Communication and induction integrated low-power-consumption method in mixed scene

Abstract

The invention relates to the technical field of wireless communication, in particular to a communication and inductance integrated low-power-consumption method in a mixed scene, which comprises the following steps of: constructing an initial optimization problem, and aiming at minimizing the power consumption of a communication and inductance integrated system in the mixed scene; and solving is carried out by using semi-definite relaxation and one-dimensional search by taking a communication minimum rate requirement, different equipment energy collection minimum requirements, a difference value of sensing power in different target directions, and a power segmentation factor and a time segmentation factor of user energy collection as constraints. The invention provides a method capable of effectively balancing communication, sensing and energy transmission for a hybrid communication and sensing integrated scene through research, the energy utilization efficiency and the resource utilization rate of a communication system are improved while low power consumption of the system is ensured, and the method is expected to make an important contribution to development and popularization of Internet of Things equipment in the future and has a wide application prospect. And the Internet of Things technology is promoted to advance to a new height.

Description

Technical Field

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

[0002] In the future 6G network, IoT devices will have an increasing demand for communication and perception 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, so the demand for more efficient communication and energy transmission technologies is becoming increasingly urgent. However, traditional communication systems have the problems of high energy consumption and low resource utilization, especially as the number of users increases, the demand for energy in communication systems becomes more and more prominent.

[0003] At the same time, for energy harvesting devices produced by different manufacturers, there are often energy harvesting users of energy splitting protocols and power splitting protocols in actual scenarios. Communication users not only need to transmit data, but also need to perceive the environment. Therefore, how to achieve a low-power integrated method of synaesthesia in such a complex mixed scenario has become a key issue in current research. Summary of the invention

[0004] The purpose of the present invention is to provide a low-power consumption method for synaesthesia integration in a mixed 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 consumption 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 of perceived power in different target directions, and the power division factor and time division factor of user energy collection as constraints. k 、v m and the power division factor β for energy harvesting k and the time division factor t m ; where w k is the beamforming vector of the base station for the kth power-splitting energy harvesting protocol user, where v m is the beamforming vector of the base station for the mth time-division energy harvesting protocol user, k = 1, 2, …, K, m = 1, 2, …, M, K and M are the total number of power-division energy harvesting protocol and time-division 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 binary 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-slicing 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 device can collect. represents the beam strength 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 difference constraint of beam strength in different sensing directions.

[0020] Furthermore, in step S2, auxiliary variables are introduced to convert the initial optimization problem into the following 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 represents 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 a one-dimensional search. 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 mixed scenarios described in the present invention can meet the situation 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 mixed 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 a new level. 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 be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0041] Reference Figure 1 In this embodiment, a low-power consumption method for synaesthesia integration 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 of perceived power in different target directions, and the power division factor and time division factor of user energy collection as constraints. k 、v m and the power division factor β for energy harvesting k and the time division factor t m ; where w k is the beamforming vector of the base station for the kth power-splitting energy harvesting protocol user, where v m is the beamforming vector of the base station for the mth time-division energy harvesting protocol user, k = 1, 2, …, K, m = 1, 2, …, M, K and M are the total number of power-division energy harvesting protocol and time-division 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 binary 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-slicing 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 strength 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 difference constraint of beam strength in different sensing directions.

[0054] Specifically, in step S2, auxiliary variables are introduced to convert the initial optimization problem into the following 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 represents 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 a one-dimensional search. 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 the 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 Internet of Things devices in the future and promote the Internet of Things technology to a new level.

[0072] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered 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 of perceived power in different target directions, and the power division factor and time division factor of user energy collection as constraints. k 、v m and the power division factor β for energy harvesting k and the time division factor t m ; where w k is the beamforming vector of the base station for 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, 2. The low-power consumption method for synaesthesia integration in a mixed scenario according to claim 1, characterized in that: 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 binary 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-slicing 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 device can collect; represents the beam strength 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 difference constraint of beam strength in different sensing directions.

3. The low-power consumption method for synaesthesia integration in a mixed scenario according to claim 2, characterized in that: In step S2, auxiliary variables are introduced to convert the initial 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 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 represents that X is a positive semidefinite matrix, and Rank(·) represents the rank of the matrix.

4. The low-power consumption method for synaesthesia integration in a mixed scenario according to claim 3 is 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。 5. 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 a one-dimensional search. In each search process, fix t m , using the Convex Optimization Toolbox for convex solving.

Images