CN111246494A - Massive MIMO antenna beam optimization method and device - Google Patents

Abstract

Embodiments of the present invention provide a Massive MIMO antenna beam optimization method and device. The method includes: acquiring the traffic distribution of current users of the target cell according to the throughput distribution of the target cell beam, and obtaining the traffic distribution of the current user in the target cell according to the noise distribution of the target cell beam and the same-frequency neighbors. The throughput distribution of the regional beams is used to obtain the traffic distribution of potential users; according to the traffic distribution of current users and the traffic distribution of potential users, the distribution of user traffic before and after adjustment by each weight is obtained. , according to the user traffic distribution before and after adjustment by each weight, obtain the expected throughput gain of the target cell beam after adjustment by each weight; according to the expected throughput corresponding to each beam weight Gain, select the weight for beam optimization for beam adjustment. Since the weights used for beam optimization are selected according to the expected throughput gain, it has high optimization efficiency and optimization accuracy.

Description

Massive MIMO antenna beam optimization method and device

technical field

Embodiments of the present invention relate to the field of mobile communications, and in particular, to a method and device for optimizing a Massive MIMO antenna beam.

Background technique

Massive MIMO technology refers to increasing the number of transmitting and receiving antennas to dozens or even hundreds of antennas on the basis of the traditional Multiple-Input Multiple-Output (MIMO) system. As a new cellular network structure, Massive MIMO system retains the advantages of traditional MIMO systems, and uses a large number of antennas to average out system noise and irrelevant inter-cell interference. The increase in the number of antennas greatly increases the system capacity, and the Massive MIMO feature can improve the cell capacity. These features determine that the Massive MIMO system has broad application prospects. In the LTE network, since the coverage direction of each cell of the LTE network is relatively fixed, there will be a situation where hotspot users are concentrated outside the lobe angle, thereby affecting the perception of users outside the lobe angle.

In the face of the above problems, the weight optimization of the broadcast beam of Massive MIMO is realized by manual adjustment, that is, the optimization is achieved by manual configuration according to the application scenario. This method cannot guarantee optimal adjustment results, and in the scenario of massive deployment of Massive MIMO, the workload of manual adjustment will be very large.

The effect of the optimization scheme for beam weights in the prior art obviously lags behind the adjustment work, so that targeted adjustment and optimization are often only performed on obviously abnormal cells, but the entire network cannot be considered. When optimization and adjustment are needed for the areas with poor results after optimization, it will lead to problems such as long optimization period and inability to achieve one step. The optimization and adjustment scheme is all processed by human judgment, so this optimization method requires a lot of time and manpower. Therefore, the current optimization method is inefficient, and the accuracy of the scheme cannot be guaranteed.

SUMMARY OF THE INVENTION

To solve the above problem, embodiments of the present invention provide a Massive MIMO antenna beam optimization method and apparatus.

In a first aspect, the present invention provides a Massive MIMO antenna beam optimization method, including: obtaining the traffic distribution of the current user of the target cell according to the throughput distribution of the target cell beam, and obtaining the traffic distribution of the current user of the target cell according to the noise distribution of the target cell beam and the same-frequency neighbors. The throughput distribution of the area beams is used to obtain the traffic distribution of potential users; according to the traffic distribution of current users and the traffic distribution of potential users, the distribution of user traffic before and after adjustment by each weight is obtained. , according to the user traffic distribution before and after each weight adjustment, obtain the expected throughput gain of the target cell beam after each weight adjustment; according to the throughput expectation corresponding to each beam weight Gain, select the weight used for beam optimization for beam adjustment.

In a second aspect, the present invention provides a Massive MIMO antenna beam optimization device, comprising: an acquisition module configured to acquire the traffic distribution of the current user of the target cell according to the throughput distribution of the target cell beam, and obtain the traffic volume distribution of the current user in the target cell according to the throughput distribution of the target cell beam, and obtain the target cell beam noise according to the target cell. Distribution and throughput distribution of co-frequency adjacent beams to obtain the traffic distribution of potential users; the processing module is used to obtain the pre-adjustment and per- A user traffic distribution after weight adjustment, according to the user traffic distribution before and after adjustment by each weight value, obtain the expected throughput gain of the target cell beam after adjustment by each weight value; output The module is used for selecting a weight for beam optimization to perform beam adjustment according to the expected throughput gain corresponding to each beam weight.

In a third aspect, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the program, the method for optimizing the Massive MIMO antenna beam of the first aspect of the present invention is implemented. A step of.

In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements the steps of the Massive MIMO antenna beam optimization method according to the first aspect of the present invention.

In the Massive MIMO antenna beam optimization method provided by the embodiment of the present invention, according to the traffic volume distribution of the current user and the traffic volume distribution of potential users, the user traffic volume distribution before and after adjustment by each weight value is obtained, And obtain the expected throughput gain of the beam adjusted by each weight value, and select the weight value for beam optimization according to the throughput expected gain corresponding to each beam weight value, so as to have high optimization efficiency and optimization accuracy.

Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

FIG. 1 is a flowchart of a Massive MIMO antenna beam optimization method provided by an embodiment of the present invention;

FIG. 2 is a structural diagram of a Massive MIMO antenna beam optimization apparatus provided by an embodiment of the present invention;

FIG. 3 is a schematic diagram of a physical structure of an electronic device according to an embodiment of the present invention.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

At present, the optimization of Massive MIMO antenna beams is mainly achieved by manual adjustment according to the application scenario, which often results in targeted adjustment and optimization of obviously abnormal cells, rather than taking into account the entire network. The optimization and adjustment scheme is all processed by human judgment, so this optimization method requires a lot of time and manpower. Therefore, the current optimization method is inefficient, and the accuracy of the scheme cannot be guaranteed.

To solve this problem, an embodiment of the present invention provides a Massive MIMO antenna beam optimization method. This method can be applied to the above scenarios of Massive MIMO antenna beam optimization. The execution subject corresponding to the method may be the base station where the Massive MIMO antenna is located, or may be an independently set Massive MIMO antenna beam optimization apparatus, which is not specifically limited in this embodiment of the present invention. For ease of description, the embodiment of the present invention takes the base station where the Massive MIMO antenna is located as an example as an example to describe the beam optimization method for the Massive MIMO antenna provided in the embodiment of the present invention.

FIG. 1 is a flowchart of a Massive MIMO antenna beam optimization method provided by an embodiment of the present invention. As shown in FIG. 1 , an embodiment of the present invention provides a Massive MIMO antenna beam optimization method, including:

101. Obtain the traffic distribution of the current user of the target cell according to the throughput distribution of the target cell beam, and obtain the traffic distribution of the potential user according to the noise distribution of the target cell beam and the throughput distribution of the same-frequency neighbor beam.

Since the data of the co-frequency adjacent cell is obtained through the eNodeB (Evolved Node B, LTE base station) where the adjacent cell is located, the process of obtaining the eNodeB where the co-frequency adjacent cell is located is also included before 101 . This embodiment of the present invention does not specifically limit the method for obtaining the eNodeB where the same-frequency neighbor cell is located, including but not limited to, selecting forward-facing first-layer and second-layer neighbor cells, selecting backward-facing first-layer neighbor cells, and filtering out neighbors with the same frequency. A cell with the same frequency as a Massive MIMO cell. Deduplication is performed according to the eNodeB ID to obtain a list of eNodeBs where the same-frequency neighbor cells of the Massive MIMO cell are located.

In 101, the target cell is the Massive MIMO cell to be optimized, and the statistics are the traffic distribution of users within the antenna lobe angle coverage of the Massive MIMO cell, as well as the services of adjacent users outside the Massive MIMO cell antenna lobe angle coverage area. The traffic distribution of the user is mainly reflected in the hotspot location of the user and the throughput generated by the corresponding user service.

The Massive MIMO antenna is composed of multiple antenna elements. The throughput distribution of the beam covered by the Massive MIMO antenna to the target cell reflects the traffic distribution of the current users in the target cell. The potential users are outside the coverage of the lobe angle. After adjusting the lobe angle through the weights, some of the potential users may be covered within the beam range. Users outside the coverage of the lobe angle will have a certain noise impact on the users within the coverage. The noise distribution of the beam can reflect the situation of potential users outside the lobe angle. Combined with the throughput distribution of adjacent beams, potential users can be obtained. business distribution.

102, according to the traffic distribution of the current user and the traffic distribution of the potential users, obtain the user traffic distribution before and after adjustment by each weight, according to the pre-adjustment and A user traffic distribution after weight adjustment is obtained, and the expected throughput gain of the target cell beam after each weight adjustment is obtained.

The traffic distribution of the current user corresponds to the user traffic distribution before the adjustment by the weight. After the beam is adjusted by the weight, it will absorb some new users from the potential users for coverage, and some of the current users may be adjusted by the weight. Variation in back lobe angle coverage without coverage. According to the traffic distribution of the current user, the traffic distribution of the potential user, and the corresponding weight, the user traffic distribution after the weight adjustment can be obtained. According to the user traffic distribution before and after the weight adjustment, and the corresponding user traffic, the throughput status of the users within the lobe angle coverage before the weight adjustment can be obtained, and the lobe angle coverage after the weight adjustment can be obtained. the throughput status of the user. According to the user throughput status before and after the weight adjustment, obtain the expected throughput gain after adjustment according to the weight relative to the throughput before adjustment.

103. According to the expected gain of the user throughput corresponding to each beam weight, select a weight for beam optimization to perform beam adjustment.

Since there are multiple weights, it is necessary to obtain the expected throughput gain corresponding to the weight according to each weight. The expected throughput gain is obtained by statistical analysis, and the process of adjusting the lobe angle according to the weight is not implemented at this time. According to each weight and the expected gain corresponding to each weight, select the weight that meets the requirements to optimize the beam, and implement the adjustment of the beam. For example, the weight with the largest expected throughput gain can be selected to optimize the beam.

According to each weight value and the corresponding expected user throughput gain, a comparison table of the relationship between the weight value and the user throughput expected gain shown in Table 1 below can be generated, which is used to select the weight value for beam optimization to perform beam adjustment. Take 2_h65_v8_tilt3 as an example to illustrate: the horizontal lobe angle of h65 is 65°, the vertical lobe angle of v8 is 8°, and tilt3 is the electrical downtilt angle. The weights are adjusted by these three parameters.

Table 1

In the Massive MIMO antenna beam optimization method provided by the embodiment of the present invention, according to the traffic volume distribution of the current user and the traffic volume distribution of potential users, the user traffic volume distribution before and after adjustment by each weight value is obtained, And obtain the expected throughput gain of the beam adjusted by each weight value, and select the weight value for beam optimization according to the throughput expected gain corresponding to each beam weight value, so as to have high optimization efficiency and optimization accuracy.

Based on the content of the above embodiment, as an optional embodiment, the traffic distribution of the current user in the target cell is obtained according to the throughput distribution of the target cell beam, and the noise distribution of the target cell beam and the same-frequency neighbor beam Throughput distribution Before acquiring the traffic distribution of potential users, the method further includes: acquiring the throughput of the beam according to the call history report (CHR) or measurement report (MR) data acquired within the preset busy hour. According to the CHR or MR data of the co-frequency adjacent cell acquired during the preset busy hours, the throughput distribution of the intra-frequency adjacent cell beam is obtained.

The CHR or MR data includes measurement data during user communication, such as user throughput and beam noise, which can be used to count the beam throughput. The preset busy hour is the time period for data collection, and the busy hour can better reflect the user's business volume distribution. For example, 19:00-23:00 in the evening is the peak period of residential business volume. Taking the collection of CHR data as an example, the relevant data of subscription to the eNodeB is shown in Table 2.

Table 2

Among them, PERIOD_INTRA_FREQ_MEASUREMENT is the statistical user frequency, PERIOD_PRIVATE_THROUGHPUT_MEASUREMENT is the statistical user throughput, PERIOD_PRIVATE_BEAM_TRAFFIC is the user beam information and BEAM_NOISE_TRACKING is the statistical noise distribution. According to the obtained CHR data of the Massive MIMO station, the throughput distribution and noise distribution of the beam can be obtained, and according to the obtained CHR data of the same-frequency adjacent cell, the throughput distribution of the potential user beam of the same-frequency adjacent cell can be obtained.

The Massive MIMO antenna beam optimization method provided by the embodiment of the present invention can effectively obtain the throughput distribution of the target cell and the same-frequency adjacent cell beams according to the CHR or MR data.

Based on the content of the above-mentioned embodiment, as an optional embodiment, according to the user traffic distribution before and after adjustment by each weight value, the expected gain of throughput after adjustment by each weight value of the beam is obtained , including: obtaining the expected throughput gain of each weight value according to the traffic statistics data of user traffic distribution before and after adjustment by each weight value.

The information in the traffic statistics data can be used to count the throughput gain. According to the traffic statistics data of user traffic distribution before and after adjustment by each weight value, the expected throughput gain of each weight value can be obtained. The relevant indicators used to calculate the throughput gain in the traffic statistics data are shown in Table 3:

table 3

The throughput gain KPI (Key Performance Indicator, key performance indicator) is used to count and calculate the expected throughput gain of each weight value, and the basic KPI is a necessary condition to be satisfied after the weight value is adjusted. The indicators and parameter values of KPIs are set as required, and the recommended collection time is one week.

Based on the content of the foregoing embodiment, as an optional embodiment, selecting a weight for beam optimization to perform beam adjustment according to the expected throughput gain corresponding to each beam weight, including: Among the expected throughput gains corresponding to the weights, the weights used for beam optimization are selected for beam adjustment.

Considering that the weight with the largest expected throughput gain is not necessarily suitable for weight adjustment, the selection of weight for beam optimization needs to meet certain screening conditions, including but not limited to:

The horizontal wave width, vertical wave width, and downtilt angle can be adjusted at most 2 at the same time, so as to avoid the absorption of a large number of edge users and the decrease in user throughput caused by the large adjustment of weights; if the user traffic in the vertical direction of the beam is widely distributed, the vertical wave should be adjusted first. If the user traffic in the horizontal direction of the beam is widely distributed, the horizontal width should be adjusted first, and the width corresponding to the adjusted weight should cover the beam with a high traffic ratio as much as possible.

The Massive MIMO antenna beam optimization method provided by the embodiment of the present invention makes beam optimization more reasonable through the limitation of screening conditions.

Based on the content of the above embodiment, as an optional embodiment, after obtaining the traffic distribution of the current user of the target cell according to the throughput distribution of the target cell beam, obtain the traffic distribution of the current user and the potential user according to the traffic distribution of the current user The method further includes: adjusting the antenna azimuth angle according to the traffic distribution of the current user before the user traffic distribution is adjusted according to each weight value and after the adjustment according to each weight value.

In the initial stage of Massive MIMO network construction, the Massive MIMO beam cannot obtain the user distribution in the cell, and the set antenna azimuth may not meet the optimal coverage. After obtaining the traffic distribution of the current user in the target cell according to the throughput distribution of the target cell beam, it is necessary to adjust the antenna azimuth according to the traffic distribution of the current user.

Signal transmission and reception at both ends of the transceiver can be described by wave departure angle and arrival angle. The angle problem is determined by the position of the mobile user. The line-of-sight direction from the base station to the user includes the departure angle (wave departure angle) of the transmitter signal and the arrival angle of the receiver channel (the angle of arrival). The wave departure angle and the arrival angle together constitute the directivity of the signal, and combined with the antenna structure, the signal steering vector is formed, and the antenna azimuth angle can be adjusted according to the signal steering vector.

Based on the content of the above embodiment, as an optional embodiment, there are multiple target cells. Accordingly, according to the expected throughput gain corresponding to each beam weight, the weight for beam optimization is selected to perform beam adjustment, including: : According to the expected throughput gain corresponding to each beam weight of each target cell, select the target cell with the highest expected throughput gain to adjust the weight, and select the corresponding weight for beam optimization to adjust the beam.

When a Massive MIMO station covers multiple cells, the cell with the highest expected gain is selected for weight adjustment. In the Massive MIMO continuous networking scenario, avoid simultaneously adjusting the adjacent Massive MIMO cells (such as the same-station cell, the adjacent sparring cell, etc.), and select the cell with the highest expected gain in the overlapping coverage area for weight adjustment.

Based on the content of the above embodiment, as an optional embodiment, it is possible to further screen whether the target cell implements antenna beam optimization, including but not limited to, the cell that implements antenna beam optimization must meet the following conditions: the weight of the cell optimizes the throughput The expected gain of traffic volume is greater than 5%; the downlink PRB utilization rate of the cell is lower than 50% (obtained from traffic statistics data); the proportion of near-end users in the cell is greater than 50% (obtained from traffic statistics data).

Based on the content of the foregoing embodiment, as an optional embodiment, the weight is calculated by a weight tool.

The weights include three parameters: horizontal lobe angle, vertical lobe angle, and electrical downtilt angle of the antenna. It is used to adjust the antenna. First, the beam steering is performed. The beam steering is a simple scanning beam form. By selecting the weights, the main beam of the antenna is directed to the target direction, and the amplitudes of the weights of the beamformer are equal. The difference between them is exp(-j2πfτ), and the weights of the antenna array are as follows:

是信号导向矢量。工作原理为：导向矢量是在信号接收过程中两个阵元接收信号间的相位差，而天线具有收发互异性，即接收过程的处理也可以运用到发送过程中，由于在接收一个θ₀方向的信号时，在阵元之间会产生相差a(θ₀)，在发送时，人为给阵元之间设定a(θ₀)，则阵元之间的干涉图自然会指θ₀方向，形成波束指向。In the formula, M is the number of array elements in the antenna array,

is the signal steering vector. The working principle is as follows: the steering vector is the phase difference between the received signals of the two array elements in the signal receiving process, and the antenna has the _mutuality of sending and receiving, that is, the processing of the receiving process can also be applied to the sending process. When the signal is sent, a phase difference a(θ ₀ ) will be generated between the array elements. When sending, artificially set a(θ ₀ ) between the array elements, the interference pattern between the array elements will naturally point to the direction of θ ₀ , forming a beam pointing.

Firstly, precoding beamforming weights and antenna joint optimization are performed for each user. Assuming that there are K users, optimize the Kth user, take SINR (Signal to Interference plus Noise Ratio, Signal to Interference plus Noise Ratio) as the optimization criterion, and simultaneously optimize the dual objectives of the antenna array and beamforming weights, The antenna array position and beamforming weights form a joint optimized array W _k = [W _k1 , W _k2 , W _k3 ], W _k1 is the beam forming weight, W _k2 , W _k3 are the antenna array element position matrix, and the user angle information and so on together to describe the channel matrix H _k of user k, then the signal-to-noise ratio SINR of the system is expressed as:

Among them, H _f is the channel matrix obtained from the transmitting end to the receiving end of other users using a fixed transmitting array, and Mσ ² is the noise matrix.

The weight can be calculated according to the above formula, and the weight tool is an existing tool that can quickly calculate the weight in line with the noise ratio SINR and generate a file containing multiple weights. Massive MIMO antenna beam optimization is performed according to each weight obtained. According to the expected throughput gain corresponding to each beam weight, the weight for beam optimization is selected for beam adjustment.

The Massive MIMO antenna beam optimization method provided by the embodiment of the present invention calculates each weight value according to the weight value tool, and the processing is fast and convenient.

FIG. 2 is a structural diagram of an apparatus for optimizing a Massive MIMO antenna beam provided by an embodiment of the present invention. As shown in FIG. 2 , the apparatus for optimizing a Massive MIMO antenna beam includes: an acquisition module 201 , a processing module 202 , and an output module 203 . The obtaining module 201 is configured to obtain the traffic distribution of the current user of the target cell according to the throughput distribution of the target cell beam, and obtain the potential user's traffic volume according to the noise distribution of the target cell beam and the throughput distribution of the same-frequency adjacent cell beam. Traffic distribution; the processing module 202 is configured to obtain the user traffic distribution before and after adjustment by each weight according to the current user's traffic distribution and the potential user's traffic distribution. A distribution of user traffic before weight adjustment and after adjustment according to each weight value, to obtain the expected throughput gain of the target cell beam after adjustment according to each weight value; the output module 203 is used for the throughput corresponding to each beam weight value The expected gain is selected, and the weight used for beam optimization is selected for beam adjustment.

The obtaining module 201 obtains the traffic volume distribution of the current user and the traffic volume distribution of the potential users, and the processing module 202 obtains the data before adjustment by each weight value and by each weight value according to the traffic volume distribution of the current user and the traffic volume distribution of the potential users. For the adjusted user traffic distribution, according to the user traffic distribution before and after adjustment by each weight, obtain the expected throughput gain of the target cell beam adjusted by each weight.

The traffic distribution of the current user corresponds to the user traffic distribution before the adjustment by the weight. After the beam is adjusted by the weight, it will absorb some new users from the potential users for coverage, and some of the current users may be adjusted by the weight. Variation in back lobe angle coverage without coverage. According to the traffic distribution of the current user, the traffic distribution of the potential user, and the corresponding weight, the user traffic distribution after the weight adjustment can be obtained. The processing module 202 can obtain the throughput status of users within the coverage of the lobe angle before the weight adjustment, and obtain the lobe angle after the weight adjustment according to the user traffic distribution before and after the weight adjustment, and the corresponding user traffic. Throughput status of users within the coverage area. According to the user throughput status before and after the weight adjustment, obtain the expected throughput gain after adjustment according to the weight relative to the throughput before adjustment.

Since there are multiple weights, it is necessary to obtain the expected throughput gain corresponding to the weight according to each weight. The expected throughput gain is obtained by statistical analysis, and the process of adjusting the lobe angle according to the weight is not implemented at this time. The output module 203 selects a weight that meets the requirements to optimize the beam according to each weight and the expected gain corresponding to each weight, and adjust the beam. For example, the weight with the largest expected throughput gain can be selected to optimize the beam.

In the Massive MIMO antenna beam optimization device provided by the embodiment of the present invention, the acquisition module acquires the traffic volume distribution of the current user and the traffic volume distribution of potential users, and the processing module acquires the users before and after adjustment by each weight value. Traffic distribution, and obtain the expected throughput gain after the beam is adjusted according to each weight. The output module selects the weight for beam optimization according to the expected throughput gain corresponding to each beam weight, so as to have high optimization efficiency and optimize accuracy.

The apparatus embodiments provided in the embodiments of the present invention are for implementing the foregoing method embodiments. For specific processes and details, please refer to the foregoing method embodiments, which will not be repeated here.

FIG. 3 is a schematic diagram of the physical structure of an electronic device according to an embodiment of the present invention. As shown in FIG. 3 , the electronic device may include: a processor (processor) 301, a communications interface (Communications Interface) 302, and a memory (memory) 303 And the bus 304, wherein the processor 301, the communication interface 302, and the memory 303 complete the mutual communication through the bus 304. The communication interface 302 may be used for information transmission of the electronic device. The processor 301 can invoke the logic instruction in the memory 303 to execute a method including the following: obtaining the traffic distribution of the current user of the target cell according to the throughput distribution of the target cell beam, and obtaining the traffic distribution of the current user of the target cell according to the noise distribution of the target cell beam and the same-frequency neighbor cell. The throughput distribution of the beam is used to obtain the traffic distribution of potential users; according to the traffic distribution of current users and the traffic distribution of potential users, the distribution of user traffic before and after adjustment by each weight is obtained, According to the user traffic distribution before and after adjustment by each weight value, obtain the expected throughput gain of the target cell beam after adjustment by each weight value; according to the expected throughput gain corresponding to each beam weight value , select the weights used for beam optimization for beam adjustment.

In addition, the above-mentioned logic instructions in the memory 303 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such understanding, the technical solution of the present invention can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above method embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes .

Embodiments of the present invention provide a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause a computer to execute the Massive MIMO antenna beam optimization method provided by the foregoing embodiments, for example, including : Obtain the traffic distribution of the current user of the target cell according to the throughput distribution of the target cell beam, and obtain the traffic distribution of potential users according to the noise distribution of the target cell beam and the throughput distribution of the same-frequency adjacent beam; The distribution of traffic volume and the traffic volume distribution of potential users are obtained, and the traffic volume distribution of users before and after adjustment by each weight value is obtained. The traffic distribution is to obtain the expected throughput gain of the target cell beam adjusted by each weight value; according to the throughput expected gain corresponding to each beam weight value, the weight value used for beam optimization is selected for beam adjustment.

The device embodiments described above are only illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in One place, or it can be distributed over multiple network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.

From the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on this understanding, the above-mentioned technical solutions can be embodied in the form of software products in essence or the parts that make contributions to the prior art, and the computer software products can be stored in computer-readable storage media, such as ROM/RAM, magnetic A disc, an optical disc, etc., includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the methods described in various embodiments or some parts of the embodiments.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that it can still be The technical solutions described in the foregoing embodiments are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A Massive MIMO antenna beam optimization method is characterized by comprising the following steps:

acquiring the traffic distribution of a current user of a target cell according to the throughput distribution of beams of the target cell, and acquiring the traffic distribution of potential users according to the noise distribution of the beams of the target cell and the throughput distribution of beams of adjacent cells with the same frequency;

according to the service distribution of the current user and the service distribution of the potential user, obtaining the service distribution of the user before the adjustment according to each weight and after the adjustment according to each weight, and obtaining the expected throughput gain of the target cell wave beam after the adjustment according to each weight according to the service distribution of the user before the adjustment according to each weight and after the adjustment according to each weight;

and selecting the weight for beam optimization to adjust the beam according to the expected throughput gain corresponding to each beam weight.

2. The method of claim 1, wherein before the obtaining the traffic distribution of the current user in the target cell according to the throughput distribution of the beam in the target cell and obtaining the traffic distribution of the potential user according to the noise distribution of the beam in the target cell and the throughput distribution of the beam in the neighboring cells with the same frequency, the method further comprises:

and acquiring throughput distribution and noise distribution of the wave beam according to CHR or MR data acquired in a preset busy hour, and acquiring throughput distribution of the wave beam of the adjacent region with the same frequency according to CHR or MR data of the adjacent region with the same frequency acquired in the preset busy hour.

3. The method of claim 1, wherein the obtaining the expected throughput gain of the beam after each weight adjustment according to the user traffic distribution before each weight adjustment and after each weight adjustment comprises:

and acquiring the expected gain of the throughput of each weight according to the speech system data distributed by the user traffic before and after the adjustment according to each weight.

4. The method of claim 1, wherein selecting the weight for beam optimization for beam adjustment according to the expected gain of throughput corresponding to each beam weight comprises:

and selecting a weight for beam optimization from the expected throughput gain corresponding to each beam weight according to the screening condition to carry out beam adjustment.

5. The method of claim 1, wherein after obtaining the traffic distribution of the current user in the target cell according to the throughput distribution of the beam in the target cell, and before obtaining the traffic distribution of the user before adjusting according to each weight and before obtaining the traffic distribution of the user after adjusting according to each weight according to the traffic distribution of the current user and the traffic distribution of the potential user, the method further comprises:

and adjusting the azimuth angle of the antenna according to the traffic distribution of the current user.

6. The method of claim 1, wherein there are multiple target cells, and wherein selecting weights for beam optimization according to expected throughput gain corresponding to each beam weight for beam adjustment comprises:

and selecting the target cell with the highest expected throughput gain for weight adjustment according to the expected throughput gain corresponding to each beam weight of each target cell, and selecting the corresponding weight for beam optimization for beam adjustment.

7. The method of claim 1, wherein the weight is calculated by a weight tool.

8. A Massive MIMO antenna beam optimization device is characterized by comprising:

the acquisition module is used for acquiring the traffic distribution of the current user of the target cell according to the throughput distribution of the beam of the target cell and acquiring the traffic distribution of the potential user according to the noise distribution of the beam of the target cell and the throughput distribution of the beam of the adjacent cell with the same frequency;

the processing module is used for acquiring the user traffic distribution before and after being adjusted according to each weight according to the traffic distribution of the current user and the traffic distribution of the potential user, and acquiring the expected throughput gain of the target cell beam after being adjusted according to each weight according to the user traffic distribution before and after being adjusted according to each weight;

and the output module is used for selecting the weight for beam optimization to carry out beam adjustment according to the expected throughput gain corresponding to each beam weight.

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the MassiveMIMO antenna beam optimization method according to any one of claims 1 to 7.

10. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, performs the steps of the Massive MIMO antenna beam optimization method according to any one of claims 1 to 7.

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