CN113613260A - Method and system for optimizing distance-distance cooperative perception delay moving edge calculation - Google Patents

Method and system for optimizing distance-distance cooperative perception delay moving edge calculation Download PDF

Info

Publication number
CN113613260A
CN113613260A CN202110926759.2A CN202110926759A CN113613260A CN 113613260 A CN113613260 A CN 113613260A CN 202110926759 A CN202110926759 A CN 202110926759A CN 113613260 A CN113613260 A CN 113613260A
Authority
CN
China
Prior art keywords
stage
distance
base station
user
far
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110926759.2A
Other languages
Chinese (zh)
Other versions
CN113613260B (en
Inventor
王大伟
贺甜蜜
李立欣
梁微
林文晟
唐晓
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Northwestern Polytechnical University
Original Assignee
Northwestern Polytechnical University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Northwestern Polytechnical University filed Critical Northwestern Polytechnical University
Priority to CN202110926759.2A priority Critical patent/CN113613260B/en
Publication of CN113613260A publication Critical patent/CN113613260A/en
Application granted granted Critical
Publication of CN113613260B publication Critical patent/CN113613260B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a method and a system for calculating and optimizing a perception delay moving edge of far and near distance cooperation, wherein the method divides a data transmission process T in a far and near distance MEC network into two stages: a first phase t1, in which the distant user FU transfers part of the task to the distant user NU and the base station BS by means of NOMA; in the second stage t2, the short-distance user NU and the long-distance user FU use the uplink NOMA to transfer the calculation tasks to the base station BS, and the base station BS executes the calculation tasks in the whole first stage t1 and the second stage t 2; the first and second stages t1 and t2 are optimized to minimize the time delay of the first and second stages t1 and t 2.

Description

Method and system for optimizing distance-distance cooperative perception delay moving edge calculation
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a method and a system for calculating and optimizing a perception delay moving edge in near-far distance cooperation.
Background
5G research advances have witnessed the emergence of more and more time-sensitive services (TSSs), such as electronic medicine, Virtual Reality (VR), and unmanned automobiles, among others. To support TSSs applications, Moving Edge Computing (MEC) is widely used. With the help of Mobile Edge Computing (MEC), it will be possible for a large number of terminal devices to connect into a complex intelligent network, and internet of things (IoT) technology will therefore evolve rapidly.
Millions of connected devices and applications can operate seamlessly at high data rates and low latency. The research on the combined application of MEC and 6G in the field of internet of things is becoming the future. Traditionally, there are two types of MEC offloading schemes: full unloading and partial unloading. In the fully offloaded model, the appliance sends the entire task in its entirety to the MEC server for further computation. In the partial offload model, a device divides its computational tasks into multiple parts and offloads part of the computational tasks to the MEC server. Due to the rapid development of the internet of things technology, the Orthogonal Multiple Access (OMA) technology has been difficult to meet the requirement of simultaneous access of a large number of mobile terminals, and how to implement a time-frequency resource block to carry more mobile terminals has become a new research direction.
Disclosure of Invention
The invention aims to provide a method and a system for calculating and optimizing a perception delay moving edge of near-far distance cooperation, which aim to solve the problem that the Orthogonal Multiple Access (OMA) technology in the prior art is difficult to meet the requirement of simultaneous access of a large number of mobile terminals.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a second aspect of the present invention, a method for computing and optimizing a perceptual delay moving edge in a near-far distance cooperation includes the following steps:
establishing a far-near distance MEC network based on the cooperative NOMA, wherein the far-near distance MEC network comprises three nodes of a far-distance user FU, a near-distance user NU and a base station BS, and the near-distance user NU is positioned between the far-distance user FU and the base station BS;
the data transmission process T in the near-far MEC network is divided into two stages: first stage t1The long-distance user FU transfers part of tasks to the short-distance user NU and the base station BS by using NOMA; second stage t2The short-range user NU and the long-range user FU use the uplink NOMA to transfer the calculation tasks to the base station BS, which is in the first phase t1And a second stage t2To perform a computational task;
establishing a mathematical model of the optimization problem for the first stage t1And a second stage t2Optimizing to make the first stage t1And a second stage t2The sum of the time delays of (a) is minimal.
Specifically, the mathematical model of the optimization problem is as follows:
Figure BDA0003209519390000021
Figure BDA0003209519390000022
Rfb,1t1+Rfb,2t2≥La1+La2,
t1+t2≤T,
tf≤T,tn≤T.
wherein L isa1Is the amount of task data, L, offloaded to the BS in the first phasea2Is the amount of task data, L, offloaded to the BS in the second stagebIs the amount of task data offloaded to NU; l iseAmount of data offloaded to BS for NU; rfnThe arrival rate of the signal at the NU of the close-range user in the first stage is obtained; rnbReceiving the NU signal arrival rate of the close-range user for the second-stage base station BS; rfb,1Is the signal arrival rate at the base station BS in the first phase; rfb,2Receiving the signal arrival rate of the remote user FU for the second stage base station BS; t is tfFor time spent on local calculations of distant user FUs, tnLocal computing for spending on near-range user NUTime of (d).
Specifically, t in the problem P1 will be optimized1Minimizing, minimum transmission delay of the first stage
Figure BDA0003209519390000023
Figure BDA0003209519390000031
Wherein the content of the first and second substances,
Figure BDA0003209519390000032
is the optimal value of the amount of task data offloaded to NU; h isfnIs the channel coefficient from FU to NU; p is a radical ofuRepresents the total power of the FU; t is tnbDelaying information from NU at BS; h isnbIs the channel coefficient from NU to BS.
Specifically, t in the problem P1 will be optimized2Minimizing, minimum propagation delay in the second stage
Figure BDA0003209519390000033
Figure BDA0003209519390000034
Figure BDA0003209519390000035
Is the optimal value of the task data volume unloaded to the BS in the second stage; h isfbIs the channel coefficient, p, from FU to BSuRepresenting the total power of FU, pnbIs the transmission power of NU to BS.
In particular, pfb,1And pfnThe transmission power unloaded by FU and NU, respectively, the relationship is expressed as:
Figure BDA0003209519390000036
α1+α21 and 0. ltoreq. alpha1≤1，0≤α2≤1。
Specifically, for the first stage, FU will amount L of dataaAnd LbSimultaneously transmitting to the BS and the NU by using NOMA, wherein the signals received by the BS and the NU are respectively as follows:
Figure BDA0003209519390000037
Figure BDA0003209519390000038
wherein, yfb,1For signals received by the base station BS, yfnA signal received for a NU; p is a radical offnTransmission power, p, offloaded to short-range users NU for long-range users FUfb,1Offloading transmission power to a base station BS for a remote user FU; the signal sent by FU to BS is xfb,1FU to NU is xfn(ii) a Noise n received by BSfb,1And the noise n received by the NUfnAre all mean 0 and variance σ2Additive white gaussian noise of (1); the BS adopts the instantaneous channel state information and has the capability of decoding the information by using SIC; the arrival rates of the signals at the BS and NU are:
Figure BDA0003209519390000041
Figure BDA0003209519390000042
in particular, for the second phase, the user FU continues to pass on the data La2Transmitted to the BS while the NU will acquire data L from the FUbAnd a calculation task LdAnd transmitting to the base station, wherein the signal received by the second stage base station BS is represented as:
Figure BDA0003209519390000043
wherein n isnbRepresenting white gaussian noise at the base station;
thus, the power from the FU is equal to the transmission power of the second stage FU, and the information rate at the FU is:
Figure BDA0003209519390000044
Figure BDA0003209519390000045
specifically, the total calculation task L of FU is represented by L ═ La1+La2+Lb+Lf；G≥Lb+Le+Ln；
Figure BDA0003209519390000046
Wherein G is the storage data capacity of NU, and F represents the maximum calculation storage capacity of the mobile edge server; c. Cf，cn，cbRespectively representing the CPU frequencies of the three nodes.
Specifically, the optimal allocation of tasks in the transmission process is as follows:
Figure BDA0003209519390000047
Figure BDA0003209519390000048
Figure BDA0003209519390000049
Figure BDA0003209519390000051
wherein the content of the first and second substances,
Figure BDA0003209519390000052
is LbThe optimum value of (d);
Figure BDA0003209519390000053
is La1The optimum value of (d);
Figure BDA0003209519390000054
is La2The optimum value of (d);
Figure BDA0003209519390000055
an optimal value for the amount of data offloaded to the BS for NU; g is the storage data capacity of NU, and F represents the maximum calculation storage capacity of the mobile edge server; c. CbRepresenting the CPU frequency of the BS node;
Figure BDA0003209519390000056
is La1And La2Optimum power distribution coefficient of distribution, LfAnd LnRespectively FU and NU for local computation.
In a second aspect of the present invention, a system for the method for optimizing computation of perceptual delay moving edges in near-far distance collaboration comprises:
the network model establishing module is used for establishing a near-far MEC network based on the cooperative NOMA, the near-far MEC network comprises three nodes of a far-distance user FU, a near-distance user NU and a base station BS, and the near-distance user NU is positioned between the far-distance user FU and the base station BS;
the data transmission module is used for dividing a data transmission process T in the near-far MEC network into two stages: first stage t1The long-distance user FU transfers part of tasks to the short-distance user NU and the base station BS by using NOMA; second stage t2The short-range user NU and the long-range user FU use the uplink NOMA to transfer the calculation tasks to the base station BS, which is in the first phase t1And a second stage t2To perform a computational task;
an optimization module forEstablishing a mathematical model of the optimization problem, and optimizing the first stage t1 and the second stage t2 to ensure that the first stage t1And a second stage t2The time delay of (a) is minimal.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention provides a cooperation process scheme divided into two stages, analyzes the distribution problem by combining power and time slot, and provides a formula expression of delay minimization. Closed form expressions are derived as the optimal power, resource block and offload parameters. The numerical calculation result shows that the format proposed by the invention is feasible and can actually reduce the information delay of the system.
Furthermore, the invention firstly analyzes the channel state and the relevant channel coefficient of each node in the system and simultaneously deduces the expressions of the signals received by each node and the transmission rate.
Further, based on the signal receiving state and the unloading rate formula, the invention provides a two-stage moving edge calculation unloading method based on the cooperation of the remote and near FUs of the non-orthogonal multiple access technology, and the effectiveness of information transmission and the calculation requirements of each node are considered so as to improve the performance of the network.
Furthermore, according to the two-stage unloading strategy, the invention further represents the power and the resource block size of each node of the system in each stage, and then obtains the transmission information delay of the system, thereby providing a mathematical tool for performance analysis.
Furthermore, according to the system transmission information delay, the invention provides an optimization method of time delay, and realizes the optimal distribution of power, resource blocks and time delay.
In summary, the present invention combines the advantages of MECs and NOMA to provide a three-node cooperative MEC system that supports two-stage NOMA, with which FUs offload computational tasks to different edge servers simultaneously. By reasonably distributing the calculation tasks of the mobile terminal and the wireless resources in the system, the system delay is reduced, and the MEC network performance is further improved.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a diagram of a model of a perceptual delay mobile edge computing network for near-far user cooperation based on NOMA technology in an embodiment of the present invention;
FIG. 2 is a diagram illustrating timeslot allocation according to an embodiment of the present invention;
fig. 3 is a diagram illustrating the relationship between transmission delay and transmission power of a remote FU according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating the relationship between transmission delay and packet size according to an embodiment of the present invention;
fig. 5 is a diagram illustrating a relationship between transmission delay and channel transmission coefficient according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
As shown in fig. 1, the present invention provides a delay-based offloading scheme for a cooperative NOMA-based near-far MEC network consisting of three nodes, a far-distance user FU, a near-distance user NU and a base station BS. NU is located between FU and BS. The transmission process T is divided into two phases T1 and T2 as shown in the slot allocation of fig. 2. In the first phase, the FU transfers part of its tasks to the NU and BS using NOMA. In the second phase, the NU and FU use uplink NOMA to transfer their computational tasks to the base station. The BS performs the calculation task in the entire time slot T.
(I) unloading model
The present embodiment assumes, in order to focus on the minimization of information delay, that three nodes of the far and near MEC network are equipped with a single antenna and operate in half duplex mode. The wireless channel follows independent and uniformly distributed rayleigh fading, with the channel conditions remaining constant in each transmission slot and varying independently in different transmission slots.
hfnIs the channel coefficient from FU to NU; h isfbIs the channel coefficient from FU to BS; h isnbIs the channel coefficient from NU to BS. Total power of FU is puIs represented by the formula pfb,1And pfnRespectively, FU and NU offloaded transmission power. The relationship is expressed as:
Figure BDA0003209519390000071
α1+α21 and 0. ltoreq. alpha1≤1，0≤α2≤1。
For the first stage, FU will amount L of dataaAnd LbAnd simultaneously transmitting to the BS and the NU by using NOMA, wherein the signals received by the user base station and the NU at the moment are respectively as follows:
Figure BDA0003209519390000081
Figure BDA0003209519390000082
wherein, yfb，1For signals received by the base station BS, yfnA signal received for a NU; p is a radical offnTransmission power, p, offloaded to short-range users NU for long-range users FUfb，1Offloading transmission power to a base station BS for a remote user FU; the signal sent by FU to BS is xfb，1FU is sent toNU signal is xfn. Noise n received by BSfb，1And the noise n received by the NUfnAre all mean 0 and variance σ2Additive White Gaussian Noise (AWGN). The BS employs instantaneous Channel State Information (CSI), with the ability to decode the information using SIC. The arrival rate of the signal is:
Figure BDA0003209519390000083
Figure BDA0003209519390000084
wherein R isfb，1Is the rate of signal arrival, R, at the first stage BSfnThe rate of signal arrival at the first phase NU; h isfbIs the channel coefficient from FU to BS, hfnIs the channel coefficient from FU to BS; p is a radical offb，1Offloading transmission power to a base station BS for a remote user FU; p is a radical offnThe transmission power to the near user NU is offloaded for the far user FU.
For the second phase, the user FU continues to pass on the data La2Transmitted to the BS while the NU will acquire data L from the FUbAnd its own computing task LdAnd transmitting to the base station. This process is simultaneously transmitted using NOMA. The signal received by the second base station BS is represented as:
Figure BDA0003209519390000085
wherein x isnbFor signals offloaded to BS by NU, xfb，2Signals offloaded to the BS for FUs; h isnbIs the channel coefficient, p, of NU to BSfb，2For the second stage FU to BS transmission power, nnbRepresenting white gaussian noise at the base station BS.
Thus, the power from the FU is equal to the transmission power of the second stage FU, i.e. pfb，2＝pu. The achievable rate of information at the FU is:
Figure BDA0003209519390000091
Figure BDA0003209519390000092
wherein R isfb，2Rate of arrival of the received FU signal for the second stage BS, RfnThe rate at which NU signals arrive for the second stage BS; h isfbIs the channel coefficient from FU to BS, hnbChannel coefficient NU to BS; p is a radical ofnbIs the transmission power of NU to BS.
(II) resource allocation model
The total computation task L of the FUs is divided into four parts, a first part La1Is to offload data to the BS in a first stage, a second part La2Is to offload data to the BS in the second stage, the third part LbIs data offloaded to NU, and a fourth portion LfIs data used for local computation. In addition, the part of offloading NU to BS is denoted as LeThe part for local computation of NU is Ln. Assume that each node has a fixed known Central Processing Unit (CPU) frequency and let cf，cn，cbRespectively representing the CPU frequencies of the three nodes. The CPU frequency versus the amount of task data can be used to represent the maximum computational memory capacity of the mobile edge server in the base station BS.
First, the total calculation task L of FU is expressed as L ═ La1+La2+Lb+Lf。
Assuming that the storage data capacity of NU is G, G ≧ Lb+Le+Ln. Let F denote the maximum computational storage capacity of the mobile edge server provided in the base station BS, i.e.
Figure BDA0003209519390000093
Thus, in the first phase, the information delay at the BS is
Figure BDA0003209519390000094
The information delay at NU is
Figure BDA0003209519390000095
The total transmission delay of the first stage is t1＝max{tfb，1，tfn}。
Thus, in the second stage, the information from the FU is delayed at the BS to
Figure BDA0003209519390000096
Information delay at BS from NU as
Figure BDA0003209519390000101
So that the total delay of the second stage is t2＝max{tfb,2,tnb}。
(III) local computation model
Let cfAnd cnThe time delay for calculating each bit of data at FU and NU is represented separately. Then the time t spent on the local calculation of the FU and the local calculation of the NUfAnd tnIs shown as
Figure BDA0003209519390000102
(IV) optimization problem
The aim of the invention is to consider minimizing the time delay of a three-node MEC network in which both near and far users participate, the problem being specifically expressed by the mathematical formula:
Figure BDA0003209519390000103
Figure BDA0003209519390000104
Rfb,1t1+Rfb,2t2≥La1+La2,
t1+t2≤T,
tf≤T,tn≤T.
wherein the first constraint is that the time for the first stage FU to unload to NU must be less than the FU to unload to BS time, and the time for the second stage NU to unload must be less than the FU unload time. The second constraint is that the FU must complete the offload task within two time slots; the third constraint is that the information delay constraint of the system must be within duration T; the fourth constraint implies that the local computation time of NU and FU must be less than the duration T.
To obtain the minimum delay of the whole system, the minimum delay is obtained
Figure BDA0003209519390000105
And
Figure BDA0003209519390000106
first, t in the problem P1 will be optimized1And (4) minimizing. t is t1And tfb,1And tfnAnd (4) correlating.
Figure BDA0003209519390000107
Figure BDA0003209519390000111
Order to
Figure BDA0003209519390000112
At this time, t is obtainedfnFor alpha1Partial derivatives of (a):
Figure BDA0003209519390000113
it can be known that k>0, therefore
Figure BDA0003209519390000114
I.e. tfnFor alpha1Is a monotonically decreasing function.
In the same way, let m be | hfn|2puCalculating tfb,1For alpha1Partial derivatives of (a):
Figure BDA0003209519390000115
it can be known that
Figure BDA0003209519390000116
I.e. tfb,1For alpha1Is a monotonically increasing function.
t1Is tfb,1And tfnThe larger of the median value, again due to the two pairs of α1The partial derivatives of (a) are monotonically increasing and monotonically decreasing. It is obvious that only when tfb,1＝tfnTime delay t of this stage1Can a minimum be reached. Namely, at this time:
Figure BDA0003209519390000117
the coefficient that minimizes the time delay can be derived from the equation
Figure BDA0003209519390000118
Figure BDA0003209519390000119
Wherein:
Figure BDA00032095193900001110
so the minimum transmission delay of the first stage
Figure BDA00032095193900001111
As can be seen from the second section,
Figure BDA0003209519390000121
from tfb,2And tnbAnd (6) determining. Wherein:
Figure BDA0003209519390000122
Figure BDA0003209519390000123
at this time, t is obtainedfb,2To pnbPartial derivatives of (a):
Figure BDA0003209519390000124
at this time, t is obtainednbTo pnbPartial derivatives of (a):
Figure BDA0003209519390000125
wherein:
Figure BDA0003209519390000126
for the same reason that
Figure BDA0003209519390000127
And is
Figure BDA0003209519390000128
tfb,2And tnbAre each relative to pnbDecreasing and increasing functions of. Thus, the minimum information delay t of the second stage2Is by tfb,2＝tnbWhat is achieved is that:
Figure BDA0003209519390000129
the optimum power for NU transmission to BS is
Figure BDA00032095193900001210
At this point, the second stage of the system
Figure BDA0003209519390000131
For resource block LaSince the number of bits per second that a user can transmit is related to the power allocated by the system, we will assign L to the number of bits per seconda1And La2Distribution as optimal power distribution coefficient
Figure BDA0003209519390000132
Thus, La1And La2The relationship between is expressed as
Figure BDA0003209519390000133
According to the relation of resource allocation, obtaining
Figure BDA0003209519390000134
To transmit more data tasks at the same time, the largest is selected
Figure BDA0003209519390000135
Then it is determined that,
Figure BDA0003209519390000136
the asterisks indicate that the maximum value is selected to ensure the validity of the data transfer.
After the relevant parameters of power allocation and resource allocation are optimized, the information transmission time delay of each stage of the system is obtained. The minimum transmission delay of the first stage is
Figure BDA0003209519390000137
The minimum transmission delay of the second stage is
Figure BDA0003209519390000138
Of a systemThe total delay is expressed as
Figure BDA0003209519390000139
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
(V) simulation verification
(1) Shown in FIG. 3 as the total power p of FUuThe variation of the system time delay is changed. Power puVarying from 10w at 0.5 average intervals to 15 w. It can be seen from the figure that the latency of the cooperation scheme proposed by the present invention is the lowest of the three schemes. Furthermore, as can be seen from the figure, with power puThe overall delay of the system as a whole shows a gradually decreasing trend. This is because when the power is increased, the amount of information data transmitted per unit time increases, and the time naturally decreases.
(2) FIG. 4 shows a calculation task amount LaVariations in the impact on system time delay. Calculated quantity LaThe range from 30Mbits to 40Mbits was 1 Mbits. At this time, the cooperative offloading scheme proposed by the present invention shows superior performance in terms of time delay. Meanwhile, it can be seen that the system delay of the three schemes is gradually increased along with the increase of the data quantity. The time delay of the system without NOMA participation is significantly greater than that of the other two systems, and the advantage of cooperation of the NOMA participation systems is realized.
(3) Fig. 5 shows the variation of the information time delay under the influence of different channel coefficients. We vary the channel coefficients between FU and BS. It can be seen from the figure that the information delay of the cooperative system proposed by the present invention, which is participated in by NOMA, is always minimal during the channel coefficient variation. In addition, as the channel coefficient gradually increases, the information time delay of each system gradually increases. Since there will be more problems such as path loss during transmission as the channel coefficient gradually increases at the same transmission speed, the time required for transmission naturally becomes longer.
In conclusion, the invention provides a three-node cooperative mobile edge calculation model consisting of a long-distance user FU, a short-distance user NU and a base station BS, and the time delay of the system is effectively reduced by the scheme along with the participation of NOMA in each stage. In order to solve the optimization problem, the problem is decomposed into a plurality of sub-problems to be solved, and the effectiveness of the scheme is proved in a final simulation link.
It will be appreciated by those skilled in the art that the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The embodiments disclosed above are therefore to be considered in all respects as illustrative and not restrictive. All changes which come within the scope of or equivalence to the invention are intended to be embraced therein.

Claims (10)

1. The method for computing and optimizing the perceptual delay moving edge in the near-far distance cooperation is characterized by comprising the following steps of:
establishing a far-near distance MEC network based on the cooperative NOMA, wherein the far-near distance MEC network comprises three nodes of a far-distance user FU, a near-distance user NU and a base station BS, and the near-distance user NU is positioned between the far-distance user FU and the base station BS;
the data transmission process T in the near-far MEC network is divided into two stages: first stage t1The long-distance user FU transfers part of tasks to the short-distance user NU and the base station BS by using NOMA; second stage t2Near distance user NU and far distance userFU uses uplink NOMA to transfer computational tasks to base station BS, which is in a first phase t1And a second stage t2To perform a computational task;
establishing a mathematical model of the optimization problem for the first stage t1And a second stage t2Optimizing to make the first stage t1And a second stage t2The sum of the time delays of (a) is minimal.
2. The method for optimizing computation of perceptual-delay moving edges for distance collaboration according to claim 1, wherein the mathematical model of the optimization problem is:
Figure FDA0003209519380000011
Figure FDA0003209519380000012
Rfb,1t1+Rfb,2t2≥La1+La2,
t1+t2≤T,
tf≤T,tn≤T.
wherein L isa1Is the amount of task data, L, offloaded to the BS in the first phasea2Is the amount of task data, L, offloaded to the BS in the second stagebIs the amount of task data offloaded to NU; l iseAmount of data offloaded to BS for NU; rfnThe arrival rate of the signal at the NU of the close-range user in the first stage is obtained; rnbReceiving the NU signal arrival rate of the close-range user for the second-stage base station BS; rfb,1Is the signal arrival rate at the base station BS in the first phase; rfb,2Receiving the signal arrival rate of the remote user FU for the second stage base station BS; t is tfFor time spent on local calculations of distant user FUs, tnTime spent on local computation of close-range users NU.
3. The method for optimizing distance-to-distance cooperative perceptual delay moving edge calculation as claimed in claim 2, wherein t in the optimization problem P11Minimizing, minimum transmission delay of the first stage
Figure FDA0003209519380000021
Figure FDA0003209519380000022
Wherein the content of the first and second substances,
Figure FDA0003209519380000023
is the optimal value of the amount of task data offloaded to NU; h isfnIs the channel coefficient from FU to NU; p is a radical ofuRepresents the total power of the FU; t is tnbDelaying information from NU at BS; h isnbIs the channel coefficient from NU to BS.
4. The method for optimizing distance-to-distance cooperative perceptual delay moving edge calculation as claimed in claim 3, wherein t in the optimization problem P12Minimizing, minimum propagation delay in the second stage
Figure FDA0003209519380000024
Figure FDA0003209519380000025
Figure FDA0003209519380000026
Is the optimal value of the task data volume unloaded to the BS in the second stage; h isfbIs the channel coefficient, p, from FU to BSuRepresenting the total power of FU, pnbIs the transmission power of NU to BS.
5. The method of claim 4, wherein p is the distance-based cooperative perceptual delay moving edge calculation optimization methodfb,1And pfnThe transmission power unloaded by FU and NU, respectively, the relationship is expressed as:
Figure FDA0003209519380000027
α1+α21 and 0. ltoreq. alpha1≤1，0≤α2≤1。
6. The method of claim 5, wherein FU assigns a data amount L to the first stageaAnd LbSimultaneously transmitting to the BS and the NU by using NOMA, wherein the signals received by the BS and the NU are respectively as follows:
Figure FDA0003209519380000028
Figure FDA0003209519380000031
wherein, yfb,1For signals received by the base station BS, yfnA signal received for a NU; p is a radical offnTransmission power, p, offloaded to short-range users NU for long-range users FUfb,1Offloading transmission power to a base station BS for a remote user FU; the signal sent by FU to BS is xfb,1FU to NU is xfn(ii) a Noise n received by BSfb,1And the noise n received by the NUfnAre all mean 0 and variance σ2Additive white gaussian noise of (1); the BS adopts the instantaneous channel state information and has the capability of decoding the information by using SIC; the arrival rates of the signals at the BS and NU are:
Figure FDA0003209519380000032
Figure FDA0003209519380000033
7. the method of claim 6, wherein for the second stage, the user FU continues to apply the data La2Transmitted to the BS while the NU will acquire data L from the FUbAnd a calculation task LdAnd transmitting to the base station, wherein the signal received by the second stage base station BS is represented as:
Figure FDA0003209519380000034
wherein n isnbRepresenting white gaussian noise at the base station;
thus, the power from the FU is equal to the transmission power of the second stage FU, and the information rate at the FU is:
Figure FDA0003209519380000035
Figure FDA0003209519380000036
8. the method of claim 7, wherein the total computation task L of FU is expressed as L ═ La1+La2+Lb+Lf；G≥Lb+Le+Ln；
Figure FDA0003209519380000037
Wherein G is the storage data capacity of NU, and F represents the maximum calculation storage capacity of the mobile edge server; c. Cf，cn，cbRespectively representing the CPU frequencies of the three nodes.
9. The method for optimizing computation of perceptual delay mobile edges for near-far distance collaboration as recited in claim 8, wherein the optimal allocation of tasks during transmission is as follows:
Figure FDA0003209519380000041
Figure FDA0003209519380000042
Figure FDA0003209519380000043
Figure FDA0003209519380000044
wherein the content of the first and second substances,
Figure FDA0003209519380000045
is LbThe optimum value of (d);
Figure FDA0003209519380000046
is La1The optimum value of (d);
Figure FDA0003209519380000047
is La2The optimum value of (d);
Figure FDA0003209519380000048
an optimal value for the amount of data offloaded to the BS for NU; g is NUThe storage capacity, F represents the maximum computing storage capacity of the mobile edge server; c. CbRepresenting the CPU frequency of the BS node;
Figure FDA0003209519380000049
is La1And La2Optimum power distribution coefficient of distribution, LfAnd LnRespectively FU and NU for local computation.
10. A system for the method for distance-cooperative perceptual-delay-shifted-edge-computation optimization of claim 1, comprising:
the network model establishing module is used for establishing a near-far MEC network based on the cooperative NOMA, the near-far MEC network comprises three nodes of a far-distance user FU, a near-distance user NU and a base station BS, and the near-distance user NU is positioned between the far-distance user FU and the base station BS;
the data transmission module is used for dividing a data transmission process T in the near-far MEC network into two stages: first stage t1The long-distance user FU transfers part of tasks to the short-distance user NU and the base station BS by using NOMA; second stage t2The short-range user NU and the long-range user FU use the uplink NOMA to transfer the calculation tasks to the base station BS, which is in the first phase t1And a second stage t2To perform a computational task;
an optimization module for establishing a mathematical model of the optimization problem, optimizing the first stage t1 and the second stage t2 to make the first stage t1And a second stage t2The time delay of (a) is minimal.
CN202110926759.2A 2021-08-12 2021-08-12 Method and system for optimizing distance-distance cooperative perception delay moving edge calculation Active CN113613260B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110926759.2A CN113613260B (en) 2021-08-12 2021-08-12 Method and system for optimizing distance-distance cooperative perception delay moving edge calculation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110926759.2A CN113613260B (en) 2021-08-12 2021-08-12 Method and system for optimizing distance-distance cooperative perception delay moving edge calculation

Publications (2)

Publication Number Publication Date
CN113613260A true CN113613260A (en) 2021-11-05
CN113613260B CN113613260B (en) 2022-08-19

Family

ID=78340560

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110926759.2A Active CN113613260B (en) 2021-08-12 2021-08-12 Method and system for optimizing distance-distance cooperative perception delay moving edge calculation

Country Status (1)

Country Link
CN (1) CN113613260B (en)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170012695A1 (en) * 2015-07-09 2017-01-12 Electronics And Telecommunications Research Institute User terminal of multi-hop relay system and method for applying non-orthogonal multiple access in the same
US20170325152A1 (en) * 2010-12-16 2017-11-09 Intel Corporaiton Relay node operation with improved data throughput
CN108920279A (en) * 2018-07-13 2018-11-30 哈尔滨工业大学 A kind of mobile edge calculations task discharging method under multi-user scene
CN110113190A (en) * 2019-04-24 2019-08-09 西北工业大学 Time delay optimization method is unloaded in a kind of mobile edge calculations scene
CN111884696A (en) * 2020-07-01 2020-11-03 广州大学 Relay cooperation mobile edge calculation method based on multiple carriers
CN111953730A (en) * 2020-07-02 2020-11-17 华北电力大学（保定） NOMA-based server cooperation edge calculation method
CN113242568A (en) * 2021-06-08 2021-08-10 重庆邮电大学 Task unloading and resource allocation method in uncertain network environment

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170325152A1 (en) * 2010-12-16 2017-11-09 Intel Corporaiton Relay node operation with improved data throughput
US20170012695A1 (en) * 2015-07-09 2017-01-12 Electronics And Telecommunications Research Institute User terminal of multi-hop relay system and method for applying non-orthogonal multiple access in the same
CN108920279A (en) * 2018-07-13 2018-11-30 哈尔滨工业大学 A kind of mobile edge calculations task discharging method under multi-user scene
CN110113190A (en) * 2019-04-24 2019-08-09 西北工业大学 Time delay optimization method is unloaded in a kind of mobile edge calculations scene
CN111884696A (en) * 2020-07-01 2020-11-03 广州大学 Relay cooperation mobile edge calculation method based on multiple carriers
CN111953730A (en) * 2020-07-02 2020-11-17 华北电力大学（保定） NOMA-based server cooperation edge calculation method
CN113242568A (en) * 2021-06-08 2021-08-10 重庆邮电大学 Task unloading and resource allocation method in uncertain network environment

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
BAOGANG LI: ""Wireless Powered Mobile Edge Computing With NOMA and User Cooperation"", 《IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY》 *
YAO XU: ""Performance of D2D Aided Uplink Coordinated Direct and Relay Transmission Using NOMA"", 《IEEE ACCESS》 *

Also Published As

Publication number Publication date
CN113613260B (en) 2022-08-19

Similar Documents

Publication Publication Date Title
CN113709201A (en) Method and communication device for computing offloading
CN111884696B (en) Relay cooperation mobile edge calculation method based on multiple carriers
CN110493854B (en) WPT-MEC network uplink and downlink resource allocation and power control mechanism based on optimization theory
Krisnamurthy et al. Polling-based media access protocols for use with smart adaptive array antennas
CN107682935B (en) Wireless self-return resource scheduling method based on system stability
CN110545584A (en) Communication processing method of full-duplex mobile edge computing communication system
US20070116010A1 (en) System and method for allocating resource and user terminal
CN111683413B (en) Method, terminal, network access device and storage medium for establishing multilink connection
CN115334675B (en) Self-adaptive time slot allocation method of directional ad hoc network
CN111212108B (en) Multi-user parallel migration method based on non-orthogonal multiple access and mobile edge computing
Zhu et al. Joint optimal allocation of wireless resource and MEC computation capability in vehicular network
CN110049473B (en) Joint wireless channel allocation and power control method for relay enhanced D2D communication
CN113891481A (en) Throughput-oriented cellular network D2D communication dynamic resource allocation method
CN113079577B (en) Resource allocation method based on coexistence scene of EMBB and URLLC
Zhang et al. Matching-based resource allocation and distributed power control using mean field game in the NOMA-based UAV networks
Altin et al. Novel OMA and hybrid NOMA schemes for MEC offloading
Xie et al. Reliable and energy-aware job offloading at terahertz frequencies for mobile edge computing
CN113613260B (en) Method and system for optimizing distance-distance cooperative perception delay moving edge calculation
Liu et al. Reliability-oriented design framework in NOMA-assisted mobile edge computing
Paymard et al. Task scheduling based on priority and resource allocation in multi-user multi-task mobile edge computing system
CN117336790A (en) Task time delay optimization method in NOMA auxiliary millimeter wave mobile edge computing system based on intelligent reflecting surface
CN115988649B (en) Inter-link time slot and power cooperative allocation method for multi-beam directional ad hoc network
CN105025565A (en) A power optimization method for a full duplex bidirectional relay system under asymmetrical speeds
CN112104737B (en) Calculation migration method, mobile computing equipment and edge computing equipment
CN115633369A (en) Multi-edge device selection method for user task and power joint distribution

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant