CN114520980A - Three-dimensional RIS (remote RIS) assisted coal mine sensing integrated network optimization method and device - Google Patents
Three-dimensional RIS (remote RIS) assisted coal mine sensing integrated network optimization method and device Download PDFInfo
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Abstract
The invention provides a three-dimensional RIS (remote integrated services) assisted coal mine general-sensing integrated network optimization method and device, and relates to the field of communication technology and coal mine safety detection. The method comprises the following steps: the method comprises the steps of establishing a coal mine communication network model with a three-dimensional intelligent surface, wherein the model comprises the three-dimensional intelligent surface, users needing communication, a sensing area, a small portable general-purpose integrated base station and a ground data center. The invention comprises two optimization algorithms: firstly, optimizing a perception communication time distribution strategy in a coal mine communication network model; and secondly, optimizing a reflection coefficient matrix of the three-dimensional intelligent surface. On the premise of ensuring the basic communication requirement of the coal mine, the coal mine safety information is sensed and detected more effectively; on the premise of achieving real-time monitoring of corresponding safety level, more information is sensed as far as possible; optimizing the RIS phase shift matrix improves channel conditions such that communication efficiency is maximized. When dangerous accidents such as collapse accidents happen to coal mines, the RIS can acquire accident information according to the change of the perception information so as to reduce casualty probability.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a coal mine general-purpose sensing integrated network optimization method and device assisted by a three-dimensional RIS.
Background
Under high data rate requirements, people have to raise the communication frequency continuously, so that the communication frequency and the radar frequency are crossed, and the frequency spectrum is crowded. On one hand, the prior art develops an efficient interference management technology so that two separately deployed systems can run smoothly without mutual interference; on the other hand, these two functions are physically integrated in one system, but they are superimposed or separated in the time, frequency or spatial domain using two sets of dedicated hardware components or two different waveforms. The two methods only integrate the communication function and the sensing function loosely, do not share hardware and spectrum resources, and can only obtain limited benefits. In view of this, joint sensing and communication techniques are more firmly integrated by sharing most of the hardware components, and furthermore, both functions use the same waveforms, aiming to jointly optimize communication and sensing performance. In this joint system, flexible trade-off between communication function and sensing function is achieved, and has been strongly researched and interested, and the results have been reported under various names, such as radar communication (RadCom), joint radar (and) communication (JRC), joint communication (JRC joint radar communication system and joint communication radar system JCR), joint communication and radar/radio sensing (JCAS), Dual Function Radar Communication (DFRC), and recently integrated sensing and communication (ISAC). The first three are generally referred to as the general joint system and may be used interchangeably. Sometimes JRC and JCR are used to distinguish between radar-centric and communication-centric designs. The term JCAS was introduced to emphasize the development of radar to a more general radio sensing application of communication center complex systems. These sensing applications go beyond traditional radar functions of location, tracking and target recognition, such as human behavior recognition and atmospheric monitoring using radio signals.
The channel is always regarded as a probabilistic process in the past for the problem of improving communication efficiency, and the prior art mainly includes: MIMO technology, millimeter wave communication, etc. These techniques are only enhanced at the transmitting end and the receiving end of the communication, and limit the development of the communication technology.
Most work on communication-aware integration has focused on the problem of time resource allocation. High spectral efficiency and communication rate are achieved by optimizing communication time and perceptual time allocation strategies. Since the communication and perception integrated system needs to periodically sense the safety information of the target area, the perception information freshness becomes an important performance index of the homonymy integrated system. Furthermore, most of the previous work on three-dimensional smart surfaces (RIS) has focused on two aspects. One is channel estimation and network deployment of RIS; the second is how to jointly optimize the RIS phase shift matrix and the transmit power so that the system is most energy efficient.
In the prior art, at least two of the devices are designed by the simulation of a combined system of free space, and the coal mine scene is not deeply researched. The channel condition can be changed by intelligently regulating the RIS phase shift matrix, so that the signal-to-noise ratio requirement can be met by smaller transmitting power. Therefore, in a coal mine scene, after a model is established, how to optimize the RIS phase shift matrix to enable the channel gain passing through the RIS to be maximum and how to determine the perception times of each region to enable the perception information time delay to be minimum under different safety levels are main problems.
Disclosure of Invention
The invention provides a three-dimensional RIS (RIS) assisted coal mine sensing integrated network optimization method and device, aiming at the problems that in the prior art, how to optimize an RIS phase shift matrix enables the channel gain passing through the RIS to be maximum, and how to determine the sensing times of each region enables the sensing information time delay to be minimum under different safety levels.
In order to solve the technical problems, the invention provides the following technical scheme:
on the one hand, the three-dimensional RIS assisted coal mine common sense integrated network optimization method is provided, and comprises the following steps:
s1: establishing a coal mine communication network model with a three-dimensional intelligent surface, wherein the model comprises a three-dimensional intelligent envelope and a sensing area;
s2: optimizing a perception region in a coal mine communication network model;
s3: and optimizing a reflection coefficient matrix of a three-dimensional intelligent surface in the coal mine communication network model.
Optionally, the coal mine communication network model in step S1 further includes:
the system comprises a data center, a small base station with sensing and communication functions; the coal mine communication network model presets at least two perception areas.
Optionally, in step S2, optimizing the sensing region in the coal mine communication network model includes:
s21: sequentially perceiving at least two preset perceiving areas according to preset perceiving times through a base station to obtain perceiving data;
s22: the auxiliary sensing data is sent to a data center through the three-dimensional intelligent surface for algorithm analysis;
s23: determining the optimal sensing times, and updating the preset sensing times through the optimal sensing times;
s24: obtaining a control signal through the updated sensing times;
s25: and sending a control signal to the base station through the data center, and sequentially perceiving the at least two perception areas by the base station according to the updated perception times to optimize the perception areas.
Optionally, obtaining perception data comprises:
perceptual data are obtained according to equation (1):
wherein K is the number of actual sensing areas, PkFor the probability that the base station perceives successfully in one area, the probability that the base station perceives successfully once in one area is as follows:
therefore, the probability of perceiving bk successes is Pk＝1-(1-qk)bkThen, the objective function may be specifically expressed as:
since the objective function cannot determine the unevenness, the objective function is functionally approximated, and the accuracy of the approximation function is verified by the fmincon function.
Optionally, in step S3, optimizing the reflection coefficient moment of the three-dimensional intelligent surface in the coal mine communication network model includes:
s31: setting a path loss value of a direct channel from a base station to a data center and a path loss setting value through an RIS channel through code simulation;
s32: constructing a target function under the condition of the set path loss value;
s33: and (4) optimizing and solving the objective function by adopting a continuous convex approximation method to complete the optimization of the reflection coefficient matrix of the three-dimensional intelligent surface in the coal mine communication network model.
Optionally, in step S32, under the condition of the set path loss value, constructing an objective function, including:
constructing an objective function as shown in the following formula (2):
y＝|gH+hHΘG|2 (2)
let hHΘG＝θHU; wherein,
when the meta objective function becomes the following formula (3):
y＝|gH+θHU|2 (3)
let v equal θ*Then the objective function becomes the following formula (4):
y＝|gH+UHv|2 (4)
optionally, in step S33, performing optimization solution on the objective function by using a continuous convex approximation method to complete optimization of the reflection coefficient matrix of the three-dimensional intelligent surface in the coal mine communication network model, including:
constructing a convex approximation function as the following formula (5):
2R((g+UHv(n-1))UHv)-|g+UHv(n-1)|2 (5)
if the function is optimizedSmall, then (g + U)Hv(n-1))UHv) must be real, at the optimum point (g + U)Hv(n-1))UHAnd the phase of the sum v must be the opposite number, then (g + U) is calculated at the current iteration point under the model constraintHv(n-1))UHThe next iteration point can be found out by analogy, the optimal solution of the convex problem is found out, and the optimization of the reflection coefficient matrix of the three-dimensional intelligent surface in the coal mine communication network model is completed.
In one aspect, the invention provides a three-dimensional RIS assisted coal mine sensory integration network optimization device, which comprises:
the model building module is used for building a coal mine communication network model with a three-dimensional intelligent surface;
the sensing region optimization module is used for optimizing a sensing region in the coal mine communication network model;
and the three-dimensional intelligent surface optimization module is used for optimizing a reflection coefficient matrix of the three-dimensional intelligent surface in the coal mine communication network model.
Optionally, the model building module is further configured to enable the coal mine communication network model to include a three-dimensional intelligent surface, a data center, and a small base station with sensing and communication functions; at least two sensing areas are preset in the coal mine communication network model.
Optionally, the sensing region optimizing module includes:
the perception data initial submodule is used for the base station to sequentially perceive at least two areas according to preset perception times to obtain perception data;
the perception data forwarding submodule is used for sending perception data to a data center through the assistance of the three-dimensional intelligent surface;
the algorithm analysis submodule is used for determining proper sensing times after algorithm analysis of the data center; obtaining a control signal through the updated sensing times;
and the perception optimization submodule is used for sending a control signal to the base station by the data center, and the base station sequentially perceives the at least two perception areas according to the updated perception times.
The technical scheme of the embodiment of the invention at least has the following beneficial effects:
in the scheme, 1, the coal mine safety information is sensed and communicated more effectively by the invention
The small-sized base station senses the number of coal gangues through radio and transmits the sensing information to the data center DC through the RIS. The invention can sense more safety information as far as possible under the condition of meeting the requirement of safety level; optimizing the RIS phase shift matrix improves channel conditions so as to maximize communication efficiency and effectively reduce information delay.
2. The invention can realize the real-time early warning of the coal mine
When a dangerous accident such as a collapse event occurs in a coal mine, the RIS can acquire accident information according to the change of the perception information: including the location of the accident and the accident risk level. Therefore, coal mine risks can be further discovered, and casualty probability is further reduced.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a three-dimensional RIS-assisted coal mine sensing integrated network optimization method provided by an embodiment of the invention;
FIG. 2 is a flow chart of a method for optimizing a coal mine sensor integrated network assisted by a three-dimensional RIS according to an embodiment of the present invention;
FIG. 3 is a scatter plot of relative positions of model components provided by an embodiment of the present invention;
FIG. 4 is a phase shift matrix optimization iteration curve provided by an embodiment of the present invention;
FIG. 5 is a model diagram of a RIS-assisted synaesthesia integration system provided by an embodiment of the present invention;
fig. 6 is a block diagram of a three-dimensional RIS-assisted coal mine sensing integrated network optimization device according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, an embodiment of the present invention provides a three-dimensional RIS-assisted coal mine sensory integration network optimization method, including:
s101: establishing a coal mine communication network model with a three-dimensional intelligent surface, wherein the model comprises a three-dimensional intelligent envelope and a sensing area;
s102: optimizing a perception region in a coal mine communication network model;
s103: and optimizing a reflection coefficient matrix of a three-dimensional intelligent surface in the coal mine communication network model.
Optionally, the coal mine communication network model in step S1 further includes:
the system comprises a data center, a small base station with sensing and communication functions; the coal mine communication network model presets at least two perception areas.
Optionally, in step S12, optimizing the sensing region in the coal mine communication network model includes:
s121: sequentially perceiving at least two preset perceiving areas according to preset perceiving times through a base station to obtain perceiving data;
s122: the auxiliary sensing data is sent to a data center through the three-dimensional intelligent surface for algorithm analysis;
s123: determining the optimal sensing times, and updating the preset sensing times through the optimal sensing times;
s124: obtaining a control signal through the updated sensing times;
s125: and sending a control signal to the base station through the data center, and sequentially perceiving the at least two perception areas by the base station according to the updated perception times to optimize the perception areas.
Optionally, obtaining perception data comprises:
perceptual data are obtained according to equation (1):
wherein K is the number of the actual sensing areas, PkFor the probability that the base station perceives successfully in one area, the probability that the base station perceives successfully once in one area is as follows:
therefore, the probability of perceiving bk successes is Pk＝1-(1-qk)bkThen, the objective function may be specifically expressed as:
since the objective function cannot determine the unevenness, the objective function is functionally approximated, and the accuracy of the approximation function is verified by the fmincon function.
Optionally, in step S13, optimizing the reflection coefficient moment of the three-dimensional intelligent surface in the coal mine communication network model includes:
s131: setting a path loss value of a direct channel from a base station to a data center and a path loss setting value through an RIS channel through code simulation;
s132: constructing a target function under the condition of the set path loss value;
s133: and (4) optimizing and solving the objective function by adopting a continuous convex approximation method to complete the optimization of the reflection coefficient matrix of the three-dimensional intelligent surface in the coal mine communication network model.
Optionally, in step S132, under the condition of the set path loss value, constructing an objective function, including:
constructing an objective function as shown in the following formula (2):
y＝|gH+hHΘG|2 (2)
let hHΘG＝θHU; wherein all characters are interpreted
When the meta objective function becomes the following formula (3):
y＝|gH+θHU|2 (3)
let v equal θ*Then the objective function becomes the following formula (4):
y＝|gH+UHv|2 (4)
optionally, in step S133, performing optimization solution on the objective function by using a successive convex approximation method to complete optimization of the reflection coefficient matrix of the three-dimensional intelligent surface in the coal mine communication network model, including:
constructing a convex approximation function as the following formula (5):
2R((g+UHv(n-1))UHv)-|g+UHv(n-1)|2 (5)
if the function is minimized, (g + U)Hv(n-1))UHv) must be real, at the optimum point (g + U)Hv(n-1))UHAnd the phase of the sum v must be the opposite number, then (g + U) is calculated at the current iteration point under the model constraintHv(n-1))UHThe next iteration point can be found out by analogy, the optimal solution of the convex problem is found out, and the optimization of the reflection coefficient matrix of the three-dimensional intelligent surface in the coal mine communication network model is completed.
As shown in fig. 2, an embodiment of the present invention provides a three-dimensional RIS-assisted coal mine sensory integration network optimization method, which specifically includes the steps of:
s201: and establishing a coal mine communication network model with a three-dimensional intelligent surface, wherein the model comprises a three-dimensional intelligent envelope and a sensing area.
The coal mine communication network model comprises a three-dimensional intelligent envelope and a sensing area, and also comprises a data center and a small base station with sensing and communication functions; the coal mine communication network model presets at least two perception areas.
In a feasible implementation mode, the invention establishes a simulation model of an assisted communication perception integrated network (RIS) in a coal mine scene, and seeks an optimal RIS phase shift matrix and an optimal perception time distribution strategy by an optimization method.
S202: sequentially perceiving at least two preset perceiving areas according to preset perceiving times through a base station to obtain perceiving data;
in one possible embodiment, obtaining the perception data includes:
perceptual data are obtained according to equation (1):
wherein K is the number of the actual sensing areas, PkFor the probability of success of the base station perception for a region, Dk.., respectively; the probability of one successful perception for a region is:
therefore, the probability of perceiving bk successes is Pk＝1-(1-qk)bkThen, the objective function may be specifically expressed as:
since the objective function cannot determine the unevenness, the objective function is functionally approximated, and the accuracy of the approximation function is verified by the fmincon function.
Since the concave-convex property cannot be judged by the objective function, the objective function is subjected to function approximation:
first, let Qk be 1-Qk, the objective function is expanded to:
second order approximation by Taylor's formula
The objective function expansion is obtained as:
y2＝aH·b+bH·A·b，a＝[D1ln Q1；D2ln Q2...Dkln Qk；]
b＝[b1；b2...bk]，A＝diag[ln Q1 2·D1/2；ln Q2 2·D2/2...ln Qk 2·Dk/2]
it is obvious that the objective function is the sum of a first order function and a second order function, and obviously, the objective function is a convex function, and can be optimized by convex optimization knowledge. The present invention uses fmincon functions to verify the correctness of the approximation function. S203: the auxiliary sensing data is sent to a data center through the three-dimensional intelligent surface for algorithm analysis;
in one possible implementation, the fmincon function is a matlab function used to solve the nonlinear multivariate function minimum, and the optimization toolset provides the fmincon function for solving the constrained optimization problem.
The calling form is as follows:
[x,fval,exitflag,output,lambda,grad,hessian]＝fmincon
(fun,x0,A,b,Aeq,beq,lb,ub,nonlcon,options)；
inputting parameters: fun is the function value to be solved; initialization of the parameter values of the x0 function fun;
linear inequality constraint A, b of parameter values
The equality of the linear constraints Aeq, beq,
upper and lower bounds lb, ub for parameter values
Nonlinear constrained nolcon
Outputting parameters: x outputs the optimum parameter value
Value of Fval output fun at X parameter
Exitflag output fmincon extra condition value
The invention uses function self-defining constraint conditions and integer approximate constraint in the aspect of integer constraint.
S204: determining the optimal sensing times, and updating the preset sensing times through the optimal sensing times;
s205: obtaining a control signal through the updated sensing times;
s206: and sending a control signal to the base station through the data center, and sequentially perceiving the at least two perception areas by the base station according to the updated perception times to optimize the perception areas.
In a possible embodiment, since the sensing process is a probability process, i.e. each successful sensing is a probability event, the greater the number of senses, the greater the corresponding probability of successful sensing. However, in a coal mine scene, the size of the safety information data of each region is different, and further the safety levels are different, and the region with a high safety level needs more sensing time to ensure the successful sensing of the data. To achieve low-latency data updates, the sum of the communication time and the sensing time is limited. Therefore, the sensing time needs to be reasonably allocated to each region. Under the condition, the problem is expressed as an optimization problem, and the objective of the optimization is to optimize a perception time distribution strategy under the constraint of data updating time and the minimum successful perception probability of each region so as to maximize perception data. In order to solve the optimization problem, the target problem is approximated to be a convex problem by Taylor quadratic expansion, and the optimal point is solved by a method of solving the minimum value of the nonlinear multivariate function.
S207: setting a path loss value of a direct channel from a base station to a data center and a path loss setting value through an RIS channel through code simulation;
in a feasible implementation mode, in a coal mine environment, wireless signals can be normally transmitted only in a limited direction; secondly, due to the tortuosity of the coal mine cavern, the effective transmission of the coal mine cavern can be guaranteed only by using the RIS. Under the condition, the invention expresses the coal mine RIS phase shift matrix problem as an optimization problem. By optimizing the reflection coefficient matrix of the RIS, the network communication efficiency is maximized. The goal is to maximize the communication rate within the maximum power constraint that can be provided. In order to solve the optimization problem, the invention uses SCA (sequential Convex Approximation) to solve, and each step has a closed-form solution.
For RIS phase shift matrix optimization, the relative positions of the system components of the present invention are shown in fig. 3, and in a coal mine scenario, it is assumed that the base station has information processing capability, and in addition, the base station can perform communication and radio perception. In the communication process, the channel condition is improved by intelligently regulating the RIS phase shift unit.
In code simulation, the base station to data center direct channel path loss is set to 10-6 and the path loss through the RIS channel is set to 1.
S208: constructing a target function under the condition of the set path loss value;
in one possible embodiment, constructing the objective function under the condition of the set path loss value includes:
constructing an objective function as shown in the following formula (2):
y＝|gH+hHΘG|2 (2)
let hHΘG＝θHU; wherein all characters are interpreted
When the meta objective function becomes the following formula (3):
y＝|gH+θHU|2 (3)
let v equal θ*Then the objective function becomes the following formula (4):
y＝|gH+UHv|2 (4)
s209: and (4) optimizing and solving the objective function by adopting a continuous convex approximation method to complete the optimization of the reflection coefficient matrix of the three-dimensional intelligent surface in the coal mine communication network model.
In one possible embodiment, a convex approximation function is constructed as shown in equation (5) below:
2R((g+UHv(n-1))UHv)-|g+UHv(n-1)|2 (5)
if the above function is minimized, (g + U)Hv(n-1))UHv) must be real, at the optimum point (g + U)Hv(n-1))UHAnd the phase of the sum v must be the opposite number, then (g + U) is calculated at the current iteration point under the model constraintHv(n-1))UHThe next iteration point can be found out by analogy, the optimal solution of the convex problem is found out, and the method is used for making an iteration curve as shown in figure 4: the ordinate is the normalized channel gain value, and the abscissa is the number of iterations. And (4) completing the optimization of the reflection coefficient matrix of the three-dimensional intelligent surface in the coal mine communication network model.
FIG. 5 is a diagram of a system model according to the present invention. The small-sized base station of the invention senses the number of coal gangues through radio and transmits the sensing information to the data center through the RIS. The invention can sense more safety information as far as possible under the condition of meeting the requirement of safety level; optimizing the RIS phase shift matrix improves channel conditions so as to maximize communication efficiency and effectively reduce information delay.
When a dangerous accident such as a collapse event occurs in a coal mine, the RIS can acquire accident information according to the change of the perception information: including the location of the accident and the accident risk level. Therefore, coal mine risks can be further discovered, and casualty probability is further reduced.
As shown in fig. 6, an embodiment of the present invention provides a three-dimensional RIS assisted coal mine sensory integration network optimization apparatus 300, including:
the model building module 301 is used for building a coal mine communication network model with a three-dimensional intelligent surface;
a perception region optimization module 302, configured to optimize a perception region in the coal mine communication network model;
and the three-dimensional intelligent surface optimization module 303 is used for optimizing a reflection coefficient matrix of a three-dimensional intelligent surface in the coal mine communication network model.
Preferably, the coal mine communication network model comprises a three-dimensional intelligent surface, a data center and a small base station with sensing and communication functions; at least two sensing areas are preset in the coal mine communication network model.
Preferably, the sensing region optimization module 302 includes:
the perception data initial submodule is used for the base station to sequentially perceive at least two areas according to preset perception times to obtain perception data;
the perception data forwarding submodule is used for sending perception data to a data center through the assistance of the three-dimensional intelligent surface;
the algorithm analysis submodule is used for determining proper sensing times after algorithm analysis of the data center; obtaining a control signal through the updated sensing times;
and the perception optimization submodule is used for sending a control signal to the base station by the data center, and the base station sequentially perceives the at least two perception areas according to the updated perception times.
Preferably, the perception data comprises:
perceptual data are obtained according to equation (1):
wherein K is the number of the actual sensing areas, PkIs the probability of success of the base station for a region sensing; the probability of one successful perception for a region is:
therefore, the probability of perceiving bk successes is Pk＝1-(1-qk)bkThen, the objective function may be specifically expressed as:
since the objective function cannot determine the unevenness, the objective function is functionally approximated, and the accuracy of the approximation function is verified by the fmincon function.
Preferably, the three-dimensional smart surface optimization module 303 comprises:
the path loss value setting submodule is used for setting a path loss value of a direct channel from the base station to the data center and a path loss setting value through the RIS channel through code simulation;
the target function constructing submodule is used for constructing a target function under the condition of the set path loss value;
and the reflection coefficient matrix optimization submodule is used for optimizing and solving the objective function by adopting a continuous convex approximation method to complete the optimization of the reflection coefficient matrix of the three-dimensional intelligent surface in the coal mine communication network model.
Preferably, the path loss value setting submodule includes:
constructing an objective function as the following formula (2):
y＝|gH+hHΘG|2 (2)
let hHΘG＝θHU; wherein all characters are interpreted
When the meta objective function becomes the following formula (3):
y＝|gH+θHU|2 (3)
let v equal θ*Then the objective function becomes the following formula (4):
y＝|gH+UHv|2 (4)
preferably, the reflection coefficient matrix optimization submodule is configured to construct a convex approximation function according to the following formula (5):
2R((g+UHv(n-1))UHv)-|g+UHv(n-1)|2 (5)
if the above function is minimized, (g + U)Hv(n-1))UHv) must be real, at the optimum point (g + U)Hv(n-1))UHThe phase of v must be opposite, then under model constraints, atCurrent iteration point calculates (g + U)Hv(n-1))UHThe next iteration point can be found out by analogy, the optimal solution of the convex problem is found out, and the optimization of the reflection coefficient matrix of the three-dimensional intelligent surface in the coal mine communication network model is completed.
In a possible embodiment, firstly, the invention senses and communicates coal mine safety information more effectively, and the small base station senses the quantity of coal gangue by radio and transmits the sensed information to the data center DC through RIS. The invention can sense more safety information as far as possible under the condition of meeting the requirement of safety level; optimizing the RIS phase shift matrix improves channel conditions so as to maximize communication efficiency and effectively reduce information delay. Secondly, the coal mine early warning real-time method can realize the coal mine early warning real-time effect; when a dangerous accident such as a collapse event occurs in a coal mine, the RIS can acquire accident information according to the change of the perception information: including the location of the accident and the accident risk level. Therefore, coal mine risks can be further discovered, and casualty probability is further reduced.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A three-dimensional RIS assisted coal mine sensing integrated network optimization method is characterized by comprising the following steps:
s1: establishing a coal mine communication network model with a three-dimensional intelligent surface, wherein the model comprises the three-dimensional intelligent surface, a sensing area, a small base station and a ground data center;
s2: optimizing a perception communication time distribution strategy in a coal mine communication perception integrated network model;
s3: and optimizing a reflection coefficient matrix of a three-dimensional intelligent surface in the coal mine communication network model to complete optimization of the coal mine communication integrated network.
2. The three-dimensional RIS assisted coal mine sensory integration network optimization method of claim 1, wherein said small base station is a portable small base station with both sensory and communication functions; the coal mine communication network model presets at least two perception areas.
3. The three-dimensional RIS assisted coal mine sensory integration network optimization method of claim 2, wherein in step S2, optimizing the sensory areas in the coal mine communication network model comprises:
s21: sequentially perceiving at least two preset perceiving areas according to preset perceiving times through a base station to obtain perceiving data;
s22: the auxiliary sensing data is sent to a data center through the three-dimensional intelligent surface for algorithm analysis;
s23: determining a lower bound of sensing times through a data center according to the regional security level, generating a control signal and sending the control signal to a base station;
s24: the base station obtains the lower bound of the sensing times of each area with different security levels; and sequentially perceiving the at least two perception areas according to the updated perception times, and optimizing the perception areas.
4. The three-dimensional RIS assisted coal mine sensory integration network optimization method of claim 3, wherein in step S3, optimizing the reflection coefficient moment of the three-dimensional intelligent surface in the coal mine communication network model comprises:
s31: setting a path loss value of a direct channel from a base station to a data center and a path loss setting value through an RIS channel through code simulation;
s32: constructing a target function under the condition of the set path loss value;
s33: and optimizing and solving the objective function by adopting a continuous convex approximation method to complete the optimization of the reflection coefficient matrix of the three-dimensional intelligent surface in the coal mine communication network model.
5. The three-dimensional RIS assisted coal mine sensory integration network optimization method of claim 3, wherein said deriving sensory data comprises:
perceptual data I is obtained according to equation (1):
wherein K is the number of the actual sensing areas, DkTotal amount of sensing data for k-th sensing region, PkFor the probability that the base station perceives successfully in one area, the probability that the base station perceives successfully once in one area is as follows:
therefore, the probability of perceiving bk successes is Pk＝1-(1-qk)bkThen, the objective function may be specifically expressed as:
since the unevenness cannot be judged by the objective function, the objective function is functionally approximated, and the accuracy of the approximation function is verified by the fmincon function.
6. The three-dimensional RIS assisted coal mine sensory integration network optimization method of claim 5, wherein said step S32, under the condition of set path loss value, constructing objective function, comprising:
constructing an objective function as shown in the following formula (2):
y＝|gH+hHΘG|2 (2)
let hHΘG＝θHU; wherein g ∈ CMRepresenting the path loss from the base station to the data center;
representing the path loss of the reconfigurable intelligent surface to the data center;
representing the path loss from the base station to the reconfigurable intelligent surface;
representing the corresponding phase shift modulation matrix.
When the meta objective function becomes the following formula (3):
y＝|gH+θHU|2 (3)
let v equal θ*Then the objective function becomes the following formula (4):
y＝|gH+UHv|2 (4)。
7. the three-dimensional RIS assisted coal mine communication integrated network optimization method of claim 5, wherein said step S33, using successive convex approximation to optimize the objective function, to complete the optimization of the reflection coefficient matrix of the three-dimensional intelligent surface in said coal mine communication network model, comprises:
constructing a convex approximation function as the following formula (5):
2R((g+UHv(n-1))UHv)-|g+UHv(n-1)|2 (5)
if the function is minimized, (g + U)Hv(n-1))UHv) must be real, at the optimum point (g + U)Hv(n-1))UHAnd the phase of the sum v must be the opposite number, then (g + U) is calculated at the current iteration point under the model constraintHv(n-1))UHFinding the next iteration point by analogy, finding the optimal solution of the convex problem, and completing the reflection coefficient moment of the three-dimensional intelligent surface in the coal mine communication network modelAnd (5) optimizing the array.
8. The utility model provides a supplementary colliery of three-dimensional RIS leads to and feels integration network optimization device which characterized in that includes:
the model building module is used for building a coal mine communication network model with a three-dimensional intelligent surface;
the sensing area optimization module is used for optimizing the sensing area in the coal mine communication network model;
and the three-dimensional intelligent surface optimization module is used for optimizing a reflection coefficient matrix of the three-dimensional intelligent surface in the coal mine communication network model.
9. The three-dimensional RIS assisted coal mine communication integrated network optimization device of claim 8, wherein the model building module is further configured to enable the coal mine communication network model to include a three-dimensional intelligent surface, a data center, a small base station with both sensing and communication functions; and presetting at least two perception areas in the coal mine communication network model.
10. The 3DRIS assisted coal mine communication integrated network optimization device of claim 8, wherein the sensing region optimization module comprises:
the perception data initial submodule is used for the base station to sequentially perceive at least two areas according to preset perception times to obtain perception data;
the perception data forwarding submodule is used for sending perception data to a data center through the assistance of the three-dimensional intelligent surface;
the algorithm analysis submodule is used for determining proper sensing times after the algorithm analysis of the data center; obtaining a control signal through the updated sensing times;
and the perception optimization submodule is used for sending a control signal to the base station by the data center, and the base station sequentially perceives the at least two perception areas according to the updated perception times.
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