CN112052086B - Multi-user safety energy-saving resource allocation method in mobile edge computing network - Google Patents
Multi-user safety energy-saving resource allocation method in mobile edge computing network Download PDFInfo
- Publication number
- CN112052086B CN112052086B CN202010740465.6A CN202010740465A CN112052086B CN 112052086 B CN112052086 B CN 112052086B CN 202010740465 A CN202010740465 A CN 202010740465A CN 112052086 B CN112052086 B CN 112052086B
- Authority
- CN
- China
- Prior art keywords
- user
- energy
- base station
- task
- optimization
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000013468 resource allocation Methods 0.000 title claims abstract description 18
- 238000005457 optimization Methods 0.000 claims abstract description 47
- 238000004364 calculation method Methods 0.000 claims abstract description 23
- 238000005265 energy consumption Methods 0.000 claims abstract description 22
- 238000005562 fading Methods 0.000 claims abstract description 4
- 238000012549 training Methods 0.000 claims abstract description 4
- 230000005540 biological transmission Effects 0.000 claims description 10
- 230000009977 dual effect Effects 0.000 claims description 7
- 230000003190 augmentative effect Effects 0.000 claims description 4
- 238000000354 decomposition reaction Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 2
- 230000000737 periodic effect Effects 0.000 claims 1
- 238000005516 engineering process Methods 0.000 description 9
- 238000004088 simulation Methods 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 238000007796 conventional method Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000005251 gamma ray Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000977 initiatory effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 238000001356 surgical procedure Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5083—Techniques for rebalancing the load in a distributed system
- G06F9/5088—Techniques for rebalancing the load in a distributed system involving task migration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
- G06F9/5072—Grid computing
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/02—Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]
-
- 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
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
- H04W52/0261—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2209/00—Indexing scheme relating to G06F9/00
- G06F2209/50—Indexing scheme relating to G06F9/50
- G06F2209/502—Proximity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2209/00—Indexing scheme relating to G06F9/00
- G06F2209/50—Indexing scheme relating to G06F9/50
- G06F2209/509—Offload
-
- 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
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
-
- 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)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Security & Cryptography (AREA)
- Mathematical Physics (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention provides a multi-user safety energy-saving resource allocation method in a mobile edge computing network, which comprises the following steps: step 1, K users of the current time slot access a base station, and the base station obtains channel state information of the users through channel training; setting time division duplexing, channel reciprocity, and channel obeying quasi-static block fading model; step 2, calculating the energy consumed by local calculation of each user and the energy consumed by task unloading of each user; step 3, constructing an optimization problem according to the energy consumed by local calculation and the energy consumed by unloading the calculation task obtained in the second step, wherein the optimization problem comprises an optimization target and constraint conditions, and the optimization target is to minimize the total energy consumption of the system; step 4, solving an optimization problem to obtain a minimum value of total energy consumption of the system and each optimization variable corresponding to the minimum value; the invention obviously reduces the energy consumption in the aspect of realizing the safety energy-saving task unloading of the MEC network.
Description
Technical Field
The invention relates to the problem of wireless communication physical layer safety transmission, in particular to a multi-user safety energy-saving resource allocation method in a mobile edge computing network.
Background
The rapid growth of wireless networks has spawned a range of computationally intensive and time-delay sensitive smart devices (e.g., tablet computers and smartphones) and new applications (e.g., augmented reality, autopilot, and tele-surgery). With the large-scale deployment of smart devices, how to accommodate them under limited resources is a challenging task. Therefore, providing an ideal quality of service for terminal devices with limited power and limited size is a problem to be solved. Mobile Edge Computing (MEC) and non-orthogonal multiple access technology (NOMA) have evolved and are considered to be the two most promising technologies in next generation wireless networks.
In MEC systems, distributed MEC servers are deployed exclusively in close proximity to end devices, which may transfer part or all of the computing tasks to the MEC servers for computation. Therefore, the MEC enables cloud computing of small, low power consumption terminal devices in a low latency, low cost manner. Compared with the traditional OMA technology, NOMA allows multiple users to access simultaneously and simultaneously in the same frequency, and eliminates the interference among the users through Serial Interference Cancellation (SIC) at a receiving end, so that the spectrum efficiency of the system can be effectively improved.
The secure transmission of information has been a concern, and because of the broadcasting characteristics of wireless communication, the computing task offloaded from the terminal is easily intercepted by an illegal eavesdropper, and physical layer security is considered as an effective secure transmission protection technology for wireless information. The method directly realizes the safe transmission of signals on a physical layer by utilizing the transmission characteristics of a wireless channel and by means of channel coding and signal processing technology.
Recently, researchers have conducted research work on physical layer security in mobile edge computing. Research results show that energy consumption can be effectively reduced by using moving edge calculation. The combination of these two techniques with NOMA technology is widely focused by students, but a troublesome problem is the decoding order of the receiver in the NOMA system. The decoding order problem for the downlink has been concluded, while no accurate theorem exists in the existing study of the decoding order for the uplink.
Disclosure of Invention
The invention aims to provide a multi-user safety energy-saving resource allocation method in a mobile edge computing network, which solves the safety and energy consumption problems in the multi-user unloading process.
In the invention, multiple users each leave part of the calculation task to be completed locally, and the other part of the task is unloaded to the base station integrated with the MEC server in a non-orthogonal multiple access mode. The invention takes the total energy consumption of the MEC network as an evaluation index, and designs the task unloading capacity, the transmitting power, the transmitting rate and the multi-user decoding sequence of the base station end of each user through joint optimization, so that the system always works in the state of minimum energy consumption under the condition of meeting specific safety requirements.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the invention provides
A multi-user safe energy-saving resource allocation method in a mobile edge computing network is based on a multi-user safe energy-saving resource allocation system in the mobile edge computing network, which comprises K users, a base station and an eavesdropper, wherein each user needs to execute L in a period T k Task amount of > 0 bits;
dividing the task quantity corresponding to the kth user into l k and Lk -l k Two parts, wherein l k Partial tasks are calculated locally; l (L) k -l k Part is unloaded to the base station through a non-orthogonal multiple access mode;
the distribution method comprises the following steps:
and 4, solving the optimization problem to obtain the minimum value of the total energy consumption of the system and each optimization variable corresponding to the minimum value.
Preferably, in step 2, the energy consumed is calculated locally by each user during the period T, by the following formula:
wherein ,
local calculation of l for kth user k The amount of energy that needs to be consumed by the bit task; c k Calculating the number of CPU revolutions per bit on behalf of the kth user; f (f) k Represents the CPU frequency; />
Representing the effective capacitance coefficient.
Preferably, in step 2, the energy consumed by the computing task is offloaded for each user during the period T, calculated separately by:
wherein ,
offloading L for kth user k -l k The energy consumption required by the bit calculation task; p is p k Representing the transmit power of the kth user.
Preferably, in step 3, the optimization objective is as follows:
the constraints include the following:
β k,l +β l,k ＝1
wherein, l= [ l ] 1 ,…l K ]Representing a local computing task amount; p= [ p ] 1 ,…,p K ]Representing the user transmit power; r is R t ＝[R t,1 ,…,R t,K ]Representing a codeword transmission rate; r is R s ＝[R s,1 ,…,R s,K ]Representing a useful information rate; beta represents the decoding sequence of the base station end; b represents the bandwidth of the system; 0 < epsilon < 1 represents the maximum tolerable safe interrupt probability; c (C) k ＝log 2 (1+γ k p k /(1+∑ l≠k β k,l γ l p l ) A) is the channel capacity of the kth user; gamma ray k ＝|h k | 2 /σ 2 ；h k The channel coefficient from the kth user to the base station; sigma (sigma) 2 Is the noise variance at the base station.
Preferably, the optimization problem is solved by the following specific methods:
converting the non-convex constraint condition in the step 3 into a convex constraint condition by adopting a continuous convex approximation method to form an optimization problem P2;
adding an augmented Lagrangian term corresponding to the equality constraint into an objective function in the optimization problem P2 to obtain an optimization problem P3;
and solving the optimization problem P3 by adopting a penalty dual decomposition method to obtain the minimum value of the total energy consumption of the system and each optimization variable value corresponding to the minimum value.
Preferably, the base station is equipped with an MEC server.
Preferably, the base station, the user and the eavesdropper are each provided with a single antenna.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a multi-user safe energy-saving resource allocation method in a mobile edge computing network, wherein a part of computing tasks are left locally by each user to finish, and the other part of tasks are unloaded to a base station integrated with an MEC server in a non-orthogonal multiple access mode; the invention takes the total energy consumption of the MEC network as an evaluation index, and designs the task unloading capacity, the transmitting power, the transmitting rate and the multi-user decoding sequence of the base station end of each user through joint optimization, so that the system always works in the state of minimum energy consumption under the condition of meeting specific safety requirements; simulation experiments prove that compared with a partial unloading method based on Orthogonal Multiple Access (OMA), the method can remarkably reduce energy consumption; this demonstrates the feasibility and superiority of the present invention in achieving safe energy-saving task offloading in MEC networks.
Furthermore, for the decoding sequence of the receiving end under the NOMA system, based on the product of the channel gain and the transmitting power, a binary variable is introduced to indicate the decoding sequence, and the decoding sequence can be obtained by solving the converted optimization problem; compared with the traditional method for defining a decoding sequence, the method is more scientific, and the obtained result is more accurate.
Furthermore, the invention introduces the safety interruption probability to measure the safety performance in the task unloading process, and the broadcasting characteristic of the wireless communication system ensures that the task unloaded to the base station by the user terminal is easily intercepted by the eavesdropping terminal, thereby causing information leakage, and therefore, the consideration of the safety problem is very important. In terms of energy consumption, although more energy is consumed in order to combat eavesdropping, safety performance is improved.
Drawings
Fig. 1 is a view of a MEC system model according to the present invention;
FIG. 2 is a flow chart of an algorithm for minimizing total energy consumption of the system according to the present invention;
fig. 3 is a simulation diagram comparing energy consumption of a conventional method without eavesdropper based on an orthogonal multiple access part unloading method and a multi-user safety energy-saving resource allocation method based on non-orthogonal multiple access according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The system used by the multi-user safety energy-saving resource allocation method in the mobile edge computing network is shown in fig. 1:
k users are set to execute respective calculation tasks in a period T, and the task quantity of each user is L k ＞0bits，k＝1,…K。
Dividing the task quantity corresponding to the kth user into l k and Lk -l k Two parts, wherein, set l k Part of the tasks are done by local computation, L k -l k And partially offloaded to a base station equipped with a Mobile Edge Computation (MEC) server by means of non-orthogonal multiple access (NOMA).
Meanwhile, an illegal eavesdropper is set in the system, and the task of unloading the user to the base station is attempted to be intercepted.
The base station, user and eavesdropper are all equipped with a single antenna.
The invention provides a multi-user safety energy-saving resource allocation method in a mobile edge computing network, which specifically comprises the following steps:
firstly, K users in the current time slot access a base station simultaneously and simultaneously in the same frequency through a NOMA technology, and the base station obtains channel state information of the users through channel training;
setting time division duplexing, channel reciprocity, and channel obeying quasi-static block fading model, i.e. channel keeps unchanged in period T, and changes independently and randomly between different periods;
assuming that the noise at the base station end is additive Gaussian white noise; meanwhile, the base station adopts a Serial Interference Cancellation (SIC) technology to eliminate the interference among users.
Step two, respectively calculating the energy consumed by local calculation of each user;
during period T, the task amount of local calculation of the kth user is l k Bit, energy required to be consumed
The method comprises the following steps:
wherein ,ck Calculating the number of CPU revolutions per bit on behalf of the kth user; f (f) k Represents the CPU frequency;
representing the effective capacitance coefficient.
Thirdly, respectively calculating the energy consumed by each user for unloading the calculation task;
in period T, the kth user unloads the energy consumption required by the computing task
The method comprises the following steps:
wherein ,pk Representing the transmit power of the kth user.
Fourthly, constructing a decoding sequence rule of multiple users at the base station end;
let the base station transmit power p for each user k And channel gain |h k | 2 The products of (2) are subjected to decoding sequencing in the order from big to small; at this time, an auxiliary variable beta is introduced k,l Indicates decoding order, specifically:
when beta is k,l When=1, the transmission power p of the kth user is represented k And channel gain |h k | 2 Is greater than the first user transmit power p l And channel gain |h l | 2 Therefore, the data of the kth user is decoded first, and then the data of the first user is decoded, and at this time, the signal of the user l causes interference to the decoding of the user k;
when beta is k,l When=0or 1, the transmission power p of the kth user is represented k And channel gain gamma k Is equal to the first user transmit power p l And channel gain |h l | 2 If the product of the first time is the same, the first time is selected to be decoded for the kth user and the first user;
to sum up, the auxiliary variable beta k,l Expressed as:
β k,l +β l,k ＝1 (4)
wherein 
The noise at the base station is equal and thus gamma is available k p k Represents |h k | 2 p k
Fifth step, the kth is calculated assuming that the eavesdropper passively eavesdrops without actively transmitting signals to interfere with the user task offloading process, and that the eavesdropper's instantaneous Channel State Information (CSI) is unknown and perfect confidentiality cannot be achievedProbability of user's safety interruption P so,k The definition is as follows:
P so,k ＝Pr{R t,k -R s,k ＜C e,k (5) wherein R t,k Is the code word transmission rate; r is R s,k Is a useful information rate; the channel capacity of the eavesdropping terminal is
The method overestimates the eavesdropping capability of the eavesdropper, and can cancel interference generated by other users when eavesdropping on user k. From the base station side, this assumption also enables secure offloading without knowledge of the eavesdropper capability and instantaneous CSI at the base station side. For simplicity, R is used hereafter s,k Instead of
R t,k Replace->
A sixth step of constructing an optimization problem according to the energy consumed by the local calculation obtained in the second step and the energy consumed by the unloading calculation task obtained in the third step, wherein the optimization problem comprises an optimization target and constraint conditions, and the optimization target is to minimize the total energy consumption of the system; the corresponding optimization problem is as follows:
β k,l +β l,k ＝1 (6i)
wherein, l= [ l ] 1 ,…l K ]Representing a local computing task amount; p= [ p ] 1 ,…,p K ]Representing the user transmit power; r is R t ＝[R t,1 ,…,R t,K ]Representing a codeword transmission rate; r is R s ＝[R s,1 ,…,R s,K ]Representing a useful information rate; beta represents the decoding sequence of the base station end; b represents the bandwidth of the system; 0 < epsilon < 1 represents the maximum tolerable safe interrupt probability; c (C) k ＝log 2 (1+γ k p k /(1+∑ l≠k β k,l γ l p l ) A) is the channel capacity of the kth user; gamma ray k ＝|h k | 2 /σ 2 ；h k The channel coefficient from the kth user to the base station; sigma (sigma) 2 Is the noise variance at the base station.
Constraint (6 b) ensures successful offloading of the computing task;
constraint (6 c) ensures that the base station successfully decodes the offload tasks;
constraints (6 d) and (6 e) ensure a low probability of information being intercepted;
constraint (6 g) represents the total computation task amount constraint;
constraint (6 h) ensures that the base station always preferentially decodes signals of users with stronger received signals;
constraint (6 i) avoids the case where two users decode at the same time.
Seventh, converting the constraint conditions in the sixth step;
from the constraints (6 c), (6 e) and (6 h), the optimization problem (P1) is a mixed integer non-convex optimization problem, which is rewritten to a convex optimization problem, and the method is specifically described as follows:
the constraint (6 c) is a non-convex constraint, and the auxiliary variable b is introduced k and πk Constraint (6 c) can be translated into:
R t,k ≤1+b k (7a)
b k π k ≤γ k p k (7c)
constraints (7 b) and (7 c) are still non-convex constraints, and by means of a continuous convex approximation, (7 b), (7 c) can be approximated as:
wherein the inequality in formulas (8) and (9) is beta to the left k,l p k At the position of
First-order taylor expansion at point b k π k At->
A first order taylor expansion at the location; i represents the ith inner iteration.
Calculating the safe break probability in constraint (6 e) can result in:
wherein de,k Is the distance between the kth user and the eavesdropping end and α is the path loss coefficient.
Introducing a series of auxiliary variables, constraint (6 e) can be equivalently transformed into the form:
π k R s,k ≤u k (11b)
φ k w k ≤γ k +γ k p k -u k (11c)
p k u k ≤w k (11d)
specific: first, an auxiliary variable phi is introduced k The safe interrupt probability constraint may be expressed as:
then introducing an auxiliary variable pi k As a means of
Can be obtained:
continuing to introduce the auxiliary variable u k As pi k R s,k Upper bound of (2) can be obtained:
π k R s,k ≤u k (14a)
φ k p k u k ≤π k +γ k p k -u k (14c)
finally, introducing the auxiliary variable w k As p k u k And (3) can be obtained by finishing the upper limit of the formula (11) through some mathematical calculation.
Wherein the inequalities (11 b) - (11 e) are still non-convex constraints, the constraint (11 e) is approximated by equation (8) and the constraints (11 b) - (11 d) can be approximated by a continuous convex approximation method:
constraints (6 h) and (6 i) are integer constraints, and an auxiliary variable mu is introduced k,l The method can obtain:
β k,l γ l p l ≤γ k p k (18a)
β k,l (1+μ k,l )＝0 (18b)
β k,l ＝μ k,l (18c)
β k,l +β l,k ＝1 (18d)
likewise, the successive convex approximation method (18 a) can be rewritten as follows:
from the above discussion, the original problem can be converted into (P2)
Where v is the set of all variables.
To solve the equation constraints (18 b) - (18 d), writing the objective function as an augmented lagrangian function form, (P2) can be translated as follows:
eighth, solving an optimization problem (P3), adopting a penalty dual decomposition method, wherein the method is based on a dual-loop structure, and updating dual variables when the constraint violation of an equation is lower than a certain threshold value in an outer loop, otherwise updating penalty parameters; in the inner loop, iteratively solving the problem of increasing Lagrangian, finding the optimal solution of the current outer loop, and obtaining the solution which is the same as the solution of the optimization (P2) when the penalty parameter approaches to 0; the algorithm flow chart shown in fig. 2 is further described below:
s1, determining the precision delta and delta 0 The method comprises the steps of carrying out a first treatment on the surface of the Maximum iteration number I and R, iteration number i=0 and r=0; threshold η=0.3 of equation constraint violation; penalty parameter ρ 0 The method comprises the steps of carrying out a first treatment on the surface of the Dual variable lambda 0 And penalty factor 0 < c < 1, and a feasible initiation point x 0 ；
S2, fixing penalty parameter rho r Dual variable lambda r The method comprises the steps of carrying out a first treatment on the surface of the In x i-1 For an initial point, solve the optimization objective (P2):
dividing the variable v into two parts, updating in turn, wherein the first part comprises mu k,l ；μ k,l Only in the objective function, mu k,l The closed-form solution of (2) is:
the second part being other variables
Specifically, mu is fixed k,l Other variables are solved by using a convex optimization tool bag CVX, and at the moment, a minimum point x is obtained i The method comprises the steps of carrying out a first treatment on the surface of the Updating iteration times i=i+1;
s3, if
Or the maximum iteration number I is reached, and S4 is switched; otherwise, turning to S2;
wherein ,
representing the optimal value of the objective function at the ith internal iteration;
s4, updating the penalty parameter rho and the dual variable lambda, specifically:
if it is
Then->
Otherwise ρ r+1 ＝cρ r ；
Updating iteration times r=r+1;
wherein ,
representing all equality constraint vectors;
s5, if
The iteration is stopped and x is output i As an approximate minimum of the original problem; otherwise, go to S2.
Finally, the optimization problem is solved and the algorithm is ended.
Simulation experiments prove the effectiveness and the realizability of the patent for minimizing the consumed energy. The bandwidth b=1 MHz, noise power σ used in the present embodiment 2 -60dB, time t=0.1 sec, path loss index α=4, cpu revolution c=1000 revolutions per bit, effective capacitance coefficient
The distance d=100 meters from the eavesdropper to the user, and the number k=3.
Fig. 3 shows the results of computer simulation of the method of the present invention, compared with the partial offloading method based on orthogonal multiple access and the conventional method without eavesdropping. The simulation parameters under the three methods are identical, wherein the abscissa in the figure represents the calculated task amount, and the ordinate represents the total energy consumed by the system. As can be seen from the figure, the OMA-portion offloading scheme is superior to the present method when the computational effort is low, because in this case the NOMA system uses more energy to cancel the inter-user interference, and after the computational effort is greater than a certain bit, the present method is superior to the OMA-portion offloading scheme, demonstrating the superiority of NOMA technology in reducing the system under computationally intensive tasks; the energy consumption of the present invention is always greater than for a solution without eavesdroppers, since a part of the energy is consumed against eavesdroppers.
Claims (7)
1. A method for multi-user safe energy-saving resource allocation in a mobile edge computing network, characterized in that the system comprises
Individual subscriber, a base station and an eavesdropper, wherein each subscriber is in period +.>
Execution of +.>
Task amount of bits;
will be the first
The task amount corresponding to the individual user is divided into +.>
and />
Two parts, wherein->
Partial tasks are calculated locally;
part is unloaded to the base station through a non-orthogonal multiple access mode;
the distribution method comprises the following steps:
step 1, the current time slotKThe method comprises the steps that a user accesses a base station, and the base station obtains channel state information of the user through channel training; setting time division duplexing, channel reciprocity, and channel obeying quasi-static block fading model;
step 2, calculating the energy consumed by local calculation of each user and the energy consumed by task unloading of each user;
step 3, constructing a decoding sequence rule of a base station end multi-user, and specifically:
decoding and sequencing the products of the transmitting power and the channel gain of each user according to the sequence from big to small;
step 4, constructing an optimization problem according to the local calculation consumed energy obtained in the step 2 and the energy consumed by the task unloading calculation and the decoding sequence rule of the base station end multi-user obtained in the step 3, wherein the optimization problem comprises an optimization target and constraint conditions, and the optimization target is to minimize the energy consumed by the system;
step 5, solving an optimization problem to obtain the minimum value of the total energy consumption of the system and each optimization variable value corresponding to the minimum value when the minimum value reaches the value, wherein each optimization variable value is in a period
Interior->
CPU revolution number, effective capacitance coefficient, th +.>
Transmit power of individual user, < >>
The locally calculated task amount of the individual user.
2. The method for multi-user secure energy-saving resource allocation in a mobile edge computing network of claim 1, whereinIn step 2, the calculation is performed by the following formulas, respectively, in a period
In, each user calculates the consumed energy locally:
wherein ,
is->
Local calculation of individual user->
The amount of energy that needs to be consumed by the bit task; />
Represents->
The individual users calculate the number of CPU revolutions required per bit; />
Represents the CPU frequency; />
Representing the effective capacitance coefficient.
3. The method for multi-user safe and energy-saving resource allocation in a mobile edge computing network according to claim 1, wherein in step 2, the method is performed in a periodic manner by the following formulas, respectively
In this, each user offloads the energy that is consumed by the computing task:
wherein ,
is->
Individual user uninstallation->
The energy consumed by the bit calculation task; />
Represents->
The transmit power of the individual users.
4. The method for multi-user safe and energy-saving resource allocation in a mobile edge computing network according to claim 1, wherein in step 3, the optimization objective is as follows:
wherein ,
representing a local computing task amount; />
Representing the user transmit power;
representing a codeword transmission rate; />
Representing a useful information rate; />
Representing the decoding sequence of the base station end; />
Representing the bandwidth of the system; />
Representing the maximum tolerable safe interrupt probability;
is->
Channel capacity of individual users; />
；/>
Is->
Channel coefficients from individual users to the base station; />
The noise variance of the base station end; />
Represents->
The individual users calculate the number of CPU revolutions required per bit; />
Representing the effective capacitance coefficient; />
Represents->
The transmit power of the individual users; />
Is->
Locally calculated task amounts for individual users; />
Is a period; />
Is->
Probability of a security outage for an individual user.
5. The method for multi-user safe and energy-saving resource allocation in a mobile edge computing network according to claim 1, wherein the method for solving the optimization problem is as follows:
adopting a continuous convex approximation method to convert the non-convex constraint condition in the step 3 into a convex constraint condition, thereby forming an optimization problem
；
In the optimization problem
Adding an augmented Lagrangian term corresponding to the equality constraint to the objective function in the model to obtain an optimization problem
；
Optimizing problem by using penalty dual decomposition method
And solving to obtain the minimum value of the total energy consumption of the system and each optimized variable value corresponding to the minimum value.
6. A multi-user secure energy-efficient resource allocation method in a mobile edge computing network according to claim 1, characterized in that the base station is equipped with a MEC server.
7. The method for multi-user safe and energy-saving resource allocation in a mobile edge computing network of claim 1, wherein the base station, the user, and the eavesdropper are each equipped with a single antenna.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010740465.6A CN112052086B (en) | 2020-07-28 | 2020-07-28 | Multi-user safety energy-saving resource allocation method in mobile edge computing network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010740465.6A CN112052086B (en) | 2020-07-28 | 2020-07-28 | Multi-user safety energy-saving resource allocation method in mobile edge computing network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112052086A CN112052086A (en) | 2020-12-08 |
| CN112052086B true CN112052086B (en) | 2023-06-20 |
Family
ID=73602525
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010740465.6A Active CN112052086B (en) | 2020-07-28 | 2020-07-28 | Multi-user safety energy-saving resource allocation method in mobile edge computing network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112052086B (en) |
Families Citing this family (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112559078B (en) * | 2020-12-22 | 2023-03-21 | 杭州电子科技大学 | Method and system for hierarchically unloading tasks of mobile edge computing server |
| CN112995129A (en) * | 2021-01-28 | 2021-06-18 | 全球能源互联网研究院有限公司 | Electric power business safety transmission method and system based on edge calculation |
| CN113630734B (en) * | 2021-08-09 | 2024-05-14 | 郑州海威光电科技有限公司 | Calculation unloading and resource allocation method for intelligent power grid power supply system |
| CN114158050B (en) * | 2021-08-24 | 2023-06-27 | 华北电力大学（保定） | Method, device and system for improving secure computing performance of WPT-MEC system |
| CN113810908B (en) * | 2021-08-24 | 2023-06-20 | 华北电力大学（保定） | MEC system safety unloading method and equipment and MEC system |
| CN113938997B (en) * | 2021-09-30 | 2024-04-30 | 中国人民解放军陆军工程大学 | Resource allocation method of secure MEC system in NOMA (non-volatile memory access) Internet of things |
| CN114363803B (en) * | 2021-12-27 | 2024-04-02 | 杭州电子科技大学 | Energy-saving multitasking distribution method and system for mobile edge computing network |
| CN114389654B (en) * | 2022-01-13 | 2022-09-20 | 郑州轻工业大学 | Mobile edge calculation safety calculation efficiency maximization method based on large-scale MIMO |
| CN114531718B (en) * | 2022-02-24 | 2023-06-06 | 南京邮电大学 | NOMA-MEC system energy consumption optimization method under imperfect CSI |
| CN114745383A (en) * | 2022-04-08 | 2022-07-12 | 浙江金乙昌科技股份有限公司 | Mobile edge calculation assisted multilayer federal learning method |
| CN115883568B (en) * | 2023-02-22 | 2023-06-02 | 云南省交通规划设计研究院有限公司 | Tunnel edge computing node deployment method and system |
| CN115866691B (en) * | 2023-02-27 | 2023-06-20 | 南京邮电大学 | Task unloading method for heterogeneous network mobile edge computing system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109548013A (en) * | 2018-12-07 | 2019-03-29 | 南京邮电大学 | A kind of mobile edge calculations system constituting method of the NOMA with anti-eavesdropping ability |
| CN109814951A (en) * | 2019-01-22 | 2019-05-28 | 南京邮电大学 | The combined optimization method of task unloading and resource allocation in mobile edge calculations network |
| CN110545584A (en) * | 2019-08-20 | 2019-12-06 | 浙江科技学院 | Communication processing method of full-duplex mobile edge computing communication system |
| CN111093226A (en) * | 2019-12-12 | 2020-05-01 | 西安电子科技大学 | Multitask parallel migration method based on non-orthogonal multiple access and mobile edge computing |
| CN111093213A (en) * | 2019-12-12 | 2020-05-01 | 西安电子科技大学 | Hot content superposition pushing and distributing method and system and wireless communication system |
| CN111245539A (en) * | 2020-01-07 | 2020-06-05 | 南京邮电大学 | NOMA-based efficient resource allocation method for mobile edge computing network |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11244242B2 (en) * | 2018-09-07 | 2022-02-08 | Intel Corporation | Technologies for distributing gradient descent computation in a heterogeneous multi-access edge computing (MEC) networks |
-
2020
- 2020-07-28 CN CN202010740465.6A patent/CN112052086B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109548013A (en) * | 2018-12-07 | 2019-03-29 | 南京邮电大学 | A kind of mobile edge calculations system constituting method of the NOMA with anti-eavesdropping ability |
| CN109814951A (en) * | 2019-01-22 | 2019-05-28 | 南京邮电大学 | The combined optimization method of task unloading and resource allocation in mobile edge calculations network |
| CN110545584A (en) * | 2019-08-20 | 2019-12-06 | 浙江科技学院 | Communication processing method of full-duplex mobile edge computing communication system |
| CN111093226A (en) * | 2019-12-12 | 2020-05-01 | 西安电子科技大学 | Multitask parallel migration method based on non-orthogonal multiple access and mobile edge computing |
| CN111093213A (en) * | 2019-12-12 | 2020-05-01 | 西安电子科技大学 | Hot content superposition pushing and distributing method and system and wireless communication system |
| CN111245539A (en) * | 2020-01-07 | 2020-06-05 | 南京邮电大学 | NOMA-based efficient resource allocation method for mobile edge computing network |
Non-Patent Citations (3)
| Title |
|---|
| Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices;Yuyi Mao;《 IEEE Journal on Selected Areas in Communications ( Volume: 34, Issue: 12, December 2016)》;20160920;3590 - 3605 * |
| 多核服务器边缘计算系统中任务卸载调度和功率分配的研究;凌雪延;《 南京邮电大学学报（自然科学版）》;20200611(第2期);81-88 * |
| 超密集网络中基于移动边缘计算的任务卸载和资源优化;张海波等;《电子与信息学报》;20190514(第05期);183-190 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112052086A (en) | 2020-12-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112052086B (en) | Multi-user safety energy-saving resource allocation method in mobile edge computing network | |
| CN111245539A (en) | NOMA-based efficient resource allocation method for mobile edge computing network | |
| CN109640320B (en) | Safe unloading method of computing task of mobile edge computing system based on mixed NOMA | |
| CN109688596A (en) | A kind of mobile edge calculations system constituting method based on NOMA | |
| CN111614419B (en) | NOMA-based mobile edge computing network task high-security unloading resource allocation method | |
| CN112333702A (en) | Optimization method for delay minimization based on safe NOMA moving edge calculation | |
| CN109150855B (en) | Robust wireless communication safety transmission method for optimizing power resources | |
| CN107528624B (en) | Design method of steady beam forming based on non-orthogonal multiple access technology | |
| CN109618336A (en) | A kind of key extraction method in frequency division duplex system | |
| CN111343712A (en) | Unmanned aerial vehicle-assisted cognitive wireless network communication safety control method | |
| CN109728865B (en) | Interception coding method based on artificial noise in large-scale antenna array | |
| CN112911587B (en) | Method for safely unloading anti-eavesdropping task by using physical layer under MEC-D2D environment | |
| Li et al. | Asynchronous activity detection for cell-free massive MIMO: From centralized to distributed algorithms | |
| Li | Fairness-aware multiuser scheduling for finite-resolution intelligent reflecting surface-assisted communication | |
| CN107172701A (en) | A kind of power distribution method of non-orthogonal multiple access system | |
| Wang et al. | Delay minimization for secure NOMA mobile-edge computing | |
| Xiao et al. | Reinforcement learning based network coding for drone-aided secure wireless communications | |
| US20220076134A1 (en) | Two-stage deep learning based secure precoder for information and artificial noise signal in non-orthogonal multiple access system | |
| CN113179113B (en) | Beam forming method and system under satellite communication multicast signal transmission mode | |
| CN113938997B (en) | Resource allocation method of secure MEC system in NOMA (non-volatile memory access) Internet of things | |
| CN113784340B (en) | Secret unloading rate optimization method and system | |
| CN113726396B (en) | High-energy-efficiency confidential transmission method of full-duplex wireless energy-carrying relay communication system | |
| Lin et al. | Channel-adaptive quantization for wireless federated learning | |
| CN114599044A (en) | Beam forming optimization method in cognitive network based on intelligent reflector technology | |
| Tiwari et al. | Latency minimization in uplink non-orthogonal multiple access-based mobile edge computing |
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 |