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CN-122001716-A - Channel estimation method, device, equipment, medium and program product

CN122001716ACN 122001716 ACN122001716 ACN 122001716ACN-122001716-A

Abstract

The application discloses a channel estimation method, a device, equipment, a storage medium and a program product, wherein the method comprises the steps of determining channel statistical characteristics required in the target channel estimation process, wherein the channel statistical characteristics are a group of parameters describing wireless channel behaviors, performing optimization target construction based on the channel statistical characteristics to obtain a target optimization function, performing iterative search in a multidimensional space based on the target optimization function to obtain target channel statistical characteristics, and performing channel estimation based on the target channel statistical characteristics to obtain a target channel estimation result.

Inventors

  • CHEN BOTAO

Assignees

  • 中移(苏州)软件技术有限公司
  • 中国移动通信集团有限公司

Dates

Publication Date
20260508
Application Date
20260114

Claims (12)

  1. 1. A method of channel estimation, the method comprising: Determining channel statistical characteristics required in the target channel estimation process, wherein the channel statistical characteristics refer to a group of parameters describing wireless channel behaviors; performing optimization target construction based on the channel statistical characteristics to obtain a target optimization function; performing iterative search of a multidimensional space based on the target optimization function to obtain the target channel statistical characteristics; and carrying out channel estimation based on the target channel statistical characteristics to obtain a target channel estimation result.
  2. 2. The method of claim 1, wherein the performing an iterative search of a multidimensional space based on the target optimization function to obtain the target channel statistics comprises: Determining a multidimensional optimization vector based on the channel statistical characteristics, and setting a search range corresponding to the optimization vector of each dimension in the multidimensional optimization vector; carrying out population initialization based on the multidimensional optimization vectors and the search ranges corresponding to the optimization vectors of each dimension to obtain a plurality of groups of multidimensional optimization vectors, wherein each group of multidimensional optimization vectors corresponds to the position of an individual in the population in a multidimensional search space; and performing iterative search of a multidimensional space based on the target optimization function and the plurality of groups of multidimensional optimization vectors to obtain the target channel statistical characteristics.
  3. 3. The method of claim 2, wherein the performing an iterative search of a multidimensional space based on the target optimization function and the plurality of sets of multidimensional optimization vectors to obtain the target channel statistical feature comprises: Iteratively executing a first process until the iteration times reach preset iteration times, obtaining an individual corresponding to a maximum fitness value in the population, and determining the statistical characteristics of the target channel based on a multidimensional optimization vector corresponding to the individual corresponding to the maximum fitness value; wherein the first process comprises: Determining a fitness value corresponding to each individual in the population based on the target optimization function and the plurality of sets of multidimensional optimization vectors; dividing the population into individual types based on the fitness value corresponding to each individual to obtain a finder type, a joiner type and an alerter type; And adjusting the number of individuals in the finder type based on the iteration times, updating each individual position in the finder type, each individual position in the enrollee type and each individual position in the alerter type based on the iteration times and/or the fitness value corresponding to each individual, and increasing the iteration times by 1.
  4. 4. A method according to claim 3, wherein said determining a fitness value for each individual in said population based on said objective optimization function and said plurality of sets of multidimensional optimization vectors comprises: carrying out channel estimation on multidimensional optimization vectors corresponding to each individual in the population to obtain channel estimation values corresponding to each individual; And inputting the channel estimation value corresponding to each individual and the target channel actual value into the target optimization function to obtain the fitness value corresponding to each individual output by the target optimization function.
  5. 5. The method of claim 4, wherein the individual type classification of the population based on the fitness value corresponding to each individual to obtain a finder type, an enrollee type, and an alerter type comprises: sorting each individual in a descending order based on the fitness value corresponding to each individual to obtain a sorted individual sequence; Dividing a first preset number of individuals in the ordered individual sequence into the finder type, dividing the rest individuals except the first preset number of individuals into the enrollee type, and arbitrarily selecting a second preset number of individuals from the ordered individual sequence and dividing the second preset number of individuals into the alerter type.
  6. 6. The method of claim 5, wherein the updating each individual location in the finder type, each individual location in the enrollee type, and each individual location in the alerter type based on the fitness value corresponding to each individual comprises: Updating each individual location in the finder type based on the number of iterations; updating each individual location in the enrollee type based on the updated individual location in the discoverer type and the fitness value corresponding to each individual in the enrollee type; Determining a population target fitness value based on fitness values corresponding to each individual in the population; updating each individual location in the alerter type based on a difference between the fitness value corresponding to each individual in the alerter type and the population target fitness value.
  7. 7. The method according to any one of claims 1 to 6, wherein the performing channel estimation based on the target channel statistics to obtain a target channel estimation result includes: determining a channel correlation matrix based on the target channel statistics; and carrying out channel estimation based on the channel correlation matrix to obtain the target channel estimation result, wherein the target channel estimation result is a channel estimation matrix of a single time slot.
  8. 8. The method of claim 7, wherein the method further comprises: storing a channel estimation matrix of the first time slot and a channel estimation matrix of a third preset number of second time slots before the first time slot; and carrying out weighted sum processing on the channel estimation matrix of the first time slot and the channel estimation matrix of the third preset number of second time slots to obtain a channel frequency domain response optimization estimation value of the first time slot.
  9. 9. A channel estimation apparatus, the apparatus comprising: a determining unit, configured to determine a channel statistical characteristic required in the target channel estimation process, where the channel statistical characteristic refers to a set of parameters describing a wireless channel behavior; the construction unit is used for carrying out optimization target construction based on the channel statistical characteristics to obtain a target optimization function; the searching unit is used for carrying out iterative search of a multidimensional space based on the target optimization function to obtain the target channel statistical characteristics; And the estimation unit is used for carrying out channel estimation based on the target channel statistical characteristics to obtain a target channel estimation result.
  10. 10. A processing device comprising a processor and a memory for storing a computer program, the processor being arranged to invoke and execute the computer program stored in the memory for performing the method according to any of claims 1 to 8.
  11. 11. A computer readable storage medium storing a computer program for causing a computer to perform the method of any one of claims 1 to 8.
  12. 12. A computer program product comprising computer program instructions for causing a computer to perform the method of any one of claims 1 to 8.

Description

Channel estimation method, device, equipment, medium and program product Technical Field The present application relates to the field of communications technologies, and in particular, to a channel estimation method, apparatus, device, storage medium, and program product. Background In modern communication systems, channel estimation is one of the key techniques for guaranteeing signal transmission quality and reliability. Particularly in the internet of things and 5G communication systems, the communication environment between devices is complex and changeable, and the accurate reception of data can be influenced by channel characteristics such as delay spread and Doppler effect. Therefore, by effectively estimating the channel state information, the signal can be compensated, thereby improving the performance of the communication system. In the related art, a channel estimation method for a 5G scene mainly relies on a known pilot signal and a specific mathematical model to perform channel parameter estimation, or uses a neural network to extract channel characteristics from a large amount of training data. However, when the methods face a dynamically changing channel environment, it is often difficult to combine the estimation accuracy and the calculation efficiency, so as to affect the accuracy and the timeliness of channel estimation. Disclosure of Invention In order to solve the above technical problems, embodiments of the present application provide a channel estimation method, apparatus, device, storage medium, and program product. The channel estimation method provided by the embodiment of the application comprises the following steps: Determining channel statistical characteristics required in the target channel estimation process, wherein the channel statistical characteristics refer to a group of parameters describing wireless channel behaviors; performing optimization target construction based on the channel statistical characteristics to obtain a target optimization function; performing iterative search of a multidimensional space based on the target optimization function to obtain the target channel statistical characteristics; and carrying out channel estimation based on the target channel statistical characteristics to obtain a target channel estimation result. The channel estimation device provided by the embodiment of the application comprises: a determining unit, configured to determine a channel statistical characteristic required in the target channel estimation process, where the channel statistical characteristic refers to a set of parameters describing a wireless channel behavior; the construction unit is used for carrying out optimization target construction based on the channel statistical characteristics to obtain a target optimization function; the searching unit is used for carrying out iterative search of a multidimensional space based on the target optimization function to obtain the target channel statistical characteristics; And the estimation unit is used for carrying out channel estimation based on the target channel statistical characteristics to obtain a target channel estimation result. The processing device provided by the embodiment of the application comprises a processor and a memory, wherein the memory is used for storing a computer program, and the processor is used for calling and running the computer program stored in the memory to execute any channel estimation method. The embodiment of the application provides a computer readable storage medium for storing a computer program, wherein the computer program makes a computer execute any one of the channel estimation methods. The computer program product provided by the embodiment of the application comprises computer program instructions, wherein the computer program instructions enable a computer to execute any one of the channel estimation methods. In the technical scheme of the embodiment of the application, the channel statistical characteristics required in the target channel estimation process are determined, wherein the channel statistical characteristics refer to a group of parameters describing the wireless channel behaviors, the optimization target construction is carried out based on the channel statistical characteristics to obtain a target optimization function, the iteration search of the multidimensional space is carried out based on the target optimization function to obtain target channel statistical characteristics, and the channel estimation is carried out based on the target channel statistical characteristics to obtain a target channel estimation result. Therefore, key parameters for describing wireless channel behaviors required by a target channel estimation process are firstly determined, then an optimization target is constructed based on the key parameters to form a target optimization function, further, iterative search is carried out in a multidimensional space according to the target o