CN117060961B - Beam selection method, device, medium and equipment of MIMO system - Google Patents
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Abstract
The invention relates to a beam selection method, a device, a medium and equipment of a MIMO system, belongs to the technical field of wireless communication, and solves the problem of beam selection of large-scale MIMO. The technical scheme of the invention mainly comprises the following steps: acquiring a grid set and a sector set associated with any grid, wherein the sector is provided with a plurality of beams, and acquiring the beam signal intensity of any beam in any grid; representing the total number of high-quality grids according to the second binary variable to determine an objective function as maximizing the total number of high-quality grids; constructing constraints among beam signal intensity, a first binary variable and a second binary variable according to preset conditions which need to be met by the high-quality grid; forming a punishment item according to the constraint, and adding the punishment item into an objective function to form a secondary unconstrained binary optimization model; solving a quadratic unconstrained binary optimization model to determine the value of each first binary variable, and determining the beam selection of each sector according to the value of the first binary variable.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a beam selection method, device, medium and equipment of a MIMO system.
Background
Multiple input multiple output (multiple input multiple output, MIMO) is an antenna system in which a plurality of antennas are used at both a transmitting end and a receiving end to form a plurality of channels between transmission and reception in order to greatly increase channel capacity. Massive MIMO is a revolutionary technology in the field of wireless communications and is considered to be one of the key technologies of 5G. It utilizes a large number of antennas at the base station to improve the coverage and capacity of the wireless communication system. By utilizing a large number of antennas, massive MIMO can provide multiple data streams simultaneously, thereby achieving higher throughput and better signal quality. However, implementing massive MIMO in 5G systems, beam selection is a significant challenge. In a MIMO system, where the transmission bandwidth is divided into subbands, the MIMO beam selection problem involves selecting a set of beams that maximize some performance criteria (e.g., the number of cells that meet a given constraint) to enhance the quality of service. Solving the MIMO beam selection problem is critical to optimizing the performance of the wireless communication system.
The prior art has difficulty in calculating a large-scale MIMO beam selection problem, and as the problem scale increases (the number of beams and the number of grids increase), the calculation complexity increases exponentially, the solving difficulty increases sharply, and the prior solving technology is difficult to finish solving in a short time. When there are hundreds of sub-beams, there are hundreds of MIMO weight sets, and it is difficult to select the best combination from the hundreds of MIMO weight sets. The current computing technology is based on optimization algorithms of a traditional computer, including greedy algorithms, branch-and-bound and simulated annealing algorithms. Greedy algorithms are simple and efficient, but may fall into a locally optimal solution. The branch-and-bound algorithm provides global optimality assurance, but is computationally expensive. Simulated annealing is a meta-heuristic optimization algorithm that gradually reduces the temperature to encourage optimization to converge to a globally optimal solution, but does not guarantee a globally optimal solution.
In summary, the prior art computing methods have difficulty in solving the beam selection problem of massive MIMO due to the computational complexity and the large number of possible combinations.
Disclosure of Invention
In view of the above analysis, the embodiments of the present invention aim to provide a method, an apparatus, a medium and a device for selecting a beam of a MIMO system, so as to solve the problem of beam selection of massive MIMO.
An embodiment of a first aspect of the present invention provides a beam selection method for a MIMO system, including the steps of:
acquiring a grid set and a sector set associated with any grid, wherein the sector is provided with a plurality of beams, and acquiring the beam signal intensity of any beam in any grid; defining a first binary variable to indicate whether any beam of any sector is selected, and defining a second binary variable to indicate whether any grid is a high quality grid;
representing a total number of high quality grids according to the second binary variable to determine an objective function to maximize the total number of high quality grids;
constructing constraints among the beam signal intensity, the first binary variable and the second binary variable according to preset conditions which the high-quality grid needs to meet, and enabling a beam selected by a sector represented by the first binary variable to enable a second binary variable value corresponding to the high-quality grid meeting the preset conditions to represent the high-quality grid when the constraints are met;
Forming a penalty term according to the constraint, and adding the penalty term into the objective function to form a secondary unconstrained binary optimization model;
solving the quadratic unconstrained binary optimization model to determine the value of each first binary variable, and determining the beam selection of each sector according to the value of the first binary variable.
In some embodiments, acquiring a set of grids and a set of sectors associated with a single grid, wherein a sector has a number of beams, acquiring beam signal strengths of any beam in any grid, comprises:
the number of grids in the acquisition grid set is m, the total number of sectors is v, the number of beams of the sectors is n, and the set of sectors associated with the ith grid is expressed asDefinitions->Representing the beam signal strength of the kth beam of the jth sector associated with the ith grid;
the first binary variable is expressed asWhen the kth beam of the jth sector is selected,/when the kth beam is selected>Otherwise->；
The second binary variable is expressed asWhen the ith sector meets said preset condition +.>Otherwise。
In some embodiments, the objective function is expressed as:
wherein->Representing a second binary variable.
In some embodiments, defining the signal strength of a grid includes a set of sector signal strengths for each of the sectors in an associated set of sectors of the grid, the sector signal strength being a maximum of the beam signal strengths of the beams selected by the sectors in the grid;
The preset conditions include:
a first condition that a first large sector signal strength of the grid exceeds a first threshold; and
and a second condition that a difference between the first large sector signal strength and the second large sector signal strength of the grid exceeds a second threshold.
In some embodiments, constructing constraints among the beam signal strength, the first binary variable and the second binary variable according to preset conditions that the high quality grid needs to meet, includes the following steps S31-S35:
s31, constraining the sector signal intensity of the grid according to the definition of the sector signal intensity through the beam signal intensity and a first binary variable, comprising:
definition of the definitionIs the firstAn auxiliary binary variable, when->For the maximum beam signal strength of the beam signal strengths of the associated jth sector of the ith grid,/th>Otherwise->；
The constraints on sector signal strength are expressed as formulas (1), (2) and (3):
；
wherein,a sector signal strength of a jth sector representing an ith trellis association; m represents a constant not less than the maximum beam signal intensity;
s32, restraining the signal intensity of the first large sector through the signal intensity of the sector, comprising:
Definition of the definitionAs a second auxiliary binary variable, when +.>When the maximum sector signal strength among the sector signal strengths of the ith grid is the +.>Otherwise->；
The constraint of the first large sector signal strength is expressed as equations (4), (5) and (6):
；
wherein,a first large sector signal strength representing an ith grid, M representing a constant not less than a maximum beam signal strength;
s33, restraining the second large sector signal strength through the sector signal strength, including:
definition of the definitionAs a third auxiliary binary variable, when +.>When the maximum sector signal strength among the sector signal strengths of the ith grid is the +.>Otherwise->；
The constraint of the second largest sector signal strength is expressed as formulas (7), (8), (9) and (10):
；
wherein,a second largest sector signal strength representing an ith grid, M representing a constant not less than a maximum beam signal strength;
s34, restraining the first binary variable according to a preset maximum beam selection number of a single sector, wherein the first binary variable is expressed as a formula (11):
；
wherein r represents a preset maximum beam selection number of a single sector;
s35, restraining the relation among the second binary variable, the first large sector signal intensity and the second large sector signal intensity according to the preset condition, wherein the relation is expressed as formulas (12) and (13):
；
Wherein,representing a first threshold,/->Representing a second threshold, M represents a constant that is not less than the maximum beam signal strength.
In some embodiments, forming a penalty term from the constraints, adding the penalty term to the objective function to form a quadratic unconstrained binary optimization model, comprising:
s41, equivalently converting the objective function into；
S42, performing secondary unconstrained constraint and multiplying the constraint by a penalty coefficient to form a penalty term;
s43, adding the penalty term into the objective function to form the quadratic unconstrained binary optimization model, wherein the quadratic unconstrained binary optimization model is expressed as a formula (16):
；
wherein lambda represents the penalty factor, each representing a relaxation variable generated when the corresponding inequality constraint is secondarily unconstrained;
and converting the shaping non-binary variables in the secondary unconstrained binary optimization model into a combination of a plurality of auxiliary binary variables through binary representation.
In some embodiments, the quadratic unconstrained binary optimization model is solved by a quantum computer.
An embodiment of a second aspect of the present invention provides a beam selection apparatus for a MIMO system, including:
the acquisition module is used for acquiring a grid set and a sector set associated with any grid, wherein the sector is provided with a plurality of beams, and acquiring the beam signal intensity of any beam in any grid; defining a first binary variable to indicate whether any beam of any sector is selected, and defining a second binary variable to indicate whether any grid is a high quality grid;
An objective function representation module for representing the total number of high-quality grids according to the second binary variable to determine an objective function to maximize the total number of high-quality grids;
the constraint construction module is used for constructing constraints among the beam signal intensity, the first binary variable and the second binary variable according to preset conditions which are required to be met by the high-quality grid, and when the constraints are met, the beam selected by the sector represented by the first binary variable can enable the second binary variable corresponding to the high-quality grid meeting the preset conditions to represent the high-quality grid;
the punishment generation module is used for forming punishment items according to the constraint, and adding the punishment items into the objective function to form a secondary unconstrained binary optimization model;
and the solving module is used for solving the quadratic unconstrained binary optimization model to determine the value of each first binary variable, and determining the beam selection of each sector according to the value of the first binary variable.
An embodiment of a third aspect of the present invention provides an electronic device, including a memory and a processor, where the memory stores a computer program that, when executed by the processor, implements a beam selection method of a MIMO system as described in any of the embodiments above.
An embodiment of a fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a beam selection method of a MIMO system as described in any of the embodiments above.
The embodiment of the invention has at least the following beneficial effects:
1. and whether the wave beam in the sector is selected to establish a first binary variable is used as a solving target, and whether the grid accords with the preset condition of the decision target or not is used as an intermediate variable, so that the representation of the optimization model is facilitated, and the construction of a secondary unconstrained binary optimization model through the binary variable is facilitated.
2. By constructing the optimization problem of beam selection as a secondary unconstrained binarization model, the solution of the model can be realized through a quantum computer, the problem that the traditional computer optimization algorithm is difficult to solve due to large complexity scale is solved, and the solution efficiency of the large-scale MIMO beam selection problem can be improved, so that the operation cost is reduced, the efficiency is improved, and the effects of saving the cost, reducing the energy consumption, saving the time and the like are achieved.
Drawings
In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the embodiments of the present description, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
Fig. 1 is a schematic flow chart of a beam selection method of a MIMO system according to an embodiment of the first aspect of the present invention;
fig. 2 is an exemplary schematic diagram of an application scenario according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a beam selection device architecture of a MIMO system according to the present invention;
fig. 4 is a schematic diagram of an electronic device architecture according to the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. It should be noted that embodiments and features of embodiments in the present disclosure may be combined, separated, interchanged, and/or rearranged with one another without conflict. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, when the terms "comprises" and/or "comprising," and variations thereof, are used in the present specification, the presence of stated features, integers, steps, operations, elements, components, and/or groups thereof is described, but the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof is not precluded. It is also noted that, as used herein, the terms "substantially," "about," and other similar terms are used as approximation terms and not as degree terms, and as such, are used to explain the inherent deviations of measured, calculated, and/or provided values that would be recognized by one of ordinary skill in the art.
The method and the device are applied to optimization of beam selection of massive MIMO.
An embodiment of a first aspect of the present invention provides a beam selection method for a MIMO system, as shown in fig. 1, including the following steps:
acquiring a grid set and a sector set associated with any grid, wherein the sector is provided with a plurality of beams, and acquiring the beam signal intensity of any beam in any grid; defining a first binary variable to indicate whether any beam of any sector is selected, and defining a second binary variable to indicate whether any grid is a high quality grid;
representing a total number of high quality grids according to the second binary variable to determine an objective function to maximize the total number of high quality grids;
constructing constraints among the beam signal intensity, the first binary variable and the second binary variable according to preset conditions which the high-quality grid needs to meet, and enabling a beam selected by a sector represented by the first binary variable to enable a second binary variable value corresponding to the high-quality grid meeting the preset conditions to represent the high-quality grid when the constraints are met; in other words, the condition of selecting which beams correspond to which grids that meet the high quality grids can be known by constraint inequality.
And forming a punishment term according to the constraint, adding the punishment term into the objective function to form a quadratic unconstrained binary optimization model, wherein the punishment term enables punishment values to be generated when the values of the first binary variable and the second binary variable do not meet the constraint.
Solving the quadratic unconstrained binary optimization model to determine the value of each first binary variable, and determining the beam selection of each sector according to the value of the first binary variable.
It should be understood that, as shown in fig. 2, the acquisition of the grid is to divide the investigation target area, such as a city area, into several grids, each grid being, for example, a physical area of 5m×5m, each grid being covered by several sectors, wherein a sector is a geographical area covered by a single antenna under one base station. The optimization problem is for example how to choose several beams for each sector when knowing the respective beam signal strength of each beam in any grid of each sector, so that the number of grids meeting the constraint is the largest, in other words the decision space of the MIMO beam selection problem is to select a subset of beams for each sector with the aim of maximizing a given performance index. In particular, the objective function is to maximize the number of high quality grids that meet specified constraints. The selected beam combination must therefore be able to provide sufficient signal coverage to meet the constraints, covering as much of the high quality grid as possible. For example, in one embodiment, there are 148 beams in each sector, and the decision problem is how to choose 8 beams for each sector so that the number of grids meeting the constraint is the largest.
In some embodiments, acquiring a set of grids and a set of sectors associated with a single grid, wherein a sector has a number of beams, acquiring beam signal strengths of any beam in any grid, comprises:
the number of grids in the acquisition grid set is m, the total number of sectors is v, the number of beams of the sectors is n, and the set of sectors associated with the ith grid is expressed asDefinitions->Representing the beam signal strength of the kth beam of the jth sector associated with the ith grid;
the first binary variable is expressed asWhen the kth beam of the jth sector is selected,/when the kth beam is selected>Otherwise->；
The second binary variable is expressed asWhen the ith sector meets said preset condition +.>Otherwise。
It should be understood that in some embodiments, for convenience of presentation, all sectors are taken as associated sectors of a grid, and if some associated sectors do not actually cover some grids, then a signal strength of zero may be indicated.
In some embodiments, the objective function is expressed as:
wherein->Representing a second binary variable, i.e. maximizing the number of high quality grids.
In some embodiments, defining the signal strength of a grid includes a set of sector signal strengths for each of the sectors in an associated set of sectors of the grid, the sector signal strength being a maximum of the beam signal strengths of the beams selected by the sectors in the grid;
The preset conditions include:
a first condition that a first large sector signal strength of the grid exceeds a first threshold; and
and a second condition that a difference between the first large sector signal strength and the second large sector signal strength of the grid exceeds a second threshold.
To build a mathematical model, the logic of the problem is first summarized as follows:
1. a subset of beams is selected for each sector.
2. A grid is covered by a number of sectors and the signal strength under a sector in the same grid is the maximum value of the signal in the grid for the beam selected for that sector.
3. The signal strength set in a grid is composed of sector signal strengths covering the grid, and whether the grid meets the constraint condition is determined according to the sector signal strength set.
The combined optimization is a mathematical optimization technology, and consists of discrete decision variables, objective functions and constraint conditions, and aims to solve the optimal values of the objective functions and the corresponding optimal solutions. If the objective function is quadratic and without constraints and the decision variables can only take 0 or 1, then this combined optimization is called a quadratic unconstrained binary optimization model (QUBO, quadratic Unconstrained Binary Optimization). The QUBO model form can be solved by a quantum annealing machine or a special quantum computer such as coherent i Xin Ji. The problem is solved in an acceleration way by using the quantum computer, so that the calculation efficiency is improved, and the problem that the complex scheduling problem is difficult to calculate or the calculation consumes long time in the prior art is solved.
In some embodiments, constructing constraints among the beam signal strength, the first binary variable and the second binary variable according to preset conditions that the high quality grid needs to meet, includes the following steps S31-S35:
s31, constraining the sector signal intensity of the grid according to the definition of the sector signal intensity through the beam signal intensity and a first binary variable, comprising:
definition of the definitionFor the first auxiliary binary variable, when +.>For the maximum beam signal strength of the beam signal strengths of the associated jth sector of the ith grid,/th>Otherwise->；
The constraints on sector signal strength are expressed as formulas (1), (2) and (3):
；
wherein,a sector signal strength of a jth sector representing an ith trellis association; m represents a constant not less than the maximum beam signal strength.
Specifically, wherein inequality (1) constrainsNot less than the signal intensity of any selected wave beam in the ith grid and the jth sector; inequality (2) constrains +.>Not more than->WhereinIf it is 1>Not more than->Otherwise constraint (2) means +.>Not more than->Since M is->Maximum value of (2), thus->Constraint (2) must hold if 0. Constraint (3) constraint- >The sum is 1, i.e. for a fixed i, j, there is only one +.>Taking 1, the rest->All take values of 0. Combining constraint (2) and constraint (3), there must be a certain unique k such that +.>Not more than->. Comprehensive constraint (1),>only the value of the beam is the maximum value of the signal intensity corresponding to the selected beam.
After constraining the sector signal strength to the maximum signal strength of the grid for only the selected beam of the sector, constraining the first large sector signal strength and the second large sector signal strength.
S32, restraining the signal intensity of the first large sector through the signal intensity of the sector, comprising:
definition of the definitionAs a second auxiliary binary variable, when +.>When the maximum sector signal strength among the sector signal strengths of the ith grid is the +.>Otherwise->；
The constraint of the first large sector signal strength is expressed as equations (4), (5) and (6):
；
wherein,the first large sector signal strength of the ith grid is represented, and M represents a constant that is not less than the maximum beam signal strength.
According to constraints (4), (5), (6), for a fixed i, the latter oneTaking 1, the constraints (4) and (5) are combined, a unique k must exist, so that +. >Only the maximum value of the signal intensity of the sector associated with the grid can be taken.
S33, restraining the second large sector signal strength through the sector signal strength, including:
definition of the definitionAs a third auxiliary binary variable, when +.>When the maximum sector signal strength among the sector signal strengths of the ith grid is the +.>Otherwise->；
The constraint of the second largest sector signal strength is expressed as formulas (7), (8), (9) and (10):
；
wherein,the second largest sector signal strength of the ith grid is represented, and M represents a constant not less than the maximum beam signal strength.
In particular, in constraint (7)Not less than->When->When in use, then->Not less than->Since M takes on the value +.>The maximum value of (2) is known to restrict (7)>The time constant is established; when->When in use, then->Not less than->. Due to->The sum is 1, i.e. there is and only one +.>Takes a value of 1, so that under the constraints (7) and (8), the +_is>Not less than->The second largest value of (b), i.e., the second largest signal strength. Constraint (9)/(x)>Not more than->When->In the time-course of which the first and second contact surfaces,then->Not more than->Otherwise->Not more than->. From the value of M it is known when +.>When the constraint (9) is constant. Constraint (10)>The sum is 2, i.e. there are only 2 +. >The value is 1, and the constraint (9) is combined to know +.>Not more than two->I.e. not exceeding some two signal strengths in the grid. In combination with constraints (7) and (8) are known, < +.>Is present and unique and can only take on the value of the second largest signal strength in the ith grid.
S34, restraining the first binary variable according to a preset maximum beam selection number of a single sector, wherein the first binary variable is expressed as a formula (11):
；
where r represents a preset maximum beam selection number for a single sector.
S35, restraining the relation among the second binary variable, the first large sector signal intensity and the second large sector signal intensity according to the preset condition, wherein the relation is expressed as formulas (12) and (13):
；
wherein,representing a first threshold,/->Representing a second threshold, M represents a constant that is not less than the maximum beam signal strength.
Specifically, in constraint (13), whenWhen the constraint left is greater than zero, the right must therefore also be greater than zero, thus +.>Must be zero. Similarly, in constraint (14), when maximum signal strength +.>And second highest signal intensity->The difference of (2) is less than +.>When (I)>Must be zero. Thus, under the constraints of constraints (13) and (14), the ith grid is a high quality grid if and only if both constraints are met.
In some embodiments, forming a penalty term from the constraints, adding the penalty term to the objective function to form a quadratic unconstrained binary optimization model, comprising:
s41, equivalently converting the objective function into；
S42, performing secondary unconstrained constraint and multiplying the constraint by a penalty coefficient to form a penalty term;
in an embodiment of the present invention, the secondary unconstrained constraint includes a secondary unconstrained constraint on equality, such as constraints (3), (6), (8) and (10), and a secondary unconstrained constraint on inequality, such as constraints (1), (2), (4), (5), (7), (9), (12) and (13).
The quadratic unconstrained constraint on the equation involves rewriting the constraint equation to the form f (x) =0, and then squaring the left side of the equation and multiplying by the penalty coefficient to form the penalty term.
The second unconstrained for the inequality constraint includes converting the introduction of a relaxation variable in the inequality constraint into an equality constraint and then into a penalty term according to the second unconstrained method of equality constraint.
S43, adding the penalty term into the objective function to form the quadratic unconstrained binary optimization model, wherein the quadratic unconstrained binary optimization model is expressed as a formula (16):
；
Wherein lambda represents the penalty factor,
each representing a relaxation variable generated when the corresponding inequality constraint is secondarily unconstrained;
and converting the shaping non-binary variables in the secondary unconstrained binary optimization model into a combination of a plurality of auxiliary binary variables through binary representation. In which non-binary variables such as the above-described relaxation variables, ith, in embodiments of the invention are shapedSector signal strength for jth sector of a trellis associationFirst large sector signal strength +.>Second largest sector Signal Strength +.>The maximum beam selection number r is preset for the middle non-binary variable and a single sector. For example, will->、/>And->The following transformations were carried out: />
。
In some embodiments, the quadratic unconstrained binary optimization model is solved by a quantum computer.
It should be appreciated that, because the constraint representation of the problem is complex, the method includes excessive binary variables when the method is converted into a quadratic unconstrained binary optimization model, and quantum bits for quantum computation are huge in number of steps and quantum computing resources are consumed greatly.
Preferably, in some preferred embodiments, there is also provided a bidirectional optimization method for beam selection of a MIMO system, including:
Acquiring a grid set and a sector set associated with any grid, wherein the sector is provided with a plurality of beams, and acquiring the beam signal intensity of any beam in any grid;
the preset conditions comprise a first condition and a second condition, the first condition is selected as a forward constraint condition, and the second condition is selected as a screening condition;
constructing a first binary variable to indicate whether any beam of any sector is selected, and constructing a second binary variable to indicate whether any grid is a high quality grid;
representing a total number of high quality grids according to the second binary variable to determine an objective function to maximize the total number of high quality grids;
constructing a constraint between a first binary variable and a second binary variable according to the forward constraint condition, and when the constraint is satisfied, if the second binary variable value corresponding to the grid represents a high-quality grid, then the grid at least satisfies the forward constraint condition;
forming a penalty term according to the constraint, and adding the penalty term into the objective function to form a secondary unconstrained binary optimization model;
solving the secondary unconstrained binary optimization model to obtain a solution set, wherein the solution set comprises a plurality of solutions which are value combinations of first binary variables;
And performing verification screening based on the beam signal intensity and the solutions according to the screening conditions, taking a plurality of solutions with the largest number of high-quality grids as screening results, and determining the beam selection of each sector according to the screening results.
Constructing a constraint between a first binary variable and a second binary variable according to the forward constraint condition, wherein the constraint comprises the following steps:
constructing a third binary variable according to the forward constraint condition, wherein the third binary variable is expressed asBeam signal intensity of kth beam of jth sector associated with ith grid +.>Greater than threshold->When (I)>Otherwise->；
Constraining the relationship of the first, second and third binary variables according to the forward constraint, expressed as equation (19):
（19）；
constraining the first binary variable according to a preset maximum beam selection number of a single sector, expressed as formula (11):
；
where r represents a preset maximum beam selection number for a single sector.
In the above preferred embodiment, forming a penalty term according to the constraint, adding the penalty term to the objective function to form a quadratic unconstrained binary optimization model includes:
Equivalent conversion of the objective function into；
Performing secondary unconstrained constraint and multiplying the constraint by a penalty coefficient to form a penalty term;
adding the penalty term to the objective function to form the quadratic unconstrained binary optimization model, expressed as equation (20):
；
wherein lambda represents the penalty factor,each representing a relaxation variable generated when the corresponding inequality constraint is secondarily unconstrained;
and converting the shaping non-binary variables in the secondary unconstrained binary optimization model into a combination of a plurality of auxiliary binary variables through binary representation.
In the above preferred embodiment, performing verification screening based on the beam signal strength and the solutions according to the screening conditions, taking a plurality of solutions with the highest number of high-quality grids as screening results, including:
s81, calculating the signal intensity of a first large sector and the signal intensity of a second large sector of each grid according to the solution and the beam signal intensity;
s82, verifying whether each grid meets the screening conditions according to the screening conditions based on the first large sector signal strength and the second large sector signal strength so as to determine the number of grids meeting the screening conditions;
looping through steps S81-S82 until at least a portion of the solution set is traversed;
And selecting a plurality of solutions with the front grid number meeting the screening condition as the screening result.
According to the preferred embodiment, when the MIMO selection problem is converted into the secondary unconstrained binary optimization model, constraint conditions considered in the problem are divided, namely, only partial constraint conditions are considered when the secondary unconstrained binary optimization model is formed, so that the number of binary variables of the secondary unconstrained binary optimization model is greatly reduced, the solving complexity is reduced, and the remaining constraint conditions are considered by screening a solving result, so that the selection optimization problem is solved.
Preferably, the QUBO model of embodiments of the present invention is suitable for use with specialized quantum computers such as quantum annealers and coherent ifers Xin Ji. Taking CIM based on degenerate optical parametric oscillator as an example, the physical machine based on the embodiment of the invention is a mixed quantum computing system consisting of an optical part and an electrical part. The electrical part comprises an FPGA, digital-to-analog/analog-to-digital conversion. The optical portion includes a laser, an amplifier, a periodically poled lithium niobate crystal, a fiber loop, and the like.
CIM operates in a manner different from conventional computers that rely on semiconductor integrated circuits. Instead, it employs optical fibers As a basic unit of computation, is called a qubit. Initial research in the academy focused on the idea of injecting a synchronizing laser i Xin Ji. Since the number of coupled lasers grows with the square of the qubit, based on a Degenerate Optical Parametric Oscillator (DOPO), the academy has proposed an improvement, using nonlinear optical crystals, two DOPO-based methods, namely optical delay line CIM and measurement feedback CIM, were developed. However, in the first approach, the overhead and the requirement for precise control are not tolerable. The scheme is based on measurement feedback CIM. Based on the QUBO form, it can be converted into an ising model and corresponding biggest cut problem. The objective function may be expressed as maximization. Defining two rotating unitary matricesAnd->. After repeating the operation p times, the following new state can be obtained:
. Then the approximate solution is obtained as follows:
1. constructing a QAOA quantum circuit, wherein the circuit comprises trainable parameters;
2. parameters in the line are initialized. Initializing a quantum state in a quantum computer;
3. operating the quantum circuit to obtain a quantum state;
4. calculating an objective function value and an expected value of Hamiltonian quantity by utilizing a quantum state in a classical computer;
5. Repeating the steps 2-4 for a plurality of times, namely measuring the same group of parameters gamma and beta for a plurality of times, so as to obtain the quantum state distribution;
6. grid searching is used to optimize parameters in the line. The results of steps 2-5 were repeated for a new set of parameters gamma, beta. After obtaining the quantum state distribution, selecting the target value with the largest value;
7. and (4) calculating an approximate solution of the target problem according to the result of the step 4.
The ultra-large-bit quantum computer can be introduced into the MIMO beam selection optimization problem, so that the actual problem is better solved, and the calculation characteristics under the background of a large example are researched.
An embodiment of the second aspect of the present invention provides a beam selection apparatus of a MIMO system, as shown in fig. 3, including:
the acquisition module is used for acquiring a grid set and a sector set associated with any grid, wherein the sector is provided with a plurality of beams, and acquiring the beam signal intensity of any beam in any grid; defining a first binary variable to indicate whether any beam of any sector is selected, and defining a second binary variable to indicate whether any grid is a high quality grid;
an objective function representation module for representing the total number of high-quality grids according to the second binary variable to determine an objective function to maximize the total number of high-quality grids;
The constraint construction module is used for constructing constraints among the beam signal intensity, the first binary variable and the second binary variable according to preset conditions which are required to be met by the high-quality grid, and when the constraints are met, the beam selected by the sector represented by the first binary variable can enable the second binary variable corresponding to the high-quality grid meeting the preset conditions to represent the high-quality grid;
the punishment generation module is used for forming punishment items according to the constraint, and adding the punishment items into the objective function to form a secondary unconstrained binary optimization model;
and the solving module is used for solving the quadratic unconstrained binary optimization model to determine the value of each first binary variable, and determining the beam selection of each sector according to the value of the first binary variable.
An embodiment of a third aspect of the present invention provides an electronic device, as shown in fig. 4, including a memory and a processor, where the memory stores a computer program, where the computer program, when executed by the processor, implements a beam selection method of a MIMO system as in any of the embodiments above.
An embodiment of a fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a beam selection method of a MIMO system as described in any of the embodiments above.
Computer-readable storage media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, read only compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by the computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transshipment) such as modulated data signals and carrier waves.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of function in order to clearly illustrate the interchangeability of hardware and software. 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.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A method for beam selection in a MIMO system, comprising the steps of:
acquiring a grid set and a sector set associated with any grid, wherein the sector is provided with a plurality of beams, and acquiring the beam signal intensity of any beam in any grid; defining a first binary variable to indicate whether any beam of any sector is selected, and defining a second binary variable to indicate whether any grid is a high quality grid;
Representing a total number of high quality grids according to the second binary variable to determine an objective function to maximize the total number of high quality grids;
constructing constraints among the beam signal intensity, the first binary variable and the second binary variable according to preset conditions which the high-quality grid needs to meet, and enabling a beam selected by a sector represented by the first binary variable to enable a second binary variable value corresponding to the high-quality grid meeting the preset conditions to represent the high-quality grid when the constraints are met;
forming a penalty term according to the constraint, and adding the penalty term into the objective function to form a secondary unconstrained binary optimization model;
solving the quadratic unconstrained binary optimization model to determine the value of each first binary variable, and determining the beam selection of each sector according to the value of the first binary variable.
2. The beam selection method of a MIMO system according to claim 1, wherein: acquiring a grid set and a sector set associated with a single grid, wherein the sector has a plurality of beams, acquiring the beam signal intensity of any beam in any grid, and the method comprises the following steps:
the number of grids in the acquisition grid set is m, the total number of sectors is v, the number of beams of the sectors is n, and the set of sectors associated with the ith grid is expressed as Definitions->Representing the beam signal strength of the kth beam of the jth sector associated with the ith grid;
the first binary variable is expressed asWhen the kth beam of the jth sector is selected,/when the kth beam is selected>Otherwise；
The second binary variable is expressed asWhen the ith sector meets said preset condition +.>Otherwise。
3. The beam selection method of a MIMO system according to claim 2, wherein: the objective function is expressed as:
wherein->Representing a second binary variable.
4. A beam selection method for a MIMO system according to claim 3 wherein: defining a grid of signal strengths including a set of sector signal strengths for each of the sectors in an associated set of sectors of the grid, the sector signal strengths being the maximum of the beam signal strengths of the beams selected by the sectors in the grid;
the preset conditions include:
a first condition that a first large sector signal strength of the grid exceeds a first threshold; and
and a second condition that a difference between the first large sector signal strength and the second large sector signal strength of the grid exceeds a second threshold.
5. The beam selection method of a MIMO system according to claim 4, wherein: constructing constraints among the beam signal intensity, the first binary variable and the second binary variable according to preset conditions to be met by the high-quality grid, wherein the constraints comprise the following steps S31-S35:
S31, constraining the sector signal intensity of the grid according to the definition of the sector signal intensity through the beam signal intensity and a first binary variable, comprising:
definition of the definitionFor the first auxiliary binary variable, when +.>Each beam of the associated jth sector of the ith gridMaximum beam signal intensity of the signal intensities, < +.>Otherwise->；
The constraints on sector signal strength are expressed as formulas (1), (2) and (3):
；
wherein,a sector signal strength of a jth sector representing an ith trellis association; m represents a constant not less than the maximum beam signal intensity;
s32, restraining the signal intensity of the first large sector through the signal intensity of the sector, comprising:
definition of the definitionAs a second auxiliary binary variable, when +.>When the maximum sector signal strength among the sector signal strengths of the ith grid is the +.>Otherwise->；
The constraint of the first large sector signal strength is expressed as equations (4), (5) and (6):
；
wherein,a first large sector signal strength representing an ith grid, M representing a constant not less than a maximum beam signal strength;
s33, restraining the second large sector signal strength through the sector signal strength, including:
Definition of the definitionAs a third auxiliary binary variable, when +.>When the maximum sector signal strength among the sector signal strengths of the ith grid is the +.>Otherwise->；
The constraint of the second largest sector signal strength is expressed as formulas (7), (8), (9) and (10):
；
wherein,a second largest sector signal strength representing an ith grid, M representing a constant not less than a maximum beam signal strength;
s34, restraining the first binary variable according to a preset maximum beam selection number of a single sector, wherein the first binary variable is expressed as a formula (11):
；
wherein r represents a preset maximum beam selection number of a single sector;
s35, restraining the relation among the second binary variable, the first large sector signal intensity and the second large sector signal intensity according to the preset condition, wherein the relation is expressed as formulas (12) and (13):
；
wherein,representing a first threshold,/->Representing a second threshold, M represents a constant that is not less than the maximum beam signal strength.
6. The beam selection method of the MIMO system according to claim 5, wherein:
forming a penalty term according to the constraint, adding the penalty term to the objective function to form a quadratic unconstrained binary optimization model, comprising:
S41, equivalently converting the objective function into；
S42, performing secondary unconstrained constraint and multiplying the constraint by a penalty coefficient to form a penalty term;
s43, adding the penalty term into the objective function to form the quadratic unconstrained binary optimization model, wherein the quadratic unconstrained binary optimization model is expressed as a formula (16):
；
wherein lambda represents the penalty factor, each representing a relaxation variable generated when the corresponding inequality constraint is secondarily unconstrained;
and converting the shaping non-binary variables in the secondary unconstrained binary optimization model into a combination of a plurality of auxiliary binary variables through binary representation.
7. The beam selection method of a MIMO system according to claim 1, wherein: and solving the secondary unconstrained binary optimization model through a quantum computer.
8. A beam selection apparatus for a MIMO system, comprising:
the acquisition module is used for acquiring a grid set and a sector set associated with any grid, wherein the sector is provided with a plurality of beams, and acquiring the beam signal intensity of any beam in any grid; defining a first binary variable to indicate whether any beam of any sector is selected, and defining a second binary variable to indicate whether any grid is a high quality grid;
An objective function representation module for representing the total number of high-quality grids according to the second binary variable to determine an objective function to maximize the total number of high-quality grids;
the constraint construction module is used for constructing constraints among the beam signal intensity, the first binary variable and the second binary variable according to preset conditions which are required to be met by the high-quality grid, and when the constraints are met, the beam selected by the sector represented by the first binary variable can enable the second binary variable corresponding to the high-quality grid meeting the preset conditions to represent the high-quality grid;
the punishment generation module is used for forming punishment items according to the constraint, and adding the punishment items into the objective function to form a secondary unconstrained binary optimization model;
and the solving module is used for solving the quadratic unconstrained binary optimization model to determine the value of each first binary variable, and determining the beam selection of each sector according to the value of the first binary variable.
9. An electronic device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, implements the beam selection method of the MIMO system of any of claims 1-7.
10. A computer readable storage medium, having stored thereon a computer program which, when executed by a processor, implements a beam selection method of a MIMO system according to any of claims 1-7.
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| EP4184804A1 (en) * | 2021-11-17 | 2023-05-24 | Nokia Solutions and Networks Oy | Algorithm for mitigation of impact of uplink/downlink beam mis-match |
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| EP4184804A1 (en) * | 2021-11-17 | 2023-05-24 | Nokia Solutions and Networks Oy | Algorithm for mitigation of impact of uplink/downlink beam mis-match |
| CN116187594A (en) * | 2023-04-27 | 2023-05-30 | 北京玻色量子科技有限公司 | Multi-target multi-task path scheduling cost optimization method, device, medium and equipment |
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