CN118555587A - Antenna weight optimization method and device - Google Patents

Abstract

The embodiment of the application provides a method and a device for optimizing antenna weights, comprising the following steps: and acquiring Reference Signal Receiving Power (RSRP) reported by each user equipment in the target area, carrying out cluster division on cells in the target area according to the RSRP to obtain a plurality of cell clusters, and optimizing the antenna weight of each cell cluster according to the distribution condition of the user equipment in the target area. The application solves the problem that the beam configuration of the high-frequency product depends on the simulation calculation or experience of the network gauge net optimizing personnel, and the user distribution is difficult to accurately acquire at high frequency, thereby acquiring the accurate user distribution and ensuring that the network coverage and the efficiency of the whole optimization area reach the optimal effect.

Description

Antenna weight optimization method and device

Technical Field

The embodiments of the present application relate to the field of communications, and more specifically, to a method and device for optimizing antenna weights.

Background Art

There are many beam configurations for high-frequency products, and the configuration depends on the simulation calculations or experience of network planners and network optimization personnel. Especially for large-scale commercial use, it will bring a huge workload for beam configuration. The measurement overhead of Direction Of Arrival (DOA) at high frequencies is too large, and the measurement accuracy and stability of DOA are relatively poor. Therefore, it is difficult to accurately obtain user distribution at high frequencies.

For models such as 128 Transmit/Receive (T/R), the number of arrays in a single channel of the active antenna unit (AAU) will increase, resulting in a narrower vertical analog beam width, reduced vertical coverage, and an increased impact of the analog beam on coverage.

Summary of the invention

The embodiments of the present application provide an antenna weight optimization method and device to at least solve the problem in the related art that the beam configuration of high-frequency products depends on the simulation calculation or experience of network planners and network optimizers, and it is difficult to accurately obtain the user distribution at high frequencies.

According to an embodiment of the present application, a method for optimizing antenna weights is provided, including: collecting reference signal received power (RSRP) reported by each user equipment in a target area, clustering cells in the target area according to RSRP to obtain multiple cell clusters, and optimizing the antenna weights of each cell cluster according to the distribution of user equipment in the target area.

According to another embodiment of the present application, an antenna weight optimization device is provided, including: a collection module for collecting the reference signal received power RSRP reported by each user equipment in the target area, a division module for clustering the cells in the target area according to RSRP to obtain multiple cell clusters, and an optimization module for optimizing the antenna weights of each cell cluster according to the distribution of user equipment in the target area.

According to another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

According to another embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

Through the present application, since the user's position is estimated based on the measurement results of the multi-beam measurement report (measurement report, referred to as MR), and the antenna weights of the high-frequency cell are adjusted using an intelligent optimization algorithm according to the user distribution, it is possible to solve the problem that the beam configuration of high-frequency products depends on the simulation calculations or experience of network planners and network optimizers, and it is difficult to accurately obtain the user distribution at high frequencies, so that the user distribution can be accurately obtained, so that the network coverage and efficiency of the entire optimization area can be optimized.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of a communication system architecture according to an embodiment of the present application;

FIG2 is a flow chart of an antenna weight optimization method according to an embodiment of the present application (I);

FIG3 is a flow chart (II) of the antenna weight optimization method according to an embodiment of the present application;

FIG4 is a flow chart (III) of the antenna weight optimization method according to an embodiment of the present application;

FIG5 is a structural block diagram of an antenna weight optimization device according to an embodiment of the present application.

DETAILED DESCRIPTION

The embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

The method provided in the embodiment of the present application can be run in a base station or other similar communication device. Taking running on a base station as an example, FIG1 is a schematic diagram of the communication system architecture of an embodiment of the present application. As shown in FIG1 , a base station 10 and multiple terminals are included, and the base station and the terminal can communicate based on a wireless channel. The base station 10 can be a base station of various types such as a macro base station, a micro base station, an eNB, etc. The base station 10 may include functional components such as an antenna system and a radio frequency unit, and the terminal may be a terminal device of various types such as a user equipment UE. It can be understood by those of ordinary skill in the art that the architecture shown in FIG1 is only for illustration, and it does not limit the architecture constituted by the above-mentioned base station and terminal. For example, the base station 10 may also include more or fewer components, or have different configurations.

In this embodiment, an antenna weight optimization method running on the above communication system architecture is provided. FIG. 2 is a flow chart (I) of the antenna weight optimization method according to an embodiment of the present application. As shown in FIG. 2 , the process includes the following steps:

Step S202, collecting the reference signal received power RSRP reported by each user equipment in the target area;

Step S204, clustering cells in the target area according to RSRP to obtain multiple cell clusters;

Step S206: Optimize the antenna weights of each cell cluster according to the distribution of user equipments in the target area.

Through the above steps, the reference signal received power RSRP reported by each user equipment in the target area is collected, and the cells in the target area are clustered according to RSRP to obtain multiple cell clusters. According to the distribution of user equipment in the target area, the antenna weight of each cell cluster is optimized, which solves the problem that the beam configuration of high-frequency products depends on the simulation calculation or experience of network planning and optimization personnel, and it is difficult to accurately obtain user distribution at high frequencies. Accurate user distribution is then obtained, so that the network coverage and efficiency of the entire optimization area are optimized.

It should be noted that the above step S202 can also collect any one or more information such as path loss (PL), timing advance (TA), uplink reference signal received power and interference plus noise ratio (SINR), and the present application does not limit this.

In one embodiment, the distribution of user equipment in the target area can be obtained based on a simulated beam scanning method, or based on the RSRP and beam gain of multiple synchronization signal block (SSB) beams reported by the user equipment.

For example, scanning parameters may be preset, including at least one of a scanning range, a scanning step, a scanning time for each step, and a total number of scanning times, and the target area may be scanned according to the scanning parameters to obtain the distribution of user devices in the target area. The distribution of user devices in the target area may also be obtained based on a probability distribution model or a correlation algorithm, and based on the RSRP and beam gain of multiple synchronization signal block SSB beams reported by the user equipment. It should be noted that the actual use is not limited to the probability distribution model and the correlation algorithm.

In one embodiment, when a probability distribution model is used to obtain the distribution of user devices in the target area, it is necessary to obtain the RSRP difference between each SSB beam and a preset reference beam, and also to obtain the beam gain difference between each SSB beam and the preset reference beam in each horizontal and vertical orientation. Based on the RSRP difference and the beam gain difference, the probability of each SSB beam in each horizontal and vertical orientation is determined through the probability distribution model, and the position with the highest probability is determined as the position of the user device.

For example: select SSB beam 0 as the reference beam, calculate the RSRP difference between each SSB beam and SSB beam 0, and obtain ΔRSRP_i. Then the RSRP difference of each beam also follows Gaussian distribution, with a mean of ΔRSRP_i and a variance of 2. At the same time, calculate the beam gain difference Δgain_i between each SSB beam and the reference SSB beam 0 at each position in the horizontal -90° to 90°, vertical -20° to 20°, with a step of 1°. At each position, take the beam gain difference Δgain_i of each beam as input, calculate the probability pb_i of each beam at this position when the mean is ΔRSRP_i and the variance is 2, and then multiply the probability of each beam to obtain the final probability pb at this position. Finally, take the position with the highest probability among all positions (including each position in the horizontal and vertical azimuths) as the final position of the user in the cell.

In one embodiment, when a correlation algorithm is used to obtain the distribution of user equipment in the target area, it is necessary to obtain the RSRP difference between each SSB beam and the preset reference beam to obtain an RSRP difference sequence, and also to obtain the beam gain difference between each SSB beam and the preset reference beam in each horizontal and vertical azimuth to obtain a beam gain difference sequence. According to the RSRP difference sequence and the beam gain difference sequence, the correlation between the RSRP difference sequence and the beam gain difference sequence in each horizontal and vertical azimuth is determined based on the correlation algorithm, and the position with the largest correlation is determined as the position of the user equipment. The correlation algorithm can be a correlation algorithm such as Euclidean distance, cosine similarity, etc., and this application does not limit this.

For example: the RSRP value reported for each SSB beam follows a Gaussian distribution, where the mean is the reported RSRP value and the variance is 1. Select SSB beam 0 as the reference beam, calculate the RSRP difference between each SSB beam and SSB beam 0, and obtain an RSRP difference sequence. Then, in the same order, calculate the beam gain difference between each SSB beam and the reference SSB beam 0 at each position in the horizontal range of -90° to 90° and vertical range of -20° to 20° with a step of 1° to obtain a beam gain difference sequence. Then calculate the correlation between the RSRP difference sequence and the beam gain difference sequence at each position, and the beam gain sequence to obtain the Euclidean distance of the RSRP difference sequence at each position. Finally, take the position where the Euclidean distance of all positions is less than the preset threshold and the correlation is the largest as the final position of the user in the cell.

In one embodiment, in order to divide the cells in the target area into cell clusters, it is necessary to first determine the overlapping coverage between each cell according to RSRP, and then cluster the cells in the target area based on the overlapping coverage to obtain the cell clusters based on the clustering algorithm. For example, the overlapping coverage can be determined as follows:

Determine the number of user equipments whose RSRP of the serving cell reported by the user equipment is greater than or equal to the overlapping coverage RSRP threshold of the serving cell reported by the user equipment, as the denominator;

The number of user equipments that meet at least two of the following conditions is used as the numerator: the number of user equipments whose RSRP of the serving cell reported by the user equipment is greater than or equal to the overlapping coverage RSRP threshold of the serving cell reported by the user equipment; the number of user equipments whose RSRP of the neighboring cell reported by the user equipment is greater than or equal to the overlapping coverage RSRP threshold of the neighboring cell reported by the user equipment; the number of user equipments whose difference between the RSRP of the neighboring cell reported by the user equipment and the RSRP of the serving cell reported by the user equipment is greater than or equal to the overlapping coverage RSRP difference threshold of the neighboring cell;

The overlapping coverage between each cell is determined according to the ratio of the numerator and the denominator.

In one embodiment, according to the obtained user equipment distribution and based on the set optimization parameters, an optimization algorithm can be used to search the weights in the weight library for each cell cluster to obtain the optimal simulated beam for each cell cluster, and then the optimal simulated beam and the antenna weight combination corresponding to the optimal simulated beam are determined based on the optimal simulated beam, and finally the optimal simulated beam and antenna weight combination are sent to each cell.

In one embodiment, according to the distribution of user equipment and based on the set optimization parameters, an optimization algorithm can be used to search the weights in the weight library for each cell cluster to obtain the optimal antenna weight combination for each cell cluster, and finally the antenna weight combination is sent to each cell.

In one embodiment, the optimization parameter may include at least one of the following: RSRP, path loss PL, timing advance TA, and signal to interference plus noise ratio SINR.

It should be noted that the above optimization parameters may vary according to actual conditions, and this application does not impose any limitation thereto.

Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of each embodiment of the present application.

FIG3 is a flow chart (II) of the antenna weight optimization method according to an embodiment of the present application, as shown in the figure, including:

Step 1: Collect data. After selecting the target area to be optimized, collect and store information such as RSRP, Path Loss (PL), Timing Advance (TA), uplink reference signal received power, and Signal To Interference Plus Noise Ratio (SINR) of multiple SSB beams reported by users in the target area network.

Step 2: Locate the user. Use the RSRP and beam gain of multiple SSB beams to estimate the user's position, where the beam gain can be simulated based on the data collected in the first step. The RSRP value reported by each SSB follows a certain probability distribution model, such as Gaussian distribution, Beta distribution, etc., taking Gaussian distribution as an example.

The RSRP value reported by each SSB follows a Gaussian distribution and is independent of each other, with the mean being the reported RSRP value and the variance being σ ² . First, a certain beam is used as the reference beam to calculate the RSRP difference ΔRSRP_i between each SSB beam and the reference beam. The RSRP difference of each beam also follows a Gaussian distribution, with a mean of ΔRSRP_i and a variance of 2σ ² . At the same time, the difference Δgain_i in beam gain between each SSB beam and the reference beam in each horizontal and vertical azimuth is calculated, where the reference beam can be any one of multiple beams, and the first beam is generally selected as the reference beam.

At each horizontal and vertical azimuth, the difference Δgain_i of the beam gain of each beam is used as input, and the probability pb_i of each beam at that position is calculated with a mean of ΔRSRP_i and a variance of 2σ ^2. The probability of each beam is then multiplied to obtain the final probability pb at that position, that is, the possible probability of the user at that position.

The above is a Bayesian estimation method based on Gaussian distribution. It can also be estimated using estimation methods based on correlation analysis, but actual use is not limited to these two methods.

Step 3: Divide the cell clusters. After collecting the RSRP information of the users in the target area, the overlapping coverage between each cell is calculated according to the following method, where the number of samples that simultaneously meet two or three of conditions 1, 2, and 3 can be used as the numerator. The best effect is achieved when the number of samples that simultaneously meet three conditions is used as the numerator.

For two cells _vi and vj, the method for cell _vi to calculate its overlapping coverage with cell vj is:

Condition 1: The RSRP reported by the user in the serving cell _vi is greater than or equal to the “serving cell coverage RSRP threshold”.

Condition 2: The RSRP reported by the user in the neighboring cell _vj is greater than or equal to the "neighboring cell overlapping coverage RSRP threshold".

Condition 3: (RSRP reported by user in neighboring cell vj - RSRP reported by user in serving cell _vi ) is greater than or equal to the "neighboring cell overlapping coverage RSRP difference threshold".

Then, the cells in the target area are clustered according to the calculated overlapping coverage. The clustering method can be used for clustering. For example, hierarchical clustering and other clustering methods are used to cluster the cells in the target area. The serving cell coverage RSRP threshold, the neighboring cell overlapping coverage RSRP threshold and the neighboring cell overlapping coverage RSRP difference threshold can be preset according to actual experience values.

Step 4: Search for antenna weight combinations suitable for each cell cluster. Set the target parameter to be optimized, which can be at least one of the following parameters: optimal RSRP, optimal SINR, optimal uplink PL, optimal TA, optimal uplink reference signal received power, optimal overlapping coverage, etc. According to the distribution of user equipment in the target area, use intelligent optimization algorithms such as ant colony algorithm, evolutionary algorithm and particle swarm algorithm for the cell cluster to search for the optimal cell cluster antenna weight combination in the weight library.

Step 5: Send antenna weight combination. Send the searched antenna weight combination to each cell in the target area.

Through the above embodiment, the user's position is estimated based on the RSRP of multiple SSB beams reported by each user, so as to obtain accurate user distribution, and the antenna weights of the high-frequency cell are adjusted using an intelligent optimization algorithm according to the user distribution, so as to achieve optimal network coverage and efficiency in the entire optimization area.

Embodiment 1:

Step 1: After selecting the target area to be optimized, collect and store the received power RSRP, path loss PL, timing advance TA, uplink reference signal received power and interference plus noise ratio SINR reported by users in the target area network.

Step 2: The RSRP value reported for each SSB beam follows a Gaussian distribution, where the mean is the reported RSRP value and the variance is 1.

Select beam 0 as the reference beam, calculate the RSRP difference between each SSB beam and beam 0, and obtain ΔRSRP_i. Then the RSRP difference of each beam also obeys Gaussian distribution, with a mean of ΔRSRP_i and a variance of 2. At the same time, calculate the beam gain difference Δgain_i between each SSB beam and the reference beam at each position from -90° to 90° horizontally and from -20° to 20° vertically with a step of 1°.

At each position, the beam gain difference Δgain_i of each beam is used as input, and the mean is ΔRSRP_i. The probability pb_i of each beam at this position when the variance is 2 is calculated, and then the probability of each beam is multiplied to obtain the final probability pb at this position.

Finally, the position with the highest probability among all positions is taken as the final position of the user in the cell.

Step 3. After collecting the received power RSRP information of users in the corresponding cells in the target area network, set the service cell coverage RSRP threshold to 90dB, the neighboring cell overlapping coverage RSRP threshold to 90dB, and the neighboring cell overlapping coverage RSRP difference threshold to 6dB. Calculate the overlapping coverage between each cell according to the following method.

For two cells _vi and _vj , the method for cell _vi to calculate its overlapping coverage with cell _vj is:

Condition 1: The RSRP reported by the user in the serving cell _vi is greater than or equal to the “serving cell coverage RSRP threshold”.

Condition 2: The RSRP reported by the user in the neighboring cell _vj is greater than or equal to the "neighboring cell overlapping coverage RSRP threshold".

Condition 3: (RSRP reported by user in neighboring cell _vj - RSRP reported by user in serving cell _vi ) is greater than or equal to the "neighboring cell overlapping coverage RSRP difference threshold".

Then, based on the calculated overlapping coverage between each cell, the hierarchical clustering method is used to divide the cell clusters.

Step 4: Set the target to be optimized as RSRP optimization. According to the distribution of user equipment in the target area, use the evolutionary algorithm to search for the optimal cell cluster antenna weight combination in the weight library for each cell cluster.

Step 5: Send the searched antenna weight combination to each cell in the target area.

Embodiment 2:

Step 1: After selecting the target area to be optimized, collect and store the received power RSRP, path loss PL, timing advance TA, uplink reference signal received power and interference plus noise ratio SINR reported by users in the target area network.

Step 2: The RSRP value reported for each SSB beam follows a Gaussian distribution, where the mean is the reported RSRP value and the variance is 2.

Select beam 0 as the reference beam, calculate the RSRP difference between each SSB beam and beam 0, and obtain ΔRSRP_i. Then the RSRP difference of each beam also obeys Gaussian distribution, with a mean of ΔRSRP_i and a variance of 4. At the same time, calculate the beam gain difference Δgain_i between each SSB beam and the reference beam at each position from -90° to 90° horizontally and from -20° to 20° vertically with a step of 1°.

At each position, the beam gain difference Δgain_i of each beam is used as input, and the mean is ΔRSRP_i. The probability pb_i of each beam at this position when the variance is 4 is calculated, and then the probability of each beam is multiplied to obtain the final probability pb at this position.

Finally, the position with the highest probability among all positions is taken as the final position of the user in the cell.

Step 3. After collecting the received power RSRP information of users in the corresponding cells in the target area network, set the serving cell coverage RSRP threshold to 100dB, the neighboring cell overlapping coverage RSRP threshold to 100dB, and the neighboring cell overlapping coverage RSRP difference threshold to 3dB. Calculate the overlapping coverage between each cell according to the following method.

For two cells _vi and _vj , the method for cell _vi to calculate its overlapping coverage with cell _vj is:

Condition 1: The RSRP reported by the user in the serving cell _vi is greater than or equal to the “serving cell coverage RSRP threshold”.

Condition 2: The RSRP reported by the user in the neighboring cell vj is greater than or equal to the "neighboring cell overlapping coverage RSRP threshold".

Condition 3: (RSRP reported by user in neighboring cell _vj - RSRP reported by user in serving cell _vi ) is greater than or equal to the "neighboring cell overlapping coverage RSRP difference threshold".

Then, based on the calculated overlapping coverage between each cell, the hierarchical clustering method is used to divide the cell clusters.

Step 4: Set the target to be optimized as SINR optimization. According to the distribution of user equipment in the target area, use the ant colony algorithm to search for the optimal cell cluster antenna weight combination in the weight library for each cell cluster.

Step 5: Send the searched antenna weight combination to each cell in the target area.

Embodiment 3:

Step 1: After selecting the target area to be optimized, collect and store the received power RSRP, path loss PL, timing advance TA, uplink reference signal received power and interference plus noise ratio SINR reported by users in the target area network.

Step 2. Select beam 0 as the reference beam, calculate the RSRP difference between each SSB beam and beam 0, and obtain an RSRP difference sequence. Then, in the same order, calculate the beam gain difference between each SSB beam and the reference beam at each position from -90° to 90° horizontally and from -20° to 20° vertically with a step of 1° to obtain a beam gain difference sequence. At the same time, calculate the correlation between the RSRP difference sequence and the beam gain difference sequence at each position.

Finally, the position with the greatest correlation among all positions is taken as the final position of the user in the cell.

Step 3. After collecting the received power RSRP information of users in the corresponding cells in the target area network, set the serving cell coverage RSRP threshold to 100dB, the neighboring cell overlapping coverage RSRP threshold to 100dB, and the neighboring cell overlapping coverage RSRP difference threshold to 3dB. Calculate the overlapping coverage between each cell according to the following method.

For two cells _vi and vj, the method for cell _vi to calculate its overlapping coverage with cell vj is:

Condition 1: The RSRP reported by the user in the serving cell _vi is greater than or equal to the “serving cell coverage RSRP threshold”.

Condition 2: The RSRP reported by the user in the neighboring cell _vj is greater than or equal to the "neighboring cell overlapping coverage RSRP threshold".

Condition 3: (RSRP reported by user in neighboring cell _vj - RSRP reported by user in serving cell _vi ) is greater than or equal to the "neighboring cell overlapping coverage RSRP difference threshold".

Then, based on the calculated overlapping coverage between each cell, the hierarchical clustering method is used to divide the cell clusters.

Step 4: Set the target to be optimized as the optimal uplink PL. According to the distribution of user equipment in the target area, use the ant colony algorithm to search for the optimal cell cluster antenna weight combination in the weight library for each cell cluster.

Step 5: Send the searched antenna weight combination to each cell in the target area.

Embodiment 4:

Step 1: After selecting the target area to be optimized, collect and store the received power RSRP, path loss PL, timing advance TA, uplink reference signal received power and interference plus noise ratio SINR reported by users in the target area network.

Step 2. Select beam 0 as the reference beam, calculate the RSRP difference between each SSB beam and beam 0, and obtain an RSRP difference sequence. Then, in the same order, calculate the beam gain difference between each SSB beam and the reference beam at each position from -90° to 90° horizontally and from -20° to 20° vertically with a step of 1° to obtain a beam gain difference sequence. At the same time, calculate the correlation between the RSRP difference sequence at each position and the beam gain difference sequence, as well as the Euclidean distance of the RSRP difference sequence at each position obtained by the beam gain sequence.

Finally, the position with the largest correlation and the Euclidean distance less than the preset threshold value among all the positions is taken as the final position of the user in the cell.

Step 3. After collecting the received power RSRP information of users in the corresponding cells in the target area network, set the serving cell coverage RSRP threshold to 100dB, the neighboring cell overlapping coverage RSRP threshold to 100dB, and the neighboring cell overlapping coverage RSRP difference threshold to 3dB. Calculate the overlapping coverage between each cell according to the following method.

For two cells _vi and vj, the method for cell _vi to calculate its overlapping coverage with cell vj is:

Condition 1: The RSRP reported by the user in the serving cell _vi is greater than or equal to the “serving cell coverage RSRP threshold”.

Condition 2: The RSRP reported by the user in the neighboring cell vj is greater than or equal to the "neighboring cell overlapping coverage RSRP threshold".

Condition 3: (RSRP reported by user in neighboring cell vj - RSRP reported by user in serving cell _vi ) is greater than or equal to the "neighboring cell overlapping coverage RSRP difference threshold".

Then, based on the calculated overlapping coverage between each cell, the hierarchical clustering method is used to divide the cell clusters.

Step 4: Set the target to be optimized as TA optimal. According to the distribution of user equipment in the target area, use the ant colony algorithm to search for the optimal cell cluster antenna weight combination in the weight library for each cell cluster.

Step 5: Send the searched antenna weight combination to each cell in the target area.

FIG4 is a flow chart (III) of the antenna weight optimization method according to an embodiment of the present application, as shown in the figure, including:

Step 1: Set the scanning configuration information of the simulated beam. Set the scanning range, scanning step, scanning time for each step, and total number of scans of the simulated beam to obtain the distribution of user equipment.

Step 2: Collect data. After selecting the target area to be optimized, collect and store information such as RSRP, DOA, PL, TA, uplink RSRP, and SINR reported by users in the target area network.

Step 3: Divide the cell clusters. After collecting the RSRP information of the users in the target area, the overlapping coverage between each cell is calculated according to the following method, where the number of samples that simultaneously meet two or three of conditions 1, 2, and 3 can be used as the numerator. The best effect is achieved when the number of samples that simultaneously meet three conditions is used as the numerator.

For two cells _vi and _vj , the method for cell _vi to calculate its overlapping coverage with cell _vj is:

Condition 1: The RSRP reported by the user in the serving cell _vi is greater than or equal to the “serving cell coverage RSRP threshold”.

Condition 2: The RSRP reported by the user in the neighboring cell _vj is greater than or equal to the "neighboring cell overlapping coverage RSRP threshold".

Condition 3: (RSRP reported by user in neighboring cell vj - RSRP reported by user in serving cell _vi ) is greater than or equal to the "neighboring cell overlapping coverage RSRP difference threshold".

Then, the cells in the target area are clustered according to the calculated overlapping coverage. The clustering method can be used for clustering. For example, hierarchical clustering and other clustering methods are used to cluster the cells in the target area. The serving cell coverage RSRP threshold, the neighboring cell overlapping coverage RSRP threshold and the neighboring cell overlapping coverage RSRP difference threshold can be preset according to actual experience values.

Step 4: Search for suitable simulated beams and antenna weight combinations for each cell cluster. Set the target parameter to be optimized, which can be at least one of the following parameters; such as optimal RSRP, optimal SINR, optimal uplink PL, optimal TA, optimal uplink reference signal received power, and optimal overlapping coverage, etc. According to the distribution of user equipment in the target area, use intelligent optimization algorithms such as ant colony algorithm, evolutionary algorithm, and particle swarm algorithm for the cell cluster to search for the optimal cell cluster simulated beam and corresponding antenna weight combination in the weight library.

Step 5: Send down the simulated beam and antenna weight combination. Send down the searched simulated beam and the corresponding antenna weight combination to each cell in the target area. Based on the sent simulated beam and antenna weight combination, the antenna weight of the simulated beam in the target area can be adjusted to achieve the best network coverage and efficiency in the entire target area.

Through the above embodiment, the complete user distribution of the cell is obtained based on simulated beam scanning, and the simulated beam of the cell and the corresponding antenna weight combination are adjusted simultaneously using an intelligent optimization algorithm according to the user distribution, so as to achieve optimal network coverage and efficiency in the entire optimized area.

Embodiment 5:

Step 1. Set the scanning range of the simulated beam to [-15°, 15°], the step size to 6°, the scanning time for each step to 10 minutes, and the total number of scans to 24.

Step 2: After selecting the target area to be optimized, collect and store the received power RSRP, DOA, path loss PL, timing advance TA, uplink reference signal received power, and interference plus noise ratio SINR reported by users in the target area network.

Step 3. After collecting the received power RSRP information of users in the corresponding cells in the target area network, set the service cell coverage RSRP threshold to 90dB, the neighboring cell overlapping coverage RSRP threshold to 90dB, and the neighboring cell overlapping coverage RSRP difference threshold to 6dB. Calculate the overlapping coverage between each cell according to the following method.

For two cells _vi and _vj , the method for cell _vi to calculate its overlapping coverage with cell _vj is:

Condition 1: The RSRP reported by the user in the serving cell _vi is greater than or equal to the “serving cell coverage RSRP threshold”.

Condition 2: The RSRP reported by the user in the neighboring cell _vj is greater than or equal to the "neighboring cell overlapping coverage RSRP threshold".

Condition 3: (RSRP reported by user in neighboring cell _vj - RSRP reported by user in serving cell _vi ) is greater than or equal to the "neighboring cell overlapping coverage RSRP difference threshold".

Then, based on the calculated overlapping coverage between each cell, the hierarchical clustering method is used to divide the cell clusters.

Step 4: Set the target to be optimized as RSRP optimization. According to the distribution of user equipment in the target area, use the evolutionary algorithm to search for the optimal cell cluster simulation beam and corresponding antenna weight combination in the weight library for each cell cluster.

Step 5: Send the searched simulated beam and the corresponding antenna weight combination to each cell in the target area. Based on the sent simulated beam and antenna weight combination, the antenna weight of the simulated beam in the target area can be adjusted to achieve the best network coverage and efficiency in the entire target area.

Embodiment 6:

Step 1. Set the scanning range of the simulated beam to [-13°, 12°], the step size to 5°, the scanning time for each step to 10 minutes, and the total number of scans to 48.

Step 2: After selecting the target area to be optimized, collect and store the received power RSRP, DOA, path loss PL, timing advance TA, uplink reference signal received power, and interference plus noise ratio SINR reported by users in the target area network.

Step 3. After collecting the received power RSRP information of users in the corresponding cells in the target area network, set the serving cell coverage RSRP threshold to 100dB, the neighboring cell overlapping coverage RSRP threshold to 100dB, and the neighboring cell overlapping coverage RSRP difference threshold to 3dB. Calculate the overlapping coverage between each cell according to the following method.

For two cells _vi and vj, the method for cell _vi to calculate its overlapping coverage with cell vj is:

Condition 1: The RSRP reported by the user in the serving cell _vi is greater than or equal to the “serving cell coverage RSRP threshold”.

Condition 2: The RSRP reported by the user in the neighboring cell vj is greater than or equal to the "neighboring cell overlapping coverage RSRP threshold".

Condition 3: (RSRP reported by user in neighboring cell vj - RSRP reported by user in serving cell _vi ) is greater than or equal to the "neighboring cell overlapping coverage RSRP difference threshold".

Then, based on the calculated overlapping coverage between each cell, the hierarchical clustering method is used to divide the cell clusters.

Step 4: Set the target to be optimized as the optimal SINR. According to the distribution of user equipment in the target area, use the evolutionary algorithm to search for the optimal cell cluster simulation beam and corresponding antenna weight combination in the weight library for each cell cluster.

Step 5: Send the searched simulated beam and the corresponding antenna weight combination to each cell in the target area. Based on the sent simulated beam and antenna weight combination, the antenna weight of the simulated beam in the target area can be adjusted to achieve the best network coverage and efficiency in the entire target area.

In this embodiment, an antenna weight optimization device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and/or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

FIG. 5 is a structural block diagram of an antenna weight optimization device according to an embodiment of the present application. As shown in FIG. 5 , the antenna weight optimization device 50 includes:

A collection module 502 is used to collect the reference signal received power RSRP reported by each user equipment in the target area;

A division module 504, configured to divide cells in the target area into clusters according to RSRP to obtain a plurality of cell clusters;

The optimization module 506 is used to optimize the antenna weights of each cell cluster according to the distribution of user equipments in the target area.

It should be noted that the above-mentioned acquisition module 502 can also be used to collect any one or more information such as path loss (PL), timing advance (TA), uplink reference signal received power and interference plus noise ratio (SINR), and the present application does not limit this.

In one embodiment, the antenna weight optimization device 50 may include one of the following:

A first determination module is used to obtain the distribution of user equipment in the target area based on simulated beam scanning;

The second determination module is used to obtain the distribution of user equipment in the target area based on the RSRP and beam gain of multiple synchronization signal block SSB beams reported by the user equipment.

In one embodiment, the second determining module may include:

A first acquisition submodule is used to obtain the RSRP difference between each SSB beam and a preset reference beam;

A second acquisition submodule is used to obtain a beam gain difference between each SSB beam and a preset reference beam in each horizontal azimuth and vertical azimuth;

The first determination submodule is used to determine the probability of each SSB beam in each horizontal and vertical orientation through a probability distribution model according to the RSRP difference and the beam gain difference, and determine the position with the highest probability as the position of the user equipment.

In one embodiment, the second determining module may further include:

The third acquisition submodule is used to obtain the RSRP difference between each SSB beam and the preset reference beam to obtain an RSRP difference sequence;

The fourth acquisition submodule is used to obtain the beam gain difference between each SSB beam and the preset reference beam in each horizontal azimuth and vertical azimuth to obtain a beam gain difference sequence;

The second determination submodule is used to determine the correlation between the RSRP difference sequence and the beam gain difference sequence at each horizontal and vertical orientation based on the RSRP difference sequence and the beam gain difference sequence based on the correlation algorithm, and determine the position with the largest correlation as the position of the user equipment.

In one embodiment, the antenna weight optimization device 50 may further include:

The scanning module is used to scan the target area according to preset scanning parameters to obtain the distribution of user equipment in the target area, wherein the scanning parameters include at least one of the following: scanning range, scanning step, scanning time of each step and total number of scanning times.

In one embodiment, the partitioning module 504 may include:

A third determination submodule is used to determine the overlapping coverage between each cell according to RSRP;

The division submodule is used to divide the cells in the target area into clusters according to the overlapping coverage and based on the clustering algorithm to obtain cell clusters.

In one embodiment, the third determining submodule may include:

The first determination sub-module determines, as a denominator, the number of user equipments whose RSRP of the serving cell reported by the user equipment is greater than or equal to the overlapping coverage RSRP threshold of the serving cell reported by the user equipment;

The second determination sub-module is used to use the number of user equipments that meet at least two of the following conditions as a numerator: the number of user equipments whose RSRP of the serving cell reported by the user equipment is greater than or equal to the overlapping coverage RSRP threshold of the serving cell reported by the user equipment; the number of user equipments whose RSRP of the neighboring area reported by the user equipment is greater than or equal to the overlapping coverage RSRP threshold of the neighboring area reported by the user equipment; the number of user equipments whose difference between the RSRP of the neighboring area reported by the user equipment and the RSRP of the serving cell reported by the user equipment is greater than or equal to the overlapping coverage RSRP difference threshold of the neighboring area;

The third determination sub-module is used to determine the overlapping coverage between each cell according to the ratio of the numerator and the denominator.

In one embodiment, the optimization module 506 may include:

The first optimization submodule is used to search the weights in the weight library using an optimization algorithm for each cell cluster according to the distribution of user equipment and the set optimization parameters, so as to obtain the optimal simulated beam for each cell cluster;

The fourth determination submodule is used to determine the antenna weight combination corresponding to the optimal simulated beam based on the optimal simulated beam of each cell cluster.

In one embodiment, the optimization module 506 may include:

The second optimization submodule is used to perform weight search in the weight library for each cell cluster using an optimization algorithm according to the distribution of user equipments and based on the set optimization parameters, so as to obtain the optimal antenna weight combination for each cell cluster.

In one embodiment, the optimization parameter may include at least one of the following: RSRP, path loss PL, timing advance TA, and signal to interference plus noise ratio SINR.

It should be noted that the above optimization parameters may vary according to actual conditions, and this application does not impose any limitation thereto.

In one embodiment, the antenna weight optimization device 50 may further include:

A first sending module is used to send the obtained optimal simulated beam and antenna weight combination of each cell cluster to each cell;

In one embodiment, the antenna weight optimization device 50 may further include:

The second sending module is used to send the obtained optimal antenna weight combination of each cell cluster to each cell.

It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

In an exemplary embodiment, the electronic device may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.

For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

An embodiment of the present application further provides a computer program product, comprising computer instructions, wherein when the computer instructions are executed by a processor, the steps in any of the above method embodiments are implemented.

Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims (16)

1. An antenna weight optimization method is characterized by comprising the following steps:

collecting Reference Signal Receiving Power (RSRP) reported by each user equipment in a target area;

carrying out cluster division on cells in the target area according to the RSRP to obtain a plurality of cell clusters;

and optimizing the antenna weight of each cell cluster according to the distribution condition of the user equipment in the target area.

2. The method of claim 1, further comprising determining a user equipment distribution in the target area according to one of:

Acquiring the distribution condition of the user equipment in the target area based on analog beam scanning;

and acquiring the distribution condition of the user equipment of the target area based on the RSRP and the beam gain of the SSB beams of the plurality of synchronous signal blocks reported by the user equipment.

3. The method of claim 2, wherein obtaining the ue distribution of the target area based on RSRP and beam gains of a plurality of SSB beams reported by the ue comprises:

acquiring an RSRP difference value between each SSB beam and a preset reference beam;

Acquiring a beam gain difference value between each SSB beam and the preset reference beam in each horizontal direction and each vertical direction;

And determining the probability of each SSB wave beam in each horizontal azimuth and each vertical azimuth through a probability distribution model according to the RSRP difference value and the wave beam gain difference value, and determining the position with the maximum probability as the position of the user equipment.

4. The method of claim 2, wherein obtaining the ue distribution of the target area based on RSRP and beam gains of a plurality of SSB beams reported by the ue comprises:

acquiring an RSRP difference value between each SSB beam and a preset reference beam to obtain an RSRP difference value sequence;

acquiring a beam gain difference value between each SSB beam and the preset reference beam in each horizontal direction and each vertical direction to obtain a beam gain difference value sequence;

and determining the correlation of the RSRP difference sequence and the beam gain difference sequence in each horizontal direction and each vertical direction based on a correlation algorithm according to the RSRP difference sequence and the beam gain difference sequence, and determining the position with the maximum correlation as the position of the user equipment.

5. The method according to claim 1, characterized in that before collecting the received power RSRP reported by each user equipment in the target area, the method comprises:

Scanning the target area according to preset scanning parameters to obtain the distribution condition of the user equipment in the target area;

wherein the scan parameters include at least one of: scan range, scan step size, scan time per step size, and total scan times.

6. The method of claim 1, wherein clustering cells in the target area according to the RSRP results in a plurality of cell clusters, comprising:

Determining overlapping coverage between each cell according to the RSRP;

And according to the overlapping coverage, clustering the cells in the target area based on a clustering algorithm to obtain the cell clusters.

7. The method of claim 6, wherein determining the overlapping coverage between each cell based on the RSRP comprises:

determining the number of user equipment with the RSRP of the service cell reported by the user equipment being greater than or equal to the overlapping coverage RSRP threshold of the service cell reported by the user equipment as a denominator;

Taking the number of the user equipment satisfying at least two of the following as a molecule: the RSRP of the service cell reported by the user equipment is larger than or equal to the number of the user equipment with the overlapping coverage RSRP threshold of the service cell reported by the user equipment; the RSRP of the neighbor cells reported by the user equipment is larger than or equal to the number of the user equipment with the overlapping coverage RSRP threshold of the neighbor cells reported by the user equipment; the difference value between the RSRP of the neighbor cell reported by the user equipment and the RSRP of the service cell reported by the user equipment is larger than or equal to the number of the user equipment with the overlapping coverage RSRP difference threshold of the neighbor cell;

And determining the overlapping coverage degree between each cell according to the ratio of the numerator to the denominator.

8. The method of claim 2, wherein optimizing the antenna weights for each of the cell clusters according to the user equipment distribution in the target area comprises:

according to the distribution condition of the user equipment, based on the set optimization parameters, carrying out weight searching on each cell cluster in a weight library by using an optimization algorithm to obtain an optimal analog beam of each cell cluster;

and determining an antenna weight combination corresponding to the optimal analog beam based on the optimal analog beam of each cell cluster.

9. The method of claim 2, wherein optimizing the antenna weights for each of the cell clusters according to the user equipment distribution in the target area comprises:

And according to the distribution condition of the user equipment, based on the set optimization parameters, carrying out weight searching on each cell cluster in a weight library by using an optimization algorithm to obtain the optimal antenna weight combination of each cell cluster.

10. The method according to claim 8 or 9, wherein the optimization parameters comprise at least one of: RSRP, path loss PL, timing advance TA, signal to interference plus noise ratio SINR.

11. The method of claim 8, wherein the method further comprises:

And transmitting the obtained optimal analog wave beam and antenna weight combination of each cell cluster to each cell.

12. The method according to claim 9, wherein the method further comprises:

and transmitting the obtained optimal antenna weight combination of each cell cluster to each cell.

13. An antenna weight optimizing apparatus, comprising:

The acquisition module is used for acquiring Reference Signal Received Power (RSRP) reported by each user equipment in the target area;

the division module is used for carrying out cluster division on the cells in the target area according to the RSRP to obtain a plurality of cell clusters;

And the optimizing module is used for optimizing the antenna weight of each cell cluster according to the distribution condition of the user equipment in the target area.

14. A computer readable storage medium, characterized in that a computer program is stored in the computer readable storage medium, wherein the computer program, when being executed by a processor, implements the steps of the method according to any of the claims 1 to 12.

15. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method as claimed in any one of claims 1 to 12 when the computer program is executed.

16. A computer program product comprising computer instructions which, when executed by a processor, implement the steps of the method as claimed in any one of claims 1 to 12.