A method for achieving reachability and rate optimization based on stacked smart metasurfaces in decellularized networks

Abstract

This invention discloses an reachability and rate optimization method based on stacked smart metasurfaces in decellularized networks. The method includes: configuring multi-layer cascaded stacked smart metasurfaces at the access point transmitter to establish an end-to-end wireless transmission link system; constructing the system's equivalent channel matrix; deriving a closed-form expression for the system's reachability and rate with respect to the phase shift matrices of each layer of the stacked smart metasurfaces at all access points; using this closed-form expression as the objective function; and maximizing the system's reachability and rate as the optimization objective. The Riemannian gradient algorithm is introduced, utilizing the geometric projection of the analytical gradient to achieve efficient cooperative iteration of the phase shifts of all atomic phases in the entire space while satisfying the unit modulus constraint of the phase shift. This invention solves the problem of high computational overhead in existing technologies, which prevent the improvement of system reachability and rate in high-speed mobile decellularized communication environments.

Description

Technical Field

[0001] This invention relates to the field of wireless communication network technology, and more specifically to an reachability and rate optimization method based on stacked smart metasurfaces in decellularized networks. Background Technology

[0002] As wireless communication technology evolves towards ultra-high reliability and low latency, high-mobility communication environments such as high-speed rail, low-altitude drones, and vehicle-to-everything (V2X) networks place higher demands on system transmission performance. The high-speed movement of mobile terminals leads to severe Doppler spread and rapidly time-varying channel characteristics in communication links. Although orthogonal time-frequency-space (OTFS) modulation technology can transform rapidly time-varying channels into quasi-static channels in the delay-Doppler domain, and decellularized massive multiple-input multiple-output (MIMO) architectures can improve coverage gain through distributed deployment of access points (APs), the combined system still requires more refined spatial beamforming techniques to further explore the potential of system achievable speeds when dealing with extreme mobile environments.

[0003] To enhance signal processing capabilities, stacked smart metasurfaces (SIMs) can be deployed at the AP (Analog Access Point) to achieve complex spatial beamforming in the analog domain using the diffraction cascade of their multilayer electromagnetic units. However, while the introduction of SIMs improves system performance, it also brings severe optimization challenges. Due to the high-dimensional phase shift search space introduced by their multilayer structure, and the strong coupling non-convex constraints between the phase shifts of each layer, the reachability and rate of the system exhibit extremely high non-convexity as a function of phase shift.

[0004] Currently, phase shift optimization for SIMs often employs greedy algorithms or block-based iterative optimization methods. However, when dealing with multi-layered cascaded physical models and stringent phase shift modulus constraints, existing methods incur significant computational overhead and cannot achieve the desired improvement in system achievable speeds in high-speed mobile decellularization environments. Summary of the Invention

[0005] The purpose of this invention is to provide an reachability and rate optimization method based on stacked smart metasurfaces in decellularized networks, in order to solve the problem that the existing technology has high computational overhead and cannot achieve the improvement of system reachability and rate in high-speed mobile decellularized communication environments.

[0006] A method for optimizing reachability and rate in decellularized networks based on stacked smart metasurfaces includes:

[0007] Step S1: Deploy multiple access points in a high-speed mobile environment to build a decellularized network architecture, and configure multi-layer cascaded stacked smart metasurfaces at the transmitter of the access points. By adopting orthogonal time-frequency control technology at the transmitter, an end-to-end wireless transmission link system is established. The system is used to obtain a quasi-static channel in the time-delay-Doppler domain.

[0008] Step S2: Based on the system established in step S1, firstly, based on the Rayleigh-Sommerfeld diffraction theory, the propagation matrix between the stacked smart metasurface layers is obtained. Then, based on the propagation matrix and the phase shift matrix of each metasurface layer, the total equivalent response operator of the stacked smart metasurface is constructed. Finally, based on the total equivalent response operator and the time delay-Doppler domain transform operator, the system equivalent channel matrix is ​​constructed.

[0009] Step S3: Based on the equivalent channel matrix, using channel statistical characteristics and matrix algebraic transformation, derive a closed-form expression for the system achievable sum rate with respect to the phase shift matrices of each layer of the stacked smart metasurface at all access points. Use this closed-form expression as the objective function. Then, with the goal of maximizing the system achievable sum rate, map all the phase shift parameters of the stacked smart metasurface to be optimized to a complex domain multidimensional torus Riemannian manifold that satisfies the unit modulus constraint.

[0010] Step S4: Calculate the Euclidean gradient of the objective function with respect to the phase shift parameters to be optimized using the closed expression, and project it onto the Riemann tangent space to obtain the Riemann gradient. Then, use the Riemann gradient to perform a collaborative update in the tangent space, and remap the updated parameters to the Riemann manifold through a shrinking mapping. After completing the phase shift optimization through iterative loops, configure the finally obtained optimal phase shift to each layer of stacked smart metasurfaces to achieve an improvement in reachability and speed in a cellular network communication environment.

[0011] The reachability and rate optimization method based on stacked smart metasurfaces in decellularized networks provided by the present invention has the following beneficial effects:

[0012] This invention transforms the fast time-varying channel in high-speed mobile environments into a quasi-static channel in the time-delay-Doppler domain by deploying a SIM at the access point and combining it with OTFS modulation technology. This effectively resists Doppler spread and lays the foundation for physical layer signal processing. Then, based on Rayleigh-Sommerfeld diffraction theory, a total equivalent response operator is constructed by stacking the propagation matrix between smart metasurface layers and the phase shift matrix of each metasurface layer. Based on the total equivalent response operator and the time-delay-Doppler domain transformation operator, the system's equivalent channel matrix is ​​constructed. Finally, a closed-form expression for the system's achievable sum rate with respect to the phase shift matrices of each SIM layer at all access points is derived. An analytical mapping relationship between phase shift parameters and system performance was established. Furthermore, with the optimization objective of maximizing the system reachability and rate, the phase shift was mapped to a complex multidimensional torus Riemannian manifold. The analytical gradient of the objective function was projected and iteratively updated in the tangent space, ensuring that the phase shift configuration always satisfies the unit modulus physical constraint under non-convex constraints. This invention fully leverages the multi-layer physical space processing capability of SIM and the anti-Doppler diversity characteristics of OTFS. Through low-overhead manifold gradient optimization, it can effectively improve the system reachability and rate in high-speed mobile decellular communication environments while reducing computational overhead. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating the reachability and rate optimization method based on stacked smart metasurfaces in decellularized networks provided by the present invention. Detailed Implementation

[0014] To facilitate understanding of the present invention, a more complete description will be given below with reference to various embodiments. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0016] Please see Figure 1 Embodiments of the present invention provide an reachability and rate optimization method based on stacked smart metasurfaces in decellularized networks, comprising steps S1-S4:

[0017] Step S1: Deploy multiple access points in a high-speed mobile environment to build a decellularized network architecture, and configure multi-layer cascaded stacked smart metasurfaces at the transmitter of the access points. By adopting orthogonal time-frequency control technology at the transmitter, an end-to-end wireless transmission link system is established. The system is used to obtain a quasi-static channel in the time-delay-Doppler domain.

[0018] This embodiment constructs a wireless transmission link system for decellularized massive MIMO transmission in high-speed mobile environments. Specifically, it deploys a wireless transmission link system in high-speed mobile environments. One access point, and configure Individual end users, to build a cellular-free network architecture, each access point configured One antenna, and each access point is connected to a block with Layered metasurface, each layer containing The stacked intelligent metasurface physical cascade of individual atoms adopts orthogonal time-frequency control technology at the transmitting end to establish an end-to-end wireless transmission link system. It converts the fast time-varying channel in the high-speed mobile environment into a quasi-static channel in the time-delay-Doppler domain and uses embedded pilot channel estimation to utilize its time-frequency diversity characteristics to counteract the strong Doppler spread and time-varying channel characteristics in the high-speed mobile environment, and maps information symbols to the time-delay-Doppler domain.

[0019] Step S2: Based on the system established in step S1, the propagation matrix between the stacked smart metasurface layers is first obtained based on Rayleigh-Sommerfeld diffraction theory. Then, based on the propagation matrix and the phase shift matrix of each metasurface layer, the total equivalent response operator of the stacked smart metasurface is constructed. Finally, based on the total equivalent response operator and the time delay-Doppler domain transform operator, the system equivalent channel matrix is ​​constructed.

[0020] Specifically, step S2 includes:

[0021] First, define the first The stacked smart metasurface of the access point. Layer and First The propagation matrix between layers is Based on Rayleigh-Sommerfeld diffraction theory, and according to the interlayer distance, dielectric properties, and unit cell distribution of the stacked smart metasurface, we obtain... The ( ) elements for:

[0022]

[0023] in, The physical meaning is the first The stacked smart metasurface of the access point. The first in the layer The first atom and the first The first in the layer The transfer coefficient between individual atoms, and These represent the spacing between elementary atoms in the horizontal and vertical directions, respectively. For the first The stacked smart metasurface of the access point. The first in the layer The first atom and the first The first in the layer The line connecting the first atom and the second atom The angle between the layers, Indicates the first The stacked smart metasurface of the access point. The first in the layer The first atom and the first The first in the layer The straight-line distance between individual atoms Represents the imaginary unit. The system wavelength;

[0024] Then, define the first The stacked smart metasurface of the access point. Phase shift matrix of the layer for:

[0025]

[0026] in, Indicates a diagonal matrix. , , They are the 1st, 2nd, and 3rd respectively. Phase shift of individual atoms;

[0027] Next, the propagation matrix and the phase shift matrix are concatenated to construct the total equivalent response operator of the stacked smart metasurface, expressed as:

[0028]

[0029] in, Indicates the first The total equivalent response operator of a stacked smart metasurface with multiple access points , , , The first The stacked smart metasurface of the access point. Layer, First Phase shift matrices of layer 1, layer 2, and layer 1. Indicates the first The stacked smart metasurface of the access point. Layer and First Propagation matrix between layers Indicates the first The stacked smart metasurface of the access point. Layer and First Propagation matrix between layers Indicates the first The propagation matrix between the first and second layers of the stacked smart metasurface with access points;

[0030] Finally, construct from the first The end user to the first The system equivalent channel matrix of each access point :

[0031]

[0032] in, For non-direct trajectory index, This represents the total number of paths that are not direct rays. For the first The propagation matrix from the first layer of the stacked smart metasurface to the surface of the access points. This is the conjugate transpose operator. For the time-delay-Doppler domain transform operator of the direct path, For the first Delay-Doppler domain transform operator for non-direct paths, For non-direct component, For direct component, This is the direct trajectory guidance vector matrix. Indicates the first Stacked smart metasurfaces at each access point This represents the Kronecker product operation.

[0033] Step S3: Based on the equivalent channel matrix, using channel statistical characteristics and matrix algebraic transformation, derive a closed-form expression for the system achievable sum rate with respect to the phase shift matrices of each layer of the stacked smart metasurface at all access points. Use this closed-form expression as the objective function. Then, with the goal of maximizing the system achievable sum rate, map all the phase shift parameters of the stacked smart metasurface to be optimized to a complex domain multidimensional torus Riemannian manifold that satisfies the unit modulus constraint.

[0034] First, combining step S2, the total equivalent response operator and spatial channel statistical characteristics are obtained. Then, using a large-scale fading decoding method, the expression for the system signal-to-interference-plus-noise ratio (SIR) with respect to the phase shift matrices of all SIM layers is derived, specifically:

[0035]

[0036] in, The phase shift matrix of all layers of the stacked smart metasurface. , For the first The signal-to-interference-plus-noise ratio of each end user is related to The expression, For the first Power of each end user For the first Power of each end user For the first The expected signal feature vector of an end user For the first The terminal user and the first Signal interference correlation matrix among individual end users For the first The direct path feature vector of each end user , as well as All are about implicit functions, For the first Noise correlation matrix of each end user This represents noise power.

[0037] Furthermore, by incorporating the system bandwidth, a closed-form expression for the system achievable sum rate with respect to the phase shift matrices of each layer of the stacked smart metasurface at all access points is obtained. for:

[0038]

[0039] in, This refers to the system bandwidth.

[0040] Use this closed expression as the objective function, and then apply it to all layers of the SIM. For each element-matrix atom, construct a complex-domain multidimensional torus manifold that satisfies the unit modulus constraint. The phase shift control range of all SIMs is strictly limited within the manifold, wherein, The dimension is The complex field space, express Each phase shift element is distributed on the unit circle of the complex plane.

[0041] Step S4: Calculate the Euclidean gradient of the objective function with respect to the phase shift parameters to be optimized using the closed expression, and project it onto the Riemann tangent space to obtain the Riemann gradient. Then, use the Riemann gradient to perform a collaborative update in the tangent space, and remap the updated parameters to the Riemann manifold through a shrinking mapping. After completing the phase shift optimization through iterative loops, configure the finally obtained optimal phase shift to each layer of stacked smart metasurfaces to achieve an improvement in reachability and speed in a cellular network communication environment.

[0042] Specifically, an analytic manifold gradient operator is constructed, and the chain rule is used to calculate the objective function with respect to the closed-form expression. Euclidean gradient Then perform tangent space projection, and... Projecting onto the Riemannian tangent space defined by the phase shift constraints of each layer's atomic atoms, we obtain the manifold gradient. The expression is:

[0043]

[0044] in, To extract the real part operator, for The complex conjugate, It represents the Hadamah accumulation. The calculation is the radial component of the Euclidean gradient perpendicular to the unit circular manifold. This formula ensures that the phase shift update direction is always tangent to the manifold surface.

[0045] Then, the Riemann gradient descent method is used to synchronously perform step updates along the manifold gradient on the phase shifts of all stacked smart metasurfaces. The point in the tangent space is remapped to the complex unit circle manifold using the shrinkage operator so that the updated phase shifts of each layer always satisfy the unit modulus constraint. The value of the updated objective function is evaluated until the performance gain fluctuation between adjacent iterations is within the preset tolerance range or the maximum number of iterations is reached. The iteration is then stopped, and the obtained optimal phase shift is assigned to the meta-atoms of each stacked smart metasurface to achieve an increase in achievable speed in a cellular network communication environment.

[0046] In summary, the reachability and rate optimization method based on stacked smart metasurfaces in decellularized networks described above has the following beneficial effects:

[0047] This invention transforms the fast time-varying channel in high-speed mobile environments into a quasi-static channel in the time-delay-Doppler domain by deploying a SIM at the access point and combining it with OTFS modulation technology. This effectively resists Doppler spread and lays the foundation for physical layer signal processing. Then, based on Rayleigh-Sommerfeld diffraction theory, a total equivalent response operator is constructed by stacking the propagation matrix between smart metasurface layers and the phase shift matrix of each metasurface layer. Based on the total equivalent response operator and the time-delay-Doppler domain transformation operator, the system's equivalent channel matrix is ​​constructed. Finally, a closed-form expression for the system's achievable sum rate with respect to the phase shift matrices of each SIM layer at all access points is derived. An analytical mapping relationship between phase shift parameters and system performance was established. Furthermore, with the optimization objective of maximizing the system reachability and rate, the phase shift was mapped to a complex multidimensional torus Riemannian manifold. The analytical gradient of the objective function was projected and iteratively updated in the tangent space, ensuring that the phase shift configuration always satisfies the unit modulus physical constraint under non-convex constraints. This invention fully leverages the multi-layer physical space processing capability of SIM and the anti-Doppler diversity characteristics of OTFS. Through low-overhead manifold gradient optimization, it can effectively improve the system reachability and rate in high-speed mobile decellular communication environments while reducing computational overhead.

Claims

1. A method for optimizing reachability and rate in decellularized networks based on stacked smart metasurfaces, characterized in that, include: Step S1: Deploy multiple access points in the mobile environment to build a decellularized network architecture, and configure multi-layer cascaded stacked smart metasurfaces at the transmitter of the access points. By adopting orthogonal time-frequency control technology at the transmitter, an end-to-end wireless transmission link system is established. The system is used to obtain a quasi-static channel in the time-delay-Doppler domain. Step S2, define the first The stacked smart metasurface of the access point. Layer and First The propagation matrix between layers is Based on Rayleigh-Sommerfeld diffraction theory, and according to the interlayer distance, dielectric properties, and unit cell distribution of the stacked smart metasurface, we obtain... The ( ) elements for: in, The physical meaning is the first The stacked smart metasurface of the access point. The first in the layer The first atom and the first The first in the layer The transfer coefficient between individual atoms, and These represent the spacing between elementary atoms in the horizontal and vertical directions, respectively. For the first The stacked smart metasurface of the access point. The first in the layer The first atom and the first The first in the layer The line connecting the first atom and the second atom The angle between the layers, Indicates the first The stacked smart metasurface of the access point. The first in the layer The first atom and the first The first in the layer The straight-line distance between individual atoms Represents the imaginary unit. The system wavelength; Definition of the first The stacked smart metasurface of the access point. Phase shift matrix of the layer for: in, Indicates a diagonal matrix. , , They are the 1st, 2nd, and 3rd respectively. Phase shift of individual atoms; By concatenating the propagation matrix and the phase shift matrix, the total equivalent response operator of the stacked smart metasurface is constructed, expressed as: in, Indicates the first The total equivalent response operator of a stacked smart metasurface with multiple access points , , , The first The stacked smart metasurface of the access point. Layer, First Phase shift matrices of layer 1, layer 2, and layer 1. Indicates the first The stacked smart metasurface of the access point. Layer and First Propagation matrix between layers Indicates the first The stacked smart metasurface of the access point. Layer and First Propagation matrix between layers Indicates the first The propagation matrix between the first and second layers of the stacked smart metasurface with access points; Construct from the first The end user to the first The system equivalent channel matrix of each access point : in, For non-direct trajectory index, This represents the total number of paths that are not direct rays. For the first The propagation matrix from the first layer of the stacked smart metasurface to the surface of the access points. This is the conjugate transpose operator. For the time-delay-Doppler domain transform operator of the direct path, For the first Delay-Doppler domain transform operator for non-direct paths, For non-direct component, For direct component, The direct trajectory guidance vector matrix, This represents the Kronecker product operation; Step S3: Based on the equivalent channel matrix, and utilizing channel statistical properties and matrix algebraic transformations, a closed-form expression for the system's achievable sum rate with respect to the phase shift matrices of each layer of the stacked smart metasurface at all access points is derived. This closed-form expression is used as the objective function, with maximizing the system's achievable sum rate as the optimization objective. All phase shift parameters of the stacked smart metasurface to be optimized are mapped to a complex-domain multidimensional torus Riemannian manifold satisfying unit modulus constraints. The closed-form expression... for: in, The phase shift matrix of all layers of the stacked smart metasurface. , For system bandwidth, For the first The signal-to-interference-plus-noise ratio of each end user is related to The expression, For the first Power of each end user For the first Power of each end user For the first The expected signal feature vector of an end user For the first The terminal user and the first Signal interference correlation matrix among individual end users For the first The direct path feature vector of each end user For the first Noise correlation matrix of each end user Noise power; Step S4: Calculate the Euclidean gradient of the objective function with respect to the phase shift parameters to be optimized using a closed-form expression, and project it onto the Riemann tangent space to obtain the Riemann gradient. Use the Riemann gradient descent method to synchronously update the phase shift of all stacked smart metasurfaces along the Riemann gradient in the Riemann tangent space, and remap the updated parameters to the Riemann manifold through shrinking mapping. After completing the phase shift optimization through iterative loops, the final optimal phase shift is configured to each layer of stacked smart metasurfaces.

2. The reachability and rate optimization method based on stacked smart metasurfaces in decellularized networks according to claim 1, characterized in that, Step S1 specifically includes: Deployment in mobile environments One access point, and configure Individual end users, to build a cellular-free network architecture, each access point configured One antenna, and each access point is connected to a block with Layered metasurface, each layer containing The stacked intelligent metasurface physical cascade of individual atoms adopts orthogonal time-frequency control technology at the transmitting end to establish an end-to-end wireless transmission link system, which converts the fast time-varying channel in the mobile environment into a quasi-static channel in the time-delay-Doppler domain and maps information symbols to the time-delay-Doppler domain.

3. The reachability and rate optimization method based on stacked smart metasurfaces in decellularized networks according to claim 2, characterized in that, In step S3, the complex field multidimensional torus Riemannian manifold The expression is: in, The dimension is The complex field space, express Each phase shift element is distributed on the unit circle of the complex plane.

4. The reachability and rate optimization method based on stacked smart metasurfaces in decellularized networks according to claim 3, characterized in that, Step S4 specifically includes: Calculate the objective function with respect to closed-form expression Euclidean gradient Then perform tangent space projection, and... Projecting onto the Riemann tangent space defined by the phase shift constraints of each layer's atomic atoms, we obtain the Riemann gradient. The expression is: in, To extract the real part operator, for The complex conjugate, It represents the Hadamardi (or Hadama) stack; Then, the Riemann gradient descent method is used to synchronously perform step updates along the Riemann gradient on the phase shifts of all stacked smart metasurfaces. The shrinkage operator is used to remap the points in the tangent space to the complex unit circle manifold so that the updated phase shifts of each layer always satisfy the unit modulus constraint. The value of the updated objective function is evaluated until the performance gain fluctuation between adjacent iterations is within the preset tolerance range or the maximum number of iterations is reached. The iteration is then stopped, and the obtained optimal phase shifts are assigned to the meta-atoms of each stacked smart metasurface to achieve an increase in achievable speed in a cellular network communication environment.

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