Beam optimization method, communication method and system in 5G power scenarios

Abstract

The present invention discloses a beam optimization method, a communication method and a system for a 5G power scenario. The present invention effectively reduces communication delay by transmitting short packets and constructs a short packet beamforming optimization model. The model aims to maximize the weighted sum of the achievable rates corresponding to all the communication users. By iteratively solving the short packet beamforming optimization model corresponding to each communication user, the short packet beamforming vector corresponding to each communication user is obtained. Signal transmission is performed based on the short packet beamforming vector, which can effectively increase the transmission rate in the power system, improve transmission reliability and meet the user's low-latency requirements.

Description

Technical Field

[0001] The present invention relates to a beam optimization method, a communication method, and a system for a 5G power scenario, and belongs to the technical field of power communication. Background Art

[0002] Fifth-generation wireless systems (5G) are designed to provide highly reliable, low-latency, and high-data-rate communication services, enabling a more stable and efficient wireless communication architecture. In smart grid construction, the application of 5G technology enables the construction of communication network systems that automate distribution networks while meeting latency-tolerant performance requirements, such as network stability. At the same time, the increasing diversity of services and user numbers are placing higher demands on communication service metrics and systems. To provide millisecond-level ultra-low latency and flexible, configurable communication services, and to meet the exponential communication demands of power terminals, grid equipment, and users in 5G scenarios, end-to-end low-latency jitter control is one of the most critical technologies in 5G scenarios.

[0003] Most existing power grid wireless communications rely on single-antenna base stations for data transmission, employing traditional time-division multiple access (TDMA) or frequency-division multiple access (FDMA). While single-antenna systems are simple to configure and install, the introduction of multiple antennas and multiple-input, multiple-output (MIMO) into power grid scenarios makes it difficult to meet the low-latency, high-reliability communication requirements of 5G power grid scenarios. Summary of the Invention

[0004] The present invention provides a beam optimization method, communication method and system for 5G power scenarios, which solves the problem that existing communication methods are difficult to meet the low-latency and high-reliability communication requirements of 5G power scenarios.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] Beam optimization methods for 5G power scenarios include:

[0007] Acquire communication information of all communication users within a preset range of the target base station, and calculate the achievable rate of the wireless channel corresponding to each communication user based on the communication information of each communication user;

[0008] According to the achievable rates corresponding to the respective communication users, a short packet beamforming optimization model corresponding to the respective communication users is constructed; wherein the short packet beamforming optimization model takes maximizing the weighted sum of the achievable rates corresponding to all the communication users as the optimization goal

[0009] Iteratively solve the short packet beamforming optimization model corresponding to each of the communication users to obtain a short packet beamforming vector corresponding to each of the communication users.

[0010] The communication information of the communication user includes the coordinates and antenna angle of the communication user.

[0011] Calculating the achievable rate of the wireless channel corresponding to each of the communication users according to the communication information of each of the communication users includes:

[0012] Calculating a wireless channel coefficient corresponding to each of the communication users according to the communication information of each of the communication users;

[0013] Calculating the signal to interference and noise ratio of the wireless channel corresponding to each of the communication users according to the wireless channel coefficient corresponding to each of the communication users;

[0014] The achievable rate of the wireless channel corresponding to each of the communication users is calculated according to the signal to interference and noise ratio corresponding to each of the communication users.

[0015] Calculating the achievable rate of the wireless channel corresponding to each of the communication users according to the signal to interference and noise ratio corresponding to each of the communication users includes:

[0016] Calculating the channel dispersion parameter corresponding to each communication user under short packet transmission according to the signal to interference and noise ratio corresponding to each communication user;

[0017] The achievable rate of the wireless channel corresponding to each of the communication users is calculated according to the signal to interference and noise ratio, the channel spread parameter, a given bit error rate requirement, and a transmission delay corresponding to each of the communication users.

[0018] The calculation formula of the signal to interference and noise ratio is:

[0019]

[0020] Wherein, M is the number of communication users, h m is the wireless channel coefficient between the target base station and the mth communication user, γ m is the signal-to-interference-and-noise ratio of the wireless channel corresponding to the mth communication user, w m The signal weight assigned by the target base station before transmitting the signal to the mth communication user, w j is the signal weight assigned by the target base station before transmitting a signal to the jth communication user, σ m is the transmission noise when the target base station transmits a signal to the mth communication user.

[0021] The calculation formula of the achievable rate is:

[0022]

[0023] Among them, Rm is the achievable rate of the wireless channel corresponding to the mth communication user, γ m is the signal-to-interference-and-noise ratio of the wireless channel corresponding to the mth communication user, V m is the channel dispersion parameter under the short packet transmission corresponding to the mth communication user, B m is the bandwidth allocated to the mth communication user, T is the given transmission delay, ε m is the given bit error rate requirement corresponding to the mth communication user, Q -1 (·) is the inverse function of the Q equation.

[0024] The calculation formula of the channel dispersion parameter under the short packet transmission corresponding to the communication user is:

[0025] V m =1-(1+γ m ) -2

[0026] Among them, γ m is the signal-to-interference-and-noise ratio of the wireless channel corresponding to the mth communication user, V m is the channel dispersion parameter under the short packet transmission corresponding to the mth communication user.

[0027] The short packet beamforming optimization model includes an objective function and constraints;

[0028] Wherein, the objective function is:

[0029]

[0030] Among them, R sum is the weighted sum of the achievable rates corresponding to the communication users, R m is the achievable rate of the wireless channel corresponding to the mth communication user, u m is the performance weight of the mth communication user, and M is the number of communication users;

[0031] The constraints are:

[0032]

[0033] R m ≥Γ m ;

[0034] Among them, P max is the maximum transmission power of the target base station, w m a signal weight assigned by the target base station before transmitting a signal to the mth communication user, Indicates w m The conjugate transpose vector of Γ mThe threshold value required for the communication user rate.

[0035] Iteratively solving the short packet beamforming optimization model corresponding to each of the communication users to obtain the short packet beamforming vector corresponding to each of the communication users includes:

[0036] Using auxiliary variable t m and a semi-positive definite variable W under rank 1 constraint m , convert the weighted sum of the reachable rates corresponding to all the communication users into the form of subtracting two concave functions, and obtain the optimized weighted sum R of the reachable rates corresponding to all the communication users sum for:

[0037]

[0038] in, γ m is the signal to interference and noise ratio of the wireless channel corresponding to the mth communication user, γ j is the signal to interference and noise ratio of the wireless channel corresponding to the jth communication user, σ m is the transmission noise when the target base station transmits a signal to the mth communication user, To define symbols, V m is the channel dispersion parameter under the short packet transmission corresponding to the mth communication user, B m is the bandwidth allocated to the mth communication user, T is the given transmission delay, ε m is the given bit error rate requirement corresponding to the mth communication user, Q -1 (·) is the inverse function of the Q equation;

[0039] For function T m and contains W m The concave function f m (W m ) to perform first-order Taylor expansion and adopt the successive iteration method to obtain the function T m and f m (W m )’s approximate function;

[0040] The function T m and contains W m The concave function f m (W m) is brought into the corresponding short packet beamforming optimization model, and the corresponding short packet beamforming optimization model is solved using an interior point iterative method to obtain a short packet beamforming matrix corresponding to the communication user; wherein a termination condition of the interior point iterative method is that a difference between a weighted sum of achievable rates corresponding to the communication user output in two consecutive iterations is less than a threshold;

[0041] Performing singular value decomposition on the short packet beamforming matrix to obtain a short packet beamforming vector corresponding to the communication user.

[0042] The communication method in the 5G power scenario, applied to the target base station, includes:

[0043] Obtain the short packet beamforming vector obtained from the beam optimization method in the 5G power scenario;

[0044] According to the short packet beamforming vector, a signal is transmitted to each communication user within a preset range.

[0045] Transmitting a signal to each communication user within a preset range according to the short packet beamforming vector includes:

[0046] The short packet beamforming vector is added as a transmission weight to the front of the signal to be transmitted, and the time division multiple access technology is used to transmit the signal to each communication user within a preset range.

[0047] The beam optimization system for 5G power scenarios includes:

[0048] A calculation module is configured to obtain communication information of all communication users within a preset range of the target base station, and calculate a achievable rate of a wireless channel corresponding to each communication user based on the communication information of each communication user;

[0049] An optimization model construction module is configured to construct a short packet beamforming optimization model corresponding to each of the communication users according to the achievable rates corresponding to each of the communication users; wherein the short packet beamforming optimization model takes maximizing the weighted sum of the achievable rates corresponding to all of the communication users as an optimization goal;

[0050] A solution module iteratively solves the short packet beamforming optimization model corresponding to each of the communication users to obtain a short packet beamforming vector corresponding to each of the communication users.

[0051] The short packet beamforming optimization model constructed by the optimization model construction module includes an objective function and constraints;

[0052] Wherein, the objective function is:

[0053]

[0054] Among them, R sum is the weighted sum of the achievable rates corresponding to the communication users, R m is the achievable rate of the wireless channel corresponding to the mth communication user, u m is the performance weight of the mth communication user, and M is the number of communication users;

[0055] The constraints are:

[0056]

[0057] R m ≥Γ m ;

[0058] Among them, P max is the maximum transmission power of the target base station, w m a signal weight assigned by the target base station before transmitting a signal to the mth communication user, Indicates w m The conjugate transpose vector of Γ m The threshold value required for the communication user rate.

[0059] The 5G communication system in the power sector, applied to the target base station, includes:

[0060] The short packet beamforming vector module obtains the short packet beamforming vector obtained by the beam optimization method in the 5G power scenario;

[0061] The transmission module transmits a signal to each communication user within a preset range according to the short packet beamforming vector.

[0062] The transmission module adds the short packet beamforming vector as a transmission weight to the front of the signal to be transmitted, and adopts time division multiple access technology to transmit the signal to each communication user within a preset range.

[0063] A computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform a beam optimization method or a communication method in a 5G power scenario.

[0064] A computing device comprises one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing a beam optimization method or a communication method in a 5G power scenario.

[0065] The beneficial effects achieved by the present invention are as follows: the present invention effectively reduces communication delay by transmitting short packets, and constructs a short packet beamforming optimization model, which aims to maximize the weighted sum of the achievable rates corresponding to all the communication users. By iteratively solving the short packet beamforming optimization model corresponding to each communication user, the short packet beamforming vector corresponding to each communication user is obtained, and signal transmission is performed based on the short packet beamforming vector, which can effectively increase the transmission rate in the power system, improve transmission reliability, and meet the user's low-latency requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 Flowchart of the beam optimization method of the present invention;

[0067] Figure 2 This is a schematic diagram of the 5G power scenario;

[0068] Figure 3 Flowchart of the communication method of the present invention. DETAILED DESCRIPTION

[0069] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0070] like Figure 1 As shown in FIG, the beam optimization method in the 5G power scenario includes the following steps:

[0071] Step 1: Acquire communication information of all communication users within a preset range of the target base station, and calculate the achievable rate of the wireless channel corresponding to each communication user based on the communication information of each communication user;

[0072] Step 2: constructing a short packet beamforming optimization model corresponding to each of the communication users according to the achievable rates corresponding to the communication users; wherein the short packet beamforming optimization model takes maximizing the weighted sum of the achievable rates corresponding to all the communication users as an optimization goal;

[0073] Step 3: Iteratively solve the short packet beamforming optimization model corresponding to each of the communication users to obtain the short packet beamforming vector corresponding to each of the communication users.

[0074] The above method effectively reduces communication delay by transmitting short packets and constructs a short packet beamforming optimization model. The model aims to maximize the weighted sum of the achievable rates corresponding to all the communication users. By iteratively solving the short packet beamforming optimization model corresponding to each communication user, the short packet beamforming vector corresponding to each communication user is obtained. Signal transmission is performed based on the short packet beamforming vector, which can effectively increase the transmission rate in the power system, improve transmission reliability, and meet the user's low latency requirements.

[0075] by Figure 2 As an example, a 5G power scenario in the example is deployed, in which a base station connected to the 5G power network is deployed. The base station is equipped with a multi-antenna array unit with a total of N t Antennas are used, and M single-antenna users are distributed in the power wireless network. To meet the low-latency, high-reliability communication requirements of multiple users, the base station uses multiple antenna array units and spatial division multiple access technology to achieve frequency division multiplexing gain.

[0076] The communication method on the base station side can be as follows:

[0077] Collect the communication information of all communication users within the preset range of the target base station, that is, collect the needs, distribution, coordinates, antenna angles, etc. of M communication users.

[0078] Since space division multiple access technology is used, the base station will add a weight before each transmission signal, that is, the transmission signal can be expressed as where w m The weight added before transmitting the signal to the mth communication user, x m is the signal transmitted to the mth communication user.

[0079] Therefore, when considering using time division multiple access technology to serve users, the transmission link between the base station and the communication user is constructed. The wireless channel coefficient corresponding to each communication user can be calculated based on the communication information of each communication user. Based on the wireless channel coefficient corresponding to each communication user, the signal-to-interference-and-noise ratio of the wireless channel corresponding to each communication user can be further calculated. The specific calculation formula can be:

[0080]

[0081] Where M is the number of communication users, h m is the wireless channel coefficient between the target base station and the mth communication user, γ m is the signal-to-interference-and-noise ratio of the wireless channel corresponding to the mth communication user, w m is the signal weight assigned by the target base station before transmitting the signal to the mth communication user, w j is the signal weight assigned by the target base station before transmitting the signal to the jth communication user, σ m is the transmission noise when the target base station transmits signals to the mth communication user. In addition, Represents the noise term in the signal received by the mth communication user.

[0082] In order to eliminate interference between multiple users in communication, a short packet beamforming scheme is used here. The channel dispersion parameter corresponding to each communication user under short packet transmission can be calculated based on the signal-to-interference-and-noise ratio corresponding to each communication user. The achievable rate of the wireless channel corresponding to each communication user can be calculated based on the signal-to-interference-and-noise ratio corresponding to the communication user, the channel dispersion parameter, a given bit error rate requirement, and transmission delay. The specific calculation formula can be:

[0083]

[0084] Among them, R m is the achievable rate of the wireless channel corresponding to the mth communication user, V m is the channel dispersion parameter under the short packet transmission corresponding to the mth communication user, V m =1-(1+γ m ) -2 , γ m is the signal to interference and noise ratio of the wireless channel corresponding to the mth communication user, B m is the bandwidth allocated to the mth communication user, T is the given transmission delay, ε m is the given bit error rate requirement corresponding to the mth communication user, Q -1 (·) is the Q equation is the inverse function of , t represents time.

[0085] In order to ensure the fairness of performance of all users in the 5G power scenario, user weights are introduced. According to the achievable rate corresponding to each communication user, a short packet beamforming optimization model corresponding to each communication user can be constructed.

[0086] The model uses the short packet beamforming optimization model to maximize the weighted sum of the achievable rates corresponding to all the communication users as the optimization goal, taking into account the base station transmission power constraint and the service quality requirement constraint provided by the base station to the communication users. The specific formula can be expressed as follows:

[0087] Objective function:

[0088]

[0089] Among them, R sum is the weighted sum of the achievable rates corresponding to the communication users, u m is the performance weight of the mth communication user, and the system uses u m To control and adjust the fairness of performance between users, M is the number of communication users;

[0090] Constraints:

[0091] 1) The transmission power of the base station in the power scenario is less than the maximum transmission base station power;

[0092]

[0093] Among them, P max is the maximum transmission power of the target base station, w m The signal weight assigned by the target base station before transmitting the signal to the mth communication user, Indicates w m The conjugate transpose vector of ;

[0094] 2) The services provided by the base station to users should meet the users' minimum service quality requirements;

[0095] R m ≥Γ m

[0096] Among them, Γ m The threshold value required for the communication user rate.

[0097] The above model is iteratively solved to obtain the optimal short packet beamforming vector, that is, the short packet beamforming vector corresponding to each communication user. The specific process can be as follows:

[0098] S1) Using auxiliary variable t m and a semi-positive definite variable W under rank 1 constraint m , convert the weighted sum of the reachable rates corresponding to all communication users into the form of subtracting two concave functions, and obtain the optimized weighted sum R of the reachable rates corresponding to all the communication users sum ;

[0099] Introducing semi-positive variables At the same time, add the rank 1 constraint rank(W m )=1;

[0100] Introduce auxiliary variable t m Handling complex scatter variables, i.e. in To define symbols;

[0101] The objective function can be transformed into the form of subtracting two concave functions:

[0102]

[0103] Among them, W m Concave function γ j is the signal to interference and noise ratio of the wireless channel corresponding to the j-th communication user;

[0104] S2) for the function T m and contains W m The concave function f m (Wm ) to perform first-order Taylor expansion and use the successive iteration method to obtain the function T m and f m (W m ), respectively denoted as and

[0105] S3) The approximate function is brought into the corresponding short packet beamforming optimization model, and the corresponding short packet beamforming optimization model is solved by the interior point method iteration method to obtain the short packet beamforming matrix corresponding to the communication user, that is, the preferred short packet beamforming matrix The termination condition of the interior point iterative method is that the difference between the weighted sum of the achievable rates corresponding to the communication users output in two consecutive iterations is less than a threshold;

[0106] S4) Optimize the short packet beamforming matrix Perform singular value decomposition to obtain the short packet beamforming vector corresponding to the communication user, that is, the preferred short packet beamforming vector

[0107] The base station can perform signal transmission according to the obtained short packet beamforming vector.

[0108] The above method effectively reduces communication delay by transmitting short packets and constructs a short packet beamforming optimization model. The model aims to maximize the weighted sum of the achievable rates corresponding to all the communication users and takes the user communication quality as a constraint. It aims to maximize the system communication performance on the basis of meeting the user's bit error rate and transmission time requirements. In the process of designing short packet beamforming, iterative optimization is continuously performed according to the base station transmission power constraint and the service quality requirement constraint provided by the base station to the user to obtain the preferred short packet beamforming vector, so that signal transmission can be performed based on the preferred short packet beamforming vector. The algorithm has low complexity and short calculation time, and can effectively increase the transmission rate in the power system, improve transmission reliability and meet the user's low latency requirements.

[0109] Based on the same technical solution, the present invention also discloses a software system corresponding to the above method, a beam optimization system for 5G power scenarios, including:

[0110] The calculation module obtains the communication information of all communication users within the preset range of the target base station, and calculates the reachable rate of the wireless channel corresponding to each communication user according to the communication information of each communication user.

[0111] An optimization model construction module constructs a short packet beamforming optimization model corresponding to each of the communication users according to the achievable rates corresponding to each of the communication users; wherein the short packet beamforming optimization model takes maximizing the weighted sum of the achievable rates corresponding to all the communication users as an optimization goal.

[0112] The short packet beamforming optimization model includes objective functions and constraints;

[0113] Wherein, the objective function is:

[0114]

[0115] Among them, R sum is the weighted sum of the achievable rates corresponding to the communication users, R m is the achievable rate of the wireless channel corresponding to the mth communication user, u m is the performance weight of the mth communication user, and M is the number of communication users;

[0116] The constraints are:

[0117]

[0118] R m ≥Γ m ;

[0119] Among them, P max is the maximum transmission power of the target base station, w m a signal weight assigned by the target base station before transmitting a signal to the mth communication user, Indicates w m The conjugate transpose vector of Γ m The threshold value required for the communication user rate.

[0120] A solution module iteratively solves the short packet beamforming optimization model corresponding to each of the communication users to obtain a short packet beamforming vector corresponding to each of the communication users.

[0121] The data processing procedures and methods of each module in the beam optimization system under the above-mentioned 5G power scenario are consistent and will not be repeated here.

[0122] Based on the above method, the present invention also discloses Figure 3 The communication method in the 5G power scenario shown includes:

[0123] A1) Obtaining a short packet beamforming vector obtained in a beam optimization method for a 5G power scenario;

[0124] A2) Add the short packet beamforming vector as a transmission weight to the signal to be transmitted, that is, Using time division multiple access technology, signals are transmitted to each communication user within a preset range. After receiving the signal, the user decodes it and obtains the signal he expects.

[0125] Based on the above communication method, the present invention also discloses a corresponding software system, a communication system in a 5G power scenario, which is applied to a target base station and includes:

[0126] The short packet beamforming vector module obtains the short packet beamforming vector obtained by the beam optimization method in the 5G power scenario;

[0127] The transmission module adds the short packet beamforming vector as a transmission weight to the front of the signal to be transmitted, and adopts time division multiple access technology to transmit the signal to each communication user within a preset range.

[0128] Based on the same technical solution, the present invention also discloses a computing device, including one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing a communication method in a 5G power scenario.

[0129] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0130] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0131] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0133] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

The beam optimization method in the 1.5G power scenario is characterized by: include: Obtaining communication information of all communication users within a preset range of the target base station, calculating a wireless channel coefficient corresponding to each communication user based on the communication information of each communication user, calculating a signal-to-interference-and-noise ratio (SINR) of the wireless channel corresponding to each communication user based on the SINR, calculating a achievable rate of the wireless channel corresponding to each communication user based on the SINR, calculating a channel dispersion parameter under short packet transmission corresponding to each communication user based on the SINR, and calculating a achievable rate of the wireless channel corresponding to each communication user based on the SINR, the channel dispersion parameter, a given bit error rate requirement, and a transmission delay; According to the achievable rate corresponding to each of the communication users, a short packet beamforming optimization model corresponding to each of the communication users is constructed; wherein the short packet beamforming optimization model takes maximizing the weighted sum of the achievable rates corresponding to all of the communication users as an optimization goal; Iteratively solve the short packet beamforming optimization model corresponding to each of the communication users to obtain a short packet beamforming vector corresponding to each of the communication users.

2. The beam optimization method in the 5G power scenario according to claim 1 is characterized in that: The communication information of the communication user includes the coordinates and antenna angle of the communication user.

3. The beam optimization method in the 5G power scenario according to claim 1 is characterized in that: The calculation formula of the signal to interference and noise ratio is: Wherein, M is the number of communication users, h m is the wireless channel coefficient between the target base station and the mth communication user, γ m is the signal-to-interference-and-noise ratio of the wireless channel corresponding to the mth communication user, w m The signal weight assigned by the target base station before transmitting the signal to the mth communication user, w j is the signal weight assigned by the target base station before transmitting the signal to the jth communication user, σ m is the transmission noise when the target base station transmits a signal to the mth communication user.

4. The beam optimization method in the 5G power scenario according to claim 1 is characterized in that: The calculation formula of the achievable rate is: Among them, R m is the achievable rate of the wireless channel corresponding to the mth communication user, γ m is the signal-to-interference-and-noise ratio of the wireless channel corresponding to the mth communication user, V m is the channel dispersion parameter under the short packet transmission corresponding to the mth communication user, B m is the bandwidth allocated to the mth communication user, T is the given transmission delay, ε m is the given bit error rate requirement corresponding to the mth communication user, Q -1 (·) is the inverse function of the Q equation.

5. The beam optimization method in the 5G power scenario according to claim 1 is characterized in that: The calculation formula of the channel dispersion parameter under the short packet transmission corresponding to the communication user is: V m =1-(1+γ m ) -2 Among them, γ m is the signal-to-interference-and-noise ratio of the wireless channel corresponding to the mth communication user, V m is the channel dispersion parameter under the short packet transmission corresponding to the mth communication user.

6. The beam optimization method in the 5G power scenario according to claim 1 is characterized in that: The short packet beamforming optimization model includes an objective function and constraints; Wherein, the objective function is: Among them, R sum is the weighted sum of the achievable rates corresponding to the communication users, R m is the achievable rate of the wireless channel corresponding to the mth communication user, u m is the performance weight of the mth communication user, and M is the number of communication users; The constraints are: R m ≥Γ m ; Among them, P max is the maximum transmission power of the target base station, w m a signal weight assigned by the target base station before transmitting a signal to the mth communication user, Indicates w m The conjugate transpose vector of Γ m The threshold value required for the communication user rate.

7. The beam optimization method in the 5G power scenario according to claim 6 is characterized in that: Iteratively solving the short packet beamforming optimization model corresponding to each of the communication users to obtain the short packet beamforming vector corresponding to each of the communication users includes: Using auxiliary variable t m and a semi-positive definite variable W under rank 1 constraint m , convert the weighted sum of the reachable rates corresponding to all the communication users into the form of subtracting two concave functions, and obtain the optimized weighted sum R of the reachable rates corresponding to all the communication users sum for: in, γ m is the signal to interference and noise ratio of the wireless channel corresponding to the mth communication user, γ j is the signal to interference and noise ratio of the wireless channel corresponding to the jth communication user, σ m is the transmission noise when the target base station transmits a signal to the mth communication user, To define symbols, V m is the channel dispersion parameter under the short packet transmission corresponding to the mth communication user, B m is the bandwidth allocated to the mth communication user, T is the given transmission delay, ε m is the given bit error rate requirement corresponding to the mth communication user, Q -1 (·) is the inverse function of the Q equation; For function T m and contains W m The concave function f m (W m ) to perform first-order Taylor expansion and adopt the successive iteration method to obtain the function T m and f m (W m )’s approximate function; The function T m and f m (W m ) is brought into the corresponding short packet beamforming optimization model, and the corresponding short packet beamforming optimization model is solved using an interior point iterative method to obtain a short packet beamforming matrix corresponding to the communication user; wherein a termination condition of the interior point iterative method is that a difference between a weighted sum of achievable rates corresponding to the communication user output in two consecutive iterations is less than a threshold; Performing singular value decomposition on the short packet beamforming matrix to obtain a short packet beamforming vector corresponding to the communication user. The communication method in the 8.5G power scenario is applied to a target base station and is characterized in that: include: Obtain a short packet beamforming vector obtained in the beam optimization method for a 5G power scenario according to any one of claims 1 to 7; According to the short packet beamforming vector, a signal is transmitted to each communication user within a preset range.

9. The communication method in the 5G power scenario according to claim 8, characterized in that: Transmitting a signal to each communication user within a preset range according to the short packet beamforming vector includes: The short packet beamforming vector is added as a transmission weight to the front of the signal to be transmitted, and the time division multiple access technology is used to transmit the signal to each communication user within a preset range. The beam optimization system in the 10.5G power scenario is characterized by: include: The calculation module obtains communication information of all communication users within a preset range of the target base station, calculates a wireless channel coefficient corresponding to each communication user based on the communication information of each communication user, calculates a signal to interference and noise ratio of the wireless channel corresponding to each communication user based on the wireless channel coefficient corresponding to each communication user, calculates a achievable rate of the wireless channel corresponding to each communication user based on the signal to interference and noise ratio corresponding to each communication user, calculates a channel dispersion parameter corresponding to each communication user under short packet transmission based on the signal to interference and noise ratio corresponding to each communication user, and calculates a achievable rate of the wireless channel corresponding to each communication user based on the signal to interference and noise ratio corresponding to each communication user, the channel dispersion parameter, a given bit error rate requirement, and a transmission delay; An optimization model construction module is configured to construct a short packet beamforming optimization model corresponding to each of the communication users according to the achievable rates corresponding to each of the communication users; wherein the short packet beamforming optimization model takes maximizing the weighted sum of the achievable rates corresponding to all of the communication users as an optimization goal; A solution module iteratively solves the short packet beamforming optimization model corresponding to each of the communication users to obtain a short packet beamforming vector corresponding to each of the communication users.

11. The 5G beam optimization system for electric power scenarios according to claim 10, characterized in that: The short packet beamforming optimization model constructed by the optimization model construction module includes an objective function and constraints; Wherein, the objective function is: Among them, R sum is the weighted sum of the achievable rates corresponding to the communication users, R m is the achievable rate of the wireless channel corresponding to the mth communication user, u m is the performance weight of the mth communication user, and M is the number of communication users; The constraints are: R m ≥Γ m ; Among them, P max is the maximum transmission power of the target base station, w m a signal weight assigned by the target base station before transmitting a signal to the mth communication user, Indicates w m The conjugate transpose vector of Γ m The threshold value required for the communication user rate. A 12.5G communication system for electric power scenarios, applied to a target base station, is characterized by including: A short packet beamforming vector module, which obtains the short packet beamforming vector obtained in the beam optimization method for the 5G power scenario according to any one of claims 1 to 7; The transmission module transmits a signal to each communication user within a preset range according to the short packet beamforming vector.

13. The 5G communication system for electric power scenarios according to claim 12, characterized in that: The transmission module adds the short packet beamforming vector as a transmission weight to the front of the signal to be transmitted, and adopts time division multiple access technology to transmit the signal to each communication user within a preset range.

14. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any one of the methods according to claims 1 to 9.

15. A computing device, characterized in that include: One or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods according to claims 1 to 9.

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