CN111246494B - Massive MIMO antenna beam optimization method and device - Google Patents
Massive MIMO antenna beam optimization method and device Download PDFInfo
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- CN111246494B CN111246494B CN201811436484.9A CN201811436484A CN111246494B CN 111246494 B CN111246494 B CN 111246494B CN 201811436484 A CN201811436484 A CN 201811436484A CN 111246494 B CN111246494 B CN 111246494B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
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- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
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- H04B7/0413—MIMO systems
Abstract
The embodiment of the invention provides a Massive MIMO antenna beam optimization method and a device, wherein the method comprises the following steps: acquiring the traffic distribution of the current user of the target cell according to the throughput distribution of the beam of the target cell, and acquiring the traffic distribution of the potential user according to the noise distribution of the beam of the target cell and the throughput distribution of the beam of the same-frequency adjacent cell; according to the service distribution of the current user and the service distribution of the potential user, obtaining the service distribution of the user before the adjustment according to each weight and after the adjustment according to each weight, and obtaining the expected throughput gain of the target cell wave beam after the adjustment according to each weight according to the service distribution of the user before the adjustment according to each weight and after the adjustment according to each weight; and selecting the weight for beam optimization to adjust the beam according to the expected throughput gain corresponding to each beam weight. Because the weight for optimizing the wave beam is selected according to the expected gain of the throughput, the optimization efficiency and the optimization accuracy are higher.
Description
Technical Field
The embodiment of the invention relates to the field of mobile communication, in particular to a Massive MIMO antenna beam optimization method and device.
Background
A Massive MIMO (Massive MIMO) technology, which increases the number of transmit/receive antennas to tens or even hundreds of antennas on the basis of a conventional Multiple-Input Multiple-Output (MIMO) system. The Massive MIMO system as a new cellular network structure keeps the advantages of the traditional MIMO system and utilizes a plurality of antennas to average out system noise and irrelevant intercell interference. The increase of the number of the antennas enables the system capacity to be greatly increased, the cell capacity can be improved by the aid of the Massive MIMO characteristic, and the characteristics determine that the Massive MIMO system has wide application prospects. In the LTE network, since the coverage direction of each cell of the LTE network is relatively fixed, a situation that hot spot users are concentrated outside a lobe angle may occur, thereby affecting user perception outside the lobe angle.
In order to solve the above problems, the broadcast beam weight optimization of Massive MIMO is implemented by manual adjustment, that is, the optimization is implemented by manual configuration according to an application scenario. The method cannot ensure that the adjustment result is optimal, and the workload of manual adjustment is large under the scene of large-scale deployment of Massive MIMO.
The effect of the optimization scheme aiming at the beam weight in the prior art is obviously lagged behind the adjustment work, so that the targeted adjustment and optimization are often performed only on obviously abnormal cells, and the whole network cannot be considered. When the optimization adjustment is needed for the area with poor effect after the optimization, the problems that the optimization period is long, the one-step operation cannot be realized and the like can be caused. The optimization adjustment scheme is processed by manual judgment, so that the optimization method needs a lot of time and labor. Therefore, the current optimization method has low efficiency, and the accuracy of the scheme cannot be guaranteed.
Disclosure of Invention
In order to solve the above problems, embodiments of the present invention provide a method and an apparatus for optimizing a beam of a Massive MIMO antenna.
In a first aspect, the present invention provides a method for optimizing beams of Massive MIMO antennas, including: acquiring the traffic distribution of a current user of a target cell according to the throughput distribution of beams of the target cell, and acquiring the traffic distribution of potential users according to the noise distribution of the beams of the target cell and the throughput distribution of beams of adjacent cells with the same frequency; according to the service distribution of the current user and the service distribution of the potential user, obtaining the service distribution of the user before the adjustment according to each weight and after the adjustment according to each weight, and obtaining the expected throughput gain of the target cell wave beam after the adjustment according to each weight according to the service distribution of the user before the adjustment according to each weight and after the adjustment according to each weight; and selecting the weight for beam optimization to adjust the beam according to the expected throughput gain corresponding to each beam weight.
In a second aspect, the present invention provides a Massive MIMO antenna beam optimization apparatus, including: the acquisition module is used for acquiring the traffic distribution of the current user of the target cell according to the throughput distribution of the beam of the target cell and acquiring the traffic distribution of the potential user according to the noise distribution of the beam of the target cell and the throughput distribution of the beam of the adjacent cell with the same frequency; the processing module is used for acquiring the user traffic distribution before and after being adjusted according to each weight according to the traffic distribution of the current user and the traffic distribution of the potential user, and acquiring the expected throughput gain of the target cell beam after being adjusted according to each weight according to the user traffic distribution before and after being adjusted according to each weight; and the output module is used for selecting the weight for beam optimization to carry out beam adjustment according to the expected throughput gain corresponding to each beam weight.
In a third aspect, the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for optimizing a Massive MIMO antenna beam according to the first aspect of the present invention when executing the computer program.
In a fourth aspect, the present invention provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method for Massive MIMO antenna beam optimization according to the first aspect of the present invention.
The Massive MIMO antenna beam optimization method provided by the embodiment of the invention obtains the user traffic distribution before and after being adjusted according to each weight according to the traffic distribution of the current user and the traffic distribution of the potential user, obtains the expected throughput gain of the beam after being adjusted according to each weight, and selects the weight for beam optimization according to the expected throughput gain corresponding to each beam weight, thereby having higher optimization efficiency and optimization accuracy.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a flowchart of a Massive MIMO antenna beam optimization method according to an embodiment of the present invention;
fig. 2 is a structural diagram of a Massive MIMO antenna beam optimization apparatus according to an embodiment of the present invention;
fig. 3 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
At present, Massive MIMO antenna beam optimization is mainly realized through manual adjustment according to application scenes, so that only obvious abnormal cells are subjected to targeted adjustment and optimization, and the whole network cannot be considered. The optimization adjustment scheme is processed by manual judgment, so that the optimization method needs a lot of time and labor. Therefore, the current optimization method has low efficiency, and the accuracy of the scheme cannot be guaranteed.
To solve the problem, the embodiment of the invention provides a Massive MIMO antenna beam optimization method. The method can be applied to the scenario of Massive MIMO antenna beam optimization. The corresponding execution main body of the method may be a base station where the Massive MIMO antenna is located, or may be an independently set Massive MIMO antenna beam optimization apparatus, which is not specifically limited in the embodiment of the present invention. For convenience of explanation, the embodiment of the present invention takes a base station where a dominant implementation is a Massive MIMO antenna as an example, and explains a beam optimization method for a Massive MIMO antenna provided in the embodiment of the present invention.
Fig. 1 is a flowchart of a method for optimizing a Massive MIMO antenna beam according to an embodiment of the present invention, and as shown in fig. 1, the embodiment of the present invention provides a method for optimizing a Massive MIMO antenna beam, including:
101, obtaining the service distribution of the current user in the target cell according to the throughput distribution of the beam in the target cell, and obtaining the service distribution of the potential user according to the noise distribution of the beam in the target cell and the throughput distribution of the beam in the adjacent regions with the same frequency.
Since the data of the same-frequency neighboring cell is obtained through an eNodeB (Evolved Node B, LTE base station) where the neighboring cell is located, the process of obtaining the eNodeB where the same-frequency neighboring cell is located is also included before 101. The method for acquiring the eNodeB of the same-frequency adjacent cells is not specifically limited in the embodiment of the invention, and includes but is not limited to selecting forward first-layer adjacent cells and second-layer adjacent cells, selecting backward first-layer adjacent cells, and screening out cells with the same frequency as Massive MIMO cells. And removing duplication according to the eNodeB ID to obtain a list of eNodeBs in which the same-frequency adjacent regions of the Massive MIMO cell are located.
In 101, a target cell is a Massive MIMO cell to be optimized, and statistics are given of traffic distribution of users within coverage of an antenna lobe angle of the Massive MIMO cell and traffic distribution of users adjacent outside the coverage area of the antenna lobe angle of the Massive MIMO cell, where the traffic distribution of users is mainly reflected in a hotspot direction of the users and a throughput generated by corresponding user services.
The Massive MIMO antenna is composed of a plurality of antenna array elements, and the throughput distribution of the Massive MIMO antenna to beams covered by the target cell reflects the traffic distribution of current users of the target cell. Potential users are covered outside the lobe angle, and partial users in the potential users can be covered in the beam range after the lobe angle is adjusted through the weight. Users outside the coverage of the lobe angle can generate certain noise influence on the users inside the coverage, the condition of potential users outside the lobe angle can be reflected through the noise distribution of the wave beam, and the service distribution condition of the potential users can be obtained by combining the throughput distribution of the wave beam in the adjacent region.
102, according to the traffic distribution of the current user and the traffic distribution of the potential user, obtaining the traffic distribution of the user before and after being adjusted according to each weight, and obtaining the expected throughput gain of the target cell beam after being adjusted according to each weight according to the traffic distribution of the user before and after being adjusted according to each weight.
The traffic distribution of the current user corresponds to the traffic distribution of the user before being adjusted according to the weight, part of new users can be absorbed from potential users for covering after the beam is adjusted according to the weight, and part of users in the current user may not be in the coverage range due to the change of the lobe angle coverage range after the weight is adjusted. According to the traffic distribution of the current user, the traffic distribution of the potential user and the corresponding weight, the traffic distribution of the user after the weight adjustment can be obtained. According to the user traffic distribution before and after the weight adjustment and the corresponding user traffic, the throughput condition of the user in the coverage range of the lobe angle before the weight adjustment can be obtained, and the throughput condition of the user in the coverage range of the lobe angle after the weight adjustment can be obtained. And according to the user throughput conditions before and after the weight adjustment, acquiring the expected gain of the throughput after the weight adjustment relative to the throughput before the adjustment.
103, according to the expected gain of the user throughput corresponding to each beam weight, selecting the weight for beam optimization to perform beam adjustment.
Because there are a plurality of weights, the expected throughput gain corresponding to each weight needs to be obtained according to the weight. The expected throughput gain is obtained by statistical analysis, and the process of adjusting the lobe angle according to the weight is not implemented at this time. And selecting the weight values meeting the requirements to carry out beam optimization according to each weight value and the expected gain corresponding to each weight value, and implementing beam adjustment. And if the weight value with the largest expected throughput gain can be selected for optimizing the beam.
According to each weight and the corresponding user throughput expected gain, a comparison table of the relationship between the weight and the user throughput expected gain shown in the following table 1 can be generated for selecting the weight for beam optimization to perform beam adjustment. 2_ h65_ v8_ tilt3 is taken as an example for explanation: the horizontal lobe angle of h65 is 65 °, the vertical lobe angle of v8 is 8 °, tilt3 is an electrical downtilt angle, and the beam is adjusted by the weight through the three parameters.
TABLE 1
According to the Massive MIMO antenna beam optimization method provided by the embodiment of the invention, the user traffic distribution before and after being adjusted according to each weight is obtained according to the traffic distribution of the current user and the traffic distribution of the potential user, the expected throughput gain of the beam after being adjusted according to each weight is obtained, and the weight for beam optimization is selected according to the expected throughput gain corresponding to each beam weight, so that the optimization efficiency and the optimization accuracy are higher.
Based on the content of the foregoing embodiment, as an optional embodiment, before obtaining the traffic distribution of the current user in the target cell according to the throughput distribution of the beam in the target cell, and obtaining the traffic distribution of the potential user according to the noise distribution of the beam in the target cell and the throughput distribution of the beam in the adjacent cells with the same frequency, the method further includes: acquiring throughput distribution and noise distribution of beams according to Call History Report (CHR) or Measurement Report (MR) data acquired in preset busy hours, and acquiring throughput distribution of beams of adjacent cells of the same frequency according to CHR or MR data acquired in preset busy hours of adjacent cells of the same frequency.
The CHR or MR data includes measurement data of user communication, such as throughput of the user and noise of the beam, and thus can be used to count the throughput of the beam. The preset busy hour is a data acquisition time period, the busy hour can reflect the traffic distribution condition of the user, for example, 19:00-23:00 is the peak period of the residential area traffic at night. Taking the collection of CHR data as an example, the subscription related data to eNodeB is shown in table 2.
TABLE 2
Wherein, PERIOD _ INTRA _ FREQ _ MEASUREMENT is statistical user frequency, PERIOD _ prior _ THROUGHPUT _ MEASUREMENT is statistical user THROUGHPUT, PERIOD _ prior _ BEAM _ propagation is user BEAM information, and BEAM _ NOISE _ TRACKING is statistical NOISE distribution. According to the obtained CHR data of the Massive MIMO station, the throughput distribution and the noise distribution of the wave beams can be obtained, and according to the obtained CHR data of the same-frequency adjacent region, the throughput distribution of the potential user wave beams of the same-frequency adjacent region can be obtained.
The Massive MIMO antenna beam optimization method provided by the embodiment of the invention can effectively obtain the throughput distribution of the target cell and the same-frequency adjacent cell beam according to the CHR or MR data.
Based on the content of the foregoing embodiment, as an optional embodiment, obtaining the expected throughput gain of the beam after being adjusted by each weight according to the user traffic distribution before being adjusted by each weight and after being adjusted by each weight, includes: and acquiring the expected gain of the throughput of each weight according to the speech system data distributed by the user traffic before and after the adjustment according to each weight.
The information in the speech system data can be used for counting throughput gain, and the expected throughput gain of each weight can be obtained according to the speech system data distributed by the user traffic before and after the adjustment according to each weight. The relevant metrics for calculating the throughput gain in the session data are shown in table 3:
TABLE 3
The throughput gain KPI (Key Performance Indicator) is used for counting and calculating the expected throughput gain of each weight, and the basic KPI is a necessary condition to be satisfied after the weight is adjusted. The indexes and parameter values of the KPI are set according to requirements, and the collection time is recommended to be one week.
Based on the content of the foregoing embodiment, as an optional embodiment, selecting a weight for beam optimization to perform beam adjustment according to a throughput expected gain corresponding to each beam weight includes: and selecting a weight for beam optimization from the expected throughput gain corresponding to each beam weight according to the screening condition to carry out beam adjustment.
Considering that the weight with the largest expected throughput gain is not necessarily suitable for weight adjustment, the weight selection for beam optimization needs to satisfy certain screening conditions, which include but are not limited to:
the horizontal wave width, the vertical wave width and the downward inclination angle are adjusted by 2 at most simultaneously, so that the reduction of user throughput rate caused by absorbing a large number of edge users due to the fact that the weight is adjusted greatly is avoided; if the user traffic in the beam vertical direction is widely distributed, the vertical bandwidth should be preferentially adjusted, and if the user traffic in the beam horizontal direction is widely distributed, the horizontal bandwidth should be preferentially adjusted, and the bandwidth corresponding to the adjustment weight covers the beam with the high traffic ratio as much as possible.
The Massive MIMO antenna beam optimization method provided by the embodiment of the invention ensures that the beam optimization is more reasonable through the limitation of screening conditions.
Based on the content of the foregoing embodiment, as an optional embodiment, after obtaining the traffic distribution of the current user in the target cell according to the throughput distribution of the beam in the target cell, before obtaining the traffic distribution of the user before adjusting according to each weight and before obtaining the traffic distribution of the user after adjusting according to each weight according to the traffic distribution of the current user and the traffic distribution of the potential users, the method further includes: and adjusting the azimuth angle of the antenna according to the traffic distribution of the current user.
The Massive MIMO wave beam can not obtain the user distribution in the cell at the initial stage of Massive MIMO network establishment, and the set azimuth angle of the antenna can not meet the optimal coverage. After the traffic distribution of the current user in the target cell is obtained according to the throughput distribution of the beam in the target cell, the azimuth angle of the antenna needs to be adjusted according to the traffic distribution of the current user.
The signal transmission and reception at the transceiving ends can be described by the departure angle and the arrival angle. The angular problem is determined by the position of the mobile subscriber, and the line-of-sight direction from the base station to the subscriber includes the departure angle (departure angle) of the signal at the transmitting end and the arrival angle (arrival angle) of the channel at the receiving end. The departure angle and the arrival angle jointly form the directivity of the signal, and then the signal guide vector is formed by combining the antenna structure, and the azimuth angle of the antenna can be adjusted according to the signal guide vector.
Based on the content of the foregoing embodiment, as an optional embodiment, there are multiple target cells, and accordingly, according to the expected throughput gain corresponding to each beam weight, selecting a weight for beam optimization to perform beam adjustment includes: and selecting the target cell with the highest expected throughput gain for weight adjustment according to the expected throughput gain corresponding to each beam weight of each target cell, and selecting the corresponding weight for beam optimization for beam adjustment.
When the Massive MIMO station covers a plurality of cells, the cell with the highest expected gain is selected to carry out weight adjustment. Under the situation of Massive MIMO continuous networking, adjacent Massive MIMO cells (such as cells in the same station, adjacent cells in opposite direction and the like) are prevented from being adjusted at the same time, and the cell with the highest expected gain is selected in an overlapped coverage area to adjust the weight.
Based on the content of the foregoing embodiments, as an optional embodiment, further screening may be performed on whether the target cell implements antenna beam optimization, including but not limited to that the cell implementing antenna beam optimization needs to satisfy the following conditions: the expected gain of the weight optimization throughput of the cell is more than 5 percent; the utilization rate of downlink PRB in busy time of the cell is lower than 50 percent (can be obtained by voice system data); the near end user of the cell is more than 50% (available via session data).
Based on the content of the above embodiment, as an optional embodiment, the weight value is calculated by a weight value tool.
The weight includes three parameters of the horizontal lobe angle, the vertical lobe angle and the electrical downtilt angle of the antenna. By adjusting the antenna, beam steering is firstly performed, the beam steering is in a simple scanning beam form, the main beam of the antenna points to a target direction by selecting weights, the amplitudes of the weights of a beam former are equal, the difference between the weights is exp (-j2 pi f tau), and the weights of the antenna array are as follows:
in the formula, M is the number of array elements in the antenna array,
is a signal steering vector. The working principle is as follows: the steering vector is the phase between two array elements receiving signal in signal receiving processThe difference is that the antenna has transmit-receive reciprocity, i.e. the processing of the receiving process can also be applied to the transmitting process, since a theta is received0In the case of directional signals, a (θ) phase difference is generated between array elements0) In transmission, a (theta) is artificially set between array elements0) The interference pattern between array elements will naturally be referred to as θ0Direction, forming beam pointing.
Firstly, precoding beam forming weight and antenna joint optimization are carried out on each user. Supposing that K users are provided, optimizing the K user, taking SINR (Signal to Interference plus Noise Ratio) as an optimization criterion, simultaneously carrying out joint optimization on an antenna array and a beam forming weight, and forming a joint optimization array W by the antenna array position and the beam forming weightk＝[Wk1，Wk2，Wk3],Wk1For beamforming weights, Wk2，Wk3Describing the channel matrix H of user k for the antenna array element position matrix and user angle informationkThen, the signal-to-noise ratio SINR of the system is expressed as:
wherein HfThe channel matrix, M sigma, is obtained from the transmitting end to other user receiving ends by using a fixed transmitting array2Is a noise matrix.
The weight value can be calculated according to the formula, and the weight value tool is an existing tool which is used at present and can quickly calculate the weight value according with the noise ratio SINR and generate a plurality of weight value files. And optimizing the Massive MIMO antenna beam according to each obtained weight. And selecting the weight for beam optimization to adjust the beam according to the expected throughput gain corresponding to each beam weight.
The Massive MIMO antenna beam optimization method provided by the embodiment of the invention calculates each weight according to the weight tool, and the processing is quick and convenient.
Fig. 2 is a structural diagram of a Massive MIMO antenna beam optimization apparatus according to an embodiment of the present invention, and as shown in fig. 2, the Massive MIMO antenna beam optimization apparatus includes: an acquisition module 201, a processing module 202 and an output module 203. The acquiring module 201 is configured to acquire traffic distribution of a current user in a target cell according to throughput distribution of a beam in the target cell, and acquire traffic distribution of a potential user according to noise distribution of the beam in the target cell and throughput distribution of beams in adjacent cells with the same frequency; a processing module 202, configured to obtain, according to traffic distribution of a current user and traffic distribution of potential users, traffic distribution of users before and after being adjusted according to each weight, and obtain, according to traffic distribution of users before and after being adjusted according to each weight, expected throughput gain of a target cell beam after being adjusted according to each weight; and the output module 203 is configured to select a weight for beam optimization to perform beam adjustment according to the expected throughput gain corresponding to each beam weight.
The obtaining module 201 obtains the traffic distribution of the current user and the traffic distribution of the potential user, the processing module 202 obtains the traffic distribution of the user before and after being adjusted according to each weight according to the traffic distribution of the current user and the traffic distribution of the potential user, and obtains the expected throughput gain of the target cell beam after being adjusted according to each weight according to the traffic distribution of the user before and after being adjusted according to each weight.
The traffic distribution of the current user corresponds to the traffic distribution of the user before being adjusted according to the weight, part of new users can be absorbed from potential users for covering after the beam is adjusted according to the weight, and part of users in the current user may not be in the coverage range due to the change of the lobe angle coverage range after the weight is adjusted. According to the traffic distribution of the current user, the traffic distribution of the potential user and the corresponding weight, the traffic distribution of the user after the weight adjustment can be obtained. The processing module 202 may obtain the throughput condition of the user within the lobe angle coverage range before weight adjustment and obtain the throughput condition of the user within the lobe angle coverage range after weight adjustment according to the user traffic distribution before and after weight adjustment and the corresponding user traffic. And according to the user throughput conditions before and after the weight adjustment, acquiring the expected gain of the throughput after the weight adjustment relative to the throughput before the adjustment.
Because there are a plurality of weights, the expected throughput gain corresponding to each weight needs to be obtained according to the weight. The expected throughput gain is obtained by statistical analysis, and the process of adjusting the lobe angle according to the weight is not implemented at this time. The output module 203 selects the weight satisfying the requirement to perform beam optimization according to each weight and the expected gain corresponding to each weight, and performs beam adjustment. For example, the weight with the largest expected throughput gain may be selected for optimization of the beam.
The Massive MIMO antenna beam optimization device provided by the embodiment of the invention has the advantages that the acquisition module acquires the service volume distribution of the current user and the service volume distribution of the potential user, the processing module acquires the user service volume distribution before and after being adjusted according to each weight, the expected throughput gain of the beam after being adjusted according to each weight is acquired, and the output module selects the weight for beam optimization according to the expected throughput gain corresponding to each beam weight, so that the Massive MIMO antenna beam optimization device has higher optimization efficiency and optimization accuracy.
The device embodiment provided in the embodiments of the present invention is for implementing the above method embodiments, and for details of the process and the details, reference is made to the above method embodiments, which are not described herein again.
Fig. 3 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device may include: a processor (processor)301, a communication Interface (communication Interface)302, a memory (memory)303 and a bus 304, wherein the processor 301, the communication Interface 302 and the memory 303 complete communication with each other through the bus 304. The communication interface 302 may be used for information transfer of an electronic device. Processor 301 may call logic instructions in memory 303 to perform a method comprising: acquiring the traffic distribution of the current user of the target cell according to the throughput distribution of the beam of the target cell, and acquiring the traffic distribution of the potential user according to the noise distribution of the beam of the target cell and the throughput distribution of the beam of the same-frequency adjacent cell; according to the service distribution of the current user and the service distribution of the potential user, obtaining the service distribution of the user before the adjustment according to each weight and after the adjustment according to each weight, and obtaining the expected throughput gain of the target cell wave beam after the adjustment according to each weight according to the service distribution of the user before the adjustment according to each weight and after the adjustment according to each weight; and selecting the weight for beam optimization to adjust the beam according to the expected throughput gain corresponding to each beam weight.
In addition, the logic instructions in the memory 303 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above-described method embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
An embodiment of the present invention provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable a computer to execute the method for optimizing a Massive MIMO antenna beam provided in the foregoing embodiment, where the method includes: acquiring the traffic distribution of the current user of the target cell according to the throughput distribution of the beam of the target cell, and acquiring the traffic distribution of the potential user according to the noise distribution of the beam of the target cell and the throughput distribution of the beam of the same-frequency adjacent cell; according to the service distribution of the current user and the service distribution of the potential user, obtaining the service distribution of the user before the adjustment according to each weight and after the adjustment according to each weight, and obtaining the expected throughput gain of the target cell wave beam after the adjustment according to each weight according to the service distribution of the user before the adjustment according to each weight and after the adjustment according to each weight; and selecting the weight for beam optimization to adjust the beam according to the expected throughput gain corresponding to each beam weight.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A Massive MIMO antenna beam optimization method is characterized by comprising the following steps:
acquiring the traffic distribution of a current user of a target cell according to the throughput distribution of beams of the target cell, and acquiring the traffic distribution of potential users according to the noise distribution of the beams of the target cell and the throughput distribution of beams of adjacent cells with the same frequency;
according to the service distribution of the current user and the service distribution of the potential user, obtaining the service distribution of the user before the adjustment according to each weight and after the adjustment according to each weight, and obtaining the expected throughput gain of the target cell wave beam after the adjustment according to each weight according to the service distribution of the user before the adjustment according to each weight and after the adjustment according to each weight;
selecting a weight for beam optimization to adjust the beam according to the expected throughput gain corresponding to each beam weight, wherein the weight is adjusted by a weight tool according to a formula
Calculated, wherein M is the number of array elements in the antenna array,
is a signal steering vector.
2. The method of claim 1, wherein before the obtaining the traffic distribution of the current user in the target cell according to the throughput distribution of the beam in the target cell and obtaining the traffic distribution of the potential user according to the noise distribution of the beam in the target cell and the throughput distribution of the beam in the neighboring cells with the same frequency, the method further comprises:
and acquiring throughput distribution and noise distribution of the wave beam according to CHR or MR data acquired in a preset busy hour, and acquiring throughput distribution of the wave beam of the adjacent region with the same frequency according to CHR or MR data of the adjacent region with the same frequency acquired in the preset busy hour.
3. The method of claim 1, wherein the obtaining the expected throughput gain of the beam after each weight adjustment according to the user traffic distribution before each weight adjustment and after each weight adjustment comprises:
and acquiring the expected gain of the throughput of each weight according to the speech statistics distributed by the user traffic before and after the adjustment according to each weight.
4. The method of claim 1, wherein selecting the weight for beam optimization for beam adjustment according to the expected gain of throughput corresponding to each beam weight comprises:
and selecting a weight for beam optimization from the expected throughput gain corresponding to each beam weight according to the screening condition to carry out beam adjustment.
5. The method of claim 1, wherein after obtaining the traffic distribution of the current user in the target cell according to the throughput distribution of the beam in the target cell, and before obtaining the traffic distribution of the user before adjusting according to each weight and before obtaining the traffic distribution of the user after adjusting according to each weight according to the traffic distribution of the current user and the traffic distribution of the potential user, the method further comprises:
and adjusting the azimuth angle of the antenna according to the traffic distribution of the current user.
6. The method of claim 1, wherein there are multiple target cells, and wherein selecting weights for beam optimization according to expected throughput gain corresponding to each beam weight for beam adjustment comprises:
and selecting the target cell with the highest expected throughput gain for weight adjustment according to the expected throughput gain corresponding to each beam weight of each target cell, and selecting the corresponding weight for beam optimization for beam adjustment.
7. A Massive MIMO antenna beam optimization device is characterized by comprising:
the acquisition module is used for acquiring the traffic distribution of the current user of the target cell according to the throughput distribution of the beam of the target cell and acquiring the traffic distribution of the potential user according to the noise distribution of the beam of the target cell and the throughput distribution of the beam of the adjacent cell with the same frequency;
the processing module is used for acquiring the user traffic distribution before and after being adjusted according to each weight according to the traffic distribution of the current user and the traffic distribution of the potential user, and acquiring the expected throughput gain of the target cell beam after being adjusted according to each weight according to the user traffic distribution before and after being adjusted according to each weight;
an output module for selecting a weight for beam optimization to adjust the beam according to the expected gain of throughput corresponding to each beam weight, wherein the weight is adjusted by a weight tool according to a formula
Calculated, wherein M is the number of array elements in the antenna array,
is a signal steering vector.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the Massive MIMO antenna beam optimization method according to any of claims 1 to 6 when executing the program.
9. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the Massive MIMO antenna beam optimization method according to any of claims 1 to 6.
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| CN114339776B (en) * | 2020-09-27 | 2023-08-15 | 中国移动通信集团浙江有限公司 | Antenna weight optimization method, device, equipment and computer readable storage medium |
| CN114390534B (en) * | 2020-10-16 | 2024-03-12 | 中国移动通信集团设计院有限公司 | Beam pattern determining method and device, electronic equipment and storage medium |
| CN112351449B (en) * | 2021-01-08 | 2022-03-11 | 南京华苏科技有限公司 | Massive MIMO single-cell weight optimization method |
| CN114374984A (en) * | 2021-12-28 | 2022-04-19 | 中国电信股份有限公司 | Beam adjustment method and device, electronic equipment and storage medium |
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