CN111464208A - Passive edge computing system based on spread spectrum communication, task unloading method and storage medium - Google Patents

Passive edge computing system based on spread spectrum communication, task unloading method and storage medium Download PDF

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CN111464208A
CN111464208A CN202010157484.6A CN202010157484A CN111464208A CN 111464208 A CN111464208 A CN 111464208A CN 202010157484 A CN202010157484 A CN 202010157484A CN 111464208 A CN111464208 A CN 111464208A
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edge computing
wireless terminal
computing system
task
user
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毕宿志
林晓辉
王晖
陈月贵
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Shenzhen University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
    • H04B1/69—Spread spectrum techniques
    • H04B1/707—Spread spectrum techniques using direct sequence modulation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J50/00—Circuit arrangements or systems for wireless supply or distribution of electric power
    • H02J50/20—Circuit arrangements or systems for wireless supply or distribution of electric power using microwaves or radio frequency waves
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J50/00—Circuit arrangements or systems for wireless supply or distribution of electric power
    • H02J50/80—Circuit arrangements or systems for wireless supply or distribution of electric power involving the exchange of data, concerning supply or distribution of electric power, between transmitting devices and receiving devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/06—TPC algorithms
    • H04W52/14—Separate analysis of uplink or downlink
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/32—TPC of broadcast or control channels
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/34—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading

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Abstract

The invention discloses a passive edge computing system based on spread spectrum communication, a task unloading method and a storage medium. Each user uses the pseudo-random sequence with the length of G as a spreading code to spread the signal to the whole broadband, so that all users share all time-frequency freedom degrees, and compared with a multi-access scheme for unloading other tasks, the DS-CDMA mode greatly improves the utilization rate of time-frequency resources. Meanwhile, in order to prevent adjacent channel interference and near-far effect, the power control is carried out on the CDMA system with limited interference. Through power control, the total interference level of the system is minimized, and therefore the total throughput of the passive edge computing system is improved. The invention aims at the optimal distribution of energy transmission and information transmission time in a passive edge computing system. The phenomenon that the energy near-far effect can cause serious performance unfairness among users at different positions is solved.

Description

Passive edge computing system based on spread spectrum communication, task unloading method and storage medium
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a passive edge computing system based on spread spectrum communications, a task offloading method, and a storage medium.
Background
The wireless power supply communication network is a new network mode, which transmits radio frequency energy signals to wireless equipment while transmitting information wireless signals, an energy receiver in the wireless equipment remotely collects energy from the radio frequency signals transmitted by an energy transmitter, and the collected energy is effectively converted into direct current signals to continuously and stably charge a battery carried by the wireless equipment, and the collected energy is used for normal information communication of the wireless equipment. The power supply technology utilizes wireless electromagnetic waves or variable electromagnetic fields to carry out energy wireless transmission, has good broadcasting characteristics, and has important application value in the fields of autonomous driving, intelligent household appliances, virtual reality, remote operations, wireless sensor networks and the like.
The radio frequency energy transmission technology solves the problems of limited battery capacity of wireless equipment, frequent manual battery replacement, frequent battery recharging and the like, and avoids the problem of high-probability interruption of wireless communication caused by the battery problem and the influence on the quality of application service. Compared with the traditional battery-powered network, the wireless power-powered communication network effectively reduces the operation cost, improves the communication performance, and has higher throughput, higher robustness and greater flexibility.
Mobile Edge Computing (MEC) is a new network architecture concept that provides information technology and cloud services. Due to the close proximity to the user, mobile edge computing can provide ultra-low latency, high bandwidth, and direct access to real-time network information. MEC allows users to offload intensive computing to nearby servers located at the edge of the wireless access network, such as cellular base stations and WiFi Access Points (APs). Compared with the traditional cloud computing mode, the MEC eliminates longer backhaul delay and enjoys lower equipment energy consumption and superior server load balancing performance.
In recent years, mobile computing has shifted from centralized mobile cloud computing to mobile edge computing, driven by the internet of things and 5G communications. The key feature of MECs is to push mobile computing, network control and storage to the network edge (e.g., base stations and access points) to enable the application of compute-intensive and delay-sensitive applications to resource-limited mobile devices. The MEC can greatly reduce delay and mobile energy consumption, solving the key challenge of realizing 5G vision.
The passive edge calculation is to remotely supply power to a user at an edge server end through a wireless energy transmission technology, and the wireless equipment does not need to manually replace a battery or charge. The passive edge computing system combines the characteristics of a wireless power supply communication network and a mobile edge computing network, and solves two basic performance limitations in an IoT network: energy limitation and low computational performance.
Existing wireless powered communication networks have a significant challenge, on the one hand, because of the significant attenuation of microwave power with distance, the received energy level may be very low at wireless devices far from the energy transmitter. The energy collected by users at different positions is greatly different, and a serious unfair performance phenomenon is caused. On the other hand, information transmission is usually related to the collected wireless energy, e.g., the wireless device needs to obtain enough energy through wireless energy transmission technology before transmitting data, and the more energy is consumed by the user data transmission process the farther away from the HAP. Unfairness caused by the "double near-far problem" of energy transmission and information transmission reduces the overall performance of the network.
Accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a passive edge computing system based on spread spectrum communication, a task offloading method and a storage medium, aiming at solving the problem of reducing the overall performance of the wireless communication network caused by the "dual near-far problem" of energy transmission and information transmission existing in the wireless communication network in the prior art.
The technical scheme adopted by the invention for solving the technical problem is as follows:
in a first aspect, an embodiment of the present invention provides a passive edge computing system based on spread spectrum communication, where the system includes: n wireless terminal users and a HAP, the HAP comprising a radio frequency energy transmitter and a mobile edge computing server; the wireless terminal user comprises an energy harvesting circuit and a battery; and broadcasting radio frequency energy to the wireless terminal user by the HAP, and charging the battery by the wireless terminal user receiving the radio frequency energy, wherein N is more than or equal to 3.
The passive edge computing system based on spread spectrum communications wherein the wireless end user further comprises wireless communications circuitry to transmit information to the HAP over a communications channel by the wireless end user, the radio frequency energy transmission sharing a common spectrum with the communications channel.
The passive edge computing system based on spread spectrum communication is characterized in that the downlink energy transmission and the uplink wireless communication work in a time division multiplexing mode.
In a second aspect, a method for task offloading of the passive edge computing system includes:
establishing user index N of task running in unloading mode1And a user index N for the task running in the local computing mode0N ═ N1UN0＝{1,…,N}；
Dividing transmission time into a phase for broadcasting radio frequency energy to the wireless terminal user by the HAP within each system time T, wherein the phase takes α T, α∈ (0,1), a phase for the wireless terminal user to offload task data to the HAP and a phase for the HAP to process the offload task data, and returning a processing result to the wireless terminal user;
based on
Figure BDA0002404603190000041
Figure BDA0002404603190000042
0≤α≤1,
xi∈ {0,1}, i ═ 1, L, n., obtaining an optimal transmission power for each offload wireless end user using a fractional planning method, each of the wireless end users performing task offload with the optimal transmission power, wherein,
Figure BDA0002404603190000043
wi>0 represents a positive weight, x, of the computing task priority of the ith wireless terminal user i1 means that the ith wireless terminal user offloads its computing task to the mobile edge computing server for execution, and x i0 means that it performs the computation task locally.
Wherein P is0Representing the radio frequency energy emission power of the HAP,. mu. ∈ (0,1) representing the energy harvesting efficiency, hiRepresenting the channel power gain of the HAP to the ith user. Noise power of all receivers is N0The total slot time is set to T-1 at-140 dBm.
Calculating the efficiency coefficient ki＝10-26N, where the number of CPU cycles required to process one bit of raw data per WD is set as Ci100. For the data offload mode, the bandwidth of uplink information transmission is 10MHz, and the spreading gain G is 128. The residual energy of the previous time frame is zero ei＝0,i＝1,...,N。
The task unloading method of the passive edge computing system is characterized in that
Figure BDA0002404603190000051
Computing indicating that a wireless terminal user is operating in a local computing modeRate of said
Figure BDA0002404603190000052
Represents the calculated rate at which the wireless terminal user is operating in an offload mode, wherein,
Figure BDA0002404603190000053
qmaxrepresenting the maximum transmission power, Pmax,iRepresenting the maximum allowed power for the ith wireless terminal user.
The task unloading method of the passive edge computing system is characterized in that for a wireless terminal user operating in an unloading mode, an objective function of power control based on a fractional programming method is as follows:
Figure BDA0002404603190000054
the task unloading method of the passive edge computing system comprises the following steps of, when P is a fixed value,
Figure BDA0002404603190000055
the task unloading method of the passive edge computing system adopts a random local search method to select the optimal user from wireless terminal users to unload the tasks, and the selection rule is as follows:
Figure BDA0002404603190000061
wherein the mode selection vector in the first iteration is represented as xlWherein x is0Representing the initial mode selection solution.
The task offloading method of the passive edge computing system, wherein the energy collected by each wireless terminal user is Ei＝μhiP0αT。
In a third aspect, a storage medium, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the above-described method.
The invention has the beneficial effects that: the invention applies the spread spectrum communication technology to a passive edge computing system, and all uninstalled users share the spectrum resources by using a Direct Sequence-Code Division Multiple access (Direct Sequence-Code Division Multiple access DS-CDMA) mode. Each user uses the pseudo-random sequence with the length of G as a spreading code to spread the signal to the whole broadband, so that all users share all time-frequency freedom degrees, and compared with a multi-access scheme for unloading other tasks, the DS-CDMA mode greatly improves the utilization rate of time-frequency resources. Meanwhile, in order to prevent adjacent channel interference and near-far effect, the power control is carried out on the CDMA system with limited interference. Through power control, the total interference level of the system is minimized, and therefore the total throughput of the passive edge computing system is improved. The invention aims at the optimal distribution of energy transmission and information transmission time in a passive edge computing system. The phenomenon that the energy near-far effect can cause serious performance unfairness among users at different positions is solved.
Drawings
Fig. 1 is a schematic diagram of a passive edge computing system based on spread spectrum communication according to the present invention.
Fig. 2 is a schematic diagram of allocation of energy and information transmission time slots in the task offloading method of the passive edge computing system based on spread spectrum communication according to the present invention.
Fig. 3 is a sample set diagram for a given mode selection.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Due to the existing wireless powered communication networks, on the one hand, the received energy level may be very low at wireless devices far away from the energy transmitter due to the significant attenuation of the microwave power with distance. The energy collected by users at different positions is greatly different, and a serious unfair performance phenomenon is caused. On the other hand, information transmission is usually related to the collected wireless energy, e.g., the wireless device needs to obtain enough energy through wireless energy transmission technology before transmitting data, and the more energy is consumed by the user data transmission process the farther away from the HAP. Unfairness caused by the "double near-far problem" of energy transmission and information transmission reduces the overall performance of the network.
In order to solve the above problems, in the embodiment of the present invention, a wireless power supply communication network is constructed, task offloading policy optimization is performed on all users, each offloading wireless terminal user uses a pseudorandom sequence with a length of G as a spreading code to spread its signal to the whole broadband, and all users share all time-frequency degrees of freedom. The utilization rate of time frequency resources is improved.
All tasks of the wireless terminal user are defined as indivisible, and a user index running in an uninstall mode and a user index running in a local computing mode of the task are established. Energy transmission and information transmission time are distributed, the transmission rate of each wireless terminal user is optimized in a time range, the optimal transmission power of each unloaded wireless terminal user is obtained by adopting a fractional programming method, and the total interference level of the system is minimized through power control, so that the total throughput of the passive edge computing system is improved.
Various non-limiting embodiments of the present invention are described in detail below with reference to the accompanying drawings.
Exemplary System
As shown in fig. 1, the present embodiment provides a passive edge computing system based on spread spectrum communication, including: n wireless terminal users and a HAP, the HAP comprising a radio frequency energy transmitter and a mobile edge computing server; the wireless terminal user comprises an energy harvesting circuit and a battery; and broadcasting radio frequency energy to the wireless terminal user by the HAP, and charging the battery by the wireless terminal user receiving the radio frequency energy, wherein N is more than or equal to 3.
Specifically, the HAP integrates a radio frequency energy transmitter, MEC server, and antenna, broadcasts radio frequency energy, and each wireless end user collects energy and charges the battery with the received energy. The wireless terminal user also includes wireless communication circuitry that transmits information to the HAP over a communication channel by the wireless terminal user, the radio frequency energy transmission sharing a common frequency spectrum with the communication channel. To avoid co-channel interference, the downlink energy transmission and uplink wireless communication operate in a Time Division Duplex (TDD) manner.
In this embodiment, all the offload users share the spectrum resources using the DS-CDMA scheme. That is, each user uses a length G pseudorandom sequence as a spreading code to spread its signal to the whole wideband, and all users share all degrees of freedom in time and frequency, so that the DS-CDMA method greatly improves the utilization rate of time and frequency resources compared with, for example, Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), orthogonal frequency division multiple access (OFDM), and non-orthogonal multiple access (NOMA).
Exemplary method
The embodiment provides a task unloading method of a passive edge computing system, which comprises the following steps:
step S10, establishing a user index N of the task running in the unloading mode1And a user index N for the task running in the local computing mode0Said
Figure BDA0002404603190000091
Specifically, in this embodiment, a binary computation offload policy is employed, where all tasks are indivisible, either running in offload mode (mode-1, such as user 1 and user 2 in FIG. 1) or running in local computation mode (mode-0, such as user 3).
Figure BDA0002404603190000092
And
Figure BDA0002404603190000093
are two non-overlapping sets representing user indices operating in mode-1 and mode-0, respectively, wherein
Figure BDA0002404603190000094
Representing the set of all users. That is, the N wireless end users in the system are divided into two parts, one part operating in a local computing mode and the other part operating in an offload mode. By adopting the binary computation offload strategy, the optimization of the computation rate is facilitated.
S20, dividing the transmission time into a phase for broadcasting the radio frequency energy to the wireless terminal user by the HAP within each system time T, the occupied time of the phase is α T, wherein α∈ (0,1), the remaining time is used for the phase for the wireless terminal user to unload the task data to the HAP and for the HAP to process the unloaded task data, and returning the processing result to the wireless terminal user;
specifically, as shown in FIG. 2, the transmission time is divided into three phases, a first phase α T for HAP broadcasting RF energy to users, where α∈ (0,1), the energy collected by each user may be denoted E, within each system duration Ti＝μhiP0α T, the second phase is mode-1 user offloading task data, and in the last phase, the HAP performs calculation processing on the data from the offloading user and returns the calculation result to the user, since the HAP has strong calculation capability and strong transmission capability and the calculation result is relatively short, the time taken for task calculation and the time taken for the calculation result to return to the user can be ignored for the convenience of calculation, and thus, the time taken for the second phase is approximately equal to (1- α) T.
S30 based on
Figure BDA0002404603190000101
Figure BDA0002404603190000102
0≤α≤1,
xi∈ {0,1}, i ═ 1, L, n., obtaining the optimal transmission power of each wireless terminal user to be offloaded by a fractional planning method, wherein each wireless terminal user performs task offloading through the optimal transmission power;wherein the content of the first and second substances,
Figure BDA0002404603190000103
wi>0 represents a positive weight, x, of the computing task priority of the ith wireless terminal user i1 means that the ith wireless terminal user offloads its computing task to the mobile edge computing server for execution, and xi0 means that it performs the computation task locally.
In particular, the offloading policy is expressed as a weighted sum of the computed rates for maximizing wireless end user over a time horizon
Figure BDA0002404603190000104
Wherein xi1 means that the ith user offloads its computational tasks to the MEC server for execution, and xi0 denotes that it performs the computation task locally, wi>0 represents a positive weight of the computing task priority of the ith user. The problem of maximizing the rate is expressed as follows:
Figure BDA0002404603190000105
Figure BDA0002404603190000111
0≤α≤1,
xi∈{0,1},i＝1,L,N.
wherein
Figure BDA0002404603190000112
Representing the calculated rate for the mode-0 user,
Figure BDA0002404603190000113
representing the calculated rate for mode-1 users.
Figure BDA0002404603190000114
Wherein q ismaxRepresenting the maximum transmission power, Pmax,iRepresenting the maximum allowed power for the ith user.
In the present embodiment, it is assumed that
Figure BDA0002404603190000115
Given, the optimal transmission power for each offload user can be derived through power control by Fractional Programming (FP). By power control, the total interference level of the system can be minimized, thereby improving the total throughput of the passive edge computing system.
In some real-time approaches, it is assumed that
Figure BDA0002404603190000116
And α are given, the computation rate per mode-0 user is fixed, and for each mode-1 user, the transmit power constraint P ismax,iIs a fixed value. The above rate maximization problem can be attributed to the power control problem by controlling the transmit power of each user only under the corresponding maximum power constraint. Performing power control based on a fractional programming method, performing secondary transformation on a power control problem to obtain a new objective function:
Figure BDA0002404603190000121
therefore, the power control problem has the following new expression:
Figure BDA0002404603190000122
when P is fixed, the best y can be found in a closed formi，
Figure BDA0002404603190000123
Further, when yiWhen fixed, f (y, P) is a concave function on P, the optimal P can be effectively obtained through numerical convex optimization, and the fractional programming method updates y in an iterative modeiAnd P, the objective function finally converges to an optimal solution. Then, for timeThe resource allocation problem can obtain an optimal value by a simple one-dimensional gold search method.
In some embodiments, the problem of strong mutual interference caused by unloading of all users and the problem of waste of edge computing resources caused by allowing all users to compute locally are solved. A random local search method may be employed to obtain an optimal set of users to offload their tasks.
Specifically, the proposed random local search algorithm iteratively updates x in a probabilistic manner, and updates only one entry at most in each iterative update process, for example, as shown in fig. 3, in which one number in each row indicates one user, that is, there are 6 wireless terminal users, and one entry updated in each iterative update process is indicated by a numerical subscript "-". Denote the mode selection vector in the l-th iteration as xlWherein x is0Representing the initial mode selection solution. In the first iteration, consider N +1 candidate solutions, with C (x)l) And (4) showing. One of the N +1 candidate solutions is selected as x in the subsequent iterationl. The selection rule is executed according to the following probability
Figure BDA0002404603190000131
The higher the probability, the greater the probability of being selected, and over time the random local search algorithm converges to an optimum value.
It will be understood by those of ordinary skill in the art that all or a portion of the processes of the methods of the embodiments described above may be implemented by a computer program that may be stored on a non-volatile computer-readable storage medium, which when executed, may include the processes of the embodiments of the methods described above, wherein any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory.
In summary, the present invention discloses a passive edge computing system based on spread spectrum communication, wherein, by applying the spread spectrum communication technology to the passive edge computing system, all the offload users share the spectrum resources in a direct sequence-Code Division Multiple Access (DS-CDMA) manner. Each user uses the pseudo-random sequence with the length of G as a spreading code to spread the signal to the whole broadband, so that all users share all time-frequency freedom degrees, and compared with a multi-access scheme for unloading other tasks, the DS-CDMA mode greatly improves the utilization rate of time-frequency resources. Meanwhile, in order to prevent adjacent channel interference and near-far effect, the power control is carried out on the CDMA system with limited interference. Through power control, the total interference level of the system is minimized, and therefore the total throughput of the passive edge computing system is improved. The invention aims at the optimal distribution of energy transmission and information transmission time in a passive edge computing system. The phenomenon that the energy near-far effect can cause serious performance unfairness among users at different positions is solved.
Meanwhile, by adopting a random local search method, a group of optimal users are obtained from the uninstalled users to uninstall their tasks, so that the problems of strong mutual interference caused by the uninstallation of all users and the waste of edge computing resources caused by allowing all users to locally compute are solved.
It is to be understood that the invention is not limited to the examples described above, but that modifications and variations may be effected thereto by those of ordinary skill in the art in light of the foregoing description, and that all such modifications and variations are intended to be within the scope of the invention as defined by the appended claims.

Claims (10)

1. A passive edge computing system based on spread spectrum communications, comprising: n wireless terminal users and a HAP, the HAP comprising a radio frequency energy transmitter and a mobile edge computing server; the wireless terminal user comprises an energy harvesting circuit and a battery; and broadcasting radio frequency energy to the wireless terminal user by the HAP, and charging the battery by the wireless terminal user receiving the radio frequency energy, wherein N is more than or equal to 3.
2. The spread-spectrum communication-based passive edge computing system of claim 1, wherein the wireless end user further comprises wireless communication circuitry to transmit information to the HAP over a communication channel by the wireless end user, the radio frequency energy transmission sharing a common spectrum with the communication channel.
3. The spread-spectrum communication-based passive edge computing system of claim 2, wherein the downlink energy transfer and the uplink wireless communication operate in a time-division-multiplexed manner.
4. A method of task offloading for a passive edge computing system of claim 1, comprising:
establishing user index N of task running in unloading mode1And a user index N for the task running in the local computing mode0Said
Figure FDA0002404603180000011
Dividing transmission time into a phase for broadcasting radio frequency energy to the wireless terminal user by the HAP within each system time T, wherein the phase takes α T, α∈ (0,1), a phase for the wireless terminal user to offload task data to the HAP and a phase for the HAP to process the offload task data, and returning a processing result to the wireless terminal user;
based on
Figure FDA0002404603180000012
Figure FDA0002404603180000021
0≤α≤1,
xi∈ {0,1}, i ═ 1, L, n., obtaining an optimal transmission power for each offload wireless end user using a fractional planning method, each of the wireless end users performing task offload with the optimal transmission power, wherein,
Figure FDA0002404603180000022
wi>0 represents a positive weight, x, of the computing task priority of the ith wireless terminal useri1 means that the ith wireless terminal user offloads its computing task to the mobile edge computing server for execution, and xi0 denotes that it performs the computation task locally, P0Representing the radio frequency energy emission power of the HAP,. mu. ∈ (0,1) representing the energy harvesting efficiency, hiRepresenting the power gain of the channel from the HAP to the ith user, the noise power of all receivers being N0-140dBm, T1 for the whole time slot, and k for the efficiency coefficienti＝10-26N, the number of CPU cycles required for each wireless end user to process one bit of raw data is set as CiFor the data offload mode, the bandwidth of uplink information transmission is 10MHz, the spreading gain G is 128, and the remaining energy of the previous time frame is zero ei＝0,i＝1,...,N。
5. The method of claim 4, wherein the task offload of the passive edge computing system is performed by a processor of the passive edge computing system
Figure FDA0002404603180000023
Representing a calculated rate at which a wireless terminal user is operating in a local calculation mode, said
Figure FDA0002404603180000024
Represents the calculated rate at which the wireless terminal user is operating in an offload mode, wherein,
Figure FDA0002404603180000025
qmaxrepresenting the maximum transmission power, Pmax,iRepresenting the maximum allowed power for the ith wireless terminal user.
6. The method of claim 5, wherein the objective function of power control based on the fractional programming for the wireless end user operating in the offload mode is:
Figure FDA0002404603180000031
7. the method of task offloading of the passive edge computing system of claim 6, wherein when P is a fixed value,
Figure FDA0002404603180000032
8. the task offloading method for passive edge computing system of claim 6, wherein the most optimal users are selected from wireless terminal users to offload their tasks by using a random local search method, and the selection rule is:
Figure FDA0002404603180000033
wherein the mode selection vector in the first iteration is represented as xlWherein x is0Representing the initial mode selection solution.
9. The method of claim 4, wherein each wireless terminal is configured to offload tasks from the passive edge computing systemThe energy collected by the house is Ei＝μhiP0αT。
10. A storage medium, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the method of any one of claims 4-9.
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