A low-power integrated method for inter-sensory perception in a mixed scene

Abstract

The application relates to the technical field of wireless communication, in particular to a mixed scene integrated sensing and communication low-power consumption method, which comprises the following steps: constructing an initial optimization problem, taking the minimum power consumption of an integrated sensing and communication system in a mixed scene as a target, and taking the minimum rate requirement of communication, the minimum energy collection requirement of different devices, the difference of sensing power in different target directions, and the power splitting factor and time splitting factor of user energy collection as constraints, and solving by using semi-definite relaxation and one-dimensional search. The application studies the mixed integrated sensing and communication scene, and proposes a method which can effectively balance communication, sensing and energy transmission, improves the energy utilization efficiency and resource utilization rate of a communication system while guaranteeing low power consumption of the system, is expected to make an important contribution to the development and popularization of future Internet of Things devices, and promotes the Internet of Things technology to a new height.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a low-power consumption method for synaesthesia integration in a hybrid scenario. Background Art

[0002] In the future of 6G networks, IoT devices will increasingly demand communication and sensing capabilities. Innovative applications such as augmented reality, smart cities, digital twins, smart factories, and autonomous driving are booming. These applications rely on wireless signals for data transmission and environmental perception, creating an increasingly urgent need for more efficient communication and energy transmission technologies. However, traditional communication systems suffer from high energy consumption and low resource utilization. This energy demand becomes increasingly prominent as the number of users increases.

[0003] At the same time, for energy-harvesting devices produced by different manufacturers, real-world scenarios often involve both energy-splitting and power-splitting protocols. Communication users not only need to transmit data but also perceive the environment. Therefore, achieving a low-power, integrated approach to synaesthesia in this complex, mixed scenario has become a key research issue. Summary of the Invention

[0004] The purpose of the present invention is to provide a low-power consumption method for synaesthesia integration in a hybrid scenario to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A low-power method for synaesthesia integration in a mixed scenario includes the following steps:

[0007] Step S1: Construct the initial optimization problem, with the goal of minimizing the power consumption of the synaesthesia integrated system in the hybrid scenario, and optimize the base station transmission beam w based on the minimum communication rate requirement, the minimum energy collection requirement of different devices, the difference in the perceived power in different target directions, and the power division factor and time division factor of the user energy collection as constraints. k 、v m and the power division factor β for energy harvesting k and time division factor t m ; where w k is the beamforming vector of the base station to the kth power-splitting energy harvesting protocol user, where v m is the beamforming vector of the base station for the mth time-splitting energy harvesting protocol user, k = 1, 2, ..., K, m = 1, 2, ..., M, K and M are the total number of power-splitting energy harvesting protocol and time-splitting energy harvesting protocol users in the hybrid scenario, respectively.

[0008] Step S2: Introducing auxiliary variables The initial optimization problem is equivalently transformed into a new expression form, where (·) H Represents the conjugate transpose of a matrix.

[0009] Step S3: Based on the problem obtained in step S2, use the semidefinite relaxation technique to discard the matrix and matrix rank constraint.

[0010] Step S4: Since t is fixed m When the problem in step S3 is a convex problem, a one-dimensional search t m , find the optimal solution to the problem.

[0011] Step S5: The optimal solution is recorded as And perform Cholesky decomposition to obtain the correlation factor between the optimal transmit beam and user energy collection,

[0012] Furthermore, in step S1, the initial optimization problem is constructed as follows:

[0013] The optimization goal is to minimize

[0014] The constraints are:

[0015]

[0016]

[0017] |P(θ P )-P(θ q )|≤P diff ,

[0018] 0<β k <1,0<t m <1, m=1,2,…,M, k=1,2,…,K

[0019] Among them, ||·|| represents the two-norm of the vector, log2(·) represents the logarithm with base 2, |·| represents the modulus value, and h k represents the channel vector from the base station to the kth power-splitting energy harvesting protocol user, denote the noise power of the user receiver of the power-splitting energy harvesting protocol and the noise power at the energy splitting location, respectively. represents the minimum communication rate requirement of the kth power-splitting energy harvesting protocol user, g m represents the channel vector from the base station to the mth time-segmented energy harvesting protocol user, denote the noise power of the user receiver of the time-splitting energy harvesting protocol and the noise power at the energy splitting point, respectively. represents the minimum communication rate requirement of the mth time-sliced ​​energy harvesting protocol user, represents the minimum energy harvesting requirement of the kth power-splitting energy harvesting protocol user, represents the minimum energy harvesting requirement of the mth time-sliced ​​energy harvesting protocol user, is a nonlinear energy harvesting model, where a and b are constants, and p max Indicates the maximum energy that the user equipment can collect. represents the beam intensity at angle θ, a(θ) is the angle of the base station antenna array in the sensing direction of θ, a(θ)=[1,e jπsinθ ,…,e jπ(N-1)sinθ ], N represents the number of base station transmitting antennas, e is the natural base, j is the imaginary unit, L is the number of radar sensing directions, P diff Indicates the beam strength difference constraint in different sensing directions.

[0020] Furthermore, in step S2, auxiliary variables are introduced to convert the initial optimization problem into the following equivalent problem:

[0021] The optimization goal is to minimize

[0022] The constraints are:

[0023]

[0024] |P(θ P )-P(θ q )|≤P diff ,

[0025] 0<β k <1,0<t m <1, m=1,2,…,M, k=1,2,…,K

[0026] W k ±0, V m ±0, m=1,2,…,M, k=1,2,…,K

[0027] Rank(W k )=1,Rank(V m )=1, m=1,2,…,M, k=1,2,…,K

[0028] Among them, P in (·) is E NLr (P in), Tr(·) represents the trace of the matrix, X±0 means that X is a positive semidefinite matrix, and Rank(·) represents the rank of the matrix.

[0029] Furthermore, in step S3, the semidefinite relaxation technique is used to discard the rank constraint and transform the optimization problem into the following problem:

[0030] The optimization goal is to minimize

[0031] The constraints are:

[0032]

[0033] |P(θ P )-P(θ q )|≤P diff ,

[0034] 0<β k <1,0<t m <1, m=1,2,…,M, k=1,2,…,K

[0035] W k ±0, V m ±0, m=1,2,…,M, k=1,2,…,K

[0036] Furthermore, in step S4, the time division factor t m Perform one-dimensional search, and in each search process, fix t m , using the Convex Optimization Toolbox for convex solving.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The low-power consumption method for integrated synaesthesia in hybrid scenarios described in the present invention meets the problem of inconsistent energy collection protocols of devices produced by multiple manufacturers in practical applications. Through this innovative low-power consumption method for integrated synaesthesia in hybrid scenarios, it is expected to make important contributions to the development and popularization of future Internet of Things devices and promote Internet of Things technology to new heights. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] Reference Figure 1 In this embodiment, a low-power consumption method for integrated synaesthesia in a mixed scenario specifically includes the following steps:

[0042] Step S1: Construct the initial optimization problem, with the goal of minimizing the power consumption of the synaesthesia integrated system in the hybrid scenario, and optimize the base station transmission beam w based on the minimum communication rate requirement, the minimum energy collection requirement of different devices, the difference in the perceived power in different target directions, and the power division factor and time division factor of the user energy collection as constraints. k 、v m and the power division factor β for energy harvesting k and time division factor t m ; where w k is the beamforming vector of the base station to the kth power-splitting energy harvesting protocol user, where v m is the beamforming vector of the base station for the mth time-splitting energy harvesting protocol user, k = 1, 2, ..., K, m = 1, 2, ..., M, K and M are the total number of power-splitting energy harvesting protocol and time-splitting energy harvesting protocol users in the hybrid scenario, respectively.

[0043] Step S2: Introducing auxiliary variables The initial optimization problem is equivalently transformed into a new expression form, where (·) H Represents the conjugate transpose of a matrix.

[0044] Step S3: Based on the problem obtained in step S2, use the semidefinite relaxation technique to discard the matrix and matrix rank constraint.

[0045] Step S4: Since t is fixed m When the problem in step S3 is a convex problem, a one-dimensional search t m , find the optimal solution to the problem.

[0046] Step S5: The optimal solution is recorded as And perform Cholesky decomposition to obtain the correlation factor between the optimal transmit beam and user energy collection,

[0047] Specifically, in step S1, the initial optimization problem is constructed as follows:

[0048] The optimization goal is to minimize

[0049] The constraints are:

[0050]

[0051] |P(θ P )-P(θ q )|≤P diff ,

[0052] 0<β k <1,0<t m <1, m=1,2,…,M, k=1,2,…,K

[0053] Among them, ||·|| represents the two-norm of the vector, log2(·) represents the logarithm with base 2, |·| represents the modulus value, and h k represents the channel vector from the base station to the kth power-splitting energy harvesting protocol user, denote the noise power of the user receiver of the power-splitting energy harvesting protocol and the noise power at the energy splitting location, respectively. represents the minimum communication rate requirement of the kth power-splitting energy harvesting protocol user, g m represents the channel vector from the base station to the mth time-segmented energy harvesting protocol user, denote the noise power of the user receiver of the time-splitting energy harvesting protocol and the noise power at the energy splitting point, respectively. represents the minimum communication rate requirement of the mth time-sliced ​​energy harvesting protocol user, represents the minimum energy harvesting requirement of the kth power-splitting energy harvesting protocol user, represents the minimum energy harvesting requirement of the mth time-sliced ​​energy harvesting protocol user, is a nonlinear energy harvesting model, where a and b are constants, and p max Indicates the maximum energy that the user equipment can collect. represents the beam intensity at angle θ, a(θ) is the angle of the base station antenna array in the sensing direction of θ, a(θ)=[1,e j πsinθ ,…,e jπ(N-1)sinθ ], N represents the number of base station transmitting antennas, e is the natural base, j is the imaginary unit, L is the number of radar sensing directions, P diff Indicates the beam strength difference constraint in different sensing directions.

[0054] Specifically, in step S2, auxiliary variables are introduced to convert the initial optimization problem into the following equivalent problem:

[0055] The optimization goal is to minimize

[0056] The constraints are:

[0057]

[0058] |P(θ P )-P(θ q )|≤P diff ,

[0059] 0<β k <1,0<t m <1, m=1,2,…,M, k=1,2,…,K

[0060] W k ±0, V m ±0, m=1,2,…,M, k=1,2,…,K

[0061] Rank(W k )=1,Rank(V m )=1, m=1,2,…,M, k=1,2,…,K

[0062] Among them, P in (·) is E NLr (P in ), Tr(·) represents the trace of the matrix, X±0 means that X is a positive semidefinite matrix, and Rank(·) represents the rank of the matrix.

[0063] Specifically, in step S3, the semidefinite relaxation technique is used to discard the rank constraint and transform the optimization problem into the following problem:

[0064] The optimization goal is to minimize

[0065] The constraints are:

[0066]

[0067] |P(θ P )-P(θ q )|≤P diff ,

[0068] 0<β k <1,0<t m <1, m=1,2,…,M, k=1,2,…,K

[0069] W k ±0, V m ±0, m=1,2,…,M, k=1,2,…,K

[0070] Specifically, in step S4, the time division factor t m Perform one-dimensional search, and in each search process, fix t m , using the Convex Optimization Toolbox for convex solving.

[0071] In summary, the present invention proposes a method that can effectively balance communication, perception and energy transmission by studying the hybrid synaesthesia integration scenario. While ensuring low power consumption of the system, it improves the energy efficiency and resource utilization of the communication system. It is expected to make important contributions to the development and popularization of future IoT devices and promote IoT technology to new heights.

[0072] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A low-power consumption method for synaesthesia integration in mixed scenarios, characterized in that: The following steps are involved: Step S1: Construct the initial optimization problem, with the goal of minimizing the power consumption of the synaesthesia integrated system in the hybrid scenario, and optimize the base station transmission beam w based on the minimum communication rate requirement, the minimum energy collection requirement of different devices, the difference in the perceived power in different target directions, and the power division factor and time division factor of the user energy collection as constraints. k 、v m and the power division factor β for energy harvesting k and time division factor t m ; where w k is the beamforming vector of the base station to the kth power-splitting energy harvesting protocol user, where v m is the beamforming vector of the base station for the mth time-splitting energy harvesting protocol user, k = 1, 2, ..., K, m = 1, 2, ..., M, K and M are the total number of power-splitting energy harvesting protocol and time-splitting energy harvesting protocol users in the hybrid scenario, respectively; Step S2: Introducing auxiliary variables The initial optimization problem is equivalently transformed into a new expression form, where (·) H represents the conjugate transpose of a matrix; Step S3: Based on the problem obtained in step S2, use the semidefinite relaxation technique to discard the matrix and matrix rank constraint; Step S4: Since t is fixed m When the problem in step S3 is a convex problem, a one-dimensional search t m , find the optimal solution to the problem; Step S5: The optimal solution is recorded as And perform Cholesky decomposition to obtain the correlation factor between the optimal transmit beam and user energy collection, In step S1, the initial optimization problem is constructed as follows: The optimization goal is to minimize The constraints are: 0<β k <1,0<t m <1,m=1,2,…,M,k=1,2,…,K Among them, ||·|| represents the two-norm of the vector, log2(·) represents the logarithm with base 2, |·| represents the modulus value, and h k represents the channel vector from the base station to the kth power-splitting energy harvesting protocol user, denote the noise power of the user receiver of the power-splitting energy harvesting protocol and the noise power at the energy splitting location, respectively. represents the minimum communication rate requirement of the kth power-splitting energy harvesting protocol user, g m represents the channel vector from the base station to the mth time-segmented energy harvesting protocol user, denote the noise power of the user receiver of the time-splitting energy harvesting protocol and the noise power at the energy splitting point, respectively. represents the minimum communication rate requirement of the mth time-sliced ​​energy harvesting protocol user, represents the minimum energy harvesting requirement of the kth power-splitting energy harvesting protocol user, represents the minimum energy harvesting requirement of the mth time-sliced ​​energy harvesting protocol user, is a nonlinear energy harvesting model, where a and b are constants, and p max Indicates the maximum energy that the user equipment can collect; represents the beam intensity at angle θ, a(θ) is the angle of the base station antenna array in the sensing direction of θ, a(θ)=[1,e j πsinθ ,…,e jπ(N-1)sinθ ], N represents the number of base station transmitting antennas, e is the natural base, j is the imaginary unit, L is the number of radar sensing directions, P diff Indicates the beam strength difference constraint in different sensing directions.

2. The low-power consumption method for synaesthesia integration in a mixed scenario according to claim 1, characterized in that: In step S2, auxiliary variables are introduced to convert the initial optimization problem into the following equivalent problem: The optimization goal is to minimize The constraints are: 0<β k <1,0<t m <1,m=1,2,…,M,k=1,2,…,K W k ±0,V m ±0,m=1,2,…,M,k=1,2,…,K Rank(W k )=1,Rank(V m )=1,m=1,2,…,M,k=1,2,…,K Among them, P in (·) is E NLr (P in ), Tr(·) represents the trace of the matrix, X±0 means that X is a positive semidefinite matrix, and Rank(·) represents the rank of the matrix.

3. The low-power consumption method for synaesthesia integration in a mixed scenario according to claim 2, characterized in that: In step S3, the semidefinite relaxation technique is used to discard the rank constraint and transform the optimization problem into the following problem: The optimization goal is to minimize The constraints are: 0<β k <1,0<t m <1,m=1,2,…,M,k=1,2,…,K W k ±0,V m ±0,m=1,2,…,M,k=1,2,…,K。 4. The low-power consumption method for synaesthesia integration in a mixed scenario according to claim 1, characterized in that: In step S4, the time division factor t m Perform one-dimensional search, and in each search process, fix t m , using the Convex Optimization Toolbox for convex solving.

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