Super dense network multi-beam directional transmission performance analysis method and system based on random geometry

Abstract

The invention provides an ultra-dense network multi-beam directional transmission performance analysis method and system based on stochastic geometry, and the method comprises the steps: carrying out the network performance index analysis of the coverage probability, the unit area spectrum efficiency and the energy efficiency for the ultra-dense network multi-beam directional transmission when a preset condition is satisfied. Modeling is carried out according to network deployment, channel characteristics, directional transmission multi-beam characteristics and the like in the ultra-dense network, expressions of the coverage probability, the unit area spectrum efficiency and the energy efficiency of the network are given through a random geometric method, and the network performance can be effectively evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of ultra-dense network performance analysis research, and in particular to an ultra-dense network multi-beam directional transmission performance analysis method and system based on random geometry. Background Art

[0002] With the commercialization of the fifth generation mobile communication system (5G), the global competition for the research of the next generation of mobile communication technology has begun. From traditional mobile terminal phones to new terminals such as sensors, vehicles, and drones, with the diversification of cellular network service objects in the 6G era, the number and density of communication nodes in the network will continue to rise. However, the dense deployment of the network also brings serious interference between cells, which is often the main reason why the network performance cannot continue to improve or even decline during dense deployment. On the other hand, as the base station deployment becomes more dense, the network energy consumption gradually increases, and the huge network energy consumption has also become one of the constraints on whether the dense deployment of cellular networks can be sustainable in the future. In order to intuitively reflect the impact of dense network deployment on network performance, the present invention proposes a method of using random geometry theory to analyze the performance scaling rate of multi-beam directional transmission systems in ultra-dense deployment networks. By studying the network performance scaling rate, it is possible to theoretically analyze the average network performance under different scenarios, so as to further optimize and explore the network deployment method, user access / pairing strategy, network parameter setting, etc.

[0003] Among the existing methods for analyzing the performance of multi-beam directional transmission in ultra-dense networks using random geometry, AlAmmouri A, Andrews JG, Baccelli F. Scaling laws of dense multi-antennacellular networks[C] / / 2020 54th Asilomar Conference on Signals, Systems, and Computers. 2020: 1041-1045. This document only considers the physically achievable path loss model and assumes that the number of transceiver antennas of the base station and the user is a function of the current base station density. The assumption that the number of transceiver antennas of the base station and the user is variable is not conducive to the deployment of the actual system and does not consider the impact of multi-beam. Chen G, Qiu L, Li Y. Stochastic geometry analysis of coordinated beamforming small cell networks with CSI delay [J]. IEEE Communications Letters, 2018, 22 (5): 1066-1069. This paper considers the coordinated beamforming between base stations to reduce the interference between cells, and explores the impact of the delay of channel state information, the base station cooperation range and the number of base station antennas on the average user rate, but lacks the analysis of multi-beam analysis and network performance limits. Wildman J, Nardelli PHJ, Latva-aho M, et al. 0n the joint impact of beamwidth and orientation error on throughput indirectional wireless poisson networks [J]. IEEE Transactions on Wireless Communications, 2014, 13 (12): 7072-7085. This paper discusses the impact of beamwidth and beam alignment error on network spatial throughput and transmission capacity in wireless self-organizing networks modeled by bipolar Poisson point process, but the bipolar Poisson point process is not suitable for modeling ultra-dense cellular networks.Di Renzo M. Stochastic geometry modeling and analysis of multi-tier millimeter wave cellular networks [J]. IEEE Transactions on Wireless Communications, 2015, 14 (9): 5038-5057. This paper adopts an occlusion model based on measured data to analyze the coverage and transmission rate in a multi-layer millimeter wave network with beam alignment errors. It also lacks analysis of multi-beam and network extreme performance, and the measured data in specific areas lacks general applicability. Yining Xu and Sheng Zhou. On the Coverage and Capacity of Ultra-Dense Networks with Directional Transmissions [J]. IEEE Wireless Communications Letters, 2020, 9 (3): 271-275. This paper only considers the analysis of ultra-dense network directional transmission performance in a single-beam scenario, and does not consider the impact of multi-beam on network performance. Summary of the invention

[0004] In view of the defects in the prior art, the object of the present invention is to provide a method and system for analyzing the performance of ultra-dense network multi-beam directional transmission based on random geometry.

[0005] According to the present invention, a method for analyzing the performance of ultra-dense network multi-beam directional transmission based on random geometry is provided, comprising: under preset conditions, performing network performance index analysis including coverage probability, spectral efficiency per unit area and energy efficiency for ultra-dense network multi-beam directional transmission.

[0006] Preferably, satisfying a preset condition includes: satisfying multiple preset conditions; wherein the multiple preset conditions include:

[0007] Condition 1: Based on the downlink transmission of the cellular network, the spatial distribution of base stations is modeled as a two-dimensional infinite plane with a density of λ B Homogeneous Poisson point process The spatial distribution of users is modeled as a density of λ U Homogeneous Poisson point process And φ U With φ B is independent;

[0008] Condition 2: All base stations use the same transmission power P B And the base station has been operating at full load;

[0009] Condition 3: The dual slope path loss model considering physically feasible factors is used to model large-scale channel fading, and its expression is:

[0010]

[0011] Among them, β2 is the path loss factor of the far field, d0 is the boundary distance between the near field and the far field; α0 represents the fixed value of the near field path loss, and r represents the distance between the base station and the user;

[0012] Condition 4: The small-scale fading between different channels is independent. The Rayleigh fading model is adopted. It is assumed that the gain h of the small-scale fading follows a negative exponential distribution with a mean of 1, which is recorded as h~exp(1)

[0013] Condition 5: When the noise in the ultra-dense network is negligible, the current ultra-dense network is an interference-limited network, and the current signal-to-interference-and-noise ratio SINR is equivalent to the signal-to-interference ratio SIR;

[0014] Condition 6: Use sector model to approximate beam pattern;

[0015] Condition 7: Use single-beam beamforming with negligible sidelobe gain and beam width θ U , the main lobe gain is N U ;

[0016] Condition 8: There is a typical user who selects the nearest base station B0 for access and whose beam direction is perfectly aligned with the location of the access base station B0. There is no beam alignment error. The main lobe beam gain of the base station and the user is superimposed through the useful signal link.

[0017] Condition 9: For the interference link between the typical user and the interfering base station, the seamless multi-beam of the interfering base station is equivalent to an omnidirectional interference source. As long as the interfering base station falls within the receiving beam range of the typical user, the typical user will inevitably be interfered; Interfering base station B i Total beamforming gain of the interfering link to a typical user For any interfering base station B i are independent and identically distributed, and their distribution is as follows:

[0018]

[0019] Preferably, the network performance index analysis including coverage probability, spectral efficiency per unit area and energy efficiency for ultra-dense network multi-beam directional transmission under the preset conditions includes:

[0020] The coverage probability includes:

[0021]

[0022] in, represents the coverage probability; θ U represents the beam width, τ represents the received signal-to-interference-noise ratio threshold required for successful communication by the user, and λ B represents the base station density, d0 represents the boundary distance between the near field and the far field, and β2 represents the path loss factor of the far field;

[0023] Preferably, the network performance index analysis including coverage probability, spectral efficiency per unit area and energy efficiency for ultra-dense network multi-beam directional transmission under the preset conditions includes:

[0024] The spectral efficiency per unit area includes:

[0025]

[0026] in, represents the spectral efficiency per unit area; k represents the number of beams, λ B represents the base station density, d0 represents the boundary distance between the near field and the far field, θ U represents the user beam width, represents the coverage probability.

[0027] Preferably, the network performance index analysis including coverage probability, spectral efficiency per unit area and energy efficiency for ultra-dense network multi-beam directional transmission under the preset conditions includes:

[0028] The energy efficiency includes:

[0029]

[0030] in, k represents the number of beams, P B represents the base station transmission power, τ represents the received signal-to-interference-noise ratio threshold required for successful communication by the user, and λ B represents the base station density, d0 represents the boundary distance between the near field and the far field, θ U represents the user beam width, β2 represents the far-field path loss factor, represents the coverage probability.

[0031] According to the present invention, a system for analyzing the performance of ultra-dense network multi-beam directional transmission based on random geometry includes: a performance analysis module for analyzing network performance indicators including coverage probability, spectral efficiency per unit area and energy efficiency for ultra-dense network multi-beam directional transmission under preset conditions.

[0032] Preferably, satisfying a preset condition includes: satisfying multiple preset conditions; wherein the multiple preset conditions include:

[0033] Condition 1: Based on the downlink transmission of the cellular network, the spatial distribution of base stations is modeled as a two-dimensional infinite plane with a density of λ B Homogeneous Poisson point process The spatial distribution of users is modeled as a density of λ U Homogeneous Poisson point process And φ U With φ B is independent;

[0034] Condition 2: All base stations use the same transmission power P B And the base station has been operating at full load;

[0035] Condition 3: The dual slope path loss model considering physically feasible factors is used to model large-scale channel fading, and its expression is:

[0036]

[0037] Among them, β2 is the path loss factor of the far field, d0 is the boundary distance between the near field and the far field; α0 represents the fixed value of the near field path loss, and r represents the distance between the base station and the user;

[0038] Condition 4: The small-scale fading between different channels is independent. The Rayleigh fading model is adopted. It is assumed that the gain h of the small-scale fading follows a negative exponential distribution with a mean of 1, which is recorded as h~exp(1)

[0039] Condition 5: When the noise in the ultra-dense network is negligible, the current ultra-dense network is an interference-limited network, and the current signal-to-interference-and-noise ratio SINR is equivalent to the signal-to-interference ratio SIR;

[0040] Condition 6: Use sector model to approximate beam pattern;

[0041] Condition 7: Use single-beam beamforming with negligible sidelobe gain and beam width θ U , the main lobe gain is N U ;

[0042] Condition 8: There is a typical user who selects the nearest base station B0 for access and whose beam direction is perfectly aligned with the location of the access base station B0. There is no beam alignment error. The main lobe beam gain of the base station and the user is superimposed through the useful signal link.

[0043] Condition 9: For the interference link between the typical user and the interfering base station, the seamless multi-beam of the interfering base station is equivalent to an omnidirectional interference source. As long as the interfering base station falls within the receiving beam range of the typical user, the typical user will inevitably be interfered; Interfering base station B i Total beamforming gain of the interfering link to a typical user For any interfering base station B i are independent and identically distributed, and their distribution is as follows:

[0044]

[0045] Preferably, the coverage probability in the performance analysis module includes:

[0046]

[0047] in, represents the coverage probability; θ U represents the beam width, τ represents the received signal-to-interference-noise ratio threshold required for successful communication by the user, and λ B represents the base station density, d0 represents the boundary distance between the near field and the far field, and β2 represents the path loss factor of the far field;

[0048] Preferably, the spectral efficiency per unit area in the performance analysis module includes:

[0049]

[0050] in, represents the spectral efficiency per unit area; k represents the number of beams, λ B represents the base station density, d0 represents the boundary distance between the near field and the far field, θ U represents the user beam width, represents the coverage probability.

[0051] Preferably, the energy efficiency in the performance analysis module includes:

[0052]

[0053] in, k represents the number of beams, P B represents the base station transmission power, τ represents the received signal-to-interference-noise ratio threshold required for successful communication by the user, and λ B represents the base station density, d0 represents the boundary distance between the near field and the far field, θ U represents the user beam width, β2 represents the far-field path loss factor, represents the coverage probability.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. The present invention realizes rapid analysis and calculation of network performance based on the expressions of coverage probability, spectral efficiency per unit area, and energy efficiency of multi-beam directional transmission in ultra-dense networks;

[0056] 2. The expression of network performance can be calculated in a short time by computer software when network parameters are given; especially for scenarios with a large number of network nodes, compared with the network simulation method, the present invention has the advantages of less time consumption and higher accuracy;

[0057] 3. From the mathematical expression of network performance, we can explore the influence trend of each network parameter on network performance and the limit performance of the network during ultra-dense deployment, so as to further optimize and explore the network deployment mode, user access / pairing strategy, network parameter setting, etc.;

[0058] 4. The present invention models the network deployment, channel characteristics, directional transmission multi-beam characteristics, etc. in ultra-dense networks, and uses random geometry methods to provide expressions for the network's coverage probability, unit area spectrum efficiency, and energy efficiency, which can effectively evaluate network performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0060] Figure 1 Schematic diagram of the multi-beam sector model when k=8.

[0061] Figure 2 The coverage probability With base station density λ B Schematic diagram of the changing relationship.

[0062] Figure 3 is the spectral efficiency per unit area With base station density λ B Schematic diagram of the changing relationship.

[0063] Figure 4 For energy efficiency With base station density λ B Schematic diagram of the changing relationship. DETAILED DESCRIPTION

[0064] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0065] Example 1

[0066] The present invention proposes a method for analyzing the performance of ultra-dense network multi-beam directional transmission based on random geometry, comprising:

[0067] Step 1: Make the following assumptions for the ultra-dense network multi-beam directional transmission system:

[0068] Assumption 1: Considering the downlink transmission of the cellular network, the spatial distribution of base stations is modeled as a two-dimensional infinite plane with a density of λ B Homogeneous Poisson point process The spatial distribution of users is modeled as a density of λ U Homogeneous Poisson point process And φ U With φ B are independent. Among them, B i represents the i-th base station, Represents a non-negative integer, U i represents the i-th user;

[0069] Assumption 2: All base stations use the same transmission power P B And the base station has been operating at full load.

[0070] Assumption 3: Based on the dual slope path loss model, considering the physically feasible factors (i.e., the path loss at distance zero is finite, the average received signal strength at any distance is not greater than the transmission power, and the total received signal power of all points on the two-dimensional infinite plane is finite), the large-scale fading modeling of the channel is performed, and its expression is:

[0071]

[0072] Among them, β2 is the path loss factor of the far field, d0 is the boundary distance between the near field and the far field; α0 represents the fixed value of the near field path loss, and r represents the distance between the base station and the user;

[0073] Assumption 4: Assume that the small-scale fading between different channels is independent, and use the Rayleigh fading model to model the small-scale fading. That is, assume that the gain h of its small-scale fading obeys a negative exponential distribution with a mean of 1, which is denoted as h~exp(1).

[0074] Assumption 5: Assume that the noise in the ultra-dense network is negligible, that is, assume that the ultra-dense network is an interference-limited network. In this case, the signal-to-interference-plus-noise ratio SINR is equivalent to the signal-to-interference ratio SIR.

[0075] Assumption 6: Use sector model to approximate beam pattern. Assume that the base station can generate k beams with the same width and main lobe gain at the same time (each beam can serve one user in one time-frequency resource block, i.e., space division multiple access) and the side lobe gain can be ignored. The width of each beam is The main lobe gain of each beam is Where N B and θ B They are respectively the main lobe gain and main lobe width when the base station generates a single beam. Figure 1 A schematic diagram of the multi-beam sector model when k=8 is given.

[0076] Assumption 7: Assume that the user adopts single-beam beamforming, its sidelobe gain is negligible, and the beam width is θ U , the main lobe gain is N U .

[0077] Assumption 8: Assume that there is a typical user who chooses to access the nearest base station B0 and whose beam direction is perfectly aligned with the location of the access base station B0, that is, there is no beam alignment error. Then the useful signal link can obtain the superposition of the main lobe beam gain of the base station and the user, that is,

[0078] Assumption 9: For the interference link between the typical user and the interfering base station, since the seamless multi-beam of the interfering base station is equivalent to an omnidirectional interference source, as long as the interfering base station falls within the receiving beam range of the typical user, the typical user will inevitably be interfered. i Total beamforming gain of the interfering link to a typical user For any interfering base station B i are independent and identically distributed, and their distribution is as follows:

[0079]

[0080] Step 2: Provide definitions for network performance indicators such as coverage probability, spectral efficiency per unit area, and energy efficiency.

[0081] Wherein, step 2 comprises the following steps:

[0082] Step 2.1: The network coverage probability is defined as the probability that the user receives a signal-to-interference-to-noise ratio greater than a certain threshold, expressed as:

[0083]

[0084] Where SINR is the signal to interference and noise ratio received by the user, τ is the minimum signal to interference and noise ratio requirement for successful reception by the user, and τ>0, that is, the receiving signal to interference and noise ratio threshold.

[0085] Step 2.2: The spectral efficiency per unit area of ​​the network is defined as the average throughput per unit bandwidth and area, expressed as:

[0086]

[0087] in, It should be noted that it is assumed here that communication can only be successful when the user receives the signal-to-interference-to-noise ratio greater than the threshold τ, assuming that the channel state information c is known and the throughput reaches the channel capacity.

[0088] Step 2.3: The energy efficiency of the network (abbreviated as energy efficiency) is defined as the average throughput carried by unit bandwidth and power, expressed as:

[0089]

[0090] The above formula does not take into account the power consumption of the user equipment, because it is almost negligible compared to the power consumption of the base station.

[0091] Step 3: Calculate the coverage probability, spectral efficiency per unit area, and energy efficiency according to the network performance indicator definition formula given in step 2. Step 3 includes the following steps:

[0092] Step 3.1: According to the definition of signal-to-interference ratio SIR, the received signal-to-interference ratio SIR of a typical user U0 accessing the nearest base station B0 is expressed as:

[0093]

[0094] Where I is the number of base stations B except B0 that a typical user U0 receives. i The interference caused is expressed as:

[0095]

[0096] In formula (7), r0 and r i They represent the distance between the typical user U0 and the nearest base station B0, the distance between the typical user U0 and the interfering base station B i distance.

[0097] Step 3.2: Under the condition that the nearest base station B0 is located at a distance r0 from the typical user U0, the coverage probability Taking the average on the whole plane, we can get formula (8):

[0098]

[0099] in is the probability density function of the distance r0.

[0100] Step 3.3: Using the distribution of small-scale fading of the channel h~exp(1), we can obtain equations (9) and (10):

[0101]

[0102] in is the cumulative interference to the typical user U0, the Laplace transform at x when r0∈(0, d0), Cumulative interference to a typical user U0, Laplace transform at x when r0∈(d0,∞).

[0103] Step 3.4: Target and Simplify to As an example, the solution process is shown in formula (11-13):

[0104]

[0105] Among them, the validity of formula (11) comes from h~exp(1), and the validity of formula (12) comes from the probability generating function of the Poisson point process and The probability distribution of , by substituting equation (1) into equation (12), we can get equation (13). Similarly, The expression of is solved through a similar process, and the result is shown in formula (14):

[0106]

[0107] Step 3.5: Substitute equation (13) into equation (9), substitute equation (14) into equation (10), and then simultaneously substituting equations (9-10) into

[0108] Substituting into formula (8), after sorting and variable replacement, we can get the coverage probability The expression of is as follows:

[0109]

[0110] in

[0111] Step 3.6: Based on the spectral efficiency per unit area The definition of can be expanded as follows:

[0112]

[0113] Among them, f in formula (16.2) SINR(x) is the probability density function of SINR, and F in formula (16.3) SINR (x) is the cumulative distribution function of SINR. That is formula (15), This can be obtained by replacing τ in equation (15) with 2y-1.

[0114] Step 3.7: Substitute equation (15) into equation (16.4) and after sorting and replacing variables, we can get the spectral efficiency per unit area: The expression of is shown in formula (17):

[0115]

[0116] in,

[0117] Step 3.8: Based on energy efficiency By substituting formula (17) into formula (5), we can get the specific expression, as shown in formula (18):

[0118]

[0119] in

[0120] Step 4: From equations (15), (17), and (18), we can see that the coverage probability of the network is Spectral efficiency per unit area Energy efficiency Depends only on the beam alignment probability Near field base station density λ B πd0 2 As well as the path loss characteristic β2, the minimum receive signal-to-interference-to-noise ratio requirement τ, and the base station transmit power P B , the number of base station beams k and other parameters. Given the values ​​of the above parameters in a real system, substituting them into equations (15), (17) and (18), the performance analysis results of the system can be obtained.

[0121] The present invention also provides an ultra-dense network multi-beam directional transmission performance analysis system based on random geometry. The ultra-dense network multi-beam directional transmission performance analysis system based on random geometry can be realized by executing the process steps of the ultra-dense network multi-beam directional transmission performance analysis method based on random geometry, that is, technical personnel in this field can understand the ultra-dense network multi-beam directional transmission performance analysis method based on random geometry as a preferred implementation of the ultra-dense network multi-beam directional transmission performance analysis system based on random geometry.

[0122] Example 2

[0123] Embodiment 2 is a preferred embodiment of Embodiment 1

[0124] For an ultra-densely deployed multi-beam directional transmission network whose performance is to be estimated, the specific values ​​of the following network parameters need to be obtained. Taking the network parameters involved in a certain application example as an example, as shown in Table 1.

[0125] Table 1 Network parameter table

[0126]

[0127] Given the values ​​of the above network parameters, the base station deployment density λ B is a variable, the number of base station beams k is an influencing factor, and the coverage probability of network performance indicators is explored. Spectral efficiency per unit area Energy efficiency The specific steps are as follows:

[0128] Step 1: Substitute the network parameter values ​​in Table 1 into equations (15), (17), and (18) to obtain the coverage probability Spectral efficiency per unit area Energy efficiency and base station deployment density λ B , the functional relationship between the number of base station beams k.

[0129] Step 2: Given variable λ B The value range of and k, in this application example, we take λ B ∈(0,10 7 ), the unit is "pieces / square kilometer"; take k∈{1, 2, 4, 8}, the unit is "pieces", in particular, k=1 represents omnidirectional transmission of the base station.

[0130] Step 3: Take λ B As the horizontal axis, As the vertical axis, draw four curves when k = 1, k = 2, k = 4, and k = 8. This application example uses MATLAB software to draw the curves. The results are as follows Figure 2 , Figure 3 , Figure 4 shown.

[0131] Step 4: The network performance trend is analyzed as follows:

[0132] 1) By Figure 2 It can be seen that the coverage probability of multi-beam space division multiple access transmission is lower than that of single beam transmission, and the coverage performance is independent of the number of beams k. This is because the coverage performance is mainly affected by the increase in the probability of interference, and the change in the useful signal strength caused by the change in beam gain is synchronized with the interference, so the influence of beam gain is offset.

[0133] 2) By Figure 3 It can be seen that as the number of beams k increases, the spectral efficiency per unit area of ​​the network gradually increases. When the base station density is small, for example, B ≤5×10 3 / km 2 At this time, the interference between cells in the network is not serious. Using more beams (k=4, 8) for space-division multiple access transmission can obtain a higher spectral efficiency per unit area than a single beam. However, when there are fewer beams (k=1, 2), the spectral efficiency of multi-beam transmission is not as good as that of single-beam transmission.

[0134] However, when the base station density is large, such as λ B >5×10 3 / km 2 , the impact of interference is more significant at this time, so the performance of single-beam transmission far exceeds that of multi-beam transmission. On the other hand, in the multi-beam scenario, the optimal base station density that maximizes the spectral efficiency per unit area decreases as the number of beams k increases. In addition, the spectral efficiency optimal base station density of multi-beam transmission is much smaller than that of single-beam transmission. And the maximum spectral efficiency per unit area that can be achieved by a single beam is much greater than that of a multi-beam scenario. For example, the maximum spectral efficiency per unit area that can be achieved by a single beam is about 1.6 times that of 8-beam transmission.

[0135] 3) By Figure 4 It can be seen that, similar to the spectral efficiency per unit area, there is also an overall trend that multi-beam transmission performance is better when base station density is low, and single-beam transmission performance is better when base station density is high; and the more beams there are in the multi-beam scenario, the better the energy efficiency performance. The difference is that the optimal energy efficiency of multi-beam is significantly greater than the optimal energy efficiency of single beam. For example, the maximum energy efficiency that can be achieved by 8-beam transmission is about 3 times that of single beam.

[0136] In summary, when the network is sparsely deployed, multi-beam spatial division multiple access transmission with more beams should be used as much as possible to improve spectral efficiency and energy efficiency, but the coverage performance is not good; when the network is densely deployed, the use of single-beam directional transmission can better reduce interference between cells and achieve better coverage, spectral efficiency and energy efficiency performance.

[0137] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0138] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for analyzing the performance of multi-beam directional transmission in ultra-dense networks based on random geometry, characterized in that: include: Under the preset conditions, network performance indicators including coverage probability, spectrum efficiency per unit area and energy efficiency are analyzed for ultra-dense network multi-beam directional transmission.

2. The method for analyzing performance of ultra-dense network multi-beam directional transmission based on random geometry according to claim 1 is characterized in that: The satisfying of the preset condition includes: satisfying multiple preset conditions; wherein the multiple preset conditions include: Condition 1: Based on the downlink transmission of the cellular network, the spatial distribution of base stations is modeled as a two-dimensional infinite plane with a density of λ B Homogeneous Poisson point process The spatial distribution of users is modeled as a density of λ U Homogeneous Poisson point process And φ U With φ B is independent; Condition 2: All base stations use the same transmission power P B And the base station has been operating at full load; Condition 3: The dual slope path loss model considering physically feasible factors is used to model large-scale channel fading, and its expression is: Among them, β2 is the path loss factor of the far field, d0 is the boundary distance between the near field and the far field; α0 represents the fixed value of the near field path loss, and r represents the distance between the base station and the user; Condition 4: The small-scale fading between different channels is independent. The Rayleigh fading model is adopted. It is assumed that the gain h of the small-scale fading follows a negative exponential distribution with a mean of 1, which is recorded as h~exp(1) Condition 5: When the noise in the ultra-dense network is negligible, the current ultra-dense network is an interference-limited network, and the current signal-to-interference-and-noise ratio SINR is equivalent to the signal-to-interference ratio SIR; Condition 6: Use sector model to approximate beam pattern; Condition 7: Use single-beam beamforming with negligible sidelobe gain and beam width θ U , the main lobe gain is N U ; Condition 8: There is a typical user who selects the nearest base station B0 for access and whose beam direction is perfectly aligned with the location of the access base station B0. There is no beam alignment error. The main lobe beam gain of the base station and the user is superimposed through the useful signal link. Condition 9: For the interference link between the typical user and the interfering base station, the seamless multi-beam of the interfering base station is equivalent to an omnidirectional interference source. As long as the interfering base station falls within the receiving beam range of the typical user, the typical user will inevitably be interfered; Interfering base station B i Total beamforming gain of the interfering link to a typical user For any interfering base station B i are independent and identically distributed, and their distribution is as follows:

3. The method for analyzing performance of ultra-dense network multi-beam directional transmission based on random geometry according to claim 1 is characterized in that: The network performance indicators including coverage probability, spectral efficiency per unit area and energy efficiency are analyzed for ultra-dense network multi-beam directional transmission under preset conditions. include: The coverage probability includes: in, represents the coverage probability; θ U represents the beam width, τ represents the received signal-to-interference-noise ratio threshold required for successful communication by the user, and λ B represents the base station density, d0 represents the boundary distance between the near field and the far field, and β2 represents the path loss factor of the far field; 4. The method for analyzing performance of ultra-dense network multi-beam directional transmission based on random geometry according to claim 1 is characterized in that: The network performance indicators including coverage probability, spectral efficiency per unit area and energy efficiency are analyzed for ultra-dense network multi-beam directional transmission under preset conditions. include: The spectral efficiency per unit area includes: in, represents the spectral efficiency per unit area; k represents the number of beams, λ B represents the base station density, d0 represents the boundary distance between the near field and the far field, θ U represents the user beam width, represents the coverage probability.

5. The method for analyzing performance of ultra-dense network multi-beam directional transmission based on random geometry according to claim 1, characterized in that: The network performance indicators including coverage probability, spectral efficiency per unit area and energy efficiency are analyzed for ultra-dense network multi-beam directional transmission under preset conditions. include: The energy efficiency includes: in, k represents the number of beams, P B represents the base station transmission power, τ represents the received signal-to-interference-noise ratio threshold required for successful communication by the user, and λ B represents the base station density, d0 represents the boundary distance between the near field and the far field, θ U represents the user beam width, β2 represents the far-field path loss factor, represents the coverage probability.

6. A super dense network multi-beam directional transmission performance analysis system based on random geometry, characterized in that: include: The performance analysis module is used to analyze network performance indicators including coverage probability, spectral efficiency per unit area and energy efficiency for ultra-dense network multi-beam directional transmission under preset conditions.

7. The ultra-dense network multi-beam directional transmission performance analysis system based on random geometry according to claim 6, characterized in that: The satisfying of the preset condition includes: satisfying multiple preset conditions; wherein the multiple preset conditions include: Condition 1: Based on the downlink transmission of the cellular network, the spatial distribution of base stations is modeled as a two-dimensional infinite plane with a density of λ B Homogeneous Poisson point process The spatial distribution of users is modeled as a density of λ U Homogeneous Poisson point process And φ U With φ B is independent; Condition 2: All base stations use the same transmission power P B And the base station has been operating at full load; Condition 3: The dual slope path loss model considering physically feasible factors is used to model large-scale channel fading, and its expression is: Among them, β2 is the path loss factor of the far field, d0 is the boundary distance between the near field and the far field; α0 represents the fixed value of the near field path loss, and r represents the distance between the base station and the user; Condition 4: The small-scale fading between different channels is independent. The Rayleigh fading model is adopted. It is assumed that the gain h of the small-scale fading follows a negative exponential distribution with a mean of 1, which is recorded as h~exp(1) Condition 5: When the noise in the ultra-dense network is negligible, the current ultra-dense network is an interference-limited network, and the current signal-to-interference-and-noise ratio SINR is equivalent to the signal-to-interference ratio SIR; Condition 6: Use sector model to approximate beam pattern; Condition 7: Use single-beam beamforming with negligible sidelobe gain and beam width θ U , the main lobe gain is N U ; Condition 8: There is a typical user who selects the nearest base station B0 for access and whose beam direction is perfectly aligned with the location of the access base station B0. There is no beam alignment error. The main lobe beam gain of the base station and the user is superimposed through the useful signal link. Condition 9: For the interference link between the typical user and the interfering base station, the seamless multi-beam of the interfering base station is equivalent to an omnidirectional interference source. As long as the interfering base station falls within the receiving beam range of the typical user, the typical user will inevitably be interfered; Interfering base station B i Total beamforming gain of the interfering link to a typical user For any interfering base station B i are independent and identically distributed, and their distribution is as follows:

8. The ultra-dense network multi-beam directional transmission performance analysis system based on random geometry according to claim 6, characterized in that: The coverage probability in the performance analysis module includes: in, represents the coverage probability; θ U represents the beam width, τ represents the received signal-to-interference-noise ratio threshold required for successful communication by the user, and λ B represents the base station density, d0 represents the boundary distance between the near field and the far field, and β2 represents the path loss factor of the far field; 9. The ultra-dense network multi-beam directional transmission performance analysis system based on random geometry according to claim 6, characterized in that: The spectral efficiency per unit area in the performance analysis module includes: in, represents the spectral efficiency per unit area; k represents the number of beams, λ B represents the base station density, d0 represents the boundary distance between the near field and the far field, θ U represents the user beam width, represents the coverage probability.

10. The ultra-dense network multi-beam directional transmission performance analysis system based on random geometry according to claim 6, characterized in that: The energy efficiency in the performance analysis module includes: in, k represents the number of beams, P B represents the base station transmission power, τ represents the received signal-to-interference-noise ratio threshold required for successful communication by the user, and λ B represents the base station density, d0 represents the boundary distance between the near field and the far field, θ U represents the user beam width, β2 represents the far-field path loss factor, represents the coverage probability.

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