CN114599044B - Intelligent reflector technology-based beam forming optimization method in cognitive network - Google Patents

Intelligent reflector technology-based beam forming optimization method in cognitive network Download PDF

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CN114599044B
CN114599044B CN202210215298.2A CN202210215298A CN114599044B CN 114599044 B CN114599044 B CN 114599044B CN 202210215298 A CN202210215298 A CN 202210215298A CN 114599044 B CN114599044 B CN 114599044B
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phase matrix
beam forming
energy efficiency
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cognitive
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CN114599044A (en
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梁微
罗薇
王大伟
李立欣
曹龙
苏坚
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Northwestern Polytechnical University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/24—Cell structures
    • H04W16/28—Cell structures using beam steering
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses a beam forming optimization method in a cognitive network based on an intelligent reflector technology, which is based on a beam forming vector of a base station and a phase matrix of an intelligent reflector, and aims at the maximum energy efficiency value of the cognitive network to construct an optimization problem and constraint conditions; determining a phase matrix according to the constraint condition, and iteratively optimizing a beam forming vector by taking the phase matrix as a known quantity; taking the converged beamforming vector as a known quantity, optimizing and verifying the phase matrix, and carrying out beamforming alternate optimization on the cognitive network based on the converged beamforming vector and the verified phase matrix; according to the invention, the cognitive user can simultaneously carry out communication tasks on the premise of meeting the service quality requirement of the authorized user, so that the communication efficiency can be improved, meanwhile, the base station and the intelligent reflecting surface are circularly optimized, and the beam forming of the base station and the phase of the intelligent reflecting surface are optimized, so that the energy efficiency value is maximized, and the operation efficiency of the communication system is improved.

Description

Intelligent reflector technology-based beam forming optimization method in cognitive network
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a beam forming optimization method in a cognitive network based on an intelligent reflector technology.
Background
In recent years, with the large-scale increase of wireless devices and the continuous increase of communication frequencies, the service range of cellular networks is continuously shrinking. Meanwhile, the number of base stations is continuously increased, but the wireless device occasionally enters a network blind area to cause connection failure. To solve this problem, intelligent Reflection Surface (IRS) devices are proposed, where it is desirable to control the phase and amplitude of the incident signal with a reflection surface containing a large number of reconfigurable passive elements, and to improve the signal transmission performance by using the coherent principle.
Most of IRS studies usually design the effect of the overall transmission rate of the cognitive system, or a discrete value method used in studying the optimal phase of the IRS, by fixing N IRS phases and finding the phase value from it that maximizes the channel gain. However, the fixed phase dispersion value may be subject to errors from the IRS optimal phase value, ultimately resulting in system inefficiency.
Disclosure of Invention
The invention aims to provide a beam forming optimization method in a cognitive network based on an intelligent reflector technology, which alternately optimizes active beam forming of a cognitive user base station and passive beam forming phases of IRS, so as to maximize the energy efficiency value of a system.
The invention adopts the following technical scheme: the method for optimizing the beam forming in the cognitive network based on the intelligent reflector technology comprises the following steps:
Based on a beam forming vector of a base station and a phase matrix of an intelligent reflecting surface, and taking the maximum energy efficiency value of a cognitive network as a target, constructing an optimization problem and constraint conditions;
determining a phase matrix according to the constraint condition, and iteratively optimizing the beam forming vector by taking the phase matrix as a known quantity until the beam forming vector converges;
taking the converged beam forming vector as a known quantity, optimizing and verifying the phase matrix until the phase matrix passes verification to obtain the phase matrix of the cognitive network;
And carrying out beam forming on the cognitive network based on the converged beam forming vector and the verified phase matrix.
Further, the optimization problem and constraints are:
s.t.W≤Pmax，
Wherein Θ is a phase matrix, w= |w 1||2+||w2||2+...+||wn||2,wn is a beamforming vector transmitted by the base station to the nth cognitive user, r=r 1+R2+...+Rn,Rn is a downlink transmission rate of the nth cognitive user, R n≥Rn,min,Rn,min is a minimum data transmission rate of the nth cognitive user, and R n,min＝log2(1+SINRn,min),SINRn,min is a minimum signal-to-interference-and-noise ratio of the nth cognitive user.
Further, iteratively optimizing the beamforming vector by taking the phase matrix as a known quantity comprises:
and taking the phase matrix as a known quantity, and adopting an SCA algorithm to iteratively optimize the beam forming vector.
Further, the convergence condition of the beamforming vector is:
t(i)-t(i-1)＜δ，
Wherein t (i) is a relaxation variable corresponding to a beamforming vector obtained by the ith iteration, t (i-1) is a relaxation variable corresponding to a beamforming vector obtained by the (i-1) th iteration, and δ is an error threshold.
Further, optimizing and verifying the phase matrix includes:
based on the optimization problem, an SDR algorithm is adopted to optimize the phase matrix, and an optimized phase matrix is obtained.
Further, the verification method for optimizing and verifying the phase matrix comprises the following steps:
calculating the energy efficiency value of the cognitive network according to the converged beam forming vector and the optimized phase matrix;
performing difference operation on the energy efficiency value and the energy efficiency value obtained after the last optimization;
And when the difference value is smaller than the energy efficiency error value, completing verification of the phase matrix.
Further, calculating the energy efficiency value of the cognitive network according to the converged beamforming vector and the verified phase matrix includes:
decomposing the verified phase matrix to obtain a phase vector of the intelligent reflecting surface;
And calculating an energy efficiency value according to the converged beam forming vector and the phase vector.
Further, when the difference value is equal to or greater than the energy efficiency error value:
the phase matrix is used as a known quantity, and the iterative optimization step of the beamforming vector is returned to continue to be executed;
Until the difference is less than the energy efficiency error value.
Another technical scheme of the invention is as follows: the device for optimizing the wave beam forming in the cognitive network based on the intelligent reflector technology comprises:
the construction module is used for constructing optimization problems and constraint conditions based on the beam forming vector of the base station and the phase matrix of the intelligent reflecting surface and aiming at the maximum energy efficiency value of the cognitive network;
The first iterative optimization module is used for determining a phase matrix according to constraint conditions, and iteratively optimizing the beam forming vector by taking the phase matrix as a known quantity until the beam forming vector converges;
the second iterative optimization module is used for optimizing the phase matrix by taking the converged beam forming vector as a known quantity, verifying the phase matrix and obtaining the phase matrix of the cognitive network through verification;
and the beam forming optimization module is used for carrying out beam forming on the cognitive network based on the converged beam forming vector and the verified phase matrix.
Another technical scheme of the invention is as follows: the beam forming optimization device in the cognitive network based on the intelligent reflector technology comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the beam forming optimization method in the cognitive network based on the intelligent reflector technology is realized when the processor executes the computer program.
The beneficial effects of the invention are as follows: according to the invention, the cognitive user can simultaneously carry out communication tasks on the premise of meeting the service quality requirement of the authorized user, so that the communication efficiency can be improved, meanwhile, the base station and the intelligent reflecting surface are circularly optimized, and the beam forming of the base station and the phase of the intelligent reflecting surface are optimized, so that the energy efficiency value is maximized, and the operation efficiency of the communication system is improved.
Drawings
Fig. 1 is a topology diagram of a non-orthogonal cognitive wireless network based on an intelligent reflection surface in an embodiment of the invention;
fig. 2 is a flowchart of a beam forming optimization method in a cognitive network based on an intelligent reflector technology according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an iterative effect in a verification embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating simulation of energy efficiency values of different methods according to the verification embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a beam forming optimization device in a cognitive network based on an intelligent reflector technology in an embodiment of the invention.
Detailed Description
The invention will be described in detail below with reference to the drawings and the detailed description.
In a spectrum sharing function (underlay) spectrum resource allocation scheme of a cognitive radio network, a cognitive user and an authorized user can use the same spectrum to transmit simultaneously, but the interference caused by the cognitive user cannot affect the normal communication of the authorized user. In this application background, the occurrence of non-orthogonal multiple access (NOMA) technology can effectively improve the spectrum access efficiency of the future mobile network. At the transmitting end, NOMA can realize multiplexing on the power domain, flexibly allocate power according to the channel quality of different users, allocate less power to users with good channel quality, and realize user separation by using the Successive Interference Cancellation (SIC) technology at the receiving end.
The invention combines the intelligent reflecting surface and the non-orthogonal multiple access technology cognitive radio network to form a novel non-orthogonal cognitive radio network (CR-NOMA based IRS) based on the intelligent reflecting surface. The maximum energy efficiency is realized in the network, which means that the trade-off between the total transmission rate of the system and the energy consumption is made, and the key of guaranteeing green communication. The phase of the IRS thus obtained is both continuously variable, the energy efficiency value is optimal, and finally the computational complexity is controllable.
In one embodiment, as shown in fig. 1, there is one cognitive user base station (CBS), two authorized users (PUs), and two Cognitive Users (CUs) in a communication cell. The cognitive user performs communication transmission by borrowing the frequency band of the authorized user, but the communication interference caused by the cognitive user to the authorized user is controllable. And because of the building blockage, the cognitive user is in a communication 'blind zone', and a 'direct communication link' from the CBS to the CU does not exist. In this context, it is necessary to use the IRS to provide a "reflective communication link" so that the CBS message can successfully reach the CU.
Because two cognitive users exist in the communication system, the expression is U k, and k=1, 2. The base station has M transmission antennas, and the transmitted active beam forming vector is expressed asThe IRS has N reflecting units, and the cognitive user receiver defaults to 1 antenna.
Obviously, in the communication process, a communication link is existed and is a reflection link of BS-IRS-User, then the equivalent channel is from BS to IRS and from IRS to U k, and the channel gain matrix is respectivelyAnd/>The IRS can change the amplitude and the phase of the input signal, and the phase matrix of the IRS isWherein, beta n is the amplitude of the IRS nth block reflection element, the value range is beta n epsilon [0,1], j is the imaginary symbol, theta n is the phase shift of the IRS nth block reflection element, and the value range is theta n epsilon [0,2 pi).
To maximize the amplitude of the signal, β n is fixed to 1. The channel between the BS-IRS-User can therefore consist of the BS-IRS channel, the phase change equivalent channel brought about by the IRS, the IRS-User channel, respectively.
In order to maximize the effective transmission efficiency of the base station and balance the problems of maximizing the sum of transmission rates and minimizing the transmission power consumption, the optimization in this embodiment aims to maximize the Energy Efficiency (EE), i.e. the number of data bits that can be transmitted per unit bandwidth and unit energy.
Therefore, the invention discloses a beam forming optimization method in a cognitive network based on an intelligent reflector technology, wherein the beam forming optimization comprises active beam forming and passive beam forming optimization. For convenience of distinction, in the following, the beamforming vector of the base station represents active beamforming, and the phase matrix of the intelligent reflection surface represents passive beamforming.
As shown in fig. 2, the method comprises the following steps: step S110, constructing optimization problems and constraint conditions based on a beam forming vector of a base station and a phase matrix of an intelligent reflecting surface and aiming at the maximum energy efficiency value of a cognitive network; step S120, determining a phase matrix according to constraint conditions, and iteratively optimizing the beam forming vector by taking the phase matrix as a known quantity until the beam forming vector converges; step S130, optimizing and verifying the phase matrix by taking the converged beam forming vector as a known quantity until the phase matrix passes verification, so as to obtain the phase matrix of the cognitive network; and step 140, carrying out beamforming on the cognitive network based on the converged beamforming vector and the verified phase matrix.
According to the invention, the cognitive user can simultaneously carry out communication tasks on the premise of meeting the service quality requirement of the authorized user, so that the communication efficiency can be improved, meanwhile, the base station and the intelligent reflecting surface are circularly optimized, and the beam forming of the base station and the phase of the intelligent reflecting surface are optimized, so that the energy efficiency value is maximized, and the operation efficiency of the communication system is improved.
In the embodiment of the invention, the authorized user refers to the user served by the network service provider to which the base station belongs, and the cognitive user borrows the base station for other network service providers to provide service, so that the communication quality of the authorized user cannot be influenced when the cognitive user is served.
When the optimization problem is constructed, the power amplification rate eta epsilon [0,1] of the base station and the total circuit power consumption P C,Pmax of the base station are defined as the maximum transmitting power which can be provided by the base station for the cognitive user (namely the maximum power which can tolerate interference of the authorized user in the system). Subsequently, using shannon's law, the optimization problem and constraints can be derived as:
s.t.W≤Pmax (1a)
Wherein Θ is a phase matrix, w= |w 1||2+||w2||2+...+||wn||2,wn is a beamforming vector transmitted by the base station to the nth cognitive user, r=r 1+R2+...+Rn,Rn is a downlink transmission rate of the nth cognitive user, R n≥Rn,min,Rn,min is a minimum data transmission rate of the nth cognitive user, and R n,min＝log2(1+SINRn,min),SINRn,min is a minimum signal-to-interference-and-noise ratio of the nth cognitive user.
In the embodiment of the present invention, as shown in fig. 1, taking two cognitive users and 1 cognitive user base station as an example, a spectrum resource allocation scheme with a spectrum sharing function (underservy) is adopted, that is, the cognitive users can transmit by using an authorized frequency band under the condition that the requirements of user service quality set by the authorized users are met and the transmission quality requirements of the authorized users are not reduced. The transmission rates are R 1 and R 2 respectively, and in order to maximize EE of the system, the optimization problem is specifically as follows:
s.t.R1≥R1,min (2a)
R2≥R2,min (2b)
||w1||2+||w2||2≤Pmax (2c)
θn∈[0,2π) (2d)
Wherein, R k,min＝log2(1+SINRk,min) is a minimum data transmission rate k=1, 2 for enabling the cognitive user to normally communicate, and the limiting conditions (2 a) and (2 b) are to ensure the normal communication of the cognitive user, that is, if the CU data transmission rate cannot reach the R k,min communication process, the problems such as interruption may occur. The limiting condition (2 c) indicates that the interference brought by the cognitive user to the authorized user cannot exceed the maximum power which can be tolerated by the authorized user, and on the other hand, the transmitting power of the cognitive user base station is limited. The constraint (2 d) states that the passive beamforming phase of the IRS should be between 0 and 2 pi. R 1 and R 2 are downlink transmission rates of each cognitive user, and w 1、w2 is a beamforming vector transmitted by a cognitive user base station to a corresponding user.
After the optimization problem is obtained, the optimization problem is solved according to the solving method. The convex optimization is used as a practical mathematical tool, and can solve the optimal values of various convex problems, and the operation solving method is widely applied to various fields. The invention relates to two optimization algorithms which are alternately operated, namely successive approximation (SCA) and semi-definite programming (SDR). The SCA is optimized by an active beam forming vector emitted by the base station, the SDR is optimized by a passive beam forming phase of the intelligent reflecting surface, and the convex problem with two optimized variables can be solved by an alternate optimization mode.
In the embodiment of the invention, firstly, a system is initialized, and meanwhile, a phase matrix theta of an intelligent reflecting surface in the system is initialized, so that a channel gain matrix from a cognitive user base station to an IRS and from the IRS to a cognitive user is established.
Next, the non-convex optimization problem of solving energy efficiency EE is converted into a suitable convex problem. In the conversion process, the solving energy efficiency EE problem is decomposed into a first optimization problem and a second optimization problem. The first optimization problem is to solve the base station active beamforming given the IRS passive beamforming phase; the second optimization problem is to solve the IRS passive beamforming phase when the base station active beamforming vector is given, and the solving difficulty can be reduced by decomposing the optimization problem.
Solving a first optimization problem, namely iteratively optimizing the beamforming vector by adopting an SCA algorithm by taking a phase matrix as a known quantity. In this process, the custom relaxation variables are { t, ρ, γ, β }. When the phase matrix Θ is given, w k needs to be optimized, and in order to facilitate observation of the concavity and convexity of the objective function, a relaxation variable t is introduced to enable the original problem to be equivalently converted into the following form:
to shift the above optimization problem from a non-convex problem to a convex problem, other relaxation variables such as ρ, γ, β, etc. will also be introduced during the process.
Initializing an active beam shaping vector w k of a cognitive user base station under the condition of a phase matrix theta of a given intelligent reflecting surface, performing iterative calculation on an initialized group of values by using an SCA algorithm, wherein in the ith iteration, the input parameters of the SCA algorithm are as followsThe output parameter is/>
In the calculation method, the convergence condition of the beam forming vector is as follows:
t(i)-t(i-1)＜δ (5)
Wherein t (i) is a relaxation variable corresponding to a beamforming vector obtained by the ith iteration, t (i-1) is a relaxation variable corresponding to a beamforming vector obtained by the (i-1) th iteration, and δ is an error threshold.
If the convergence condition is met, stopping the SCA iterative operation to obtain an active beam shaping vectorIf the requirements are not met, will/>The optimization of w k continues as an input value for the next SCA iteration.
In the second optimization problem conversion process, for convenience of expression, the phase vector v introduced into the intelligent reflecting surface represents the original phase matrix Θ. Subsequently, to facilitate the use of the semi-definite relaxation algorithm, the optimization variable is modified to a quadratic form, for example, the phase vector V of the intelligent reflection surface becomes a matrix form v=vv H. Then, at a given base station, the active beam shaping vectorAnd then solving by utilizing an SDR algorithm to obtain an optimal value V * in the form of an intelligent reflecting surface phase matrix. Then, decomposing from the phase matrix V * by using a singular value decomposition or Gaussian randomization method to obtain the intelligent reflection surface phase vector V *.
That is, based on the second optimization problem, the SDR algorithm is adopted to optimize the phase matrix, so as to obtain an optimized phase matrix. When the phase matrix is optimized, the phase matrix is optimized through the downlink transmission rate of the cognitive user in the optimization problem.
Specifically, according to the channel model between CBS and CU, the received signals at two CUs can be obtained as:
where s k represents the signal used by the user U k for carrying information, k=1, 2.
In order to decode the received information by using the successive interference cancellation technique (SIC) in the non-orthogonal multiple access technique (NOMA) at the user receiving end, it is necessary to assume that the channels of the two CUs are strong and weak, i.e., U 1 is a strong channel user, U 2 is a weak channel user, and the decoding order at the user receiving end is (U 2,U1). Thus, the signal-to-interference-and-noise ratio when decoding U 2 information at the receiving end of U 1 is:
Wherein σ 2 is the power of the additive gaussian noise.
After decoding the information of U 2 at the receiving end of U 1, deleting the information and then decoding the information of U 1, thereby obtaining the following signal-to-interference-and-noise ratio:
After the receiving end of U 2 decodes the effective information required by itself, it is unnecessary to decode the information of U 1, so that the signal-to-interference-and-noise ratio obtained by decoding at the receiving end of U 2 is phi 2,2. In order to meet the requirement that the signal-to-interference-and-noise ratio of two CUs meets the minimum signal-to-interference-and-noise ratio of a system, the shannon law is utilized to obtain the information transmission rate expressions of different CUs:
R1＝log2(1+φ1,1) (9)
R2＝min{log2(1+φ1,2),log2(1+φ2,2)} (10)
Wherein,
Therefore, only the downlink transmission rate to the cognitive user in the optimization problem is considered, and the following optimization problem is proposed for simple operation:
s.t.R1≥R1,min (11a)
R2≥R2,min (11b)
θn∈[0,2π) (11c)
According to the optimization variables And v *, the energy efficiency value EE can be calculated, but since the beamforming matrix and the intelligent reflecting surface phase value may not be the global optimum at this time, the calculated energy efficiency value may not be the optimum, and therefore, a corresponding convergence condition needs to be set to determine whether convergence is achieved.
As a specific implementation manner, the verification method for optimizing and verifying the phase matrix comprises the following steps:
Calculating the energy efficiency value of the cognitive network according to the converged beam forming vector and the verified phase matrix; performing difference operation on the energy efficiency value and the energy efficiency value obtained by the last optimization; and when the difference value is smaller than the energy efficiency error value, completing verification of the phase matrix, considering the energy efficiency value calculated by the algorithm to be converged to an optimal value, ending the solving process, and storing the calculated energy efficiency value EE.
Further, calculating the energy efficiency value of the cognitive network according to the converged beamforming vector and the verified phase matrix includes: decomposing the verified phase matrix to obtain a phase vector of the intelligent reflecting surface; and calculating an energy efficiency value according to the converged beam forming vector and the phase vector.
In addition, when the difference value is greater than or equal to the energy efficiency error value: the phase matrix is used as a known quantity, and the iterative optimization step of the beamforming vector is returned to continue to be executed; until the difference is less than the energy efficiency error value.
According to the invention, by considering the energy efficiency, the balance between the energy consumption and the communication quality is achieved, and the cognitive user and the authorized user simultaneously carry out the communication task on the premise of meeting the service quality requirement of the authorized user, so that the energy efficiency is maximized in the power domain. Meanwhile, an alternating algorithm of continuous convex approximation and semi-definite programming is provided. In the process of optimizing the energy efficiency, the beam forming of the base station is optimized, and meanwhile, the phase of the intelligent reflecting surface is optimized, so that the obtained energy efficiency value is maximum.
The method can reduce the emission power of the base station of the cognitive user, if the beam forming is not carried out, the random emission power of the base station wastes energy, and the energy efficiency value is reduced instead. Meanwhile, the interference of the cognitive users to the authorized users can be reduced by controlling the power transmitted by the base station, and more cognitive users can fully utilize the cognitive radio communication system.
In addition, the scheme of the embodiment of the invention is verified through simulation. In the process of evaluating whether the energy efficiency result of the cognitive user is stable, according to the flow design of the invention, the communication system can realize the aim of maximum energy efficiency value by optimizing the beam forming of the base station and the phase mode of the intelligent reflecting surface. The CVX can be used for calculating the optimal value of the convex problem, and if the energy efficiency value does not reach the convergence value, the base station beam forming and the intelligent reflecting surface phase are indicated to have a room for optimization.
Simulation conditions: the cognitive radio network is assumed to comprise 1 cognitive user base station and 2 cognitive users. P max is set to 30dBm; SINR k,min is set to 10dB; p C is set to 10dBm; η is set to 0.7. In addition, the distance between CBS and IRS is 10m, and the distance between IRS and CU is 10m and 28m respectively. As shown in fig. 3, it can be seen intuitively that the energy efficiency value converges to the maximum value after several iterations.
According to the optimization contrast value of the energy efficiency maximization method under the condition of considering different algorithms, according to a simulation result (shown in fig. 4), the ordinate is the energy efficiency value of a cognitive user, and the abscissa is the different intelligent reflecting surface numbers. In the simulation result of fig. 4, the Optimal scheme refers to the energy efficiency value calculated by adopting the SCA-SDR algorithm; the Random Phase scheme refers to optimizing CBS active beam forming by adopting SCA algorithm, and calculating an energy efficiency value by a Random IRS passive beam forming Phase; the Fixed Phase scheme refers to optimizing CBS active beamforming by adopting SCA algorithm, and fixing IRS passive beamforming Phase to be pi calculated energy efficiency value. As the number of IRS elements increases, the energy efficiency value calculated based on the Optimal scheme increases. And compared with other schemes, the energy efficiency value of the intelligent reflection pixel increases very little as the number of the intelligent reflection pixel increases. Because the random and fixed smart reflector phases do not increase the channel gain of their system, even improper phases can cause destructive interference of the beams, ultimately resulting in a drop in energy efficiency. And the Optimal scheme enables the beams of the CBS and the IRS to be matched, and finally constructive interference is achieved, and finally the effect of energy efficiency increase is achieved.
Therefore, the method is applicable to the problem of optimizing the energy efficiency value under different intelligent reflection pixel numbers, and the effect is obviously better than the energy efficiency value calculated by a random phase method and a fixed phase method.
The invention also discloses a device for optimizing the beam forming in the cognitive network based on the intelligent reflector technology, which is shown in fig. 5 and comprises the following steps: the construction module 210 is configured to construct an optimization problem and a constraint condition based on a beamforming vector of the base station and a phase matrix of the intelligent reflecting surface, and with a maximum energy efficiency value of the cognitive network as a target; a first iterative optimization module 220, configured to determine a phase matrix according to a constraint condition, and iteratively optimize a beamforming vector by using the phase matrix as a known quantity until the beamforming vector converges; the second iterative optimization module 230 is configured to optimize and verify the phase matrix with the converged beamforming vector as a known quantity until the phase matrix passes verification, thereby obtaining a phase matrix of the cognitive network. The beamforming optimization module 240 performs beamforming on the cognitive network based on the converged beamforming vector and the validated phase matrix.
It should be noted that, because the content of information interaction and execution process between modules of the above apparatus is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be found in the method embodiment section, and will not be described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to perform all or part of the above-described functions. The functional modules in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
The invention also discloses a beam forming optimization device in the cognitive network based on the intelligent reflector technology, which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the beam forming optimization method in the cognitive network based on the intelligent reflector technology is realized when the processor executes the computer program.
The device can be a computing device such as a desktop small computer, a notebook computer, a palm computer, a cloud server and the like. The means may include, but is not limited to, a processor, a memory. Those skilled in the art will appreciate that the apparatus may include more or fewer components, or certain components may be combined, or different components, for example, may also include input-output devices, network access devices, etc.
The Processor may be a central processing unit (Central Processing Unit, CPU), which may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may in some embodiments be an internal storage unit of the device, such as a hard disk or a memory of the device. The memory may in other embodiments also be an external storage device of the apparatus, such as a plug-in hard disk provided on the apparatus, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), etc. Further, the memory may also include both an internal storage unit and an external storage device of the apparatus. The memory is used to store an operating system, application programs, boot loader (BootLoader), data, and other programs, etc., such as program code for the computer program, etc. The memory may also be used to temporarily store data that has been output or is to be output.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments. Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

Claims (5)

1. The method for optimizing the beam forming in the cognitive network based on the intelligent reflector technology is characterized by comprising the following steps of:
Based on a beam forming vector of a base station and a phase matrix of an intelligent reflecting surface, and taking the maximum energy efficiency value of a cognitive network as a target, constructing an optimization problem and constraint conditions;
determining a phase matrix according to the constraint condition, and iteratively optimizing the beam forming vector by taking the phase matrix as a known quantity until the beam forming vector converges;
Optimizing and verifying the phase matrix by taking the converged beam forming vector as a known quantity until the phase matrix passes verification to obtain the phase matrix of the cognitive network;
carrying out beam forming on the cognitive network based on the converged beam forming vector and the verified phase matrix;
the optimization problem and constraint conditions are as follows:
s.t. W≤Pmax，
Wherein Θ is a phase matrix, w= |w 1||2+||w2||2+...+||wn||2,wn is a beamforming vector transmitted by the base station to the nth cognitive user, r=r 1+R2+...+Rn,Rn is a downlink transmission rate of the nth cognitive user, R n≥Rn,min,Rn,min is a minimum data transmission rate of the nth cognitive user, R n,min＝log2(1+SINRn,min),SINRn,min is a minimum signal-to-interference-and-noise ratio of the nth cognitive user, P C is total circuit power consumption of the base station, and P max is a maximum transmission power which can be provided by the base station for the cognitive user;
Optimizing and verifying the phase matrix includes:
Based on the optimization problem, optimizing the phase matrix by adopting an SDR algorithm to obtain an optimized phase matrix;
The verification method for optimizing and verifying the phase matrix comprises the following steps:
calculating the energy efficiency value of the cognitive network according to the converged beam forming vector and the verified phase matrix;
performing difference operation on the energy efficiency value and the energy efficiency value obtained after the last optimization;
when the difference value is smaller than the energy efficiency error value, completing verification of the phase matrix;
The energy efficiency value of the cognitive network is calculated according to the converged beam forming vector and the verified phase matrix, and the energy efficiency value comprises the following steps:
decomposing the verified phase matrix to obtain a phase vector of the intelligent reflecting surface;
Calculating the energy efficiency value according to the converged beam forming vector and the phase vector;
When the difference value is greater than or equal to the energy efficiency error value:
returning to the iterative optimization step of the beamforming vector to continue to execute by taking the phase matrix as a known quantity;
until the difference is less than the energy efficiency error value.
2. The method for optimizing beamforming in a cognitive network based on intelligent reflector technology as claimed in claim 1, wherein iteratively optimizing the beamforming vector with the phase matrix as a known quantity comprises:
and taking the phase matrix as a known quantity, and adopting an SCA algorithm to iteratively optimize the beam forming vector.
3. The method for optimizing beam forming in a cognitive network based on intelligent reflector technology as claimed in claim 2, wherein the convergence condition of the beam forming vector is:
t(i)-t(i-1)＜δ，
Wherein t (i) is a relaxation variable corresponding to a beamforming vector obtained by the ith iteration, t (i-1) is a relaxation variable corresponding to a beamforming vector obtained by the (i-1) th iteration, and δ is an error threshold.
4. Wave beam forming optimizing device in cognitive network based on intelligent reflector technique, its characterized in that includes:
the construction module is used for constructing optimization problems and constraint conditions based on the beam forming vector of the base station and the phase matrix of the intelligent reflecting surface and aiming at the maximum energy efficiency value of the cognitive network;
the first iterative optimization module is used for determining a phase matrix according to the constraint condition, and iteratively optimizing the beam forming vector by taking the phase matrix as a known quantity until the beam forming vector converges;
The second iterative optimization module is used for optimizing and verifying the phase matrix by taking the converged beam forming vector as a known quantity, and the phase matrix passes the verification to obtain the phase matrix of the cognitive network;
The beam forming optimization module is used for carrying out beam forming on the cognitive network based on the converged beam forming vector and the verified phase matrix;
the optimization problem and constraint conditions are as follows:
s.t. W≤Pmax，
Wherein Θ is a phase matrix, w= |w 1||2+||w2||2+...+||wn||2,wn is a beamforming vector transmitted by the base station to the nth cognitive user, r=r 1+R2+...+Rn,Rn is a downlink transmission rate of the nth cognitive user, R n≥Rn,min,Rn,min is a minimum data transmission rate of the nth cognitive user, R n,min＝log2(1+SINRn,min),SINRn,min is a minimum signal-to-interference-and-noise ratio of the nth cognitive user, P C is total circuit power consumption of the base station, and P max is a maximum transmission power which can be provided by the base station for the cognitive user;
Optimizing and verifying the phase matrix includes:
Based on the optimization problem, optimizing the phase matrix by adopting an SDR algorithm to obtain an optimized phase matrix;
The verification method for optimizing and verifying the phase matrix comprises the following steps:
calculating the energy efficiency value of the cognitive network according to the converged beam forming vector and the verified phase matrix;
performing difference operation on the energy efficiency value and the energy efficiency value obtained after the last optimization;
when the difference value is smaller than the energy efficiency error value, completing verification of the phase matrix;
The energy efficiency value of the cognitive network is calculated according to the converged beam forming vector and the verified phase matrix, and the energy efficiency value comprises the following steps:
decomposing the verified phase matrix to obtain a phase vector of the intelligent reflecting surface;
Calculating the energy efficiency value according to the converged beam forming vector and the phase vector;
When the difference value is greater than or equal to the energy efficiency error value:
returning to the iterative optimization step of the beamforming vector to continue to execute by taking the phase matrix as a known quantity;
until the difference is less than the energy efficiency error value.
5. The beam forming optimization device in the cognitive network based on the intelligent reflector technology comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and is characterized in that the beam forming optimization method in the cognitive network based on the intelligent reflector technology as claimed in any one of claims 1-4 is realized when the processor executes the computer program.
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Publication number Priority date Publication date Assignee Title
CN115278707B (en) * 2022-06-15 2023-12-15 东莞理工学院 NOMA terahertz network energy efficiency optimization method based on intelligent reflector assistance

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110278017A (en) * 2019-06-27 2019-09-24 广东工业大学 A kind of multi-antenna wireless energy transmission system and method based on intelligent reflecting surface
CN112073107A (en) * 2020-09-17 2020-12-11 南通大学 Multi-group and multicast combined beam forming algorithm design based on intelligent reflecting surface
CN112803978A (en) * 2020-12-31 2021-05-14 齐鲁工业大学 Intelligent surface MISO system joint beam forming method based on successive approximation
CN112929067A (en) * 2021-02-04 2021-06-08 重庆邮电大学 SCA-based IRS-NOMA system low-complexity beam forming method
CN112929068A (en) * 2021-02-04 2021-06-08 重庆邮电大学 SDR-based IRS-NOMA system beam forming optimization method
CN113225108A (en) * 2021-03-18 2021-08-06 北京邮电大学 Robust beam forming method for assisting multi-cell coordinated multi-point transmission by intelligent reflector
CN113315547A (en) * 2021-05-28 2021-08-27 北京邮电大学 Robust joint transmission beam optimization method for intelligent reflecting surface assisted multiple cells
CN113645635A (en) * 2021-08-12 2021-11-12 大连理工大学 Design method of intelligent reflector-assisted high-energy-efficiency unmanned aerial vehicle communication system
CN113691295A (en) * 2021-08-18 2021-11-23 北京理工大学 IRS-based interference suppression method in heterogeneous network
CN113949427A (en) * 2021-09-10 2022-01-18 中国人民解放军国防科技大学 Multi-user wireless network safety energy efficiency optimization design method and system

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108809397B (en) * 2018-06-27 2020-06-30 东南大学 High-efficiency digital-analog hybrid beam forming method, device and equipment in multi-antenna system

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110278017A (en) * 2019-06-27 2019-09-24 广东工业大学 A kind of multi-antenna wireless energy transmission system and method based on intelligent reflecting surface
CN112073107A (en) * 2020-09-17 2020-12-11 南通大学 Multi-group and multicast combined beam forming algorithm design based on intelligent reflecting surface
CN112803978A (en) * 2020-12-31 2021-05-14 齐鲁工业大学 Intelligent surface MISO system joint beam forming method based on successive approximation
CN112929067A (en) * 2021-02-04 2021-06-08 重庆邮电大学 SCA-based IRS-NOMA system low-complexity beam forming method
CN112929068A (en) * 2021-02-04 2021-06-08 重庆邮电大学 SDR-based IRS-NOMA system beam forming optimization method
CN113225108A (en) * 2021-03-18 2021-08-06 北京邮电大学 Robust beam forming method for assisting multi-cell coordinated multi-point transmission by intelligent reflector
CN113315547A (en) * 2021-05-28 2021-08-27 北京邮电大学 Robust joint transmission beam optimization method for intelligent reflecting surface assisted multiple cells
CN113645635A (en) * 2021-08-12 2021-11-12 大连理工大学 Design method of intelligent reflector-assisted high-energy-efficiency unmanned aerial vehicle communication system
CN113691295A (en) * 2021-08-18 2021-11-23 北京理工大学 IRS-based interference suppression method in heterogeneous network
CN113949427A (en) * 2021-09-10 2022-01-18 中国人民解放军国防科技大学 Multi-user wireless network safety energy efficiency optimization design method and system

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Joint Beamforming and Phase Shift Design in Downlink UAV Networks with IRS-Assisted NOMA;Shiyu Jiao等;Journal of Communications and Information Networks;20200630;全文 *
基于辅助相位优化的交替投影阵列综合算法;万川;韩玉兵;盛卫星;马晓峰;张仁李;;电子学报;20171115(11);全文 *
毫米波分离型子阵列的低复杂度混合波束成型传输设计;欧飞飞;何世文;薛春林;徐琴珍;杨绿溪;;信号处理;20160925(09);全文 *

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