CN111225384B

CN111225384B - Uplink interference modeling method, interference determining method and device

Info

Publication number: CN111225384B
Application number: CN201811419267.9A
Authority: CN
Inventors: 彭涛; 董卫国; 袁雁南; 段然; 刘欣; 曹佳琪; 齐志强
Original assignee: China Mobile Communications Group Co Ltd; China Mobile Communications Ltd Research Institute
Current assignee: China Mobile Communications Group Co Ltd; China Mobile Communications Ltd Research Institute
Priority date: 2018-11-26
Filing date: 2018-11-26
Publication date: 2023-05-09
Anticipated expiration: 2038-11-26
Also published as: CN111225384A

Abstract

The embodiment of the invention discloses an uplink interference modeling method, an interference determining method and an uplink interference determining device. The method comprises the following steps: acquiring wireless resource allocation data corresponding to first user equipment; the wireless resource allocation data characterizes wireless resource allocation conditions of other user equipment belonging to different base stations with the first user equipment relative to the first user equipment; obtaining network measurement data corresponding to the wireless resource allocation data; and learning and training are carried out based on the wireless resource allocation data and the network measurement data, so as to obtain an uplink interference prediction model.

Description

Uplink interference modeling method, interference determining method and device

Technical Field

The present invention relates to wireless communication technologies, and in particular, to an uplink interference modeling method, an interference determining method, and an apparatus.

Background

The interference matrix plays a crucial role for resource allocation. At present, the method for acquiring the interference matrix mainly comprises the following two steps: one is to establish an interference matrix based on the sweep data, and the other is to establish an interference matrix based on measurement report messages of the mobile phone. The method for establishing the interference matrix based on the sweep frequency data requires physical equipment deployment and is inconvenient to implement. The method for establishing the interference matrix based on the mobile phone measurement report message only comprises interference information with strong interference signals, and when the mobile phone measurement report message is in a dense network with a large number of user equipment, the interference information only comprises information of a plurality of adjacent cells with strong interference signals, so that the interference information is not complete enough, and the established interference matrix has a certain error, so that effective interference avoidance cannot be performed, and a practical scene cannot be well met.

Disclosure of Invention

In order to solve the existing technical problems, the embodiment of the invention provides an uplink interference modeling method, an interference determining method and an interference determining device.

In order to achieve the above object, the technical solution of the embodiment of the present invention is as follows:

the embodiment of the invention provides an uplink interference modeling method, which comprises the following steps:

acquiring historical wireless resource allocation data corresponding to first user equipment; the historical wireless resource allocation data represents the historical wireless resource allocation situation of other user equipment belonging to different base stations with the first user equipment relative to the first user equipment;

acquiring historical network measurement data corresponding to the historical wireless resource allocation data;

and learning and training based on the historical wireless resource allocation data and the historical network measurement data to obtain a first uplink interference prediction model.

In the above scheme, the learning training is performed based on the historical radio resource allocation data and the historical network measurement data to obtain a first uplink interference prediction model, including:

and training the preset neural network by taking the historical wireless resource allocation data and the historical network measurement data as input data of the preset neural network to obtain a first uplink interference prediction model.

In the above scheme, the method further comprises: acquiring historical traffic information of the first user equipment;

and carrying out learning training based on the historical wireless resource allocation data, the historical network measurement data and the historical traffic information to obtain a second uplink interference prediction model.

In the above solution, the learning training based on the historical radio resource allocation data, the historical network measurement data and the historical traffic information to obtain a second uplink interference prediction model includes:

and training the preset neural network by taking the historical wireless resource allocation data, the historical network measurement data and the historical traffic information as input data of the preset neural network to obtain a second uplink interference prediction model.

In the above scheme, the method further comprises: and obtaining first interference information of the user equipment based on the first uplink interference prediction model.

In the above solution, the obtaining the first interference information of the ue based on the first uplink interference prediction model includes:

acquiring wireless resource allocation data corresponding to the user equipment; the wireless resource allocation data characterizes wireless resource allocation conditions of other user equipment belonging to different base stations relative to the user equipment;

And obtaining interference information of the user equipment based on the wireless resource allocation data and the first uplink interference prediction model.

In the above scheme, the method further comprises: and obtaining second interference information of the user equipment based on the second uplink interference prediction model.

In the above solution, the obtaining the second interference information of the ue based on the second uplink interference prediction model includes: acquiring wireless resource allocation data and traffic information corresponding to the user equipment; the wireless resource allocation data characterizes wireless resource allocation conditions of other user equipment belonging to different base stations relative to the user equipment;

and obtaining second interference information based on the wireless resource allocation data, the traffic information and the second uplink interference prediction model.

The embodiment of the invention also provides an interference determination method, which comprises the following steps:

acquiring wireless resource allocation data corresponding to the second user equipment; the wireless resource allocation data characterizes wireless resource allocation conditions of other user equipment belonging to different base stations with the second user equipment relative to the second user equipment;

And obtaining first interference information of the second user equipment based on the wireless resource allocation data and a preset first uplink interference prediction model.

In the above scheme, the method further comprises: acquiring service volume information corresponding to the second user equipment;

and obtaining second interference information of the second user equipment based on the wireless resource allocation data, the traffic information and a preset second uplink interference prediction model.

In the above scheme, the method further comprises: acquiring historical wireless resource allocation data corresponding to first user equipment; the historical wireless resource allocation data represents the historical wireless resource allocation situation of other user equipment belonging to different base stations with the first user equipment relative to the first user equipment;

Acquiring historical traffic information of the first user equipment;

In the above solution, the obtaining radio resource allocation data corresponding to the second ue includes: the first wireless resource allocation data corresponding to the second user equipment is obtained; the first radio resource allocation data characterizes the radio resource allocation situation of a third user equipment under different base stations to which the second user equipment belongs relative to the second user equipment;

the obtaining the interference information of the second ue based on the radio resource allocation data and a preset uplink interference prediction model includes:

and obtaining interference information of the third user equipment to the second user equipment based on the first radio resource allocation data and a preset uplink interference prediction model.

The embodiment of the invention also provides an uplink interference modeling device, which comprises: a first acquisition unit and a first modeling unit; wherein,,

The first obtaining unit is used for obtaining historical wireless resource allocation data corresponding to the first user equipment; the historical wireless resource allocation data represents the historical wireless resource allocation situation of other user equipment belonging to different base stations with the first user equipment relative to the first user equipment; the method is also used for obtaining historical network measurement data corresponding to the historical wireless resource allocation data;

the first modeling unit is configured to perform learning training based on the historical radio resource allocation data and the historical network measurement data, and obtain a first uplink interference prediction model.

In the above scheme, the first modeling unit is configured to use the historical radio resource allocation data and the historical network measurement data as input data of a preset neural network, train the preset neural network, and obtain a first uplink interference prediction model.

In the above solution, the first obtaining unit is further configured to obtain historical traffic information of the first user equipment;

the first modeling unit is further configured to perform learning training based on the historical radio resource allocation data, the historical network measurement data, and the historical traffic information, and obtain a second uplink interference prediction model.

In the above scheme, the first modeling unit is configured to train the preset neural network by using the historical radio resource allocation data, the historical network measurement data and the historical traffic information as input data of the preset neural network, so as to obtain a second uplink interference prediction model.

In the above scheme, the apparatus further includes a first determining unit, configured to obtain first interference information of the user equipment based on the first uplink interference prediction model obtained by the first modeling unit.

In the above scheme, the device further comprises a second acquisition unit;

the second obtaining unit is configured to obtain radio resource allocation data corresponding to the user equipment; the wireless resource allocation data characterizes wireless resource allocation conditions of other user equipment belonging to different base stations relative to the user equipment;

the first determining unit is configured to obtain interference information of the ue based on the radio resource allocation data obtained by the second obtaining unit and the first uplink interference prediction model.

In the above scheme, the device further includes a first determining unit, configured to obtain second interference information of the user equipment based on the second uplink interference prediction model obtained by the first modeling unit.

In the above scheme, the device further comprises a second acquisition unit;

the second obtaining unit is configured to obtain radio resource allocation data and traffic information corresponding to the user equipment; the wireless resource allocation data characterizes wireless resource allocation conditions of other user equipment belonging to different base stations relative to the user equipment;

the first determining unit is configured to obtain second interference information based on the radio resource allocation data, the traffic information, and the second uplink interference prediction model obtained by the second obtaining unit.

The embodiment of the invention also provides an interference determination device, which comprises: a third acquisition unit and a second determination unit; wherein,,

the third obtaining unit is configured to obtain radio resource allocation data corresponding to the second user equipment; the wireless resource allocation data characterizes wireless resource allocation conditions of other user equipment belonging to different base stations with the second user equipment relative to the second user equipment;

the second determining unit is configured to obtain first interference information of the second user equipment based on the radio resource allocation data and a preset first uplink interference prediction model.

In the above solution, the third obtaining unit is further configured to obtain traffic information corresponding to the second user equipment;

the second determining unit is further configured to obtain second interference information of the second user equipment based on the radio resource allocation data, the traffic information and a preset second uplink interference prediction model.

In the above scheme, the device further comprises a fourth acquisition unit and a second modeling unit; wherein,,

the fourth obtaining unit is configured to obtain historical radio resource allocation data corresponding to the first user equipment; the historical wireless resource allocation data represents the historical wireless resource allocation situation of other user equipment belonging to different base stations with the first user equipment relative to the first user equipment; the method is also used for obtaining historical network measurement data corresponding to the historical wireless resource allocation data;

the second modeling unit is configured to perform learning training based on the historical radio resource allocation data and the historical network measurement data, and obtain a first uplink interference prediction model.

The fourth obtaining unit is configured to obtain historical radio resource allocation data corresponding to the first user equipment; the historical wireless resource allocation data represents the historical wireless resource allocation situation of other user equipment belonging to different base stations with the first user equipment relative to the first user equipment; the method is also used for obtaining the historical traffic information of the first user equipment; the method is also used for obtaining historical network measurement data corresponding to the historical wireless resource allocation data;

the second modeling unit is configured to perform learning training based on the historical radio resource allocation data, the historical network measurement data, and the historical traffic information, and obtain a second uplink interference prediction model.

In the above solution, the third obtaining unit is configured to obtain first radio resource allocation data corresponding to the second user equipment; the first radio resource allocation data characterizes the radio resource allocation situation of a third user equipment under different base stations to which the second user equipment belongs relative to the second user equipment;

the second determining unit is configured to obtain interference information of the third user equipment to the second user equipment based on the first radio resource allocation data and a preset uplink interference prediction model obtained by the third obtaining unit.

The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, implements the steps of the uplink interference modeling method according to the embodiment of the invention; or,

the program when executed by a processor implements the steps of the interference determination method according to the embodiments of the present invention.

The embodiment of the invention also provides an uplink interference modeling device which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the uplink interference modeling method according to the embodiment of the invention when executing the program.

The embodiment of the invention also provides an interference determination device which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the interference determination method according to the embodiment of the invention when executing the program.

According to the uplink interference modeling method, the interference determining method and the device provided by the embodiment of the invention, on one hand, an uplink interference prediction model is trained by using a learning training mode through historical wireless resource allocation data and historical network measurement data; on the other hand, the uplink interference prediction model is used for determining interference information, on the premise that physical equipment deployment and complete measurement report of users are not needed, a large amount of radio resource allocation data and network measurement data generated in the scheduling process are utilized, a complete and accurate uplink user interference modeling scheme is provided through big data analysis and a machine learning algorithm, implementation is simple, the implementation is closer to an actual network scene, and real-time, efficient, high-precision and complete interference prediction is realized.

Drawings

Fig. 1 is a flow chart of an uplink interference modeling method according to an embodiment of the present invention;

fig. 2 is a schematic diagram of an indoor networking model applied to an uplink interference modeling method according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of modeling principle of an uplink interference modeling method according to an embodiment of the present invention;

fig. 4 is a flow chart of an interference determination method according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of an interference prediction error using an interference determination method according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of another interference prediction error using an interference determination method according to an embodiment of the present invention;

fig. 7 is a schematic diagram of a composition structure of an uplink interference modeling apparatus according to an embodiment of the present invention;

FIG. 8 is a schematic diagram of another component structure of an uplink interference modeling apparatus according to an embodiment of the present invention;

FIG. 9 is a schematic diagram of another structure of an uplink interference modeling apparatus according to an embodiment of the present invention;

fig. 10 is a schematic diagram of a composition structure of an interference determination device according to an embodiment of the present invention;

fig. 11 is a schematic diagram of another composition structure of an interference determination device according to an embodiment of the present invention;

fig. 12 is a schematic diagram of a hardware composition structure of a device according to an embodiment of the present invention.

Detailed Description

The invention will be described in further detail with reference to the accompanying drawings and specific examples.

The embodiment of the invention provides an uplink interference modeling method, and fig. 1 is a flow diagram of the uplink interference modeling method according to the embodiment of the invention; as shown in fig. 1, the method includes:

step 101: acquiring historical wireless resource allocation data corresponding to first user equipment; the historical wireless resource allocation data characterizes historical wireless resource allocation conditions of other user equipment belonging to different base stations with the first user equipment relative to the first user equipment.

Step 102: and obtaining historical network measurement data corresponding to the historical wireless resource allocation data.

Step 103: and learning and training based on the historical wireless resource allocation data and the historical network measurement data to obtain a first uplink interference prediction model.

In this embodiment, the historical radio resource allocation data and the historical network measurement data are both historical data for model training, and may be understood as sample data.

As an example, the historical radio Resource allocation data is a historical usage of each Resource Block (RB) of other user equipments belonging to different base stations with the first user equipment, and the historical radio Resource allocation data may indicate whether the other user equipments belonging to different base stations with the first user equipment use the same Resource as the first user equipment.

In this embodiment, the historical network measurement data characterizes uplink interference conditions of other user equipment belonging to different base stations with the first user equipment to the first user equipment. As an example, the historical network measurement data table may be represented by a historical uplink signal to interference plus noise ratio (UL-SINR, up Link-Signal to Interference plus Noise Ratio).

In one implementation, the historical radio resource allocation data and/or historical network measurement data of the embodiments of the present invention are obtained based on a particular channel model. Fig. 2 is a schematic diagram of an indoor networking model applied to an uplink interference modeling method according to an embodiment of the present invention; as shown in fig. 2, it is assumed that two rows of rooms are provided on both sides of a corridor 20 m wide, each row having 10 rooms, each room having a size of 10m×10m, and each room being equipped with one small base station (SBS, small Base Station) for a total of 40 small base stations, each small base station being connected to 2 User Equipments (UEs), i.e., UEs ₁ 、UE ₂ Registered with SBS ₁ ，UE ₃ 、UE ₄ Registered with SBS ₂ ……UE _i 、UE _i+1 Registered with SBS _i/2 … … UEs belonging to the same small base station only allocate orthogonal resources. The small base stations and the UE are randomly deployed in corresponding rooms, the minimum distance between the small base stations is 8m, the system bandwidth of the whole model is 10 megahertz (MHz) (comprising 50 RBs), and the available bandwidth of each small base station is 10MHz. The transmission power of the small base station is 20dBm.

Of course, the indoor channel model shown in fig. 2 is only an example, and other dense networking models may be suitable for the embodiments of the present invention.

According to the embodiment of the invention, a machine learning algorithm is adopted to train historical wireless resource allocation data and historical network measurement data, and a first uplink interference prediction model is obtained. As an example, the machine learning algorithm may be specifically any neural network algorithm, and in this embodiment, the learning training based on the historical radio resource allocation data and the historical network measurement data to obtain the first uplink interference prediction model includes: and training the preset neural network by taking the historical wireless resource allocation data and the historical network measurement data as input data of the preset neural network to obtain a first uplink interference prediction model. And training the preset neural network by taking the historical wireless resource allocation data and the historical network measurement data as input data of the preset neural network to obtain a first uplink interference prediction model.

In fig. 2, a total of 40 small base stations are in the network, each small base station is accessed to 2 UEs, namely UEs ₁ 、UE ₂ Registered with SBS ₁ ，UE ₃ 、UE ₄ Registered with SBS ₂ ……UE _i 、UE _i+1 Registered with SBS _i/2 … …; UEs registered under the same small base station allocate only orthogonal resources.

With UE _i For example, the input data includes the UE _i Historical UL-SINR per RB per transmission time interval (TTI, transmission Time Interval), and corresponding TTI corresponding to UE on RB _i Historical radio resource allocation conditions for UEs that may form interference. UE (user Equipment) _i+1 With UE _i Registered with the same small cell, so the UE _i+1 Not as UE _i Is subject to interference by the user for analysis. UE (user Equipment) _i Is to divide SBS _i/2 UEs registered by other small base stations, i.e. including UEs ₁ 、UE ₂ ……UE _i-1 、UE _i+2 ……UE ₈₀ . Taking the historical radio resource allocation conditions corresponding to the UE as characteristic attributes, wherein the attribute values are {0,1}, and 1 is represented in TTI _t Other corresponding UEs and UEs _i Using the same resources (i.e., using the same RB); 0 represents in TTI _t Other corresponding UEs and UEs _i The same resource is not used (i.e., the same RB is not used). Next, with TTI _t For example, as shown in table 1.

TABLE 1

Wherein RB is ₂ 、…RB ₇ 、RB ₉ Representing UE _i In TTI _t The RB used. The last column data is indicated in the TTI _t ，UE _i Other column data representation may be for the UE _i Other UEs forming interference.

Inputting data as shown in table 1 as input data into the neural network; FIG. 3 is a schematic diagram of modeling principle of an uplink interference modeling method according to an embodiment of the present invention; as shown in fig. 3, the number of neurons in the neural network input layer is all possible for the UE _i The number of interfering users, exemplified with reference to fig. 2, is 78; the number of neurons in the middle hidden layer can be adjusted according to the root mean square error (RMSE, root Mean Square Error) under different number configurations. And optimizing and adjusting relevant parameters of a hidden layer in the neural network through learning and training, so as to obtain a first uplink interference prediction model.

Taking fig. 2 as an example, an object of an embodiment of the present invention is to determine an interference relationship between any two UEs belonging to different base stations, such as UEs _i For example, prediction divide and UE _i UE registered in same small base station _i+1 In addition, the UE _i Interference strength with other UEs. And repeating the operation on 80 UE in the networking to finally obtain a prediction model.

In an alternative embodiment of the invention, the method further comprises: acquiring historical traffic information of the first user equipment; and carrying out learning training based on the historical wireless resource allocation data, the historical network measurement data and the historical traffic information to obtain a second uplink interference prediction model.

In this embodiment, in the training process of the uplink interference prediction model, the historical traffic information is used as the training of the model according to the participation model in the model training in addition to the historical radio resource allocation data and the historical network measurement data. As an example, the historical traffic information may be packet data convergence protocol (PDCP, packet Data Convergence Protocol) throughput information.

Optionally, the learning training based on the historical radio resource allocation data, the historical network measurement data and the historical traffic information, to obtain a second uplink interference prediction model, includes: and training the preset neural network by taking the historical wireless resource allocation data, the historical network measurement data and the historical traffic information as input data of the preset neural network to obtain a second uplink interference prediction model.

In this embodiment, data as shown in table 1 and historical traffic information (such as historical PDCP throughput) are input into a neural network as input data, and relevant parameters of a hidden layer in the neural network are optimized and adjusted through learning and training, so as to obtain a second uplink interference prediction model.

In an alternative embodiment of the invention, the method further comprises: and obtaining first interference information of the user equipment based on the first uplink interference prediction model.

The obtaining the first interference information of the user equipment based on the first uplink interference prediction model includes: acquiring wireless resource allocation data corresponding to the user equipment; the wireless resource allocation data characterizes wireless resource allocation conditions of other user equipment belonging to different base stations relative to the user equipment; and obtaining interference information of the user equipment based on the wireless resource allocation data and the first uplink interference prediction model.

In an alternative embodiment of the invention, the method further comprises: and obtaining second interference information of the user equipment based on the second uplink interference prediction model.

The obtaining the second interference information of the user equipment based on the second uplink interference prediction model includes: acquiring wireless resource allocation data and traffic information corresponding to the user equipment; the wireless resource allocation data characterizes wireless resource allocation conditions of other user equipment belonging to different base stations relative to the user equipment; and obtaining second interference information based on the wireless resource allocation data, the traffic information and the second uplink interference prediction model.

The determining process of the interference information in this embodiment may refer to the following embodiments specifically, and will not be described herein.

The embodiment of the invention also provides an interference determination method, and fig. 4 is a schematic flow chart of the interference determination method in the embodiment of the invention; as shown in fig. 4, the method includes:

step 201: acquiring wireless resource allocation data corresponding to the second user equipment; the radio resource allocation data characterizes radio resource allocation conditions of other user equipment belonging to different base stations with the second user equipment relative to the second user equipment.

Step 202: and obtaining first interference information of the second user equipment based on the wireless resource allocation data and a preset first uplink interference prediction model.

As an example, the radio resource allocation data is a per Resource Block (RB) usage of other user equipments belonging to different base stations than the first user equipment, and may indicate whether the other user equipments belonging to different base stations than the first user equipment use the same resource as the first user equipment.

In this embodiment, the first interference information is obtained by inputting the obtained radio resource allocation data into a preset first uplink interference prediction model. As an example, the first interference information may be UL-SINR.

In an alternative embodiment of the invention, the method further comprises: acquiring historical wireless resource allocation data corresponding to first user equipment; the historical wireless resource allocation data represents the historical wireless resource allocation situation of other user equipment belonging to different base stations with the first user equipment relative to the first user equipment; acquiring historical network measurement data corresponding to the historical wireless resource allocation data; and learning and training based on the historical wireless resource allocation data and the historical network measurement data to obtain a first uplink interference prediction model.

In this embodiment, the modeling process of the first uplink interference prediction model may be specifically described with reference to the foregoing embodiment, which is not described herein again.

In an alternative embodiment of the invention, the method further comprises: acquiring service volume information corresponding to the second user equipment; and obtaining second interference information of the second user equipment based on the wireless resource allocation data, the traffic information and a preset second uplink interference prediction model.

As an example, the traffic information may be PDCP throughput information.

In an alternative embodiment of the invention, the method further comprises: acquiring historical wireless resource allocation data corresponding to first user equipment; the historical wireless resource allocation data represents the historical wireless resource allocation situation of other user equipment belonging to different base stations with the first user equipment relative to the first user equipment; acquiring historical traffic information of the first user equipment; acquiring historical network measurement data corresponding to the historical wireless resource allocation data; and carrying out learning training based on the historical wireless resource allocation data, the historical network measurement data and the historical traffic information to obtain a second uplink interference prediction model.

In this embodiment, the modeling process of the second uplink interference prediction model may be specifically described with reference to the foregoing embodiment, which is not described herein again.

Taking a first uplink interference prediction model as an example, according to UE _i Corresponding input data under different interference conditions (such as a single interference user, a plurality of interference users and the like) is input into the first uplink interference prediction model, and corresponding UL-SINR is output. As an example, single interference user data is input into a first uplink interference prediction model, resulting in a corresponding UL-SINR. Wherein a single interference user indicates that there is only one UE to UE _i Forming interference; correspondingly, the multi-interference user indicates that there are multiple UEs to UE _i Creating interference. The input data and the output data are as shown in table 2Each characteristic attribute represents a potential for a UE _i Other UEs forming interference, each data representing a certain TTI UE _i 1 denotes the interference user situation in TTI _t Other corresponding UEs and UEs _i Using the same resources (i.e., using the same RB), 0 indicates that in TTI _t Other corresponding UEs and UEs _i The same resources are not used (i.e. the same RBs are not used), so each data in table 2 characterizes a certain TTI, UE _i Sharing an RB with a certain UE, and inputting the data into a first uplink interference prediction model to obtain the UE _i UL-SINR when forming interference with the UE.

TABLE 2

Based on this, the obtaining radio resource allocation data corresponding to the second ue includes: the first wireless resource allocation data corresponding to the second user equipment is obtained; the first radio resource allocation data characterizes the radio resource allocation situation of a third user equipment under different base stations to which the second user equipment belongs relative to the second user equipment; the obtaining the interference information of the second ue based on the radio resource allocation data and a preset uplink interference prediction model includes: and obtaining interference information of the third user equipment to the second user equipment based on the first radio resource allocation data and a preset uplink interference prediction model.

The same applies to the case of multiple interference users, which will be the case for the UE _i Data of a plurality of other UE forming interference is used as input data to be input into a first uplink interference prediction model, and the UE can be obtained _i UL-SINR when forming interference with a plurality of other UEs.

In the analysis of UE _i The UL-SINR at single user interference does not distinguish between RBs and TTIs, specifically, regardless of the UE _i The UL-SINR is the same (without considering the influence of fastfading) as the interfering user uses which RB in which TTI, so that the UE is constructed _i When interference relation with other UERB and TTI are not considered.

According to the interference determination scheme of the embodiment of the invention, as the same-frequency interference is mainly considered in the embodiment, and the transmitting power of the user equipment can be detected in the small base station, the interference intensity average value and the UE detected by the small base station can be obtained by counting the multiple average values of the interference signal intensity _i The multiple averages of the signal strengths of (a) to obtain the received signal strength average. The UL-SINR actual value is the ratio of the latter to the former.

Single-user interference UL-SINR prediction is performed for 80 UEs in the scenario shown in fig. 2, and 78 possible interference UEs for each UE are used to analyze 80×78=6240 prediction errors. As can be seen from fig. 5, approximately 75% of the prediction error is less than 0.5dB, and approximately 95% of the prediction error is less than 1dB.

And the user equipment performs sequencing from small to large by multiplexing the actual UL-SINR of the same resource respectively, obtains average errors after 80 user equipment are sequenced according to the actual UL-SINR, and analyzes the average errors. As can be seen from fig. 6, the larger the actual UL-SINR, the model prediction error is in an upward trend, but the error is not more than 0.55dB. Thus, the stronger the interference, the smaller the UL-SINR, and the higher the accuracy of the prediction. Simulation proves that the uplink interference prediction model provided by the embodiment has very high accuracy of receiving the UL-SINR by the single interference user, and particularly has very high prediction accuracy on the UL-SINR of multiplexing resources of the strong interference user.

Training an uplink interference prediction model by using a learning training mode through historical wireless resource allocation data and historical network measurement data; on the other hand, the uplink interference prediction model is used for determining interference information, on the premise that physical equipment deployment and complete measurement report of users are not needed, a large amount of radio resource allocation data and network measurement data generated in the scheduling process are utilized, a complete and accurate uplink user interference modeling scheme is provided through big data analysis and a machine learning algorithm, implementation is simple, the implementation is closer to an actual network scene, and real-time, efficient, high-precision and complete interference prediction is realized.

The embodiment of the invention also provides an uplink interference modeling device, and fig. 7 is a schematic diagram of the composition structure of the uplink interference modeling device according to the embodiment of the invention; as shown in fig. 7, the apparatus includes: a first acquisition unit 31 and a first modeling unit 32; wherein,,

the first obtaining unit 31 is configured to obtain historical radio resource allocation data corresponding to a first user equipment; the historical wireless resource allocation data represents the historical wireless resource allocation situation of other user equipment belonging to different base stations with the first user equipment relative to the first user equipment; the method is also used for obtaining historical network measurement data corresponding to the historical wireless resource allocation data;

The first modeling unit 32 is configured to perform learning training based on the historical radio resource allocation data and the historical network measurement data, and obtain a first uplink interference prediction model.

In an optional embodiment of the present invention, the first modeling unit 32 is configured to train the preset neural network to obtain a first uplink interference prediction model by using the historical radio resource allocation data and the historical network measurement data as input data of the preset neural network.

In an alternative embodiment of the present invention, the first obtaining unit 31 is further configured to obtain historical traffic information of the first user equipment;

the first modeling unit 32 is further configured to perform learning training based on the historical radio resource allocation data, the historical network measurement data, and the historical traffic information, and obtain a second uplink interference prediction model.

In an optional embodiment of the present invention, the first modeling unit 32 is configured to train the preset neural network to obtain a second uplink interference prediction model by using the historical radio resource allocation data, the historical network measurement data, and the historical traffic information as input data of the preset neural network.

In an alternative embodiment of the present invention, as shown in fig. 8, the apparatus further includes a first determining unit 33, configured to obtain first interference information of the user equipment based on the first uplink interference prediction model obtained by the first modeling unit.

In an alternative embodiment of the invention, as shown in fig. 9, the apparatus further comprises a second acquisition unit 34;

the second obtaining unit 34 is configured to obtain radio resource allocation data corresponding to the ue; the wireless resource allocation data characterizes wireless resource allocation conditions of other user equipment belonging to different base stations relative to the user equipment;

the first determining unit 33 is configured to obtain interference information of the ue based on the radio resource allocation data obtained by the second obtaining unit 34 and the first uplink interference prediction model.

In an alternative embodiment of the present invention, as shown in fig. 8, the apparatus further includes a first determining unit 33, configured to obtain second interference information of the user equipment based on the second uplink interference prediction model obtained by the first modeling unit.

The second obtaining unit 34 is configured to obtain radio resource allocation data and traffic information corresponding to the ue; the wireless resource allocation data characterizes wireless resource allocation conditions of other user equipment belonging to different base stations relative to the user equipment;

the first determining unit 33 is configured to obtain second interference information based on the radio resource allocation data, the traffic information, and the second uplink interference prediction model obtained by the second obtaining unit 34.

In the embodiment of the present invention, the first obtaining unit 31, the first modeling unit 32, the second obtaining unit 34 and the first determining unit 33 in the apparatus may be implemented by a central processing unit (CPU, central Processing Unit), a digital signal processor (DSP, digital Signal Processor), a micro control unit (MCU, microcontroller Unit) or a programmable gate array (FPGA, field-Programmable Gate Array) in practical applications.

It should be noted that: in the uplink interference modeling apparatus provided in the above embodiment, only the division of each program module is used for illustration, and in practical application, the processing allocation may be performed by different program modules according to needs, that is, the internal structure of the apparatus is divided into different program modules, so as to complete all or part of the processing described above. In addition, the uplink interference modeling apparatus provided in the above embodiment and the uplink interference modeling method embodiment belong to the same concept, and detailed implementation processes of the uplink interference modeling apparatus are referred to the method embodiment, which is not repeated herein.

The embodiment of the invention also provides an interference determination device, and fig. 10 is a schematic diagram of a composition structure of the interference determination device in the embodiment of the invention; as shown in fig. 10, the apparatus includes: a third acquisition unit 41 and a second determination unit 42; wherein,,

the third obtaining unit 41 is configured to obtain radio resource allocation data corresponding to the second user equipment; the wireless resource allocation data characterizes wireless resource allocation conditions of other user equipment belonging to different base stations with the second user equipment relative to the second user equipment;

the second determining unit 42 is configured to obtain first interference information of the second ue based on the radio resource allocation data and a preset first uplink interference prediction model.

In an optional embodiment of the present invention, the third obtaining unit 41 is further configured to obtain traffic information corresponding to the second user equipment;

the second determining unit 42 is further configured to obtain second interference information of the second user equipment based on the radio resource allocation data, the traffic information, and a preset second uplink interference prediction model.

In an alternative embodiment of the invention, as shown in fig. 11, the apparatus further comprises a fourth acquisition unit 43 and a second modeling unit 44; wherein,,

The fourth obtaining unit 43 is configured to obtain historical radio resource allocation data corresponding to the first user equipment; the historical wireless resource allocation data represents the historical wireless resource allocation situation of other user equipment belonging to different base stations with the first user equipment relative to the first user equipment; the method is also used for obtaining historical network measurement data corresponding to the historical wireless resource allocation data;

the second modeling unit 44 is configured to perform learning training based on the historical radio resource allocation data and the historical network measurement data, and obtain a first uplink interference prediction model.

the fourth obtaining unit 43 is configured to obtain historical radio resource allocation data corresponding to the first user equipment; the historical wireless resource allocation data represents the historical wireless resource allocation situation of other user equipment belonging to different base stations with the first user equipment relative to the first user equipment; the method is also used for obtaining the historical traffic information of the first user equipment; the method is also used for obtaining historical network measurement data corresponding to the historical wireless resource allocation data;

The second modeling unit 44 is configured to perform learning training based on the historical radio resource allocation data, the historical network measurement data, and the historical traffic information, and obtain a second uplink interference prediction model.

In an optional embodiment of the present invention, the third obtaining unit 41 is configured to obtain first radio resource allocation data corresponding to the second user equipment; the first radio resource allocation data characterizes the radio resource allocation situation of a third user equipment under different base stations to which the second user equipment belongs relative to the second user equipment;

the second determining unit 42 is configured to obtain interference information of the third user equipment to the second user equipment based on the first radio resource allocation data obtained by the third obtaining unit 41 and a preset uplink interference prediction model.

In the embodiment of the present invention, the third obtaining unit 41, the second determining unit 42, the fourth obtaining unit 43 and the second modeling unit 44 in the apparatus may be implemented by CPU, DSP, MCU or FPGA in practical application.

It should be noted that: in the interference determination device provided in the above embodiment, only the division of each program module is used for illustration, and in practical application, the process allocation may be performed by different program modules according to needs, that is, the internal structure of the device is divided into different program modules, so as to complete all or part of the processes described above. In addition, the interference determining device and the interference determining method provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the interference determining device and the interference determining method are detailed in the method embodiments and are not repeated herein.

The embodiment of the present invention further provides an apparatus, and fig. 12 is a schematic diagram of a hardware composition structure of the apparatus according to the embodiment of the present invention, as shown in fig. 12, where the apparatus includes a memory 52, a processor 51, and a computer program stored in the memory 52 and capable of running on the processor 51.

Optionally, the device may specifically be an uplink interference modeling device in the embodiment of the present invention; the steps of the uplink interference modeling method according to the embodiment of the present invention are implemented when the processor 51 executes the program.

Optionally, the apparatus may be specifically an interference determining apparatus in the embodiment of the present invention; the processor 51 implements the steps of the interference determination method according to the embodiment of the present invention when executing the program.

Alternatively, the various components in the device may be coupled together by a bus system 53. It will be appreciated that the bus system 53 is used to enable connected communications between these components. The bus system 53 includes a power bus, a control bus, and a status signal bus in addition to the data bus. But for clarity of illustration the various buses are labeled as bus system 53 in fig. 11.

It will be appreciated that the memory 52 can be either volatile memory or nonvolatile memory, and can include both volatile and nonvolatile memory. Wherein the nonvolatile Memory may be Read Only Memory (ROM), programmable Read Only Memory (PROM, programmable Read-Only Memory), erasable programmable Read Only Memory (EPROM, erasable Programmable Read-Only Memory), electrically erasable programmable Read Only Memory (EEPROM, electrically Erasable Programmable Read-Only Memory), magnetic random access Memory (FRAM, ferromagnetic random access Memory), flash Memory (Flash Memory), magnetic surface Memory, optical disk, or compact disk Read Only Memory (CD-ROM, compact Disc Read-Only Memory); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be random access memory (RAM, random Access Memory), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available, such as static random access memory (SRAM, static Random Access Memory), synchronous static random access memory (SSRAM, synchronous Static Random Access Memory), dynamic random access memory (DRAM, dynamic Random Access Memory), synchronous dynamic random access memory (SDRAM, synchronous Dynamic Random Access Memory), double data rate synchronous dynamic random access memory (ddr SDRAM, double Data Rate Synchronous Dynamic Random Access Memory), enhanced synchronous dynamic random access memory (ESDRAM, enhanced Synchronous Dynamic Random Access Memory), synchronous link dynamic random access memory (SLDRAM, syncLink Dynamic Random Access Memory), direct memory bus random access memory (DRRAM, direct Rambus Random Access Memory). The memory 52 described in embodiments of the present invention is intended to comprise, without being limited to, these and any other suitable types of memory.

The method disclosed in the above embodiment of the present invention may be applied to the processor 51 or implemented by the processor 51. The processor 51 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 51 or by instructions in the form of software. The processor 51 may be a general purpose processor, DSP, or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The processor 51 may implement or perform the methods, steps and logic blocks disclosed in embodiments of the present invention. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in the embodiment of the invention can be directly embodied in the hardware of the decoding processor or can be implemented by combining hardware and software modules in the decoding processor. The software modules may be located in a storage medium in a memory 52. The processor 51 reads information in the memory 52 and, in combination with its hardware, performs the steps of the method as described above.

In an exemplary embodiment, the apparatus may be implemented by one or more application specific integrated circuits (ASIC, application Specific Integrated Circuit), DSP, programmable logic device (PLD, programmable Logic Device), complex programmable logic device (CPLD, complex Programmable Logic Device), FPGA, general purpose processor, controller, MCU, microprocessor, or other electronic component for performing the aforementioned methods.

The embodiment of the invention also provides a computer readable storage medium, on which the computer program is stored.

Alternatively, the computer-readable storage medium may be applied to the uplink interference modeling apparatus of the embodiment of the present invention; the program when executed by the processor implements the steps of the uplink interference modeling method according to the embodiment of the present invention.

Alternatively, the computer-readable storage medium may be applied to the interference determination device of the embodiment of the present invention; the program when executed by a processor implements the steps of the interference determination method according to the embodiments of the present invention.

It is to be understood that the "first" and "second" in the embodiments of the present invention are used merely for distinguishing the same nouns in different embodiments, and are not used for limiting the nouns in any way. For example, the first user equipment and the second user equipment may be the same user equipment or different user equipment, which is not limited in this embodiment.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.

The units described as separate units may or may not be physically separate, and units 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 units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware associated with program instructions, where the foregoing program may be stored in a computer readable storage medium, and when executed, the program performs steps including the above method embodiments; and the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.

Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.

The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An uplink interference modeling method, characterized in that the method comprises:

and learning and training based on the historical radio resource allocation data and the historical network measurement data to obtain a first uplink interference prediction model, wherein the first uplink interference prediction model is used for determining first interference information of the user equipment.

2. The method of claim 1, wherein the learning training based on the historical radio resource allocation data and the historical network measurement data to obtain a first uplink interference prediction model comprises:

3. The method according to claim 1, wherein the method further comprises: acquiring historical traffic information of the first user equipment;

4. The method of claim 3, wherein the learning training based on the historical radio resource allocation data, the historical network measurement data, and the historical traffic information to obtain a second uplink interference prediction model comprises:

5. The method according to claim 1 or 2, characterized in that the method further comprises:

and obtaining first interference information of the user equipment based on the first uplink interference prediction model.

6. The method of claim 5, wherein the obtaining the first interference information for the user device based on the first uplink interference prediction model comprises:

7. The method according to claim 3 or 4, characterized in that the method further comprises:

and obtaining second interference information of the user equipment based on the second uplink interference prediction model.

8. The method of claim 7, wherein the obtaining second interference information for the user device based on the second uplink interference prediction model comprises:

acquiring wireless resource allocation data and traffic information corresponding to the user equipment; the wireless resource allocation data characterizes wireless resource allocation conditions of other user equipment belonging to different base stations relative to the user equipment;

9. A method of interference determination, the method comprising:

acquiring first interference information of the second user equipment based on the wireless resource allocation data and a preset first uplink interference prediction model; and the first uplink interference prediction model is obtained by learning and training based on historical wireless resource allocation data and historical network measurement data.

10. The method according to claim 9, wherein the method further comprises: acquiring service volume information corresponding to the second user equipment;

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

12. The method according to claim 10, wherein the method further comprises:

acquiring historical traffic information of the first user equipment;

13. The method according to any one of claims 9 to 12, wherein the obtaining radio resource allocation data corresponding to the second user equipment includes:

the first wireless resource allocation data corresponding to the second user equipment is obtained; the first radio resource allocation data characterizes the radio resource allocation situation of a third user equipment under different base stations to which the second user equipment belongs relative to the second user equipment;

14. An uplink interference modeling apparatus, the apparatus comprising: a first acquisition unit and a first modeling unit; wherein,,

the first modeling unit is configured to perform learning training based on the historical radio resource allocation data and the historical network measurement data, and obtain a first uplink interference prediction model, where the first uplink interference prediction model is used to determine first interference information of the user equipment.

15. The apparatus of claim 14, wherein the first modeling unit is configured to train a preset neural network to obtain a first uplink interference prediction model by using the historical radio resource allocation data and the historical network measurement data as input data of the preset neural network.

16. The apparatus of claim 14, wherein the first obtaining unit is further configured to obtain historical traffic information of the first user device;

17. The apparatus of claim 16, wherein the first modeling unit is configured to train a preset neural network to obtain a second uplink interference prediction model using the historical radio resource allocation data, the historical network measurement data, and the historical traffic information as input data for the preset neural network.

18. The apparatus according to claim 14 or 15, further comprising a first determining unit configured to obtain first interference information of a user equipment based on the first uplink interference prediction model obtained by the first modeling unit.

19. The apparatus of claim 18, further comprising a second acquisition unit;

20. The apparatus according to claim 16 or 17, further comprising a first determining unit configured to obtain second interference information of a user equipment based on the second uplink interference prediction model obtained by the first modeling unit.

21. The apparatus of claim 20, further comprising a second acquisition unit;

22. An interference determination device, the device comprising: a third acquisition unit and a second determination unit; wherein,,

the second determining unit is configured to obtain first interference information of the second user equipment based on the radio resource allocation data and a preset first uplink interference prediction model; and the first uplink interference prediction model is obtained by learning and training based on historical wireless resource allocation data and historical network measurement data.

23. The apparatus of claim 22, wherein the third obtaining unit is further configured to obtain traffic information corresponding to the second user equipment;

24. The apparatus according to claim 22, further comprising a fourth acquisition unit and a second modeling unit; wherein,,

25. The apparatus according to claim 23, further comprising a fourth acquisition unit and a second modeling unit; wherein,,

26. The apparatus according to any one of claims 22 to 25, wherein the third obtaining unit is configured to obtain first radio resource allocation data corresponding to the second user equipment; the first radio resource allocation data characterizes the radio resource allocation situation of a third user equipment under different base stations to which the second user equipment belongs relative to the second user equipment;

27. A computer readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor realizes the steps of the uplink interference modeling method according to any of claims 1 to 8; or,

the program when executed by a processor implements the steps of the interference determination method of any one of claims 9 to 13.

28. An uplink interference modeling apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the uplink interference modeling method of any of claims 1 to 8 when the program is executed by the processor.

29. An interference determination device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the interference determination method according to any one of claims 9 to 13 when the program is executed by the processor.