Intelligent reflector auxiliary network parameter configuration method based on time correlation

Abstract

The invention provides an intelligent reflector auxiliary network parameter configuration method based on time correlation. And the intelligent reflecting surface supplementarily covers the base station and provides service for the user. Specifically, according to the method, features of the intelligent reflecting surface are included in time correlation analysis, random movement of a user under a time-varying cascade channel and adjacent time slots of the intelligent reflecting surface is considered, and the influence of time fluctuation on the performance of the intelligent reflecting surface auxiliary network is analyzed. By considering the randomness of small-scale fading, the dual-path loss characteristic of the intelligent reflecting surface and the array gain of the intelligent reflecting surface, the time-varying cascade channel is represented. Besides, the time correlation is reflected by measuring the correlation of users among different time slots, the network performance of large-scale time is represented by the joint coverage rate of multiple time slots based on the time correlation, and the network performance of the large-scale time is calculated according to the change relation of the joint coverage rate along with deployment parameters. And obtaining the deployment density and the number of service units of the intelligent reflecting surface when the joint coverage performance is optimal.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a deployment design of a dynamic network based on time correlation in an intelligent reflecting surface (IRS) assisted network. Background Art

[0002] With the rapid development of intelligent reflective surface technology, this technology has shown great potential in improving the performance of wireless communication networks. Intelligent reflective surface is an artificial structure that can dynamically control the reflection of electromagnetic waves. By adjusting the phase of the reflective unit, IRS can flexibly change the propagation environment of the wireless channel at the physical level, thereby enhancing signal quality, expanding coverage and reducing interference. Due to these unique advantages, IRS is regarded as one of the key technologies to improve the performance of future wireless communication networks, especially in complex environments or scenarios with insufficient coverage, such as densely populated urban areas, indoor signal blind spots, and remote and remote areas.

[0003] In the field of communications, IRS can be used as a tool to assist network resource management and optimize network performance by adjusting the reflected signal path. For example, in coverage blind spots or restricted environments, IRS can redirect signals to make up for the shortcomings of traditional base stations, enhance signal coverage, or reduce non-line-of-sight (NLOS) transmission problems caused by obstacles. In addition, IRS can also significantly reduce the signal quality degradation caused by multipath effects and interference, which is crucial to improving user experience and ensuring communication quality.

[0004] Although IRS has significant advantages in improving network performance, it still faces some challenges in actual deployment. First, IRS itself does not have active transmission capabilities, so it must rely on base stations or other active devices to transmit signals. Second, the performance optimization of IRS requires accurate channel state information (CSI), which places higher requirements on system design and resource allocation. In addition, the deployment location, angle, and phase control strategy of IRS need to be optimized in multi-dimensional space to achieve the best network performance.

[0005] When used in conjunction with ground base stations, IRS can enhance signal strength and coverage by adjusting the reflection path. However, due to the passive characteristics of IRS, the system design must fully consider its coupling relationship with the environment, such as the relative position relationship between the reflection surface and the user and base station, the obstruction of buildings, and even the dynamic changes of the environment. Especially in large-scale networks, the deployment of IRS needs to consider the complex interactions between multiple users, multiple base stations, and multiple IRSs, as well as the time correlation of channel states.

[0006] When IRS assists dynamic user networks, due to the spatial dependence between IRS and base stations and the static deployment of ground base stations, the network topology is stable. This stability introduces time correlation, that is, the correlation of network status between different time slots. For sites, the degree of correlation between network status in the current time slot and the historical time slot can be obtained according to the size of time correlation, thereby guiding network resource scheduling. On the other hand, due to the change of cascade link channel energy in the three-dimensional spatial channel, different obstruction conditions of obstacles, and independent multipath fading between different time slots, the performance at the user is fluctuating in time; the dynamic nature of users leads to changes in spatial distribution and link status, which makes the network performance at the user time-varying; since the network performance at the user fluctuates in time, the instantaneous rate can no longer represent the average rate of the network, and the instantaneous performance indicators cannot reflect the actual communication quality at the user. Therefore, the deployment of IRS-assisted networks based on the coverage and rate of a single time slot cannot achieve optimal deployment. It is necessary to consider the impact of large-scale time network performance based on time correlation on the deployment of IRS-assisted networks. Summary of the invention

[0007] The present invention considers an intelligent reflective surface-assisted network deployment method based on time correlation, and the intelligent reflective surface provides supplementary coverage to the coverage blind area of ​​the base station to provide services to users. Specifically, the present invention incorporates the IRS characteristics into the time correlation analysis, takes into account the time-varying cascade channel of the IRS and the random movement of users in adjacent time slots, so as to analyze the impact of time fluctuations on the performance of the IRS-assisted network. The time-varying cascade channel is characterized by considering the randomness of small-scale fading, the dual-path loss characteristics of the IRS, and the array gain of the IRS. In addition, the present invention quantifies the time correlation of the IRS by the signal power correlation coefficient at different spatial positions in adjacent time slots. Furthermore, the size of the time correlation is reflected by measuring the correlation between users in different time slots. Based on the time correlation, the joint coverage rate of multiple time slots is used to characterize the network performance of large-scale time. According to the relationship between the joint coverage rate and the deployment parameters, the deployment density and the number of IRS service units when the joint coverage performance is optimized are obtained.

[0008] The deployment method of the intelligent reflective surface auxiliary network of the present invention comprises the following steps:

[0009] Step 200, determining the range of parameters such as the deployment density of IRS and base stations, the number of IRS service units and user moving speed according to the environmental parameters of the target area to be covered by the intelligent reflective surface network.

[0010] According to the scenario of IRS-assisted network downlink transmission, two independent Poisson point processes are used to model the distribution of base stations and IRS respectively. The distribution of base stations is set to Φ B , whose density is λ B, the distribution of IRS is set to Φ I , whose density λ I The base station and the user equipment are both configured as single antennas, and each IRS has N reflection units.

[0011] According to the dynamics of the user, the random waypoint model is used for analysis. The user randomly selects an angle direction from time slot t1 to time slot t2 and moves a distance v.

[0012] Step 210: introduce a time correlation factor, and obtain the time correlation magnitude by calculating the correlation of useful signals in different time slots.

[0013] The mean and variance of the useful signal of the nearest IRS serving a typical user can be represented by the cascaded channel, which for the nth unit in the IRS is calculated as:

[0014]

[0015] in is the path loss from the base station to the IRS, is the path loss from IRS to user, and m is the shape parameter of small-scale fading (Nakagami distribution).

[0016] The central limit theorem is used to calculate the mean and variance of the signal of an IRS with n units. The results are expressed as:

[0017]

[0018] The calculation formula of the first-order moment and second-order moment of the IRS service signal is:

[0019]

[0020] After obtaining the accumulated signal of the user, the signal correlation coefficient ζ of different time slots is calculated S (t1, t2) to obtain the time correlation of the network state, the correlation coefficient ζ S The calculation formula of (t1, t2) is:

[0021]

[0022] Step 220: Obtain received signals y and y of typical users, and calculate their Laplace transforms according to the interference signal.

[0023] The signal received by the user includes the signal passing through the nearest base station, the signal formed by the nearest IRS beam, and the signal generated by the remaining IRS scattering, which can be expressed as:

[0024]

[0025] By assuming and The calculation formula for the first-order moment and the second-order moment of the received signal and is:

[0026]

[0027] The interference signals to the user include the signals of the non-serving base station and the signals of the non-serving base station reflected by the IRS, which can be expressed as:

[0028]

[0029] Further, the calculation formula is:

[0030]

[0031] The Laplace transform of the received interference signal can be expressed as:

[0032]

[0033] where ζ i represents small-scale fading, δ1 represents interference parameter, λ B Indicates the base station density.

[0034] Step 230, based on the time correlation of the network, the large-scale time coverage performance of the IRS-assisted network is obtained, and according to the change of the large-scale time coverage performance with the base station deployment parameters and the IRS parameters, the optimal IRS-assisted network deployment plan is obtained.

[0035] The joint coverage ratio P cov To accurately characterize the network performance of large-scale time, the joint coverage is defined as the probability that the received signal-to-noise ratio of users in multiple time slots is simultaneously higher than the signal-to-noise ratio threshold, which is calculated as shown in formula (11):

[0036]

[0037]

[0038] in, T is the coverage threshold, W is the noise power, k s is the shape parameter, θ s is the scale parameter.

[0039] After determining the range of base station and IRS deployment parameters through known environmental parameters, the group parameter settings that optimize the large-scale temporal coverage performance of the network are determined based on the changes in the joint coverage as the number of IRS service units in the group, the IRS and base station deployment density, the coverage threshold and the user's moving speed change, and the optimal IRS-assisted network deployment is obtained.

[0040] Beneficial Effects

[0041] In view of the insufficient coverage of ground base stations, the present invention proposes a network deployment method using IRS auxiliary services for dynamic users. The use of IRS auxiliary services effectively solves the problems of insufficient signal coverage and severe interference in traditional networks. Deployment parameters are set according to actual environmental conditions: the deployment location of the IRS is determined according to the user's mobile range, and the IRS deployment density and the number of service units are given according to time correlation and joint coverage, providing guidance for the deployment of IRS in actual scenarios.

[0042] The time correlation factor is introduced to obtain the correlation between network states in different time slots through the correlation coefficient of the accumulated interference in different time slots. When the IRS-assisted network is transmitting, the conversion of the channel cascade link between different time slots and the dynamic adjustment of the IRS lead to the fluctuation of the link status and the change of the network topology, making the instantaneous performance indicators unable to reflect the actual communication quality. Therefore, a joint coverage based on time correlation is proposed to more accurately reflect the actual network performance.

[0043] By analyzing the relationship between the joint coverage rate and the IRS deployment density and the number of IRS units, the deployment parameter settings are determined when the network has the best large-scale temporal coverage performance. After determining the deployment parameter settings, the system parameters of the IRS auxiliary network are adjusted according to the relationship between the joint coverage rate and the deployment density of ground base stations and IRS and the number of IRS service units to achieve the optimal IRS deployment strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the intelligent reflector auxiliary network model based on time correlation of the present invention;

[0045] Figure 2 It is a flowchart of the algorithm implementation of the present invention;

[0046] Figure 3 It is a graph showing the relationship between the signal correlation coefficient and the user's moving speed;

[0047] Figure 4 It is a graph showing the relationship between the joint coverage and the user's moving speed. DETAILED DESCRIPTION

[0048] Aiming at the insufficient coverage of ground base stations, the present invention proposes a deployment method of IRS auxiliary base stations to serve dynamic users. The network model is shown in the attached figure. Figure 1 Attached Figure 1The subgraph in gives the network topology of IRS-assisted network serving dynamic users: there are areas where base stations have insufficient coverage, and IRSs are randomly distributed on the ground to provide services to users in these target areas; mobile users obey the random waypoint model, and users randomly choose an angle direction from time slot t1 to time slot t2 and move a distance v; users are served by the nearest base station and the nearest IRS, and no switching occurs between time slot 1 and time slot 2; Figure 1 As shown in the figure, in the IRS-assisted network, the user's cumulative signal mainly comes from two parts: the signal of the serving base station and the signal of the serving base station reflected by the IRS; the user's cumulative interference comes from two parts: the interference of the non-serving base station and the interference of the non-serving base station reflected by the IRS.

[0049] When the IRS assists the network to serve dynamic users, the mutual conversion of the channel energy of the cascaded links and the change of the position of the dynamic users lead to the fluctuation of the links and the fluctuation of the service rate at the users. In order to more accurately characterize the actual network performance, the joint coverage of multiple time slots is used to measure the large-scale time network performance. The joint coverage is defined as the probability that the received signal-to-noise ratio of multiple time slots exceeds the threshold at the same time. The necessity of using the joint coverage to characterize the large-scale time performance of the network depends on the time correlation of the network state. In the scenario considered by the present invention, the time correlation of the network state is introduced by the spatial dependence between different time slots of dynamic users and the stability caused by the static deployment of base stations and IRS. The time correlation is measured by the correlation coefficient of the cumulative interference at users in different time slots. On this basis, the joint coverage of multiple time slots at the user is obtained. According to the relationship between the change of the joint coverage and the deployment parameters, the optimal parameter setting is determined.

[0050] The algorithm flow of this case is as follows Figure 2 As shown, the specific implementation steps are:

[0051] Step 300, obtain the environmental parameters of the target area covered by the intelligent reflective surface network, determine the range of parameters such as the deployment density of IRS and base stations, the number of IRS service units and the user moving speed. According to the scenario of IRS-assisted network downlink transmission, two independent Poisson point processes are used to model the distribution of base stations and IRSs respectively. The distribution of base stations is set to Φ B , whose density is λ B , the distribution of IRS is set to Φ I , whose density λ I According to the user's dynamics, the user obeys the random waypoint model, randomly chooses an angle direction from time slot t1 to time slot t2, and moves a distance v.

[0052] Step 310, introduce the time correlation factor, and obtain the time correlation magnitude by calculating the correlation of useful signals in different time slots. The mean and variance of useful signals of typical users served by IRS can be represented by IRS cascade channels, and the time correlation magnitude is obtained by calculating the correlation coefficient of accumulated signals in different time slots, which reflects the correlation degree of network status between different time slots.

[0053] Step 320, obtain the useful signal and of a typical user, and calculate the Laplace transform of the interference signal. The signal and received by the user include the signal passing through the nearest base station, the signal formed by the nearest IRS beam, and the signal generated by the remaining IRS scattering. The Laplace transform of the interference signal can be obtained by L I (x) = E{e -sI}Obtained.

[0054] Step 330, based on the time correlation of the network, obtain the large-scale time coverage performance of the IRS auxiliary network, and obtain the optimal IRS auxiliary network deployment plan according to the change of the large-scale time coverage performance with the base station deployment parameters and IRS parameters. By analyzing the relationship between the joint coverage rate and the IRS deployment density and the number of IRS units, the deployment parameter settings of the network when the large-scale time coverage performance is optimal are determined. After determining the deployment parameter settings, the system parameters of the IRS auxiliary network are adjusted according to the relationship between the joint coverage rate and the deployment density of the ground base station and IRS and the number of IRS service units to achieve the optimal IRS deployment strategy.

[0055] The simulation results are shown in the attached Figure 3 and attached Figure 4 Attached Figure 3 The relationship between the signal correlation coefficient and the number of IRS service units N, the small-scale fading parameter m, and the distance v between two time slots of the user is given in Appendix. Figure 3 In the figure, the distance between users in two time slots is taken as the horizontal axis. It can be seen that the signal correlation coefficient decreases as the distance between users in time slots increases. This is because as the distance increases, the difference in the transmission environment between users in the two time slots becomes greater, resulting in a smaller correlation in the transmission link. In addition, the difference in the transmission distance from the base station to the user between the two time slots becomes larger, resulting in a smaller correlation in the fading conditions of the signals in the two time slots. Figure 3The three solid lines in the figure compare the signal correlation coefficients under the same number of IRS service units. It can be seen that the signal correlation becomes stronger as the small-scale fading parameter increases. This is because the larger the small-scale fading parameter, the better the channel quality, which makes the fading correlation of the two time slot signals larger. It can also be seen in the figure that as the number of IRS service units increases, the signal correlation coefficient decreases. This is because as the number of IRS units increases, the number of sub-channels served by IRS increases, and the variance of the cascade link signal power increases, resulting in a decrease in signal correlation. When the position of the users in the two time slots is zero, the signal correlation is strong, which means that for stationary or low-speed users, the performance of the two time slots has a strong correlation. In the actual scenario studied in the present invention, since the gap between the two time slots is very small, the distance between the user positions also tends to zero, and the signal correlation coefficient is large, indicating that the network status of different time slots has a strong time correlation, so the joint coverage rate is used to characterize the large-scale time coverage performance of the network.

[0056] Attached Figure 4 The relationship between the joint coverage and the number of IRS service units, IRS deployment density and user moving speed is given for a known coverage threshold. It can be observed that as the speed v increases, the joint coverage probability gradually decreases. This is because higher mobility will lead to worse channel conditions, especially in a rapidly changing wireless environment, where the impact of signal fading and multipath effects will be more significant, thereby reducing the stability and quality of the communication link. At the same time, the IRS density λ I The increase in has a positive impact on the joint coverage probability. Specifically, a higher IRS density means that more IRS devices are deployed in the network, which can provide the ability to reflect and enhance signals over a larger range. The signal strength and coverage are improved, and the chances of users receiving enhanced signals at different locations are increased. Therefore, even at higher mobile speeds, the rate at which the joint coverage probability of the network decreases is relatively slow. In addition, the increase in the number of IRS units N also has a positive impact on the joint coverage probability. More reflection units mean that the IRS can more finely adjust and control the phase of the incident signal, thereby achieving more efficient beamforming. This optimized signal reflection can better counteract the degradation of channel quality caused by the increase in user mobility, thereby slowing down the downward trend of the joint coverage probability to a certain extent.

Claims

1. A method for configuring network parameters based on intelligent reflective surface assistance based on time correlation, characterized in that: include: The base station is deployed with the assistance of an intelligent reflective surface, and ground environmental parameters are obtained. The number of service units of the intelligent reflective surface, the deployment density, and the range of the deployment density of the base station are determined according to the environmental parameters and the user's moving speed. The intelligent reflective surface provides services to users in its coverage area, and the users will be served by the base station and the intelligent reflective surface at the same time. A time correlation factor is introduced to reflect the degree of correlation of the network status. Based on the time correlation, the joint coverage rate of multiple time slots is used to characterize the large-scale time-averaged network performance. The joint coverage rate is used as the key indicator. According to the change of the joint coverage rate with the deployment density of the intelligent reflective surface and the number of service units, the optimal deployment density and the optimal number of service units are obtained, thereby completing the deployment of the intelligent reflective surface-assisted network.

2. The method according to claim 1, characterized in that It is necessary to deploy intelligent reflective surfaces to assist base stations. The distribution of intelligent reflective surfaces and base stations is independent. Intelligent reflective surfaces are deployed in horizontal planes. The deployment density and number of service units of intelligent reflective surfaces are related to environmental parameters and user movement speed.

3. The method according to claim 1 or 2, characterized in that: The service signals received by users include: service signals from base stations and service signals from IRS, and the calculation formula is: According to the signal power in different time slots, the change of the user's received power is obtained.

4. The method according to claim 3, characterized in that Users will be affected by service signals in different time slots, and a time correlation factor is introduced, that is, the correlation coefficient ζ of the cumulative interference I between different time slots S (t1, t2), the calculation formula is: According to the size of the signal correlation coefficient, the correlation degree of the network status between different time slots is obtained.

5. The method according to claim 1, characterized in that The user will be affected by interference signals, including: interference signals from non-serving base stations and interference signals from non-serving base stations reflected by IRS. The calculation formula is: The Laplace transform of the interference signal is obtained by Laplace transform, and its calculation formula is:

6. The method according to claim 1 or 4, characterized in that: Based on time correlation, the joint coverage P of multiple time slots is used cov To characterize the coverage performance of large-scale time, the calculation formula is: After determining the range of base station and IRS deployment parameters through known environmental parameters, the group parameter settings that optimize the large-scale temporal coverage performance of the network are determined based on the changes in the joint coverage as the number of IRS service units in the group, the IRS and base station deployment density, the coverage threshold and the user's moving speed change, and the optimal IRS-assisted network deployment is obtained.

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