阅读说明：本技术 基于粒子群与遗传算法的自适应直接序列扩频通信方法、系统和介质 (Self-adaptive direct sequence spread spectrum communication method, system and medium based on particle swarm and genetic algorithm ) 是由 杨迎泽 荣介奇 李烁 刘伟荣 高凯 张晓勇 蒋富 顾欣 刘勇杰 彭军 黄志武 于 2021-09-29 设计创作，主要内容包括：本发明公开了一种基于混合粒子群遗传算法的自适应直接序列扩频通信方法、系统和介质,其中方法包括：步骤1,基于多种环境条件和多种通信条件,构建扩频因子条件向量和扩频因子表达式；步骤2,将扩频因子条件向量抽象表示为粒子的地址,将扩频因子表达式作为适应度函数,采用混合粒子群遗传算法求解最优的扩频因子；步骤3,将求解得到的最优扩频因子作为通信双方最新的扩频因子,对需要传输的数据进行直接序列扩频通信。本发明能根据通信和环境条件快速求解最优扩频因子,增强通信系统的抗干扰性、提高带宽资源利用效率。(The invention discloses a self-adaptive direct sequence spread spectrum communication method, a system and a medium based on a hybrid particle swarm genetic algorithm, wherein the method comprises the following steps: step 1, constructing a spreading factor condition vector and a spreading factor expression based on various environmental conditions and various communication conditions; step 2, abstractly expressing the conditional vector of the spread spectrum factor as the address of the particle, taking the spread spectrum factor expression as a fitness function, and solving the optimal spread spectrum factor by adopting a hybrid particle swarm genetic algorithm; and 3, taking the obtained optimal spreading factor as the latest spreading factor of the two communication parties, and carrying out direct sequence spread spectrum communication on the data needing to be transmitted. The invention can quickly solve the optimal spread spectrum factor according to the communication and environmental conditions, enhance the anti-interference performance of the communication system and improve the utilization efficiency of bandwidth resources.)

1. A self-adaptive direct sequence spread spectrum communication method based on a hybrid particle swarm genetic algorithm is characterized by comprising the following steps:

step 1, constructing a spreading factor condition vector and a spreading factor expression based on various environmental conditions and various communication conditions;

step 2, abstractly expressing the conditional vector of the spread spectrum factor as the address of the particle, taking the spread spectrum factor expression as a fitness function, and solving the optimal spread spectrum factor by adopting a hybrid particle swarm genetic algorithm;

and 3, taking the obtained optimal spreading factor as the latest spreading factor of the two communication parties, and carrying out direct sequence spread spectrum communication on the data needing to be transmitted.

2. The method according to claim 1, wherein the step 2 of solving the optimal spreading factor by using the hybrid particle swarm optimization is as follows:

step A1, initializing the mixed particle group and genetic parameters, including setting the size N of the particle group, the initial position and initial velocity of each particle, and the genetic variation probability p_mAnd genetic crossover probability p_c(ii) a Wherein, the current spreading factor value of both communication parties is used as the initial fitness value of each particle in the particle swarm and the individual optimal solution p_ibestAnd a global optimal solution p_gbest；

Step A2, randomly selecting two particles in the particle swarm as parent 1 and parent 2, and generating 2 random numbers p₁And p₂，p₁∈(0,1)，p₂∈(0,1)；

If p is₁＜p_cPerforming cross operation on the parent 1 and the parent 2 to generate a child 1 and a child 2; otherwise, directly using the parent 1 and the parent 2 as child 1 and child 2 without operating;

if p is₂＜p_mCarrying out mutation operation on the offspring 1 and the offspring 2;

step A3, calculating fitness value f for the filial generation 1 and the filial generation 2 obtained by the genetic variation of the step A2_iThe obtained fitness value f_iAnd individual optimal solution p_ibestMaking a comparison if f_i＞p_ibestThen f is_iAssigning to the individual an optimal solution p_ibest(ii) a Otherwise, the probability p of random number to be generated randomly is₃And the probability of genetic variation p_mMaking a comparison if p₃＜p_mThen f will remain_iAssigning to the individual optimal solution p_ibestOtherwise, maintaining the individual optimal solution p_ibestThe change is not changed;

step A4, obtaining the optimal solution p of the individual filial generation 1 and the filial generation 2 through the genetic variation of the step A2_ibestAnd the global optimal solution p_gbestMaking a comparison if p_ibest＞p_gbestThen p will be_ibestAssign to the global optimal solution p_gbestOtherwise, the global optimal solution p is maintained_gbestThe change is not changed;

step A5, iteratively updating the velocity v of each particle i according to the following formula_iSum positionPut x_i：

x_i(t+1)＝x_i(t)+v_i(t+1)

Where ω is the inertial weight, c₁,c₂Is a learning factor, r₁,r₂Is a random number, T represents the current iteration number, T_maxTo the maximum number of iterations, p_ibest(t) represents the individual optimal solution for the ith particle, p_gbest(t) represents the global optimal solution, ω, for the particle swarm_maxAnd ω_minRespectively representing a preset maximum weight and a preset minimum weight;

and step A6, returning to step A2 until the iteration update reaches the maximum iteration times or meets the convergence condition, and outputting the global optimal solution as the optimal spreading factor.

3. The method of claim 1, wherein the spreading factor is expressed in terms of a plurality of environmental conditions and a plurality of communication conditions as:

wherein f represents a spreading factor; s_jA quantized value, alpha, representing the j-th environmental condition_jRepresenting the interference degree of the jth environmental condition on the communication, and M representing the number of the environmental conditions affecting the communication; w_kA quantized value, beta, representing the kth communication condition_kThe interference degree of the K-th communication condition on the communication is shown, and the K is the number of the types of the communication conditions affecting the communication.

4. The method of claim 1, wherein the method for updating the optimal spreading factor by both communication parties comprises:

step B1, the sending end uses the piggyback mechanism to transmit the optimal spreading factor to the receiving end;

step B2, when the receiving end feeds back the confirmation information to the sending end, the receiving end feeds back the timing information for changing the set spreading factor to the sending end;

and step B3, the sending end updates the spreading factor of the communication between itself and the other side to the optimal spreading factor according to the received timing information and the receiving end according to the set timing information.

5. The method of claim 4, wherein the transmitting end uses the optimal spreading factor, and adopts a method of modulation before spreading to modulate and spread the data to be transmitted; the receiving end uses the optimal spread spectrum factor to despread the received data and demodulate the received data to obtain the originally transmitted data.

6. The method of claim 4, wherein the transmitting end uses the optimal spreading factor, and adopts a method of spreading first and then modulating to modulate and spread the data to be transmitted; the receiving end uses the optimal spread spectrum factor to demodulate and then despread the received data to obtain the originally transmitted data.

7. An adaptive direct sequence spread spectrum communication system based on a hybrid particle swarm genetic algorithm, comprising:

a spreading factor construction module configured to: constructing a spreading factor condition vector and a spreading factor expression based on various environmental conditions and various communication conditions;

an adaptive spreading factor solution module to: abstractly expressing the condition vectors of the spread spectrum factors as the addresses of the particles, taking the spread spectrum factor expression as a fitness function, and solving the optimal spread spectrum factor by adopting a hybrid particle swarm genetic algorithm;

a direct sequence spread spectrum communication module to: and taking the obtained optimal spreading factor as the latest spreading factor of both communication parties, spreading and modulating the data to be transmitted, and despreading and demodulating the received data.

8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1 to 6.

Technical Field

The invention belongs to the technical field of network communication, and particularly relates to a self-adaptive direct sequence spread spectrum communication method, a self-adaptive direct sequence spread spectrum communication system and a self-adaptive direct sequence spread spectrum communication medium based on a hybrid particle swarm genetic algorithm.

Background

With the progress of science and technology and the development of society, people's demand for high-speed video image data transmission service is increasing. Meanwhile, the security and reliability of communication are also receiving attention. The spread spectrum communication system can improve the confidentiality and the anti-interference performance of the communication system, wherein the direct sequence spread spectrum communication system becomes a research hotspot due to simple structure and easy realization, and the design of a transceiver is the key point of the design of the direct sequence spread spectrum communication system.

A block diagram of a typical system for direct sequence spread spectrum is shown in fig. 1. It mainly inserts two processes of spreading and despreading on the conventional digital communication system, as shown in the dotted-line box part of fig. 1. The essence of spread spectrum is a modulation of the signal that spreads the spectrum of the interfering signal with a low power spectral density. After the receiving end is subjected to narrow-band filtering, most of interference signals spread over a very wide bandwidth are filtered, the energy of the residual part is very low, and useful signals are compressed and restored into narrow-band signals and smoothly pass through a filter, so that the signal-to-noise ratio is increased. In addition, because receiving is carried out according to the correlation principle, the transmitting side and the receiving side must use the identical spread spectrum code sequence, and the spread spectrum communication can still normally work under the condition of negative signal-to-noise ratio, thereby having stronger security and confidentiality.

The existing spread spectrum technology uses a fixed spread spectrum factor for communication, and a system treats all receiving ends equally, and does not consider the environment and communication conditions around the receiving ends, which can cause the problems of poor spectrum allocation, partial communication blockage, waste of bandwidth resources and the like. Especially in an environment with high interference, the bandwidth is equally allocated by the same spreading factor, which results in that all receiving ends cannot normally communicate.

Disclosure of Invention

The invention provides a self-adaptive direct sequence spread spectrum communication method, a system and a medium based on a hybrid particle swarm genetic algorithm, which can quickly solve an optimal spread spectrum factor according to communication and environmental conditions and meet the requirements of communication rate and environment.

In order to achieve the technical purpose, the invention adopts the following technical scheme:

a self-adaptive direct sequence spread spectrum communication method based on a hybrid particle swarm genetic algorithm comprises the following steps:

step 1, constructing a spreading factor condition vector and a spreading factor expression based on various environmental conditions and various communication conditions;

step 2, abstractly expressing the conditional vector of the spread spectrum factor as the address of the particle, taking the spread spectrum factor expression as a fitness function, and solving the optimal spread spectrum factor by adopting a hybrid particle swarm genetic algorithm;

and 3, taking the obtained optimal spreading factor as the latest spreading factor of the two communication parties, and carrying out direct sequence spread spectrum communication on the data needing to be transmitted.

Further, the method for solving the optimal spreading factor by adopting the hybrid particle swarm genetic algorithm in the step 2 comprises the following steps:

step A1, initializing the mixed particle group and genetic parameters, including setting the size N of the particle group, the initial position and initial velocity of each particle, and the genetic variation probability p_mAnd genetic crossover probability p_c(ii) a Wherein, the current spreading factor value of both communication parties is used as the initial fitness value of each particle in the particle swarm and the individual optimal solution p_ibestAnd a global optimal solution p_gbest；

Step A2, randomly selecting two particles in the particle swarm as parent 1 and parent 2, and generating 2 random numbers p₁And p₂，p₁∈(0,1)，p₂∈(0,1)；

If p is₁＜p_cPerforming cross operation on the parent 1 and the parent 2 to generate a child 1 and a child 2; otherwise, directly using the parent 1 and the parent 2 as child 1 and child 2 without operating;

if p is₂＜p_mCarrying out mutation operation on the offspring 1 and the offspring 2;

step A3, calculating fitness value f for the filial generation 1 and the filial generation 2 obtained by the genetic variation of the step A2_iThe obtained fitness value f_iAnd individual optimal solution p_ibestMaking a comparison if f_i＞p_ibestThen f is_iAssigning to the individual an optimal solution p_ibest(ii) a Otherwise, the probability p of random number to be generated randomly is₃And the probability of genetic variation p_mMaking a comparison if p₃＜p_mThen f will remain_iAssigning to the individual optimal solution p_ibestOtherwise, maintaining the individual optimal solution p_ibestThe change is not changed;

step A4, obtaining the genetic variation through the step A2The obtained filial generation 1 and filial generation 2 have their individual optimum solution p_ibestAnd the global optimal solution p_gbestMaking a comparison if p_ibest＞p_gbestThen p will be_ibestAssign to the global optimal solution p_gbestOtherwise, the global optimal solution p is maintained_gbestThe change is not changed;

step A5, iteratively updating the velocity v of each particle i according to the following formula_iAnd position x_i：

x_i(t+1)＝x_i(t)+v_i(t+1)

Where ω is the inertial weight, c₁,c₂Is a learning factor, r₁,r₂Is a random number, T represents the current iteration number, T_maxTo the maximum number of iterations, p_ibest(t) represents the individual optimal solution for the ith particle, p_gbest(t) represents the global optimal solution, ω, for the particle swarm_maxAnd ω_minRespectively representing a preset maximum weight and a preset minimum weight;

and step A6, returning to step A2 until the iteration update reaches the maximum iteration times or meets the convergence condition, and outputting the global optimal solution as the optimal spreading factor.

Further, the spreading factor is expressed in terms of various environmental conditions and various communication conditions as:

wherein f represents a spreading factor; s_jA quantized value, alpha, representing the j-th environmental condition_jRepresenting the interference degree of the jth environmental condition on the communication, and M representing the number of the environmental conditions affecting the communication; w_kA quantized value, beta, representing the kth communication condition_kThe interference degree of the K-th communication condition on the communication is shown, and the K is the number of the types of the communication conditions affecting the communication.

Further, the method for updating the optimal spreading factor by both communication parties comprises the following steps:

step B1, the sending end uses the piggyback mechanism to transmit the optimal spreading factor to the receiving end;

step B2, when the receiving end feeds back the confirmation information to the sending end, the receiving end feeds back the timing information for changing the set spreading factor to the sending end;

and step B3, the sending end updates the spreading factor of the communication between itself and the other side to the optimal spreading factor according to the received timing information and the receiving end according to the set timing information.

Further, the transmitting end uses the optimal spread spectrum factor and adopts a method of modulating first and then spreading spectrum to modulate and spread spectrum process the data to be transmitted; the receiving end uses the optimal spread spectrum factor to despread the received data and demodulate the received data to obtain the originally transmitted data.

Further, the sending end uses the optimal spread spectrum factor, and adopts a method of firstly spreading and then modulating to modulate and spread spectrum process the data to be transmitted; the receiving end uses the optimal spread spectrum factor to demodulate and then despread the received data to obtain the originally transmitted data.

An adaptive direct sequence spread spectrum communication system based on a hybrid particle swarm genetic algorithm, comprising:

a spreading factor construction module configured to: constructing a spreading factor condition vector and a spreading factor expression based on various environmental conditions and various communication conditions;

an adaptive spreading factor solution module to: abstractly expressing the condition vectors of the spread spectrum factors as the addresses of the particles, taking the spread spectrum factor expression as a fitness function, and solving the optimal spread spectrum factor by adopting a hybrid particle swarm genetic algorithm;

a direct sequence spread spectrum communication module to: and taking the obtained optimal spreading factor as the latest spreading factor of both communication parties, spreading and modulating the data to be transmitted, and despreading and demodulating the received data.

A computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the hybrid particle swarm genetic algorithm-based adaptive direct sequence spread spectrum communication method of any of the above.

Has the advantages that:

1. the invention uses the hybrid particle swarm genetic algorithm to solve the optimal spread spectrum factor meeting the environment and communication requirements, can be adaptively applied to any two communication nodes in a communication system, realizes the anti-interference performance of the communication system and ensures the communication rate.

2. The invention can respectively package the spread spectrum and the de-spread spectrum into modules, thereby automatically solving the optimal spread spectrum factor according to the hybrid particle swarm genetic algorithm and synchronously adjusting the two communication parties.

Drawings

FIG. 1 is a schematic diagram of a direct sequence spread spectrum system;

FIG. 2 is a detailed flow chart of a hybrid particle swarm genetic algorithm;

fig. 3 is an adaptive spreading factor update process;

FIG. 4 is a diagram of first spreading, then QPSK modulation, first demodulation, and then despreading;

FIG. 5 is a diagram of QPSK modulation followed by spreading, despreading followed by demodulation;

fig. 6 is a schematic diagram of a process of resisting narrowband interference in a spread spectrum system;

FIG. 7 is a schematic diagram of a direct sequence spread spectrum waveform;

fig. 8 is a schematic diagram of a complete communication flow of the communication node having both transmitting and receiving functions.

Detailed Description

The following describes embodiments of the present invention in detail, which are developed based on the technical solutions of the present invention, and give detailed implementation manners and specific operation procedures to further explain the technical solutions of the present invention.

Spread spectrum communication is a communication method that transmits information using a radio frequency signal that is much wider than the frequency band of the original signal itself. In a spread spectrum communication system, a transmitter spreads the bandwidth of an original signal using a specific modulation method to obtain a spread spectrum signal. The receiving end processes the received spread spectrum signal and restores it to the original bandwidth signal. Compared with narrow-band communication, the spread spectrum communication has a series of unique advantages of strong anti-interference capability, low interception rate, code division multiple access, signal concealment and the like, so that the spread spectrum communication is widely applied to the military and civil fields.

Interference signals are a very serious threat to wireless communication systems, and particularly in military communication, the anti-interference capability of a spread spectrum system relying on a fixed spreading factor is insufficient; and the spreading factor can not be infinitely enlarged, otherwise, the problems of reduced transmission rate, low bandwidth utilization rate and the like can be caused. In a wireless network, the environmental conditions and communication conditions of nodes at different positions are different, and some nodes at different positions may be subjected to higher interference except for the influence of distance factors, and serious unreasonable channel resource allocation can be caused by setting a fixed spreading factor and considering all nodes as inappropriate.

Aiming at the problem, the invention mainly researches and researches on adaptively allocating the spreading factor to the nodes in the system, so that the system can adaptively adjust the spreading factor of each node according to the difference between the environmental condition and the communication condition of each node, thereby utilizing the bandwidth resource to the maximum extent under the condition of ensuring the communication safety and the anti-interference.

Therefore, the invention provides a self-adaptive direct sequence spread spectrum communication method, system and medium based on a hybrid particle swarm genetic algorithm, which can self-adaptively solve and obtain the optimal spread spectrum factor according to the environmental conditions and the communication conditions of each node, so as to furthest utilize the bandwidth resources of the whole communication system under the condition of ensuring the communication safety and the anti-interference. The performance of spread spectrum is determined based on the spreading factor, and the bit error rate is lower along with the increase of the spreading factor, namely the communication reliability is better, the self-adaptive direct sequence spread spectrum communication method of the embodiment of the invention comprises the following steps:

step 1, constructing a spreading factor condition vector and a spreading factor expression based on various environmental conditions and various communication conditions.

It is assumed that there are M kinds of environmental conditions affecting communication, including the density of obstacles, the density of metal objects, the dryness of air, the wind strength, the amount of rain and snow, the temperature, the air pollutant content, and the like. The quantized value of the jth environmental condition is denoted S_jThe degree of interference to the communication is represented as alpha_j. The communication conditions affecting the communication are K, including the communication conditions affecting the wireless communication, such as the surrounding electromagnetic environment, the receiver sensitivity, the system anti-interference performance, the software error correction capability, the channel occupation condition, the antenna aging degree, the antenna height and the like. The quantization value of the kth communication condition is represented as W_kThe degree of interference to the communication is expressed as beta_k。

The spreading factor condition vector, which is composed of all environmental conditions and communication conditions, can be expressed as:

p＝(S₁,…,S_M,W₁,…,W_K)；

the spreading factor expression with respect to all environmental and communication conditions can be expressed as:

and 2, abstractly expressing the conditional vector of the spreading factor as the address of the particle, taking the spreading factor expression as a fitness function, and solving the optimal spreading factor by adopting a hybrid particle swarm genetic algorithm. Referring to fig. 2, a specific process for solving the optimal spreading factor is as follows:

step A1, initializing the mixed particle group and genetic parameters, including setting the size N of the particle group, the initial position and initial velocity of each particle, and the genetic variation probability p_mAnd genetic crossover probability p_c(ii) a Wherein, the current spreading factor value of both communication parties is used as the initial fitness value of each particle in the particle swarm and the individual optimal solution p_ibestAnd a global optimal solution p_gbest；

Step A2, random in particle populationTwo particles are selected as parent 1 and parent 2 and 2 random numbers p are generated₁And p₂，p₁∈(0,1)，p₂∈(0,1)；

If p is₁＜p_cPerforming cross operation on the parent 1 and the parent 2 to generate a child 1 and a child 2; otherwise, directly using the parent 1 and the parent 2 as child 1 and child 2 without operating;

if p is₂＜p_mCarrying out mutation operation on the offspring 1 and the offspring 2;

step A3, calculating fitness value f for the filial generation 1 and the filial generation 2 obtained by the genetic variation of the step A2_iThe obtained fitness value f_iAnd individual optimal solution p_ibestMaking a comparison if f_i＞p_ibestThen f is_iAssigning to the individual an optimal solution p_ibest(ii) a Otherwise, the probability p of random number to be generated randomly is₃And the probability of genetic variation p_mMaking a comparison if p₃＜p_mThen f will remain_iAssigning to the individual optimal solution p_ibestOtherwise, maintaining the individual optimal solution p_ibestThe change is not changed;

step A4, obtaining the optimal solution p of the individual filial generation 1 and the filial generation 2 through the genetic variation of the step A2_ibestAnd the global optimal solution p_gbestMaking a comparison if p_ibest＞p_gbestThen p will be_ibestAssign to the global optimal solution p_gbestOtherwise, the global optimal solution p is maintained_gbestThe change is not changed;

step A5, iteratively updating the velocity v of each particle i according to the following formula_iAnd position x_i：

x_i(t+1)＝x_i(t)+v_i(t+1)

Where ω is the inertial weight, c₁,c₂Is a learning factor, r₁,r₂Is a random number, T represents the current iteration number, T_maxTo the maximum number of iterations, p_ibest(t) represents the individual optimal solution for the ith particle, p_gbest(t) represents the global optimal solution, ω, for the particle swarm_maxAnd ω_minRespectively representing a preset maximum weight and a preset minimum weight;

and step A6, returning to step A2 until the iteration update reaches the maximum iteration times or meets the convergence condition, and outputting the global optimal solution as the optimal spreading factor.

And 3, taking the obtained optimal spreading factor as the latest spreading factor of the two communication parties, and carrying out direct sequence spread spectrum communication on the data needing to be transmitted.

In order to ensure that both communication parties trigger the update of the spreading factor at the same time, both communication parties update the optimal spreading factor by using a timing device, as shown in fig. 3, the specific method is as follows:

step B1, the sending end uses the piggyback mechanism to transmit the optimal spreading factor to the receiving end;

step B2, when the receiving end feeds back the confirmation information to the sending end, the receiving end feeds back the timing information for changing the set spreading factor to the sending end;

and step B3, the sending end updates the spreading factor of the communication between itself and the other side to the optimal spreading factor according to the received timing information and the receiving end according to the set timing information.

In addition, there are two methods for performing direct sequence spread spectrum communication on data to be transmitted: the first mode is that the sending end uses the optimal spread spectrum factor and adopts the method of modulation and spread spectrum, and the data to be transmitted is modulated and spread spectrum processed; and then the receiving end uses the optimal spread spectrum factor to despread and demodulate the received data to obtain the originally transmitted data. The second mode is that the sending end uses the optimal spread spectrum factor and adopts a method of firstly spreading and then modulating to modulate and spread spectrum process the data to be transmitted; and then the receiving end uses the optimal spread spectrum factor to demodulate and then despread the received data to obtain the originally transmitted data.

In the method of first spreading and then modulating, as shown in fig. 4, a random binary sequence, a spreading sequence generation module and a time domain spreading module are designed and generated, and the spreading function can be realized by performing xor operation on the spreading sequence and the spread information code. In the scheme of modulation-first and spreading-second, as shown in fig. 5, since the modulated signal is an analog signal and is represented by an imaginary number, the information code needs to be split into a real part and an imaginary part for time domain spreading, respectively, so as to implement the spreading function.

Therefore, the specific algorithm steps for setting the spreading module at the transmitting end are as follows:

c1, the test generates a spreading sequence. And generating a spreading sequence in an exclusive-or mode, generating a spreading sequence group and storing the spreading sequence group. And after the unipolar spread spectrum sequence is converted into the bipolar spread spectrum sequence, performing fast Fourier transform on the bipolar spread spectrum sequence. And setting a frequency sequence and sampling according to the sampling frequency.

And C2, randomly generating original data and spreading and coding. And randomly generating a binary bit stream as source data, performing exclusive or operation on the source data and the spreading sequence in A1, and performing direct sequence spreading.

The effect of spreading is to "chop" its symbols before transmitting the signal; the effect of despreading is to recover the symbols after reception. Here c (t) is called spreading code, which is typically a pseudo-random (spreading) code (random known sequence) with a value of ± 1. Its symbols are called chips or "chips", the chip width being denoted T_c. Usually T_c＝T_sand/L is the code width of a baseband signal, L is a spreading factor and is an integer which is more than or equal to 1.

Direct sequence spreading is actually a modulo-2 addition process of source data a (t) and spreading code c (t), if the source data a (t) and the spreading code c (t) are both-1 or both 1, then the spreading result d (t) is-1, otherwise the spreading result is 1. In fig. 6, the signals of the transmitting end are: s (t) a (t) c (t) cos (2 pi f)₀t+θ₀). In the formula (f)₀For modulating the frequency of the carrier wave, theta₀Is the initial phase of the modulated carrier.

C3, performing QPSK modulation. And setting a sampling frequency, and converting the bit stream which is originally an impulse signal and is modulated by a spread spectrum sequence into a rectangular signal. At the receiving end after transmission over the wireless channel:

S'(t)＝a(t-t_d)c(t-t_d)cos(2π(f₀+f_d)(t-t_d)+θ₀) + N (t). In the formula, t_dFor time delay of the signal, f_dFor Doppler shift, N (t) is the noise in the channel. The receiver can recover the original data of the signal source after performing de-spreading demodulation operation on the received signal.

Suppose the information code sequence is [ -11-1]The spreading code is [ -111-11 [ -11 ]]Then pass throughThe carrier modulated waveform of (2) is shown in fig. 7. The waveforms are shown as a (t), c (t), d (t), S (t), respectively.

When the receiving end demodulates and spreads, it needs to generate a spreading sequence and implement a despreading function. In the scheme of designing the first demodulation and the second despreading, the spread spectrum sequence and the received information code are subjected to XOR operation, and then the despreading function can be realized through a despreading module. In the scheme of despreading first and then demodulating, because the modulated signal is an analog signal and is represented by an imaginary number, the received analog information code needs to be split into a real part and an imaginary part, and the real part and the imaginary part are respectively subjected to exclusive or operation and then pass through a despreading module, so that the despreading function is realized. The specific algorithm steps are as follows:

d1, performing QPSK demodulation. After the spread and modulated analog signal is transmitted through a gaussian channel signal, the signal is demodulated through QPSK, and the analog signal is converted into a digital signal.

And D2, despreading the spread spectrum signal. And multiplying the spread spectrum signal by a despreading sequence, and finally carrying out sampling judgment on the despread signal. An important feature of spread spectrum systems is the ability to combat narrowband interference. Assuming that there is a narrow-band interference in the channel, the despread output signal is:

S'(t)＝a(t-t_d)c(t-t_d)cos(2π(f₀+f_d)(t-t_d)+θ₀)+N(t)+J(t)c(t-t_d)

the spectrum of J (t) is spread into a flat shape, while its power spectral density is reduced by a factor of L, so that J (t) c (t-t)_d) Equivalent to general white noise. After subsequent narrow-band filtering, the effect becomes minimal. The whole process is shown in fig. 6.

In general, nodes in a communication system have both transmission and reception functions, and therefore, a direct sequence spread spectrum communication method for each node in the communication system is shown in fig. 8: for the communication data to be sent out, the method is firstly used for direct sequence spread spectrum, then the subsequent steps of modulation, frequency mixing and the like are carried out, and finally the communication data are sent out through an antenna; for the communication data to be received, the conventional filtering, analog-to-digital conversion, signal amplification, frequency mixing, demodulation and other steps are firstly carried out, and then the data obtained in the steps are subjected to de-spreading processing according to the method of the invention, so as to obtain the real communication data.

The above embodiments are preferred embodiments of the present application, and those skilled in the art can make various changes or modifications without departing from the general concept of the present application, and such changes or modifications should fall within the scope of the claims of the present application.