CN110545584A - Communication processing method of full-duplex mobile edge computing communication system - Google Patents
Communication processing method of full-duplex mobile edge computing communication system Download PDFInfo
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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- H04L5/00—Arrangements affording multiple use of the transmission path
- H04L5/14—Two-way operation using the same type of signal, i.e. duplex
- H04L5/16—Half-duplex systems; Simplex/duplex switching; Transmission of break signals non-automatically inverting the direction of transmission
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
- H04W52/0203—Power saving arrangements in the radio access network or backbone network of wireless communication networks
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- H—ELECTRICITY
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- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
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- H—ELECTRICITY
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- H04W52/18—TPC being performed according to specific parameters
- H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
- H04W52/267—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the information rate
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- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
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- H—ELECTRICITY
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- H04W72/04—Wireless resource allocation
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
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- 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
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- 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
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Abstract
The invention discloses a communication processing method of a full-duplex mobile edge computing communication system. The full-duplex mobile edge computing communication system comprises a plurality of mobile devices and a base station with a mobile edge computing function server; a joint communication and computing resource optimization model is established and divided into two submodels for respectively processing and solving, and joint optimization allocation is carried out on two computing resources, namely an unloading task and a processor frequency, and two communication resources, namely a sending power and a sending rate. The communication processing method provided by the invention has the advantage of remarkably reducing the energy consumption of the system on the premise of ensuring the delay and power constraint of the system.
Description
Technical Field
the invention relates to the technical field of wireless communication, and aims to optimize transmitting power, calculation tasks, CPU frequency and transmission rate resources in a cellular network under the background of full-duplex mobile edge calculation communication.
Background
with the advent of the world of everything interconnection and big data, mobile communication networks face the problem of a dramatic increase in mobile data traffic and flow. Ultra-low time delay, ultra-high efficiency, ultra-high reliability and ultra-high density connection become the necessary requirements of future mobile communication systems, and the development and implementation of 5G systems are accelerated. Many new application scenarios, such as AR/VR, automatic driving, industrial internet, internet of vehicles, smart cities, smart agriculture, etc., have raised higher requirements on 5G key technologies in terms of time delay, energy efficiency, reliability, etc., and the existing mobile network architecture and terminal device have been unable to meet these ever-increasing demands.
Based on the above realistic background, to further exploit the potential capabilities of mobile devices, Full-duplex (FD) communication technology is capable of sending and receiving information on the same frequency at the same time. The node equipped with the Full Duplex technology supports the amplification forwarding transmission, and can effectively solve the problems of insufficient resource utilization rate and time delay (Riihonen T, Werner S, and Wichman R. hybrid Full-Duplex/Half-Duplex Relay with Transmit Power Adaptation, IEEE Transactions on Wireless Communications,2011, 10(9):3074 + 3085; namely, Riihonen T, Werner S and Wichman R. transmission Power self-adaptive hybrid Full Duplex/Half Duplex relay, IEEE Wireless communication, 2011,10(9):3074 + 3085). Especially, with the continuous improvement of the technology for eliminating self-interference, the full-duplex technology has been widely applied to relays, small cellular networks and end-to-end devices to ensure the quality of service. On the other hand, while many mobile devices benefit from the development of CPUs and flash memory, their computing power is very limited for future computing tasks. Based on this, the Mobile Edge Computing (MEC) is used for Computing and storing the data, so that the problems of higher time delay and higher energy consumption in the data return link caused by the traditional centralized processing mode can be solved. In addition, the MEC deploys computation, storage, caching and the like of mass data at the network edge, which greatly reduces energy consumption, improves user experience, and greatly reduces network congestion rates of the transmission network and the core network (Kongyue, task migration strategy research in a mobile edge computing environment, university of Western-Anoei, 2018). However, the battery of the mobile device limits the development of MECs because the energy consumed by the heavy local computing task is extremely poor for the user experience. Therefore, the technology of using energy collection technology to enable the mobile device to continuously collect energy from the surrounding environment solves the problem of insufficient energy supply of the communication device in the Wireless network, and especially in recent years, the technology of Simultaneous Information and energy Transfer (SWIPT) is proposed, which divides the radio frequency signal into two parts, one part is used for Information transmission, and the other part is used for energy collection, and provides a feasible solution for the excessive network energy consumption (varsheny L R, transportation Information and energy Information theory, namely, varsheny L R, which simultaneously transmits Information and energy, and the IEEE International Information theory forum).
currently, some researchers are beginning to combine full-duplex technology with MEC technology and solve the problem of mobile device battery capacity limitation through energy harvesting, but how to jointly consider energy management control, computational offloading policy, and resource optimization configuration has not received sufficient attention.
disclosure of Invention
The invention provides a communication processing method of a full-duplex mobile edge computing communication system, aiming at solving the problems of time delay, energy consumption and resource shortage in the data transmission and computing process. The communication processing method provided by the invention has the advantage of remarkably reducing the energy consumption of the system on the premise of ensuring the delay and power constraint of the system.
The technical scheme of the invention comprises the following steps:
s1, system model establishing:
in a wireless cellular network, a full-duplex mobile edge computing communication system is formed by a Base Station (BS) with one multi-antenna and a plurality of mobile User Equipment (UE) with a single antenna, wherein the Base station works in a full-duplex mode, and the mobile user equipment works in a half-duplex mode; a Mobile Edge Computing (MEC) server is arranged at a base station, and a power decomposition receiver (PS receiver) is arranged at Mobile user equipment;
In each current time slot, each mobile user equipment unloads a part of calculation tasks and sends the calculation tasks to a base station through an uplink, the base station sends the information processing result of the previous time slot to the mobile user equipment corresponding to the information processing result through a downlink, the base station receives the unloaded calculation tasks and then carries out edge calculation processing through a mobile edge calculation server to obtain the information processing result, and the information processing result is sent to the mobile user equipment corresponding to the information processing result through the downlink in the next time slot in the form of radio frequency signals; the wireless radio frequency signal comprises information and energy. The mobile user equipment decodes one part of the received information processing result through a power decomposition receiver to obtain a calculation result of the unloaded calculation task, and the other part of the information processing result is collected as energy for energy consumption of local calculation;
the mobile user equipment sends uplink to the base station, and the base station sends downlink to the mobile user equipment.
The time slot refers to the time required for completing one uplink or downlink transmission.
the full-duplex mobile edge computing communication system of the present invention includes a plurality of mobile devices and a base station with a mobile edge computing function server. The base station is equipped with multiple antennas and operates in a full duplex mode, i.e., can simultaneously send and receive computing tasks. The mobile user equipment is equipped with a single antenna, works in a half-duplex mode, can calculate local calculation tasks by using a self-contained battery, can also unload partial calculation tasks to the base station, and even can unload all the calculation tasks to the base station so as to reduce the problem of energy shortage. In addition, the mobile user equipment may also receive energy from the base station to charge its battery.
the mobile user equipment is equipment such as a mobile phone.
s2, modeling a joint communication and computing resource optimization model:
Establishing a joint communication and computing resource optimization model, wherein the joint communication and computing resource optimization model is composed of total energy consumption of a system consisting of communication resources (including uplink and downlink transmission power and uplink transmission rate) and computing resources (energy consumption of uplink unloaded computing tasks and local computing CPU frequency), and the system energy consumption is the difference between the energy consumption of each mobile user equipment unloading computing task uplink transmission, the energy consumption of each mobile user equipment local computing, the energy consumption of a base station sending wireless radio frequency signals and the energy sum collected by each mobile user equipment user; therefore, the aim of minimizing the energy consumption of the system is achieved by jointly optimizing communication resources and computing resources;
S3, a step of decomposing a joint communication and computing resource optimization model:
decomposing the joint communication and computing resource optimization model into two submodels, namely a local computing CPU frequency optimization submodel and a grouping optimization submodel;
s4, solving a joint communication and computing resource optimization model:
Solving a local calculation CPU frequency optimization sub-model by adopting a theoretical derivation mode to obtain the optimal local calculation CPU frequency of the mobile user equipment; and solving the grouping optimization submodel by adopting an interior point method to obtain the optimal transmission power of the uplink and the downlink, the optimal transmission rate of the uplink and the optimal uplink unloading calculation task, so as to distribute and set the communication relationship between the base station and the mobile user equipment. The method is implemented by allocating the optimal local computing CPU frequency of the mobile user equipment, the optimal uplink transmission power, the optimal uplink transmission rate and the optimal uplink unloading computing task to the mobile user equipment, and allocating the optimal downlink transmission power to the base station for setting.
in S2, the step of building a joint communication and computing resource optimization model includes:
s21, uplink model: k mobile user equipment with single antenna unloads calculation tasks to a base station in time slots;
in the time slot ti of the ith mobile user equipment, the ith mobile user equipment UEi unloads the calculation task Lui to the base station, and the signal received by the base station is:
wherein, the signal received by the base station from the ith mobile user equipment is represented; the power transmitted by the ith uplink mobile user equipment to the base station through the uplink, namely the transmission power of the uplink, and the power transmitted by the base station to the jth downlink mobile user equipment through the downlink, namely the transmission power of the downlink; and an uplink sending signal and a downlink sending signal respectively, wherein the uplink sending signal refers to a signal when the mobile user equipment unloads a part of the calculation task and sends the calculation task to the base station through an uplink, and the downlink sending signal refers to a signal when the base station sends an information processing result to the corresponding mobile user equipment through a downlink in the next time slot by using a radio frequency signal; the uplink transmission signal and the downlink transmission signal are signals with normalized power, that is, nB is white Gaussian noise at the base station and represents a complex matrix, M represents the number of antennas of the base station, and H0 represents the channel matrix of an uplink channel and the channel matrix of a self-interference channel at the base station respectively, wherein the uplink channel refers to a channel in an uplink, and the self-interference channel is caused by the full-duplex characteristic of the base station, that is, simultaneous transceiving;
then, the received power and the signal-to-interference-and-noise ratio at the base station are obtained through processing calculation, and are respectively as follows:
piu represents the received power of the signal received by the base station from the ith mobile user equipment, represents the signal-to-interference-and-noise ratio of the signal received by the base station from the ith mobile user equipment, tr {. cndot } is the trace operation for solving the matrix, and superscript H represents the matrix conjugate transpose and represents the noise power of white Gaussian noise nB;
in the uplink, the maximum feasible rate theoretical value is calculated by adopting the following formula:
wherein, B is the signal transmission bandwidth of the full-duplex mobile edge computing communication system;
Obtaining the transmission delay of the uplink and the energy consumption of the uplink unloading calculation tasks of all K mobile user equipment according to the transmission rate of the uplink and the uplink unloading calculation task Lui as total energy consumption Euo of the uplink unloading calculation tasks, wherein the energy consumption is energy consumption:
Wherein, the transmission rate of the ith mobile user equipment for sending the unloaded calculation tasks to the base station through the uplink is represented;
s22, downlink model:
The received signal at the jth mobile user equipment is obtained by the following formula:
The system comprises a mobile user equipment, a downlink channel matrix and a mobile user equipment, wherein the mobile user equipment is a white gaussian noise of jth mobile user equipment, represents a noise of the white gaussian noise, represents a transmission power of an uplink of the jth mobile user equipment and represents the channel matrix of the downlink channel;
then, the following formula is used to obtain the reception power of the jth mobile user equipment as:
the jth mobile user equipment converts the portion of the signal received from the base station into information by a power decomposition receiver, wherein the portion is beta (beta is more than or equal to 0 and less than or equal to 1), and the rest (1-beta) portion is converted into energy; the process obtains the energy consumption collected by all K mobile user devices as total energy consumption collected by the mobile user device Euh, as follows:
wherein, the transmission delay of the downlink is represented, and the calculation is as follows:
wherein, γ represents the weight factor of the transmission delay of the uplink, γ is more than or equal to 0 and less than or equal to 1, which represents that the downlink transmission signal time of the base station does not exceed the uplink unloading calculation task time of each user, because the downlink transmission rate of the actual system is much greater than the uplink transmission rate;
And calculating the sum of the energy consumption of the base station in each time slot as the total energy consumption EBS transmitted by the base station by adopting the following formula:
S23, local calculation model:
The local calculation time delay and the local calculation energy consumption of the ith mobile user equipment are calculated and obtained by adopting the following formulas:
wherein, C represents the period of 1bit data calculated by the CPU of the mobile user equipment, fin represents the frequency of the ith mobile user equipment in the nth CPU period and satisfies the condition that fin is more than or equal to 0 and less than or equal to fimax, fimax represents the maximum CPU frequency, kappa is the effective capacitance coefficient of the CPU of the mobile user equipment, and Li is the total calculation task of the ith mobile user equipment;
and then, calculating local calculation energy consumption of all K mobile user equipment by adopting the following formula as local calculation total energy consumption Elocal:
S24, joint communication and computing resource optimization model:
According to the total energy consumption Euo of the uplink unloading calculation task, the local calculation total energy consumption Elocal, the total energy consumption Euh collected by the mobile user equipment and the total energy consumption EBS sent by the base station, the total energy consumption of the full-duplex mobile edge calculation communication system is obtained as follows:
E＝E+E+E-E
the following joint communication and computing resource optimization models are established with the aim of minimum total energy consumption:
wherein Lu ═ Lu1, Lu2,..., LuK, Lu denotes the mobile user device offload computation task vector; ru represents a transmission rate vector for the mobile user equipment to send offloaded computational tasks to the base station over the uplink; pu represents the transmission power vector of the mobile user equipment in the uplink, and the pu represents the upper limit and the lower limit of the transmission power of the uplink respectively; pd represents the transmission power vector of the mobile user equipment in the downlink, which is respectively the upper limit and the lower limit of the transmission power of the downlink; fn represents the frequency vector of the mobile user equipment in the nth CPU period, and fmax represents the maximum CPU frequency of the ith mobile user equipment; i belongs to { 1.,. K }, j belongs to { 1.,. K }, and T is the total time slot of the full-duplex mobile edge computing communication system;
In the above formula, the first constraint indicates that the energy collected by the user should be no less than the energy consumed by the user (including offloading the computing task and local computing); the second constraint condition indicates that the task unloaded by the user should not be more than the total calculation task of the user; the third constraint indicates that the actual transmission rate of the user should not be greater than the maximum theoretical transmission rate; the fourth to eighth constraints represent a power constraint, a transmission time constraint and a CPU frequency constraint, respectively.
In S3, the joint communication and computing resource optimization model is specifically divided into the following two submodels:
S3A) local computation CPU frequency optimization submodel
and dividing a model with the following minimum energy consumption target as a local computation CPU frequency optimization submodel:
Γ＝E+E-E
S3B) grouping optimization submodel
Constructing a model with the following minimum energy consumption target as a grouping optimization sub-model:
the step of solving the joint communication and computing resource optimization model of S4 specifically includes:
S41, establishing the following conditions for optimal local calculation of the CPU frequency:
Wherein, the frequency used by the ith mobile user equipment to process one CPU cycle is shown, and the frequency used by the ith mobile user equipment to process C (Li-Lui) CPU cycle (namely, the CPU cycle required by the local computing task) is shown; when all frequencies are optimal at the same time, the optimal CPU frequency parameter fi is used for representing the optimal frequencies;
To minimize the system power consumption of the local compute CPU frequency optimization submodel, it is desirable that fi be as small as possible.
second constraint processing transformation of CPU frequency optimization submodel according to local computation
obtaining a lower bound of an optimal CPU frequency parameter fi as an optimal local computing CPU frequency by adopting the following formula:
And S42, according to the result of the step S41, substituting the optimal local computing CPU frequency into the grouping optimization submodel, and grouping the parameters pu, pd, Ru and Lu of the grouping optimization submodel for iterative optimization solution to obtain the optimal parameters and Luopt, thereby obtaining the optimal resource allocation mode.
the step of performing iterative optimization solution on the S42 grouping specifically comprises the following steps:
S421, using the transmission power vector pu of the uplink mobile user equipment, the transmission rate vector Ru of the uplink mobile user equipment and the transmission power vector pd of the downlink mobile user equipment as a first group of jointly optimized variables, setting the uplink unloading calculation task vector Lu as a fixed variable, and processing the packet optimization submodel by adopting an interior point method to obtain the optimal uplink transmission rate vector and the optimal downlink transmission power vector
s422, substituting the first group of optimized values obtained in the S421 into the grouping optimization submodel, and processing the grouping optimization submodel by adopting an interior point method to obtain an optimal uplink unloading calculation task vector Luopt;
And S423, returning to S421, and performing a second iteration optimization, wherein the iteration is continued until the method converges.
the convergence condition of the method is that the difference of the total energy consumption of the system of the two iterations is less than 10 < -4 >.
the invention minimizes the total energy consumption of the system by a calculation task optimization allocation method for jointly and optimally allocating the local calculation CPU frequency, the uplink and downlink transmission power, the uplink transmission rate and the uplink unloading. And finally, performing grouping iterative optimization on the optimization problem obtained by modeling by using a problem decomposition and interior point method to obtain an optimal resource allocation scheme and achieve the effect of minimizing the total energy consumption of the system.
The invention establishes a communication and calculation resource optimal allocation problem, divides the problem into two submodels, processes and solves the submodels separately, and performs combined optimal allocation on two calculation resources of unloading tasks and processor frequency and two communication resources of sending power and sending rate.
compared with the prior art, the invention has the advantages that:
1) The full-duplex MEC communication system established by the invention is composed of a base station supporting a multi-antenna full-duplex (FD) technology and a plurality of mobile user equipment supporting power decomposition. The mobile user equipment may offload part of the computation task to the base station and be computed by the MEC server. Meanwhile, the base station may transmit the calculation result from the MEC server to the corresponding received mobile subscriber based on the FD technique.
in addition, the mobile user equipment (UE side) may receive the result of the calculation task and may also be powered by harvesting energy through a power decomposition SWIPT technique.
2) the method for optimizing the combined communication and computing resources can reduce the total energy consumption of the system to the maximum extent by optimizing resources such as transmission power, CPU frequency, transmission rate and unloading computing tasks.
3) the solution of the joint communication and computing resource optimization model provided by the invention optimizes the target value by adopting a decomposition problem and grouping iteration method, can effectively solve the problem of high computation complexity of the original problem, and has lower energy consumption advantage compared with other two comparison schemes.
in addition, the invention also provides reference for other related problems in the same field, can be expanded and extended on the basis of the reference, is applied to technical schemes of other methods in the same field, and has very wide application prospect.
in general, the communication resource optimization processing method provided by the invention effectively reduces the energy consumption of the system, has the advantage of effective communication under the condition of energy limitation, has excellent use effect and has very high use and popularization values.
drawings
FIG. 1 is a logic flow diagram of a communication processing method of the present invention.
Fig. 2 is a system model of the communication processing method of the present invention.
FIG. 3 is a process diagram of the convergence performance of the method of the present invention.
FIG. 4 is a graph comparing the impact of different computing tasks on system energy consumption.
fig. 5 is a graph comparing the effect of different antenna numbers on system energy consumption.
Detailed Description
the following detailed description of the embodiments of the present invention is provided in connection with the accompanying drawings for the purpose of facilitating understanding and understanding of the technical solutions of the present invention.
The technical solution of the invention is further illustrated below in connection with an embodiment of the invention carried out according to a complete method and the accompanying drawings thereof:
fig. 1 is a logic flow diagram of a communication processing method of a full-duplex mobile edge computing communication system according to the present invention. As shown in fig. 1, firstly, parameter values are initialized, and the parameters to be initialized include an unloading calculation task, uplink and downlink transmission power and uplink transmission rate; then solving the submodel I, and calculating the optimal local CPU frequency of the theory; and then solving a second sub-model, wherein the optimization is carried out in two groups, and firstly, optimizing a first group of variables by adopting an interior point method: uplink transmission power, downlink transmission power and uplink transmission rate; and optimizing a second group of variables by adopting an interior point method again: unloading the calculation task and the uplink transmission rate; the two groups of optimization iteration are carried out until the method is converged and the iteration is finished, so that the final resource allocation result is obtained. So far, the energy consumption of the whole system is minimized, and the method is finished.
Fig. 2 is a system model diagram of a communication processing method of a full-duplex mobile edge computing communication system according to the present invention. A wireless cellular network deploys a multi-antenna base station and multiple single-antenna mobile user equipment. The base station operates in full duplex mode and the mobile user equipment operates in half duplex mode. In addition, an edge computing server (MEC server) is located at the base station and a power splitting receiver (PS receiver) is located at the user side. In the time slot ti of the ith mobile user equipment, the ith mobile user equipment unloads a part of calculation tasks Lui to the base station through an uplink, an MEC server at the base station processes the calculation tasks, and the base station sends a radio frequency signal containing information and energy to a downlink user j. The downlink jth mobile user equipment decodes the received radio frequency signal through a part of PS receivers to obtain a calculation task result processed by the MEC server, and a part of the calculation task result is collected as energy for local calculation.
the convergence process of the method processing of the embodiment is shown in fig. 3, the convergence speed of the method is only iteration for four times, and the effectiveness and complexity of the method are low, so that the method has a wide application prospect.
fig. 4 shows the relationship between the average energy consumption of the system and the calculation task size L when the number of mobile user equipments K is 2 and the number of base station antennas M is 6. And selecting another two schemes, namely a parameter fixing scheme and a full unloading scheme, to compare with the scheme of the invention. In all three scenario scenarios, the average energy consumption increases with the increase of the computational workload L, and the present invention is superior to the parameter-fixed scenario and the full offload scenario. In addition, compared with the parameter fixing scheme, the performance of the calculation task full unloading scheme is obviously improved, and the application of resource allocation to the system performance can be greatly improved.
fig. 5 compares the impact on system performance when the number of mobile user equipment K is 2 and the number of different antennas M is taken to be 4, 6, 8 respectively, with the abscissa being the total computational task. Parameter fixing scheme since the parameters are fixed, it can be found that the reduction in the number of antennas has little effect on the total energy consumption of the system. However, for the full offload scheme and the method proposed by the present invention, when the number of antennas increases, the system power consumption decreases significantly and better performance gains can be obtained.
Therefore, the communication processing method of the full-duplex mobile edge computing communication system provided by the invention has excellent use effect and high use and popularization values.
In summary, the invention establishes a communication processing method of a full-duplex mobile edge computing communication system aiming at the problems of time delay, energy consumption and resource shortage generated in the data transmission and computing processes, constructs a joint communication and computing resource optimization allocation problem to minimize the total energy consumption of the system, and provides a progressive method for solving an optimal solution by combining packet iterative optimization and an interior point method.
in addition, the invention also provides reference for other related problems in the same field, can be expanded and extended on the basis of the reference, is applied to technical schemes of other methods in the same field, and has very wide application prospect.
equivalent structural changes made by those skilled in the art according to the contents of the specification and the drawings are included in the scope of the invention.
Claims (5)
1. A communication processing method of a full-duplex mobile edge computing communication system is characterized by comprising the following steps:
S1, system model establishing:
in a wireless cellular network, a full-duplex mobile edge computing communication system is formed by a Base Station (BS) with one multi-antenna and a plurality of mobile User Equipment (UE) with a single antenna, wherein the Base station works in a full-duplex mode, and the mobile user equipment works in a half-duplex mode; a Mobile Edge Computing (MEC) server is arranged at a base station, and a power decomposition receiver (PS receiver) is arranged at Mobile user equipment; in each current time slot of the wireless cellular network, each mobile user equipment unloads a part of calculation tasks and sends the calculation tasks to the base station through an uplink, the base station carries out edge calculation processing after receiving the unloaded calculation tasks to obtain information processing results, and the information processing results are sent to corresponding mobile user equipment through a downlink in the next time slot in the form of wireless radio frequency signals; the mobile user equipment decodes one part of the received wireless radio frequency signal through a power decomposition receiver to obtain a calculation result of the unloaded calculation task, and the other part of the wireless radio frequency signal is collected as energy for energy consumption of local calculation;
S2, modeling a joint communication and computing resource optimization model:
Establishing a joint communication and computing resource optimization model, wherein the joint communication and computing resource optimization model is composed of total energy consumption of a system consisting of communication resources (including uplink and downlink transmission power and uplink transmission rate) and computing resources (uplink unloaded computing tasks and local computing CPU frequency), and the energy consumption of the system comprises the energy consumption of uplink transmission of the computing tasks unloaded by each mobile user equipment, the energy consumption of local computing of each mobile user equipment, the energy consumption of wireless radio frequency signals sent by a base station and the difference between the energy consumption and the energy collected by each mobile user equipment user;
s3, a step of decomposing a joint communication and computing resource optimization model:
Decomposing the joint communication and computing resource optimization model into two submodels, namely a local computing CPU frequency optimization submodel and a grouping optimization submodel;
s4, solving a joint communication and computing resource optimization model:
solving a local calculation CPU frequency optimization submodel by adopting a theoretical derivation mode to obtain the optimal local calculation CPU frequency of the mobile user equipment; and solving the grouping optimization submodel by adopting an interior point method to obtain the optimal transmission power of the uplink and the downlink, the optimal transmission rate of the uplink and the optimal uplink unloading calculation task, so as to distribute and set the communication relationship between the base station and the mobile user equipment.
2. the communication processing method of claim 1, wherein the method further comprises:
In S2, the step of building a joint communication and computing resource optimization model includes:
s21, uplink model: k mobile user equipment with single antenna unloads calculation tasks to a base station in time slots;
in the time slot ti of the ith mobile user equipment, the ith mobile user equipment UEi unloads a calculation task Lui to the base station, and the signals received by the base station are:
the power transmitted by the ith uplink mobile user equipment to the base station through the uplink is the power transmitted by the base station to the jth downlink mobile user equipment through the downlink; and an uplink sending signal and a downlink sending signal respectively, wherein the uplink sending signal is a signal when the mobile user equipment unloads a part of the calculation task and sends the calculation task to the base station through an uplink, and the downlink sending signal is a signal when the base station sends an information processing result to the corresponding mobile user equipment through a downlink in the next time slot by using a radio frequency signal; the uplink transmission signal and the downlink transmission signal satisfy nB is Gaussian white noise at the base station and represents a complex matrix, M represents the number of antennas of the base station, and the sum of the noise power and H0 represents a channel matrix of an uplink channel and a channel matrix of a self-interference channel at the base station respectively;
Then, the received power and the signal-to-interference-and-noise ratio at the base station are obtained through processing calculation, and are respectively as follows:
piu represents the received power of the signal received by the base station from the ith mobile user equipment, represents the signal-to-interference-and-noise ratio of the signal received by the base station from the ith mobile user equipment, tr {. cndot } is the trace operation of the matrix, and the superscript H represents the conjugate transpose of the matrix and represents the noise power of white Gaussian noise nB;
in the uplink, the maximum feasible rate theoretical value is calculated by adopting the following formula:
wherein, B is the signal transmission bandwidth of the full-duplex mobile edge computing communication system;
obtaining the transmission delay of the uplink and the energy consumption of the uplink offload computation tasks of all the K mobile user equipments as total energy consumption of the uplink offload computation tasks Euo according to the transmission rate of the uplink and the uplink offload computation task Lui:
Wherein, the transmission rate of the ith mobile user equipment for sending the unloaded calculation task to the base station through the uplink is represented;
S22, downlink model:
the received signal at the jth mobile user equipment is obtained by the following formula:
wherein, the white gaussian noise of the jth mobile user equipment represents the noise of the white gaussian noise, represents the transmission power of the uplink of the jth mobile user equipment, and represents the channel matrix of the downlink channel;
Then, the following formula is used to obtain the reception power of the jth mobile user equipment as:
the jth mobile user equipment converts the portion of the signal received from the base station into information by a power decomposition receiver, wherein the portion is beta (beta is more than or equal to 0 and less than or equal to 1), and the rest (1-beta) portion is converted into energy; the process obtains the energy consumption collected by all K mobile user devices as total energy consumption collected by the mobile user device Euh, as follows:
Wherein, the transmission delay of the downlink is represented, and the calculation is as follows:
wherein gamma represents a weight factor of the transmission delay of the uplink, and gamma is more than or equal to 0 and less than or equal to 1;
And calculating the sum of the energy consumption of the base station in each time slot as the total energy consumption EBS transmitted by the base station by adopting the following formula:
s23, local calculation model:
The local calculation time delay and the local calculation energy consumption of the ith mobile user equipment are calculated and obtained by adopting the following formulas:
wherein C represents the period of 1bit data calculated by the CPU of the mobile user equipment, fin represents the frequency of the ith mobile user equipment in the nth CPU period and satisfies the condition that fin is more than or equal to 0 and less than or equal to fimax, fimax represents the maximum CPU frequency, kappa is the effective capacitance coefficient of the CPU of the mobile user equipment, and Li is the total calculation task of the ith mobile user equipment;
and then, calculating local calculation energy consumption of all K mobile user equipment by adopting the following formula as local calculation total energy consumption Elocal:
S24, joint communication and computing resource optimization model:
According to the uplink unloading calculation task total energy consumption Euo, the local calculation total energy consumption Elocal, the total energy consumption collected by the mobile user equipment Euh and the total energy consumption EBS sent by the base station, the total energy consumption of the full-duplex mobile edge calculation communication system is obtained as follows:
E＝E+E+E-E
the following joint communication and computing resource optimization models are established with the aim of minimum total energy consumption:
wherein Lu ═ Lu1, Lu2,..., LuK, Lu denotes the mobile user device offload computation task vector; ru represents a transmission rate vector for the mobile user equipment to send offloaded computational tasks to the base station over the uplink; pu represents the transmission power vector of the mobile user equipment in the uplink, and the pu represents the upper limit and the lower limit of the transmission power of the uplink respectively; pd represents the transmission power vector of the mobile user equipment in the downlink, which is respectively the upper limit and the lower limit of the transmission power of the downlink; fn represents the frequency vector of the mobile user equipment in the nth CPU period, and fmax represents the maximum CPU frequency of the ith mobile user equipment; i ∈ { 1., K }, j ∈ { 1., K }, and T is the total time slot of the full-duplex mobile edge computing communication system.
3. the communication processing method of claim 2, wherein the method further comprises: in S3, the joint communication and computing resource optimization model is specifically divided into the following two submodels:
S3A) local computation CPU frequency optimization submodel
And dividing a model with the following minimum energy consumption target as a local computation CPU frequency optimization submodel:
Γ＝E+E-E
S3B) grouping optimization submodel
constructing a model with the following minimum energy consumption target as a grouping optimization sub-model:
4. the communication processing method of claim 1, wherein the method further comprises: the step of solving the joint communication and computing resource optimization model of S4 specifically includes:
s41, establishing the following conditions for optimal local calculation of the CPU frequency:
wherein fi1 represents the frequency used by the ith mobile user equipment to process one CPU cycle, and represents the frequency used by the ith mobile user equipment to process C (Li-Lui) CPU cycle; when all frequencies are optimal at the same time, the optimal CPU frequency parameter fi is used for representing the optimal frequencies;
obtaining a lower bound of an optimal CPU frequency parameter fi as an optimal local calculation CPU frequency by adopting the following formula:
and S42, according to the result of the step S41, bringing the optimal local computing CPU frequency into the grouping optimization submodel, and grouping the parameters pu, pd, Ru and Lu of the grouping optimization submodel for iterative optimization solution to obtain the optimal parameters and Luopt, thereby obtaining the optimal resource allocation mode.
5. The communication processing method of claim 4, wherein the method further comprises the steps of: the step of performing iterative optimization solution on the S42 grouping specifically comprises the following steps:
s421, using the transmission power vector pu of the uplink mobile user equipment, the transmission rate vector Ru of the uplink mobile user equipment and the transmission power vector pd of the downlink mobile user equipment as a first group of jointly optimized variables, setting the uplink unloading calculation task vector Lu as a fixed variable, and processing the packet optimization submodel by adopting an interior point method to obtain the optimal uplink transmission rate vector and the optimal downlink transmission power vector
s422, substituting the first group of optimized values obtained in the S421 into the grouping optimization submodel, and processing the grouping optimization submodel by adopting an interior point method to obtain an optimal uplink unloading calculation task vector Luopt;
and S423, returning to S421 again, and performing a second iteration optimization, wherein the iteration is continued until the method is converged.
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Application publication date: 20191206 |