Stacked intelligent metasurface enhanced communication and calculation fusion method

Abstract

The invention discloses a stacked intelligent metasurface enhanced communication and calculation fusion method, and belongs to the field of wireless communication. A multi-antenna base station provided with a stacked intelligent metasurface is arranged in the center of a cell, and user equipment with a plurality of single antennas is accessed to the base station through an uplink. User equipment is divided into communication users and computing users according to different service requirements, and the communication users and the computing users send respective signals to a base station simultaneously through wireless channels. A base station regulates and controls electromagnetic waves by configuring a superatom phase of a multi-layer structure of a stacked intelligent metasurface, a wireless channel is converted into a controllable task guiding medium, and then a corresponding receiver is designed, so that high-precision aggregation is carried out on signals of all calculation users, and meanwhile high-quality decoding is carried out on signals of all communication users. According to the method disclosed by the invention, the problems of inherent interference and resource competition among heterogeneous services are relieved by utilizing the capability of finely remodeling the propagation environment of the stacked metasurface, and an effective communication and calculation fusion method is provided for a wireless network.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of wireless communication, and particularly relates to a stacked intelligent metasurface enhanced communication and computing fusion method. BACKGROUND

[0002] With the rapid evolution of the sixth generation mobile communication network, the function of the network is changing from pure information transmission to deep integration of communication and computing. Future wireless networks not only need to support high-speed data connection of a large number of devices, but also need to carry growing computing-intensive applications such as distributed artificial intelligence, industrial Internet of Things and extended reality. In this context, air computing, as an emerging technology, can directly realize efficient aggregation and functional computing of multiple data at the physical layer by utilizing the waveform superposition characteristics of wireless multiple access channels, significantly reducing computing delay and communication overhead, and becoming one of the key enabling technologies to support future ubiquitous intelligent services.

[0003] However, in actual communication and computing fusion networks, there are serious resource competition and endogenous interference problems between heterogeneous services. Traditional communication tasks aim to maximize information transmission rate or reliability by eliminating inter-user interference, while air computing tasks require strict alignment of multiple signals at the receiving end to achieve precise coherent superposition. This structural conflict in objectives makes it difficult to simultaneously meet the high service quality requirements of communication users and the high precision requirements of computing tasks under limited network resources. Existing solutions based on single-layer intelligent metasurface enhancement often face the bottleneck of insufficient degrees of freedom and limited performance.

[0004] Compared with traditional single-layer metasurfaces, stacked intelligent metasurfaces are composed of multiple layers of cascaded super-atom arrays, forming an artificial neural network at the physical layer, which can perform more fine-grained layer-by-layer manipulation of electromagnetic waves and has powerful wavefront reshaping and analog signal processing capabilities. Therefore, designing a stacked intelligent metasurface enhanced computing and communication fusion method for future wireless networks can effectively separate and customize the regulation of communication signals and computing signals, which is expected to solve the problem of multi-service conflict and resource competition in the network, thereby improving the overall performance of the system. SUMMARY

[0005] The present application aims to overcome the defects in the prior art and provide a stacked intelligent metasurface enhanced communication and computing fusion method.

[0006] The specific technical solutions adopted by the present application are as follows: In a first aspect, the present application provides a stacked intelligent metasurface enhanced communication and computing fusion method, which is as follows: S1: the base station contains N root antennas, and there are K single antenna users accessing the base station through uplink; the base station is equipped with oneL Layers, each layer has M A stacked intelligent metasurface composed of superatoms, each superatom providing an independently tunable phase. ;in, , ; S2: Based on different business needs K Each single-antenna user is divided into individual communication users and There are 3 computing users, denoted as the communication user set and the computing user set, respectively. and Each communication user has a communication task, which involves transmitting independent communication signals. Used to implement decoding, Each computing user transmits locally pre-processed computing signals. joint participation J A separate, in-flight computing task is used to perform data aggregation. ; S3: The base station interacts with single-antenna users to obtain the... k Channel state information of a single-antenna user to a stacked smart metasurface on the base station Simultaneously, based on the Rayleigh-Sommerfeld diffraction theory, the first... l layer to the ( l+ 1) Transmission matrix between layers And the transmission matrix from the last metasurface layer to the base station antenna array , ; S4: Based on channel state information The base station modulates the superatomic phase of stacked smart metasurfaces. Design of wave domain beamforming matrix To reshape the channel environment and obtain the first k Equivalent channel matrix from a single-antenna user to the base station ,in At the same time, all single-antenna users simultaneously transmit their signals to the base station via the uplink. S5: Based on the received signal after beamforming in the wave domain, the base station designs the communication receiver. Decode the desired communication data and design the computing receiver. Estimate the first j The desired computational results for an aerial computing task .

[0007] Preferably, in S3, the transmission matrix and The specific method for obtaining it is as follows: The matrix elements are ;in, For the area of ​​the superatom, Wavelength; and The first l The first layer m From the (th superatom to the (th) l+ 1) The first layer m’ The propagation angle and propagation distance of each superatom ; The matrix elements are ;in, and The first on the last metasurface m The first superatom and the base station antenna array n The propagation angle and propagation distance between the antennas.

[0008] Preferably, in S4, the superatomic phase The specific control methods are as follows: S411: Randomly initialize superatomic phases ,in It is a random function; at the same time, the first... l Diagonal phased array of layered metasurfaces ,in ; S412: Define variables Calculate the total mean square error of the base station estimation. Among them, the first j The mean square error between the estimated and expected results of an aerial computing task is expressed as: , For the first k Transmit power of a single antenna user Indicates from the first k The equivalent channel state matrix from a single-antenna user to the base station and They represent the diagonal phased arrays respectively. The forward and backward propagation matrices participate in the first... j The subset of computing users for each aerial computing task is , This represents the noise variance of the base station receiver; S413: Based on the signal-to-interference-plus-noise ratio of the communication signal received by the base station. ,make ,in For the first iMinimum signal-to-interference-plus-noise ratio required by each communication user; constraints By rearranging and expanding, we obtain ;in , ; S414: Objective function obtained from S412 Using the continuous convex approximation method, the approximate convex objective function is obtained. ;in This indicates a function with its imaginary part taken. Describe the objective function In the t Round Iteration Point Top Find the result of the first-order partial derivative; S415: Restrictions on S413 Using the continuous convex approximation method, we obtain ;in Represents a constraint function At the iteration point Top Find the result of the first-order partial derivative; S416: For variables Let the modulus of each of its elements be... ; Indicates 1 to M The set of natural numbers; S417: Call the CVX convex optimization toolkit to find the objective function. The solution with the smallest value Next, regarding Perform the projection operation to obtain the normalized solution. ; S418: Using the block coordinate descent method, repeat steps S412-S417 to configure the phase of the next metasurface layer, until the... L The metasurface configuration is complete, and... Phase is obtained by conversion .

[0009] Preferably, in S4, the first k Transmit power of a single antenna user The specific allocation method is as follows: S421: Increase transmission power satisfy ,in For the first k Maximum transmit power budget for each user; simultaneously, new variables are introduced. And satisfy ; S422: Obtain transmit power The relevant calculation of mean square error function Meanwhile, based on the signal-to-interference-plus-noise ratio of the communication signal, ; S423: Using the Lagrange multiplier method and the Kuhn-Tak condition, find the objective function that makes the objective function... The solution with the minimum value yields the transmit power. .

[0010] Preferably, in S5, the communication receiver and computing receiver The specific design method is as follows: Optimal communication receiver Optimal computing receiver ;in For dimension N The identity matrix.

[0011] In a second aspect, the present invention provides a computer program product, including a computer program / instruction that, when executed by a processor, enables the implementation of the stacked smart metasurface-enhanced communication and computing fusion method as described in any of the first aspects.

[0012] Thirdly, a computer-readable storage medium storing a computer program that, when executed by a processor, implements the stacked smart metasurface-enhanced communication and computing fusion method as described in any of the first aspects.

[0013] Fourthly, a computer electronic device, including a memory and a processor; The memory is used to store computer programs; The processor is configured to, when executing the computer program, implement the stacked smart metasurface-enhanced communication and computing fusion method as described in any of the first aspects.

[0014] Compared with the prior art, the present invention has the following advantages: This invention effectively enhances system performance and solves the inherent interference and resource contention problems between heterogeneous communication and computing services. The algorithms for stacked intelligent metasurface phase configuration, transmit power allocation, and receiver design proposed in this invention have advantages such as fast convergence speed and low computational complexity. Attached Figure Description

[0015] Figure 1 This is a system block diagram of a stacked smart metasurface-enhanced communication and computing fusion method; Figure 2The performance comparison of the proposed method is conducted under different conditions, including whether the base station is equipped with a stacked smart metasurface (SIM) and the different maximum transmit power and minimum signal-to-interference-plus-noise ratio (SNR) requirements for users. (With SIM, the base station is equipped with a stacked smart metasurface; without SIM, the base station is not equipped with a stacked smart metasurface; the maximum transmit power for users ranges from 25dBm to 35dBm, and the minimum required SNR is 0dB, 4dB, and 8dB, respectively). Figure 3 The performance comparison of the proposed method is shown under different stacking numbers of smart metasurface layers and the number of superatoms per layer (the number of stacked smart metasurface layers ranges from 1 to 8, and the number of superatoms per layer is 36, 64, 81 and 100, respectively). Detailed Implementation

[0016] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. Technical features in various embodiments of the present invention can be combined accordingly without mutual conflict.

[0017] This invention provides a method for enhancing communication and computing fusion using stacked smart metasurfaces, as shown in the system block diagram below. Figure 1 As shown, a multi-antenna base station equipped with a stacked smart metasurface is deployed at the center of the cell. The adjustable stacked smart metasurface is used to reshape the channel environment, while multiple single-antenna user equipments (UEs) access the base station via uplink. The UEs are divided into a communication user subset requiring high-quality communication and an airborne computing user subset requiring high-precision computation results. They simultaneously transmit their respective signals to the base station via a wireless channel. The base station modulates the electromagnetic waves by configuring the multi-layered superatomic phase of the stacked smart metasurface, transforming the wireless channel into a controllable, task-oriented medium. It then designs corresponding receivers to perform high-precision aggregation of signals from all computing users and high-quality decoding of signals from all communication users. In other words, these signals are received by the base station after passing through the phase-modulated stacked smart metasurface. The base station designs a communication receiver to decode the communication signals and a computing receiver to estimate the desired computation results.

[0018] The specific technical solution of the communication and computing fusion method of the present invention is as follows: S1: A system containing [a certain element] is arranged in the system for implementing the communication and computing fusion method of the present invention. N Root antenna base station, simultaneously KEach single-antenna user accesses the base station via the uplink. The base station is equipped with one... L Layers, each layer has M A stacked smart metasurface composed of superatoms is used to enhance system performance, with each superatom providing an independently tunable phase. ;in, l For the first l Stacked smart metasurfaces and , m For the first m a superatom and .

[0019] S2: Based on different business needs K Each single-antenna user is divided into individual communication users and There are 3 computing users, denoted as the communication user set and the computing user set, respectively. and Each communication user has a communication task, requiring them to transmit an independent communication signal. ( This is used to achieve reliable decoding; and each computation requires the user to transmit a locally pre-processed computation signal. ( ), to participate J A separate aerial computing task is used to achieve high-precision data aggregation. For the first... j There are three aerial computing tasks, and the subset of computing users participating in the task is denoted as . .

[0020] S3: The base station interacts with single-antenna users to obtain the... k Channel state information of a single-antenna user to a stacked smart metasurface on the base station Simultaneously, based on the Rayleigh-Sommerfeld diffraction theory, the first... l layer to the ( l+ 1) Transmission matrix between layers And the transmission matrix from the last metasurface layer to the base station antenna array .

[0021] In a preferred embodiment of the present invention, in this step, the transmission matrix and The specific method for obtaining it is as follows: The first stacked smart metasurface l layer to the first l+1 Inter-layer transfer matrix The matrix elements are ;in, For the area of ​​the superatom, Wavelength; and The first l The first layer m From the (th superatom to the (th) l+ 1) The first layer m’ The propagation angle and propagation distance of each superatom ; Transmission matrix from the last layer of the stacked smart metasurface to the base station antenna array The matrix elements are ;in, and The first on the last metasurface m The first superatom and the base station antenna array n The propagation angle and propagation distance between the antennas.

[0022] S4: Based on channel state information The base station modulates the superatomic phase of stacked smart metasurfaces. Design of wave domain beamforming matrix To reshape the channel environment and obtain the first k Equivalent channel matrix from a single-antenna user to the base station ,in Simultaneously, all single-antenna users transmit their signals to the base station via the uplink, with the first... k The transmit power of each user is .

[0023] In a preferred embodiment of the present invention, in this step, the superatomic phase... The method of controlling the superatomic phase of stacked smart metasurfaces The specific steps for configuring the system are as follows: S411: Randomly initialize superatomic phases ,in It is a random function. Also, define the first... l Diagonal phased array of layered metasurfaces ,in .

[0024] S412: For the first l The problem of phase configuration of layered metasurfaces, defining variables. Calculate the total mean square error of the base station estimation. Among them, the first j The mean square error between the estimated and expected results of an aerial computing task is expressed as: , For the first kTransmit power of a single antenna user Indicates from the first k The equivalent channel state matrix from a single-antenna user to the base station and They represent the diagonal phased arrays respectively. The forward and backward propagation matrices participate in the first... j The subset of computing users for each aerial computing task is , This represents the noise variance of the base station receiver.

[0025] S413: Based on the signal-to-interference-plus-noise ratio of the communication signal received by the base station. ,make ,in For the first i The minimum signal-to-interference-plus-noise ratio required by each communication user. (Regarding constraints) By rearranging and expanding, we obtain ;in , .

[0026] S414: Objective function obtained from S412 Using the continuous convex approximation method, the approximate convex objective function is obtained. ;in This indicates a function with its imaginary part taken. Describe the objective function In the t Round Iteration Point Top Find the result of the first-order partial derivative.

[0027] S415: Restrictions on S413 Using the continuous convex approximation method, we obtain ;in Represents a constraint function At the iteration point Top Find the result of the first-order partial derivative.

[0028] S416: For variables Let the modulus of each element be less than or equal to 1, that is... ; Indicates 1 to M The set of natural numbers.

[0029] S417: Call the CVX convex optimization toolkit to find the objective function. The solution with the smallest value is obtained. Next, regarding Perform the projection operation to obtain the normalized solution. .

[0030] S418: Using the block coordinate descent method, repeat steps S412-S417 to configure the phase of the next metasurface layer, until the... L The metasurface configuration is complete, and... Phase is obtained by conversion .

[0031] In a preferred embodiment of the present invention, in this step, the first... k Transmit power of a single antenna user The specific allocation method is as follows: S421: Increase transmission power satisfy ,in For the first k Maximum transmit power budget for each user; simultaneously, new variables are introduced. And satisfy .

[0032] S422: Obtain transmit power The relevant calculation of mean square error function Meanwhile, based on the signal-to-interference-plus-noise ratio of the communication signal, .

[0033] S423: Using the Lagrange multiplier method and the Kuhn-Tak condition, find the objective function that makes the objective function... The solution with the minimum value yields the transmit power. .

[0034] S5: Based on the received signal after beamforming in the wave domain, the base station designs the communication receiver. Decode the desired communication data and design the computing receiver. Estimate the first j The desired computational results for an aerial computing task .

[0035] In a preferred embodiment of the present invention, in this step, the communication receiver and computing receiver The specific design method is as follows: Optimal communication receiver Optimal computing receiver ;in For dimension N The identity matrix.

[0036] Computer simulations show that, Figure 2As shown, this invention proposes a stacked smart metasurface-enhanced communication and computing fusion method. The higher the user's transmit power, the lower the required signal-to-interference-plus-noise ratio (SNR) of the communication signal, and the smaller the total mean square error of the computation. Furthermore, the performance of the stacked smart metasurface (SIM)-enhanced communication and computing fusion system far exceeds that of a system without SIM enhancement, with the total mean square error of the computation reduced by up to 95%. Figure 3 This indicates that in the method proposed in this invention, the total mean square error decreases as the number of metasurface layers and the number of superatoms in each metasurface layer increases. However, when the number of layers or superatoms is too large, it can lead to overfitting, similar to the situation in artificial intelligence networks, resulting in an increase in the mean square error.

[0037] This invention utilizes the ability of stacked metasurfaces to finely reshape the propagation environment, alleviating the inherent interference and resource competition problems between heterogeneous services, and providing an effective communication and computing integration method for wireless networks.

[0038] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, all technical solutions obtained through equivalent substitution or transformation fall within the protection scope of the present invention.

Claims

1. A method for enhancing communication and computing through stacked smart metasurfaces, characterized in that, Specifically as follows: S1: Base station contains N One antenna, simultaneously K A single-antenna user accesses the base station via an uplink; the base station is equipped with one L Layers, each layer has M A stacked intelligent metasurface composed of superatoms, each superatom providing an independently tunable phase. ;in, , ; S2: Based on different business needs K Each single-antenna user is divided into individual communication users and There are 3 computing users, denoted as the communication user set and the computing user set, respectively. and Each communication user has a communication task, which involves transmitting independent communication signals. Used to implement decoding, Each computing user transmits locally pre-processed computing signals. joint participation J A separate, in-flight computing task is used to perform data aggregation. ; S3: The base station interacts with single-antenna users to obtain the... k Channel state information of a single-antenna user to a stacked smart metasurface on the base station Simultaneously, based on the Rayleigh-Sommerfeld diffraction theory, the first... l layer to the ( l+ 1) Transmission matrix between layers And the transmission matrix from the last metasurface layer to the base station antenna array , ; S4: Based on channel state information The base station modulates the superatomic phase of stacked smart metasurfaces. Design of wave domain beamforming matrix To reshape the channel environment and obtain the first k Equivalent channel matrix from a single-antenna user to the base station ,in At the same time, all single-antenna users simultaneously transmit their signals to the base station via the uplink. S5: Based on the received signal after beamforming in the wave domain, the base station designs the communication receiver. Decode the desired communication data and design the computing receiver. Estimate the first j The desired computational results for an aerial computing task .

2. The method for enhancing communication and computing with stacked smart metasurfaces according to claim 1, characterized in that, In S3, the transmission matrix and The specific method for obtaining it is as follows: The matrix elements are ;in, For the area of ​​the superatom, Wavelength; and The first l The first layer m From the (th superatom to the (th) l+ 1) The first layer m’ The propagation angle and propagation distance of each superatom ; The matrix elements are ;in, and The first on the last metasurface m The first superatom and the base station antenna array n The propagation angle and propagation distance between the antennas.

3. The method for enhancing communication and computing with stacked smart metasurfaces according to claim 1, characterized in that, In S4, the superatomic phase The specific control methods are as follows: S411: Randomly initialize superatomic phases ,in It is a random function; at the same time, the first... l Diagonal phased array of layered metasurfaces ,in ; S412: Define variables Calculate the total mean square error of the base station estimation. Among them, the first j The mean square error between the estimated and expected results of an aerial computing task is expressed as: , For the first k Transmit power of a single antenna user Indicates from the first k The equivalent channel state matrix from a single-antenna user to the base station and They represent the diagonal phased arrays respectively. The forward and backward propagation matrices participate in the first... j The subset of computing users for each aerial computing task is , This represents the noise variance of the base station receiver; S413: Based on the signal-to-interference-plus-noise ratio of the communication signal received by the base station. ,make ,in For the first i Minimum signal-to-interference-plus-noise ratio required by each communication user; constraints By rearranging and expanding, we obtain ;in , ; S414: Objective function obtained from S412 Using the continuous convex approximation method, the approximate convex objective function is obtained. ;in This indicates a function with its imaginary part taken. Describe the objective function In the t Round Iteration Point Top Find the result of the first-order partial derivative; S415: Restrictions on S413 Using the continuous convex approximation method, we obtain ;in Represents a constraint function At the iteration point Top Find the result of the first-order partial derivative; S416: For variables Let the modulus of each of its elements be... ; Indicates 1 to M The set of natural numbers; S417: Call the CVX convex optimization toolkit to find the objective function. The solution with the smallest value Next, regarding Perform the projection operation to obtain the normalized solution. ; S418: Using the block coordinate descent method, repeat steps S412-S417 to configure the phase of the next metasurface layer, until the... L The metasurface configuration is complete, and... Phase is obtained by conversion .

4. The method for enhancing communication and computing with stacked smart metasurfaces according to claim 1, characterized in that, In S4, the first k Transmit power of a single antenna user The specific allocation method is as follows: S421: Increase transmission power satisfy ,in For the first k Maximum transmit power budget for each user; simultaneously, new variables are introduced. And satisfy ; S422: Obtain transmit power The relevant calculation of mean square error function Meanwhile, based on the signal-to-interference-plus-noise ratio of the communication signal, ; S423: Using the Lagrange multiplier method and the Kuhn-Tak condition, find the objective function that makes the objective function... The solution with the minimum value yields the transmit power. .

5. The method for enhancing communication and computing with stacked smart metasurfaces according to claim 1, characterized in that, In S5, the communication receiver and computing receiver The specific design method is as follows: Optimal communication receiver Optimal computing receiver ;in For dimension N The identity matrix.

6. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it can realize the communication and computing fusion method enhanced by stacked smart metasurfaces as described in any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the stacked smart metasurface-enhanced communication and computing fusion method as described in any one of claims 1 to 5.

8. A computer electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is configured to, when executing the computer program, implement the stacked smart metasurface-enhanced communication and computing fusion method as described in any one of claims 1 to 5.

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