High-energy-efficiency semantic communication method, system and equipment based on stacked metasurfaces

Abstract

The invention relates to a stacked metasurface-based high-energy-efficiency semantic communication method, system and device, and the method comprises the steps: setting a minimum semantic rate target, a minimum semantic similarity threshold value and maximum transmission power, taking the maximization of semantic energy efficiency as a target function, and achieving the high-energy-efficiency semantic communication of a wireless channel under the condition of a non-ideal angle error of the wireless channel. A to-be-solved optimization model with a digital beam former and a metasurface phase shift matrix as optimization variables is constructed by taking a minimum semantic rate target, a minimum semantic similarity threshold, maximum transmitting power and amplitude normalization of each reflection unit of the stacked metasurface as constraint conditions; a non-ideal channel model is based on an angle error, wireless channel non-ideal angle error parameters are discretized, and finally an optimal digital beam former is solved by using a semantic maximization and minimization method and an optimal metasurface phase shift matrix is solved by using a cyclic coordinate descent algorithm for semantic communication optimization of a communication system. Therefore, the semantic energy efficiency maximization of the stacked metasurface in communication application is realized.

Description

Technical Field

[0001] The present invention relates to the field of semantic communication technology, and in particular to a high-energy-efficiency semantic communication method, system and device based on stacked metasurfaces. Background Art

[0002] Multiple-input multiple-output (MIMO) communication systems have been widely used in fifth-generation (5G) wireless networks to improve spectrum efficiency and support massive connectivity. However, MIMO systems are driven by the use of hundreds of antennas in base stations (BSs), which results in prohibitive hardware costs and power consumption due to the large number of radio frequency (RF) chains required for each antenna. Although the development of analog-to-digital hybrid MIMO communication systems has greatly reduced the number of required RF chains, it still requires a large number of phase shifters, resulting in low energy efficiency (EE).

[0003] With the rise of two emerging innovative technologies, namely reconfigurable smart metasurfaces (RIS) and semantic communication (SemCom), energy-saving holographic MIMO communications can be achieved. Specifically, smart metasurfaces are artificial surfaces composed of a large number of low-cost meta-atoms, which can apply phase shifts to electromagnetic waves and reconfigure the wireless propagation environment in an energy-saving manner, thereby promoting the use of large-scale arrays. In addition, semantic communication eliminates irrelevant information and transmits compressed key information, thereby reducing system power consumption and improving energy efficiency EE. Most existing research works on smart metasurfaces rely on single-layer architectures, which have been proven to expand service coverage, enhance physical layer security, and improve achievable rate / energy efficiency. However, studies have shown that the beamforming design freedom of single-layer smart metasurfaces is limited and multi-user interference cannot be suppressed. In order to overcome the above shortcomings, the industry has proposed the stacked metasurface (SM) technology, which vertically stacks multiple metasurfaces to form a stacked metasurface with a multi-layer structure. However, how to maximize the semantic energy efficiency of stacked metasurfaces in communication applications has become one of the important technical issues to be solved in the field. Summary of the invention

[0004] In view of the problems existing in the above-mentioned traditional technologies, a high-energy-efficiency semantic communication method, a communication system and a computer device based on stacked metasurfaces are provided.

[0005] In order to achieve the above objectives, the embodiments of the present invention adopt the following technical solutions: On the one hand, a high-energy-efficiency semantic communication method based on a stacked metasurface is provided, which is applied to a communication system, wherein the communication system includes a base station configured with a digital antenna, a stacked metasurface, and a plurality of user-end devices configured with a single antenna; The energy-efficient semantic communication method includes the following steps: Setting the minimum semantic rate target, minimum semantic similarity threshold, and maximum transmit power of the communication system; Taking the maximization of semantic energy efficiency of the communication system as the objective function, under the condition of non-ideal angle error of the wireless channel, with the minimum semantic rate target, minimum semantic similarity threshold, maximum transmission power and amplitude normalization of each reflective unit of the stacked metasurface as constraints, an optimization model to be solved with digital beamformer and metasurface phase shift matrix as optimization variables is constructed; Construct a non-ideal channel model based on angle error and discretize the non-ideal angle error of the wireless channel into parameters; The optimal digital beamformer is solved using the semantic maximization-minimization method, and the optimal metasurface phase shift matrix is ​​solved using the cyclic coordinate descent algorithm; The communication system is optimized for semantic communication using the optimal digital beamformer and the optimal metasurface phase shift matrix.

[0006] On the other hand, a communication system is also provided, comprising a base station configured with a digital antenna, a stacked metasurface, and a plurality of user-end devices configured with a single antenna, wherein the communication system uses an optimal digital beamformer and an optimal metasurface phase shift matrix to perform semantic communication optimization; wherein obtaining the optimal digital beamformer and the optimal metasurface phase shift matrix comprises the steps of: Setting the minimum semantic rate target, minimum semantic similarity threshold, and maximum transmit power of the communication system; Taking the maximization of semantic energy efficiency of the communication system as the objective function, under the condition of non-ideal angle error of the wireless channel, with the minimum semantic rate target, minimum semantic similarity threshold, maximum transmission power and amplitude normalization of each reflective unit of the stacked metasurface as constraints, an optimization model to be solved with digital beamformer and metasurface phase shift matrix as optimization variables is constructed; Construct a non-ideal channel model based on angle error and discretize the non-ideal angle error of the wireless channel into parameters; The optimal digital beamformer is solved using the semantic maximization-minimization method, and the optimal metasurface phase shift matrix is ​​solved using the cyclic coordinate descent algorithm; The communication system is optimized for semantic communication using the optimal digital beamformer and the optimal metasurface phase shift matrix.

[0007] On the other hand, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned high-energy-efficiency semantic communication method based on stacked metasurfaces when executing the computer program.

[0008] One of the above technical solutions has the following advantages and beneficial effects: The above-mentioned high-energy-efficiency semantic communication method, system and device based on stacked metasurfaces, by setting the minimum semantic rate target, the minimum semantic similarity threshold and the maximum transmission power, takes the maximization of semantic energy efficiency as the objective function, and under the condition of non-ideal angular error of the wireless channel, takes the minimum semantic rate target, the minimum semantic similarity threshold, the maximum transmission power and the normalization of the amplitude of each reflection unit of the stacked metasurface as constraints, constructs an optimization model to be solved with a digital beamformer and a metasurface phase shift matrix as optimization variables, and discretizes the non-ideal channel model based on the angular error. Finally, the semantic maximization minimization method is used to solve the optimal digital beamformer and the cyclic coordinate descent algorithm is used to solve the optimal metasurface phase shift matrix for semantic communication optimization of the communication system, thereby realizing the maximization of the semantic energy efficiency of the stacked metasurface in communication applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the conventional technology, the drawings required for use in the embodiments or the conventional technology descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0010] Figure 1 1 is a flow chart of a high energy-efficient semantic communication method based on a stacked metasurface in one embodiment; Figure 2 FIG. 1 is a schematic diagram of the architecture of a communication system in an embodiment. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and Examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0012] It should be noted that the reference to "embodiment" in this article means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The presentation of this phrase at various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It will be appreciated by those skilled in the art that the embodiments described herein may be combined with other embodiments. The term "and / or" used in the specification and appended claims of the present invention refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0013] The following will describe the implementation of the present invention in detail with reference to the accompanying drawings in the embodiment diagram of the present invention.

[0014] In one embodiment, a high-energy-efficiency semantic communication method based on a stacked metasurface is provided, which is applied to a communication system, wherein the communication system includes a base station configured with a digital antenna, a stacked metasurface, and a plurality of user-end devices configured with a single antenna. Figure 1 As shown, the above-mentioned high-energy-efficiency semantic communication method based on stacked metasurfaces may include the following processing steps S10 to S18: S10, setting a minimum semantic rate target, a minimum semantic similarity threshold, and a maximum transmission power of the communication system; S12, taking the maximization of the semantic energy efficiency of the communication system as the objective function, under the condition of non-ideal angle error of the wireless channel, with the minimum semantic rate target, the minimum semantic similarity threshold, the maximum transmission power and the normalization of the amplitude of each reflection unit of the stacked metasurface as constraints, constructs an optimization model to be solved with the digital beamformer and the metasurface phase shift matrix as optimization variables; S14, constructing a non-ideal channel model based on angle error and discretizing the non-ideal angle error of the wireless channel into parameters; S16, solving the optimal digital beamformer using a semantic maximization-minimization method, and solving the optimal metasurface phase shift matrix using a cyclic coordinate descent algorithm; S18, semantic communication optimization of the communication system using the optimal digital beamformer and the optimal metasurface phase shift matrix.

[0015] It can be understood that the high energy-efficient semantic communication method based on stacked metasurfaces provided in this embodiment can be applied to Figure 2 In the communication system application environment shown in the figure, the communication system can be equipped with A base station (which can be recorded as BS) with digital antennas, a A layers of intelligent super surface and each layer of intelligent super surface has The stacked metasurface (which can be denoted as SM) of uniform planes and KThere are user-end devices with a single antenna (referred to as User1 to User K ).

[0016] You can define collections , and ; The channel between the digital antenna of the base station and the first layer of the stacked metasurface is , No. a Layer-by-layer supersurface and a The channel between the +1-layer stacked metasurfaces is , the base station and the k The channel between the user end devices is Using the semantic transfer framework based on deep learning, the original sentence Through neural network Mapped into semantic symbols, that is .remember For the k User semantic symbols are transmitted through a digital beamformer before transmission. Processing. a The metasurface phase shift matrix of the stacked metasurface is Therefore, the semantic beamformer transmitted to the user end device can be expressed as Therefore, k The signal received at the user terminal device is ,in, is additive noise, is the average noise power. The equivalent analog beamformer generated by SM is defined as ,Right now:

[0017] DeepSC Receiver Received a degraded semantic symbol After that, extract and restore the original sentence In order to evaluate the quality of semantic communication, the semantic rate (suts / s) is widely used, namely:

[0018] in, For the original sentence The expected amount of semantic information in , is the bandwidth, is the average number of semantic symbols per word, For the original sentence The expected number of words, Sentences for recovery With the original sentence The semantic similarity between . For the k The signal-to-noise ratio of a user terminal device is:

[0019] However, It mainly depends on the neural network used , its black box nature makes it impossible to accurately characterize it. A generalized logic function can handle this problem, which converts the semantic rate Maps to:

[0020] in, , , and is a non-negative parameter. And the total power consumption of the communication system considered is:

[0021] in, For efficiency, is the power required by the RF chain, is the power consumed by each meta-atom, is the power consumption of the baseband processor. Therefore, the semantic energy efficiency is expressed as:

[0022] The above-mentioned high-energy-efficiency semantic communication method based on stacked metasurfaces sets the minimum semantic rate target, the minimum semantic similarity threshold and the maximum transmission power, and takes the maximization of semantic energy efficiency as the objective function. Under the condition of non-ideal angular error of the wireless channel, the minimum semantic rate target, the minimum semantic similarity threshold, the maximum transmission power and the normalization of the amplitude of each reflection unit of the stacked metasurface are used as constraints. An optimization model to be solved with a digital beamformer and a metasurface phase shift matrix as optimization variables is constructed. The non-ideal channel model based on the angle error and the non-ideal angular error parameters of the wireless channel are discretized. Finally, the semantic maximization minimization method is used to solve the optimal digital beamformer and the cyclic coordinate descent algorithm is used to solve the optimal metasurface phase shift matrix for semantic communication optimization of the communication system, thereby realizing the maximization of the semantic energy efficiency of stacked metasurfaces in communication applications.

[0023] Specifically, the above-mentioned high-energy-efficiency semantic communication method based on stacked metasurfaces may include the following processing steps: Step 1: Set a minimum semantic rate target for the communication system , minimum semantic similarity threshold And the maximum transmit power .

[0024] Step 2: Semantic Energy Efficiency Maximize the objective function, in the non-ideal angle error of the wireless channel Under the condition of minimum semantic rate target , minimum semantic similarity threshold , Maximum transmit power The amplitude of each reflection unit of the smart metasurface is normalized as a constraint condition, and the user-end device k Desired Digital Beamformer and metasurface phase shift matrix To optimize the variables, build the optimization model to be solved:

[0025] Among them, C1 to C4 represent various constraint conditions respectively.

[0026] Step 3: Construct a non-ideal channel model based on angle error and transform the non-ideal angle error of the wireless channel into Discretize the parameters.

[0027] Specifically, let the channel belongs to a given uncertainty set, namely , and are the upper and lower limits of the azimuth, and are the upper and lower limits of the elevation angle respectively. Non-ideal angle error of the wireless channel The interval angles within are uniformly chosen as:

[0028] in, and They are and The number of samples, , .thus In the worst case CSI (channel state information) . Make the signal-to-noise ratio can be restated as:

[0029] Step 4: Use the semantic maximization and minimization method to solve the optimal digital beamformer (which can be recorded as ), and the optimal metasurface phase shift matrix (which can be recorded as ).

[0030] In one embodiment, in the process of solving the optimal digital beamformer using the semantic maximization-minimization method, a semantic proxy function is used to transform the untractable quasi-convexity in the digital optimization sub-problem into a tractable quasi-convexity.

[0031] Specifically, firstly, the given metasurface phase shift matrix is ​​used Designing a Digital Beamformer , the corresponding digital optimization sub-problem can be expressed as:

[0032]

[0033] in, , the goal is to convert the unprocessable quasi-convex Transform it into a tractable quasi-convex, thereby solving the corresponding numerical optimization subproblem. In the following proposition, we propose Semantic proxy function: Proposition 1 (Semantic Proxy Function): Given a stationary point , quasi-convex It can be approximated as ,Right now:

[0034] in, And the intermediate parameters:

[0035] This Proposition 1 can be proved by sufficient experimental examples and curve fitting. By using Proposition 1, As A simple function, namely:

[0036] However, the denominator With the form of sum and ratio, the corresponding digital optimization subproblem is still difficult to solve. Therefore, the secondary transformation is used for processing, namely:

[0037] in, is an auxiliary variable introduced, and Therefore, the objective function can be further rewritten as Finally, the fractional semantic energy efficiency form is processed using the Dinkelbach method (an efficient algorithm for solving fractional programming problems). To elaborate, by introducing an additional variable , the fractional Sem-EE is converted into a non-fractional function, namely:

[0038] Obviously, this is a given and The non-homogeneous separable QCQP (Quadratic ConstrainedQuadratic Programming) problem can be solved efficiently using CVX (a commonly used Matlab convex optimization solver). and Can be updated to:

[0039] Among them, the superscript represents the number of iterations. In this iterative framework, the digital beamformer can be found .

[0040] In the right After optimization, the focus is on designing the SM metasurface phase shift matrix , and its corresponding simulation optimization subproblem is given by:

[0041] Obviously, due to the multiple constraints of quality of service (QoS) and the unit module constraints of meta-atoms, solving the above simulation optimization sub-problems is challenging. Therefore, we first add multiple QoS constraints to the non-negative Lagrange multiplier The simulation optimization subproblem is then rewritten as:

[0042] Among them, the intermediate parameters are as follows:

[0043] Then, we turned to the CCD (conjugate gradient method) algorithm to process the unit module constraints of the meta-atom, and thus obtained the optimal closed-form solution. Specifically, you can first Expands to:

[0044] in, . It should be noted that due to the Hermitian matrix The unit module constraints of and meta-atoms make the second equation valid. Therefore, Can be decomposed into N These scalar subproblems can be updated using the following CCD framework:

[0045] Here Then, solve the above equation A scalar subproblem. Ignoring the constant term, It can be expressed as:

[0046] Obviously, the optimal closed-form solution is:

[0047] However, the above CCD algorithm is based on a fixed non-negative Lagrange multiplier Therefore, we turn to find the optimal , which can be obtained by the following complementary relaxation conditions:

[0048] in, . K binary search can be used to search for the optimal For the sake of simplicity, we can refer to the existing K binary search for similar understanding. Finally, by and Perform iterative optimization to obtain the optimal , to obtain the optimal metasurface phase shift matrix .

[0049] In one embodiment, a communication system is also provided, including a base station configured with a digital antenna, a stacked metasurface, and a plurality of user-end devices configured with a single antenna, wherein the communication system uses an optimal digital beamformer and an optimal metasurface phase shift matrix to perform semantic communication optimization. The acquisition of the optimal digital beamformer and the optimal metasurface phase shift matrix includes the steps of: Setting the minimum semantic rate target, minimum semantic similarity threshold, and maximum transmit power of the communication system; Taking the maximization of semantic energy efficiency of the communication system as the objective function, under the condition of non-ideal angle error of the wireless channel, with the minimum semantic rate target, minimum semantic similarity threshold, maximum transmission power and amplitude normalization of each reflective unit of the stacked metasurface as constraints, an optimization model to be solved with digital beamformer and metasurface phase shift matrix as optimization variables is constructed; Construct a non-ideal channel model based on angle error and discretize the non-ideal angle error of the wireless channel into parameters; The optimal digital beamformer is solved using the semantic maximization-minimization method, and the optimal metasurface phase shift matrix is ​​solved using the cyclic coordinate descent algorithm; The communication system is optimized for semantic communication using the optimal digital beamformer and the optimal metasurface phase shift matrix.

[0050] The above-mentioned communication system sets the minimum semantic rate target, the minimum semantic similarity threshold and the maximum transmission power, takes the maximization of semantic energy efficiency as the objective function, and under the condition of non-ideal angular error of the wireless channel, takes the minimum semantic rate target, the minimum semantic similarity threshold, the maximum transmission power and the normalization of the amplitude of each reflection unit of the stacked metasurface as constraints. It constructs an optimization model to be solved with the digital beamformer and the metasurface phase shift matrix as optimization variables, and discretizes the non-ideal channel model based on the angular error and the non-ideal angular error parameters of the wireless channel. Finally, the semantic maximization minimization method is used to solve the optimal digital beamformer and the cyclic coordinate descent algorithm is used to solve the optimal metasurface phase shift matrix for semantic communication optimization of the communication system, thereby realizing the maximization of the semantic energy efficiency of the stacked metasurface in communication applications.

[0051] In one embodiment, in the process of solving the optimal digital beamformer using the semantic maximization-minimization method, a semantic proxy function is used to transform the untractable quasi-convexity in the digital optimization sub-problem into a tractable quasi-convexity.

[0052] It can be understood that for the specific limitations of the above-mentioned communication system, please refer to the corresponding limitations of the high-efficiency semantic communication method based on stacked metasurfaces mentioned above, which will not be repeated here.

[0053] In one embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor implementing the following processing steps when executing the computer program: setting a minimum semantic rate target, a minimum semantic similarity threshold, and a maximum transmission power of a communication system; taking maximization of the semantic energy efficiency of the communication system as an objective function, under the condition of non-ideal angular error of a wireless channel, taking the minimum semantic rate target, the minimum semantic similarity threshold, the maximum transmission power, and the normalization of the amplitudes of each reflection unit of the stacked metasurface as constraints, constructing an optimization model to be solved with a digital beamformer and a metasurface phase shift matrix as optimization variables; constructing a non-ideal channel model based on an angular error and discretizing the parameters of the non-ideal angular error of the wireless channel; using a semantic maximization minimization method to solve the optimal digital beamformer, and using a cyclic coordinate descent algorithm to solve the optimal metasurface phase shift matrix; and using the optimal digital beamformer and the optimal metasurface phase shift matrix to perform semantic communication optimization on the communication system.

[0054] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus dynamic random access memory (RambusDRAM, referred to as RDRAM) and interface dynamic random access memory (DRDRAM).

[0055] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0056] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of protection of the invention. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the attached claims.

Claims

1. A high-energy-efficiency semantic communication method based on stacked metasurfaces, characterized in that: Applied to a communication system, the communication system comprising a base station configured with a digital antenna, a stacked metasurface, and a plurality of user-end devices configured with a single antenna; The energy-efficient semantic communication method comprises the following steps: Setting the minimum semantic rate target, minimum semantic similarity threshold, and maximum transmit power of the communication system; Taking maximizing the semantic energy efficiency of the communication system as the objective function, under the condition of non-ideal angle error of the wireless channel, taking the minimum semantic rate target, the minimum semantic similarity threshold, the maximum transmission power and the amplitude normalization of each reflection unit of the stacked metasurface as constraints, constructing an optimization model to be solved with a digital beamformer and a metasurface phase shift matrix as optimization variables; Construct a non-ideal channel model based on angle error and discretize the non-ideal angle error of the wireless channel into parameters; The optimal digital beamformer is solved using the semantic maximization-minimization method, and the optimal metasurface phase shift matrix is ​​solved using the cyclic coordinate descent algorithm; The communication system is optimized for semantic communication using an optimal digital beamformer and an optimal metasurface phase shift matrix.

2. The high-energy-efficiency semantic communication method based on stacked metasurfaces according to claim 1 is characterized in that: In the process of solving the optimal digital beamformer using the semantic maximization-minimization method, the semantic proxy function is used to transform the untractable quasi-convexity in the digital optimization subproblem into a tractable quasi-convexity.

3. A communication system, characterized in that: The invention comprises a base station configured with a digital antenna, a stacked metasurface, and a plurality of user-end devices configured with a single antenna, wherein the communication system uses an optimal digital beamformer and an optimal metasurface phase shift matrix to perform semantic communication optimization; wherein obtaining the optimal digital beamformer and the optimal metasurface phase shift matrix comprises the steps of: Setting the minimum semantic rate target, minimum semantic similarity threshold, and maximum transmit power of the communication system; Taking maximizing the semantic energy efficiency of the communication system as the objective function, under the condition of non-ideal angle error of the wireless channel, taking the minimum semantic rate target, the minimum semantic similarity threshold, the maximum transmission power and the amplitude normalization of each reflection unit of the stacked metasurface as constraints, constructing an optimization model to be solved with a digital beamformer and a metasurface phase shift matrix as optimization variables; Construct a non-ideal channel model based on angle error and discretize the non-ideal angle error of the wireless channel into parameters; The optimal digital beamformer is solved using the semantic maximization-minimization method, and the optimal metasurface phase shift matrix is ​​solved using the cyclic coordinate descent algorithm; The communication system is optimized for semantic communication using an optimal digital beamformer and an optimal metasurface phase shift matrix.

4. The communication system according to claim 3, characterized in that: In the process of solving the optimal digital beamformer using the semantic maximization-minimization method, the semantic proxy function is used to transform the untractable quasi-convexity in the digital optimization subproblem into a tractable quasi-convexity.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the energy-efficient semantic communication method according to claim 1 or 2 are implemented.

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