CN117675460B - Anti-interference method, equipment and storage medium of wireless communication system - Google Patents

Abstract

The application discloses an anti-interference method, equipment and a storage medium of a wireless communication system, which determine a target coding mode based on a target transmission rate; determining a target communication channel based on the interference power of each channel; constructing an optimization model based on nonlinear generalized control in a continuous time domain; constructing a sliding mode interference observer to estimate the system state and the interference state to obtain a control error estimated value and a total interference estimated value; calculating a control error estimated value and a lumped interference estimated value to obtain a control error predicted value, a control input predicted value and a target input predicted value; and carrying out weighted combination on the control error predicted value, the control input predicted value and the target input predicted value to obtain a target control law. The anti-interference method of the wireless communication system provided by the application can quickly converge without pre-predicting or sensing the characteristics of unknown interference, and reduce the influence of the unknown interference, so that the wireless communication system can stably communicate.

Description

Anti-interference method, device and storage medium for wireless communication system

Technical Field

The present application relates to the field of wireless communication technology, and more specifically, to an anti-interference method, device and storage medium for a wireless communication system.

Background technique

In the past 20 years, wireless communication technologies such as software radio and cognitive radio have developed rapidly, enabling wireless communication systems to gradually have the ability to sense the electromagnetic environment and adjust their own transmission power, communication frequency and other communication parameters as needed, greatly enhancing the ability of wireless communication systems to adapt to complex and harsh interference environments. However, there are many types of interference, and new interference is emerging in an endless stream. Wireless communication systems may be subject to unknown interference that they have never encountered before, making it difficult to maintain the reliability of communication information transmission.

The unknown interference to wireless communication systems generally has the following characteristics: the characteristics of unknown interference are often difficult to predict in advance or perceive in real time; the time-varying speed of unknown interference is often much faster than the adjustment speed of wireless communication system parameters; unknown interference is generally bounded in energy or power, and segmented and continuous in the time domain; wireless communication systems often face different unknown interference from multiple sources.

Most of the existing wireless communication system anti-interference is based on machine learning technologies such as reinforcement learning and deep learning. When the system is interfered with, the trained or learned model determines the system parameters of the communication system based on the characteristics of the interference, and then adjusts based on the system parameters. The model needs to be iterated and trained in the early stage. Since the model needs to be iterated or trained based on the interference, when the communication system is subject to unknown interference, the perception and adaptation speed of the unknown interference is slow. Therefore, it is difficult for the communication system to maintain stable communication under unknown interference with unknown characteristics, rapid changes, and multiple sources. In the case of unknown interference, the reliability and effectiveness of the communication system cannot be guaranteed.

Summary of the invention

In view of this, the present application provides an anti-interference method for a wireless communication system, which is used to solve the problem that the communication system is difficult to maintain stable communication when subjected to unknown interference in the existing anti-interference method for wireless communication system.

To achieve the above objectives, the proposed solution is as follows:

An anti-interference method for a wireless communication system, comprising:

Obtaining the target transmission rate of the wireless communication system and the interference power of each channel;

Determining a target encoding mode based on the target transmission rate;

Determining a target communication channel based on the interference power of each channel;

Based on continuous-time domain nonlinear generalized control, construct an optimization model of the wireless communication system;

The sliding mode interference observer estimates the system state of the wireless communication system and the interference state of the unknown interference to obtain a control error estimation value and an aggregate interference estimation value;

Calculate the control error estimate and the lumped interference estimate to obtain a control error prediction value, a control input prediction value, and a target input prediction value;

The control error prediction value, the control input prediction value and the target input prediction value are substituted into the optimization model for weighted combination to obtain a target control law.

Preferably, constructing the optimization model of the wireless communication system based on continuous-time domain nonlinear generalized control includes:

According to the linear relationship between the signal to interference and noise ratio of each channel and the bit error rate of each channel, the state equation of the wireless communication system is modeled to obtain the state equation:

Wherein, _xn (t) is the system state variable of the nth channel of the wireless communication system at time t, _yn,BER (t) is the bit error rate of the nth channel of the wireless communication system at time t, _Ci and _Di are constants under the ith modulation and coding mode, _u1 (t)= _up (t) _-PJn (t)(5), _uP (t) is the transmit power control output, and _PJn (t) is the interference plus noise power in the passband of the nth channel;

Construct the control error equation: e(t)＝ _yr,BER (t) _-yn,BER (t),e _i (t)＝e ⁽ⁱ⁾ (t),i∈N(9), yr _,BER (t) is the target bit error rate, e(t) is the control error, and e ⁽ⁱ⁾ (t) is the i-th derivative of e(t);

Construct the lumped interference equation system:

Where w(t) is the aggregate interference of unknown interference and wireless communication system uncertainty, b(t) is the control gain, which satisfies bmin<b(t)<bmax, bmin and bmax are positive constants, _b0 is the nominal value of b(t), is the _n1- order derivative of the bit error rate, is the first-order derivative of the bit error rate _yn,BER (t), is the n _1- order derivative of the target bit error rate y _r,BER (t), and is given by express;

Based on continuous-time domain nonlinear generalized control, an optimization model of the wireless communication system is constructed:

Among them, T>0 is the control period, Q>0 is the weight of the control error, R≥0 is the weight of the control input, and u _r,P (t) is the steady-state control output.

Preferably, the sliding mode disturbance observer estimates the system state and the disturbance state to obtain a control error estimate and a lumped disturbance estimate, including:

The sliding mode disturbance observer is expressed by the formula:

Make an estimate and get the control error estimate. and are the estimates of the n ₁ -1 and i-th order derivatives of e(t), respectively. is the estimated value of wn ₁ (t) when n ₁ = 0, is the estimated value of the i+1-order derivative of e(t), u ₁ ^* (t) is the target control law, and _vi (t) is the sliding mode disturbance observer gain;

Assuming that there are known constants L ≥ 0 and m∈N ₊ , such that |w ^(m) (t)| ≤ L, the sliding mode disturbance observer is expressed by the formula:

Estimation is performed to obtain the aggregate interference estimate, where and Aggregate interference The estimated value when n ₁ = j and n ₁ = m-1, for Estimated value when n ₁ =j+1.

Preferably, the calculating the control error estimate and the lumped interference estimate to obtain a control error prediction value, a control input prediction value and a target input prediction value includes:

Substituting the control error estimate into the Taylor series of the control error:

The predicted value can be calculated as:

Define the decision variables:

U(t)＝[u ₁ (t) u ₁ ⁽¹⁾ (t) … u ₁ ^(r) (t)] ^T (26)

When the wireless communication system is relatively low in order, the wireless communication system is always asymptotically stable by selecting any controller parameters (Q, R, r, T). Substituting r = 0 into the control error Taylor series, the control error prediction value is obtained:

When r = 0, the decision variable U(t) = u ₁ (t), let the following variables be:

in:

W ₂ (t) = w ₂ (t) (29)

Get the control error prediction value: is the predicted value of the control error, r∈N is the control order;

Based on the Taylor series of the control input and the Taylor series of the target input, the predicted values of the control input and the target input can be obtained as

in, is the predicted value of the control input, The predicted value for the target input.

Preferably, the step of substituting the control error prediction value, the control input prediction value and the target input prediction value into the optimization model for weighted combination to obtain a target control law comprises:

Substitute the control error prediction value, the control input prediction value, and the target input prediction value into:

The target control law is obtained as:

Where _k0 and _k1 are the optimal gains when the control order r is zero.

Preferably, the determining the target communication channel based on the interference power of each channel includes:

The channel with the smallest interference power is used as the target communication channel.

An anti-interference device for a wireless communication system, comprising: a memory and a processor;

The memory is used to store programs;

The processor is used to execute the program to implement the various steps of the anti-interference method of the wireless communication system as described above.

A storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, each step of the anti-interference method of the wireless communication system as described above is implemented.

It can be seen from the above technical scheme that the anti-interference method of the wireless communication system provided by the embodiment of the present application obtains the target transmission rate of the wireless communication system and the interference power of each channel; then determines the target coding mode based on the target transmission rate; determines the target communication channel based on the signal-to-noise ratio of each channel; constructs an optimization model of the communication system based on continuous time domain nonlinear generalized control; constructs a sliding mode interference observer to estimate the system state and interference state, and obtains the control error estimate and the lumped interference estimate; calculates the control error estimate and the lumped interference estimate to obtain the control error prediction value, the control input prediction value and the target input prediction value; substitutes the control error prediction value, the control input prediction value and the target input prediction value into the optimization model for weighted combination to obtain the target control law. The anti-interference method of the wireless communication system provided by the present application uses a sliding mode interference observer to generate an estimate of the system state affected by interference. Then, by using the estimate to predict the future tracking error and steady-state control input, the optimization model optimizes the control strategy of the system. There is no need to predict or perceive the characteristics of unknown interference in advance, and there is no special requirement for the time-varying nature of unknown interference. When the interference is bounded, it can converge quickly and reduce the impact of unknown interference, so that the wireless communication system can communicate stably.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

FIG1 is a flow chart of an anti-interference method for a wireless communication system provided in an embodiment of the present application;

FIG2 is a graph showing a change in bit error rate versus signal to interference noise ratio according to an embodiment of the present application;

FIG3 is a block diagram of the hardware structure of an anti-interference device of a wireless communication system provided in an embodiment of the present application.

Detailed ways

The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

The wireless communication system includes a transmitter and a receiver, and the wireless communication system has the ability to adaptively adjust power, modulation and coding mode, and channel. The wireless communication frequency band is divided into n non-overlapping channels. The transmission power of the wireless system at time t is P _s (t)∈[P _smin ,P _smax ], where P _smin and P _smax are the maximum and minimum transmission powers of the wireless communication system, respectively. The wireless communication system has M modulation and coding modes, corresponding to M transmission rates. Before each communication, the wireless communication system can establish communication by giving the target communication channel, modulation and coding mode, and transmission power according to the technical solution of the present application.

First, an anti-interference method for a wireless communication system provided in an embodiment of the present application is introduced in conjunction with FIG. 1. As shown in FIG. 1, the method may include:

Step S01: obtaining a target transmission rate of a wireless communication system and interference power of each channel.

Specifically, the target transmission rate _yr,Rate (t) of the wireless communication system is determined, the interference plus noise power of each channel of the wireless communication system is obtained through broadband spectrum sensing, and the interference power P _Jn (t) of each channel is obtained.

Step S02: determining a target encoding method based on a target transmission rate.

Specifically, a modulation coding scheme whose transmission rate is consistent with the target transmission rate is selected as the target coding scheme.

Step S03: determining a target communication channel based on the interference power of each channel.

Specifically, the channel with the least unknown interference can be selected as the communication channel. Therefore, the channel with the least interference power can be selected as the target communication channel from the interference powers of the channels sensed by the broadband spectrum, and the target communication channel outputs u _ch (t).

Step S04: constructing an optimization model of the communication system based on continuous-time domain nonlinear generalized control.

Specifically, the curve of the bit error rate changing with the signal to noise ratio under different modulation and coding modes of the wireless communication system is shown in FIG2. When the bit error rate _Pe ≤ 10 ^-3 , the general bit error rate curve will enter the "waterfall area". At this time, the relationship between the bit error rate and the channel signal to noise ratio can be approximated as a linear relationship. Therefore, a straight line equation can be used to approximate the relationship between the signal to noise ratio and the bit error rate of a certain channel under a certain modulation and coding mode. The signal to noise ratio SJNR of the nth channel is the system state variable _xn (t); then the relationship between the signal to noise ratio SJNR and the bit error rate BER under the M modulation and coding modes of the nth channel at time t can be approximated as:

Where i∈{1,…,M}, _Ci and _Di are constants under the i-th modulation and coding mode, and _Xi is the SJNR threshold under the i-th modulation and coding mode. If at time t, the transmission power _Ps (t) of the communication signal and the interference plus noise power _PJn (t) in the n-th channel are both in dBm, and the free space propagation loss is not considered, then at time t, the SJNR of the n-th channel can be expressed as:

_xn (t)= _SJNRn (t)= _Ps (t) _-PJn (t) (2)

When the target communication channel control output u _ch (t) = n, the change of the wireless communication system state under the i-th modulation and coding mode can be expressed by the following formula:

Wherein, u _P (t) is the transmit power control output variable, and B _p is the control parameter of u _P (t). According to formula (2), the dynamic characteristic parameter A = -1, and the control parameter B _P = 1. Therefore, combining formula (1) and formula (3), the state equation of the wireless communication system can be modeled as follows:

make

u ₁ (t) = u _p (t) - P _{J n} (t) (5)

Furthermore, according to the linear relationship between the signal-to-interference-noise ratio of each channel and the bit error rate of each channel, the state equation (4) of the communication system is simplified to:

According to formula (6), we can get:

in, is the second-order derivative of y _n,BER (t), is the first-order derivative of u ₁ (t), _which is defined by equation (5). Let:

in, is the first-order derivative of y _n,BER (t).

Construct the control error equation:

e(t)＝y _r,BER (t)-y _n,BER (t),e _i (t)＝e ⁽ⁱ⁾ (t),i∈N (9)

Where y _r,BER (t) is the target bit error rate, e(t) is the control error, and e ⁽ⁱ⁾ (t) is the i-th order derivative of e(t).

Since the target bit error rate _yr,BER (t) of the wireless communication system is usually a constant, the _n1- order derivative of the control error e(t) must exist and be bounded, and the control error satisfies:

in, represents the first-order derivative of e _i (t), w(t) is the aggregate interference of unknown interference and system uncertainty, is the _n1- th order derivative of w(t).

Construct the lumped interference equation system:

Where b(t) is the control gain, which satisfies b _min ＜b(t)＜b _max . b _min and b _max are positive constants, which are the upper and lower bounds of b(t). b ₀ is the nominal value of b(t). is the _n1- order derivative of the bit error rate, is the first-order derivative of the bit error rate _yn,BER (t), is the n _1- order derivative of the target bit error rate y _r,BER (t), and is given by express.

Based on continuous-time domain nonlinear generalized control, an optimization model of the communication system is constructed:

Among them, T>0 is the control period, Q>0 is the weight of the control error, R≥0 is the weight of the control input, and u _r,P (t) is the steady-state control output.

Step S05: The sliding mode interference observer estimates the system state of the wireless communication system and the interference state of the unknown interference.

Specifically, the sliding mode interference observer can observe the system state of the wireless communication system and the interference state of the unknown interference to obtain the control error estimate and the lumped interference estimate. Assuming that there are known constants L≥0 and m∈N ₊ , such that |w ^(m) (t)|≤L, the sliding mode interference observer is calculated by the formula:

The control error is estimated to obtain the estimated value of the control error, where: and are the estimates of the n ₁ -1 and i-th order derivatives of e(t), respectively. for The estimated value when n ₁ = 0, is the estimated value of the i+1-th order derivative of e(t), u ₁ ^* (t) is the target control law, and _vi (t) is the sliding mode disturbance observer gain.

Assuming that there are known constants L ≥ 0 and m∈N ₊ , such that |w ^(m) (t)| ≤ L, the sliding mode disturbance observer is expressed by the formula:

further,

_vi (t)=K(t)sign[ _vi-1 (t)] (20)

in is the system output The estimated value of can be expressed as The aggregate interference is estimated to obtain the aggregate interference estimate value. is the aggregate interference estimate, K(t) is the switching gain,

When i=0, equation (14) is simplified to:

When n ₁ = 2, m = 1, equation (15) is simplified to:

Formula (17) is simplified to:

in, is the estimated value of each order derivative of the control error and the estimated value of the lumped interference, is the target control law.

Step S06, calculating the control error estimation value and the aggregate interference estimation value.

Specifically, a control error prediction value, a control input prediction value, and a target input prediction value may be calculated based on the Taylor series.

Substitute the control error estimate into the Taylor series for the control error:

Calculation yields:

Define the decision variables:

U(t)＝[u ₁ (t) u ₁ ⁽¹⁾ (t) … u ₁ ^(r) (t)] ^T (26)

Considering that the wireless communication system is relatively low in order, for any selected controller parameters (Q, R, r, T), the wireless communication system is always asymptotically stable. Substituting r = 0 into the control error Taylor series, the control error prediction value can be obtained:

When r = 0, the decision variable U(t) = u ₁ (t), let the following variables be:

in:

W ₂ (t) = w ₂ (t) (29)

In summary, after calculation, the control error prediction value can be obtained as follows:

Considering the existence of external unknown disturbances and system uncertainty, W(t) is a correction value for the traditional prediction method based on Taylor series expansion. Similar to steps (24)-(31) for solving the control error prediction value, the prediction values of the control input and target input can be obtained based on the Taylor series of the control input and the Taylor series of the target input:

in, is the predicted value of the control input, The predicted value for the target input.

Step S07: Substitute the control error prediction value, the control input prediction value and the target input prediction value into the optimization model for weighted combination.

Specifically, the control error prediction value, control input prediction value, and target input prediction value are substituted into the optimization model to obtain:

in,

right Taking partial derivatives about U, we get:

in, Can make Therefore, the decision variables become:

Taking the first row of decision variables U ^* (t) as the target control law, the target control law can be obtained as:

Wherein, u ₁ ^* (t) represents the target control law under disturbance environment, _{k 0} and k ₁ are the optimal gains when r = 0.

The anti-interference method of the wireless communication system provided in the embodiment of the present application obtains the target transmission rate of the wireless communication system and the interference power of each channel; then determines the target coding mode based on the target transmission rate; determines the target communication channel based on the signal-to-noise ratio of each channel; constructs an optimization model of the communication system based on continuous time domain nonlinear generalized control; constructs a sliding mode interference observer to estimate the system state and interference state, and obtains the control error estimate and the lumped interference estimate; calculates the control error estimate and the lumped interference estimate to obtain the control error prediction value, the control input prediction value and the target input prediction value; substitutes the control error prediction value, the control input prediction value and the target input prediction value into the optimization model for weighted combination to obtain the target control law. The anti-interference method of the wireless communication system provided in the present application uses a sliding mode interference observer to generate an estimate of the system state affected by interference. Then, by using the estimate to predict the future tracking error and steady-state control input, the optimization model optimizes the control strategy of the system. There is no need to predict or perceive the characteristics of unknown interference in advance, and there is no special requirement for the time-varying nature of unknown interference. When the interference is bounded, it can converge quickly and reduce the impact of unknown interference, so that the wireless communication system can communicate stably.

The anti-interference method of the wireless communication system provided in the embodiment of the present application can be applied to the anti-interference device of the wireless communication system. Figure 3 shows a hardware structure block diagram of the anti-interference device of the wireless communication system. Referring to Figure 3, the hardware structure of the device may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4;

In the embodiment of the present application, the number of the processor 1, the communication interface 2, the memory 3, and the communication bus 4 is at least one, and the processor 1, the communication interface 2, and the memory 3 communicate with each other through the communication bus 4;

The processor 1 may be a central processing unit CPU, or an application-specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention, etc.;

The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory;

The memory stores a program, and the processor can call the program stored in the memory, and the program is used to implement each processing flow in the anti-interference scheme of the wireless communication system.

An embodiment of the present application further provides a storage medium, which may store a program suitable for execution by a processor, wherein the program is used to implement various processing flows in the anti-interference scheme of the aforementioned wireless communication system.

Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims (4)

1. An anti-interference method for a wireless communication system, comprising:

Acquiring a target transmission rate of a wireless communication system and interference power of each channel;

determining a target coding mode based on the target transmission rate;

Determining a target communication channel based on the interference power of each channel;

constructing an optimization model of the wireless communication system based on continuous time domain nonlinear generalized control;

the sliding mode interference observer estimates the system state of the wireless communication system and the interference state of unknown interference to obtain a control error estimated value and a total interference estimated value;

Calculating the control error estimated value and the lumped interference estimated value to obtain a control error predicted value, a control input predicted value and a target input predicted value;

Substituting the control error predicted value, the control input predicted value and the target input predicted value into the optimization model for weighted combination to obtain a target control law;

the constructing an optimization model of the wireless communication system based on the continuous time domain nonlinear generalized control comprises the following steps:

modeling a state equation of the wireless communication system according to the linear relation between the signal-to-interference-plus-noise ratio of each channel and the error rate of each channel to obtain the state equation:

Wherein x _n (t) is a system state variable of an nth channel of the wireless communication system at a time t, y _n,BER (t) is a time t, error rates of the nth channel of the wireless communication system, C _i and D _i are constants in an ith modulation coding mode, u ₁(t)＝u_p(t)-P_Jn(t)(5),u_P (t) is a transmission power control output, and P _Jn (t) is interference plus noise power in a passband of the nth channel;

Constructing a control error equation: e (t) =y _r,BER(t)-y_n,BER(t),e_i(t)＝e⁽ⁱ⁾(t),i∈N (9),y_r,BER (t) is the target error rate, e (t) is the control error, e ⁽ⁱ⁾ (t) is the i-th derivative of e (t);

Constructing a lumped interference equation set:

Where w (t) is the lumped interference of the unknown interference and the uncertainty of the wireless communication system, b (t) is the control gain, b _min＜b(t)＜b_max,b_min and b _max are normal numbers, b ₀ is the nominal value of b (t), Is the n ₁ th order derivative of the bit error rate,As the first derivative of the bit error rate y _n,BER (t),N ₁ derivative of the target bit error rate y _r,BER (t) and is calculated byA representation;

Based on continuous time domain nonlinear generalized control, constructing an optimization model of the wireless communication system:

Wherein T > 0 is a control period, Q > 0 is the weight of a control error, R is more than or equal to 0 is the weight of a control input, and u _γ,P (T) is a steady-state control output;

The sliding mode interference observer estimates the system state and the interference state to obtain a control error estimated value and a total interference estimated value, and the method comprises the following steps:

The sliding mode interference observer is represented by the formula:

estimating the error to obtain a control error estimated value, AndEstimates of the derivatives of the n ₁ -1 and i orders of e (t), respectively,Is thatAn estimate at n ₁ =0,For the estimated value of the i+1 derivative of e (t), u ₁ ^* (t) is the target control law, v _i (t) is the sliding mode interference observer gain;

Assuming that there are known constants L.gtoreq.0 and m.epsilon.N ₊ such that |w ^(m) (t) |.ltoreq.L, the sliding mode disturbance observer passes the formula:

estimating to obtain a lumped interference estimated value, wherein, AndRespectively lumped interferenceAn estimated value at n ₁ =j and n ₁ =m-1,Is thatAn estimated value at n ₁ =j+1;

The calculating the control error estimated value and the lumped interference estimated value to obtain a control error predicted value, a control input predicted value and a target input predicted value includes:

Substituting the control error estimation value into a taylor series of the control error:

the predicted value is calculated to obtain:

defining decision variables:

When the relative order of the wireless communication system is low, selecting any controller parameter (Q, R, R, T), enabling the wireless communication system to be always asymptotically stable, and substituting r=0 into a control error Taylor series to obtain a control error prediction value:

when r=0, the decision variable U (t) =u ₁ (t), let the following variables be:

Wherein:

W₂(t)＝w₂(t) (29)

Obtaining a control error predicted value: r epsilon N is the control order for the predicted value of the control error;

The control input Qin Le series and the target input Qin Le series can be used for obtaining the predicted value of the target input as follows

Wherein, In order to control the predicted value of the input,A predicted value input for the target;

Substituting the control error predicted value, the control input predicted value and the target input predicted value into the optimization model for weighted combination to obtain a target control law, wherein the method comprises the following steps:

substituting the control error predicted value, the control input predicted value and the target input predicted value into the following formula:

The target control law is obtained as follows:

Where k ₀ and k ₁ are the optimal gains when the control order r is zero, respectively.

2. The method of interference avoidance in a wireless communication system according to claim 1 wherein said determining a target communication channel based on the interference power of said channels comprises:

And taking the channel with the minimum interference power as the target communication channel.

3. An anti-interference device for a wireless communication system, comprising: a memory and a processor;

The memory is used for storing programs;

the processor configured to execute the program to implement the steps of the anti-interference method of the wireless communication system according to any one of claims 1-2.

4. A storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the interference suppression method of a wireless communication system according to any of claims 1-2.