A super large scale MIMO system beam focusing method based on adaptive delay-phase structure

Abstract

The application provides a kind of based on adaptive time delay-phase structure's super large scale MIMO system beam focusing method, it is related to radio communication technical field, including the following steps: constructing adaptive time delay-phase structure suitable for super large scale MIMO system, and establishing near-field beam focusing joint optimization mathematical model;Through maximum ratio transmission solving all digital optimal beam focusing matrix;Switch matrix optimization problem is converted into two-part graph matching problem, and optimal matching matrix is solved using hungarian algorithm;Through riemann spectrum conjugate gradient method solving phase shift matrix;Utilize gradient descent method to optimize time delay matrix;Least square method is used to optimize digital precoding matrix;Through alternate iteration, base station beam focusing matrix is given, and the beam focusing of super large scale MIMO system is completed.The structure provided in the application can be realized with switch network and the dynamic sharing of antenna array element, so as to effectively enhance the adaptive ability of hybrid precoding to channel environment.

Description

Technical Field

[0001] This invention relates to the field of radio communication technology, and more particularly to a beam focusing method for a very large-scale MIMO system based on an adaptive delay-phase structure. Background Technology

[0002] With the future sixth-generation mobile communication (6G) th The demands for spectral and energy efficiency in Generation Mobile Networks (6G) have led to the development of Ultra-Large-Scale MIMO (Multiple Input Multiple Output), a further evolution of Massive MIMO technology, becoming a key technology for future wireless communication. By deploying antennas with ultra-large-scale arrays, higher beam gain and improved beam spatial resolution can be achieved, resulting in higher spectral efficiency. However, the large array aperture increases the Rayleigh distance to hundreds of meters, potentially extending 6G wireless communication into the near-field region beyond the far field. Furthermore, it can cause the signal delay between array elements to become non-negligible compared to the communication symbol period, leading to beam splitting and reduced communication speed and quality. Therefore, beam focusing, which focuses signals to specific locations, effectively reduces interference and attenuation, and improves wireless transmission performance, is considered a key technology for future wireless communication. In addition, to eliminate beam splitting, True Time Delay (TDD) devices compensate for delay by generating a phase shift proportional to the product of the time delay and the subcarrier frequency, thereby improving signal transmission performance. Therefore, research on beam focusing methods for ultra-large-scale MIMO systems based on TTD plays a crucial role in the future development of 6G wireless communication.

[0003] The time-delay-phase structure based on TTD can compensate for time delay, eliminate signal interference, and improve signal transmission gain by optimizing the time delay matrix, phase shift matrix, and digital precoding matrix. Patent application number "202510522582.8" discloses a "terahertz antenna structure and precoding method based on hybrid single and double-layer time delay lines." This method uses a single-layer time delay line structure in the middle part of the antenna where small time delays are required, and a double-layer time delay line structure at both ends where large time delays are required. Patent application number "202311044911.X" discloses a "terahertz beamforming structure and method based on dynamic grouped subarrays and true time delay." This method consists of antennas in subarrays and radio frequency links connected to the subarrays. The radio frequency links and subarrays are grouped, and within each group, the radio frequency links and each subarray are fully connected through true time delay and phase shifters. Currently, related structures mainly involve fixed time-delay-phase and antenna connections, making it difficult to adaptively adjust the connection method according to changes in channel conditions to meet diverse user communication needs.

[0004] Combining the multi-parameter coupling characteristics of beam focusing optimization in ultra-large-scale MIMO systems, the constant modulus constraints of the time delay and phase shift matrices, and the 0-1 integer programming problem of the switching matrix, beam focusing optimization methods based on manifold optimization and graph matching theory have become feasible means to solve the above problems. On the one hand, current manifold optimization algorithms mainly obtain the accurate Riemann gradient by calculating the Euclidean gradient, and then use conjugate gradient descent algorithms to solve the asymptotic optimal solution of the optimization problem. Patent application number "202310309860.2" discloses a "large-scale MIMO downlink precoding manifold optimization method". This method sets precoders based on satisfying total power constraints, user power constraints, and antenna-by-antenna power constraints on different Riemann submanifolds, transforming the constrained optimization problem in Euclidean space into an unconstrained optimization problem in manifold space. Based on this, a Riemann conjugate gradient method is provided to design precoders that satisfy different constraints on the manifold. Patent application number "202310933943.9" discloses a "conjugate gradient beamforming generation method based on manifold optimization." This method transforms the problem model of minimizing interference signal energy into a problem model on a complex circular manifold, and solves the problem model on the complex circular manifold using the gradient descent method. On the other hand, graph matching theory is currently considered an effective means to solve integer programming problems. Patent application number "201611077272.7" discloses a "resource allocation method for physical layer security scenarios in full-duplex cellular networks." This method transforms the resource allocation problem into a bipartite graph matching problem and uses a resource allocation method based on bipartite graph matching to match resource blocks. Patent application number "202210851325.5" discloses a "user access control method based on bipartite graph matching in mobile edge computing networks." This method transforms the optimization problem model into a bipartite graph matching problem of optimizing channel allocation, and uses the Hungarian algorithm to solve the bipartite graph matching problem of optimizing channel allocation to obtain the maximum matching value.

[0005] The above analysis shows that utilizing switching networks to achieve dynamic connection of time delay, phase, and antenna, and adaptively adjusting the connection method to meet diverse user communication needs, is a key technical problem that urgently needs to be solved for ultra-large-scale MIMO systems. Based on this, providing corresponding beam focusing methods tailored to structural characteristics is crucial. Summary of the Invention

[0006] In view of the technical problems mentioned in the background, a beam focusing method for ultra-large-scale MIMO systems based on an adaptive time delay-phase structure is provided to achieve high-precision, high-speed data transmission.

[0007] The technical means employed in this invention are as follows:

[0008] A beam focusing method for an ultra-large-scale MIMO system based on an adaptive time-delay-phase structure includes the following steps:

[0009] Step 1: Construct an adaptive time delay-phase structure suitable for ultra-large-scale MIMO systems and establish a joint optimization mathematical model for near-field beam focusing;

[0010] Step 2: Solve for the all-digital optimal beam focusing matrix using maximum ratio transmission;

[0011] Step 3: Transform the switch matrix optimization problem into a bipartite graph matching problem, and use the Hungarian algorithm to solve for the optimal matching matrix;

[0012] Step 4: Solve for the phase shift matrix using the Riemann spectral conjugate gradient method;

[0013] Step 5: Optimize the time delay matrix using the gradient descent method;

[0014] Step 6: Optimize the digital precoding matrix using the least squares method;

[0015] Step 7: By iteratively applying steps 3-6, the base station beam focusing matrix is ​​obtained, thus completing the beam focusing of the ultra-large-scale MIMO system.

[0016] Further, in step 1, the adaptive time delay-phase structure is: Root radio frequency chain, A delay unit and A phase shifter through The switch network is connected to Root antenna; among which, Indicates the number of delay units connected to each RF chain; Indicates the number of switches;

[0017] The mathematical model for joint optimization of near-field beam focusing, expressed by minimizing the residual criterion, is as follows:

[0018]

[0019]

[0020]

[0021]

[0022]

[0023] in, This represents a fully digital beam focusing matrix. Indicates the maximum transmission power; , Represents the analog phase-shifting matrix. This represents the phase shift vector of the phase shifter; Represents the time delay matrix. This represents the delay vector of the delay unit. Indicates the first The delay of each delay unit Indicates the maximum delay that the delay device can compensate for; Represents the switch matrix. This represents the baseband digital precoding vector. Indicates the first Each element.

[0024] Furthermore, in step 2, the optimal beam focusing matrix is ​​obtained by using the near-field channel matrix and the maximum transmission ratio. ;in This represents the channel matrix between the base station and the user.

[0025] Furthermore, step 3 includes the following steps:

[0026] Step 31: The switch matrix optimization problem is transformed into:

[0027] ;

[0028] ;

[0029] Step 32: Expand the objective function to simplify the problem to maximization. ;Will Defined as a weight matrix;

[0030] Step 33, when yes multiples of integers, let , expansion for ,in, Represents the weight matrix. Represent the expanded weight matrix; transform the problem into... Solve for the minimum weight matching on the cost matrix;

[0031] Step 34: Use the Hungarian algorithm to perform the allocation and construct the selection matrix. .

[0032] Furthermore, step 4 includes the following steps:

[0033] Step 41: The phase shift matrix parameters form a complex circular flow. The optimization problem is transformed into:

[0034]

[0035]

[0036] Step 42: Calculate the Euclidean gradient ;Will Orthogonal projection onto manifold The corresponding Riemann gradient is obtained. for: ;

[0037] Step 43: Update the search direction:

[0038] ;

[0039] in, Represents a vector transformation function; This represents the Polak-Ribiere parameter. Indicates the Riemann spectral parameters;

[0040] Step 44: Execute the rollback operator : Update the points Mapped to the origin In the same manifold space:

[0041] ;

[0042] ;

[0043] in, Representing a manifold midpoint tangent space; Indicates the Armijo backtracking search step size; and It satisfies the inequality The smallest non-negative integer.

[0044] Furthermore, step 5 includes the following steps:

[0045] Step 51: Transform the time delay matrix optimization problem into:

[0046] ;

[0047] Step 52: Calculate the derivative with respect to the time delay parameter:

[0048] ;

[0049] in, ;

[0050] Step 53: Update latency parameters ; This represents the learning rate.

[0051] Furthermore, in step 6, the digital precoding vector is solved using least squares. .

[0052] Compared with the prior art, the present invention has the following advantages:

[0053] This invention achieves dynamic connection between the RF chain, time delay, phase shift, and antenna through a switching network, effectively enhancing the system's adaptability to the channel environment and providing an important foundation for improving the system's spectral efficiency. In addition, the proposed beam focusing method utilizes the 0-1 characteristics of the switching matrix and the diagonal characteristics of the time delay and phase shift matrices to effectively optimize the switching matrix, phase shift matrix, and time delay matrix, resulting in high spectral efficiency. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a schematic diagram of the overall process of the present invention.

[0056] Figure 2 This is the adaptive time delay-phase structure diagram of the present invention.

[0057] Figure 3 This is a schematic diagram of the convergence simulation of the present invention.

[0058] Figure 4 This is a schematic diagram of the simulation results of the present invention (I).

[0059] Figure 5 This is a schematic diagram of the simulation results of the present invention (II). Detailed Implementation

[0060] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0061] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0062] like Figure 1-5 As shown, this invention provides a beam focusing method for a very large-scale MIMO system based on an adaptive time-delay-phase structure, comprising the following steps:

[0063] Step 1: Construct an adaptive time-delay-phase structure suitable for ultra-large-scale MIMO systems and establish a joint optimization mathematical model for near-field beam focusing; in Step 1, the adaptive time-delay-phase structure is... Root radio frequency chain, One delay unit ( (Indicates the number of delayers connected to each RF chain) and A phase shifter through The switch network is connected to root antenna ( (Indicates the number of switches);

[0064] The mathematical model for joint optimization of near-field beam focusing, expressed by minimizing the residual criterion, is as follows:

[0065]

[0066]

[0067]

[0068]

[0069]

[0070] in, This represents a fully digital beam focusing matrix. Indicates the maximum transmission power; , Represents the analog phase-shifting matrix. This represents the phase shift vector of the phase shifter; Represents the time delay matrix. This represents the delay vector of the delay unit. Indicates the first The delay of each delay unit Indicates the maximum delay that the delay device can compensate for; Represents the switch matrix. This represents the baseband digital precoding vector. Indicates the first One element;

[0071] Step 2: Solve for the all-digital optimal beam focusing matrix using maximum ratio transmission; obtain the optimal beam focusing matrix using the near-field channel matrix and maximum ratio transmission. ,in This represents the channel matrix between the base station and the user.

[0072] Step 3: Transform the switching matrix optimization problem into a bipartite graph matching problem, and use the Hungarian algorithm to solve for the optimal matching matrix; Step 3 includes the following steps:

[0073] Step 31: The switch matrix optimization problem is transformed into:

[0074] ;

[0075] ;

[0076] Step 32: Expand the objective function to simplify the problem to maximization. ;Will Defined as a weight matrix;

[0077] Step 33, when yes multiples of integers, let , expansion for ,in, Represents the weight matrix. Represent the expanded weight matrix; transform the problem into... Solve for the minimum weight matching on the cost matrix;

[0078] Step 34: Use the Hungarian algorithm to perform the allocation and construct the selection matrix. .

[0079] Step 4: Solve for the phase shift matrix using the Riemann spectral conjugate gradient method; Step 4 includes the following steps:

[0080] Step 41: The phase shift matrix parameters form a complex circular flow. The optimization problem is transformed into:

[0081]

[0082]

[0083] Step 42: Calculate the Euclidean gradient ;Will Orthogonal projection onto manifold The corresponding Riemann gradient is obtained. for: ;

[0084] Step 43: Update the search direction:

[0085] ;

[0086] in, Represents a vector transformation function; This represents the Polak-Ribiere parameter. Indicates the Riemann spectral parameters;

[0087] Step 44: Execute the rollback operator : Update the points Mapped to the origin In the same manifold space:

[0088] ;

[0089] ;

[0090] in, Representing a manifold midpoint tangent space; Indicates the Armijo backtracking search step size; and It satisfies the inequality The smallest non-negative integer.

[0091] Step 5: Optimize the time delay matrix using the gradient descent method; Step 5 includes the following steps:

[0092] Step 51: Transform the time delay matrix optimization problem into:

[0093] ;

[0094] Step 52: Calculate the derivative with respect to the time delay parameter:

[0095] ;

[0096] in, ;

[0097] Step 53: Update latency parameters ; This represents the learning rate.

[0098] Step 6: Optimize the digital precoding matrix using the least squares method; in step 6, the digital precoding vector is solved using the least squares method. .

[0099] Step 7: By iteratively applying steps 3-6, the base station beam focusing matrix is ​​obtained, thus completing the beam focusing of the ultra-large-scale MIMO system.

[0100] To verify the effectiveness of the invention, the inventors also conducted the following simulation experiments:

[0101] Simulation conditions: For a very large-scale MIMO system with an adaptive delay-phase structure, the simulation experiment assumes that the transmitter is equipped with a uniform linear array of 256 antenna elements, there are 10 radio frequency chains, each radio frequency chain is connected to 5 delay units, and the distance between adjacent antenna elements is set to half a wavelength.

[0102] Figure 3 The relationship between residuals and the number of iterations is presented to demonstrate the convergence of the proposed beam focusing algorithm. Simulation results show that the algorithm can achieve convergence in approximately 10 iterations. Figure 4 The relationship between spectral efficiency and SNR is given for different numbers of adders. More adders result in better spectral efficiency. Figure 5 The relationship between spectral efficiency and SNR under the proposed adaptive time-delay-phase structure and the traditional time-delay-phase structure is presented. Simulation results show that when the number of adders reaches 64, performance comparable to the time-delay-phase-antenna fixed connection structure is achieved. Therefore, based on the relevant results, the structure and beam focusing method of this invention can achieve fast convergence and high spectral efficiency.

[0103] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. In the above embodiments of the present invention, the descriptions of each embodiment have their own emphasis; parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. It should be understood that the disclosed technical content in the several embodiments provided in this application can be implemented in other ways.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A beam focusing method for an ultra-large-scale MIMO system based on an adaptive time-delay-phase structure, characterized in that, Includes the following steps: Step 1: Construct an adaptive time-delay-phase structure suitable for ultra-large-scale MIMO systems and establish a joint optimization mathematical model for near-field beam focusing; in Step 1, the adaptive time-delay-phase structure is... Root radio frequency chain, A delay unit and A phase shifter through The switch network is connected to Root antenna; among which, This indicates the number of delay units connected to each RF chain; Indicates the number of switches; The mathematical model for joint optimization of near-field beam focusing, expressed by minimizing the residual criterion, is as follows: in, This represents a fully digital beam focusing matrix. Indicates the maximum transmission power; , Represents the analog phase-shifting matrix. This represents the phase shift vector of the phase shifter; Represents the time delay matrix. This represents the delay vector of the delay unit. Indicates the first The delay of each delay unit Indicates the maximum delay that the delay device can compensate for; Represents the switch matrix. This represents the baseband digital precoding vector. Indicates the first One element; Step 2: Solve for the all-digital optimal beam focusing matrix using maximum ratio transmission; Step 3: Transform the switching matrix optimization problem into a bipartite graph matching problem, and use the Hungarian algorithm to solve for the optimal matching matrix; Step 3 includes the following steps: Step 31: The switch matrix optimization problem is transformed into: ； ； Step 32: Expand the objective function to simplify the problem to maximization. ;Will Defined as a weight matrix; Step 33, when yes multiples of integers, let , expansion for ,in, Represents the weight matrix. Represent the expanded weight matrix; transform the problem into Solve for the minimum weight matching on the cost matrix; Step 34: Use the Hungarian algorithm to perform the allocation and construct the selection matrix. ; Step 4: Solve for the phase shift matrix using the Riemann spectral conjugate gradient method; Step 5: Optimize the time delay matrix using the gradient descent method; Step 6: Optimize the digital precoding matrix using the least squares method; Step 7: By iteratively applying steps 3-6, the base station beam focusing matrix is ​​obtained, thus completing the beam focusing of the ultra-large-scale MIMO system.

2. The beam focusing method for an ultra-large-scale MIMO system based on an adaptive time-delay-phase structure according to claim 1, characterized in that, In step 2, the optimal beam focusing matrix is ​​obtained by using the near-field channel matrix and the maximum ratio transmission. ;in This represents the channel matrix between the base station and the user.

3. The beam focusing method for an ultra-large-scale MIMO system based on an adaptive time-delay-phase structure according to claim 1, characterized in that, Step 4 includes the following steps: Step 41: The phase shift matrix parameters form a complex circular flow. The optimization problem is transformed into: Step 42: Calculate the Euclidean gradient ;Will Orthogonal projection onto manifold The corresponding Riemann gradient is obtained. for: ; Step 43: Update the search direction: ； in, Represents a vector transformation function; This represents the Polak-Ribiere parameter. Indicates the Riemann spectral parameters; Step 44: Execute the rollback operator : Update the points Mapped to the origin In the same manifold space: ； ； in, Representing a manifold midpoint tangent space; Indicates the Armijo backtracking search step size; and It satisfies the inequality The smallest non-negative integer.

4. The beam focusing method for an ultra-large-scale MIMO system based on an adaptive time-delay-phase structure according to claim 1, characterized in that, Step 5 includes the following steps: Step 51: Transform the time delay matrix optimization problem into: ； Step 52: Calculate the derivative with respect to the time delay parameter: ； in, ; Step 53: Update latency parameters ; This represents the learning rate.

5. The beam focusing method for an ultra-large-scale MIMO system based on an adaptive time-delay-phase structure according to claim 1, characterized in that, In step 6, the digital precoding vector is solved using least squares. .

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