A Fusion Method of Positioning, Communication and Computing

Abstract

The present invention discloses a method for integrating positioning, communication and computing. The system consists of a control center, several multi-antenna base stations with edge computing servers deployed, and multiple single-antenna mobile terminals with limited computing, and there are multiple positioning targets at the same time. The mobile terminal offloads the tasks that need to be calculated to one of the base stations by transmitting mutually orthogonal positioning-communication dual-function signals, and performs target positioning at the same time. After receiving the signal, the base station extracts the positioning information and transmits it to the control center to estimate the target position on the one hand, and decodes the signal to obtain the task data on the other hand, and sends it to the mobile edge computing server for calculation. After the calculation is completed, the base station transmits the result back to the corresponding mobile terminal. The present invention provides an effective method for integrating positioning, communication and computing for wireless networks.

Description

Technical Field

[0001] The present invention relates to the field of wireless communications, and in particular to a method for integrating positioning, communication and calculation. Background Art

[0002] In recent years, a series of intelligent applications such as smart cities, autonomous driving, and immersive human-computer interaction have been continuously developed. In the future, emerging data-driven businesses have put forward higher requirements for wireless networks. In the existing 5G mobile communication system, the positioning function, communication function, and computing function work independently. This separated working mode will undoubtedly cause waste of hardware resources and wireless spectrum resources, and will lead to problems such as high information processing latency, thus failing to meet the needs of emerging wireless services. Therefore, the future 6G wireless network is no longer a simple pipeline for transmitting data, but also a network that can perceive everything, connect everything, and have endogenous intelligence, with deep integration of perception, communication, and computing for the full interconnection of man, machine, and object.

[0003] In this case, the future 6G network will not only need to use positioning technology for real-time target detection and environmental perception, but also use ultra-high broadband communication to transmit positioning data to widely distributed computing nodes for intelligent analysis, processing and control, so as to achieve ideal end-to-end performance. On the one hand, the communication system and the positioning system have great similarities in RF transceivers, channel characteristics and signal processing, which has given rise to the development of integrated positioning and communication technology. On the other hand, the rise of artificial intelligence technology represented by deep learning and distributed learning has played a great role in the fields of intelligent information processing, greatly increasing the possibility of deep integration of positioning, communication and computing.

[0004] Therefore, designing a fusion method of positioning, communication, and computing for future 6G wireless networks can make full use of hardware resources and frequency resources to jointly improve positioning accuracy, transmission quality, and computing efficiency, thereby meeting the needs of future intelligent emerging services. Summary of the invention

[0005] In order to solve the problems of limited computing of mobile terminals, separation of positioning-communication-computing functions, low resource utilization, etc., the present invention proposes a method for integrating positioning, communication and computing.

[0006] The specific technical solutions adopted by the present invention are as follows:

[0007] A method for integrating positioning, communication and calculation comprises the following steps:

[0008] 1) In the system for implementing the fusion method, N base stations with K antennas are arranged, each base station is connected to the control center through an optical fiber, and M single-antenna mobile terminals with limited computation access the wireless network through these base stations. At the same time, there are L positioning targets in the system;

[0009] 2) The base station and the mobile terminal exchange information to obtain mobile terminal information, which includes the channel state information between the base station and the mobile terminal, the location information of the mobile terminal, and the size and amount of calculation tasks; where the channel state information of the mth mobile terminal reflected from the lth positioning target to the kth antenna of the nth base station is h m,l,n,k , the position of the mth mobile terminal in the three-dimensional Cartesian coordinate system is The computing task size of the mth mobile terminal is D m , the task computation amount is C m , m∈[1,M],l∈[1,L],n∈[1,N],k∈[1,K];

[0010] 3) The base station sends the acquired mobile terminal information and its own location information to the control center, where the position of the nth base station in the three-dimensional Cartesian coordinate system is The control center obtains the time and energy required by the mobile terminal to complete the computing task based on the prior information and takes it as the target of the computing task. At the same time, it calculates the Cramer-Rao lower bound of the positioning target and takes it as the target of the positioning task.

[0011] 4) The control center selects a base station for task offloading and allocates transmission power for the mobile terminal according to the goals of the mobile terminal computing task and the positioning task, allocates computing resources for the offloading task to the mobile edge computing server of the base station, and sends the formed decision to the base station; the base station receives the decision information and then sends it to the mobile terminal;

[0012] 5) The mobile terminal transmits mutually orthogonal positioning-communication dual-function signals according to the decision made by the control center, offloading the tasks that need to be calculated to the designated base station and performing target positioning at the same time;

[0013] 6) After receiving the signal, the base station extracts the positioning information and sends it to the control center for estimating the target position. On the other hand, it decodes the signal to obtain the task data and sends it to the mobile edge computing server for calculation. After the calculation is completed, the result is sent back to the corresponding mobile terminal.

[0014] Based on the above technical solution, some of the steps can be implemented in the following preferred manner.

[0015] Preferably, in step 3), the control center calculates the time and energy required for the mobile terminal to complete the calculation task and the Cramer-Rao lower bound of the positioning target by:

[0016] 3.a) The time required for the mth mobile terminal to offload the task to the nth base station to complete the computing task is in is the data transmission speed from the mth mobile terminal to the nth base station, β m is the signal bandwidth of the mth mobile terminal, p m is the transmission power of the mth mobile terminal, o m,l,n,k is the reflection coefficient of the kth antenna from the mth mobile terminal to the nth base station via the lth positioning target, σ 2 is Gaussian white noise, The computing resources allocated by the server of the nth base station for the task of the mth mobile terminal, |·| 2 Represents the square of the absolute value.

[0017] 3.b) The energy required for the mth mobile terminal to offload the task to the nth base station to complete the computing task is E m,n The calculation method is where k n Energy coefficient calculated for the nth base station CPU;

[0018] 3.c) Cramer-Rao lower bound ξ for the lth location target l The calculation method is where trace(·) is the trace of the matrix, is the Fisher information matrix of the lth positioning target, c is the speed of light, (x l ,y l ,z l ) is the position of the lth target in the three-dimensional Cartesian coordinate system estimated by the control center in the last cycle, is the distance from the mth mobile terminal to the lth target, is the distance from the nth base station to the lth target.

[0019] Preferably, in step 4), the control center selects a base station for task offloading and allocates transmit power for the mobile terminal, and the decision method for allocating computing resources for the offloading task to the mobile edge computing server of the base station is:

[0020] 4.a) Initialize task offloading based on the distance between the mobile device and the base station to select θ m,n , that is, the mth mobile device selects the nearest nth base station for task offloading, let θ m,n =1, initialize the transmission power of the mobile device Initialize server computing resources Initialize auxiliary variables in is the maximum transmission power of the mth mobile device, F n is the total computing resource of the nth base station, Indicates the base station number selected when the mth mobile device performs task offloading;

[0021] 4.b) Introduce auxiliary variables based on task offloading selection strategy and Then the time required for the mth mobile device to complete the computing task is The total energy required by all mobile devices to complete computing tasks is At the same time, the auxiliary variable matrix is ​​introduced Then there is in,

[0022] 4.c) According to the maximum transmit power limit of the mobile terminal, let

[0023] 4.d) According to the computing resource constraints of the base station, let and 0≤f m θ m,n ≤F n ;

[0024] 4.e) According to the time limit for the mobile terminal to complete the computing task, let t m ≤τ m , where τ m The maximum tolerable delay for the mth mobile terminal to complete the computing task;

[0025] 4.f) According to the positioning accuracy limit of the target, let and trace(Θ l )≤η l , where η l is the positioning accuracy threshold of the lth positioning target, and I is the unit matrix with a dimension of 3;

[0026] 4.g) Find the total energy E required for all mobile devices to complete the computing task total The solution with the smallest value of m and f m ;

[0027] 4.h) If the total energy E total Convergence, then the transmit power of the mobile device is obtained and computing resources allocated to the server Otherwise, And jump back to step 4.b) and continue the cycle until the total energy E is obtained. total p in the convergence state m and

[0028] 4.i) Based on the transmit power p of the mobile device obtained in step 4.h) m and computing resources allocated to the server Recalculate the time T required for the mobile terminal to complete the computing task according to the calculation method in step 3). m,n and energy E m,n ;

[0029] 4.j) According to the task offloading policy restrictions, let and θ m,n ∈{0,1};

[0030] 4.k) According to the base station calculation resource constraints, let

[0031] 4.1) According to the time limit for the mobile terminal to complete the computing task, let

[0032] 4.m) Find the total energy required for all mobile devices to complete the computing task The solution with the smallest value of is the unloading strategy θ m,n ;

[0033] 4.n) If the total energy E T Convergence, then we get p m , and θ m,n Otherwise, jump back to step 4.b) and continue the cycle until the total energy E is obtained. T p in the convergence state m , and θ m,n .

[0034] Preferably, in step 4.g), the solution is obtained by using the interior point method or by directly calling the CVX toolkit.

[0035] Preferably, the branch and bound method is used to solve the problem in step 4.m).

[0036] The invention has the following beneficial effects: the fusion method of positioning, communication and computing proposed in the invention solves a series of problems caused by limited computing of mobile devices, high task computing delay, high energy consumption, and separation of positioning-communication-computing functions. The joint optimization algorithm of task offloading selection, mobile device power allocation and server computing resource allocation proposed in the invention has the advantages of low task computing delay, low energy consumption, high positioning accuracy, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a system block diagram of the fusion of positioning, communication and computing;

[0038] Figure 2It is the performance comparison of the proposed method under different positioning accuracy requirements and number of mobile terminals;

[0039] Figure 3 The performance of the proposed method is compared under different positioning accuracy requirements, delay requirements for completing the computing task, and the number of antennas in the base station. DETAILED DESCRIPTION

[0040] In this embodiment, the system block diagram of the large-scale access method integrating computing and communication is as follows: Figure 1 As shown in the figure, the system consists of a control center, N base stations with K antennas and M single-antenna mobile terminals with limited computation, and there are L positioning targets. The mobile terminal offloads the computational tasks to one of the base stations by transmitting mutually orthogonal positioning-communication dual-function signals, and performs target positioning at the same time. After receiving the signal, the base station extracts the positioning information and transmits it to the control center for estimating the target position. On the other hand, it decodes the signal to obtain the task data and sends it to the mobile edge computing server for calculation. After the calculation is completed, the base station transmits the result back to the corresponding mobile terminal.

[0041] The specific technical solutions adopted in this embodiment are as follows:

[0042] A method for integrating positioning, communication and calculation comprises the following steps:

[0043] 1) In the system for implementing the fusion method, N base stations with K antennas are arranged, each base station is connected to a control center via an optical fiber, and M computationally limited single-antenna mobile terminals access the wireless network through these base stations. At the same time, there are L positioning targets in the system.

[0044] 2) The base station and the mobile terminal exchange information to obtain mobile terminal information, which includes the channel state information between the base station and the mobile terminal, the location information of the mobile terminal, and the size and amount of calculation tasks; where the channel state information of the mth mobile terminal reflected from the lth positioning target to the kth antenna of the nth base station is h m,l,n,k , the position of the mth mobile terminal in the three-dimensional Cartesian coordinate system is The computing task size of the mth mobile terminal is D m , the task computation amount is C m , m∈[1,M],l∈[1,L],n∈[1,N],k∈[1,K].

[0045] 3) The base station sends the acquired mobile terminal information and its own location information to the control center, where the position of the nth base station in the three-dimensional Cartesian coordinate system is The control center obtains the time and energy required by the mobile terminal to complete the computing task based on the prior information and takes it as the target of the computing task. At the same time, it calculates the Cramer-Rao lower bound of the positioning target and takes it as the target of the positioning task.

[0046] In this embodiment, in the above step 3), the control center calculates the time and energy required for the mobile terminal to complete the calculation task and the Cramer-Rao lower bound of the positioning target by:

[0047] 3.a) The time required for the mth mobile terminal to offload the task to the nth base station to complete the computing task is in is the data transmission speed from the mth mobile terminal to the nth base station, β m is the signal bandwidth of the mth mobile terminal, p m is the transmission power of the mth mobile terminal, o m,l,n,k is the reflection coefficient of the kth antenna from the mth mobile terminal to the nth base station via the lth positioning target, σ 2 is Gaussian white noise, The computing resources allocated by the server of the nth base station for the task of the mth mobile terminal, |·| 2 Represents the square of the absolute value.

[0048] 3.b) The energy required for the mth mobile terminal to offload the task to the nth base station to complete the computing task is E m,n The calculation method is where k n Energy coefficient calculated for the nth base station CPU;

[0049] 3.c) Cramer-Rao lower bound ξ for the lth location target l The calculation method is where trace(·) is the trace of the matrix, is the Fisher information matrix of the lth positioning target, c is the speed of light, (x l ,y l ,z l ) is the position of the lth target in the three-dimensional Cartesian coordinate system estimated by the control center in the last cycle, is the distance from the mth mobile terminal to the lth target, is the distance from the nth base station to the lth target.

[0050] 4) The control center selects the base station for task offloading and allocates transmission power for the mobile terminal according to the goals of the mobile terminal computing task and the positioning task, allocates computing resources for the offloading task to the mobile edge computing server of the base station, and sends the formed decision to the base station; the base station sends the decision information to the mobile terminal after receiving it.

[0051] In this embodiment, in the above step 4), the control center selects a base station for task offloading and allocates transmit power for the mobile terminal, and the decision method for allocating computing resources for the offloading task to the mobile edge computing server of the base station is:

[0052] 4.a) Initialize task offloading based on the distance between the mobile device and the base station to select θ m,n , that is, the mth mobile device selects the nearest nth base station for task offloading, let θ m,n =1, initialize the transmission power of the mobile device Initialize server computing resources Initialize auxiliary variables in is the maximum transmission power of the mth mobile device, F n is the total computing resource of the nth base station, Indicates the base station number selected when the mth mobile device performs task offloading;

[0053] 4.b) Introduce auxiliary variables based on task offloading selection strategy and Then the time required for the mth mobile device to complete the computing task is The total energy required by all mobile devices to complete computing tasks is At the same time, the auxiliary variable matrix is ​​introduced Then there is in,

[0054] 4.c) According to the maximum transmit power limit of the mobile terminal, let

[0055] 4.d) According to the computing resource constraints of the base station, let and 0≤f m θ m,n ≤F n ;

[0056] 4.e) According to the time limit for the mobile terminal to complete the computing task, let t m ≤τ m , where τ m The maximum tolerable delay for the mth mobile terminal to complete the computing task;

[0057] 4.f) According to the positioning accuracy limit of the target, let and trace(Θ l )≤η l , where η l is the positioning accuracy threshold of the lth positioning target, and I is the unit matrix with a dimension of 3;

[0058] 4.g) Use the interior point method or directly call the CVX toolkit to solve and find the total energy E required for all mobile devices to complete the computing task total The solution with the smallest value of m and f m ;

[0059] 4.h) If the total energy E total Convergence, then the transmit power of the mobile device is obtained and computing resources allocated to the server Otherwise, And jump back to step 4.b) and continue the cycle until the total energy E is obtained. total p in the convergence state m and

[0060] 4.i) Based on the transmit power p of the mobile device obtained in step 4.h) m and computing resources allocated to the server Recalculate the time T required for the mobile terminal to complete the computing task according to the calculation method in step 3). m,n and energy E m,n ;

[0061] 4.j) According to the task offloading policy restrictions, let and θ m,n ∈{0,1};

[0062] 4.k) According to the base station calculation resource constraints, let

[0063] 4.1) According to the time limit for the mobile terminal to complete the computing task, let

[0064] 4.m) Use the branch and bound method to solve the total energy required for all mobile devices to complete the computing task The solution with the smallest value of is the unloading strategy θ m,n ;

[0065] 4.n) If the total energy E T Convergence, then we get p m , and θ m,nOtherwise, jump back to step 4.b) and continue the cycle until the total energy E is obtained. T p in the convergence state m , and θ m,n .

[0066] 5) The mobile terminal transmits mutually orthogonal positioning-communication dual-function signals according to the decision made by the control center, offloading the tasks that need to be calculated to the designated base station and performing target positioning at the same time.

[0067] 6) After receiving the signal, the base station extracts the positioning information and sends it to the control center for estimating the target position. On the other hand, it decodes the signal to obtain the task data and sends it to the mobile edge computing server for calculation. After the calculation is completed, the result is sent back to the corresponding mobile terminal.

[0068] Computer simulations show that Figure 2 As shown, in the integrated method of positioning, communication and calculation proposed by the present invention, the lower the positioning accuracy requirement, the less energy is needed to complete the calculation task. Furthermore, with the number of mobile terminals, the positioning accuracy can be improved to a certain extent. Figure 3 It shows that in the method proposed by the present invention, the lower the tolerable delay for completing the computing task, the more energy is consumed. Moreover, as the number of antennas increases, the performance can be significantly improved. Therefore, the present invention provides an effective fusion method of positioning, communication and computing for wireless networks.

Claims

1. A method for integrating positioning, communication and computing, characterized in that: The steps include: 1) In the system for implementing the fusion method, N base stations with K antennas are arranged, each base station is connected to the control center through an optical fiber, and M single-antenna mobile terminals with limited computation access the wireless network through these base stations. At the same time, there are L positioning targets in the system; 2) The base station and the mobile terminal exchange information to obtain mobile terminal information, which includes the channel state information between the base station and the mobile terminal, the location information of the mobile terminal, and the size and amount of calculation tasks; where the channel state information of the mth mobile terminal reflected from the lth positioning target to the kth antenna of the nth base station is h m,l,n,k , the position of the mth mobile terminal in the three-dimensional Cartesian coordinate system is The computing task size of the mth mobile terminal is D m , the task computation amount is C m , m∈[1,M],l∈[1,L],n∈[1,N],k∈[1,K]; 3) The base station sends the acquired mobile terminal information and its own location information to the control center, where the position of the nth base station in the three-dimensional Cartesian coordinate system is The control center obtains the time and energy required by the mobile terminal to complete the computing task based on the prior information and takes it as the target of the computing task. At the same time, it calculates the Cramer-Rao lower bound of the positioning target and takes it as the target of the positioning task. 4) The control center selects a base station for task offloading and allocates transmission power for the mobile terminal according to the goals of the mobile terminal computing task and the positioning task, allocates computing resources for the offloading task to the mobile edge computing server of the base station, and sends the formed decision to the base station; the base station receives the decision information and then sends it to the mobile terminal; 5) The mobile terminal transmits mutually orthogonal positioning-communication dual-function signals according to the decision made by the control center, offloading the tasks that need to be calculated to the designated base station and performing target positioning at the same time; 6) After receiving the signal, the base station extracts the positioning information and sends it to the control center for estimating the target position. On the other hand, it decodes the signal to obtain the task data and sends it to the mobile edge computing server for calculation. After the calculation is completed, the result is sent back to the corresponding mobile terminal. In step 4), the control center selects a base station for task offloading and allocates transmission power for the mobile terminal, and the decision method for allocating computing resources for the offloading task to the mobile edge computing server of the base station is: 4.a) Initialize task offloading based on the distance between the mobile device and the base station to select θ m,n , that is, the mth mobile device selects the nearest nth base station for task offloading, let θ m,n =1, initialize the transmission power of the mobile device Initialize server computing resources Initialize auxiliary variables in is the maximum transmission power of the mth mobile device, F n is the total computing resource of the nth base station, Indicates the base station number selected when the mth mobile device performs task offloading; 4.b) Introduce auxiliary variables based on task offloading selection strategy and Then the time required for the mth mobile device to complete the computing task is The total energy required by all mobile devices to complete computing tasks is At the same time, the auxiliary variable matrix is ​​introduced Then there is in, 4.c) According to the maximum transmit power limit of the mobile terminal, let 4.d) According to the computing resource constraints of the base station, let and 0≤f m θ m,n ≤F n ; 4.e) According to the time limit for the mobile terminal to complete the computing task, let t m ≤τ m , where τ m The maximum tolerable delay for the mth mobile terminal to complete the computing task; 4.f) According to the positioning accuracy limit of the target, let and trace(Θ l )≤η l , where η l is the positioning accuracy threshold of the lth positioning target, and I is the unit matrix with a dimension of 3; 4.g) Find the total energy E required for all mobile devices to complete the computing task total The solution with the smallest value of m and f m ; 4.h) If the total energy E total Convergence, then the transmit power of the mobile device is obtained and computing resources allocated to the server Otherwise, And jump back to step 4.b) and continue the cycle until the total energy E is obtained. total p in the convergence state m and 4.i) Based on the transmit power p of the mobile device obtained in step 4.h) m and computing resources allocated to the server Recalculate the time T required for the mobile terminal to complete the computing task according to the calculation method in step 3). m,n and energy E m,n ; 4.j) According to the task offloading policy restrictions, let and θ m,n ∈{0,1}; 4.k) According to the base station calculation resource constraints, let 4.1) According to the time limit for the mobile terminal to complete the computing task, let 4.m) Find the total energy required for all mobile devices to complete the computing task The solution with the smallest value of is the unloading strategy θ m,n ; 4.n) If the total energy E T Convergence, then we get p m , and θ m,n Otherwise, jump back to step 4.b) and continue the cycle until the total energy E is obtained. T p in the convergence state m , and θ m,n .

2. A method for integrating positioning, communication and calculation according to claim 1, characterized in that: In step 3), the control center calculates the time and energy required for the mobile terminal to complete the calculation task and the Cramer-Rao lower bound of the positioning target as follows: 3.a) The time required for the mth mobile terminal to offload the task to the nth base station to complete the computing task is in is the data transmission speed from the mth mobile terminal to the nth base station, β m is the signal bandwidth of the mth mobile terminal, p m is the transmission power of the mth mobile terminal, o m,l,n,k is the reflection coefficient of the kth antenna from the mth mobile terminal to the nth base station via the lth positioning target, σ 2 is Gaussian white noise, The computing resources allocated by the server of the nth base station for the task of the mth mobile terminal, | 2 represents the square of the absolute value; 3.b) The energy required for the mth mobile terminal to offload the task to the nth base station to complete the computing task is E m,n The calculation method is where k n Energy coefficient calculated for the nth base station CPU; 3.c) Cramer-Rao lower bound ξ for the lth location target l The calculation method is where trace(·) is the trace of the matrix, is the Fisher information matrix of the lth positioning target, c is the speed of light, (x l ,y l ,z l ) is the position of the lth target in the three-dimensional Cartesian coordinate system estimated by the control center in the last cycle, is the distance from the mth mobile terminal to the lth target, is the distance from the nth base station to the lth target.

3. The method for integrating positioning, communication and calculation according to claim 1, characterized in that: In step 4.g), the solution is obtained by using the interior point method or directly calling the CVX toolkit.

4. The method for integrating positioning, communication and calculation according to claim 1, characterized in that: In step 4.m), the branch and bound method is used to solve the problem.

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