CN-122026968-A - Beamforming method and device for honeycomb-free MIMO (multiple input multiple output) sense-on-all integrated system

CN122026968ACN 122026968 ACN122026968 ACN 122026968ACN-122026968-A

Abstract

The application relates to the technical field of communication and discloses a beamforming method and equipment of a honeycomb-free MIMO (multiple input multiple output) sense integrated system, wherein the signal-to-interference-plus-noise ratio of each user is determined according to a beamforming matrix corresponding to each transmitting AP and channel information between each transmitting AP and each user, a weighted sum rate calculation formula is generated based on the signal-to-interference-plus-noise ratio of each user and user weight, a target beamforming matrix meeting constraint conditions and maximizing the weighted sum rate is determined based on the weighted sum rate calculation formula, the constraint conditions comprise that the power of the beamforming matrix corresponding to each transmitting AP in the target beamforming matrix is smaller than or equal to the maximum transmitting power, the perceived signal-to-interference-plus-noise ratio of a received signal of all receiving APs is larger than or equal to a perceived threshold, and the signal-to-interference-noise ratio of each user is larger than or equal to the communication threshold. The method has the beneficial effect of improving the effectiveness of beam forming.

Inventors

ZHANG CHUAN
YANG BOHAN
ZHOU WENYUE
Tan Zeqiong
TAN XIAOSI
HUANG YONGMING

Assignees

紫金山实验室

Dates

Publication Date: 20260512
Application Date: 20260212

Claims (10)

1. A method of beamforming for a non-cellular MIMO-generic integrated system, the method comprising: determining the signal-to-interference-plus-noise ratio of each user according to the beam forming matrix corresponding to each transmitting AP and the channel information between each transmitting AP and each user, and generating a weighted sum rate calculation formula based on the signal-to-interference-plus-noise ratio of each user and preset user weight; optimizing the beam forming matrix based on the weighting and rate calculation formula, and determining the beam forming matrix which meets the constraint condition and maximizes the weighting and rate as a target beam forming matrix; the constraint condition comprises that the power of a beam forming matrix corresponding to each transmitting AP in the target beam forming matrix is smaller than or equal to the maximum transmitting power, the perceived signal-to-noise ratio of the received signals of all receiving APs is larger than or equal to a perceived threshold, and the signal-to-interference-plus-noise ratio of each user is larger than or equal to a communication threshold.
2. The method of claim 1, wherein optimizing the beamforming matrix based on the weighted sum rate calculation formula and determining the beamforming matrix that satisfies the constraint and maximizes the weighted sum rate as the target beamforming matrix comprises: Performing Lagrange dual conversion on the weighted sum rate calculation formula, and introducing a first auxiliary variable to obtain a first objective function; in a first iteration process, obtaining an output beam forming matrix of the first objective function in each iteration round and the first auxiliary variable based on the constraint condition; And acquiring the output beam forming matrix as a target beam forming matrix when the first objective function converges or reaches a first preset iteration number.
3. The method of claim 2, wherein deriving the first auxiliary variable and the output beamforming matrix of the first objective function at each iteration round based on the constraint comprises: Determining a second updated formula for the beamforming matrix based on the fixed first auxiliary variable; determining an updated beamforming matrix according to a second updating formula of the beamforming matrix and the constraint condition; determining a first updating formula of a first auxiliary variable based on the updated beamforming matrix; and determining the updated first auxiliary variable according to a first updating formula of the first auxiliary variable.
4. A method according to claim 3, wherein determining an updated beamforming matrix according to the second update formula of the beamforming matrix and the constraint comprises: Performing secondary transformation on a second updated formula of the beam forming matrix, and introducing a second auxiliary variable to obtain a second objective function; alternately updating the beamforming matrix and the second auxiliary variable in the second objective function based on the constraint in a second iterative process; Acquiring the beam forming matrix as an output beam forming matrix when the second objective function converges or reaches a second preset iteration number; wherein the second iterative process is an intermediate step of the first iterative process.
5. The method of claim 4, wherein alternately updating the beamforming matrix and the second auxiliary variable in the second objective function based on the constraint comprises: converting the second objective function and the constraint condition into a third updated formula of the beamforming matrix while fixing a second auxiliary variable; Converting the third updating formula and the constraint condition into a semi-definite programming problem, and solving to obtain the updated beam forming matrix; Converting the second objective function and the constraint condition into a fourth updated formula of the second auxiliary variable based on the updated beamforming matrix; And converting the fourth updating formula into a semi-definite programming problem, and solving to obtain the updated second auxiliary variable.
6. The method of any of claims 1-5, wherein generating a weighted sum rate calculation formula based on the signal-to-interference-and-noise ratio of each of the users and a preset user weight comprises: and using the user weight of each user to carry out weighted summation on the logarithmic information of the signal-to-interference-and-noise ratio of each user, and generating the weighted summation rate calculation formula.
7. The method according to any one of claims 1-5, wherein the channel information between each of the transmitting APs and each of the users includes a downlink channel vector and a receiving end noise variance of each of the transmitting APs to each of the users; Determining the signal-to-interference-and-noise ratio of each user according to the beamforming matrix corresponding to each transmitting AP and the channel information between each transmitting AP and each user, wherein the method comprises the following steps: According to the downlink channel vector from each transmitting AP to the first user and the beam forming vector of the first transmitting symbol of all transmitting APs to the first user, calculating to obtain a first parameter; calculating a second parameter according to the downlink channel vector from each transmitting AP to the first user and the beam forming vector of all transmitting APs to a second transmitting symbol, wherein the second transmitting symbol is other transmitting symbols of the transmitting APs except the first transmitting symbol; calculating the sum of all the second parameters, and combining the noise variance of the receiving end to obtain a third parameter; And taking the ratio of the first parameter to the third parameter as the signal-to-interference-and-noise ratio of the first user.
8. A beamforming apparatus of a non-cellular MIMO pass-through integrated system, the apparatus comprising: The condition generation module is used for determining the signal-to-interference-plus-noise ratio of each user according to the beam forming matrix corresponding to each transmitting AP and the channel information between each transmitting AP and each user, and generating a weighted sum rate calculation formula based on the signal-to-interference-plus-noise ratio of each user and preset user weight; The optimization calculation module is used for optimizing the beam forming matrix based on the weighting and rate calculation formula and determining the beam forming matrix which meets the constraint condition and maximizes the weighting and rate as a target beam forming matrix; the constraint condition comprises that the power of a beam forming matrix corresponding to each transmitting AP in the target beam forming matrix is smaller than or equal to the maximum transmitting power, the perceived signal-to-noise ratio of the received signals of all receiving APs is larger than or equal to a perceived threshold, and the signal-to-interference-plus-noise ratio of each user is larger than or equal to a communication threshold.
9. A computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1 to 7.
10. A computer program product comprising computer instructions for causing a computer to perform the method of any one of claims 1 to 7.

Description

Beamforming method and device for honeycomb-free MIMO (multiple input multiple output) sense-on-all integrated system Technical Field The present application relates to the field of communications technologies, and in particular, to a beamforming method and apparatus for a cellular MIMO communication system. Background The rapid development of wireless communication technology has driven the revolution of social communication means. A Cell-FREE INTEGRATED SENSING AND Communication (CF-ISAC) system is one of the key technical directions of 6G. In non-cellular integrated sensing and communication (CF-ISAC) system research, beamforming design is critical to achieving optimal system performance. Accurate Channel State Information (CSI) is a necessary condition for designing an optimal beamforming scheme. However, the actual wireless channel is inevitably subject to noise, fading and other interference, resulting in the inability to acquire perfect CSI, thereby compromising the effectiveness of the beamforming design. Disclosure of Invention The application provides a beam forming method and device of a honeycomb-free MIMO (multiple input multiple output) sense integrated system, which solve the technical problem of poor beam forming effectiveness under imperfect CSI (channel state information) and achieve the technical effect of improving the beam forming effectiveness. In order to achieve the above purpose, the main technical scheme adopted by the application comprises the following steps: In a first aspect, an embodiment of the present application provides a beamforming method of a cellular MIMO-free integrated system, including: Determining the signal-to-interference-plus-noise ratio of each user according to the beam forming matrix corresponding to each transmitting AP and the channel information between each transmitting AP and each user, and generating a weighted sum rate calculation formula based on the signal-to-interference-plus-noise ratio of each user and preset user weight; optimizing the beam forming matrix based on the weighting and rate calculation formula, and determining the beam forming matrix which meets the constraint condition and maximizes the weighting and rate as a target beam forming matrix; the constraint condition comprises that the power of a beam forming matrix corresponding to each transmitting AP in the target beam forming matrix is smaller than or equal to the maximum transmitting power, the perceived signal-to-noise ratio of the received signals of all receiving APs is larger than or equal to a perceived threshold, and the signal-to-interference-plus-noise ratio of each user is larger than or equal to a communication threshold. In this embodiment, by means of jointly optimizing the beamforming matrix and the weighted sum rate calculation and satisfying the multidimensional constraint conditions of power, perceived signal-to-noise ratio and communication signal-to-interference-and-noise ratio, the collaborative optimization effect of the non-cellular integrated perception and communication system for considering the communication efficiency, perception reliability and user fairness in the non-ideal channel environment is achieved In a second aspect, an embodiment of the present application provides a beamforming apparatus of a cellular-free MIMO passband integrated system, the apparatus comprising: the condition generation module is used for determining the signal-to-interference-and-noise ratio of each user according to the beam forming matrix corresponding to each transmitting AP and the channel information between each transmitting AP and each user, and generating a weighted sum rate calculation formula based on the signal-to-interference-and-noise ratio of each user and preset user weight; The optimization calculation module is used for optimizing the beam forming matrix based on the weighting and rate calculation formula and determining the beam forming matrix which meets the constraint condition and maximizes the weighting and rate as a target beam forming matrix; the constraint condition comprises that the power of a beam forming matrix corresponding to each transmitting AP in the target beam forming matrix is smaller than or equal to the maximum transmitting power, the perceived signal-to-noise ratio of the received signals of all receiving APs is larger than or equal to a perceived threshold, and the signal-to-interference-plus-noise ratio of each user is larger than or equal to a communication threshold. In a third aspect, an embodiment of the present application provides a computer device, including a memory and a processor, where the memory and the processor are communicatively connected to each other, and the memory stores computer instructions, and the processor executes the computer instructions, thereby performing the method according to any one of the foregoing embodiments. In a fourth aspect, embodiments of the present application provide a computer-readable sto