Sum rate increasing method suitable for flow-state antenna-assisted symbiotic communication system

Abstract

The invention discloses a sum rate increasing method applicable to a flow antenna-assisted symbiotic communication system, which comprises the following steps of: firstly, establishing a channel model among a base station, user equipment and a backscattering device; then, the signal propagation distance difference between the user equipment and the backscattering equipment is determined through the position of the flow state antenna and the pitch angle and the azimuth angle of the transmission path; then, calculating the phase difference of each signal path from the user equipment to the base station and the phase difference of each signal path from the backscattering equipment to the base station according to the propagation distance difference; and finally, optimizing a flow antenna position and a beam forming parameter through a particle swarm algorithm to improve the sum rate of the symbiotic communication system. According to the invention, the particle swarm optimization is utilized to find a better flow state antenna position and a better beam forming vector, the system sum rate can be improved, the space diversity and multiplexing effects are increased, the service quality of a user is improved, and the effect is better than that of a traditional fixed position antenna.

Description

Technical Field

[0001] The present invention belongs to the technical field of mobile communications, and in particular relates to a method for improving the sum rate applicable to a fluid antenna-assisted symbiotic communication system. Background Art

[0002] The development of the fluid antenna system (FAS) stems from the integration of traditional solid-state antennas and intelligent material technologies. The fluid antenna realizes dynamic regulation of the antenna shape and electromagnetic characteristics through a deformable fluid structure. The fluid antenna usually consists of a conductive liquid encapsulated in a flexible container, and the flow and distribution of the liquid are controlled by an external driving device. This antenna can adjust its position on the mobile plane in real time under different working environments to adapt to changing communication requirements. The fluid antenna can be deployed in real time to a position with more favorable channel conditions to achieve a higher spatial diversity gain. Optimizing the antenna position can improve the overall efficiency of the wireless communication system. This ability to dynamically adjust the antenna position can significantly enhance the performance of the wireless communication system against fading.

[0003] Symbiotic Radio (SR) is an emerging communication concept and technical framework, aiming to achieve optimized utilization of resources, adaptive adjustment of the network, and improvement of the overall communication efficiency through establishing cooperation among different communication systems. With the proliferation of wireless devices, the electromagnetic space has become increasingly crowded and complex. In the case of limited radio resources, adapting to an increasing number of wireless devices has become a challenging task. The backscatter device (BD) is one of the core technologies for realizing symbiotic communication. Different from traditional active communication devices, the backscatter device does not need to emit electromagnetic waves by itself, but realizes data transmission by reflecting and modulating radio signals from an external source. Therefore, the backscatter device can save frequency band resources and avoid introducing additional interference. It can effectively solve the problem of resource limitation in the wireless communication system. However, in existing backscatter device-assisted symbiotic communications, traditional fixed-position antennas are used, which will result in limitations in spatial diversity and spatial degrees of freedom. With the miniaturization development of Internet of Things devices, using multiple antennas on mobile devices will lead to an increase in antenna coupling, making antenna arrangement very difficult. Summary of the Invention

[0004] Object of the Invention: The object of the present invention is to provide a method for improving the sum rate applicable to a fluid antenna-assisted symbiotic communication system. By using fluid antennas at the backscatter device and the user terminal, and adopting the particle swarm optimization algorithm to optimize the antenna position and beamforming vector, the system sum rate can be effectively improved, and the ability of the system to resist channel fading can be enhanced.

[0005] Technical solution: A sum rate improvement method for a fluid antenna-assisted coexisting communication system of the present invention includes establishing a channel model, calculating the signal propagation distance difference, calculating the phase difference of each signal path, calculating the weighted sum rate according to the direct link and the indirect link, and optimizing the sum rate of the coexisting communication system through the particle swarm optimization algorithm, including the following steps:

[0006] Step S1. In the system model, communication is carried out between the base station BS, the user equipment UE, and the backscatter device BD through a wireless channel; a fluid antenna is equipped in each of the user equipment and the backscatter device, and multiple antennas are equipped at the base station end; the fluid antenna is used to enhance the channel between the user equipment and the base station.

[0007] Step S2. Determine the signal propagation distance difference between the user equipment and the backscatter device through the position of the fluid antenna and the elevation angle and azimuth angle of the transmission path.

[0008] Step S3. Calculate the phase difference of each signal path from the user equipment to the base station and from the backscatter device to the base station according to the signal propagation distance difference in S2.

[0009] Step S4. Based on the phase difference in S2, establish a direct link for communication between the user equipment and the base station and an indirect link for the user signal to reach the base station through the backscatter device according to the formula, and obtain a weighted sum rate formula.

[0010] Step S5. Optimize the position of the fluid antenna and the beamforming parameters through the particle swarm optimization algorithm to improve the sum rate of the coexisting communication system.

[0011] Further, in step S1, the system model is specifically:

[0012] Step S1.1. The direct link channel between the user and the base station is h, and the indirect link for the user signal to reach the base station through the backscatter device is g; the base station is equipped with M antennas, the user is equipped with a fluid antenna with L Tx ports, and the backscatter device is equipped with a fluid antenna with L BD ports; space diversity reception is adopted at the base station end.

[0013] Further, in step S2, the signal propagation distance difference calculation method is as follows:

[0014] Step S2.1. The signal propagation distance difference between the user end signal propagation and the position [0,0] is:

[0015] ρ Tx,l (z 1 ) = x 1 sinθ Tx,l cosφ Tx,l +y 1 cosθTx,l

[0016] is the position of the client-side fluid antenna, represents a real number, θ Tx,l ∈ [0, π], φ Tx,l ∈ [0, π] respectively represent the elevation angle and azimuth angle at which the l-th path arrives at the first antenna of the base station;

[0017] Step S2.2. The signal propagation distance difference between the signal propagation of the backscatter device and the position [0, 0] is:

[0018] ρ BD,l (z 2 ) = x 2 sinθ BD,l cosφ BD,l +y 2 cosθ BD,l

[0019] The vector is the position of the fluid antenna of the backscatter device, represents a real number, θ BD,l ∈ [0, π], φ BD,l ∈ [0, π] respectively represent the elevation angle and azimuth angle at which the l-th path arrives at the first antenna of the base station.

[0020] Furthermore, in step S3, the calculation method of the phase difference is as follows:

[0021] Step S3.1. The direct link transmit field response matrix received by the base station antenna from the user to the base station is:

[0022]

[0023] where, T represents vector transpose, λ is the wavelength, e is the natural constant, j is the imaginary unit, ρ TR,l (z 1 ) is the propagation distance difference of the user signal received at the base station; the transmit field response matrix from the user to the base station is expressed as:

[0024]

[0025] is a complex number, L Tx is the number of ports of the fluid antenna at the user side, M is the number of base station antennas, representing the phase difference of each path received by the base station from the user side;

[0026] Step S3.2. The transmit field response matrix received by the base station antenna from the backscatter device to the base station is:

[0027]

[0028] Among them, T represents vector transpose, λ is the wavelength, e is the natural constant, j is the imaginary unit, and ρ BD,l (z 2 ) is the propagation distance difference of the signal received by the base station from the backscattering device; the transmit field response matrix from the backscattering device to the base station is expressed as:

[0029]

[0030] is a complex number, L BD is the number of fluid state antenna ports at the backscattering device end, M is the number of base station antennas, representing the phase difference of each path from the backscattering device end received by the base station.

[0031] Furthermore, in step S4, establishing a direct link between the user equipment and the base station and an indirect link for the user signal to reach the base station through the backscattering device according to the formula, and obtaining the weighted sum rate formula, specifically:

[0032] Step S4.1, represents the path response vector of the channel from the user to the base station in the direct link, T represents vector transpose, is the path loss coefficient, L Tx is the number of signal path quantities, i.e., the number of fluid state antenna ports at the user end; the direct link channel H is the conjugate transpose of the matrix, A TR is the transmit field response matrix from the user to the base station;

[0033] Step S4.2, represents the path response vector of the channel from the backscattering device to the base station, T represents vector transpose, is the path loss coefficient, L BD is the number of fluid state antenna ports at the backscattering device end; represents the channel matrix between the backscattering device and the base station, H is the conjugate transpose of the matrix, A BR is the transmit field response matrix from the backscattering device to the base station;

[0034] Step S4.3, the channel from the user end to the backscattering is where is the transmit field response vector of the user end, T represents vector transpose, λ is the wavelength, e is the natural constant, j is the imaginary unit, ρ Tx,l (z 1 ) is the signal propagation distance difference between the signal propagation of the user end and the position [0,0]; is the receive field response vector of the backscattering device, L Tx is the number of fluid state antenna ports at the user end, L BD is the number of fluid state antenna ports at the backscattering device end, ρBD,l (z 2 ) is the signal propagation distance difference between the signal propagation at the backscatter device end and the position [0,0]. represents the path response matrix between the user and the backscatter device, and all elements are path loss coefficients; the indirect link g = cg 1 , g 1 represents the channel matrix from the backscatter device to the base station.

[0035] Step S4.4: The fluid antenna at the user end is used to enhance the direct link h, and the fluid antenna of the backscatter device is used to enhance the channel g from the backscatter device to the base station 1 ; after determining the positions of the two fluid antennas, the transmit field response vector a Tx (z 1 ) and the receive field response vector a BD (z 2 ) of the backscatter device are constant; since the path loss coefficient remains unchanged, c remains unchanged;

[0036] Step S4.5: Optimize it to the weighted sum rate, and its formula is:

[0037]

[0038] where, P is the transmit power, ρ is the weight of the coexisting communication direct link, and the range is [0,1], v = ιh + χg is the beamforming vector, and its modulus ||v|| 2 is 1, ι, χ are low-complexity beamforming vector parameters, J is the number of direct link continuous signal periods required to complete one indirect link communication, is the mathematical expectation, h is the direct link channel, α represents the power reflection coefficient of the backscatter device, g is the indirect link channel, σ 2 is the Gaussian white noise, and H is the matrix conjugate transpose.

[0039] Further, in step S5, the particle swarm algorithm is specifically as follows:

[0040] Step S5.1: Use the particle swarm algorithm to optimize the sum rate in S4, set the initial population size I, that is, the maximum number of particles searched in the fluid antenna position and the beamforming vector, set the particle velocity v, the particle velocity limit αlimit, that is, the particle velocity range, to prevent the particle from being unable to accurately find the optimal solution due to too long a flight distance; set the particle range limit βlimit, and the first four variables x 1 , y 1 , x 2 , y 2The position of the fluid antenna is in the range of [-A / 2, A / 2], where A is the active range of the fluid antenna. The last two variables ι and χ are low-complexity beamforming vector parameters, with a range of [0.5, 1.5].

[0041] Step S5.2: Set the range of the adaptive inertia weight so that μ is linearly adjusted with the iteration. Set the learning factors c1 and c2 to control the influence of the particle's personal best and the global best.

[0042] Step S5.3: Update the particle velocity and particle position after obtaining the fitness.

[0043] Step S5.4: Use the sum rate calculated in S4 as the fitness of the particle. Compare the current fitness of the particle with its historical best fitness. If the current fitness is higher, update the historical best position of the particle to the current position. If the current fitness is lower than the historical best fitness, keep the historical best position unchanged. If the current position is higher than the limit, set the position on the boundary αlimit. Similarly, if the current particle velocity is higher than the limit, select the maximum velocity.

[0044] Step S5.5: Determine whether the termination condition is met, that is, whether the number of iterations has reached the maximum number of iterations L ite , if it is satisfied, exit the algorithm; if not, continue to enter the loop.

[0045] Furthermore, Step S5.3 is specifically as follows:

[0046] A community is composed of I particles, and the i-th particle is represented as a vector; denoted as:

[0047] X i =(x i1 ,y i1 ,x i2 ,y i2 ,ι,χ)

[0048] i = 1, 2,..., N. The velocity of the i-th particle is also a 6-dimensional vector, denoted as:

[0049] V i =(v i1 ,v i2 ,…v i6 ), i = 1, 2,..., N

[0050] When the i-th particle in the t-th generation evolves to the i+1-th generation, the particle velocity and position are updated according to the following formula:

[0051] V ij (t + 1)=μV ij (t)+c 1 r 1(t)[P ij (t)-X ij (t)]+c 2 r 2 (t)[P gj (t)-X ij (t)]

[0052] X ij (t + 1) = X ij (t)+V ij (t + 1)

[0053] μ is the self-inertia parameter, j represents the j-th variable in the vector, [P ij (t)-X ij (t)] is the movement towards the individual extreme value, [P gj (t)-X ij (t)] is the movement towards the population extreme value, r 1 (t) and r 2 (t) are random numbers between [0, 1].

[0054] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the steps of the method of the present invention.

[0055] The present invention also discloses a computer-readable storage medium, on which a computer program / instructions are stored. When the computer program / instructions are executed by a processor, the steps of the method of the present invention are implemented.

[0056] The present invention also discloses a computer program product, including a computer program / instructions. When the computer program / instructions are executed by a processor, the steps of the method of the present invention are implemented.

[0057] Advantageous effects: Compared with the prior art, the present invention has the following remarkable advantages:

[0058] Centering around the user, the present invention uses a multi-population optimization algorithm to find the optimal parameter configuration, which can increase the sum rate received at the base station end, improve the service quality of users, and enhance the system's performance against fading. Compared with traditional fixed-position antennas, it can move the position to avoid deep fading to effectively improve the diversity effect and enhance the channel gain. In the simulation, the system performance has a significant improvement compared with traditional antennas. When the signal-to-noise ratio is 70 dB, the sum rate of the optimized fluid antenna-assisted symbiotic communication system can be 3.69 bps / Hz higher than that of the traditional fixed-position antenna-assisted symbiotic communication system. Description of the Drawings

[0059] Figure 1 It is a schematic diagram of a fluid antenna-assisted symbiotic communication system;

[0060] Figure 2 Flow chart of the sum rate improvement method for a fluid antenna-assisted coexisting communication system;

[0061] Figure 3 Flow chart of the sum rate calculation for a fluid antenna-assisted coexisting communication system;

[0062] Figure 4 Comparison chart of the sum rates between a fluid antenna-assisted coexisting communication system and a traditional fixed-position antenna-assisted coexisting communication system. Specific implementation mode

[0063] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0064] The fluid antenna-assisted coexisting communication system model of this embodiment is as Figure 1 shown. Assume that a user deploying a fluid antenna sends signals to a multi-antenna base station through a direct link and an indirect link with a backscatter device deploying a fluid antenna. The number of base station antennas is M, the number of fluid antenna ports at the user end is Tx, and the number of fluid antenna ports deployed on the backscatter device is BD. The direct link channel is h, the channel from the user to the backscatter device is c, and the channel from the backscatter device to the base station is g 1 .

[0065] This example is based on a method for improving the sum rate of a fluid antenna-assisted coexisting communication system, including establishing a channel model, calculating the signal propagation distance difference, calculating the phase difference of each signal path, calculating the weighted sum rate according to the direct link and the indirect link, and optimizing the sum rate of the coexisting communication system through a particle swarm algorithm. The process is as Figure 2 shown. It includes the following steps:

[0066] Step S1.1: The direct link channel between the user and the base station is h, and the indirect link for the user signal to reach the base station through the backscatter device is g. The base station is equipped with M antennas, the user is equipped with a fluid antenna with L Tx ports, and the backscatter device is equipped with a fluid antenna with L BD ports. Spatial diversity reception is adopted at the base station end.

[0067] After establishing the channel model, calculate the propagation distance difference of each signal path, including the following steps:

[0068] Step S2.1: The signal propagation distance difference between the user end signal propagation and the position [0,0] is ρ Tx,l (z 1 ) = x 1 sinθ Tx,l cosφ Tx,l +y 1 cosθ Tx,l , is the position of the user - side fluid - state antenna, represents a real number. θ Tx,l ∈[0, π], φ Tx,l ∈[0, π] respectively represent the elevation angle and azimuth angle of the l - th path arriving at the first antenna of the base station;

[0069] Step S2.2: The signal propagation distance difference between the signal propagation of the backscatter device and the position [0, 0] is ρ BD,l (z 2 ) = x 2 sinθ BD,l cosφ BD,l +y 2 cosθ BD,l , the vector is the position of the fluid - state antenna of the backscatter device, represents a real number. θ BD,l ∈[0, π], φ BD,l ∈[0, π] respectively represent the elevation angle and azimuth angle of the l - th path arriving at the first antenna of the base station.

[0070] After calculating the signal path propagation distance difference, calculate the phase difference of each signal path, including the following steps:

[0071] Step S3.1: The direct - link transmit - field response matrix received by the base - station antenna from the user - side to the base - station is T represents vector transpose, λ is the wavelength, e is the natural constant, j is the imaginary unit, ρ TR,l (z 1 ) is the propagation distance difference of the user - signal received at the base - station end. The transmit - field response matrix from the user to the base - station is expressed as is a complex number, L Tx is the number of ports of the user - side fluid - state antenna, M is the number of base - station antennas, representing the phase difference of each path received by the base - station end from the user - side;

[0072] Step S3.2: The transmit - field response matrix received by the base - station antenna from the backscatter device to the base - station is T represents vector transpose, λ is the wavelength, e is the natural constant, j is the imaginary unit, ρ BD,l (z 2 ) is the propagation distance difference of the signal received by the base - station end from the backscatter device. The transmit - field response matrix from the backscatter device to the base - station can be expressed as is a complex number, L BD is the number of ports of the fluid - state antenna at the backscatter - device end, M is the number of base - station antennas. It represents the phase difference of each path received by the base - station end from the backscatter - device end.

[0073] After calculating the phase difference of each path in the computational signal path, calculate the weighted sum rate, including the following steps:

[0074] Step S4.1 Denote the path response vector of the user-to-base station channel in the direct link, T represents vector transpose, is the path loss coefficient, L Tx is the number of signal path, i.e., the number of fluid state antenna ports at the user end. The direct link channel H is the conjugate transpose of the matrix, A TR is the transmit field response matrix from the user to the base station;

[0075] Step S4.2 Denote the path response vector of the backscatter device-to-base station channel, T represents vector transpose, is the path loss coefficient, L BD is the number of fluid state antenna ports at the backscatter device end. Denote the channel matrix between the backscatter device and the base station, H is the conjugate transpose of the matrix, A BR is the transmit field response matrix from the backscatter device to the base station.

[0076] Step S4.3, the channel from the user end to the backscatter is where is the transmit field response vector of the user end, T represents vector transpose, λ is the wavelength, e is the natural constant, j is the imaginary unit, ρ Tx,l (z 1 ) is the signal propagation distance difference between the signal propagation at the user end and the position [0,0]; is the receive field response vector of the backscatter device, L Tx is the number of fluid state antenna ports at the user end, L BD is the number of fluid state antenna ports at the backscatter device end, ρ BD,l (z 2 ) is the signal propagation distance difference between the signal propagation at the backscatter device end and the position [0,0]; Denote the path response matrix between the user and the backscatter device, and all elements are path loss coefficients; the indirect link g = cg 1 , g 1 denotes the channel matrix between the backscatter device and the base station;

[0077] Step S4.4, the fluid state antenna at the user end is used to enhance the direct link h, and the fluid state antenna of the backscatter device is used to enhance the channel g from the backscatter device to the base station 1 . Therefore, after determining the positions of the above two fluid state antennas, the transmit field response vector a of the user end Tx (z 1)with the receiving field response vector a of the backscattering device BD (z 2 ) is constant. Since the path loss coefficient remains unchanged, c is considered invariant.

[0078] Step S4.5 is optimized to the weighted sum rate, and its formula is:

[0079]

[0080] where P is the transmit power. ρ is the weight of the coexisting communication direct link, and its range is [0,1]. v = ιh + χg is the beamforming vector, and its modulus ||v|| 2 is 1, and ι, χ are low-complexity beamforming vector parameters. J is the number of direct link continuous signal periods required to complete an indirect link communication, is the mathematical expectation. h is the direct link channel, and α represents the power reflection coefficient of the backscattering device. g is the indirect link channel, and σ 2 is the Gaussian white noise, and H is the matrix conjugate transpose.

[0081] Finally, the particle swarm algorithm is used to optimize the fluid antenna position and the beamforming vector to obtain a larger weighted sum rate, including the following steps:

[0082] Step S5.1: Use the particle swarm algorithm to optimize the sum rate in S4. Set the initial population size I, that is, the maximum number of particles searched in the fluid antenna position and the beamforming vector. Set the particle velocity v and the particle velocity limit αlimit, that is, the particle velocity range, to prevent the particles from being unable to accurately find the optimal solution due to flying too far. Set the particle range limit βlimit. The first four variables x 1 , y 1 , x 2 , y 2 are the fluid antenna positions, and the range is [-A / 2, A / 2], where A is the active range of the fluid antenna. The last two variables ι, χ are low-complexity beamforming vector parameters, and the range is [0.5, 1.5];

[0083] Step S5.2: Set the range of the adaptive inertia weight so that μ is linearly adjusted with the iteration. Set the learning factors c1 and c2 to control the influence of the particle by the individual best value and the group best value;

[0084] Step S5.3: Update the particle velocity and the particle position after obtaining the fitness. A community is composed of I particles, and the i-th particle is represented as a vector. Denote it as X i =(x i1 , y i1 , x i2 , y i2, ι, χ), i = 1, 2, …, N. The velocity of the i-th particle is also a 6-dimensional vector, denoted as: V i =(v i1 , v i2 , … v i6 ), i = 1, 2, …, N. When the i-th particle in the t-th generation evolves to the (i + 1)-th generation, the particle velocity and position are updated according to the following equations:

[0085] V ij (t + 1)= μV ij (t)+ c 1 r 1 (t)

P ij (t)- X ij (t)

P gj (t)- X ij (t)

[0086] X ij (t + 1)= X ij (t)+ V ij (t + 1)

[0087] μ is the self-inertia parameter, j represents the j-th variable in the vector,

P ij (t)- X ij (t)

[0088] Step S5.4: Use the sum rate calculated in S4 as the fitness of the particle. Compare the fitness of the current particle with its historical best fitness. If the current fitness is higher, update the historical best position of the particle to the current position. If the current fitness is lower than the historical best fitness, keep the historical best position unchanged. If the current position is higher than the limit, set the position on the boundary αlimit. Similarly, if the current particle velocity is higher than the limit, select the maximum velocity.

[0089] Step S5.5: Determine whether the termination condition is satisfied, whether the number of iterations has reached the maximum number of iterations L ite . If satisfied, exit the algorithm; if not, continue to enter the loop.

[0090] As Figure 4 shown, when the basic signal-to-noise ratio is the same, the optimized fluid antenna-assisted symbiotic communication system has a greater improvement in sum rate than the traditional fixed-position antenna-assisted symbiotic communication system.

[0091] The present invention takes the user as the center, uses a multi-population optimization algorithm to find the optimal parameter configuration, can increase the sum rate received at the base station side, improve the service quality of users, and enhance the performance of the system against fading, and the effect is better than that of traditional fixed-position antennas.

Claims

1. A method for improving the sum rate of a flow antenna assisted symbiotic communication system, characterized in that: The steps include: Step S1, in the system model, the base station BS, the user equipment UE and the backscattering device BD communicate through a wireless channel; the user equipment and the backscattering device are each equipped with a flow antenna, and the base station is equipped with multiple antennas; the flow antenna is used to enhance the channel between the user equipment and the base station; Step S2: determining the signal propagation distance difference between the user equipment and the backscattering device through the flow antenna position and the elevation angle and azimuth angle of the transmission path; Step S3: Calculate the phase difference of each signal path from the user equipment to the base station and from the backscattering device to the base station according to the signal propagation distance difference in S2; Step S4: Based on the phase difference in S2, a direct link for communication between the user equipment and the base station and an indirect link for the user signal to reach the base station through the backscattering device are established according to a formula, and a weighted sum rate formula is obtained; Step S5: Optimize the flow antenna position and beamforming parameters through particle swarm algorithm to improve the sum rate of the symbiotic communication system.

2. According to claim 1, a method for improving the sum rate applicable to a flow antenna assisted symbiotic communication system is characterized in that: In step S1, the system model is specifically: Step S1.1, the direct link channel between the user and the base station is h, and the indirect link for the user signal to reach the base station through the backscatter device is g; The base station is equipped with M antennas, and the user is equipped with L Tx The backscatter device is equipped with a flow antenna with L BD A flow antenna with 2 ports; spatial diversity reception is used at the base station end.

3. The method for improving the sum rate of a flow antenna assisted symbiotic communication system according to claim 1, characterized in that: In step S2, the signal propagation distance difference is calculated as follows: Step S2.1: The signal propagation distance difference between the user end signal propagation and the position [0,0] is: r Tx,l (z1)=x1sinθ Tx,l cosφ Tx,l +y1cosθ Tx,l is the location of the user-side streaming antenna, represents a real number, θ Tx,l ∈[0,π],φ Tx,l ∈[0,π] respectively represent the elevation angle and azimuth angle of the lth path reaching the first antenna of the base station; Step S2.2, the difference in signal propagation distance between the backscattering device signal propagation and the position [0,0] is: r BD,l (z2)=x2sinθ BD,l cosφ BD,l +y2cosθ BD,l vector is the position of the backscatter device flow antenna, represents a real number, θ BD,l ∈[0,π],φ BD,l ∈[0,π] respectively represent the elevation angle and azimuth angle of the lth path reaching the first antenna of the base station.

4. The method for improving the sum rate of a flow antenna assisted symbiotic communication system according to claim 1, characterized in that: In step S3, the phase difference is calculated as follows: Step S3.1: The base station antenna receives the direct link transmission field response matrix from the user end to the base station: Where T represents the vector transpose, λ is the wavelength, e is the natural constant, j is the imaginary unit, ρ TR,l (z1) is the propagation distance difference of the user signal received by the base station; the transmission field response matrix from the user to the base station is expressed as: is a plural number, L Tx is the number of flow antenna ports at the user end, M is the number of base station antennas, and represents the phase difference between the base station end and each path of the user end; Step S3.2: The base station antenna receives the transmission field response matrix of the backscatter device to the base station: Where T represents the vector transpose, λ is the wavelength, e is the natural constant, j is the imaginary unit, ρ BD,l (z2) is the propagation distance difference of the backscatter device signal received by the base station end; the transmission field response matrix from the backscatter device to the base station is expressed as: is a plural number, L BD is the number of flow antenna ports at the backscatter device end, M is the number of base station antennas, and represents the phase difference of each path received by the base station end at the backscatter device end.

5. The method for improving the sum rate of a flow antenna assisted symbiotic communication system according to claim 1, characterized in that: In step S4, the direct link for communication between the user equipment and the base station and the indirect link for the user signal to reach the base station through the backscattering device are established according to the formula, and a weighted sum rate formula is obtained, which is specifically: Step S4.1: represents the path response vector of the user-to-base station channel in the direct link, T represents the vector transpose, is the path loss coefficient, L Tx is the number of channel paths, i.e. the number of flow antenna ports at the user end; direct link channel H is the matrix conjugate transpose, A TR is the transmission field response matrix from the user to the base station; Step S4.2: represents the path response vector of the backscatter device to the base station channel, T represents the vector transpose, is the path loss coefficient, L BD is the number of flow antenna ports at the backscatter device end; represents the channel matrix between the backscatter device and the base station, H is the matrix conjugate transpose, A BR is the transmission field response matrix from the backscatter device to the base station; Step S4.3: The channel from the user end to the backscatter is in is the transmitting field response vector of the user end, T represents the vector transpose, λ is the wavelength, e is the natural constant, j is the imaginary unit, ρ Tx,l (z1) is the signal propagation distance difference between the user end signal propagation and the position [0,0]; is the receiving field response vector of the backscattering device, L Tx is the number of flow antenna ports at the user end, L BD is the number of flow antenna ports at the backscattering device end, ρ BD,l (z2) is the signal propagation distance difference between the signal propagation at the backscattering device end and the position [0,0]; represents the path response matrix between the user and the backscatter device, and its elements are all path loss coefficients; the indirect link g = cg1, g1 represents the channel matrix between the backscatter device and the base station; Step S4.4, the flow antenna of the user end is used to enhance the direct link h, and the flow antenna of the backscatter device is used to enhance the channel g1 from the backscatter device to the base station; after determining the positions of the two flow antennas, the transmission field response vector a of the user end Tx (z1) and the receiving field response vector a of the backscattering device BD (z2) is constant; since the path loss coefficient does not change, c does not change; Step S4.5, optimize to weighted sum rate, the formula is: Where P is the transmit power, ρ is the weight of the symbiotic communication direct link, ranging from [0,1], and v = ιh + χg is the beamforming vector, whose modulus ||v|| 2 is 1, ι,χ are low complexity beamforming vector parameters, J is the direct link continuous signal period required to complete an indirect link communication, is the mathematical expectation, h is the direct link channel, α is the power reflection coefficient of the backscatter device, g is the indirect link channel, σ 2 is Gaussian white noise, and H is the matrix conjugate transpose.

6. The method for improving the sum rate of a flow antenna assisted symbiotic communication system according to claim 1, characterized in that: In step S5, the particle swarm algorithm is specifically as follows: Step S5.1, use the particle swarm algorithm to optimize the sum rate in S4, set the initial population size I, that is, the maximum number of particles to be found in the flow antenna position and the beamforming vector, set the particle speed v, the particle speed limit αlimit, that is, the particle speed range, to prevent the particles from being unable to accurately find the optimal solution due to the long flight distance; set the particle range limit βlimit, the first four variables x1, y1, x2, y2 in the algorithm are the flow antenna positions, the range is [-A / 2, A / 2], A is the flow antenna activity range, and the last two variables ι, χ are low-complexity beamforming vector parameters, the range is [0.5, 1.5]; Step S5.2, setting the range of the adaptive inertia weight so that μ is linearly adjusted with iteration, and setting the learning factors c1 and c2 to control the influence of the individual maximum value and the group maximum value on the particles; Step S5.3, after obtaining the fitness, update the particle velocity and particle position; Step S5.4, the sum rate calculated by S4 is used as the fitness of the particle, and the fitness of the current particle is compared with its historical best fitness; if the current fitness is higher, the historical best position of the particle is updated to the current position; if the current fitness is lower than the historical best fitness, the historical best position is kept unchanged; if the current position is higher than the limit, the position is set on the boundary αlimit, and similarly, if the current particle speed is higher than the limit, the maximum speed is selected; Step S5.5: Determine whether the termination condition is met and whether the number of iterations reaches the maximum number of iterations L ite If it is satisfied, the algorithm exits; if it is not satisfied, the loop continues.

7. A method for improving the sum rate of a flow antenna assisted symbiotic communication system according to claim 6, characterized in that: Step S5.3 is specifically as follows: A colony is composed of I particles, where the i-th particle is represented as a vector; denoted as: X i =(x i1 ,y i1 ,x i2 ,y i2 ,i,x) i=1,2,…,N, the velocity of the i-th particle is also a 6-dimensional vector, denoted as: V i =(v i1 ,v i2 ,…v i6 ),i=1,2,…,N When the i-th particle of the t-th generation evolves to the i+1-th generation, the particle speed and position are updated according to the following formula: V ij (t+1)=μV ij (t)+c1r1(t)[P ij (t)-X ij (t)]+c2r2(t)[P gj (t)-X ij (t)】 X ij (t+1)=X ij (t)+V ij (t+1) μ is its own inertia parameter, j represents the jth variable in the vector, [P ij (t)-X ij (t)] is moving towards the individual extreme value, [P gj (t)-X ij (t)] is moving towards the population extreme value, r1(t) and r2(t) are random numbers between [0,1].

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method of claim 1.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to claim 1 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to claim 1 are implemented.

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