A method for power allocation in an active IRS-assisted mobile communication system

Abstract

The application discloses an active IRS-assisted mobile communication system power distribution method, comprising the following steps: step 1, obtaining CSI of BS-user link, IRS-user link and BS-IRS link, making CSI of all links available at IRS, establishing user QoS requirement and active IRS power margin model; step 2, establishing an optimization model, minimizing the transmission power at the base station under the condition of meeting the user QoS requirement and the maximum power margin of the active IRS; step 3, solving the optimization model by using a bilinear transformation and an inner approximation algorithm to obtain a power distribution scheme. The method can achieve considerable power saving.

Description

Technical Field

[0001] This invention belongs to the key technology field of mobile communication, and in particular relates to a power allocation method for a mobile communication system assisted by an active IRS. Background Technology

[0002] Intelligent Reflecting Surfaces (IRS) originate from software-defined metamaterials. They consist of numerous reconfigurable elements, each controlled by an intelligent software controller to phase-shift incident signals, thereby improving wireless channel quality and rebuilding a smart and green radio environment for future wireless networks. In improving communication system performance, IRS research primarily focuses on rate optimization, power optimization, and efficiency optimization, with efficiency optimization encompassing both energy efficiency (EE) and spectral efficiency (SE).

[0003] Traditional passive IRS components constructively superimpose and enhance received signals from different paths by passively reflecting the incident signal and intelligently adjusting the phase shift. Active IRSs differ from passive IRSs in their hardware structure; to amplify the signal, they are equipped with phase-shifting circuits and reflective amplifiers (such as current-inverting converters). Unlike low-power passive IRSs, active IRS base stations and amplifiers can consume similar power, meaning that power consumption in active IRSs is no longer negligible.

[0004] Most existing research on IRS considers the introduction of passive IRS, which can only reflect signals with passive loads (positive resistance). Operating in full-duplex (FD) mode, it does not amplify / process noise and self-interference, thus offering higher spectral and energy efficiency than traditional active relays. However, in practice, due to the two-path loss effect, the path loss of the base station-IRS-user link is often more severe than that of an unobstructed direct link. Therefore, the extent to which deploying passive IRS can improve system performance is limited. To address these challenges, a new IRS structure, the active IRS, can be introduced into traditional mobile communication scenarios. Specifically, an active IRS is equipped with a reflective amplifier supported by an additional power supply, which can reflect and amplify the reflected signal by manipulating programmable IRS elements. When it comes to active IRS, the signals received from various elements of the IRS are amplified separately. Furthermore, it can effectively compensate for path losses without complicating the IRS design. In practice, to achieve the potential gains facilitated by active IRS, it is necessary to allocate the limited available power to each active IRS element in an appropriate manner. Summary of the Invention

[0005] The technical problem to be solved by this invention is: how to achieve power allocation in an active IRS-assisted mobile communication system.

[0006] To address the aforementioned technical problems, this invention provides a power allocation method for an active IRS-assisted mobile communication system, comprising the following steps:

[0007] Step 1: Obtain the Channel State Information (CSI) of the BS-User Link, IRS-User Link, and BS-IRS Link, so that the CSI of all links is available at the IRS, and establish the user QoS requirements and active IRS power tolerance model.

[0008] Step 2: Establish an optimization model to minimize the transmit power at the base station while meeting the user's QoS (Quality of Service) requirements and the maximum power tolerance of the active IRS.

[0009] Step 3: Solve the optimization model using bilinear transformation and inner approximation algorithms to obtain the power allocation scheme.

[0010] The beneficial effects achieved by this invention are as follows: The method of this invention considers the deployment of active IRS in mobile communication systems. Unlike traditional passive IRS, each IRS element is equipped with an amplifier. This leads to the resource allocation algorithm design problem in multi-user communication systems. By jointly optimizing the beamforming vector at the base station, the amplification factor of the active IRS, and the phase shift, the base station transmit power is minimized. To solve the formulaic non-convex optimization problem, this invention employs a low-complexity algorithm based on bilinear transformation and inner approximation, guaranteeing convergence to a local optimum and achieving considerable power savings. Furthermore, the method of this invention allows the active IRS to simultaneously adjust its phase and amplify the amplitude of the reflected incident signal with the support of an additional power supply, effectively combating the performance degradation caused by the two-path loss effect in IRS-assisted communication systems. Attached Figure Description

[0011] Figure 1 This is a model diagram of an active IRS-assisted MISO mobile communication system. Detailed Implementation

[0012] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading the present invention, any modifications of the present invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0013] Example 1

[0014] As attached Figure 1 The diagram shows an active IRS-assisted multi-user multiple-input single-output (MISO) mobile communication system with N base stations. sThere are 1 antenna serving 1 K single-antenna users. Consider installing an active IRS on the surrounding walls to improve link performance. The IRS has M reflective elements to assist communication transmission for base station users. The IRS is connected to an additional power supply. Equipped with an integrated active reflective amplifier, each reflective element can independently and flexibly change the phase shift of the incident signal while amplifying the reflected signal to achieve more effective beamforming gain.

[0015] A power allocation method for an active IRS-assisted mobile communication system includes the following steps:

[0016] Step 1: Obtain the Channel State Information (CSI) of the BS-User Link, IRS-User Link, and BS-IRS Link, so that the CSI of all links is available at the IRS, and establish the user QoS requirements and active IRS power tolerance model.

[0017] Step 2: Establish an optimization model to minimize the transmit power at the base station while meeting the user's QoS (Quality of Service) requirements and the maximum power tolerance of the active IRS.

[0018] Step 3: Solve the optimization model using bilinear transformation and inner approximation algorithms to obtain the power allocation scheme.

[0019] In step 1, the specific steps are as follows:

[0020] 1) The user sends a pilot signal, and the base station and IRS perform channel estimation to obtain channel state information (CSI) for the base station-user link and the IRS-user link, respectively;

[0021] 2) The base station sends pilot signals, and the IRS performs channel estimation to obtain the CSI of the base station-IRS link;

[0022] (21) The baseband equivalent channels of the BS-IRS link, BS-user link, and IRS-user link can be represented as follows: and N s Where M is the number of base station antennas, and M is the number of reflective elements in the IRS. Represent the complex matrix space of a×b;

[0023] (22) Transmitted signal vector at the base station in This represents the transmitted beamforming vector at user k. The corresponding transmission symbol is represented, which is independent on user k and follows a distribution with a mean of 0 and a variance of unit variance;

[0024] (23) Using a reflective amplifier driven by an external power supply, the signal reflected and amplified by an active IRS is represented as y:

[0025] y = ΑΘGx + ΑΘD + n

[0026] Where the matrix and Let represent the amplification factor matrix of the active IRS, and diag(a) denote the elements as vectors extracted from the main diagonal elements of matrix a. Let a represent the space of positive real-valued matrices. M This represents the amplification factor of the Mth element in the IRS. The phase shift of the Mth reflecting element in the IRS is n, where n is the static noise. This represents the phase shift matrix at the IRS; the noise at the IRS is divided into two categories: dynamic noise and static noise, where dynamic noise... It has a variance of Additive white Gaussian noise (AWGN) is generated due to power amplification; static noise. The variance is Additive white Gaussian noise (AWGN) is static noise that is not affected by matrix A, and its power is often much smaller than that of dynamic noise.

[0027] 3) The base station sends the provided channel information to the IRS through the set control link, so that the CSI of all channels is available at the IRS;

[0028] 4) The interference-to-noise ratio (SINR) of the received signal for user k k It is derived from the following formula:

[0029]

[0030] w r This represents the transmitted beamforming vector at user r. This represents the noise power at the k-th user, where K is the set of users, the superscript H indicates matrix transpose, a single vertical line indicates the magnitude of the vector, and double vertical lines indicate the norm.

[0031] 5) The amplification power of the active IRS is

[0032]

[0033] ||A|| F Denotes the F-norm of matrix A;

[0034] In step 2, while satisfying the user's QoS (Quality of Service) requirements and the maximum power tolerance of the active IRS, the transmit power at the base station is minimized. This involves jointly optimizing the beamforming vector at the base station, the IRS amplification factor matrix of the active IRS, and the phase shift matrix at the IRS, resulting in the following optimization model:

[0035] 1) Optimization objective: To minimize the transmit power at the base station while meeting user QoS requirements and the maximum power tolerance of the IRS.

[0036]

[0037] (PI) represents problem 1, min the following w k A and Θ represent the beamforming vector of user k, the amplification matrix of the active IRS, and the reflection phase shift matrix of the active IRS, respectively.

[0038] 2) The constraints are as follows:

[0039] 21) Meet the user's QoS requirements and the minimum interference-plus-noise ratio (SINR) required for the user's received signal, where γ k It is the minimum SINR required by user k, which is usually γ. k ≥0,

[0040]

[0041]

[0042] 22) The amplification power of an active IRS shall not exceed the maximum power tolerance P. A

[0043]

[0044] 3) The optimization variable is the transmit beamforming vector w at the base station. k The amplification coefficient matrix A and the phase shift matrix Θ at the active IRS are non-convex due to the high coupling of optimization variables and the presence of fractional constraints. Therefore, a low-complexity iterative algorithm is proposed using a method based on bilinear transformation and inner approximation (IA). The algorithm can converge to a local optimum of the optimization problem.

[0045] In step 3, the optimization model (P1) is transformed into a form that is easy to solve using bilinear transformation and inner approximation;

[0046] Perform a bilinear transformation:

[0047] In the existing optimization problem, matrices A and Θ in problem (P1) appear as a product. Therefore, the product term AΘ is rewritten as...

[0048]

[0049] To solve the quadratic term in the SINR formula, define

[0050] Then there is

[0051]

[0052]

[0053] Where Tr() is the trace of the matrix; same G represents the equivalent channel of the BS-IRS link;

[0054] The optimized model is transformed into the following:

[0055] (1) The constraint objective is

[0056]

[0057] (2) The constraints are:

[0058] To meet the user's QoS requirements, use the minimum interference plus noise ratio (SINR) required for the received signal.

[0059]

[0060] The amplification power of the active IRS shall not exceed the maximum power tolerance P. A :

[0061]

[0062] Newly defined optimization variable W k The following conditions need to be met:

[0063]

[0064]

[0065] Where Rank(W) k ) is matrix W k The rank; >= indicates greater than or equal to 0;

[0066] To further solve the optimization problem (P2), it is necessary to address the optimization variable W in the constraints. k The coupling between the matrix Φ and the optimization variable W kThe rank-one constraint (a rank-one constraint is a constraint with rank 1), i.e. First, rewrite the coupling terms:

[0067]

[0068] The right side of the equation contains the optimization variable W. r The bilinear function of matrix Φ, i.e., GW r G H Φ H G r,k It remains nonconvex. To avoid this problem, a new optimization variable, Q, is further defined. r =W r G H Φ H ;

[0069] Solving using the inner approximation (IA):

[0070] After performing the bilinear transformation described above, the right-hand side term mentioned above will be... Rewritten as follows:

[0071]

[0072] To solve this problem, an iterative internal approximation method is used. and Constructing the first-order Taylor approximation and Their respective global underestimation solutions:

[0073]

[0074]

[0075] Where Φ (i) and These are all intermediate solutions obtained in the i-th iteration, where the superscript i represents the iteration exponent of the optimization variable. This allows us to approximate the constraints in the optimization problem (P1):

[0076] The optimization problem to be solved in the (i+1)th iteration of the IA algorithm based on the inner approximation is as follows:

[0077] (1) Optimization objective:

[0078]

[0079] F(W k Regarding W k The function, symbol Indicates congruence;

[0080] (2) Constraints

[0081] The approximation of the constraints in problem (P1) ensures that the IRS amplification power does not exceed the power tolerance:

[0082]

[0083] An approximation of the constraints in problem (P1) that satisfies the user's QoS requirements:

[0084]

[0085] γ k Let r ∈ K, where {k} indicates that r belongs to the user set K, but the value of r cannot be k.

[0086] Optimization variable W k The following conditions need to be met:

[0087]

[0088]

[0089] It can be seen that the only obstacle to solving (P3) effectively is W. k To make the optimization problem convex by applying the rank-one constraint and removing the constraint from the formula, we use the semidefinite relaxation method (SDR). This yields a relaxed version of the optimization problem, resulting in a standard convex optimization problem, which is then solved using a convex programming solver (such as CVX). The beamforming vector w can be obtained. k ...

[0090] Table 1 shows the iterative algorithms based on IA.

[0091] Table 1

[0092]

[0093] The present invention discloses a power allocation method for an active IRS-assisted communication system. The active IRS utilizes an additional power source to amplify reflected signals. The objective is to minimize the base station's (BS) transmit power by jointly designing the base station beamformer and the IRS reflection matrix, while considering user QoS requirements and the maximum power tolerance of the active IRS. Since the optimization variables are highly coupled in the non-convex optimization problem under consideration, it is difficult to obtain the corresponding global optimum. As a compromise, iterative methods using bilinear transformation, internal approximation, and semidefinite relaxation techniques are employed to ensure convergence to a local optimum of the problem, thereby obtaining the optimal power allocation scheme.

[0094] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for power allocation in an active IRS-assisted mobile communication system, characterized in that, The method comprises the following steps: Step 1, obtaining CSI of BS-user link, IRS-user link and BS-IRS link, making CSI of all links available at IRS, establishing user QoS requirement and active IRS power margin model; Step 2, establishing an optimization model, minimizing the transmission power at the base station under the condition of meeting the user QoS requirement and the maximum power margin of the active IRS; Step 3, solving the optimization model by using the algorithm of bilinear transformation and interior approximation to obtain a power allocation scheme; In step 1, the specific steps are as follows: 1) The user sends a pilot signal, and the base station and the IRS perform channel estimation to obtain the CSI of the base station-user link and the IRS-user link respectively; 2) The base station sends a pilot signal, and the IRS performs channel estimation to obtain the CSI of the base station-IRS link; 3) The base station sends the provided channel information to the IRS through a set control link, so that the CSI of all channels is available at the IRS; 4) Received signal to interference plus noise ratio, SINR, of user k k is derived from the following equation: w r denotes the transmit beamforming vector at user r, denotes the noise power at the kth user, K is the set of users, the superscript H denotes the matrix transpose, the single vertical bar denotes the modulus of a vector, and the double vertical bar denotes the norm. 5) The amplification power of the active IRS is ||A|| F denotes the F-norm of the matrix A; In step 2): The baseband equivalent channels of the BS-IRS link, the BS-user link, and the IRS-user link are denoted by and N s is the number of base station antennas, M is the number of reflecting elements of the IRS, denotes the space of a x b complex matrices; Transmit signal vector at the base station where denotes the transmit beamforming vector at user k, denotes the corresponding transmit symbol, which is independent over users k, subject to a distribution with mean 0 and variance unity variance. The signal reflected and amplified by the active IRS is represented as y using a reflective amplifier driven by an external power supply: y = AΘHGx + AΘHD + n where matrix and denotes the amplification matrix of the active IRS, diag(a) denotes a vector whose elements are extracted from the main diagonal elements of the matrix a, denotes the space of positive real-valued matrices, a M denotes the amplification factor of the Mth element of the IRS, is the phase shift of the Mth reflecting element of the IRS, n is the static noise, denotes the phase shift matrix at the IRS; The noise at the IRS comprises two categories, i.e., dynamic noise and static noise, wherein the dynamic noise is an additive white Gaussian noise with variance ; the static noise is an additive white Gaussian noise with variance ; In step 2, the beamforming vector at the base station, the IRS amplification coefficient matrix of the active IRS and the phase shift matrix at the IRS are jointly optimized, which is converted into the following optimization model: 1) Optimization goal: minimize the transmission power at the base station under the condition of meeting the user QoS requirement and the maximum power margin of the IRS (P1) represents Problem 1, min the w below k A, Θ represent the beamforming vector of user k, the amplification coefficient matrix of active IRS and the reflection phase shift matrix of active IRS, respectively 2) The constraint conditions are as follows: 21) to meet the user's QoS requirement, and to meet the minimum Signal to Interference plus Noise Ratio (SINR) required by the user to receive the signal, where γ k is the minimum SINR required by user k, and typically has γ k ≥ 0, 22) the amplified power of the active IRS is not greater than the maximum power tolerance P A 3) the optimization variable is the transmit beamforming vector w at the base station k the amplification matrix A of the active IRS and the phase shift matrix Θ at the IRS In step 3, the specific steps are as follows: Perform bilinear transformation: The matrix A and the matrix Θ in problem (P1) appear in the form of a product, and the product item AΘ is rewritten as To solve the quadratic term of the SINR formula, define then have where Tr() is the trace of a matrix; Similarly G denotes the equivalent channel of the BS-IRS link; The optimization model is converted into the following: (1) The constraint target is (2) The constraint condition is: The minimum interference plus noise ratio (SINR) required for the user to receive the signal meets the user QoS requirement: The amplified power of the active IRS does not exceed the maximum power tolerance P A : The newly defined optimization variable W k Conditions to be satisfied: where Rank(W k ) is the rank of the matrix W k . >= indicates greater than or equal to 0; To further solve the optimization problem (P2), one needs to address the coupling between the optimization variable W k and the matrix Φ as well as the rank-one constraint on the optimization variable W k , i.e. First, rewrite the coupling term: The right side of the equation contains the optimization variable W r and the bilinear function of matrix Φ, i.e. GW r G H Φ H G r,k , is still non-convex, further redefine a new optimization variable Q r = W r G H Φ H ; Solve by interior approximation: After taking the bilinear transformation, the above right-hand side term is rewritten as By the method of iterative interior approximation, using and first order Taylor approximation of and the respective global underestimating solutions: where Φ (i) and are the intermediate solutions obtained in the i-th iteration, the superscript i denotes the iteration index of the optimization variable, resulting in an approximation of the constraints in the optimization problem (P1): The optimization problem to be solved in the i+1 iteration based on the interior approximation (IA) algorithm is as follows: (1) Optimization goal: F(W k ) about W k , the symbol represents congruence; (2) Constraint condition Approximation of the constraint in problem (P1) so that the IRS amplification power does not exceed the power margin: Approximation of the constraint in problem (P1) to meet the user QoS requirement: γ k denotes the minimum SINR required by user k, r e K \ {k} denotes that r belongs to the user set K, but the value of r cannot be k, Optimization variable W k Conditions to be met: Applying the SDR method and removing the constraints from the formulation results in a standard convex optimization problem that is solved by a convex program solver due to results in a beamforming vector w k .

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