Capacity evaluation method of code division multiple access spread spectrum communication system

Abstract

The invention provides a capacity evaluation method for a code division multiple access spread spectrum communication system, which comprises the following steps of: selecting a reference user according to user configuration information of the code division multiple access spread spectrum communication system, calculating a cross correlation coefficient between spread spectrum codes of the reference user and other users, and analyzing random characteristics of the cross correlation coefficient; obtaining a signal intensity ratio relation of system users according to the system user distribution and the beam directional diagram; based on the signal strength ratio relation, the random characteristic, the system communication requirement and the non-ideal power control, the failure probability of the reference user is deduced and calculated; and calculating a curve that the failure probability of the reference user changes along with the number of the system users through a conditional probability mode, and selecting a target failure probability according to a system communication requirement to obtain a system user capacity estimation value corresponding to the target failure probability. In this way, the capacity of the multi-beam code division multiple access system can be evaluated more accurately.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a capacity evaluation method for a code division multiple access spread spectrum communication system. Background Art

[0002] Since the 1980s, ground cellular wireless communication technology has developed rapidly, and has gone through the construction of 2G, 3G, 4G, and 5G systems at a rate of one generation every ten years. In air communication systems, especially narrowband air mobile communication systems, the development achievements of ground communication technology have always been fully absorbed and learned from. The communication system standards and specifications formed by ground cellular networks have also been widely transplanted and applied to air mobile communication systems in the past 20 years.

[0003] The continuous growth of communication volume and the emergence of new services have put forward the demand for capacity expansion of the structure of wireless networks. Mobile communication technology meets the high throughput, service integration and flexibility requirements of mobile communication systems by using spectrum as efficiently as possible and managing radio resources flexibly. Spread spectrum code division multiple access, or CDMA, is a mature multiple access method system. As the air interface standard of the third generation of mobile communications, it has been widely used and used in various digital cellular mobile and wireless personal communication systems. It can also provide high spectrum efficiency in air multi-beam communication systems and has broad application prospects. Spread spectrum code division multiple access scheme is a scheme that distributes the transmitted signal over a wider frequency band, which is much wider than the minimum bandwidth required to transmit the information to be sent. In spread spectrum CDMA, all users use the same bandwidth, but each transmitter is assigned a different sequence.

[0004] Since all users in the system use the same frequency resources, different spreading sequences are used to distinguish between users, and user signals interfere with each other. Although different spreading sequences usually have good cross-correlation properties, it is difficult to achieve perfect synchronization between users. As the number of users increases, the capacity of the CDMA system is inevitably affected by multiple access interference, also known as the soft capacity of the system. Soft capacity makes the system capacity evaluation of CDMA communication systems a complex issue. Ground systems usually use a simple modeling method based on measured data to evaluate endogenous interference, while the multi-beam aerial system model is quite different from the ground cellular cells. First, the overlapping area of ​​ground cells is limited, and the mutual interference is small. The interference of other cells can be simply estimated by the interference factor. The multi-beam antenna needs to meet the gain and coverage conditions. The interference between its multiple beams is obvious and cannot be simply estimated. Secondly, the long delay caused by the distance affects the power control effect, and the power control has a certain error. Non-ideal power control, the cross-correlation properties between user spreading codes and the endogenous interference of the system will directly affect the capacity of the CDMA system. The rough estimation by traditional methods cannot meet the needs of capacity evaluation of multi-beam aerial CDMA systems. Summary of the invention

[0005] The object of the present invention is to provide a capacity assessment method for a code division multiple access spread spectrum communication system, which can achieve more accurate capacity assessment of a multi-beam code division multiple access system.

[0006] In order to achieve the above object, the present invention provides a capacity evaluation method for a code division multiple access spread spectrum communication system, comprising the steps of:

[0007] Selecting a reference user according to user configuration information of the code division multiple access spread spectrum communication system, calculating a mutual correlation coefficient between the reference user and the spread spectrum codes of other users, and analyzing the random characteristics of the mutual correlation coefficient;

[0008] According to the system user distribution and the beam pattern, a signal strength ratio relationship of the system users is obtained; and based on the signal strength ratio relationship, the random characteristics, the system communication requirements and the non-ideal power control, a failure probability of the reference user is calculated;

[0009] The curve of the failure probability of the reference user changing with the number of system users is calculated by conditional probability, and the target failure probability is selected according to the system communication requirement to obtain the estimated value of the system user capacity corresponding to the target failure probability.

[0010] Furthermore, after calculating the curve of the failure probability of the reference user changing with the number of system users by conditional probability and selecting the target failure probability according to the system communication requirements to obtain the estimated value of the system user capacity corresponding to the target failure probability, the method further includes:

[0011] Determine whether the user configuration information has changed, and if so, recalculate the estimated value of the system user capacity based on the updated user configuration information.

[0012] Optionally, the failure probability is the probability that the communication quality is less than a set threshold. Suppose the failure probability is P outage ,but:

[0013]

[0014] And the target transmission quality under non-ideal power control is:

[0015]

[0016] Where (i, j, k) represents the i-th user of the j-th beam of the k-th system; P 0 is the received energy under ideal power control, R is the transmission rate of a single user, B is the channel bandwidth, η is the noise energy, α i ,j, kis the mutual correlation coefficient, I is the multiple access interference energy; is the variable of non-ideal power control.

[0017] Optionally, the multiple access interference energy of the return signal is:

[0018]

[0019] in, It is represented by the gain coefficient caused by the directional pattern of the reference beam and the interference beam corresponding to the return signal; θ i,j,k It is expressed as the deviation angle of user (i, j, k) from the center of its corresponding interference beam, It is expressed as the deviation angle of user (i, j, k) compared to the reference beam center.

[0020] Furthermore, the interference statistical variables of the complex multi-users of the return signal are discretized by using a discrete probability numerical analysis method to discretize the non-ideal power control effect of the interference, and a joint discrete probability analysis is performed with the discrete mutual correlation coefficient; or

[0021] The complex multi-user interference statistical variables of the return signal are obtained by statistically approximating the discrete mutual correlation coefficient so that the mutual correlation coefficient is equivalent to a new logarithm-to-Gaussian distributed random variable, and after combining it with the non-ideal control influence, the sum of the multi-users is approximated as a Gaussian variable.

[0022] Optionally, the multiple access interference energy of the forward signal is:

[0023]

[0024] in, θ is the gain ratio of the user's corresponding transmitting beam corresponding to the forward signal at the user's position and the reference position; i,j,k It is expressed as the deviation angle of user (i, j, k) from the center of its corresponding interference beam, It is expressed as the deviation angle of user (i, j, k) compared to the reference beam center.

[0025] Furthermore, the interference statistical variables of the complex multi-users of the forward signal are discretized by using a discrete probability numerical analysis method to discretize the non-ideal power control effect of the interference, and a joint discrete probability analysis is performed with the discrete mutual correlation coefficient; or

[0026] The complex multi-user interference statistical variables of the forward signal are obtained by statistically approximating the discrete mutual correlation coefficient so that the mutual correlation coefficient is equivalent to a new logarithm-to-Gaussian distributed random variable, and after combining with the non-ideal control influence, the sum of the multi-users is approximated as a Gaussian variable.

[0027] Furthermore, the conditional probability formula of the failure probability is:

[0028]

[0029] in, A represents the probability that the communication channel is directly blocked, and 1-A represents the probability that the communication channel is not directly blocked.

[0030] Optionally, the curve of the failure probability of the reference user calculated by conditional probability as a function of the number of system users includes:

[0031] By modeling the random variables of power control, a relationship model between the failure probability and the number of users is established, and a change curve is constructed based on the relationship model.

[0032] The capacity assessment method of the code division multiple access spread spectrum communication system described in the present invention derives the probability of system user failure under different numbers of users by analyzing the correlation of spread spectrum codes between users, the influence of non-ideal power control and link interference. Compared with the existing assessment method, it can achieve more accurate capacity assessment of the multi-beam code division multiple access system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A flow chart showing the steps of the capacity assessment method of the code division multiple access spread spectrum communication system provided by one embodiment of the present invention;

[0034] Figure 2 A flow chart showing the steps of a capacity assessment method for a code division multiple access spread spectrum communication system provided by another embodiment of the present invention;

[0035] Figure 3 A schematic structural diagram of a bidirectional link of a spread spectrum communication system is shown. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0037] It should be noted that references to "one embodiment", "embodiment", "example embodiment", etc. in this specification refer to the embodiment described, which may include specific features, structures or characteristics, but not every embodiment must include these specific features, structures or characteristics. In addition, such expressions do not refer to the same embodiment. Furthermore, when describing specific features, structures or characteristics in conjunction with an embodiment, whether or not there is an explicit description, it has been shown that it is within the knowledge of those skilled in the art to combine such features, structures or characteristics into other embodiments.

[0038] In addition, certain words are used in the specification and subsequent claims to refer to specific components or parts. Those with ordinary knowledge in the relevant field should understand that manufacturers can use different nouns or terms to refer to the same component or part. This specification and subsequent claims do not use differences in names as a way to distinguish components or parts, but use differences in the functions of components or parts as the criteria for distinction. "Including" and "including" mentioned throughout the specification and subsequent claims are open-ended terms and should be interpreted as "including but not limited to". In addition, the word "connected" here includes any direct and indirect electrical connection means. Indirect electrical connection means include connection through other devices.

[0039] See also Figure 3 In the spread spectrum communication scenario of multi-beam CDMA, each beam is distributed with a certain number of users, and each user communicates signals with the system. The signals of other users other than the target reference user will also affect the received signal of the reference user; wherein, the signals of each user are spread by their own spread spectrum code sequences, and the arrival time of each communication signal is random. The capacity evaluation method of the code division multiple access spread spectrum communication system provided by the present invention is specifically applied to the multi-beam, multi-user air communication scenario, and the communication signal of the target signal is received by the receiving end through signal despreading and demodulation processing; in addition to the target user, there are multiple interfering users in the scenario, and all users adopt the spread spectrum communication system. Without loss of generality, user communication adopts their own spread spectrum code.

[0040] Figure 1 A capacity assessment method for a code division multiple access spread spectrum communication system provided by an embodiment of the present invention is shown, and the method comprises the following steps:

[0041] S101: According to the user configuration information of the code division multiple access spread spectrum communication system, a reference user is selected, the mutual correlation coefficient between the reference user and the spread spectrum codes of other users is calculated, and the random characteristics of the mutual correlation coefficient are analyzed. Specifically, the user configuration information is the user spread spectrum code / scrambling code configuration. In the code division multiple access spread spectrum communication system, each user is assigned a specific spread spectrum code or scrambling code; the role of these spread spectrum codes / scrambling codes is to expand the user's original signal to a wider frequency band so that the signals of different users can be distinguished from each other during the transmission process; the configuration of the spread spectrum code / scrambling code is usually determined according to the design requirements of the system and the number of users to ensure the performance and capacity of the system.

[0042] In order to analyze the performance of the system, a specific user is usually selected as a reference user; the reference user can be any user in the system, and a representative user is usually selected, such as a user at the center of the system or a user with specific business needs.

[0043] In actual communication systems, due to the influence of factors such as channel fading, noise and interference, the cross-correlation properties between spread spectrum codes are usually random; this random property makes the performance and capacity of the system also random and difficult to accurately predict. In order to better understand and analyze the performance of the system, this embodiment calculates the cross-correlation properties of the spread spectrum codes of each other user and the spread spectrum code of the reference user, and then further summarizes the random characteristics of the cross-correlation properties through statistical analysis.

[0044] S102: According to the system user distribution and beam pattern, the signal strength ratio relationship of the system users is obtained; and based on the signal strength ratio relationship, random characteristics, system communication requirements and non-ideal power control, the failure probability of the reference user is calculated. That is, this step is specifically based on the probability intermediate value calculation of the correlation between non-ideal power control and spread spectrum code, the signal strength ratio relationship of the system users is obtained from the system user distribution and beam pattern, and then based on the user signal strength relationship, random characteristics, system communication requirements and non-ideal power control, the intermediate variable is derived and calculated.

[0045] By considering the system user distribution and beam pattern, the signal strength ratio relationship between different users can be obtained; this is because the user's position and the characteristics of the beam pattern determine the signal strength received by each user; specifically, by comparing the signal strengths of different users, the relative size relationship between them can be determined; after obtaining the signal strength ratio relationship of the system users, the failure probability of the reference user is further derived and calculated based on the obtained signal strength ratio relationship, random characteristics, system communication requirements and non-ideal power control.

[0046] S103: Calculate the curve of the failure probability of the reference user changing with the number of system users by conditional probability, and select the target failure probability according to the system communication requirements to obtain the estimated value of the system user capacity corresponding to the target failure probability. That is, in this step, based on the conditional probability analysis of the link quality required by the students, obtain the curve of the failure probability of the reference user changing with the number of system users; then select the target failure probability based on the system communication requirements to obtain the system user capacity evaluation result corresponding to the target failure probability.

[0047] In specific implementation, the communication system is assumed to have multiple beams, and the sub-satellite spot beam is the reference beam for capacity calculation. At this time, the average value of the reference beam's deviation angle from the antenna center of all beams is the smallest, and other surrounding beams are interference beams, causing interference to the reference beam. The return received signal contains reference user information, user interference signals in the same beam, and user interference signals in different beams of the same system. In more complex scenarios, it may also contain interference signals from other systems in the constellation and passive intermodulation interference caused by shared transmission and reception.

[0048] At this time, the user interruption probability of the system is expressed as the probability that the quality is less than the set threshold, that is, the failure probability is the probability that the communication quality is less than the set threshold. Let the failure probability be P outage ,but:

[0049]

[0050] Among them, E b / N 0 Characterizes the transmission quality of the target, usually expressed by the ratio of energy per bit to interference noise spectral density:

[0051] Among them, B is the channel bandwidth, R is the transmission rate of a single user, C is the received carrier energy, I is all co-channel interference, that is, multiple access interference energy, and η is the noise energy.

[0052] The ratio of the spread spectrum bandwidth to the rate is only a statistic of the interference after the spread spectrum processing, which is suitable for analyzing the impact of thermal noise. However, the impact of the actual interference after despreading needs to be specific to the correlation between the spread spectrum codes. Therefore, it is preferred to rewrite the expression of the transmission energy as follows:

[0053]

[0054] Among them, α i,n is a random variable of the cross-correlation coefficient of the multiple access interference, which is determined by the correlation property of the spreading codes between users, and I is the interfering user power; different from the traditional method that uses the spreading factor to simply consider the impact of multiple access interference, this embodiment introduces the cross-correlation random coefficient α i,n Conduct refined analysis.

[0055] Non-ideal power control will also affect system capacity, and non-ideal power control errors need to be considered for link quality analysis and system capacity assessment. In a CDMA system, each user undergoes power control to ensure that the signal energy received in the corresponding beam is equal; however, even under unobstructed direct conditions, there is still a certain power control error, which is specifically set as an exponential Gaussian distribution, such as γ = e δ , where δ is zero mean and variance is σ δ Gaussian random variable. In this embodiment, in this system, the target transmission quality under non-ideal power control is:

[0056]

[0057] Where (i, j, k) represents the i-th user of the j-th beam of the k-th system; P 0 is the received energy under ideal power control, R is the transmission rate of a single user, B is the channel bandwidth, η is the noise energy, α i ,j, kis the mutual correlation coefficient, I is the multiple access interference energy; is the variable of non-ideal power control.

[0058] Furthermore, the multiple access interference energy of the return signal is:

[0059]

[0060] in, It is represented by the gain coefficient caused by the directional pattern of the reference beam and the interference beam corresponding to the return signal; θ i,j,k It is expressed as the deviation angle of user (i, j, k) from the center of its corresponding interference beam, It is expressed as the deviation angle of user (i, j, k) compared to the reference beam center.

[0061] In interference beam j, the user signal is power-controlled based on the interference beam center and distance, that is, the transmission energy has an additional proportional factor 1 / G j (θ i,j,k ).

[0062] The computational complexity required to analyze different cross-correlation properties for each interfering user is extremely high, so this embodiment provides two random interference analysis methods. First, a discrete probability numerical analysis method is used to calculate the non-ideal power control effect of the interference. Discretization and correlation coefficient α i,j,k Perform joint discrete probability analysis; the mutual correlation coefficient α of a single user i,j,k Impact of non-ideal power control The probability density function of the product is:

[0063]

[0064] Where Ncorr represents the number of numerical states of cross-correlation, P a (·) is the probability corresponding to the cross-correlation value, f e (z / α) is The probability density function of .

[0065] The probability density function of the random variable z of the total interference of the system can be obtained by convolution of the above single user variables, using the beam statistical mean Replace the β in each beam i,j,k .

[0066]

[0067] Convolution calculation can be quickly calculated using fast Fourier transform, but due to the high complexity of polynomial calculation, it is difficult to obtain an analytical solution. Therefore, it is recommended to After discretization, numerical calculations are performed.

[0068] Second, the discrete correlation coefficient α i,j,k Perform statistical approximation to make it equivalent to a new logarithm of a Gaussian distributed random variable The impact of non-ideal power control After merging, since the multiplication of the two values ​​is reflected as the addition of exponential terms, the sum of the two Gaussian variables forms a new Gaussian random variable

[0069] When the number of users N is large enough, the sum of multiple logarithmic Gaussian distributions can also be approximated as a Gaussian random variable. Normalization The variance and mean of energy are expressed as:

[0070]

[0071] in:

[0072] h represents the conversion factor of natural logarithm and 10 logarithm h=ln10 / 10, A and 1-A represent the probability of the communication channel being directly blocked and not being directly blocked, respectively, and have power control variances of direct blocking (the power control variance σ in this embodiment) δs and the unoccluded variance σ δus (σ in this embodiment δs >σ δus ).

[0073] At this time, the random variable α of the cross-correlation property and the non-ideal control power control error term The probability density function of the product can be approximately equivalent to a Gaussian random variable:

[0074] Different probability density functions f can be obtained by the above two interference random analysis methods. z (z).

[0075] The failure probability can be specifically expressed as:

[0076]

[0077] in The random variable ζ on the right side of the inequality is:

[0078]

[0079] When referring to the user Under certain conditions, that is, the conditional probability under the condition of ζ being determined can be obtained from the probability density function:

[0080]

[0081] where f z (z) The probability density function of the numerical calculation method (1) can be selected, or the Gaussian distribution probability density function of the approximate method (2) can be used to obtain the value.

[0082] The final outage probability formula can be obtained by integrating different error probability densities and conditional probabilities:

[0083]

[0084] Furthermore, the curve of the failure probability of the reference user changing with the number of system users by conditional probability includes: establishing a relationship model between the failure probability and the number of users by modeling the random variable of power control, and constructing a change curve based on the relationship model. After establishing the relationship between the failure probability and the number of users by modeling the random variable of power control, the system capacity under a certain failure probability (such as 1%) can be analyzed.

[0085] For the system communication capacity in the forward case, the same idea can be used to analyze the system capacity. The analysis of the forward capacity is slightly different. The power control makes the effective energy reaching each user as equal as possible to reduce the mutual interference between users. Compared with the beam proportional factor β in the return link, (i,j,k) is the gain ratio of the corresponding beam to the reference beam of a terminal at a certain location. In the forward link analysis, the beam scaling factor is β (i,j,k) It is the gain ratio of the transmitting beam corresponding to a user at a certain position at the user position and the reference position, that is, the gain ratio of the same beam at different positions, while considering the distance loss change, expressed as:

[0086] Among them, for the high-orbit system, the distance loss changes little and can be handled approximately.

[0087] Due to the orthogonality of the forward orthogonal code, when each user is perfectly synchronized, there is no multi-user interference in this beam; if non-ideal factors such as the multipath effect in the transmission path are considered, the orthogonality of multi-user multi-access interference in the same beam in the forward link is expressed as the orthogonality factor, and the total interference is expressed as:

[0088]

[0089] in, θ is the gain ratio of the user's corresponding transmitting beam corresponding to the forward signal at the user's position and the reference position; i,j,k It is expressed as the deviation angle of user (i, j, k) from the center of its corresponding interference beam, It is represented as the deviation angle of user (i, j, k) compared to the reference beam center. The probability analysis method of the interference of the forward signal in this embodiment is the same as that in the return scenario.

[0090] The statistical characteristics of the non-ideal power control variables and in this beam change, which is expressed as:

[0091]

[0092] Based on the statistical distribution of the above formula, the conditional probability formula of the interruption probability is:

[0093]

[0094] in, A represents the probability that the communication channel is directly blocked, and 1-A represents the probability that the communication channel is not directly blocked.

[0095] The relationship between the outage probability and the number of users N in the forward communication CDMA system is obtained similarly to the return link process.

[0096] This embodiment aims at the capacity estimation problem of multi-beam spread spectrum code division multiple access communication system, and based on the analysis and modeling of the intrinsic interference of CDMA air mobile communication system including multi-beam interference and multi-user multiple access interference, and combined with the system characteristics such as long delay non-ideal power control, the capacity of air CDMA communication system under the system intrinsic interference is finely analyzed. The method provided in this embodiment derives the relationship between system user capacity and user failure probability based on probability analysis and spread spectrum code characteristics, which can more accurately evaluate the capacity of air CDMA communication system and support the design and demonstration of air spread spectrum CDMA system.

[0097] Figure 2 A capacity assessment method for a code division multiple access spread spectrum communication system provided by another embodiment of the present invention is shown. Based on the above embodiment, this embodiment further includes, after step S103:

[0098] S104: Determine whether the user configuration information has changed. If it has changed, recalculate the estimated value of the system user capacity based on the updated user configuration information. That is, when the user configuration in the system changes and the capacity result is no longer accurate, based on the updated user configuration information, return to re-execute steps S101 to S103 to calculate the relevant values ​​to complete the capacity evaluation update. The calculation method of the relevant values ​​in this embodiment refers to the content of the above embodiment, which will not be repeated here.

[0099] In summary, the capacity assessment method of the code division multiple access spread spectrum communication system provided by the present invention is based on the modeling of system intrinsic interference, and takes into account the influence of non-ideal power control and the mutual correlation of user spread spectrum codes. Compared with the method of only using rough estimation of the spreading factor, the system capacity can be more accurately analyzed based on the failure probability; and, considering the complexity of fine analysis, the present invention proposes an approximate analysis method in which the mutual correlation of user spread spectrum codes is approximated as a logarithmic Gaussian random variable and the interference probability analysis of multiple users is approximated as a Gaussian variable, which can reduce the complexity of the capacity estimation method.

[0100] The method according to the present invention can be implemented on a computer as a computer-implemented method, or implemented in dedicated hardware, or implemented in a combination of the two. The executable code or part thereof for the method according to the present invention can be stored on a computer program product. The example of a computer program product includes a memory device, an optical storage device, an integrated circuit, a server, online software, etc. Preferably, the computer program product includes a non-temporary program code component stored on a computer-readable medium so as to perform the method according to the present invention when the program product is executed on a computer.

[0101] In a preferred embodiment, the computer program comprises computer program code means adapted to perform all the steps of the method according to the invention when the computer program is run on a computer.Preferably, the computer program is embodied on a computer readable medium.

[0102] Of course, the present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A capacity evaluation method for a code division multiple access spread spectrum communication system, characterized in that: Includes steps: Selecting a reference user according to user configuration information of the code division multiple access spread spectrum communication system, calculating a mutual correlation coefficient between the reference user and the spread spectrum codes of other users, and analyzing the random characteristics of the mutual correlation coefficient; According to the system user distribution and the beam pattern, a signal strength ratio relationship of the system users is obtained; and based on the signal strength ratio relationship, the random characteristics, the system communication requirements and the non-ideal power control, a failure probability of the reference user is calculated; The curve of the failure probability of the reference user changing with the number of system users is calculated by conditional probability, and the target failure probability is selected according to the system communication requirement to obtain the estimated value of the system user capacity corresponding to the target failure probability.

2. The capacity evaluation method of a code division multiple access spread spectrum communication system according to claim 1, characterized in that: After calculating the curve of the failure probability of the reference user changing with the number of system users by conditional probability and selecting the target failure probability according to the system communication requirements to obtain the estimated value of the system user capacity corresponding to the target failure probability, the method further includes: Determine whether the user configuration information has changed, and if so, recalculate the estimated value of the system user capacity based on the updated user configuration information.

3. The capacity evaluation method of a code division multiple access spread spectrum communication system according to claim 1, characterized in that: The failure probability is the probability that the communication quality is less than the set threshold. Let the failure probability be P outage ,but: And the target transmission quality under non-ideal power control is: Where (i, j, k) represents the i-th user of the j-th beam of the k-th system; P0 is the received energy under ideal power control, R is the transmission rate of a single user, B is the channel bandwidth, η is the noise energy, α i ,j, k is the mutual correlation coefficient, I is the multiple access interference energy; is the variable of non-ideal power control.

4. The capacity evaluation method of a code division multiple access spread spectrum communication system according to claim 3, characterized in that: The multiple access interference energy of the return signal is: in, It is represented by the gain coefficient caused by the directional pattern of the reference beam and the interference beam corresponding to the return signal; θ i,j,k It is expressed as the deviation angle of user (i, j, k) from the center of its corresponding interference beam, It is expressed as the deviation angle of user (i, j, k) compared to the reference beam center.

5. The capacity evaluation method of a code division multiple access spread spectrum communication system according to claim 4, characterized in that: The interference statistical variables of the complex multi-users of the return signal are discretized by using a discrete probability numerical analysis method to discretize the non-ideal power control effect of the interference, and a joint discrete probability analysis is performed with the discrete mutual correlation coefficient; or The complex multi-user interference statistical variables of the return signal are obtained by statistically approximating the discrete mutual correlation coefficient so that the mutual correlation coefficient is equivalent to a new logarithm-to-Gaussian distributed random variable, and after combining it with the non-ideal control influence, the sum of the multi-users is approximated as a Gaussian variable.

6. The capacity evaluation method of a CDMA spread spectrum communication system according to claim 3, characterized in that: The multiple access interference energy of the forward signal is: in, θ is the gain ratio of the user's corresponding transmitting beam corresponding to the forward signal at the user's position and the reference position; i,j,k It is expressed as the deviation angle of user (i, j, k) from the center of its corresponding interference beam, It is expressed as the deviation angle of user (i, j, k) compared to the reference beam center.

7. The capacity evaluation method of a code division multiple access spread spectrum communication system according to claim 6, characterized in that: The interference statistical variables of the complex multi-users of the forward signal are discretized using a discrete probability numerical analysis method to discretize the non-ideal power control effect of the interference, and a joint discrete probability analysis is performed with the discrete mutual correlation coefficient; or The complex multi-user interference statistical variables of the forward signal are obtained by statistically approximating the discrete mutual correlation coefficient so that the mutual correlation coefficient is equivalent to a new logarithm-to-Gaussian distributed random variable, and after combining with the non-ideal control influence, the sum of the multi-users is approximated as a Gaussian variable.

8. The capacity evaluation method of a code division multiple access spread spectrum communication system according to claim 4 or 6, characterized in that: The conditional probability formula of the failure probability is: in, A represents the probability that the communication channel is directly blocked, and 1-A represents the probability that the communication channel is not directly blocked.

9. The capacity evaluation method of a code division multiple access spread spectrum communication system according to claim 1, characterized in that: The curve of calculating the failure probability of the reference user as a function of the number of system users by conditional probability includes: By modeling the random variables of power control, a relationship model between the failure probability and the number of users is established, and a change curve is constructed based on the relationship model.

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