CN111314938A - Optimization method for time-frequency domain resource allocation of cellular network of single cell - Google Patents

Optimization method for time-frequency domain resource allocation of cellular network of single cell Download PDF

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CN111314938A
CN111314938A CN202010112316.5A CN202010112316A CN111314938A CN 111314938 A CN111314938 A CN 111314938A CN 202010112316 A CN202010112316 A CN 202010112316A CN 111314938 A CN111314938 A CN 111314938A
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energy consumption
resource block
power
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CN111314938B (en
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林世俊
王叶苹
石江宏
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Xiamen University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • 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
    • H04W52/0274—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level by switching on or off the equipment or parts thereof
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/06—TPC algorithms
    • H04W52/14—Separate analysis of uplink or downlink
    • H04W52/146—Uplink power control
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/241—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account channel quality metrics, e.g. SIR, SNR, CIR, Eb/lo
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—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]
    • H04W52/265—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the quality of service QoS
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0446—Resources in time domain, e.g. slots or frames
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • H—ELECTRICITY
    • 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
    • H04W72/543—Allocation or scheduling criteria for wireless resources based on quality criteria based on requested quality, e.g. QoS
    • 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 relates to an optimization method for time-frequency domain resource allocation of a cellular network for a single cellular cell, which converts a power consumption optimization model of the cellular network into a time-frequency domain combined energy consumption minimization model of a single cellular user uplink transmission system, relates to resource allocation and transmission power control, firstly obtains the optimal transmission power of each cellular user on each frequency spectrum according to the idea of Lagrange dual algorithm, and then solves the optimization model by combining a cooperative game theory. Compared with common single-dimensional resource allocation, the time-frequency domain resource allocation optimization method greatly reduces the energy consumption of the system.

Description

Optimization method for time-frequency domain resource allocation of cellular network of single cell
Technical Field
The invention relates to the technical field of cellular network resource allocation, in particular to an optimization method for time-frequency domain resource allocation of a cellular network of a single cell.
Background
With the rapid development of mobile multimedia services and related applications, multimedia services such as high-definition video and online live broadcast are increased explosively, and the large-flow characteristic of the multimedia services brings huge pressure to a core network and spectrum resources of an operator; the 5G is a multi-service multi-technology fusion network which is provided for meeting the explosive growth of mobile data flow and the connection requirement of mass equipment, and has the characteristics of high speed, ubiquitous network, low power consumption and low time delay. Therefore, cellular network resources are efficiently and flexibly distributed, network performance can be improved, system energy consumption is reduced, and sustainable development is realized.
However, in the prior art of resource allocation in a cellular network, only single-dimensional resource allocation is performed in the frequency domain or the time domain to improve the performance of the system, where pure time domain resource allocation does not consider frequency selective fading on subcarriers, and pure frequency domain resource allocation does not consider that different qualities of Service (QoS) required by users for different applications may cause resource waste or resource shortage.
In view of the above, the present invention is directed to a resource allocation problem of the cellular network, and a related art is proposed.
Disclosure of Invention
In view of the problems in the prior art, an object of the present invention is to provide an optimization method for time-frequency domain resource allocation in a cellular network for a single cell, which combines resource allocation and transmit power control to reduce system energy consumption to the maximum.
In order to achieve the purpose, the invention adopts the technical scheme that:
a method for optimizing time-frequency domain resource allocation of a cellular network for a single cell, the method comprising the steps of:
step 1, solving the sum of energy consumption of all cellular users in a cellular network under respective resource allocation;
by using the energy consumption in the whole communication system as the sum formula of the energy consumption of each cellular user, the total energy consumption in the communication system can be expressed as:
Figure BDA0002390448630000021
wherein E isiFor cellular user i energy consumption, PiThe transmit power for cellular user i, denoted as
Figure BDA0002390448630000022
siPi,cirFor a fixed power of the circuit during the on-time of the cellular user i at transmission, (T-s)i)Pi,idleIdle power for cellular user i for the remaining time; m is the number of cellular users, siFor the equipment on time of cellular user i, s is more than or equal to 0iT is less than or equal to T, each single cellular user in the cellular network opens own sending equipment only when transmitting to the base station; t is the number of time slots in the transmission of the resource block, Pi,cirIs the power consumed by other circuit blocks when a cellular user transmits resources in the uplink; when the cellular user has no data to transmit or has all data transmitted, it turns off all transmitting circuit blocks to save energy, but the energy consumption caused by the leakage current is called idle power of cellular user, and is denoted as Pi,idle；
Figure BDA0002390448630000031
X and P in (1) respectively refer to resource allocation variable sets to be optimized by the problem
Figure BDA0002390448630000032
And power variable set P ═ P (P)i,k)，
Figure BDA0002390448630000033
For the allocation situation of the t time slot of the cellular user i on the k subcarrier of the resource block, Ri,minRepresenting a minimum rate requirement representing cellular user i;
cellular network interworkingThere is no interference phenomenon between multiple cellular users on different sub-carriers in one time slot, ri,kThe achievable rate for cellular user i on subcarrier k can be expressed as
Figure BDA0002390448630000034
Wherein p isi,kTransmitting power of cellular user i on subcarrier K, K belongs to K, i belongs to M and N0Power spectral density, g, of white gaussian noisei,kChannel gain for cell i on subcarrier k, W is the bandwidth of the subcarrier;
step 2, defining a matrix
Figure BDA0002390448630000035
The matrix is K multiplied by T and represents the resource allocation condition of the cellular user i in one resource block; according to
Figure BDA0002390448630000036
Can make
Figure BDA0002390448630000037
Counting the time of transmission of the user i on each subcarrier, and transferring the optimization model of the formula (1) as follows:
Figure BDA0002390448630000038
Figure BDA0002390448630000039
step 3, obtaining the optimal transmission power P ═ P (P) of each cellular user on each subcarrier through a Lagrange dual algorithmi,k) To meet the respective rate requirements of the cellular users;
decomposing the problem corresponding to the formula (4) into M independent sub-problems, writing the optimization model of each sub-problem into a corresponding Lagrangian function, and setting the current iteration function in the optimization model of each sub-problemThe number of iterations count is 0, the maximum number of iterations count ax, and the convergence error e is e-10(ii) a For p in each iterationi,kThe deviation derivative equation is 0, then
Figure BDA0002390448630000041
Wherein,
Figure BDA0002390448630000042
the solution of the optimization model corresponding to the current ith sub-problem, i.e. the transmission power of the user i on the sub-channel k, lambdaiIs a dual variable in a Lagrangian function;
and 4, updating Lagrangian dual variables by using a sub-gradient method:
defining a very small step size munIn order to ensure the convergence of the optimal value of the sub-gradient method, the Lagrange dual variable is updated according to the following formula:
Figure BDA0002390448630000043
λi(n+1)＝[λi(n)+μnd(λi(n))]+,i＝1,2,...,M (7)；
in each iteration, the formula (5) is updated by using the updated dual variable, and finally, the solution of the optimization model corresponding to the subproblem is converged into a unique optimal solution;
step 5, if the relative dual gap | | | lambdai(n)-λiIf (n-1) | ≦ e or the current iteration number exceeds the countmax, the iteration is stopped, and the feasible solution P of the optimal transmission power obtained under the condition that the current cellular user is distributed in the resource block is (P) |i,k) The method is applied to a communication system, and the total energy consumption of all cellular users of the current system is calculated by the formula (1); otherwise, continuing the step 3-4;
step 6, adjusting and optimizing the resource allocation of the cellular users through a cooperative game theory;
defining a maximum failure frequency fail _ max, randomly extracting a resource block, acquiring a subcarrier and a time slot corresponding to the resource block and a cellular User _ ori allocated to the resource block at present, randomly allocating the resource block to any User _ new except an original User again, updating resource occupation lists of the two users, obtaining corresponding optimal transmitting power by using the steps 3-5, and comparing the total system energy consumption E _ new after reallocation with the total system energy consumption E _ ori in the original system, wherein the comparison conditions are divided into three types:
if E _ new < E _ ori, removing the resource block from the original User _ ori resource occupation list, adding the resource block into the User _ new resource occupation list, updating the total energy consumption of the system, and setting the current failure frequency fail to be 0;
if E _ new is equal to E _ ori, updating the current failure times;
if E _ new > E _ ori, removing the resource block from the User _ new resource occupation list, and adding the resource block into the resource occupation list of the User _ ori;
and if the current failure times reach the maximum failure times, stopping the game theory, and obtaining the final user allocation condition and the respective optimal sending power of the users, so that the total energy consumption of the system users is minimum.
After the scheme is adopted, the power consumption optimization model of the cellular network is converted into the energy consumption minimization model of the single cellular user uplink transmission system combined with the time-frequency domain, the resource allocation and the transmission power control are related, the optimal transmission power of each cellular user on each frequency spectrum is obtained according to the idea of Lagrange dual algorithm, and then the optimization model is solved by combining the cooperative game theory. Compared with common single-dimensional resource allocation, the time-frequency domain resource allocation optimization method greatly reduces the energy consumption of the system.
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FIG. 1 is a schematic diagram of a single cellular subscriber communication system architecture;
FIG. 2 is an illustration of resource allocation for a single cellular user;
fig. 3 is a flow chart of single cell user control power.
Detailed Description
The invention discloses an optimization method for time-frequency domain resource allocation of a cellular network of a single cell, which aims to optimize system energy consumption in the cellular network of a plurality of cellular users in the single cell, considers that the single cellular user can only be used for transmission at any moment on any frequency spectrum, the user can occupy a plurality of time periods on a plurality of sub-channels for transmission, the total energy consumption of all the users of the system is not only related to the allocation condition of the cellular users in the resource block, but also depends on the respective transmission power of the cellular users, and simultaneously considers the factors of the two aspects, optimizes the transmission power of each user of the system on each frequency spectrum on the premise of ensuring the communication quality of each user, and adjusts the resource allocation condition of the user to minimize the total energy consumption of the system.
As shown in fig. 1, a cellular network consists of one base station and M cellular users. M cellular users share one resource block, wherein the resource block has K orthogonal subcarriers (Subcarriers) and T time slots, the T time slots form data transmission time of Ts, the bandwidth of each Subcarrier is W, and each user has different channel gains and powers on different subcarriers. As shown in fig. 2, any time period on each subcarrier may be allocated to any one user, and the remaining time of the subcarrier may be shared among users, that is, one user may occupy multiple time periods on multiple subcarriers. All channel information is known, and a base station may allocate a suitable frequency band to different users in a total Resource Block (RB) consisting of 12 subcarriers continuously in a frequency domain and a time slot in a time domain, and determine the transmit power of each user in the frequency band, i.e., the corresponding time, so that each user can complete its transmission in one RB.
The optimization method specifically comprises the following steps:
step 1, the sum of energy consumption of all cellular users in the cellular network under respective resource allocation is obtained.
In the uplink transmission link of the cellular network, the energy consumption of each user i can be divided into three parts, namely transmission power consumption Pi(ii) a Second, fixed power consumption s of the circuit during the on time when the user is using for transmissioniPi,cirAnd thirdly idle power consumption (T-s) in the remaining timei)Pi,idle. Wherein s isi,0≤siT is less than or equal to the opening time of the equipment of the cellular user i, and each single cellular user in the cellular network opens own sending equipment only when transmitting to the base station.
By using the energy consumption in the whole communication system as the sum formula of the energy consumption of each cellular user, the total energy consumption in the communication system can be expressed as:
Figure BDA0002390448630000071
in the formula (1), siPi,cirFor a fixed power of the circuit during the on-time of the cellular user i at transmission, (T-s)i)Pi,idleIdle power for cellular user i for the remaining time; m is the number of cellular users, siFor the equipment on time of cellular user i, s is more than or equal to 0iT is less than or equal to T, each single cellular user in the cellular network opens own sending equipment only when transmitting to the base station; t is the number of time slots in the transmission of the resource block, Pi,cirIs the power consumed by other circuit blocks when a cellular user transmits resources in the uplink; when the cellular user has no data to transmit or has all data transmitted, it turns off all transmitting circuit blocks to save energy, but the energy consumption caused by the leakage current is called idle power of cellular user, and is denoted as Pi,idle；
Figure BDA0002390448630000081
X and P in (1) respectively refer to resource allocation variable sets to be optimized by the problem
Figure BDA0002390448630000082
And power variable set P ═ P (P)i,k)，
Figure BDA0002390448630000083
For the allocation situation of the t time slot of the cellular user i on the k subcarrier of the resource block, Ri,minIndicating the minimum rate requirement for the cellular user i.
PiCan be represented by the following formula:
Figure BDA0002390448630000084
definition matrix
Figure BDA0002390448630000085
The matrix is K multiplied by T and represents the distribution condition of the T-th time slot of the user i on the K-th subcarrier; there is no interference phenomenon between multiple cellular users on different sub-carriers in the same time slot in cellular network, ri,kThe achievable rate for user i on subcarrier k can be expressed as
Figure BDA0002390448630000086
Wherein p isi,kFor the transmitting power of the user i on the subcarrier K, K belongs to K, i belongs to M and N0Power spectral density, g, of white gaussian noisei,kThe channel gain on subcarrier k for user i.
Step 2, according to the possible resource allocation situation of the cellular user in one resource block
Figure BDA0002390448630000087
Can make
Figure BDA0002390448630000088
Counting the transmission time of the user i on each subcarrier, converting the three-dimensional resource allocation matrix into a two-dimensional resource allocation matrix, and transforming the optimization problem into:
Figure BDA0002390448630000089
Figure BDA00023904486300000810
step 3, obtaining the optimal transmission power P ═ P (P) of each cellular user on each subcarrier through a Lagrange dual algorithmi,k) To meet the respective rate requirements of the cellular users,
decomposing the problem corresponding to the formula (4) into M independent sub-problems, writing an optimization model of each sub-problem into a corresponding Lagrangian function, setting the current iteration number count to be 0, the maximum iteration number to be count max and the convergence error e to be e in the optimization model of each sub-problem-10(ii) a For p in each iterationi,kThe deviation derivative equation is 0, then
Figure BDA0002390448630000091
Wherein,
Figure BDA0002390448630000092
the solution of the optimization model corresponding to the current ith sub-problem, i.e. the transmission power of the user i on the sub-channel k, lambdaiIs referred to as the dual variable in the lagrange function.
And 4, updating Lagrange dual variables by using a sub-gradient method:
defining a very small step size munIn order to ensure the convergence of the optimal value of the sub-gradient method, the Lagrange dual variable is updated according to the following formula:
Figure BDA0002390448630000093
λi(n+1)＝[λi(n)+μnd(λi(n))]+,i＝1,2,...,M (7)
step 5, if the relative dual gap | | | lambdai(n)-λiIf (n-1) | ≦ e or the current iteration number exceeds the countmax, the iteration is stopped, and the feasible solution P of the transmission power obtained under the condition that the current cellular user is allocated in the resource block is (P ═i,k) The method is applied to a communication system, and the total energy consumption of all cellular users of the current system is calculated by the formula (1); else it is continuedAnd (5) continuing to step 3 and step 4.
Steps 2-5 belong to a known resource allocation and power control optimization part, and a flow chart of the part is shown in fig. 3.
And 6, adjusting and optimizing the resource allocation of the cellular users through the cooperative game theory.
Defining a maximum failure frequency fail _ max as 50, randomly extracting a small resource block from a resource block, knowing the corresponding subcarrier and time slot, and the User _ ori to which the small resource block is currently allocated, randomly allocating the resource block to any User _ new except the original User, updating the resource occupation lists of the two users, obtaining corresponding optimal transmission power by using the steps 3, 4 and 5, comparing the total system energy consumption E _ new after reallocation with the original system energy consumption E _ ori, and dividing the comparison into three types:
if E _ new < E _ ori, removing the resource block from the original User _ ori resource occupation list, adding the resource block into the User _ new resource occupation list, updating the total energy consumption of the system, and setting the current failure frequency fail to be 0;
if E _ new is equal to E _ ori, updating the current failure times;
and if E _ new > E _ ori, removing the resource block from the User _ new resource occupation list and adding the resource block into the resource occupation list of the User _ ori.
And if the current failure times reach the maximum failure times, stopping the game theory, and obtaining the final user allocation condition and the respective optimal transmission power thereof so as to minimize the total energy consumption of the system users.
The key point of the invention is that the invention converts the power consumption optimization model of the cellular network into the energy consumption minimization model of the single cellular user uplink transmission system combined with the time-frequency domain, relates to the resource allocation and the transmission power control, firstly obtains the optimal transmission power of each cellular user on each frequency spectrum according to the idea of Lagrange dual algorithm, and then solves the optimization model by combining the cooperative game theory. And experiments prove that compared with common single-dimensional resource allocation, the time-frequency domain resource allocation optimization method greatly reduces the energy consumption of the system by 8-25%.
The above description is only exemplary of the present invention and is not intended to limit the technical scope of the present invention, so that any minor modifications, equivalent changes and modifications made to the above exemplary embodiments according to the technical spirit of the present invention are within the technical scope of the present invention.

Claims (1)

1. A method for optimizing time-frequency domain resource allocation for a cellular network of a single cell, characterized by: the optimization method comprises the following steps:
step 1, solving the sum of energy consumption of all cellular users in a cellular network under respective resource allocation;
by using the energy consumption in the whole communication system as the sum formula of the energy consumption of each cellular user, the total energy consumption in the communication system can be expressed as:
Figure FDA0002390448620000011
Figure FDA0002390448620000012
Figure FDA0002390448620000013
Figure FDA0002390448620000014
wherein E isiFor cellular user i energy consumption, PiThe transmit power for cellular user i, denoted as
Figure FDA0002390448620000015
siPi,cirFor a fixed power of the circuit during the on-time of the cellular user i at transmission, (T-s)i)Pi,idleIdle power for cellular user i for the remaining time; m is the number of cellular users, siFor the equipment on time of cellular user i, s is more than or equal to 0iT is less than or equal to T, each single cellular user in the cellular network opens own sending equipment only when transmitting to the base station; t is the number of time slots in the transmission of the resource block, Pi,cirIs the power consumed by other circuit blocks when a cellular user transmits resources in the uplink; when the cellular user has no data to transmit or has all data transmitted, it turns off all transmitting circuit blocks to save energy, but the energy consumption caused by the leakage current is called idle power of cellular user, and is denoted as Pi,idle；
Figure FDA0002390448620000021
X and P in (1) respectively refer to resource allocation variable sets to be optimized by the problem
Figure FDA0002390448620000022
And power variable set P ═ P (P)i,k)，
Figure FDA0002390448620000023
For the allocation situation of the t time slot of the cellular user i on the k subcarrier of the resource block, Ri,minRepresenting a minimum rate requirement representing cellular user i;
there is no interference phenomenon between multiple cellular users on different sub-carriers in the same time slot in cellular network, ri,kThe achievable rate for cellular user i on subcarrier k can be expressed as
Figure FDA0002390448620000024
Wherein p isi,kTransmitting power of cellular user i on subcarrier K, K belongs to K, i belongs to M and N0Power spectral density, g, of white gaussian noisei,kChannel gain for cell i on subcarrier k, W is the bandwidth of the subcarrier;
step 2, defining a matrix
Figure FDA0002390448620000025
The matrix is K multiplied by T and represents the resource allocation condition of the cellular user i in one resource block; according to
Figure FDA0002390448620000026
Can make
Figure FDA0002390448620000027
Counting the time of transmission of the user i on each subcarrier, and transferring the optimization model of the formula (1) as follows:
Figure FDA0002390448620000028
Figure FDA0002390448620000029
step 3, obtaining the optimal transmission power P ═ P (P) of each cellular user on each subcarrier through a Lagrange dual algorithmi,k) To meet the respective rate requirements of the cellular users;
decomposing the problem corresponding to the formula (4) into M independent sub-problems, writing an optimization model of each sub-problem into a corresponding Lagrangian function, setting the current iteration number count to be 0, the maximum iteration number to be count max and the convergence error e to be e in the optimization model of each sub-problem-10(ii) a For p in each iterationi,kThe deviation derivative equation is 0, then
Figure FDA0002390448620000031
Wherein,
Figure FDA0002390448620000032
for the solution of the optimization model corresponding to the current ith sub-problem,i.e. the transmission power, lambda, of user i on subchannel kiIs a dual variable in a Lagrangian function;
and 4, updating Lagrangian dual variables by using a sub-gradient method:
defining a very small step size munIn order to ensure the convergence of the optimal value of the sub-gradient method, the Lagrange dual variable is updated according to the following formula:
Figure FDA0002390448620000033
λi(n+1)＝[λi(n)+μnd(λi(n))]+,i＝1,2,...,M (7)；
in each iteration, the formula (5) is updated by using the updated dual variable, and finally, the solution of the optimization model corresponding to the subproblem is converged into a unique optimal solution;
step 5, if the relative dual gap | | | lambdai(n)-λiIf (n-1) | ≦ e or the current iteration number exceeds the countmax, the iteration is stopped, and the feasible solution P of the optimal transmission power obtained under the condition that the current cellular user is distributed in the resource block is (P) |i,k) The method is applied to a communication system, and the total energy consumption of all cellular users of the current system is calculated by the formula (1); otherwise, continuing the step 3-4;
step 6, adjusting and optimizing the resource allocation of the cellular users through a cooperative game theory;
defining a maximum failure frequency fail _ max, randomly extracting a resource block, acquiring a subcarrier and a time slot corresponding to the resource block and a cellular User _ ori allocated to the resource block at present, randomly allocating the resource block to any User _ new except an original User again, updating resource occupation lists of the two users, obtaining corresponding optimal transmitting power by using the steps 3-5, and comparing the total system energy consumption E _ new after reallocation with the total system energy consumption E _ ori in the original system, wherein the comparison conditions are divided into three types:
if E _ new < E _ ori, removing the resource block from the original User _ ori resource occupation list, adding the resource block into the User _ new resource occupation list, updating the total energy consumption of the system, and setting the current failure frequency fail to be 0;
if E _ new is equal to E _ ori, updating the current failure times;
if E _ new > E _ ori, removing the resource block from the User _ new resource occupation list, and adding the resource block into the resource occupation list of the User _ ori;
and if the current failure times reach the maximum failure times, stopping the game theory, and obtaining the final user allocation condition and the respective optimal sending power of the users, so that the total energy consumption of the system users is minimum.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114071476A (en) * 2021-11-03 2022-02-18 厦门大学 Time-frequency two-dimensional communication resource allocation method based on energy consumption
CN115002814A (en) * 2022-06-09 2022-09-02 中国联合网络通信集团有限公司 Resource load determination method, device and storage medium

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103619024A (en) * 2013-11-27 2014-03-05 北京邮电大学 Method for distributing spectrum resources between cellular users and D2D users in same cell
CN105472751A (en) * 2015-12-24 2016-04-06 山东大学 Method for allocating joint resources of D2D communication system based on cellular network
CN107708157A (en) * 2017-11-22 2018-02-16 重庆邮电大学 Intensive small cell network resource allocation methods based on efficiency
WO2018120935A1 (en) * 2016-12-31 2018-07-05 山东大学 Resource allocation and energy management method for collaborative cellular network
CN109842931A (en) * 2019-03-13 2019-06-04 南京邮电大学 A kind of D2D cellular system resources distribution method based on NOMA
CN110012509A (en) * 2019-04-11 2019-07-12 重庆邮电大学 Resource allocation methods based on user mobility in a kind of 5G small cell network
WO2020034218A1 (en) * 2018-08-17 2020-02-20 Oppo广东移动通信有限公司 Discontinuous transmission method and device

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103619024A (en) * 2013-11-27 2014-03-05 北京邮电大学 Method for distributing spectrum resources between cellular users and D2D users in same cell
CN105472751A (en) * 2015-12-24 2016-04-06 山东大学 Method for allocating joint resources of D2D communication system based on cellular network
WO2018120935A1 (en) * 2016-12-31 2018-07-05 山东大学 Resource allocation and energy management method for collaborative cellular network
CN107708157A (en) * 2017-11-22 2018-02-16 重庆邮电大学 Intensive small cell network resource allocation methods based on efficiency
WO2020034218A1 (en) * 2018-08-17 2020-02-20 Oppo广东移动通信有限公司 Discontinuous transmission method and device
CN109842931A (en) * 2019-03-13 2019-06-04 南京邮电大学 A kind of D2D cellular system resources distribution method based on NOMA
CN110012509A (en) * 2019-04-11 2019-07-12 重庆邮电大学 Resource allocation methods based on user mobility in a kind of 5G small cell network

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
TANHUI LIU等: "《Resource Allocation for Device-to-Device》", 《IEEE》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114071476A (en) * 2021-11-03 2022-02-18 厦门大学 Time-frequency two-dimensional communication resource allocation method based on energy consumption
CN115002814A (en) * 2022-06-09 2022-09-02 中国联合网络通信集团有限公司 Resource load determination method, device and storage medium

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