CN118714615B - Method, device and equipment for simultaneously unloading tasks and communicating for Internet of things users - Google Patents
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Abstract
The invention discloses a method, a device and equipment for simultaneously unloading tasks and communicating by an Internet of things user, and relates to the technical field of wireless communication. In a mobile edge computing network scene, based on task unloading and communication processes of users of the Internet of things, working parameters of a base station, a transmission and reflection intelligent surface and an unmanned aerial vehicle are taken as optimization variables, at least partial operation parameters of the edge computing network are taken as constraints according to the edge computing network, weighted sum of minimum task unloading capacity and minimum communication data quantity of each user of the Internet of things is maximized as an optimization target, a service optimization model is constructed and solved, and the edge computing network operates under the working parameters obtained by solving. The mobile edge computing network can simultaneously give consideration to the computing task unloading requirement and the communication requirement of the users of the Internet of things in the operation process, so that the users of the Internet of things can efficiently carry out task unloading and simultaneously realize effective communication with other users of the Internet of things.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a method, a device and equipment for simultaneously unloading tasks and communicating by an internet of things user.
Background
Currently, with the continued advancement of wireless network technology, the number of internet of things (Internet of Things, ioT) devices has increased significantly, resulting in large amounts of data that need to be processed in real-time over wireless networks. In addition, many internet of things applications have been suggested to require low latency and intensive computing power, such as autopilot, augmented and virtual reality, and ubiquitous gaming, among others. These situations present a significant challenge to traditional communication networks based on a central cloud computing framework. To effectively address this challenge, mobile edge computing (Mobile Edge Computing, MEC) technology has become an advantageous solution by extending the computing power of the central cloud to the network edge. This allows for efficient data processing within close range, thereby improving overall performance. Thus, MEC technology has attracted considerable attention in both academia and industry.
Conventional MEC networks typically place edge servers at predetermined ground locations, such as base stations and Access Points (APs), which result in two major disadvantages: (1) Ensuring high quality of service for users in remote areas or blocked by obstructions has become challenging. (2) Terrestrial MEC networks typically experience severe signal degradation during data transmission, particularly affecting uplink data transmission performance. The drone technology provides a promising solution to the above challenges by exploiting its controllable motion flexibility. This functionality enables the drone to quickly navigate to any point in 3D space and increases the likelihood of establishing a direct Line of Sight (LoS) with the user. Accordingly, many unmanned aerial vehicle assisted MEC strategies have been proposed to effectively utilize the advantages provided by unmanned aerial vehicles.
Unmanned aerial vehicle applications show promise in improving the computational power of MEC networks. However, current drone-based MEC schemes are developed to accommodate unpredictable and random wireless propagation environments, resulting in significant limitations in task offloading efficiency. In order to break the performance bottleneck caused by uncontrollable wireless channels, reconfigurable smart surface (Reconfigurable Intelligent Surface, RIS) technology can be seen as a very promising solution. RIS is an electromagnetic metamaterial with the ability to dynamically modify the electromagnetic properties (phase and amplitude) of the input signal, helping to create end-to-end tunable virtual channels. Accordingly, various wireless communication systems have utilized RIS to enhance performance, such as MEC networks. However, conventional RIS requires the base station and the user to be on the same side of the RIS, which would limit the coverage of the system. To break through this limitation, a transmissive and reflective smart surface (STAR-RIS) has been proposed. Unlike conventional RIS, STAR-RIS shows the ability to split an incoming signal into two parts, one of which reflects in the direction of the incoming signal and the other transmits in the opposite direction. This function allows the coverage area of the STAR-RIS to be achievedTwice as much as a conventional RIS. Thus, STAR-RIS has great potential for application, and has been applied to various wireless communication systems.
However, in an actual MEC network, coexistence of users facing computation and communication is a ubiquitous phenomenon, and current MEC schemes focus on improving computing performance of a system through various policies, and neglecting effectiveness of communication between users and other users in the network.
Disclosure of Invention
Based on the foregoing, it is necessary to provide a method, an apparatus and a device for task offloading and communication of an internet of things user at the same time.
The invention adopts the following technical scheme:
The invention provides a method for simultaneously unloading tasks and communicating by users of the Internet of things, which is characterized in that in a mobile edge computing network scene comprising a base station, a transmission intelligent surface, a reflection intelligent surface, an unmanned aerial vehicle, a single antenna and a plurality of user receivers, wherein the base station is provided with a plurality of antennae, the mobile edge computing server is provided with the transmission intelligent surface, the single antenna is provided with the mobile edge computing server, the unmanned aerial vehicle is provided with the single antenna, the user receivers are provided with the single antenna, the task unloading and communicating processes of the users of the Internet of things are based on the task unloading and communicating processes of the users of the Internet of things, the intelligent surface of the base station and the unmanned aerial vehicle are used as optimization variables, at least part of operation parameters of the network are calculated according to the edges, the task unloading requirements and the communication requirements of the users of the Internet of things are used as constraints, the weighted sum of the minimum task unloading capacity and the minimum communication data of all users of the Internet of the things is used as an optimization target, a service optimization model is constructed and solved, and the edge computing network is operated under the working parameters obtained by solving.
The invention provides a device for simultaneously unloading tasks and communicating by an Internet of things user, and each module in the system is used for executing the method for simultaneously unloading tasks and communicating by the Internet of things user.
The invention provides a computer readable storage medium, wherein the storage medium stores a computer program which is executed by a processor to realize the method for simultaneously unloading and communicating tasks for users of the Internet of things.
The invention provides a computer device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method for simultaneously unloading and communicating tasks for the Internet of things user when executing the program.
The at least one technical scheme adopted by the invention can achieve the following beneficial effects:
According to the technical scheme, the mobile edge computing network can simultaneously give consideration to the computing task unloading requirement and the communication requirement of the Internet of things user in the operation process, so that the Internet of things user can efficiently carry out task unloading and simultaneously realize effective communication with other Internet of things users.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a schematic flow chart of a method for task offloading and communication of an Internet of things user at the same time;
fig. 2 is a schematic diagram of a mobile edge computing network structure according to the present invention;
FIG. 3 is a schematic diagram of a weighted sum of minimum task load capacity and minimum communication data capacity of each Internet of things user and a change relation simulation of the number of intelligent units on STAR-RIS;
fig. 4 is a schematic diagram of an effect simulation of the number of time slots on the weighted sum of the minimum task load capacity and the minimum communication data volume of each internet of things user.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to specific embodiments of the present invention and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the current study, most MEC schemes in question ignore a key aspect. In particular, these MEC schemes focus on improving the computational performance of the system through various policies, while ignoring the availability of communications between users within the network and other regional users. In fact, the coexistence of computing-oriented and communication-oriented users is a common phenomenon in practical MEC networks, which will utilize the same spectrum resources to implement the respective computing and communication services. Therefore, in order to mitigate the conflict between computing-centric services and communication-centric services, it is critical to develop an efficient solution to manage both types of services. This will promote the overall quality of service of the MEC network to the user.
In accordance with the previously discussed advantages regarding drones and STAR-RIS, the present invention proposes a MEC scheme that is enabled by a STAR-RIS assisted drone while taking into account user offloading tasks and communication requirements in order to jointly optimize active beamforming of user transmitters, time planning of task offloading and communication, resource allocation, passive beamforming of STAR-RIS, and drone trajectories.
The following describes in detail the technical solutions provided by the embodiments of the present invention with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for task offloading and communication of an internet of things user at the same time, which is applied to a mobile edge computing network comprising a base station, a transmitting and reflecting intelligent surface, a single-antenna unmanned aerial vehicle and a plurality of single-antenna user receivers, wherein the base station comprises a transmitter with a plurality of antennas and a mobile edge computing server, the transmitting and reflecting intelligent surface comprises a plurality of intelligent units, and the unmanned aerial vehicle is provided with the mobile edge computing server. The method specifically comprises the following steps:
S101: at least a portion of the task offloading signals are reflected to the base station via the transmissive and reflective intelligent surface based on the user receiver and the remaining task offloading signals are transmitted to the drone to offload the computing tasks to the base station and the drone.
In practical applications, users of the internet of things generally utilize the same spectrum resources to achieve respective task offloading and communication services. Based on this, in one or more embodiments of the present invention, the present invention may determine the operating parameters of each part of the mobile edge computing network by simultaneously considering the task offloading and the communication to perform optimization solutions based on the constructed base station including the transmitter of N t antennas and the MEC server, the STAR-RIS with M intelligent units, K single-antenna user receivers, and one single-antenna drone loaded with the MEC server, with the sum of the weights of the minimum task offloading amount and the minimum communication data amount as an optimization target, with the service quality required by the users of the internet of things for the task offloading and the communication as a constraint. Fig. 2 is a schematic diagram of a mobile edge computing network according to the present invention.
Each internet of things user can manage tasks to be offloaded through the transmission and reflection intelligent surfaces simultaneously based on the user receiver, a part of task offloading signals are reflected to the base station, the rest task offloading signals are transmitted to the UAV, and then the base station and the UAV decode and offload calculation tasks according to the received signals and utilize the MEC server to perform task calculation.
In general, each internet of things user has two service requirements, one is a computing-oriented service, i.e. offloading its own computing task to a base station or an MEC server of an unmanned aerial vehicle, and the other is a communication-oriented service. For the service facing the computation, the tasks of the users of the Internet of things are allowed to be simultaneously unloaded to MEC servers at the base station and the unmanned aerial vehicle through the reflection and transmission modes of the STAR-RIS, so that the users of the Internet of things can save energy and effectively process the tasks with intensive computation and sensitive delay. On the other hand, communication-oriented services require that the Internet of things user and the base station establish uplink communication to perform traditional information transmission. It is generally assumed that two services cannot be performed simultaneously due to the constraint that each internet of things user has only one antenna.
S102: and constructing a service optimization model by taking working parameters of the base station, the transmission and reflection intelligent surface and the unmanned aerial vehicle as optimization variables, calculating at least partial operation parameters of the network according to edges, taking task unloading requirements and communication requirements of users of the Internet of things as constraints, and taking a weighted sum of the minimum task unloading capacity and the minimum communication data volume of each user of the Internet of things as an optimization target.
Based on the task offloading and communication process of the Internet of things user, the server can construct a non-convex optimization problem with the weight sum of the minimum offloading task and the minimum communication data volume as an optimization target and with the service quality constraint of the computing capacity and the communication capacity of the Internet of things user and the amplitude and phase constraint of the STAR-RIS as constraint conditions.
Aiming at the characteristic that the unmanned aerial vehicle adopts a single antenna, a time division multiple access (Time division multiple access, TDMA) protocol is utilized to process the service facing calculation for the users of the Internet of things, so that the offloading tasks are effectively distributed and managed. Specifically, the total service time can be equally divided into a plurality of time slots by taking the position of the unmanned aerial vehicle as a constraint, and the time slots obtained by dividing are determined by the following formula: . These intervals are small enough to ensure that the position of the drone can be considered constant in each time slot. N represents an equal number of parts by weight, Representing the slot size.
And dividing each time slot according to the number of the user receivers to obtain the amount of time allocated to each Internet of things user for unloading tasks in each time slot. Each slot is further subdivided into K sub-slots, which are assigned to individual internet of things users to offload their computational task load. For duration of each sub-slotRepresentation, where variablesRepresenting the amount of time for task offloading allocated to the kth internet of things user in the nth time slot, wherein the constraint of the amount of time for task offloading for each internet of things user is represented as:
Wherein k represents any Internet of things user, and the set User identification set representing Internet of things and setRepresenting a set of slot identities.
The channel between the drone and the internet of things user can be determined by: In the formula, Representing the distance between the user and the drone,A path loss index value representing the path loss between the user and the drone,Indicating the path loss occurring over a standard distance of 1 meter,Representing the carrier wavelength(s),Represents the channel between the nth time slot unmanned aerial vehicle and the kth internet of things user,Representing the rice channel of the nth time slot,The rice factor is represented by the formula,A non-line-of-sight channel component representing the nth slot channel,The line-of-sight channel component representing the nth slot channel, which may be expressed as:
wherein, AndRespectively representing the abscissa and the ordinate of the drone in the nth time slot,AndRespectively representing the abscissa and the ordinate of the kth Internet of things user in the nth time slot.AndAnd respectively representing the position vectors of the unmanned plane and the kth Internet of things user in the nth time slot.
The channel between the base station and the drone may be determined by:。
in the formula, the corner mark H represents conjugate transposition, Representing the distance between the base station and the drone,A path loss index value representing the path loss between the base station and the drone,A steering vector representing the n-th slot plane array,A steering vector representing the nth slot linear array, specifically:
Because the distance between the unmanned aerial vehicle and the STAR-RIS is relatively short, the association relationship between the unmanned aerial vehicle and the STAR-RIS is represented by adopting a near-field channel in consideration of the spherical wave characteristic of an electromagnetic field. The near field channel between the drone and the smart surface of the base station may be determined by:
In the formula, Representing the distance between the drone and the mth intelligent unit of the transmissive and reflective intelligent surface simultaneously. From the consistent spatial relationship between the drone antenna and the STAR-RIS unit, it can be deduced that the channelIs not changed in characteristic.
When the kth internet of things user is selected to offload its computing tasks to the MEC server, the signal-to-interference-and-noise ratio at the base station and the drone when the internet of things user is selected to offload its computing tasks to the mobile edge computing server may be determined by:
In the formula, The unified transmitting power for all the users of the internet of things,Indicating the signal-to-interference-and-noise ratio of the base station when the kth internet of things user of the nth time slot is selected to offload its computing tasks to the mobile edge computing server,Represents the channel between the nth time slot unmanned aerial vehicle and the jth Internet of things user,Representing the signal-to-interference-and-noise ratio at the drone when the kth internet of things user of the nth time slot is selected to offload its computing tasks to the mobile edge computing server,Is the receiving precoding which is allocated to the kth Internet of things user by the nth time slot base station,The reflection coefficient and transmission coefficient of the smart surface are transmitted and reflected simultaneously for the nth time slot,Indicating the magnitude of the nth time slot,Indicating the phase of the nth slot,Representing the noise power received by the base station,Representing the noise power received by the drone. According to the expression of the SINR, the unloading rate of each Internet of things user in different time slots can be determined by the following expression:
Where B is the communication bandwidth, Representing the offloading rate obtained at the base station by the kth internet of things user at the nth time slot,And the unloading rate obtained by the kth Internet of things user at the unmanned aerial vehicle in the nth time slot is represented.
In one or more embodiments of the present invention, it is assumed that the internet of things user does not use local computing to complete tasks under the constraints of limited resources and energy conservation goals. Therefore, the internet of things user needs to transfer his computational workload to MEC servers located at the base station and drone. To be used forAndRespectively representing the data quantity which needs to be processed at the base station and the unmanned aerial vehicle by the user k of the Internet of things at the nth time slot. Considering the limitation on the achievable offloading rate, the offloading rate constraint of each internet of things user at the base station and the unmanned aerial vehicle corresponding to the information causal constraint can be expressed as:
In the nth time period, through The CPU frequency representing the task of the nth slot base station for processing the kth internet of things user,Representing the CPU frequency of the nth time slot unmanned aerial vehicle for processing the task of the kth internet of things user, the CPU frequency constraint of the base station and the unmanned aerial vehicle for processing the task of the kth internet of things user can be expressed as:
In the formula, Representing the highest CPU frequency provided by the base station,Representing the highest CPU frequency provided by the drone. It is critical to ensure proper order of task processing. In one or more embodiments, it is assumed that the base station and drone only receive the task of delivery in the first time slot, without any calculation. In addition, it is assumed that the internet of things user will stop offloading tasks in the last time period, i.e. In order to ensure that the task delegated by the kth internet of things user can be fully processed within the specified task period, the processing constraint of the task offloaded by the internet of things user can be expressed as:
In the formula, Represents the CPU frequency of the ith slot drone for handling tasks for the kth internet of things user,Represents the CPU frequency of the ith slot drone for handling tasks for the kth internet of things user,Representing the CPU cycles required to process 1 bit of data at the base station,Representing the CPU cycles required to process 1 bit of data at the drone,Is thatIs a subset of the group.
Order theThe minimum task number unloaded by the Internet of things user in the total service time is represented, and the index is used for evaluating the computing capability of the system for effectively processing the computing tasks and ensuring fair resource allocation among all Internet of things users. In addition, to ensure that the minimum computing requirements of each internet of things user are met, the minimum computing requirement constraints that meet the task offloading of the internet of things user can be expressed as:
In the formula, And the minimum calculation task which needs to be unloaded by the user of the Internet of things is indicated.
Each internet of things user requires that the STAR-RIS and unmanned aerial vehicle assisted air platform provide it with two services, a computation oriented service and a communication oriented service, in a given time slot. In particular, when the kth internet of things user communicates with the base station, the signal-to-interference-and-noise ratio of the signal received by the base station when the kth internet of things user communicates with the base station can be determined by the following formula:
according to the expression of the SINR, the communication signal of the kth Internet of things user is interfered by the (k-2) communication signal and a task unloading signal. In practice, considering that a given time slot is small enough, the SINR of the base station remains unchanged when k internet of things users choose to offload the computational tasks to or establish communication with the base station. The communication rate of each internet of things user when communicating with the base station can be determined by:
Thus, the rate can be used And the communication rate of the kth Internet of things user in the nth time slot is represented.
Order theRepresenting the minimum communication data volume transmitted between users of the internet of things at the total service time, satisfying the minimum communication data volume requirement constraint of the communication of the users of the internet of things can be represented as:
，，
In the formula, The minimum communication data volume required by the users of the Internet of things.Is an important performance index for measuring the communication capacity and fairness of all internet of things users of the MEC network under consideration. In order to ensure that the minimum communication requirement of each Internet of things user is met.
Based on this, a service optimization model can be constructed with the weighted sum of the minimum task load and the minimum communication data volume of each internet of things user as an optimization target by the following formula:
wherein, A weighting factor representing service priority with emphasis on task offloading,A weighting factor representing priority of service with emphasis on communication,The optimization variables are assigned for the time and,Representing the computing resource allocation optimization variables of the base station and the drone,Representing the active beamforming optimization variables of the base station,Representing simultaneous transmission and reflection intelligent surface passive beamforming optimization variables,Represents the trajectory optimization variables of the unmanned aerial vehicle,Representing the position vector of the nth slot drone,Represents a velocity vector of the unmanned aerial vehicle,Representing the maximum speed of the drone,Representing the acceleration vector of the unmanned aerial vehicle,Indicating the maximum acceleration of the unmanned aerial vehicle,The starting position of the unmanned aerial vehicle is indicated,And indicating the end position of the unmanned aerial vehicle.
Further, in one or more embodiments of the present invention, the above-described optimization problem is a maximum and minimum problem, which is difficult to directly solve using a conventional convex optimization algorithm. To solve this problem, two auxiliary variables can be introduced firstAndRespectively replaceAnd. Such an alternative may translate the max-min optimization problem into an equivalent max optimization problem. The transformed optimization problem can be re-expressed as:
the server mentioned in the present invention may be a server provided in a mobile edge computing network, or a device such as a desktop, a notebook, etc. capable of executing the inventive arrangements. For convenience of explanation, only the server is used as the execution subject.
S104: and solving a service optimization model, determining working parameters of the base station, the transmission and reflection intelligent surface and the unmanned aerial vehicle, and enabling an edge computing network to operate under the working parameters obtained by solving.
For the optimization problem obtained by the construction, generally, the optimization variables can comprise time allocation optimization variables, calculation resource allocation optimization variables of the base station and the unmanned aerial vehicle, intelligent surface passive beam forming optimization variables and unmanned aerial vehicle track optimization variables.
When solving the optimization problem, the server can solve and decompose the service optimization model into four sub-problems according to an iteration strategy, and iteratively solve the four sub-problems to determine working parameters of the base station, the transmission and reflection intelligent surface and the unmanned aerial vehicle;
The first sub-problem is to determine the active beamforming optimization variable of the preferred base station under the conditions of setting time allocation optimization variables, calculation resource allocation optimization variables of the base station and the unmanned aerial vehicle, and passive beamforming optimization variables of the transmission and reflection intelligent surface and unmanned aerial vehicle track optimization variables.
The second sub-problem is to determine a preferred time allocation optimization variable and a calculation resource allocation optimization variable of the base station and the unmanned aerial vehicle under the condition that an active beamforming optimization variable of the base station, a passive beamforming optimization variable of the transmission and reflection intelligent surface and an unmanned aerial vehicle track optimization variable are set.
The third sub-problem is to determine the optimal simultaneous transmission and reflection intelligent surface passive beamforming optimization variable and the base station and unmanned aerial vehicle computing resource allocation optimization variable according to the optimal solutions of the first sub-problem and the second sub-problem under the condition that the time allocation optimization variable, the base station active beamforming optimization variable and the unmanned aerial vehicle track optimization variable are set.
The fourth sub-problem is to determine the optimal variable of the computing resource allocation of the base station and the unmanned aerial vehicle and the optimal variable of the unmanned aerial vehicle track according to the optimal solutions of the first sub-problem, the second sub-problem and the third sub-problem under the condition that the optimal variable of time allocation, the optimal variable of the active beam forming of the base station and the optimal variable of the passive beam forming of the transmissive and reflective intelligent surface are set.
Specifically, taking the service optimization model in step S103 as an example for illustration, the above-mentioned problem can be divided into four sub-problems using an alternation strategy, the first sub-problem being that given，，AndIs designed under the condition of (1)The second sub-problem is that, given，AndIs designed under the condition of (1)AndThe first sub-problem and the second sub-problem are convex problems. The third sub-problem is to，AndFixing solutions according to the first and second convex problemsFor a pair ofAndThe fourth sub-problem is to perform optimal design，AndFixing the solution to the first, second and third non-convex problemsAndAnd (5) performing optimal design. The third sub-problem and the fourth sub-problem are non-convex problems.
Wherein the first sub-problem can be represented by the following formula:
The above optimization problem can be divided into Individual sub-problems. Therefore, a method of solving the above-described optimization problem can be regarded as simultaneously solvingSub-problems. For the followingThe sub-problem can be expressed as:
The second sub-problem can be represented by the following formula:
the third sub-problem can be represented by the following formula:
In the formula, ,,Wherein, 。
Based onAnd，、AndCan be re-expressed as:
wherein, AndRespectively isAndAt the position ofFirst order taylor expansion at.。Representation and representationIs associated with the largest eigenvalue of the set.AndIs a penalty factor.
The fourth sub-problem can be represented by the following formula:
When further solving the four sub-problems, in one or more embodiments of the present invention, the server may solve the first sub-problem according to a preset initial feasible solution or a previous iteration preferred solution to determine an update solution of the active beamforming optimization variable of the base station, and solve the second sub-problem to determine an update solution of the time allocation optimization variable and a first update solution of the computing resource allocation optimization variables of the base station and the unmanned aerial vehicle.
According to the updating solution of the active beam forming optimization variable of the base station, the updating solution of the time allocation optimization variable, the first updating solution of the computing resource allocation optimization variable of the base station and the unmanned aerial vehicle, the simultaneous transmission and reflection intelligent surface passive beam forming optimization variable optimization solution and the unmanned aerial vehicle track optimization solution in the preset initial feasible solution or the upper iteration optimization solution, solving a third sub-problem until the third sub-problem converges, and determining the updating solution of the simultaneous transmission and reflection intelligent surface passive beam forming optimization variable and the second updating solution of the computing resource allocation optimization variable of the base station and the unmanned aerial vehicle.
According to the updated solution of the active beamforming optimization variable of the base station, the updated solution of the passive beamforming optimization variable of the transmissive and reflective intelligent surface, the updated solution of the time allocation optimization variable and the second updated solution of the computing resource allocation optimization variable of the base station and the unmanned aerial vehicle, solving a fourth sub-problem to determine the updated solution of the unmanned aerial vehicle track optimization variable and the third updated solution of the computing resource allocation optimization variable of the base station and the unmanned aerial vehicle;
and taking the updated solution of the time allocation optimization variable, the updated solution of the unmanned aerial vehicle track optimization variable, the updated solution of the active beamforming optimization variable of the base station, the updated solution of the passive beamforming optimization variable of the transmission and reflection intelligent surface and the third updated solution of the computing resource allocation optimization variable of the base station and the unmanned aerial vehicle as iterative optimization solutions of the iterative computation of the round.
Specifically, the method comprises the following steps:
Definition of the definition Representing the difference between the target values of two adjacent iterations whenLess than a predefined accuracy thresholdWhen the algorithm converges.
Algorithm 1: setting an initial feasible pointPenalty coefficientAnd calculateDefining tolerance thresholdAndSetting an external loop iteration index; When (when)Or (b)The following cycle was repeated at this time:
According to given set Is used to solve the first sub-problem, updating with the resulting solution。
According to given setIs used to solve the second sub-problem, updating with the resulting solution。
Setting an inner circulationWhen (when)Or (b)The following second layer cycle is repeated at this time: according to given setSolving a third convex problem, updating with the obtained solutionCalculating from the obtained resultUpdating，And make. The second layer cycle is ended.
Updating with the resulting solution。
According to given setIs used to solve the fourth convex problem, updating with the resulting solution。
Calculation of，; Ending the cycle to obtain the optimal。
After the service optimization model is solved, the edge computing network can be operated under the working parameters obtained by solving.
Based on the method for simultaneously task unloading and communication of the Internet of things user shown in fig. 1, in a mobile edge computing network scene comprising a base station with a transmitter with a plurality of antennas and a mobile edge computing server, an unmanned aerial vehicle with a single antenna with the mobile edge computing server and a plurality of user receivers with single antennas, according to the task unloading and communication process of the Internet of things user, the working parameters of the base station, the intelligent surface with the transmission and reflection and the unmanned aerial vehicle are taken as optimization variables, at least part of the operation parameters of the network are calculated according to the edges, the task unloading requirement and the communication requirement of the Internet of things user are taken as constraints, the weighted sum of the minimum task unloading capacity and the minimum communication data quantity of each Internet of things user is maximized as an optimization target, a service optimization model is constructed and solved, so that the edge computing network operates under the working parameters obtained by solving.
The mobile edge computing network can simultaneously give consideration to the computing task unloading requirement and the communication requirement of the users of the Internet of things in the operation process, so that the users of the Internet of things can efficiently carry out task unloading and simultaneously realize effective communication with other users of the Internet of things. Compared with the traditional RIS-assisted MEC scheme, the MEC scheme has great advantages in improving the computing capacity and the communication capacity of the MEC network.
FIG. 3 is a schematic diagram of a weighted sum of minimum task load and minimum traffic data for users of the Internet of things and a change in the number of intelligent units on a STAR-RIS according to the present invention. From the simulation results, it can be found that the weight sum of the calculated and communicated data in all the scenarios gradually increases with the increase of the number M of intelligent units. This phenomenon can be attributed to the fact that more intelligent units provide more flexibility for the reprofiling propagation environment. However, due to system setup limitations, growth rates gradually decrease as the number of intelligent units integrated by the STAR-RIS increases. It is worth emphasizing that the proposed MEC scheme has superior performance gains compared to the other three reference MEC schemes. The reasons can be summarized as follows: (1) Compared with the MEC scheme provided by the invention, the scheme without track optimization cannot obtain the performance gain caused by the improvement of the channel condition between the unmanned aerial vehicle and the Internet of things user or BS through track optimization. (2) The performance improvement of the MEC solution of the present invention over the traditional RIS-assisted solution is due to the enhanced regulatory capabilities of STAR-RIS. Unlike conventional RIS, STAR-RIS can effectively modify the phase and amplitude of the incident signal, thereby enabling more flexible and efficient operation. This also shows the great potential for STAR-RIS use in wireless communication systems. (3) Compared with other MEC schemes, the MEC scheme adopting unified time slot division has the least improvement in performance, because such time scheduling can lead to the fact that additional time cannot be allocated for the Internet of things users experiencing high channel quality to offload tasks, which indicates the importance of reasonable time allocation.
Fig. 4 is a schematic diagram of the effect simulation of the time slot number on the weighted sum of the minimum task load capacity and the minimum communication data volume of each internet of things user in the present invention. As can be seen from fig. 4, again, all schemes exhibit a consistent increase with increasing value of the number of time slots N, but the rate of increase decreases because the internet of things users are allocated more time to offload their tasks and communicate their information to the base station. In addition, the MEC network provided by the invention obtains the highest performance gain, and the scheme with equal time slot partitioning still provides the lowest performance gain. The performance gap between the scheme provided by the invention and the scheme lacking track optimization is larger and larger, and the unmanned aerial vehicle has more enough time to improve the position of the unmanned aerial vehicle, so that high-quality channel connection with the Internet of things user or the base station is ensured. Thus, the computational and communication capabilities of the MEC system will be enhanced.
When the method for simultaneously unloading tasks and communicating tasks of the Internet of things user provided by the invention is applied, the method can be executed without the sequence of the steps shown in fig. 1, and the execution sequence of the steps can be determined according to the needs.
The method for simultaneously task offloading and communicating for the internet of things user provided by the one or more embodiments of the present invention further provides a corresponding device for simultaneously task offloading and communicating for the internet of things user based on the same thought, including:
the system comprises a base station, an intelligent surface, a single-antenna unmanned aerial vehicle and a plurality of single-antenna user receivers, wherein the base station comprises a transmitter with a plurality of antennas and a mobile edge computing server, the intelligent surface of the base station comprises a plurality of intelligent units, and the unmanned aerial vehicle is provided with the mobile edge computing server;
The user receiver is used for reflecting at least part of task unloading signals to the base station through the transmission and reflection intelligent surface and transmitting the rest task unloading signals to the unmanned aerial vehicle so as to unload the computing tasks to the base station and the unmanned aerial vehicle;
The base station, the simultaneous transmission and reflection intelligent surface and the unmanned aerial vehicle are configured to take working parameters of the base station, the simultaneous transmission and reflection intelligent surface and the unmanned aerial vehicle as optimization variables, calculate at least partial operation parameters of the network according to edges, take task unloading requirements and communication requirements of users of the Internet of things as constraints, take weighted sum of minimum task unloading capacity and minimum communication data quantity of each user of the Internet of things as optimization targets, construct a service optimization model and solve the obtained corresponding working parameters.
The specific limitation of the device for simultaneously task offloading and communication by the internet of things user can be referred to the limitation of the method for simultaneously task offloading and communication by the internet of things user in the above, and will not be repeated here. All or part of the modules in the device for simultaneously unloading tasks and communicating by the Internet of things user can be realized by software, hardware and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
The invention also provides a computer readable storage medium storing a computer program, the computer program is used for executing the method for simultaneously unloading and communicating tasks for the internet of things user provided by the above figure 1.
The invention also provides a computer device, which comprises a processor, an internal bus, a network interface, a memory and a nonvolatile memory, and can also comprise hardware needed by other services. The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to realize the method for simultaneously unloading and communicating tasks for the internet of things user provided by the figure 1.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the present invention.
Claims (9)
1. The method is applied to a mobile edge computing network comprising a base station, a simultaneous transmission and reflection intelligent surface, a single-antenna unmanned aerial vehicle and a plurality of single-antenna user receivers, wherein the base station comprises a transmitter with a plurality of antennas and a mobile edge computing server, the simultaneous transmission and reflection intelligent surface comprises a plurality of intelligent units, and the unmanned aerial vehicle is provided with the mobile edge computing server; the method comprises the following steps:
Reflecting at least part of task unloading signals to a base station through a transmission and reflection intelligent surface based on a user receiver and transmitting the rest task unloading signals to an unmanned aerial vehicle so as to unload the computing tasks to the base station and the unmanned aerial vehicle;
The method comprises the steps of taking working parameters of a base station, a transmission intelligent surface, a reflection intelligent surface and an unmanned aerial vehicle as optimization variables, calculating at least partial operation parameters of a network according to edges, taking task unloading requirements and communication requirements of users of the Internet of things as constraints, and taking a weighted sum of the minimum task unloading capacity and the minimum communication data volume of each user of the Internet of things as an optimization target to construct a service optimization model;
and solving a service optimization model, determining working parameters of the base station, the transmission and reflection intelligent surface and the unmanned aerial vehicle, and enabling an edge computing network to operate under the working parameters obtained by solving.
2. The method for simultaneously task offloading and communicating by an internet of things user according to claim 1, wherein the constraint of meeting the task offloading requirement and the communication requirement of the internet of things user specifically comprises:
The method comprises the steps of equally dividing the total service time into a plurality of time slots by taking the position constancy of the unmanned aerial vehicle as constraint, and determining the time slots obtained by dividing by the following formula: ；
dividing each time slot according to the number of user receivers to obtain the amount of time allocated to each Internet of things user for unloading tasks in each time slot;
determining a channel between the unmanned aerial vehicle and the Internet of things user through the following steps: ；
The channel between the base station and the drone is determined by: ；
The near field channel between the drone and the simultaneous transmissive and reflective smart surface is determined by:
，，；
determining the signal-to-interference-and-noise ratio at the base station and the unmanned aerial vehicle when the user of the internet of things is selected to offload its computing tasks to the mobile edge computing server by:
，
，
，
，；
the unloading rate of each Internet of things user in different time slots is determined by the following steps:
，；
determining the signal-to-interference-and-noise ratio of a signal received by a base station when an Internet of things user communicates with the base station by:
；
Determining the communication rate of the Internet of things user when communicating with the base station through the following steps:
；
Determining constraint conditions meeting task unloading requirements and communication requirements of users of the Internet of things;
wherein, Indicating the total service time, N indicating the number of aliquots,The time slot size is indicated as such,Indicating the path loss occurring over a standard distance of 1 meter,Representing the carrier wavelength, n represents any time slot,Representing a set of time slots andK represents any Internet of things user and ，Representing user set of Internet of things，Represents the channel between the nth time slot unmanned aerial vehicle and the kth internet of things user,Represents the channel between the nth time slot unmanned aerial vehicle and the jth Internet of things user,Representing the rice channel and the channel is indicated,The rice factor is represented by the formula,A non-line-of-sight channel component representing the nth slot channel,A line-of-sight channel component representing the nth slot channel,Representing the distance between the user and the drone,A path loss index value representing the path loss between the user and the drone,Representing the distance between the base station and the drone,A path loss index value representing the path loss between the base station and the drone,Representing the channel between the nth time slot base station and the drone,A steering vector representing the n-th slot plane array,A steering vector representing the nth slot linear array,Representing the near field channel between the drone and the transmissive and reflective smart surfaces simultaneously,Representing the distance between the drone and the mth intelligent unit of the transmissive and reflective intelligent surface simultaneously,The unified transmitting power for all the users of the internet of things,Indicating the signal-to-interference-and-noise ratio of the base station when the kth internet of things user of the nth time slot is selected to offload its computing tasks to the mobile edge computing server,Representing the signal-to-interference-and-noise ratio at the drone when the kth internet of things user of the nth time slot is selected to offload its computing tasks to the mobile edge computing server,Is the receiving precoding which is allocated to the kth Internet of things user by the nth time slot base station,The reflection coefficient and transmission coefficient of the smart surface are transmitted and reflected simultaneously for the nth time slot,Indicating the magnitude of the nth time slot,Indicating the phase of the nth slot,Representing the noise power received by the base station,Representing the noise power received by the drone,Representing the offloading rate obtained at the base station by the kth internet of things user at the nth time slot,Representing the unloading rate obtained by the kth Internet of things user at the unmanned aerial vehicle in the nth time slot, B is the communication bandwidth,Representing the signal-to-interference-and-noise ratio of the signal received by the base station when the kth Internet of things user communicates with the base station in the nth time slot,And the communication rate of the kth Internet of things user in the nth time slot when communicating with the base station is represented, and the corner mark H represents the conjugate transpose.
3. The method for simultaneously task offloading and communicating by an internet of things user according to claim 2, wherein determining constraint conditions for meeting task offloading requirements and communication requirements of the internet of things user specifically comprises:
The time amount constraint of each internet of things user for offloading tasks is expressed as:
；
the offloading rate constraints of each internet of things user at the base station and the drone are expressed as:
,；
The CPU frequency constraints of the base station and the unmanned aerial vehicle for processing tasks of each internet of things user are expressed as:
；
the processing constraint of the task offloaded by the internet of things user is expressed as:
，；
The minimum computational demand constraint for meeting the task offloading of the internet of things user is expressed as:
；
the minimum communication data volume requirement constraint meeting the internet of things user communication is expressed as:
，，；
wherein, And represents the amount of time allocated to the kth internet of things user in the nth time slot for offloading tasks,Indicating the data volume that the kth Internet of things user needs to process at the base station in the nth time slot,Indicating the data volume that the kth Internet of things user needs to process at the unmanned aerial vehicle in the nth time slot,The CPU frequency representing the task of the nth slot base station for processing the kth internet of things user,Represents the CPU frequency of the ith slot drone for handling tasks for the kth internet of things user,Represents the CPU frequency of the nth slot drone for handling tasks of the kth internet of things user,Represents the CPU frequency of the ith slot drone for handling tasks for the kth internet of things user,Representing the highest CPU frequency provided by the base station,Representing the highest CPU frequency provided by the drone,Representing the CPU cycles required to process 1 bit of data at the base station,Representing the CPU cycles required to process 1 bit of data at the drone,Is thatIs selected from the group consisting of,The minimum task number unloaded by the internet of things user in the total service time is represented,Representing the minimum computing task that the internet of things user needs to offload,Represents the minimum amount of communication data transmitted between users of the internet of things at the total service time,The minimum communication data volume required by the users of the Internet of things.
4. The method for simultaneous task offloading and communication of internet of things users as claimed in claim 3, wherein said constructing a service optimization model with a weighted sum of a minimum task offloading amount and a minimum communication data amount of each internet of things user as an optimization objective specifically comprises:
Constructing a service optimization model by taking a weighted sum of the minimum task unloading capacity and the minimum communication data quantity of each Internet of things user as an optimization target, wherein the weighted sum is maximized: ；
wherein, A weighting factor representing service priority with emphasis on task offloading,A weighting factor representing priority of service with emphasis on communication,The optimization variables are assigned for the time and,Representing the computing resource allocation optimization variables of the base station and the drone,Representing the active beamforming optimization variables of the base station,Representing simultaneous transmission and reflection intelligent surface passive beamforming optimization variables,Represents the trajectory optimization variables of the unmanned aerial vehicle,Representing the position vector of the nth slot drone,Represents a velocity vector of the unmanned aerial vehicle,Representing the maximum speed of the drone,Representing the acceleration vector of the unmanned aerial vehicle,Indicating the maximum acceleration of the unmanned aerial vehicle,The starting position of the unmanned aerial vehicle is indicated,And indicating the end position of the unmanned aerial vehicle.
5. The method for simultaneous task offloading and communication of an internet of things user according to claim 1, wherein the optimization variables include a time allocation optimization variable, a base station and unmanned aerial vehicle computing resource allocation optimization variable, an intelligent surface passive beamforming optimization variable and an unmanned aerial vehicle trajectory optimization variable;
The service optimization model solving method is used for determining working parameters of the base station, the intelligent surface capable of transmitting and reflecting simultaneously and the unmanned aerial vehicle, and specifically comprises the following steps:
according to the iteration strategy, solving and decomposing the service optimization model into four sub-problems, and iteratively solving the four sub-problems to determine working parameters of the base station, the transmission and reflection surfaces and the unmanned aerial vehicle;
The first sub-problem is to determine the active beamforming optimization variable of the preferred base station under the conditions of setting time allocation optimization variables, calculation resource allocation optimization variables of the base station and the unmanned aerial vehicle, and passive beamforming optimization variables of the transmission and reflection intelligent surface and unmanned aerial vehicle track optimization variables;
The second sub-problem is to determine a preferred time allocation optimization variable and a calculation resource allocation optimization variable of the base station and the unmanned aerial vehicle under the condition that an active beam forming optimization variable of the base station, a passive beam forming optimization variable of the transmission and reflection intelligent surface and an unmanned aerial vehicle track optimization variable are set;
The third sub-problem is to determine the optimal simultaneous transmission and reflection intelligent surface passive beamforming optimization variable and the base station and unmanned aerial vehicle computing resource allocation optimization variable according to the optimal solutions of the first sub-problem and the second sub-problem under the condition that the time allocation optimization variable, the base station active beamforming optimization variable and the unmanned aerial vehicle track optimization variable are set;
The fourth sub-problem is to determine the optimal variable of the computing resource allocation of the base station and the unmanned aerial vehicle and the optimal variable of the unmanned aerial vehicle track according to the optimal solutions of the first sub-problem, the second sub-problem and the third sub-problem under the condition that the optimal variable of time allocation, the optimal variable of the active beam forming of the base station and the optimal variable of the passive beam forming of the transmissive and reflective intelligent surface are set.
6. The method for simultaneous task offloading and communication of an internet of things user according to claim 5, wherein the iteratively solving four sub-problems specifically comprises:
According to a preset initial feasible solution or a previous iteration optimal solution, solving a first sub-problem to determine an updated solution of an active beamforming optimization variable of the base station, and solving a second sub-problem to determine an updated solution of a time allocation optimization variable and a first updated solution of a calculation resource allocation optimization variable of the base station and the unmanned aerial vehicle;
According to the updating solution of the active beam forming optimization variable of the base station, the updating solution of the time allocation optimization variable, the first updating solution of the computing resource allocation optimization variable of the base station and the unmanned aerial vehicle, the simultaneous transmission and reflection intelligent surface passive beam forming optimization variable optimization solution and the unmanned aerial vehicle track optimization solution in the preset initial feasible solution or the upper iteration optimization solution, solving a third sub-problem until the third sub-problem is converged, and determining the updating solution of the simultaneous transmission and reflection intelligent surface passive beam forming optimization variable and the second updating solution of the computing resource allocation optimization variable of the base station and the unmanned aerial vehicle;
According to the updated solution of the active beamforming optimization variable of the base station, the updated solution of the passive beamforming optimization variable of the transmissive and reflective intelligent surface, the updated solution of the time allocation optimization variable and the second updated solution of the computing resource allocation optimization variable of the base station and the unmanned aerial vehicle, solving a fourth sub-problem to determine the updated solution of the unmanned aerial vehicle track optimization variable and the third updated solution of the computing resource allocation optimization variable of the base station and the unmanned aerial vehicle;
and taking the updated solution of the time allocation optimization variable, the updated solution of the unmanned aerial vehicle track optimization variable, the updated solution of the active beamforming optimization variable of the base station, the updated solution of the passive beamforming optimization variable of the transmission and reflection intelligent surface and the third updated solution of the computing resource allocation optimization variable of the base station and the unmanned aerial vehicle as iterative optimization solutions of the iterative computation of the round.
7. An apparatus for simultaneous task offloading and communication by an internet of things user, comprising:
A base station, a simultaneous transmission and reflection intelligent surface, a single-antenna unmanned aerial vehicle and a plurality of single-antenna user receivers, wherein the base station comprises a transmitter with a plurality of antennas and a mobile edge calculation server, the simultaneous transmission and reflection intelligent surface comprises a plurality of intelligent units, and the unmanned aerial vehicle is provided with the mobile edge calculation server;
The user receiver is used for reflecting at least part of task unloading signals to the base station through the transmission and reflection intelligent surface and transmitting the rest task unloading signals to the unmanned aerial vehicle so as to unload the computing tasks to the base station and the unmanned aerial vehicle;
The base station, the simultaneous transmission and reflection intelligent surface and the unmanned aerial vehicle are configured to take working parameters of the base station, the simultaneous transmission and reflection intelligent surface and the unmanned aerial vehicle as optimization variables, calculate at least partial operation parameters of the network according to edges, take task unloading requirements and communication requirements of users of the Internet of things as constraints, take weighted sum of minimum task unloading capacity and minimum communication data quantity of each user of the Internet of things as optimization targets, construct a service optimization model and solve the obtained corresponding working parameters.
8. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1-6.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any of the preceding claims 1-6 when executing the program.
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