CN117580180A - Communication computing storage multi-domain resource allocation method for end-to-end low-delay information delivery - Google Patents
Communication computing storage multi-domain resource allocation method for end-to-end low-delay information delivery Download PDFInfo
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Abstract
The invention discloses a general sense computing storage multi-domain resource allocation method for end-to-end low-delay information delivery, which performs joint optimization allocation on communication, sensing, calculation and cache resources aiming at an end-to-end whole flow from information acquisition to result delivery, and comprises the following steps: step one, establishing a general sense calculation storage fusion network model enabled by the unmanned aerial vehicle, and expressing end-to-end information delivery delay; describing the problem of multi-domain resource allocation of general sense computing oriented to information delivery delay minimization; decomposing the original problem into sub-carrier allocation and power control, computing resource allocation, unmanned aerial vehicle deployment and cache update sub-problems; and step four, designing an optimization algorithm of four sub-problems by combining methods such as matched game, lagrange multiplier method, continuous approximate convex optimization and the like, and obtaining a joint allocation result of the universal computing multi-domain resources. The invention innovatively considers the communication, sensing, calculation and caching processes of the prior splitting treatment comprehensively, and realizes the efficient cooperation of cross-domain resources.
Description
Technical Field
The invention relates to the technical field of communication perception calculation integration, in particular to a general perception calculation storage multi-domain resource allocation method facing end-to-end low-delay information delivery.
Background
The mobile information network in the 6G age is not a simple data transmission channel any more, but a resource fusion network with multiple functions of communication, perception, calculation and storage. Meanwhile, the emerging intelligent information service provides new requirements for acquisition of perceived data and processing results thereof, such as instantaneity, reliability and the like, so as to meet the data access requirements of massive users.
To reduce the delivery latency of the perceived information processing results, network resource scheduling should focus on the overall process of information generation from the sensing node until delivery to the end user, where the communication, perception, computation, and caching are strongly coupled. Specifically, the sensing node collects data through the joint design of communication and perception, and the perception information needs to be further stored and processed by the edge nodes of the foundation or the empty foundation, and finally transmitted to the terminal user. However, the traditional resource arrangement mode is used for splitting and treating the general calculation and storage process, and the mode lacking cross-domain resource collaborative management is difficult to realize the unification of performance indexes, and is also difficult to ensure the high-timeliness delivery of data.
In this regard, the invention provides a method for allocating the general sense computing multi-domain resources facing the end-to-end low-delay information delivery, which aims at a series of enabling technologies of a deep integration 6G wireless network, including an ISAC, mobile edge computing and air-ground integrated network, and further designs an optimal allocation method for the general sense computing multi-domain resources. In order to solve the non-convex nonlinear optimization problem of multi-domain resource strong coupling efficiently, the method adopts the means of block coordinate reduction, matched game, continuous convex approximation, heuristic method and the like to reasonably approximate and transform the non-convex problem, and then approximates the optimal solution of the problem in a low-complexity mode.
Disclosure of Invention
The invention discloses a general sense computing storage multi-domain resource allocation method for end-to-end low-delay information delivery, which aims at the end-to-end full flow from information acquisition, cache update, data uploading and calculation processing of a sensing terminal to user side result delivery, and performs joint optimization allocation on communication, sensing, calculation and cache resources to ensure timeliness of information delivery, and comprises the following steps: firstly, establishing a general sense calculation storage fusion network model of unmanned aerial vehicle enabling, deducing time delay and energy consumption of sensing mutual information, data transmission and edge calculation, and cache updating capacity constraint, and expressing end-to-end information delivery time delay on the basis; describing the problem of multi-domain resource allocation of general sense computing towards information delivery delay minimization, and jointly optimizing the 3D position, subcarrier allocation, power control, cache update and computing resource allocation of the unmanned aerial vehicle, wherein the constraint of perceived accuracy, communication service quality and equipment energy consumption is considered; decomposing the original problem into four iteratively solved sub-problems by a block coordinate descent method, wherein the sub-problems are respectively subcarrier allocation and power control, calculation resource allocation, unmanned aerial vehicle deployment and cache update; and step four, designing an optimization algorithm of four sub-problems by combining methods such as matched game, lagrange multiplier method, continuous approximate convex optimization, heuristic method and the like, and finally obtaining a joint allocation result of the general sense computing storage multi-domain resources in an iterative manner to realize end-to-end low-delay information delivery. The specific process is as follows:
the unmanned aerial vehicle enabled general sense computation fusion network model provided by the invention comprises M unmanned aerial vehicles, N ISAC devices and a large number of terminal users which are distributed at random. Specifically, the ISAC device transmits an integrated beam aware target while uploading the awareness information to the drone. The unmanned aerial vehicle is provided with an edge server to buffer data collected by the ISAC equipment and perform calculation processing, and then the processing result is delivered to the terminal user. Data authoring D for ISAC device n acquisition n ＝{L n ,λ n }, wherein L n Lambda for uploading data volume n The computational load for processing the data. The mutual information calculation perceived by the ISAC device transmitting the integrated beam is as
Wherein J refers to the number of consecutive OFDM symbols, T s For the symbol period, B is the bandwidth of the subcarrier,representing the signal-to-interference-and-noise ratio of ISAC device n at subcarrier k. In addition, the communication delay and the energy consumption of the ISAC device for uploading data to the unmanned plane m are respectively expressed as
Wherein L is n For data volume, p n For the ISAC device to transmit power,is the channel gain, where l n And h m Respectively the horizontal coordinates, z, of the unmanned aerial vehicle and the equipment m For the unmanned plane height, the denominator of the time delay represents the communication rate, and the time delay and the energy consumption of the unmanned plane for carrying out edge processing on the data uploaded by the ISAC equipment are calculated as
Wherein lambda is n To calculate the load, w m,n ∈[0,1]Computing resource ratio, F, allocated to drone m to ISAC device n m Computing resources are available for the drone. In addition, the buffer capacity of the unmanned plane is limited, and part of ISAC equipment needs to be selected to update the perception information, and the buffer capacity is restricted to write
Wherein c m,n E {0,1} is the cache update decision of the unmanned aerial vehicle, c m,n =1 represents UAVm update perception data D n The perceived data D uploaded by ISAC device n at this time n Waiting to be processed in the edge server cached in UAvm, otherwiseIndicating that the device n is located in the coverage area of the drone m. Further, feelThe delivery time delay of the known information is calculated as
Wherein b n,k E {0,1} is a subcarrier allocation strategy, b n,k =1 means ISAC device n occupies subcarrier k, whereas b n,k ＝0。T max The maximum delay corresponding to the information which is not updated.
On the basis, defineRepresenting 3D position deployment of UAV, +.>Indicating subcarrier allocation, +.>Indicating power control +.>Representing a cache update that is to be performed,representing computing resource allocation, and further describing the problem of general sense computing memory multi-domain resource allocation facing to information delivery delay minimization as
Wherein χ is n Representing the number of requests for information to be collected by ISAC device n, C 1 ～C 3 Representing perceived accuracy, communication rate, and energy consumption constraints, respectively, of an ISAC device, whereinFor minimum mutual information requirement, +.>For minimum rate requirement, +.>For maximum energy budget. C (C) 4 Calculating for the edges of the UAV that the energy consumption cannot be greater than the energy budget +.>C 5 Meaning collision constraints of UAV, C 6 To deploy a high constraint, wherein Z min And Z max Respectively minimum and maximum height. C (C) 7 ～C 9 Respectively, the subcarrier allocation factors are binary, and an ISAC device can only occupy one subcarrier, and at most one subcarrier is +.>And multiplexing the devices. C (C) 10 Transmit power constraint for device, +.>Representing an upper power limit. C (C) 11 ～C 13 The buffer update factors are respectively represented as binary, the perception data of one device can be buffered by only one UAV, and the buffer capacity and the update range of the UAV are restricted. C (C) 14 And C 15 Representing the computational resource allocation ratio as a continuum with the available computational power constraints of the UAV, respectively.
The problem solving can be divided into the following steps:
1) Decomposing the original problem into four iteratively solved sub-problems by a block coordinate descent method, wherein the first sub-problem is a joint subcarrier allocation and power control sub-problem and is described as
In order to solve the sub-problem, the ISAC device and the sub-carrier can be regarded as two matched sides in many-to-one mode, and power control under any sub-carrier allocation is optimized through game theory, so that perceived interference and communication interference among devices multiplexing the same sub-carrier are relieved. More specifically, we fix the power p of the other devices in each game n′ To optimize p n Until each of the adjacent two game rounds is calculatedThe difference between the utility functions of the individual devices is small enough to represent a Nash equilibrium that converges to power control. Each game adopts the Lagrangian multiplier method to solve the power control problem of a single user. Furthermore, an objective function (network utility) under the matching is calculated according to the power control result, and the subcarrier allocation result is continuously optimized through the matching exchange to improve the network utility until the reasonable power control and subcarrier allocation result is converged.
2) Solving the problem of computing resource allocation sub-at the edge of the unmanned aerial vehicle is described as
The sub-problem is a strict convex optimization problem and is directly solved by adopting an interior point method.
3) Solving the horizontal coordinate optimization sub-problem of the unmanned aerial vehicle, and introducing auxiliary variablesAnd reconstruct the sub-problem as
Approximation of non-convex constraint C using first order Taylor expansion 5 And C 16 The following are provided:
2[h m (l-1)-h m′ (l-1)] T [h m (l)-h m′ (l)]-||h m (l-1)-h m′ (l-1)|| 2 ≥(d min ) 2
wherein,and further, adopting a continuous convex approximation method to iteratively solve a convex optimization problem to obtain an optimization result of the horizontal coordinate of the unmanned aerial vehicle.
4) Solving the cache update and unmanned aerial vehicle highly optimized sub-problem is described as
Solving the sub-problem using a heuristic grid search and greedy algorithm, for any given drone altitude, which may cover ISAC devices, the contribution of each device to the objective function is calculated and updated as follows:
and preferentially selecting the equipment with large contribution to carry out cache updating until capacity constraint is reached. More specifically, define a collectionUpdating a higher priority data set with a cache capacity allowance, wherein +.>Furthermore, the cache update policy of the unmanned plane m can be expressed as
Thereafter, the optimal cache update strategy is selected under the comparison of different unmanned aerial vehicle heightsAnd selecting the height of the unmanned aerial vehicle corresponding to the minimum value and the buffer decision as the optimal solution of the SP 4.
5) And iteratively solving the four groups of optimization variables in a block coordinate descending mode until the change of an objective function in two adjacent iterations is smaller than a preset threshold value, carrying out iteration convergence, and finally obtaining a joint allocation result of the general sense computing storage multi-domain resource to realize end-to-end low-delay information delivery.
The technical method of the invention has the following advantages:
firstly, the invention innovatively considers the communication, sensing, calculating and caching processes of the prior splitting treatment, designs a resource arrangement method facing to the end-to-end low-delay information delivery, and realizes the efficient coordination of cross-domain resources. Secondly, the method minimizes information delivery latency under perceived quality, communication rate, terminal energy consumption and buffering capacity constraints, thus achieving a good tradeoff between communication, perception, computation and buffering cross-domain performance.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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In order to more clearly illustrate the embodiments of the present invention or the technical methods in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained according to the drawings without inventive effort for a person having ordinary skill in the art.
Fig. 1 is a diagram of unmanned aerial vehicle deployment and cache update optimization results.
Fig. 2 is a graph of information delivery delay versus the number of ISAC devices.
Fig. 3 is a graph of information delivery delay versus unmanned computing resources.
Fig. 4 is a graph showing the change of the information delivery delay with respect to the buffer capacity of the unmanned aerial vehicle.
Detailed Description
The invention provides an unmanned aerial vehicle RIS (RIS-enabled communication) converged network track and phase shift optimization method, and an embodiment is described in detail below with reference to the accompanying drawings.
The present invention is embodied in a 500m area comprising 50 ISAC devices, 5 drones and 300 end users. The perceived symbol period is 5 mus, the number of symbols is 10, the number of carriers is 15, and the bandwidth is 360kHz. The perceived data size is [0.1,0.2] Mbits, the calculation load is [0.2,1] Gcycles, and the unmanned aerial vehicle buffer capacity and the calculation resource distribution are 0.6Mbps and 10GHz. The lower limit of the perceived mutual information is [10,40] bits, the communication rate requirement is 0.5Mbps, the maximum power of the ISAC equipment is 23dBm, and the energy budget is [0.2,1] J.
The specific implementation steps are as follows:
1) Based on the implementation scenario parameters described above, a request for location and user information of the ISAC device is generated and a channel gain between the ISAC device and the drone is calculated. Thereafter, the mutual information perceived by the ISAC device transmitting the integrated beam is calculated as
Wherein J refers to the number of consecutive OFDM symbols, T s For the symbol period, B is the bandwidth of the subcarrier,representing the signal-to-interference-and-noise ratio of ISAC device n at subcarrier k. In addition, the communication delay and the energy consumption of the ISAC device for uploading data to the unmanned plane m are respectively expressed as
Wherein L is n For data volume, p n For the transmission power of the ISAC equipment, the denominator of the time delay represents the communication rate, and the time delay and the energy consumption of the unmanned plane for carrying out edge processing on the data uploaded by the ISAC equipment are calculated as
Wherein lambda is n To calculate the load, w m,n Computing resource ratio, F, allocated to drone m to ISAC device n m Is available to unmanned aerial vehicleComputing resources. In addition, the buffer capacity of the unmanned plane is limited, and part of ISAC equipment needs to be selected to update the perception information, and the buffer capacity is restricted to write
Wherein c m,n For the cache update decisions of the drone,indicating that the device n is located in the coverage area of the drone m. Further, the delivery delay of the perceived information is calculated as
Wherein b n,k Strategy for subcarrier allocation, T max The maximum delay corresponding to the information which is not updated.
2) Definition of the definitionRepresenting 3D position deployment of UAV, +.>Indicating subcarrier allocation, +.>Indicating power control +.>Representing a cache update that is to be performed,representing computing resource allocation, and further describing the problem of general sense computing memory multi-domain resource allocation facing to information delivery delay minimization as
Wherein χ is n Indicating ISAC device n acquisition messagesNumber of requests for information, C 1 ～C 3 Representing perceived accuracy, communication rate, and energy consumption constraints, respectively, of an ISAC device, whereinFor minimum mutual information requirement, +.>For minimum rate requirement, +.>For maximum energy budget. C (C) 4 Calculating for the edges of the UAV that the energy consumption cannot be greater than the energy budget +.>C 5 Meaning collision constraints of UAV, C 6 To deploy a high constraint, wherein Z min And Z max Respectively minimum and maximum height. C (C) 7 ～C 9 Respectively, the subcarrier allocation factors are binary, and an ISAC device can only occupy one subcarrier, and at most one subcarrier is +.>And multiplexing the devices. C (C) 10 Transmit power constraint for device, +.>Representing an upper power limit. C (C) 11 ～C 13 The buffer update factors are respectively represented as binary, the perception data of one device can be buffered by only one UAV, and the buffer capacity and the update range of the UAV are restricted. C (C) 14 And C 15 Representing the computational resource allocation ratio as a continuum with the available computational power constraints of the UAV, respectively.
3) And entering a general sense computing multi-domain resource optimization allocation flow. First, the original problem is decomposed into four iteratively solved sub-problems by a block coordinate descent method, wherein the first sub-problem is a joint subcarrier allocation and power control sub-problem, and is described as
In order to solve the sub-problem, ISAC equipment and sub-carriers can be regarded as two matched sides from many to one, power control under any sub-carrier allocation is optimized through game theory, and then sub-carrier allocation results are continuously optimized through matching exchange until convergence is achieved on reasonable power control and sub-carrier allocation results;
secondly, solving the problem of edge computing resource allocation sub-at the unmanned aerial vehicle, which is described as
The sub-problem is a strict convex optimization problem, and is directly solved by adopting an interior point method;
thirdly, solving the horizontal coordinate optimization sub-problem of the unmanned aerial vehicle, and introducing auxiliary variablesAnd reconstruct the sub-problem as
Approximation of non-convex constraint C using first order Taylor expansion 5 And C 16 Further, an optimization result of the horizontal coordinate of the unmanned aerial vehicle is obtained by adopting a continuous convex approximation method;
finally, solve the cache update and unmanned aerial vehicle highly optimized sub-problem, described as
Solving the sub-problem using a heuristic grid search and greedy algorithm, for any given drone altitude, which may cover ISAC devices, the contribution of each device to the objective function is calculated and updated as follows:
and preferentially selecting the equipment with large contribution to carry out cache updating until capacity constraint is reached.
4) And iteratively solving the four groups of optimization variables in a block coordinate descending mode until the change of an objective function in two adjacent iterations is smaller than a preset threshold value, carrying out iteration convergence, and finally obtaining a joint allocation result of the general sense computing storage multi-domain resource to realize end-to-end low-delay information delivery.
Fig. 1 illustrates the results of drone deployment and cache update optimization, and it can be seen that the drone approaches and covers the cache updated ISAC device, providing a high quality communication link for data upload. Furthermore, the higher the drone, the greater its coverage, which indicates that more cached information can be updated, but this also reduces the channel gain. After the joint optimization of the unmanned aerial vehicle position and the cache update is carried out by the method provided by the invention, each unmanned aerial vehicle can achieve good balance between the coverage area and the channel gain.
Fig. 2 shows the change in information delivery delay versus the number of ISAC devices, with an increase in information delivery delay and number of devices seen to rise. This is because the user can request more types of awareness data and the amount of resources that can be allocated to each ISAC device decreases, resulting in a decrease in user satisfaction. In addition, compared with other reference algorithms, the method provided by the invention obviously reduces the end-to-end information delivery delay. Specifically, the initial solution does not reasonably allocate the general sense computing resources, and thus good performance cannot be obtained. Subcarrier resources cannot be reasonably allocated to ISAC devices in a random matching method, resulting in communication and perceived interference. The average allocation of computing resources, the deployment of fixed drones, and the delivery latency of greedy cached information are also high, since the update of the user's cache does not conform to the actual user request.
Fig. 3 illustrates the change in information delivery latency relative to the unmanned computing resources, and it can be seen that as the computing resources increase, the information delivery latency decreases because more edge resources are available to process the awareness data. In addition, the method provided by the invention realizes better performance compared with a reference algorithm. Numerical results show that the method is better than the initial solution, random matching, calculation resource average distribution, fixed unmanned aerial vehicle deployment and greedy caching methods by 26.2%, 10.7%, 5.7%, 14.1% and 23.1%.
Fig. 4 shows the change of the information delivery delay relative to the buffer capacity of the unmanned aerial vehicle, and it can be seen that the trend of the information delivery delay decreasing gradually becomes gentle as the buffer capacity increases. The reason for this is that when the buffer capacity is large enough, the information delivery delay is limited by computational resources and energy budgets. The phenomenon shows that in an actual resource limited system, the general sense computing memory resource should be reasonably scheduled, so that the resource efficiency and the system performance are improved. In addition, the method reduces the information delivery delay by 23.5%, 13.1%, 4.6%, 9.8% and 19.5% compared with 5 reference algorithms.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention referred to in this disclosure is not limited to the specific combination of features described above, but encompasses other technical approaches which may be made by any combination of features described above or equivalents thereof without departing from the spirit of the invention. Such as those described above, are provided in the present disclosure in place of, but not limited to, features having similar functions.
Claims (6)
1. The invention discloses a general sense computing storage multi-domain resource allocation method for end-to-end low-delay information delivery, which aims at the end-to-end full flow from information acquisition, cache update, data uploading and calculation processing of a sensing terminal to user side result delivery, and performs joint optimization allocation on communication, sensing, calculation and cache resources to ensure timeliness of information delivery, and comprises the following steps:
step 1, establishing a general sense calculation storage fusion network model of unmanned aerial vehicle enabling, deducing time delay and energy consumption of sensing mutual information, data transmission and edge calculation, and cache updating capacity constraint, and expressing end-to-end information delivery time delay on the basis;
step 2, describing a general sense computing storage multi-domain resource allocation problem oriented to information delivery delay minimization, and jointly optimizing a 3D position, subcarrier allocation, power control, cache update and computing resource allocation of an unmanned aerial vehicle, wherein the constraint of perceived accuracy, communication service quality and equipment energy consumption is considered;
step 3, decomposing the original problem into four iteratively solved sub-problems by a block coordinate descent method, wherein the sub-problems are respectively subcarrier allocation and power control, calculation resource allocation, unmanned aerial vehicle deployment and cache update;
and 4, designing an optimization algorithm of four sub-problems by combining methods such as matched game, lagrange multiplier method, continuous approximate convex optimization, heuristic method and the like, and finally obtaining a joint allocation result of the general sense computing storage multi-domain resources in an iterative mode to realize end-to-end low-delay information delivery.
2. The method for end-to-end low latency information delivery-oriented general sense computing memory multi-domain resource allocation according to claim 1, wherein in step 1, indexes of communication sensing integrated (Integrated Sensing and Communication, ISAC) equipment, unmanned aerial vehicle and sub-carriers are n, m and k respectively, and further mutual information that ISAC equipment transmits integrated wave beams to sense is calculated as
Wherein J refers to the number of consecutive OFDM symbols, T s For the symbol period, B is the bandwidth of the subcarrier,the signal-to-interference-and-noise ratio of the ISAC device n at the subcarrier k is represented, in addition, by the communication delay and the energy consumption of the ISAC device for uploading data to the drone m, respectively, as
Wherein L is n For data volume, p n For the transmission power of the ISAC equipment, the denominator of the time delay represents the communication rate, and the time delay and the energy consumption of the unmanned plane for carrying out edge processing on the data uploaded by the ISAC equipment are calculated as
Wherein lambda is n To calculate the load, w m,n Computing resource ratio, F, allocated to drone m to ISAC device n m The available computing resources for the unmanned aerial vehicle, in addition, the buffer capacity at the unmanned aerial vehicle is limited, and part of ISAC equipment needs to be selected to update the perception information, and the buffer capacity is restricted to write
Wherein c m,n For the cache update decisions of the drone,indicating that the device n is located in the coverage area of the unmanned plane m, and further, calculating the delivery time delay of the perception information as follows
Wherein b n,k Strategy for subcarrier allocation, T max The maximum delay corresponding to the information which is not updated.
3. The unmanned aerial vehicle enabled through-computation fusion network model of claim 2, definingRepresenting 3D position deployment of UAV, +.>Indicating the allocation of the sub-carriers,indicating power control +.>Representing a cache update->Representing computing resource allocation, and further describing the problem of general sense computing memory multi-domain resource allocation facing to information delivery delay minimization as
P1:
Wherein χ is n Representing the number of requests for information to be collected by ISAC device n, C 1 ～C 3 Representing perceived accuracy, communication rate, and energy consumption constraints, respectively, of an ISAC device, whereinFor minimum mutual information requirement, +.>For minimum rate requirement, +.>For maximum energy budget. C (C) 4 Calculating for the edges of the UAV that the energy consumption cannot be greater than the energy budget +.>C 5 Meaning collision constraints of UAV, C 6 To deploy a high constraint, wherein Z min And Z max Respectively minimum and maximum height. C (C) 7 ～C 9 Respectively, the subcarrier allocation factors are binary, and an ISAC device can only occupy one subcarrier, and at most one subcarrier is +.>And multiplexing the devices. C (C) 10 Transmit power constraint for device, +.>Representing an upper power limit. C (C) 11 ～C 13 Representing the buffer update factor as binary, respectively, the perceived data of a device can only be buffered by one UAV, and UAV buffer capacity and update range constraints. C (C) 14 And C 15 Representing the computational resource allocation ratio as a continuum with the available computational power constraints of the UAV, respectively.
4. The information delivery delay minimization oriented general sense computing multi-domain resource allocation problem according to claim 3, wherein the solving step is as follows:
first, the original problem is decomposed into four iteratively solved sub-problems by a block coordinate descent method, wherein the first sub-problem is a joint subcarrier allocation and power control sub-problem, and is described as SP1:
C 7 ～C 10 ,
in order to solve the sub-problem, ISAC equipment and sub-carriers can be regarded as two matched sides from many to one, power control under any sub-carrier allocation is optimized through game theory, and then sub-carrier allocation results are continuously optimized through matching exchange until convergence is achieved on reasonable power control and sub-carrier allocation results;
secondly, solving the problem of edge computing resource allocation sub-at the unmanned aerial vehicle, wherein the problem is described as SP2:
C 14 ～C 15 ,
the sub-problem is a strict convex optimization problem, and is directly solved by adopting an interior point method;
thirdly, solving the horizontal coordinate optimization sub-problem of the unmanned aerial vehicle, and introducing auxiliary variablesAnd reconstruct the sub-problem to SP3:>
approximation of non-convex constraint C using first order Taylor expansion 5 And C 16 Further, an optimization result of the horizontal coordinate of the unmanned aerial vehicle is obtained by adopting a continuous convex approximation method;
finally, solve the cache update and unmanned aerial vehicle altitude optimization sub-problem, described as SP4:
solving the sub-problem using a heuristic grid search and greedy algorithm, for any given drone altitude, which may cover ISAC devices, the contribution of each device to the objective function is calculated and updated as follows:
and preferentially selecting the equipment with large contribution to carry out cache updating until capacity constraint is reached.
5. The optimization method of four sub-problems according to claim 4, wherein the four groups of optimization variables are solved iteratively by adopting a block coordinate descending mode until the objective function change in two adjacent iterations is smaller than a preset threshold value, the iteration converges, and finally, a joint allocation result of the general sense computing storage multi-domain resource is obtained, and the end-to-end low-delay information delivery is realized.
6. The invention innovatively considers the communication, sensing, calculation and caching processes of the prior splitting treatment, designs a resource arrangement method facing to the end-to-end low-delay information delivery, and realizes the efficient coordination of cross-domain resources; the method minimizes information delivery delay under the constraints of perceived quality, communication rate, terminal energy consumption and buffering capacity, thereby achieving a good compromise between communication, perception, calculation and buffering cross-domain performance.
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