Intelligent reflector assisted uplink transmission method and device based on power control

Abstract

According to the intelligent reflecting surface auxiliary uplink transmission method and equipment based on power control, the signal is enhanced by adjusting the intelligent reflecting surface unit, so that the coverage performance of uplink transmission is effectively improved. Specifically, the invention provides a power control model for the uplink transmission of the IRS auxiliary network, the constraint relation between power control and IRS deployment is represented, and uplink signal power distribution is deduced again through stochastic geometry. According to the invention, the power control uplink transmission of the ground user in the IRS auxiliary network under the influence of the IRS cascade channel is analyzed. In addition, an analysis framework is established by using a Poisson point process, and a theoretical expression of a network performance index in an uplink of the IRS-assisted network is calculated. The network performance of an IRS auxiliary uplink is reflected by measuring coverage rates under different system parameters, the deployment density and the number of IRS service units when the joint coverage performance is optimal are obtained, and the tradeoff relation between IRS auxiliary network system deployment parameters and uplink power compensation is represented.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a deployment design of uplink transmission considering power control in an intelligent reflecting surface (IRS) assisted network. Background Art

[0002] With the rapid development of IRS technology, it has shown great potential in improving the performance of wireless communication networks. IRS is an artificial structure that can dynamically control the reflection of electromagnetic waves. By adjusting the phase of the reflection unit, IRS can flexibly change the propagation characteristics of the wireless channel, thereby improving signal quality, expanding coverage and reducing interference. For this reason, IRS is widely regarded as a key technology for improving the performance of future wireless communication networks, especially for complex or insufficiently covered scenarios such as densely populated urban areas, indoor signal blind spots and remote areas.

[0003] In the field of communications, IRS is a tool to assist in network resource management, improving network performance by optimizing reflected signal paths. For example, in environments with insufficient coverage or limited signals, IRS can redirect signals to make up for the coverage defects of traditional base stations, enhance signal transmission, or reduce non-line-of-sight (NLOS) transmission problems caused by obstacles. In addition, IRS can significantly alleviate the impact of multipath effects and interference on signal quality, which is crucial to improving user experience and ensuring communication quality.

[0004] Although IRS has significant potential in improving network performance, its actual deployment still faces many challenges. First, IRS cannot actively transmit signals and must rely on base stations or other active devices. Second, the optimization of IRS performance depends on accurate channel state information (CSI), which places higher requirements on system design and resource allocation. In addition, factors such as the deployment location, reflection angle, and phase control strategy of IRS need to be comprehensively optimized in multi-dimensional space to achieve optimal 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] In an IRS-assisted uplink communication network, the array gain of the IRS significantly increases the channel power, but it also widens the gap between channel powers at different distances. Therefore, a reasonable uplink power control strategy is needed to fully utilize the performance gain brought by the IRS. In order to optimize the uplink network performance, it is necessary to consider how to reasonably set the power allocation strategy and IRS deployment parameters to achieve more efficient uplink communication network coverage. Summary of the invention

[0007] The present invention discloses a method and device for uplink transmission assisted by an intelligent reflector based on power control, which enhances the signal by adjusting the intelligent reflector unit, thereby effectively improving the coverage performance of the uplink transmission. Specifically, the present invention proposes a power control model for uplink transmission of an IRS-assisted network, characterizes the constraint relationship between power control and IRS deployment, and re-derives the uplink signal power distribution through random geometry. The present invention analyzes the fractional power control uplink transmission of ground users in an IRS-assisted network under the influence of an IRS cascade channel. In addition, the user distribution is redefined and calculated, an analysis framework is established using a Poisson point process, and the Laplace transform of the uplink interference under an IRS cascade channel is derived. By using a random geometry method, the theoretical expression of the network performance index in the uplink of the IRS-assisted network is calculated. The network performance of the IRS-assisted uplink is reflected by measuring the coverage rate under different system parameters. According to the relationship between the coverage rate and the deployment parameters, the deployment density and the number of IRS service units when the joint coverage performance is optimal are obtained, and the trade-off relationship between the deployment parameters of the IRS-assisted network system and the uplink power compensation is characterized.

[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 power control according to the environmental parameters of the target area 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 user equipment are both set to single antenna, and each IRS has N reflection units. The power control size is:

[0011]

[0012] where ρ is the power allocation factor, v kis the distance between the kth user and the slave base station, α is the path loss parameter, and ε is the power compensation factor.

[0013] Step 210, calculating the power of the useful uplink transmission signal under power control.

[0014] For a typical user, the signal is transmitted to the nearest base station and served by the nearest IRS0. In the case of IRS0 beamforming, the calculation formula of the user's uplink power using the central limit theorem is:

[0015]

[0016] For the remaining IRS, the signal received from the user is scattered, and the calculation formula is:

[0017]

[0018] The calculation formula for the uplink transmission power received by the base station under the user's power control is:

[0019]

[0020] Assumptions and The first-order moment calculation formula of the average signal received power is:

[0021]

[0022] The second-order moment calculation formula of the average signal received power is:

[0023]

[0024] The gamma function Γ(k,θ) is used to approximate the service signal power distribution, and the calculation formula is:

[0025]

[0026] Step 220, calculate the interference signals and, and calculate the Laplace transform thereof according to the interference signals.

[0027] The uplink interference signals received by the user include the signals of non-service users and the signals of non-service users 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] in represents the distance distribution from the interfering user to the corresponding base station, represents the interference parameter.

[0034] Step 230, based on the uplink coverage of the network, obtain the system performance of the IRS-assisted uplink network under power control, and obtain the optimal IRS-assisted uplink network deployment plan according to the changes in coverage performance with base station deployment parameters, IRS parameters and power control strategy.

[0035] With coverage P cov To accurately characterize the network performance over a large time scale, the coverage rate is defined as the probability that the received signal-to-noise ratio at the user is higher than the signal-to-noise ratio threshold, which is calculated as follows:

[0036]

[0037] Where, Y = T (I + W) / θ S , T is the coverage threshold, W is the noise power, k s is the shape parameter, θ S is the scale parameter.

[0038] After determining the range of base station and IRS deployment parameters through known environmental parameters, the parameter settings for optimal uplink coverage performance of the network are determined based on changes in coverage as the number of IRS service units, IRS and base station deployment density, coverage threshold, and power control parameters change, thereby obtaining the optimal IRS assisted network deployment.

[0039] Beneficial Effects

[0040] The present invention aims at the channel power gap of IRS-assisted uplink transmission and proposes a deployment method of an uplink IRS-assisted network under power control. The uplink power control effectively solves the problem of uneven power distribution of the IRS-assisted uplink network. The deployment parameters are set according to the actual environment: the reasonable configuration of IRS deployment density, number of service units and power control is given according to the coverage rate of the uplink IRS-assisted network, which provides guidance for network deployment in actual scenarios.

[0041] The power control factor is introduced to adopt distance-related transmission power for users at different distances. When the IRS-assisted uplink network is transmitted, the channel cascade link and IRS array gain lead to fluctuations in link conditions and channel power differences under different network topologies, making it impossible to fully utilize the system performance by using uniform control power. Therefore, IRS-assisted uplink transmission under power control is considered to make better use of the network performance of uplink transmission.

[0042] By analyzing the relationship between coverage and IRS deployment density, number of units, and power control factors, the deployment parameter settings of the network are determined when the coverage performance is optimal. After the deployment parameter settings are determined, the system parameters of the IRS-assisted uplink network are adjusted according to the relationship between coverage and base station density, IRS deployment density, number of service units, and power control factors to achieve the optimal network deployment strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of an uplink intelligent reflector-assisted network model considering power control of the present invention;

[0044] Figure 2 It is a flowchart of the algorithm implementation of the present invention;

[0045] Figure 3 This is a graph showing the relationship between coverage and the deployment density of smart reflective surfaces;

[0046] Figure 4 This is a graph showing how coverage changes with the number of service units of the smart reflective surface. DETAILED DESCRIPTION

[0047] The present invention aims at the channel power gap of IRS-assisted uplink transmission and proposes a deployment method of an uplink IRS-assisted network under power control. The network model is shown in the attached figure. Figure 1 Attached Figure 1 The sub-figure in Figure 1 shows the network topology of IRS-assisted uplink transmission considering power control: for areas where user uplink transmission has insufficient coverage, IRSs are randomly distributed on the ground to provide services for users in these target areas; distance-dependent transmission power is used for users at different distances to make better use of the network performance of uplink transmission; users transmit uplink to the nearest base station and are provided with services by the nearest IRS; Figure 1 As shown in the figure, in the IRS-assisted uplink transmission network, the accumulated signal received by the base station mainly comes from two parts: the signal directly sent by the served user and the signal reflected by the IRS; the accumulated interference of the user comes from two parts: the interference of non-served users and the interference of non-served users reflected by the IRS.

[0048] When the IRS-assisted uplink transmission network serves dynamic users, the channel cascade link and IRS array gain lead to fluctuations in link conditions and channel power differences under different network topologies, making it impossible to fully utilize system performance by using uniform control power. In order to fully utilize the network performance of uplink transmission, IRS-assisted uplink transmission under power control is used. In the scenario considered by the present invention, the network performance state is determined by the average coverage rate of users in space, and the spatial average coverage rate of typical users is used to measure the network performance of IRS-assisted uplink transmission. Determine the optimal parameter settings based on the relationship between coverage and deployment parameters.

[0049] The algorithm flow of this case is as follows Figure 2 As shown, the specific implementation steps are:

[0050] 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, etc. According to the scenario of IRS-assisted network uplink 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 Using uplink power control, the power control setting for user k is

[0051] Step 310, obtain the sum of useful signals received by the base station, and calculate the power of the useful signal for uplink transmission under power control. For a typical user, the signal is transmitted to the nearest base station, and the nearest IRS0 provides service. The accumulated signal received by the base station mainly comes from two parts: the signal directly sent by the served user, and the signal reflected by the IRS. The calculation formula for the uplink transmission power received by the base station under the power control of the user is:

[0052] Step 320, obtain the interference signal and received by the base station, 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 calculation formula can be expressed as The Laplace transform of the interference signal can be obtained by To obtain.

[0053] Step 330, based on the coverage index of the uplink network, obtain the coverage performance of the IRS-assisted uplink network under power control, and obtain the optimal IRS-assisted network deployment plan according to the changes in network coverage performance with base station deployment parameters, IRS parameters and power control strategy. By analyzing the relationship between coverage and IRS deployment density and the number of IRS units, the deployment parameter settings of the network when the coverage performance is optimal are determined. After determining the deployment parameter settings, adjust the system parameters of the IRS-assisted network under uplink power control according to the relationship between coverage and power control factors to achieve the optimal IRS deployment strategy.

[0054] The simulation results are shown in the attached Figure 3 and attached Figure 4 Attached Figure 3 The curves of coverage probability changing with IRS density under different power compensation factors and signal-to-noise ratio thresholds T are shown. It can be clearly seen that when the signal-to-noise ratio threshold is small, the influence of the power compensation factor on the coverage probability shows a trend of first increasing and then decreasing, and this trend will not change with the increase of IRS density. This is because, under low signal-to-noise ratio thresholds, the reflection path of the IRS helps to improve the transmission efficiency of the signal, thereby enhancing the coverage performance. However, when the IRS density reaches a certain level, an excessively high power compensation factor will bring more uplink interference, thereby offsetting the benefits brought by signal enhancement. Therefore, the increase in coverage probability is not endless, but there is a peak, that is, the optimal IRS density. On the other hand, when the signal-to-noise ratio threshold is large, the curve changes more complicated. It can be observed in the figure that the two curves with a power compensation factor of 0 and a power compensation factor of 0.5 intersect within a certain IRS density range. This crossing phenomenon shows that as the IRS density increases, using a larger power compensation factor will reduce the coverage performance. The reason behind this is that as the density of IRS increases, the number of IRS reflection paths in the network will increase accordingly. Although these reflection paths can increase the strength of useful signals, they also introduce more interference signals. These interference signals will affect the overall network performance, especially when the signal-to-noise ratio threshold is high, the negative impact of additional interference signals on the coverage probability is particularly significant. More specifically, the increase in IRS reflection paths means that there are more transmission paths in the network, which can improve the transmission efficiency of the signal to a certain extent, especially in the case of low power compensation factors, the system can use these reflection paths to effectively enhance the useful signal. However, when the power compensation factor is high, these additional paths not only enhance the useful signal, but also aggravate the uplink interference, thereby affecting the overall coverage performance of the system. Therefore, when the signal-to-noise ratio threshold is large, the case where the power compensation factor is 0.5 may introduce more interference than expected, resulting in a decrease in coverage performance.

[0055] Attached Figure 4 The curve of coverage probability changing with the number of IRS reflection units N under different power compensation factors and IRS density conditions is shown. It can be seen from the data in the figure that when the IRS density increases, the appropriate increase in the power compensation factor can significantly improve the saturated coverage probability corresponding to the number of IRS reflection units. Specifically, when the number of reflection units N = 2000, the coverage probability can achieve a gain of 16.7%, while when the number of reflection units is 0, a coverage performance gain of 16.5% can be achieved only through power compensation. In addition, by simply increasing the number of IRS reflection units to 1000, a 60.7% coverage performance improvement can be achieved, which shows that when designing the network, it is necessary to balance the relationship between the number of IRS reflection units, hardware cost, and uplink power compensation. Further analysis shows that the deployment of IRS and the optimization of power control strategies play a key role in improving network performance. By accurately adjusting the IRS reflection path, the path loss of the signal during transmission can be effectively reduced, thereby reducing the transmission power requirement of the user equipment. This not only optimizes the signal transmission efficiency, but also reduces the power consumption pressure of the user terminal and extends the battery life of the device. Therefore, appropriate deployment of IRS and reasonable design of power compensation strategy can significantly improve the coverage performance and energy efficiency of uplink communication without significantly increasing hardware costs, providing an important reference for future network design.

Claims

1. The present invention provides a method and device for uplink transmission assisted by an intelligent reflector based on power control, characterized in that: include: The signal is enhanced by adjusting the intelligent reflection surface unit, thereby effectively improving the coverage performance of uplink transmission; specifically, the present invention proposes a power control model for IRS-assisted network uplink transmission, which characterizes the constraint relationship between power control and IRS deployment; the present invention analyzes the fractional power control uplink transmission of ground users in the IRS-assisted network under the influence of the IRS cascade channel; in addition, the user distribution is redefined and calculated, an analysis framework is established using the Poisson point process, and the theoretical expression of the network performance index in the IRS-assisted network uplink is calculated; the network performance of the IRS-assisted uplink is reflected by measuring the coverage rate under different system parameters, and according to the relationship between the coverage rate and the deployment parameters, the deployment density and the number of IRS service units that optimize the joint coverage performance are obtained, characterizing the trade-off relationship between the IRS-assisted network system deployment parameters and the uplink power compensation.

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 the horizontal plane. The deployment density and number of service units of intelligent reflective surfaces are related to environmental parameters. For users at different distances, distance-related transmission power is used, and the calculation formula is:

3. The method according to claim 1 or 2, characterized in that: The uplink signal received by the base station includes: the signal of the served user and the service signal of the IRS reflected user, and the calculation formula is: According to the signal power in different time slots, the change of the user's received power is obtained; the gamma function Γ(k,θ) is used to approximate the service signal power distribution, and the calculation formula is:

4. The method according to claim 1, characterized in that: The base station will be affected by interference signals, including interference signals from non-service users and interference signals from IRS reflecting non-service users. The calculation formula is: The Laplace transform of the interference signal is obtained by Laplace transform, and its calculation formula is:

5. The method according to claim 1 or 4, characterized in that: Based on time correlation, coverage rate is used to characterize the coverage performance of the uplink transmission network, and its 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 network coverage performance are determined based on the changes in coverage as the number of IRS service units in the group, the IRS and base station deployment density, the coverage threshold and the power control factor, and the optimal IRS assisted network deployment is obtained.

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