step and the current search direction are not in the same tangent plane, so the search direction needs to be transformed. Iteration is performed based on the gradient descent method. During iteration, the direction of iteration must first be determined, assuming the current point is θ. (t) At this point, t represents the number of iterations, and the iterative expression for the search direction can be obtained as follows:

[0071]

[0072] Where, d (t) and d (t-1) These represent the search directions of the current and previous iterations, respectively, and η1 represents the conjugate gradient update parameter, used to determine the amount of additional directional change. The vector transfer function, which realizes the transformation of the search direction between tangent planes, is defined as follows:

[0073]

[0074] The objective function along direction d (t) The points descend, but their final position remains in the tangent space. Therefore, a contraction process is needed to bring the points outside the manifold space back to the manifold plane. This process corresponds to the mapping from the tangent space to the manifold, thus determining the target point in the manifold space. The contraction iterative formula is as follows:

[0075]

[0076] Where η2 represents the shrinkage step size that ensures sufficient descent. Through iterative updates of the above process, the solution of the objective function is finally determined, the optimal reflection phase matrix of the IRS is obtained, and the optimal passive beamforming vector of the smart reflector is determined.

[0077] In the simulation, the path loss parameter α was set to 3, the room dimensions were 20m × 20m × 4m, the AP height was fixed at 2m, the IRS height was fixed at 4m, the UE height was fixed at 1m, and the size of each reflection unit in the IRS was half the wavelength of the operating frequency, with a bandwidth of 160MHz. To account for the randomness of channel conditions and user location during the simulation, the simulation was repeated 500 times, and the average value was taken to obtain the final simulation result.

[0078] Figure 4 The graph shows a comparison of the target user communication rate between the interference suppression and rate enhancement method for indoor FTTR based on intelligent reflectors provided in this embodiment of the invention and a baseline scheme. The horizontal axis represents the square root of the number of reflective units in the IRS. The two baseline schemes are: no IRS and IRS using random phase, with 3 interfering access points (APs). The graph shows that the provided method can effectively improve the target user's rate. When an IRS is deployed but uses random phase, the target user's rate may decrease as the number of IRS reflective units increases, because random phase may enhance or reduce the strength of the target signal.

[0079] Figure 5The graph illustrates the relationship between the target user communication rate and the number of interfering access points (APs) in the scene for the interference suppression and rate enhancement method for indoor FTTR based on intelligent reflective surfaces provided in this embodiment of the invention, and the baseline scheme. The horizontal axis represents the number of interfering APs, and the number of reflective units is 64. As can be seen from the graph, when the number of interfering users is small, the proposed method can significantly improve the rate; however, as the number of interfering users increases, the improvement effect gradually decreases. This is because when the number of interfering users is small, the IRS can significantly reduce the impact of interfering APs, but as the number of interfering users increases, the effect of the IRS becomes less significant.

[0080] The user rate enhancement device for the intelligent reflective surface assisted FTTR system provided by the present invention is described below. The user rate enhancement device for the intelligent reflective surface assisted FTTR system described below can be referred to in correspondence with the user rate enhancement method for the intelligent reflective surface assisted FTTR system described above.

[0081] This invention provides a user rate enhancement device for an intelligent reflector-assisted FTTR system, comprising:

[0082] The first processing module is used to determine the signal-to-interference-plus-noise ratio (SIR) of the target user based on the beamforming vectors of the target AP and each interfering AP, and the reflection phase matrix of the IRS, so as to characterize the rate of the target user; wherein the target AP is the AP serving the target user.

[0083] The second processing module is used to maximize the rate of the target user and obtain the optimal reflection phase matrix of the IRS.

[0084] This invention provides a user rate enhancement system for an intelligent reflector-assisted FTTR system, comprising: a computer-readable storage medium and a processor;

[0085] The computer-readable storage medium is used to store executable instructions;

[0086] The processor is configured to read executable instructions stored in the computer-readable storage medium and execute the method as described in any of the above embodiments.

[0087] This invention provides a computer-readable storage medium storing computer instructions that cause a processor to perform the method described in any of the above embodiments.

[0088] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for enhancing the user rate of an intelligent reflector-assisted FTTR system, characterized in that, include: S1, based on the beamforming vectors of the target AP and each interfering AP, and the reflection phase matrix of the IRS, determine the signal-to-interference-plus-noise ratio (SIR) of the target user to characterize the rate of the target user; wherein, the target AP is the AP serving the target user; S2, maximize the rate of the target user to obtain the optimal reflection phase matrix of the IRS; The target user's rate v= ;in, For the target user's signal-to-interference-plus-noise ratio, , The channel matrix from the IRS to the target user. Here is the reflection phase matrix of the IRS. Let be the phase shift of the i-th reflecting element of the IRS, i = 1, 2, ..., M, where M is the number of reflecting elements in the IRS. The channel matrix from the target AP to the IRS. The channel matrix from the target AP to the target user. The beamforming vector of the target AP. The channel matrix of the k-th interfering AP after reflection by the IRS to the target user. Let be the channel matrix from the k-th interfering AP to the IRS. Let k be the direct channel matrix from the interfering AP to the target user. Let be the beamforming vector of the k-th interfering AP. K represents noise, and K represents the number of interfering APs. In step S2, an objective function and constraints are set, and the objective function is maximized under the constraints to maximize the rate of the target user. The objective function is: ; The constraints are as follows: ; in, , , , , , , ; Before step S1, the following are also included: by Given the constraints, solve the objective function. To obtain the beamforming vector of the target AP. ;in, Let P be the channel matrix from the target AP to the target user, and let P be the maximum transmit power of the target AP.

2. The method as described in claim 1, characterized in that, The objective function is solved using the Riemannian gradient descent method or the branch and bound method.

3. The method as described in claim 2, characterized in that, When solving the objective function using the Riemannian manifold gradient descent method, the following steps are included: 1) Solve for the Euclidean gradient of the objective function. ;in, 2) Calculate the Riemann gradient ;in, , For Hadama accumulation, for conjugate, Indicates taking the real part; 3) Based on the Riemann gradient, determine the iterative expression for the search direction and the shrinking iterative formula for iterative updating, to obtain the optimal reflection phase matrix of the IRS; wherein, the iterative expression is: The shrinkage iteration formula is , For the number of iterations, and These represent the search directions for the current and previous iterations, respectively. For vector transfer functions, , These are the conjugate gradient update parameters and the shrinkage step size, respectively.

4. The method as described in claim 1, characterized in that, The heights of the intelligent reflective surface, the target AP, the interfering AP, and the UE decrease sequentially.

5. A user rate enhancement device for an intelligent reflective surface-assisted FTTR system, characterized in that, include: The first processing module is used to determine the signal-to-interference-plus-noise ratio of the target user based on the beamforming vectors of the target AP and each interfering AP and the reflection phase matrix of the IRS, so as to characterize the rate of the target user. The target AP is the AP that serves the target user. The second processing module is used to maximize the rate of the target user in order to obtain the optimal reflection phase matrix of the IRS; The target user's rate v= ;in, For the target user's signal-to-interference-plus-noise ratio, , The channel matrix from the IRS to the target user. Here is the reflection phase matrix of the IRS. Let be the phase shift of the i-th reflecting element of the IRS, i = 1, 2, ..., M, where M is the number of reflecting elements in the IRS. The channel matrix from the target AP to the IRS. The channel matrix from the target AP to the target user. The beamforming vector of the target AP. The channel matrix of the k-th interfering AP after reflection by the IRS to the target user. Let be the channel matrix from the k-th interfering AP to the IRS. Let k be the direct channel matrix from the interfering AP to the target user. Let be the beamforming vector of the k-th interfering AP. K represents noise, and K represents the number of interfering APs. The second processing module maximizes the rate of the target user by setting an objective function and constraints, and maximizing the objective function under the constraints. The objective function is: ; The constraints are as follows: ; in, , , , , , , ; The first processing module is also used for: by Given the constraints, solve the objective function. To obtain the beamforming vector of the target AP. ;in, Let P be the channel matrix from the target AP to the target user, and let P be the maximum transmit power of the target AP.

6. A user rate enhancement system for an intelligent reflector-assisted FTTR system, characterized in that, include: Computer-readable storage media and processors; The computer-readable storage medium is used to store executable instructions; The processor is configured to read executable instructions stored in the computer-readable storage medium and execute the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a processor to perform the method as described in any one of claims 1-4.

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