Antenna weight optimization method and apparatus

Abstract

The embodiment of the application provides an antenna weight optimization method and device, comprising: collecting reference signal receiving power (RSRP) reported by each user equipment in a target area, performing cluster division on cells in the target area according to the RSRP to obtain a plurality of cell clusters, and optimizing antenna weights of each cell cluster according to user equipment distribution in the target area. Through the application, the problem that user distribution is difficult to accurately obtain under high frequency because high-frequency product beam configuration depends on simulation calculation or experience of network planning and optimization personnel is solved, and then accurate user distribution is obtained, so that network coverage and efficiency of the whole optimization area are optimized.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of communication, in particular to an antenna weight optimization method and device. BACKGROUND

[0002] High frequency products have more beam configurations, and the configurations depend on simulation calculation or experience of network planning and optimization personnel. Especially in large-scale commercial use, it will bring huge beam configuration workload. Direction of arrival (DOA) measurement is too expensive at high frequency, and the measurement accuracy and stability of DOA are relatively poor, so it is difficult to accurately obtain the user distribution at high frequency.

[0003] For 128 transmit / receive (T / R) models, the number of array elements of an active antenna unit (AAU) single channel increases, which narrows the analog beam width in the vertical direction, reduces the vertical coverage, and increases the influence of the analog beam on coverage. SUMMARY

[0004] Embodiments of the present application provide an antenna weight optimization method and device to at least solve the problem that high frequency product beam configuration depends on simulation calculation or experience of network planning and optimization personnel, and it is difficult to accurately obtain the user distribution at high frequency in the related art.

[0005] According to an embodiment of the present application, an antenna weight optimization method is provided, comprising: collecting reference signal received power (RSRP) reported by each user equipment in a target area, performing cluster division on cells in the target area according to the RSRP to obtain a plurality of cell clusters, and optimizing antenna weights of each cell cluster according to user equipment distribution in the target area.

[0006] According to another embodiment of the present application, an antenna weight optimization device is provided, comprising: a collection module configured to collect reference signal received power (RSRP) reported by each user equipment in a target area, a division module configured to perform cluster division on cells in the target area according to the RSRP to obtain a plurality of cell clusters, and an optimization module configured to optimize antenna weights of each cell cluster according to user equipment distribution in the target area.

[0007] According to still another embodiment of the present application, a computer readable storage medium is also provided, and the computer readable storage medium stores a computer program, wherein the computer program is configured to execute the steps in any of the method embodiments when running.

[0008] According to another embodiment of the present application, an electronic device is also provided, which comprises a memory and a processor, the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0009] According to the present application, since the position of a user is estimated based on the measurement result of a multi-beam measurement report (MR), and the antenna weight of a high-frequency cell is adjusted according to the user distribution using an intelligent optimization algorithm, the problem that the user distribution is difficult to accurately obtain under high frequency because the high-frequency product beam configuration depends on the simulation calculation or experience of network planning and optimization personnel can be solved, the user distribution is accurately obtained, and the network coverage and efficiency of the entire optimization area are optimized. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 is a communication system architecture schematic diagram of an embodiment of the present application;

[0011] Figure 2 is a flowchart of an antenna weight optimization method according to an embodiment of the present application (one);

[0012] Figure 3 is a flowchart of an antenna weight optimization method according to an embodiment of the present application (two);

[0013] Figure 4 is a flowchart of an antenna weight optimization method according to an embodiment of the present application (three);

[0014] Figure 5 is a structural block diagram of an antenna weight optimization device according to an embodiment of the present application. DETAILED DESCRIPTION

[0015] Hereinafter, the embodiments of the present application will be described in detail with reference to the accompanying drawings and in conjunction with embodiments.

[0016] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence.

[0017] The method provided by the embodiments of the present application can be run in a base station or similar communication device. Taking the case of running on a base station, Figure 1 is a communication system architecture schematic diagram of an embodiment of the present application, such as Figure 1As shown, the communication system architecture includes a base station 10 and a plurality of terminals, and the base station and the terminals can communicate with each other based on a wireless channel. The base station 10 can be a macro base station, a micro base station, an eNB, or the like, and the base station 10 can include functional components such as an antenna system, a radio frequency unit, and the like. The terminals can be user equipment (UE) or the like. Those skilled in the art can understand that Figure 1 The architecture shown is merely illustrative, and does not limit the architecture of the base station and the terminals described above. For example, the base station 10 can include more or fewer components, or have a different configuration.

[0018] In this embodiment, an antenna weight optimization method for the communication system architecture described above is provided, Figure 2 is a flowchart of an antenna weight optimization method according to an embodiment of the present application, as shown in Figure 2 The flowchart includes the following steps:

[0019] In step S202, the reference signal received power (RSRP) reported by each user equipment in a target area is collected.

[0020] In step S204, the cells in the target area are clustered based on the RSRP to obtain a plurality of cell clusters.

[0021] In step S206, the antenna weights of each cell cluster are optimized based on the distribution of user equipment in the target area.

[0022] Through the above steps, the reference signal received power (RSRP) reported by each user equipment in a target area is collected, the cells in the target area are clustered based on the RSRP to obtain a plurality of cell clusters, and the antenna weights of each cell cluster are optimized based on the distribution of user equipment in the target area. This solves the problem that the user distribution is difficult to accurately obtain in high frequency, and the beam configuration of high frequency products relies on the simulation calculation or experience of network planning and optimization personnel. Furthermore, accurate user distribution is obtained, and the network coverage and efficiency of the entire optimization area are optimized.

[0023] It should be noted that the step S202 can also collect any one or more of the following information: path loss (PL), timing advance (TA), uplink reference signal received power, and signal to interference plus noise ratio (SINR). The present application does not limit this.

[0024] In one embodiment, the user equipment distribution in the target area can be obtained based on a simulated beam scanning manner or based on the RSRP and beam gain of multiple SSB beams reported by the user equipment.

[0025] For example, scanning parameters can 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 is scanned according to the scanning parameters to obtain the user equipment distribution in the target area. The user equipment distribution in the target area can also be obtained according to a probability distribution model or a correlation algorithm and based on the RSRP and beam gain of multiple SSB beams reported by the user equipment. It should be noted that the two methods are not the only methods used in practice.

[0026] In one embodiment, when the probability distribution model is used to obtain the user equipment distribution in the target area, the RSRP difference between each SSB beam and a preset reference beam needs to be obtained, and the beam gain difference between each SSB beam and the preset reference beam in each horizontal and vertical direction also needs to be obtained. According to the RSRP difference and the beam gain difference, the probability of each SSB beam in each horizontal and vertical direction is determined by the probability distribution model, and the position with the maximum probability is determined as the position of the user equipment.

[0027] For example, SSB beam 0 is selected as the reference beam, the RSRP difference between each SSB beam and SSB beam 0 is calculated to obtain ΔRSRP_i. The RSRP difference of each beam also follows a Gaussian distribution, with a mean of ΔRSRP_i and a variance of 2. Meanwhile, the beam gain difference Δgain_i of each SSB beam and the reference SSB beam 0 in each position from -90° to 90° horizontally and from -20° to 20° vertically with a step of 1° is calculated. In each position, the beam gain difference Δgain_i of each beam is taken as the input, the probability pb_i of each beam in the position under the condition that the mean is ΔRSRP_i and the variance is 2 is calculated, and then the probabilities of each beam are multiplied to obtain the final probability pb in the position. Finally, the position with the maximum probability in all positions (including each position in the horizontal and vertical directions) is taken as the final position of the user in the cell.

[0028] In an embodiment, when a correlation algorithm is used to obtain the user equipment distribution in the target area, the RSRP difference between each SSB beam and the preset reference beam needs to be obtained to obtain an RSRP difference sequence, and the beam gain difference between each SSB beam and the preset reference beam in each horizontal and vertical direction needs to be obtained to obtain a beam gain difference sequence. According to the RSRP difference sequence and the beam gain difference sequence, the correlation of the RSRP difference sequence and the beam gain difference sequence in each horizontal and vertical direction is determined based on the correlation algorithm, and the position with the maximum correlation is determined as the position of the user equipment. The correlation algorithm can be a Euclidean distance, a cosine similarity, etc., which is not limited in the present application.

[0029] 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. SSB beam 0 is selected as the reference beam, the RSRP difference between each SSB beam and SSB beam 0 is calculated to obtain an RSRP difference sequence. Then, the beam gain difference between each SSB beam and the reference SSB beam 0 in the horizontal -90° to 90° and vertical -20° to 20°, with a step of 1°, is calculated to obtain a beam gain difference sequence. Then, the correlation of the RSRP difference sequence and the beam gain difference sequence in each position is calculated, and the Euclidean distance of the RSRP difference sequence in each position is obtained from the beam gain sequence. Finally, the position with the maximum correlation and the Euclidean distance less than a preset threshold value in all positions is taken as the final position of the user in the cell.

[0030] In an embodiment, in order to divide the cells in the target area into cell clusters, the overlapping coverage between each cell needs to be determined according to the RSRP, and then the cells in the target area are divided into clusters based on the clustering algorithm to obtain the cell clusters according to the overlapping coverage. For example, the overlapping coverage can be determined in the following manner:

[0031] The number of user equipment whose reported RSRP of the serving cell is greater than or equal to the reported overlapping coverage RSRP threshold of the serving cell is taken as the denominator;

[0032] The number of user equipment satisfying at least two of the following conditions is taken as the numerator: the number of user equipment whose reported RSRP of the serving cell is greater than or equal to the reported overlapping coverage RSRP threshold of the serving cell; the number of user equipment whose reported RSRP of the neighboring cell is greater than or equal to the reported overlapping coverage RSRP threshold of the neighboring cell; and the number of user equipment whose reported RSRP of the neighboring cell is greater than or equal to the reported RSRP of the serving cell, and the difference between the reported RSRP of the neighboring cell and the reported RSRP of the serving cell is greater than or equal to the overlapping coverage RSRP difference threshold of the neighboring cell;

[0033] The overlapping coverage between each cell is determined according to the ratio of the numerator and the denominator.

[0034] In one embodiment, according to the obtained user equipment distribution, the optimal antenna weight combination of each cell cluster can be obtained by searching the weight value in the weight value library based on the set optimization parameters and using an optimization algorithm, and finally the optimal antenna weight combination is downloaded to each cell.

[0035] In one embodiment, according to the user equipment distribution, the optimal antenna weight combination of each cell cluster can be obtained by searching the weight value in the weight value library based on the set optimization parameters and using an optimization algorithm, and finally the optimal antenna weight combination is downloaded to each cell.

[0036] In one embodiment, the optimization parameters can include at least one of RSRP, path loss PL, time advance TA, and signal to interference plus noise ratio SINR.

[0037] It should be noted that the above optimization parameters can be different according to actual conditions, which are not limited in the present application.

[0038] From the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be realized by means of software and necessary general hardware platform, and of course it can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes a plurality of instructions for making a terminal device (which can be a mobile phone, computer, server, or network device, etc.) execute the methods of various embodiments of the present application.

[0039] Figure 3 is a flowchart of the antenna weight optimization method according to an embodiment of the present application, as shown, which includes:

[0040] First step: data collection. After selecting the target area to be optimized, the reference signal received power RSRP, path loss PL, timing advance TA, uplink reference signal received power, and signal to interference plus noise ratio SINR of the multiple SSB beams reported by the users in the network of the target area are collected and stored.

[0041] Second step: positioning the user's location. Using the RSRP of multiple SSB beams and the beam gain to estimate the user's location, where the beam gain can be simulated according to 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.

[0042] The RSRP value reported by each SSB follows a Gaussian distribution and is independent of each other, where the mean is the reported RSRP value and the variance is σ 2 . First, take a certain beam as the reference beam, calculate the RSRP difference ΔRSRP_i of each SSB beam and the reference beam, then 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, calculate the difference Δgain_i of the beam gain of each SSB beam and the reference beam in each horizontal and vertical direction. The reference beam can be any one of the multiple beams, and the first beam is generally selected as the reference beam.

[0043] In each horizontal and vertical direction, take the difference Δgain_i of the beam gain of each beam as input, calculate the probability pb_i of each beam at this location under the condition that the mean is ΔRSRP_i and the variance is 2σ 2 , and then multiply the probability of each beam to get the final probability pb at this location, i.e. the probability of the user at this location.

[0044] The above is the method of Bayesian estimation based on Gaussian distribution. Other estimation methods such as correlation analysis based estimation method can also be used for estimation, but the actual use is not limited to these two methods.

[0045] Third step: dividing the cell cluster. After collecting the received power RSRP information of the users in the target area, calculate the overlapping coverage between each cell according to the following method, where the number of samples that meet conditions 1, 2 and 3 at the same time can be used as the numerator, and when the number of samples that meet all three conditions is used as the numerator, the effect is the best.

[0046] For two cells v i and vj, the method for calculating the overlapping coverage of v i and vj is:

[0047]

[0048] Condition 1: The RSRP reported by the user in the serving cell v i is greater than or equal to the "serving cell coverage RSRP threshold".

[0049] Condition 2: The RSRP reported by the user in the neighboring cell v jRSRP of the user reported in the serving cell v

[0050] Condition 3: (RSRP of the user reported in the neighboring cell vj - RSRP of the user reported in the serving cell v i RSRP) is greater than or equal to the "neighboring cell overlapping coverage RSRP difference threshold".

[0051] Then, the cells in the target area are clustered according to the calculated overlapping coverage, and the clustering method such as hierarchical clustering can be used for clustering the cells in the target area, wherein 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 the experience value in practice.

[0052] Fourth step: search for the antenna weight combination suitable for each cell cluster. Set the target parameter to be optimized, which can be at least one of the following parameters: RSRP optimal, SINR optimal, uplink PL optimal, TA optimal, uplink reference signal received power optimal, and overlapping coverage optimal, etc. According to the user equipment distribution in the target area, the intelligent optimization algorithm such as ant colony algorithm, evolution algorithm, and particle swarm algorithm is used to search for the optimal antenna weight combination of the cell cluster in the weight library.

[0053] Fifth step: issue the antenna weight combination. For each cell in the target area, issue the searched antenna weight combination.

[0054] Through the above embodiment, the position of the user is estimated based on the RSRP of multiple SSB beams reported by each user, so as to obtain accurate user distribution, and the antenna weight of the high-frequency cell is adjusted using the intelligent optimization algorithm according to the user distribution, so as to make the network coverage and efficiency of the entire optimization area optimal.

[0055] Embodiment 1:

[0056] Step 1: After selecting the target area to be optimized, collect and store the information such as the received power RSRP, path loss PL, time advance TA, uplink reference signal received power, and signal to interference plus noise ratio SINR reported by the users in the network of the target area.

[0057] Step 2: The RSRP value reported for each SSB beam is taken according to the Gaussian distribution, wherein the mean value is the reported RSRP value, and the variance is 1.

[0058] Select beam 0 as the reference beam, calculate the RSRP difference value of each SSB beam and beam 0, and obtain ΔRSRP_i. The RSRP difference value of each beam is also subject to a Gaussian distribution, with a mean of ΔRSRP_i and a variance of 2. At the same time, the beam gain difference value Δgain_i of each SSB beam and the reference beam at each position from horizontal -90° to 90° and vertical -20° to 20° with a step of 1° is calculated.

[0059] At each position, the beam gain difference value Δgain_i of each beam is taken as input, the probability pb_i of each beam at the position under the condition of mean ΔRSRP_i and variance 2 is calculated, and then the probability of each beam is multiplied to obtain the final probability pb at the position.

[0060] Finally, the position with the maximum probability at all positions is taken as the final position of the user in the cell.

[0061] Step3, after collecting the received power RSRP information of the user in the target area network in the corresponding cell, set the service cell coverage RSRP threshold to 90dB, the neighbor cell overlapping coverage RSRP threshold to 90dB, and the neighbor cell overlapping coverage RSRP difference threshold to 6dB. Calculate the overlapping coverage degree between each cell according to the following method.

[0062] For v i , v j two cells, cell v i Calculate the overlapping coverage degree of v j . The method for calculating the overlapping coverage degree of v

[0063]

[0064] Condition 1: The RSRP of the user reported in the service cell v i is greater than or equal to the "service cell coverage RSRP threshold".

[0065] Condition 2: The RSRP of the user reported in the neighbor cell v j is greater than or equal to the "neighbor cell overlapping coverage RSRP threshold".

[0066] Condition 3: (the RSRP of the user reported in the neighbor cell v j - the RSRP of the user reported in the service cell v i ) is greater than or equal to the "neighbor cell overlapping coverage RSRP difference threshold".

[0067] Then, according to the calculated overlapping coverage degree between each cell, the method of hierarchical clustering is used to divide the cell cluster.

[0068] Step4, set the target to be optimized as RSRP optimization, and search for the optimal cell cluster antenna weight combination in the weight library using an evolutionary algorithm according to the user equipment distribution in the target area.

[0069] Step5, issue the searched antenna weight combination to each cell in the target area.

[0070] Embodiment 2:

[0071] Step1, after selecting the target area to be optimized, collect and store the received power RSRP, path loss PL, time advance TA, uplink reference signal received power, and signal to interference plus noise ratio SINR information reported by users in the target area network.

[0072] Step2, the RSRP value reported by each SSB beam follows a Gaussian distribution, where the mean is the reported RSRP value and the variance is 2.

[0073] Select beam 0 as the reference beam, calculate the RSRP difference between each SSB beam and beam 0, and get ΔRSRP_i. The RSRP difference of each beam also follows a Gaussian distribution, with a mean of ΔRSRP_i and a variance of 4. At the same time, calculate the beam gain difference Δgain_i of each SSB beam and the reference beam at each position with a step size of 1° from -90° to 90° horizontally and -20° to 20° vertically.

[0074] At each position, use the beam gain difference Δgain_i of each beam as input to calculate the probability pb_i of each beam at that position with a mean of ΔRSRP_i and a variance of 4. Then multiply the probability of each beam to get the final probability pb at that position.

[0075] Finally, take the position with the maximum probability at all positions as the final position of the user in the cell.

[0076] Step3, 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 100dB, the neighbor cell overlapping coverage RSRP threshold to 100dB, and the neighbor cell overlapping coverage RSRP difference threshold to 3dB. Calculate the overlapping coverage degree between each cell according to the following method.

[0077] For two cells, cell v i , cell v j , the method for calculating the overlapping coverage degree between cell v i and cell v j is as follows:

[0078]

[0079] Condition 1: The RSRP of the user reported in the serving cell v i is greater than or equal to the "serving cell coverage RSRP threshold".

[0080] Condition 2: The RSRP of the user reported in the neighbor cell vj is greater than or equal to the "neighbor cell overlapping coverage RSRP threshold".

[0081] Condition 3: (The RSRP of the user reported in the neighbor cell v j - The RSRP of the user reported in the serving cell v i ) is greater than or equal to the "neighbor cell overlapping coverage RSRP difference threshold".

[0082] Then, according to the calculated overlapping coverage between each cell, the method of hierarchical clustering is used to divide the cell cluster.

[0083] Step 4, set the target to be optimized as the optimal SINR, and 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.

[0084] Step 5, issue the searched antenna weight combination to each cell in the target area.

[0085] Embodiment 3:

[0086] Step 1, after selecting the target area to be optimized, collect and store the information of the received power RSRP, path loss PL, time advance TA, uplink reference signal received power and signal to interference plus noise ratio SINR reported by the users in the target area network.

[0087] Step 2, select beam 0 as the reference beam, calculate the RSRP difference value of each SSB beam and beam 0, and obtain an RSRP difference value sequence. Then, calculate the beam gain difference value of each SSB beam and the reference beam at each position from horizontal -90° to 90° and vertical -20° to 20° with 1° as the step, and obtain a beam gain difference value sequence. At the same time, calculate the correlation of the RSRP difference value sequence and the beam gain difference value sequence at each position.

[0088] Finally, take the position with the maximum correlation at all positions as the final position of the user on the cell.

[0089] Step 3, after collecting the received power RSRP information of the users in the corresponding cell in the target area network, set the serving cell coverage RSRP threshold to 100 dB, the neighbor cell overlapping coverage RSRP threshold to 100 dB, and the neighbor cell overlapping coverage RSRP difference threshold to 3 dB, and calculate the overlapping coverage between each cell according to the following method.

[0090] For v i , vj two cells, cell v i The method for calculating its overlapping coverage with cell vj is:

[0091]

[0092] Condition 1: The RSRP reported by the user in the serving cell v i is greater than or equal to the "serving cell coverage RSRP threshold".

[0093] Condition 2: The RSRP reported by the user in the neighboring cell v j is greater than or equal to the "neighboring cell overlapping coverage RSRP threshold".

[0094] Condition 3: (The RSRP reported by the user in the neighboring cell v j - The RSRP reported by the user in the serving cell v i ) is greater than or equal to the "neighboring cell overlapping coverage RSRP difference threshold".

[0095] According to the calculated overlapping coverage between each cell, the method of hierarchical clustering is used to divide the cell cluster.

[0096] Step 4, set the target to be optimized as the optimal uplink PL, according to the distribution of user equipment in the target area, for each cell cluster, use the ant colony algorithm to search the optimal cell cluster antenna weight combination in the weight library.

[0097] Step 5, issue the searched antenna weight combination to each cell in the target area.

[0098] Embodiment 4:

[0099] Step 1, after selecting the target area to be optimized, collect and store the information of the received power RSRP, path loss PL, time advance TA, uplink reference signal received power and signal to interference plus noise ratio SINR reported by the user in the target area network.

[0100] Step 2, select beam 0 as the reference beam, calculate the RSRP difference value of each SSB beam and beam 0, and get an RSRP difference value sequence. Then calculate the beam gain difference value of each SSB beam and the reference beam at each position from horizontal -90° to 90° and vertical -20° to 20° with 1° step, and get a beam gain difference value sequence. At the same time, calculate the correlation of the RSRP difference value sequence and the beam gain difference value sequence at each position, and the Euclidean distance of the RSRP difference value sequence at each position obtained by the beam gain sequence.

[0101] Finally, the position with the smallest Euclidean distance less than the preset threshold and the largest correlation is taken as the final position of the user in the cell.

[0102] Step 3: After collecting the RSRP information of the user in the target area network in the corresponding cell, the RSRP threshold of the serving cell coverage is set to 100 dB, the RSRP threshold of the adjacent cell overlapping coverage is set to 100 dB, and the RSRP difference threshold of the adjacent cell overlapping coverage is set to 3 dB. The overlapping coverage degree between each cell is calculated according to the following method.

[0103] For two cells v i and vj, the overlapping coverage degree between the two cells is calculated according to the following method. i

[0104]

[0105] Condition 1: The RSRP of the user in the serving cell v i is greater than or equal to the RSRP threshold of the serving cell coverage.

[0106] Condition 2: The RSRP of the user in the adjacent cell vj is greater than or equal to the RSRP threshold of the adjacent cell overlapping coverage.

[0107] Condition 3: (The RSRP of the user in the adjacent cell vj - the RSRP of the user in the serving cell v i ) is greater than or equal to the RSRP difference threshold of the adjacent cell overlapping coverage.

[0108] According to the calculated overlapping coverage degree between each cell, the hierarchical clustering method is used to divide the cell cluster.

[0109] Step 4: Set the target to be optimized as the optimal TA, and according to the distribution of the 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.

[0110] Step 5: The searched antenna weight combination is sent to each cell in the target area.

[0111] Figure 4 According to the flowchart of the antenna weight optimization method of the embodiment of the application (three), as shown in the figure, it includes:

[0112] Step 1: Set the scanning configuration information of the simulated beam. Set the scanning range, scanning step, scanning time of each step, and total scanning times of the simulated beam, etc. to obtain the user equipment distribution.

[0113] ​Second step: Collecting data. After selecting the target area to be optimized, collect the information of reference signal received power (RSRP), direction of arrival (DOA), path loss (PL), time advance (TA), uplink reference signal received power and signal to interference plus noise ratio (SINR) reported by users in the target area network.

[0114] Third step: Dividing cell clusters. After collecting the RSRP information of users in the target area, calculate the overlapping coverage between each cell according to the following method, where the number of samples that meet conditions 1, 2 and 3 at the same time can be used as the numerator, and the number of samples that meet all three conditions at the same time is the best.

[0115] For v i , v j Two cells, cell v i Calculate the overlapping coverage method between it and cell v j .

[0116]

[0117] Condition 1: The RSRP reported by the user in the serving cell v i is greater than or equal to the "serving cell coverage RSRP threshold".

[0118] Condition 2: The RSRP reported by the user in the neighboring cell v j is greater than or equal to the "neighboring cell overlapping coverage RSRP threshold".

[0119] Condition 3: (The RSRP reported by the user in the neighboring cell vj - the RSRP reported by the user in the serving cell v i ) is greater than or equal to the "neighboring cell overlapping coverage RSRP difference threshold".

[0120] Then, according to the calculated overlapping coverage, the cells in the target area are clustered, and the clustering method such as hierarchical clustering can be used to cluster the cells in the target area, where 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 the actual experience value.

[0121] Fourth step: Searching for suitable simulation beams and antenna weight combinations for each cell cluster. Set the target parameters to be optimized, which can be at least one of the following parameters: RSRP optimization, SINR optimization, uplink PL optimization, TA optimization, uplink reference signal received power optimization and overlapping coverage optimization, 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 to search for the optimal cell cluster simulation beam and corresponding antenna weight combination in the weight library.

[0122] Step 5: Distribute simulated beam and antenna weight combinations. The searched simulated beam and corresponding antenna weight combinations are distributed to each cell in the target area. Based on these distributed simulated beam and antenna weight combinations, antenna weights can be adjusted for the simulated beams in the target area, achieving optimal network coverage and efficiency across the entire target area.

[0123] 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 according to the user distribution using an intelligent optimization algorithm, so as to optimize the network coverage and efficiency of the entire optimization area.

[0124] Example 5:

[0125] 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.

[0126] Step 2: After selecting the target area to be optimized, collect and store information such as 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.

[0127] Step 3. After collecting the RSRP information of users in the corresponding cells in the target area network, set the serving cell coverage RSRP threshold to 90dB, the neighboring cell overlap coverage RSRP threshold to 90dB, and the neighboring cell overlap coverage RSRP difference threshold to 6dB. Calculate the overlap coverage between each cell according to the following method.

[0128] For v i 、v j Two communities, community v i Calculate it with cell v j The overlap coverage method is:

[0129]

[0130] Condition 1: User reported in serving cell v i The RSRP of the service cell is greater than or equal to the "serving cell coverage RSRP threshold".

[0131] Condition 2: User reported v in neighboring area j The RSRP of the neighboring cell is greater than or equal to the "neighboring cell overlapping coverage RSRP threshold".

[0132] Condition 3: (user reported in the neighboring area v j RSRP - User reported in the serving cell vi RSRP) is greater than or equal to the "adjacent cell overlapping coverage RSRP difference threshold".

[0133] According to the calculated overlapping coverage between each cell, the method of hierarchical clustering is used to divide the cell cluster.

[0134] Step 4, set the target to be optimized as RSRP optimization, and 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.

[0135] Step 5, issue the searched simulation beam and corresponding antenna weight combination to each cell in the target area. Thus, the antenna weight adjustment of the simulation beam in the target area can be based on the issued simulation beam and antenna weight combination, so that the network coverage and efficiency of the entire target area are optimized.

[0136] Embodiment 6:

[0137] Step 1, set the scanning range of the simulation beam to [-13°, 12°], the step size to 5°, the scanning time of each step to 10 minutes, and the total scanning times to 48.

[0138] Step 2, after selecting the target area to be optimized, collect and store the information of the received power RSRP, DOA, path loss PL, time advance TA, uplink reference signal received power, and signal to interference plus noise ratio SINR reported by the users in the target area network.

[0139] Step 3, after collecting the received power RSRP information of the users in the corresponding cell in the target area network, set the service cell coverage RSRP threshold to 100 dB, the adjacent cell overlapping coverage RSRP threshold to 100 dB, and the adjacent cell overlapping coverage RSRP difference threshold to 3 dB. Calculate the overlapping coverage between each cell according to the following method.

[0140] For two cells v i and v i , the method for calculating the overlapping coverage between v and v

[0141] is as follows.

[0142] Condition 1: The RSRP reported by the user in the service cell v i is greater than or equal to the "service cell coverage RSRP threshold".

[0143] Condition 2: The RSRP reported by the user in the adjacent cell v is greater than or equal to the "adjacent cell overlapping coverage RSRP threshold".

[0144] Condition 3: (User reported RSRP in the neighbor cell vj - User reported RSRP in the serving cell v i is greater than or equal to "neighbor cell overlapping coverage RSRP difference threshold".

[0145] According to the calculated overlapping coverage between each cell, the method of hierarchical clustering is used to divide the cell cluster.

[0146] Step 4, set the target to be optimized as SINR optimal, according to the distribution of user equipment in the target area, for each cell cluster, search the optimal cell cluster simulation beam and the corresponding antenna weight combination in the weight library using evolutionary algorithm.

[0147] Step 5, each cell in the target area is issued with the searched simulation beam and the corresponding antenna weight combination. Thus, the antenna weight adjustment of the simulation beam in the target area can be made based on the issued simulation beam and antenna weight combination, so that the network coverage and efficiency of the entire target area reach the optimal.

[0148] In the embodiment, an antenna weight optimization device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and has been described above. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware, or a combination of software and hardware is also possible and is contemplated.

[0149] Figure 5 is a structural block diagram of the antenna weight optimization device according to the embodiment of the application, as shown in Figure 5 The antenna weight optimization device 50 comprises:

[0150] The acquisition module 502 is configured to acquire the reference signal received power (RSRP) reported by each user equipment in the target area.

[0151] The division module 504 is configured to divide the cells in the target area into a plurality of cell clusters according to the RSRP.

[0152] The optimization module 506 is configured to optimize the antenna weight of each cell cluster according to the distribution of user equipment in the target area.

[0153] It should be noted that the collection module 502 can also be used to collect any one or more of path loss (PL), timing advance (TA), uplink reference signal received power, and signal to interference plus noise ratio (SINR), which is not limited herein.

[0154] In one embodiment, the antenna weight optimization apparatus 50 can include one of the following:

[0155] The first determination module is configured to obtain the user equipment distribution of the target area based on the simulated beam sweeping.

[0156] The second determination module is configured to obtain the user equipment distribution of the target area based on the RSRP and beam gain of the plurality of SSB beams reported by the user equipment.

[0157] In one embodiment, the second determination module can include:

[0158] The first obtaining sub-module is configured to obtain the RSRP difference between each SSB beam and the preset reference beam.

[0159] The second obtaining sub-module is configured to obtain the beam gain difference between each SSB beam and the preset reference beam in each horizontal and vertical direction.

[0160] The first determination sub-module is configured to determine the probability of each SSB beam in each horizontal and vertical direction by a probability distribution model according to the RSRP difference and the beam gain difference, and determine the position with the maximum probability as the position of the user equipment.

[0161] In one embodiment, the second determination module can further include:

[0162] The third obtaining sub-module is configured to obtain the RSRP difference between each SSB beam and the preset reference beam, and obtain a sequence of RSRP differences.

[0163] The fourth obtaining sub-module is configured to obtain the beam gain difference between each SSB beam and the preset reference beam in each horizontal and vertical direction, and obtain a sequence of beam gain differences.

[0164] The second determination sub-module is configured to determine the correlation of the sequence of RSRP differences and the sequence of beam gain differences in each horizontal and vertical direction based on a correlation algorithm according to the sequence of RSRP differences and the sequence of beam gain differences, and determine the position with the maximum correlation as the position of the user equipment.

[0165] In one embodiment, the antenna weight optimization apparatus 50 can further comprise:

[0166] a scanning module, configured to scan the target area according to preset scanning parameters to obtain the user equipment distribution in the target area, wherein the scanning parameters comprise at least one of the following: scanning range, scanning step, scanning time of each step, and total scanning times.

[0167] In one embodiment, the dividing module 504 can comprise:

[0168] a third determining sub-module, configured to determine the overlapping coverage degree between each cell according to the RSRP;

[0169] a dividing sub-module, configured to perform cluster division on the cells in the target area based on a clustering algorithm according to the overlapping coverage degree to obtain cell clusters.

[0170] In one embodiment, the third determining sub-module can comprise:

[0171] a first determining sub-sub-module, configured to 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 a denominator;

[0172] a second determining sub-sub-module, configured to determine the number of user equipments satisfying at least two of the following 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 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; and 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;

[0173] a third determining sub-sub-module, configured to determine the overlapping coverage degree between each cell according to the ratio of the numerator and the denominator.

[0174] In one embodiment, the optimization module 506 can comprise:

[0175] a first optimization sub-module, configured to perform weight search in the weight library for each cell cluster based on an optimization algorithm according to the user equipment distribution and set optimization parameters to obtain the optimal simulation beam of each cell cluster;

[0176] a fourth determining sub-module, configured to determine the antenna weight combination corresponding to the optimal simulation beam based on the optimal simulation beam of each cell cluster.

[0177] In one embodiment, the optimization module 506 can comprise:

[0178] The second optimization submodule is configured to search for the optimal antenna weight combination of each cell cluster in the weight library by using an optimization algorithm based on the set optimization parameters according to the user equipment distribution.

[0179] In an embodiment, the optimization parameters can include at least one of RSRP, path loss PL, time advance TA, and signal to interference plus noise ratio SINR.

[0180] It should be noted that the optimization parameters described above can vary according to actual conditions, which are not limited in the present application.

[0181] In an embodiment, the antenna weight optimization device 50 can further include:

[0182] The first issuing module is configured to issue the optimal analog beam and the antenna weight combination of each cell cluster to each cell.

[0183] In an embodiment, the antenna weight optimization device 50 can further include:

[0184] The second issuing module is configured to issue the optimal antenna weight combination of each cell cluster to each cell.

[0185] It should be noted that the above-mentioned modules can be realized by software or hardware, and for the latter, the following implementation manners can be used, but are not limited thereto: the above-mentioned modules are located in the same processor; or the above-mentioned modules are located in different processors in any combination.

[0186] Embodiments of the present application also provide a computer readable storage medium, which stores a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

[0187] In an exemplary embodiment, the above-mentioned computer readable storage medium can include, but is not limited to, a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various computer program storage media.

[0188] Embodiments of the present application also provide an electronic device, which includes a memory storing a computer program and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.

[0189] In one example embodiment, the electronic device described above can further include a transmission device connected to the processor, and an input / output device connected to the processor.

[0190] The specific examples in the embodiments can refer to the examples described in the above embodiments and exemplary implementation, which will not be repeated here.

[0191] The embodiments of the present application also provide a computer program product, including computer instructions, characterized in that the computer instructions are executed by a processor to implement the steps in any of the method embodiments described above.

[0192] Obviously, those skilled in the art should understand that the modules or steps of the present application described above can be realized by general computing devices, which can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, which can be realized by program codes executable by the computing devices, so that they can be stored in storage devices and executed by the computing devices, and in some cases, the steps shown or described can be executed in different order, or they can be manufactured into individual integrated circuit modules, or multiple modules or steps can be manufactured into a single integrated circuit module. Thus, the present application is not limited to any particular hardware and software combination.

[0193] The above is only the preferred embodiment of the present application, and is not intended to limit the present application. Those skilled in the art can make various modifications and changes to the present application. Any modification, equivalent replacement, improvement, etc. within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing antenna weights, characterized in that: include: Collect the reference signal received power (RSRP) reported by each user equipment in the target area; Clustering the cells in the target area according to the RSRP to obtain a plurality of cell clusters; Obtain an RSRP difference between each synchronization signal block SSB beam and a preset reference beam, obtain a beam gain difference between each SSB beam and the preset reference beam in each horizontal and vertical direction, and obtain a user equipment distribution in the target area based on the RSRP difference and the beam gain difference; The acquiring, based on the RSRP difference and the beam gain difference, the distribution of user equipment in the target area includes: determining, according to the RSRP difference and the beam gain difference, a probability of each SSB beam in each horizontal and vertical orientation using a probability distribution model, and determining a position with the highest probability as the position of the user equipment; The antenna weight of each cell cluster is optimized according to the distribution of the user equipment in the target area.

2. The method according to claim 1, characterized in that The obtaining of the user equipment distribution in the target area further includes: The user equipment distribution situation in the target area is obtained based on the simulated beam scanning.

3. The method according to claim 1, characterized in that Acquiring user equipment distribution in the target area based on the RSRP difference and the beam gain difference further includes: Based on the RSRP difference, obtaining an RSRP difference sequence; Based on the beam gain differences, obtaining 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 orientation and vertical orientation is determined based on a correlation algorithm, and the position with the maximum correlation is determined as the position of the user equipment.

4. The method according to claim 1, wherein Before collecting the RSRP reported by each user equipment in the target area, the method includes: Scan the target area according to preset scanning parameters to obtain the distribution of user equipment in the target area; The scanning parameters include at least one of the following: scanning range, scanning step, scanning time of each step, and total number of scans.

5. The method according to claim 1, wherein Clustering cells in the target area according to the RSRP to obtain a plurality of cell clusters, including: Determine the overlapping coverage between each cell according to the RSRP; According to the overlapping coverage, the cells in the target area are clustered based on a clustering algorithm to obtain the cell cluster.

6. The method according to claim 5, characterized in that Determining the overlapping coverage between each cell according to the RSRP includes: 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, and use this number as the denominator; The number of user equipments that meets at least two of the following conditions is used as the numerator: the number of user equipments for which the 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 for which the 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 for which the 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 to the denominator.

7. The method according to claim 1, characterized in that Optimizing antenna weights of each cell cluster according to distribution of user equipment in the target area includes: According to the user equipment distribution and based on the set optimization parameters, an optimization algorithm is used to search for weights in a weight library for each cell cluster to obtain an optimal simulated beam for each cell cluster; Based on the optimal simulated beam of each cell cluster, an antenna weight combination corresponding to the optimal simulated beam is determined.

8. The method according to claim 1, characterized in that Optimizing antenna weights of each cell cluster according to distribution of user equipment in the target area includes: According to the user equipment distribution and based on the set optimization parameters, an optimization algorithm is used to search for weights in a weight library for each cell cluster to obtain the optimal antenna weight combination for each cell cluster.

9. The method according to claim 7 or 8, characterized in that The optimization parameters include at least one of the following: RSRP, path loss PL, timing advance TA, and signal to interference plus noise ratio SINR.

10. The method according to claim 7, characterized in that The method further comprises: The obtained optimal simulated beam and antenna weight combination of each cell cluster is sent to each cell.

11. The method according to claim 8, characterized in that The method further comprises: The obtained optimal antenna weight combination of each cell cluster is sent to each cell.

12. An antenna weight optimization device, characterized in that: include: A collection module is used to collect the reference signal received power RSRP reported by each user equipment in the target area; a division module, configured to cluster cells in the target area according to the RSRP to obtain a plurality of cell clusters; A second determination module is configured to obtain an RSRP and a beam gain of each synchronization signal block SSB beam, and obtain a user equipment distribution in the target area based on the RSRP and the beam gain of each SSB beam; The second determination module includes: a first acquisition submodule, a second acquisition submodule, and a first determination submodule; The first acquisition submodule is configured to obtain an RSRP difference between each SSB beam and a preset reference beam; The second 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; The first determining submodule is configured to determine, based on the RSRP difference and the beam gain difference, the probability of each SSB beam in each horizontal and vertical orientation through a probability distribution model, and determine the position with the highest probability as the position of the user equipment; An optimization module is used to optimize the antenna weight of each cell cluster according to the distribution of the user equipment in the target area.

13. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method described in any one of claims 1 to 11 when executed by a processor.

14. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 11 are implemented.

15. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the steps of the method described in any one of claims 1 to 11 are implemented.

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