Multi-user uplink MISO URLLC system resource allocation method

Abstract

This invention discloses a resource allocation method for a multi-user uplink MISO URLLC system, belonging to the field of communication resource allocation. This method considers a multi-user uplink MISO URLLC system and minimizes the average decoding error probability of users by alternately optimizing the receiver vector at the base station and the transmit power of the users. Compared with traditional resource allocation methods, this invention can achieve a lower average decoding error probability of users under the same user transmit power budget conditions. Furthermore, this invention has lower computational complexity, which is beneficial for engineering implementation.

Description

Technical Field

[0001] This invention relates to the field of communication resource allocation technology, and in particular to a resource allocation method for a multi-user uplink MISO URLLC system. Background Technology

[0002] With the development of wireless communication technology, higher requirements have been placed on the reliability and latency of communication systems. Therefore, Ultra Reliable and Low-Latency Communication (URLLC) has become a research hotspot in the field of wireless communication.

[0003] Unlike traditional Shannon information theory based on infinite block length analysis, URLLC primarily focuses on decoding error probability performance under finite block length transmission. Therefore, it is necessary to consider the allocation of communication resources while minimizing the decoding error probability. Summary of the Invention

[0004] This invention provides a resource allocation method for a multi-user uplink MISO URLLC system. Compared with traditional resource allocation methods, this invention can achieve a lower average decoding error probability per user under the same user transmit power budget conditions. Furthermore, this invention has lower computational complexity, which is beneficial for engineering implementation.

[0005] This invention provides a method for allocating resources in a multi-user uplink MISO URLLC system, comprising the following steps: initializing user transmit power and base station receiver vector, and constructing a problem to minimize the average decoding error probability of users; alternately optimizing the base station receiver vector and user transmit power with minimizing the average decoding error probability of users as the optimization objective; and obtaining the MISO URLLC system resource allocation result through multiple iterations until the optimization objective meets the optimization termination condition.

[0006] In one embodiment of the present invention, the problem of minimizing the average user decoding error probability is constructed as follows: in, Indicates the number of users. Indicates the first The decoding error probability of a user is expressed as: , The Q function is represented by the expression: , This represents the natural exponential function. Represents the natural logarithm. Represents the logarithm to the base 2. For the first The signal-to-interference-plus-noise ratio (SIR) for each user is defined as: , and They represent the first Individual user base station receiver vector and transmit power, This indicates finding the modulus of a complex number. Indicates base station and user The channel between, Indicates noise power. Describing the L2 norm of a vector, Indicates the first The achievable rate for each user , Indicates the first The number of bits of information sent by each user Indicates the length of a finite block. , This represents the user's maximum transmit power. This represents the maximum probability of a user decoding error.

[0007] In one embodiment of the present invention, initializing the user transmit power and the base station receiver vector includes: Initialize the user transmit power to its maximum value. , No. The base station receiver vector for each user can be initialized as follows: ,in Represents a unit array.

[0008] In one embodiment of the present invention, minimizing the average decoding error probability of users is the optimization objective, including: The optimization objective is: in, ,constraint By constraints Equivalent transformation yields, This represents the minimum value of the user's signal-to-interference-plus-noise ratio (SINR).

[0009] In one embodiment of the present invention, the base station receiver vector and the user transmit power are alternately optimized. With user transmit power To solve an approximation of the problem of minimizing the average user decoding error probability, where The update formula is: , This indicates the latest update. The transmit power of each user is updated by solving the following convex optimization problem. : The optimization objective is: in, , This indicates the latest update. Base station receiver vector for each user, , , , , , , .

[0010] In one embodiment of the present invention, the optimization termination condition includes: The iteration rounds reach the first preset threshold; or The optimization objective is that the difference between the iteration result of the current round and the iteration result of the previous round is less than a second preset threshold.

[0011] The resource allocation method for a multi-user uplink MISO URLLC system, as described in this invention, minimizes the average decoding error probability of users while meeting the maximum transmit power limit for each user by alternately optimizing the base station receiver vector and user transmit power. Compared to traditional resource allocation methods that maximize the minimum user signal-to-interference-plus-noise ratio, this invention achieves a lower average decoding error probability for users under the same maximum transmit power limit, with lower computational complexity, making it easier to implement in engineering.

[0012] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0013] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart illustrating a multi-user uplink MISO URLLC system resource allocation method according to an embodiment of the present invention; Figure 2 The figure shows the simulation results of a multi-user uplink MISO URLLC system resource allocation method provided by an embodiment of the present invention. Detailed Implementation

[0014] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0015] Embodiments of the present invention provide a resource allocation method for a multi-user uplink MISO URLLC system. The method is designed for a multi-user uplink MISO URLLC system and aims to alternately optimize the base station receiver vector and user transmit power to minimize the average decoding error probability of users.

[0016] Figure 1 This is a flowchart illustrating a multi-user uplink MISO URLLC system resource allocation method according to an embodiment of the present invention.

[0017] like Figure 1 As shown, the multi-user uplink MISO URLLC system resource allocation method includes the following steps: In step S101, the user transmit power and base station receiver vector are initialized, and a problem is constructed to minimize the user's average decoding error probability.

[0018] In one embodiment of the present invention, the problem of minimizing the average user decoding error probability is constructed as follows: The optimization objective is: The constraints are: in, Indicates the number of users. Indicates the first The decoding error probability of a user is expressed as: ,in, The Q function is represented by the expression: , This represents the natural exponential function. Represents the natural logarithm. Represents the logarithm to the base 2. For the first The signal-to-interference-plus-noise ratio (SIR) for each user is defined as: ,in, and They represent the first Individual user base station receiver vector and transmit power, This indicates finding the modulus of a complex number. Indicates base station and user The channel between, Indicates noise power. Describing the L2 norm of a vector, Indicates the first The achievable rate for each user , Indicates the first The number of bits of information sent by each user Indicates the length of a finite block. , This represents the user's maximum transmit power. This represents the maximum probability of a user decoding error.

[0019] In an embodiment of the present invention, the user transmit power can be initialized to the maximum transmit power value. , No. The base station receiver vector for each user can be initialized as follows: ,in This represents the identity matrix, indicating that initialization is complete.

[0020] In step S102, with the goal of minimizing the average decoding error probability of the user, the base station receiver vector and the user transmit power are alternately optimized.

[0021] In embodiments of the present invention, the base station receiver vector and user transmit power are alternately optimized by solving an approximation of the problem that minimizes the average user decoding error probability: The optimization objective is: The constraints are: in, ,constraint The above constraints can be used to... Equivalent transformation yields, This represents the minimum value of the user's signal-to-interference-plus-noise ratio (SINR).

[0022] In embodiments of the present invention, the approximation of the problem of minimizing the average user decoding error probability can be achieved by alternately optimizing the base station receiver vector. With user transmit power To solve, where The update formula is: , This indicates the latest update. Transmit power of each user; Update by solving the following convex optimization problem: The optimization objective is: The constraints are: in, , This indicates the latest update. Base station receiver vector for each user, , , , , , , .

[0023] In step S103, the resource allocation result of the MISO URLLC system is obtained through multiple rounds of iteration until the optimization objective meets the optimization termination condition.

[0024] Repeat step S102 until the optimization termination condition is met. The optimization termination condition includes: the number of iterations reaches a first preset threshold; or the difference between the iteration result of the current iteration and the iteration result of the previous iteration is less than a second preset threshold, that is, the objective function converges, the iteration ends, and the optimized resource allocation result is obtained.

[0025] To verify the correctness and advancement of the method in this embodiment, a simulation experiment was conducted.

[0026] Consider a multi-user uplink MISO URLLC system, where one base station communicates with multiple single-antenna users distributed within a ring-shaped area centered on the base station. The channel between the base station and the users is modeled as a Rayleigh channel, and the parameters involved in the simulation are shown in Table 1. Table 1 Simulation Experiment Parameter Table in, For common logarithms, Indicates the base station and the first The distance between users.

[0027] To further illustrate the effects of the present invention, this embodiment also simulates the existing method for maximizing the minimum user signal-to-interference-plus-noise ratio as a comparison.

[0028] Figure 2 The simulation results show the comparison, with the horizontal axis representing "maximum user transmit power (dBm)" and the vertical axis representing "average user decoding error probability". The simulation results demonstrate that the multi-user uplink MISO URLLC system resource allocation method proposed in this embodiment achieves a lower average user decoding error probability compared to the traditional method of maximizing the minimum user signal-to-interference-plus-noise ratio, under the same maximum user transmit power.

[0029] The resource allocation method for a multi-user uplink MISO URLLC system proposed in this invention considers a multi-user uplink MISO URLLC system and minimizes the average decoding error probability of users by alternately optimizing the receiver vector at the base station and the transmit power of users. Compared with traditional resource allocation methods, this invention can achieve a lower average decoding error probability of users under the same user transmit power budget, and the invention has lower computational complexity, which is beneficial for engineering implementation.

[0030] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0031] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0032] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

Claims

1. A resource allocation method for a multi-user uplink MISO URLLC system, characterized in that, Includes the following steps: Initialize the user transmit power and the base station receiver vector, and construct a problem to minimize the user's average decoding error probability; With the goal of minimizing the average decoding error probability of users, the base station receiver vector and user transmit power are alternately optimized; Through multiple rounds of iteration until the optimization objective meets the optimization termination condition, the resource allocation result of the MISO URLLC system is obtained; The problem of minimizing the average user decoding error probability is constructed as follows: in, Indicates the number of users. Indicates the first The decoding error probability of a user is expressed as: , The Q function is represented by the expression: , This represents the natural exponential function. Represents the natural logarithm. Represents the logarithm to the base 2. For the first The signal-to-interference-plus-noise ratio (SIR) for each user is defined as: , and They represent the first Individual user base station receiver vector and transmit power, This indicates finding the modulus of a complex number. Indicates base station and user The channel between, Indicates noise power. Describing the L2 norm of a vector, Indicates the first The achievable rate for each user , Indicates the first The number of bits of information sent by each user Indicates the length of a finite block. , This represents the user's maximum transmit power. This represents the maximum probability of a user decoding error.

2. The method according to claim 1, characterized in that, Initializing user transmit power and base station receiver vectors includes: Initialize the user transmit power to its maximum value. , No. The base station receiver vector for each user can be initialized as follows: ,in Represents a unit array.

3. The method according to claim 1, characterized in that, The optimization objective is to minimize the average decoding error probability of users, including: The optimization objective is: in, ,constraint Constrained by claim 1 Equivalent transformation yields, This represents the minimum value of the user's signal-to-interference-plus-noise ratio (SINR).

4. The method according to claim 3, characterized in that, Alternately optimize base station receiver vector and user transmit power, by alternately optimizing base station receiver vector. With user transmit power To solve an approximation of the problem of minimizing the average user decoding error probability, where The update formula is: , This indicates the latest update. The transmit power of each user is updated by solving the following convex optimization problem. : The optimization objective is: in, , This indicates the latest update. Base station receiver vector for each user, , , , , , , .

5. The method according to claim 3, characterized in that, The optimization termination conditions include: The iteration rounds reach the first preset threshold; or The optimization objective is to ensure that the difference between the current iteration result and the previous iteration result is less than a second preset threshold.

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