CN116470938B - IRS auxiliary communication service quality fairness combined beam forming optimization method and device - Google Patents
IRS auxiliary communication service quality fairness combined beam forming optimization method and device Download PDFInfo
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Abstract
The invention provides a joint beam forming optimization method for IRS auxiliary communication service quality fairness, which comprises the steps of deploying a plurality of groups of communication scenes under the assistance of IRS; establishing a mathematical optimization model with fair service quality of a plurality of groups of users under the assistance of IRS; converting the partial optimization problem of the mathematical optimization model into a subtractive optimization sub-problem for iterative solution; decoupling optimization variables of the subtractive optimization sub-problem and carrying out iterative solution of the round flow; converting the non-convex and unsmooth optimization sub-problem of the decoupled transmitting beam forming into a convex lower bound function, and obtaining a base station transmitting beam forming closed solution in an iterative form through an LDD method; solving the decoupling IRS reflection phase shift single-mode constraint non-convex optimization sub-problem by adopting a penalty function method and an SCA method; and outputting the transmit beam forming and IRS reflection phase shift optimization strategy of the joint base station. The invention designs the low-complexity combined beam forming optimization method with fair service quality for the terminal user, which can obviously improve the network.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a joint beamforming optimization method for IRS auxiliary communication service quality fairness.
Background
The 5G age of 1000-fold network capacity improvement and wireless network connection of 1000 billions of devices have been gradually realized, which benefits from a variety of advanced physical layer technologies, such as ultra-dense small cell networks, large-scale multiple-input multiple-output (mMIMO), millimeter wave communication, and the like. These technologies also suffer from critical issues such as high implementation complexity, high hardware cost, and extremely high energy consumption.
In addition, while 5G physical layer technology is generally well suited to space-time varying wireless transmission environments, the transmission of such signals is random in nature and largely uncontrollable. Based on the above elicitations, intelligent reflector-assisted communication techniques have been developed as a promising new technology for dynamically configuring wireless network propagation environments. Specifically, the IRS is a plane consisting of a large number of low cost passive reflective elements, each capable of independently inducing amplitude and phase changes to the incident signal, thereby cooperatively achieving fine-grained three-dimensional reflected beam forming. In sharp contrast to existing transmitter, receiver wireless link adaptation techniques, IRSs actively configure the wireless channel between them through highly controllable and intelligent signal reflection. This provides a new degree of freedom for further improving wireless link performance and paves the way for implementing intelligent and programmable wireless environments. By properly adjusting the three-dimensional passive beam forming, the signals reflected by the IRS can be subjected to enhanced superposition with signals of other paths so as to improve the expected signal power of the receiver or destructively cancel unwanted signals such as co-channel interference and the like. Since the IRS eliminates the use of transmit radio frequency chains, operating only in short distances, it can be densely deployed, has scalable cost and low power consumption characteristics, and does not require complex interference management between passive IRSs.
IRS has wide application scene and practical significance. Such as: when a user is in a communication blind zone, i.e. its direct link with the serving base station is severely blocked by an obstacle. At this point, deploying IRSs with an explicit relationship to the base station and the user helps the smart signal bypass the obstacle, creating a virtual line-of-sight link between them. This is particularly useful for coverage extension of millimeter wave communications that are extremely susceptible to indoor blockage. In addition, IRS can also be used in application scenarios that improve physical layer security. For example, when the link distance from the base station to the eavesdropper is smaller than the distance from the eavesdropper to the legal user or the eavesdropper and the legal user are in the same direction, if the IRS is deployed near the eavesdropper, the signals from the base station at the eavesdropper can be counteracted by tuning the signals reflected by the IRS, so that information leakage is effectively reduced, and high-density communication is realized. IRS may also be used to enhance signal reception by edge users deployed at the cell edge or to suppress co-channel interference by neighboring base stations. In addition, the IRS can be applied to the Internet of things network to realize Simultaneous Wireless Information and Power Transmission (SWIPT) of various devices, and the large aperture of the IRS is utilized to compensate a large amount of power loss transmitted to nearby Internet of things devices in a long distance through passive beam forming, so that the wireless power transmission efficiency is improved. It should be noted that the above applications related to IRS assisted communications must effectively configure the phase offset of their reflecting units to mine the performance gain of the IRS, otherwise introducing the IRS may be counterproductive.
Currently, most IRS related applications focus on wireless network unicast traffic patterns, where service providers transmit each independent data stream to their own users, which can cause serious co-channel interference to active co-channel users nearby, while service providers are also faced with tremendous data stream pressures. Therefore, for the IRS auxiliary communication multi-group and multi-broadcast application scenario, it is necessary to design a low-complexity joint beamforming optimization method with fair service quality for the terminal user and improve the received SINR of the user with the worst link quality in the network.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to overcome the problems existing in the prior art, and provide a joint beamforming optimization method with fair IRS auxiliary communication service quality, which designs a low-complexity joint beamforming optimization algorithm with fair service quality of terminal users aiming at IRS auxiliary communication multi-group and multicast application scenes and improves the receiving SINR of users with worst link quality in a network.
In order to solve the technical problems, the invention provides a joint beamforming optimization method for IRS auxiliary communication service quality fairness, which comprises the following steps:
s1, arranging a plurality of groups of communication scenes under the assistance of IRS (inter-radio space control) at a base station;
s2, establishing a mathematical optimization model with fair service quality of a plurality of groups of users under the assistance of IRS (inter-range request service) based on the scene, wherein the mathematical optimization model has two constraint conditions, the first constraint condition is the maximum total transmitting power constraint of a base station, and the second constraint is the IRS reflection phase offset single-mode constraint;
s3, converting the partial optimization problem of the mathematical optimization model into a subtractive optimization sub-problem for iterative solution;
S4, decoupling the optimization variables of the subtractive optimization sub-problem and carrying out iteration solution on the loop, wherein the method comprises the steps of converting the decoupled emission beam forming non-convex and non-smooth optimization sub-problem into a convex lower bound function, obtaining an iterative base station emission beam forming closed solution through an LDD (laser direct structuring) method, and solving the decoupled IRS reflection phase shift single-mode constraint non-convex optimization sub-problem through a penalty function and an SCA (sequence control algorithm) method;
S5, outputting a combined base station transmitting beam forming and IRS reflection phase shift optimization strategy.
In one embodiment of the present invention, in step S2, the mathematical optimization model is:
In the method, in the process of the invention, W denotes the transmit beamforming matrix of the base station,Representing IRS reflection unit phase shift diagonal matrix, S i representing user I received signal power, I i representing user I received noise and interference power, gamma i representing user I signal-to-interference-noise ratio weight factor, w l representing base station to user group l transmitting beam forming vector, g representing user group number, P max representing base station maximum transmitting power, theta n representing IRS reflection unit n phase,/>N represents the number of IRS reflective elements,/>Representing the channel gain from the base station to user i in group l, H representing the conjugate transpose of the vector, w j representing the transmit beamforming vector of the base station to user group j, σ i representing the noise power value at user i end,/>Representing a set of all user groups,/>Representing all user sets belonging to the first group.
In one embodiment of the present invention, in step S3, a method for converting a partial optimization problem of the mathematical optimization model into a subtractive optimization sub-problem for iterative solution includes:
Converting the partial optimization problem of the mathematical optimization model into a subtractive optimization sub-problem by using a GFP algorithm, and carrying out iterative solution on the subtractive optimization sub-problem, wherein the subtractive optimization sub-problem is as follows:
where τ represents the minimum receive weight signal-to-interference-and-noise ratio iteration parameter for the network user.
The reduced optimization sub-problem is converted into a smooth optimization sub-problem by introducing a relaxation variable j as follows:
s.t.(1),(2)
In the method, in the process of the invention, Representing a set of all user groups,/>Representing all user sets belonging to the first group.
In one embodiment of the present invention, in step S4, a method for decoupling optimization variables of the subtractive optimization sub-problem and performing iterative round robin solution includes:
decoupling and converting the optimization variable of the smooth optimization sub-problem into two optimization sub-problems by using an AO method, wherein the first optimization sub-problem and the second optimization sub-problem are respectively as follows:
s.t.(1)
max||y||1
s.t.(2),
y≥0,(5)
where y 1 represents a norm of the vector y, Is an introduced relaxation variable, y i represents the signal-to-noise margin for user i, and H i (Φ) represents the total channel gain of the base station to user i.
In one embodiment of the present invention, in step S4, a method for solving a single-mode constrained non-convex optimization sub-problem of the decoupled IRS reflection phase offset by using a penalty function and a SCA method includes:
The SCA method is adopted to replace the left function of the first optimization sub-problem by a convex lower bound function, wherein the convex lower bound function is as follows:
Wherein Re {.cndot.S } represents the real part, Representing in the r-th iteration of the algorithm, the transmit beamforming vector of the base station for user group l, λ i(Hi (Φ), represents the unique positive eigenvalue of matrix H i (Φ), I represents the identity matrix;
recording the convex lower bound function of the second constraint condition of the first optimization sub-problem as A i, converting the first optimization sub-problem into the following convex optimization problem and iteratively solving:
based on the convex optimization problem, the LDD method is utilized to obtain a base station transmitting beam forming closed solution as follows:
In the method, in the process of the invention, A i' is described by A i/>Substitution w l results, α and β i being the dual variables of the constraint.
In one embodiment of the present invention, the dual variables α and β i in the base station transmit beamforming closed-form solution are updated by the following sub-gradient method:
Where pi (t), ζ i (t) represents the iteration step of the sub-gradient method.
In one embodiment of the present invention, in step S4, a method for solving a single-mode constrained non-convex optimization sub-problem of the decoupled IRS reflection phase offset by using a penalty function and a SCA method includes:
Definition u= [ u 1,…,uN]H ] wherein The left side of the second optimization sub-problem is converted to the following form:
Wherein:
The second optimization sub-problem is converted into the following sub-problem:
s.t.(5)
where y 1 represents a norm of the vector y, Is an introduced relaxation variable, y i represents the signal-to-noise margin for user i;
the suboptimal solution is obtained by iterative approximation by using the following lower bound inequality joint SCA method:
where μ i,j represents the unique positive eigenvalue in matrix R i(wj);
relaxing the single-mode constraint of the second optimization sub-problem to a penalty function The sub-problem is converted into the following form:
s.t.(5)
In the formula, penalty function Forcing/>
The sub-problem is converted into the following objective function form approximation and iterative solution by adopting the SCA method:
the sub-problem is And adopting a CVX toolbox to optimize and solve.
The invention further provides a processor, and the processor executes the program to realize the steps of the method.
The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method when executing the program.
Also, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method described above.
Compared with the prior art, the technical scheme of the invention has the following advantages:
the invention provides a joint beamforming optimization method for fair service quality of IRS auxiliary communication, which is based on GFP, SCA and other technologies to design a low-complexity joint beamforming optimization method for fair service quality of terminal users.
Drawings
In order that the invention may be more readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof that are illustrated in the appended drawings.
Fig. 1 is a schematic flow chart of a joint beamforming optimization method with fair IRS auxiliary communication service quality.
FIG. 2 is a schematic diagram of an IRS-assisted multi-group, multicast MISO simulation scenario in accordance with an embodiment of the present invention.
FIG. 3 is a flowchart of an implementation algorithm of an embodiment of the present invention.
Fig. 4 is a schematic diagram of convergence performance of an algorithm according to an embodiment of the present invention.
Fig. 5 is a diagram showing a relationship between a minimum SINR of a user and a maximum transmit power of a base station according to an embodiment of the present invention.
Fig. 6 is a diagram showing the relationship between the minimum SINR of the user and the number of IRS reflection units according to the embodiment of the present invention.
Fig. 7 is a diagram showing a relationship between a minimum SINR of a user and the number of transmit antennas of a base station according to an embodiment of the present invention.
Fig. 8 is a diagram showing a relationship between a minimum SINR and the number of user groups according to an embodiment of the present invention.
Fig. 9 is a diagram showing a relationship between a minimum SINR of users and the number of users in a group according to an embodiment of the present invention.
Fig. 10 is a diagram showing a relationship between a minimum SINR of a user and the number of IRS reflection units at different maximum transmission powers of base stations according to an embodiment of the present invention.
Fig. 11 is a diagram showing a relationship between a minimum SINR of a user and the number of IRS reflection units under different numbers of transmitting antennas of a base station according to an embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and specific examples, which are not intended to be limiting, so that those skilled in the art will better understand the invention and practice it.
Referring to fig. 1, an embodiment of the present invention provides a joint beamforming optimization method for IRS-assisted communication service quality fairness, including the following steps:
s1, arranging a plurality of groups of communication scenes under the assistance of IRS (inter-radio space control) at a base station;
s2, establishing a mathematical optimization model with fair service quality of a plurality of groups of users under the assistance of IRS (inter-range request service) based on the scene, wherein the mathematical optimization model has two constraint conditions, the first constraint condition is the maximum total transmitting power constraint of a base station, and the second constraint is the IRS reflection phase offset single-mode constraint;
s3, converting the partial optimization problem of the mathematical optimization model into a subtractive optimization sub-problem for iterative solution;
S4, decoupling the optimization variables of the subtractive optimization sub-problem and carrying out iteration solution on the loop, wherein the method comprises the steps of converting the decoupled emission beam forming non-convex and non-smooth optimization sub-problem into a convex lower bound function, obtaining an iterative base station emission beam forming closed solution through an LDD (laser direct structuring) method, and solving the decoupled IRS reflection phase shift single-mode constraint non-convex optimization sub-problem through a penalty function and an SCA (sequence control algorithm) method;
S5, outputting a combined base station transmitting beam forming and IRS reflection phase shift optimization strategy.
The invention provides a joint beamforming optimization method for fair service quality of IRS auxiliary communication, which is based on GFP, SCA and other technologies to design a low-complexity joint beamforming optimization method for fair service quality of terminal users.
To illustrate and verify the benefits of the present invention in detail, the following examples of the scene are set forth.
Fig. 2 is a schematic diagram of an IRS assisted communication multi-group, multicast MISO simulation scenario. For convenience, the present embodiment is illustrated using a rectangular coordinate system, where the base station AP is located (-100M, 0M) with M transmit antennas and the IRS with N reflection units is located at (5M, -5M). All end users are randomly and evenly distributed in a circle with a centre of a circle (5 m,5 m) and a radius of 5 m. For this simulation scenario, the present embodiment sets the radio link large-scale fading model to PL (D) =c 0(d/D0)-p, where C 0 = -30dB is the path loss with reference distance D 0 =1m, D is the link distance, and p is the path loss index. The path loss indexes of the AP-IRS link, the AP-user direct link and the IRS-user link are respectively set as follows: p AI＝2.2,pAu＝4,pIu = 2. For small scale fading, the present embodiment considers an AP-IRS link, IRS-user link rice (Rician) fading channel model, where the rice fading factor is 10. The AP-user direct link is modeled as Rayleigh (Rayleigh) fading. Assume that the Additive White Gaussian Noise (AWGN) power received by the user terminal isWithout loss of generality, this embodiment assumes that the information reception priorities of all users in the network are the same and 1, namely: /(I)In the subsequent simulation verification example, this embodiment assumes two users per group unless specifically stated. Other simulation parameters will be apparent from the description of the simulation map.
Based on the simulation scene and the parameter setting, the embodiment of the invention provides a case of deploying IRS auxiliary communication in a traditional wireless communication network, and the low-complexity combined base station transmitting beam forming and IRS reflection phase shift optimizing method provided by the case can obviously improve SINR of users with worst link quality in the network. Figure 3 shows a flow chart of an algorithm of an embodiment of the invention. The invention converts the original non-convex partial optimization problem into a subtractive optimization sub-problem for iterative solution through a GFP algorithm with a super linear convergence rate; aiming at the subtractive sub-problem in the GFP algorithm, the invention adopts the AO technology design algorithm to decouple the optimization variable and alternately and iteratively solve the optimization variable; aiming at the non-convex and unsmooth optimization sub-problems of the decoupled emission beam forming, the invention adopts the optimization relaxation and SCA technology, converts the optimization relaxation and SCA technology into a convex lower bound function, analyzes the convex lower bound function through an LDD method to obtain an optimization strategy closed solution in an iterative form, and ensures that the optimization strategy closed solution can be converged to a local optimal solution of the original optimization sub-problem by a design algorithm; for the single-mode constraint non-convex optimization sub-problem of the decoupled IRS reflection phase offset, the invention adopts a punishment function method and SCA technology to design an iterative algorithm to approach and solve. And finally outputting suboptimal combined base station transmitting beam forming and IRS reflection phase shift optimization strategies through an algorithm flow shown in fig. 3. The specific implementation steps are as follows:
(1) Establishing the following mathematical optimization model To solve for a joint beamforming strategy for QoS fairness:
In the method, in the process of the invention, W denotes the transmit beamforming matrix of the base station,Representing the IRS reflection unit phase shift diagonal matrix,S i represents the received signal power of user I, I i represents the received noise and interference power of user I, gamma i represents the signal-to-interference-and-noise ratio weight factor of user I, w l represents the transmit beamforming vector of the base station to user group l, g represents the number of user groups, P max represents the maximum transmit power of the base station, θ n represents the phase of IRS reflection unit n,/>N represents the number of IRS reflective elements,/>Representing the channel gain from the base station to user i in group l, H representing the conjugate transpose of the vector, w j representing the transmit beamforming vector of the base station to user group j, σ i representing the noise power value at user i end,/>Representing a set of all user groups,/>Representing all user sets belonging to the first group. The first constraint represents the maximum total transmit power constraint of the base station and the second constraint is the IRS reflection phase offset single mode constraint.
(2) Further, the above non-convex optimization problem was transformed and solved iteratively based on the following GFP algorithm (algorithm 1).
Firstly, initializing the maximum iteration times T max and the iteration precision e of an algorithm, and setting iteration indexes t=0 and T (T) =0;
Step 1: repeat
Step 2: for a given t (t), solving a sub-problem based on algorithm 3To obtain { W (t), Φ (t) };
Step 3:
step 4: then
Step 5:
Step 6: break
Step 7: else (else)
Step 8: order the
Step 9: end if
Step 10: until T is not less than T max
Wherein sub-problemsThe following is shown:
(3) Due to the sub-problem The present invention is achieved by introducing a relaxation variable/>Convert it to the following smooth optimization problem/>
s.t.(1),(2)
(4) For the purpose ofThe invention uses AO technology to convert the optimized variable decoupling into two sub-problem design algorithms to be solved in turn and iterated. Namely: when given any one of the possible phase shift matrices F, then the relation/>Optimizing sub-problem/>The following are provided:
s.t.(1)
(5) Since constraint (3) of the above sub-problem is non-convex, the present invention replaces the left-hand function of inequality constraint (3) with a convex lower bound function as follows using SCA technique.
The right side of the inequality is recorded as A i, the sub-problemCan be converted into the following convex optimization problem/>And iteratively solving:
s.t.(1)
(6) Aiming at the optimization problem of the convex, the LDD is utilized to calculate the following closed solution of the base station transmitting beam forming:
In the method, in the process of the invention, A i' is described by A i/>Substitution w l.
The dual variables alpha and beta in the closed solution can be updated by the following sub-gradient method:
Where pi (t), ζ i (t) is the iteration step of the sub-gradient method.
(7) Based on the above solution, the W sum is shaped for a given transmit beamThe feasibility check sub-problem on the IRS phase shift matrix F can be translated into the following optimization sub-problem/>, based on the signal-to-noise marginTo accelerate its optimization process and obtain a phase shift matrix of sufficient efficiency while improving the minimum SINR of the network, the specific sub-problems are as follows:
max||y||1
s.t.(2),
y≥0,(5)
Wherein y 1 represents a norm of the vector y, Is the introduced relaxation variable, y i represents the signal-to-noise margin for user i.
(8) Definition u= [ u 1,…,uN]H ] whereinThe left side of inequality (4) can be converted into the following form: /(I)
Wherein the method comprises the steps of
Sub-problemsCan be converted into the following sub-problems/>
s.t.(5)
(9) The above sub-problem cannot be directly solved optimally due to the non-convex nature of the single-mode constraints (6) and constraints (7). For the non-convex characteristic of the constraint (7) inequality left function, the suboptimal solution can be obtained by iterative approximation of the following lower bound inequality joint SCA technique:
Where μ i,j represents the unique positive eigenvalue in matrix R i(wj). For the single-mode constraint in constraint (6), the present invention penalizes the function and relaxes the single-mode constraint to Then sub-problem/>Can be converted into the following/>Form:
s.t.(5)
/>
Wherein, P is a positive penalty factor, penalty function Forcing/>Note that when P is small, the sub-problem/>Maximizing the snr margin when P is large, the goal is to find a viable solution for IRS phase offset.
(10) To deal withThe invention uses SCA techniques (i.e., at a given/>The point performs first-order taylor expansion) to be converted into the following objective function form approximation and iterative solution:
Thus, the first and second substrates are bonded together, Can be converted into the following sub-problems/>
s.t.(5),(8),(9)
Sub-problemsIs a convex optimization problem and can be directly solved by adopting a CVX tool box.
(11) Sub-problemsThe solution of (2) can be an iterative solution of the sub-problem/>, using algorithm 2 as followsAnd a suboptimal solution is obtained. Algorithm 2 is specifically as follows:
Step 1: the iteration index t=0 and p=1e-3 are initialized. Let u t＝ur.
Step 2: repeat
Step 3: by solving sub-problemsUpdating u t+1;
Step 4: let t=t+1;
Step 5: until Is converged by the objective function value of (a);
Step 6: output u r+1＝ut.
(12) Based on the above steps and description, the invention proposes and discloses an optimization algorithm (algorithm 3) for combining base station transmitting beam forming and IRS reflection phase shift to solveIn particular,/>All of the optimization variables in (a)Is divided into two parts/>And { phi } and pass through the sub-problem/>And/>Is updated by iterative solution in turn until convergence. Algorithm 3 is specifically as follows:
step 1: the outer layer iteration index tau=0, the inner layer iteration index t=0 and the iteration threshold epsilon > 0 are initialized.
The feasible solution u 0,W0 is initialized, and the initial value alpha (0), beta (0) of the dual variable is set.
Step 2: repeat
Step 3: constructing a diagonal matrix phi r from u r;
step 4: let t=0;
step 5: repeat
Step 6: based on a given W r,Φr, updates according to equations (a) and (b)
Step 7: updating α (t+1), β (t+1) based on equations (c) and (d), respectively;
Step 8: let t=t+1;
Step 9: unitil alpha, beta converges;
Step 10: based on given And u r, updating u r+1 according to algorithm 2;
step 11: let r=r+1;
step 12:
In order to verify the performance gain of the method of the invention, the invention compares the performance of the proposed scheme with the following two reference schemes, reference scheme one, based on the above steps: the IRS-free scheme (No IRS), namely the simulation scene is a traditional wireless communication scene, IRS is not deployed, and only the transmitting beam forming of a base station end is optimized; reference scheme II: IRS random phase shift scheme (RPS) in which the phase shift of all reflective elements of the IRS Is subject to a uniform distribution over 0,2 pi.
Fig. 4 is a schematic diagram of the convergence performance of the algorithm according to the embodiment, where the number of AP transmit antennas and the number of IRS reflection units are m=4 and n=8, respectively. The result shows that the minimum SINR value of the network user of the proposal is increased iteratively and can be converged rapidly. This is because algorithm 2 performs phase shift optimization by maximizing the SINR margin, and can find a more efficient optimization strategy in each iteration to improve the minimum SINR of the network user, thereby speeding up the iteration process.
Fig. 5 shows the relationship between the minimum SINR of the implementation case network user and the maximum transmit power at the AP, where m=4, g=2. First, it can be observed that the minimum network signal-to-noise ratio of the schemes increases with increasing P max. In addition, the minimum SINR of the users of the proposed scheme is obviously superior to that of the other two reference schemes, and particularly when N is larger, the larger P max is, the larger the performance gain of the proposed scheme is, which shows that IRS has strong capability of enhancing signals and suppressing interference. Furthermore, the performance of the RPS scheme is similar to that of the NoIRS scheme, which means that the performance gain of deploying IRS can only be obtained by the transmit and reflect joint beamforming optimization design.
Fig. 6 shows the variation of the minimum SINR of an embodiment network user versus the number of IRS reflection units, where P max = 30dbm and m = 4. The results show that as N increases, the performance gain of the minimum network SINR of the proposed solution gradually increases, indicating that an IRS with a larger N can obtain a larger reflected beamforming gain by focusing more signal power and suppressing more interference power on the IRS-user link simultaneously. On the other hand, the RPS scheme performs almost the same as the IRS-free scheme, because the randomly configured phase shifted IRS can boost or suppress the received signal power at the user.
Fig. 7 depicts the variation of the minimum SINR of an embodiment network user with the number of AP transmit antennas, where P max = 30dbm and g = 2. It can be seen that the minimum SINR for the network users for both schemes increases with increasing M, the more transmit antennas, the higher the spatial diversity gain. It is also observed that the network user minimum SINR value increases with increasing N. This also verifies that the larger the reflective element size in the IRS, the higher the reflective beamforming gain. Furthermore, it can be observed that the performance gain provided by this scheme becomes smaller as M increases, which is reasonable because the performance gain provided by the transmit antenna array dominates.
Fig. 8 and 9 are graphs of minimum SINR of an embodiment network user as a function of the number of groups and the number of users per group, respectively, where m=8, n=16, p max =40 dBm. As can be seen from fig. 7, the network user minimum SINR values for all schemes are decreasing as the number of groups increases. This is because more active groups will cause more severe co-channel interference. Similarly, in fig. 8, the network user minimum SINR value shows the same trend as the number of users per group increases. However, due to the joint design of the proposed scheme for transmitting and reflecting beam forming, the performance of this scheme is improved by 3-5 dB over other schemes. Both figures verify that deploying IRSs in a multi-group, multicast network can achieve significant reflected beam forming gain.
Fig. 10 illustrates the variation of the minimum SINR of the network user with N for the implementation case under different P max settings, with m=4, g=2. It can be observed that the proposed solution can obtain the same performance as the No IRS solution at P max =30 dBm or P max =33 dBm, when P max =27 dBm and n=10 or n=19; furthermore, the proposed scheme can achieve the same performance as the No IRS scheme at P max =30 dBm, when P max =25 dBm and n=17.
Fig. 11 shows the variation of the minimum SINR of the network user with N for the implementation case with different numbers of AP transmit antennas M, and P max = 30dbm, g = 2. As can be seen, the performance of the proposed scheme can reach the same performance as the NoIRS scheme under m=16 (or m=20) condition with the combination of m=12 and n=9 (or n=16). Furthermore, the proposed scheme can obtain the same performance as the No IRS scheme at m=16 under the combined condition of m=8 and n=20. The performance analysis in fig. 9 and 10 shows that deploying some IRSs with low cost reflection units in a conventional network can achieve good reflection beamforming gain while also reducing the power consumption of the base station and the number of relatively expensive transmit antennas.
Corresponding to the above method embodiments, the present invention further provides a computer device, including:
a memory for storing a computer program;
And the processor is used for realizing the steps of the joint beamforming optimization method with fair IRS auxiliary communication service quality when executing the computer program.
In an embodiment of the present invention, the processor may be a central processing unit (Central Processing Unit, CPU), an asic, a dsp, a field programmable gate array, or other programmable logic device, etc.
The processor may invoke a program stored in the memory, and in particular, the processor may perform operations in an embodiment of a joint beamforming optimization method for IRS assisted communication quality of service fairness.
The memory is used to store one or more programs, which may include program code including computer operating instructions.
In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device or other volatile solid state storage device.
Corresponding to the above method embodiment, the embodiment of the present invention further provides a computer readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the above method for optimizing joint beamforming with IRS-assisted communication service quality fairness are implemented.
The following are to be noted: the above-mentioned steps are carried out,
IRS is INTELLIGENT REFLECTING surface, intelligent reflecting surface
LDD is Lagrangiandual decomposition, lagrange dual decomposition
SCA is Successive Convex Approximation, continuous convex approximation
GFP Generalized Fractional Programming, generic split plan
AO is ALTERNATING OPTIMIZATION, alternatively optimized
The CVX is an optimization tool box carried by Matlab software, and the name is called CVX tool box.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations and modifications of the present invention will be apparent to those of ordinary skill in the art in light of the foregoing description. It is not necessary here nor is it exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present invention.
Claims (8)
1. A joint beam forming optimization method for IRS auxiliary communication service quality fairness is characterized in that: comprising the following steps:
s1, arranging a plurality of groups of communication scenes under the assistance of IRS (inter-radio space control) at a base station;
s2, establishing a mathematical optimization model with fair service quality of a plurality of groups of users under the assistance of IRS (inter-range request service) based on the scene, wherein the mathematical optimization model has two constraint conditions, the first constraint condition is the maximum total transmitting power constraint of a base station, and the second constraint is the IRS reflection phase offset single-mode constraint;
s3, converting the partial optimization problem of the mathematical optimization model into a subtractive optimization sub-problem for iterative solution;
S4, decoupling the optimization variables of the subtractive optimization sub-problem and carrying out iteration solution on the loop, wherein the method comprises the steps of converting the decoupled emission beam forming non-convex and non-smooth optimization sub-problem into a convex lower bound function, obtaining an iterative base station emission beam forming closed solution through an LDD (laser direct structuring) method, and solving the decoupled IRS reflection phase shift single-mode constraint non-convex optimization sub-problem through a penalty function and an SCA (sequence control algorithm) method;
s5, outputting a combined base station transmitting beam forming and IRS reflection phase shift optimization strategy;
in step S2, the mathematical optimization model is:
In the method, in the process of the invention, W denotes the transmit beamforming matrix of the base station,Representing IRS reflection unit phase shift diagonal matrix, S i representing user I received signal power, I i representing user I received noise and interference power, gamma i representing user I signal-to-interference-noise ratio weight factor, w l representing base station to user group l transmitting beam forming vector, g representing user group number, P max representing base station maximum transmitting power, theta n representing IRS reflection unit n phase,/>N represents the number of IRS reflective elements,/>Representing the channel gain from the base station to user i in group l, H representing the conjugate transpose of the vector, w j representing the transmit beamforming vector of the base station to user group j, σ i representing the noise power value at user i end,/>Representing a set of all user groups,/>Representing all user sets belonging to the first group;
In step S3, the method for converting the partial optimization problem of the mathematical optimization model into the subtractive optimization sub-problem for iterative solution includes:
Converting the partial optimization problem of the mathematical optimization model into a subtractive optimization sub-problem by using a GFP algorithm, and carrying out iterative solution on the subtractive optimization sub-problem, wherein the subtractive optimization sub-problem is as follows:
wherein, tau represents the iterative parameter of the minimum receiving weight signal-to-interference-and-noise ratio of the network user;
By introducing relaxation variables The transformation of the subtractive optimization sub-problem into the smooth optimization sub-problem is as follows:
s.t.(1),(2)
In the method, in the process of the invention, Representing a set of all user groups,/>Representing all user sets belonging to the first group.
2. The method for optimizing the joint beamforming with fair quality of service for the RS-assisted communication according to claim 1, wherein the method comprises the steps of: in step S4, the method for decoupling the optimization variables of the subtractive optimization sub-problem and performing iterative solution of the loop includes:
decoupling and converting the optimization variable of the smooth optimization sub-problem into two optimization sub-problems by using an AO method, wherein the first optimization sub-problem and the second optimization sub-problem are respectively as follows:
s.t.(1)
s.t.(2),
y≥0,(5)
where y 1 represents a norm of the vector y, Is an introduced relaxation variable, y i represents the signal-to-noise margin for user i, and H i (Φ) represents the total channel gain of the base station to user i.
3. The IRS assisted communication quality of service fair joint beamforming optimization method according to claim 2, wherein: in step S4, the decoupled transmit beamforming non-convex and non-smooth optimization sub-problem is converted into a convex lower bound function, and the method for obtaining the iterative form base station transmit beamforming closed solution by the LDD method includes:
The SCA method is adopted to replace the left function of the first optimization sub-problem by a convex lower bound function, wherein the convex lower bound function is as follows:
Wherein Re {.cndot.S } represents the real part, Representing in the r-th iteration of the algorithm, the transmit beamforming vector of the base station for user group l, λ i(Hi (Φ), represents the unique positive eigenvalue of matrix H i (Φ), I represents the identity matrix;
recording the convex lower bound function of the second constraint condition of the first optimization sub-problem as A i, converting the first optimization sub-problem into the following convex optimization problem and iteratively solving:
s.t.(1)
based on the convex optimization problem, the LDD method is utilized to obtain a base station transmitting beam forming closed solution as follows:
In the method, in the process of the invention, A' i is described by A i/>Substitution w l results, α and β i being the dual variables of the constraint.
4. The IRS assisted communication quality of service fair joint beamforming optimization method according to claim 3, wherein: the dual variable alpha and beta i in the base station transmitting beam forming closed solution is updated by the following sub-gradient method:
Where pi (t), ζ i (t) represents the iteration step of the sub-gradient method.
5. The IRS assisted communication quality of service fair joint beamforming optimization method according to claim 3, wherein: in step S4, a method for solving the decoupled IRS reflection phase shift single-mode constraint non-convex optimization sub-problem by using a penalty function and an SCA method includes:
Definition u= [ u 1,…,uN]H ] wherein The left side of the second optimization sub-problem is converted to the following form:
Wherein:
The second optimization sub-problem is converted into the following sub-problem:
s.t.(5)
where y 1 represents a norm of the vector y, Is an introduced relaxation variable, y i represents the signal-to-noise margin for user i;
the suboptimal solution is obtained by iterative approximation by using the following lower bound inequality joint SCA method:
where μ i,j represents the unique positive eigenvalue in matrix R i(wj);
relaxing the single-mode constraint of the second optimization sub-problem to a penalty function The sub-problem is converted into the following form:
s.t.(5)
In the formula, penalty function Forcing/>
The sub-problem is converted into the following objective function form approximation and iterative solution by adopting the SCA method:
the sub-problem is And adopting a CVX toolbox to optimize and solve.
6. A processor, characterized by: the processor, when executing a program, implements the steps of the method of any one of claims 1 to 5.
7. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized by: the processor, when executing the program, implements the steps of the method of any one of claims 1 to 5.
8. A computer-readable storage medium having stored thereon a computer program, characterized by: which program, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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