CN110493862B - Resource allocation and energy management method for hybrid energy supply heterogeneous cloud wireless access network - Google Patents

Resource allocation and energy management method for hybrid energy supply heterogeneous cloud wireless access network Download PDF

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CN110493862B
CN110493862B CN201910723703.XA CN201910723703A CN110493862B CN 110493862 B CN110493862 B CN 110493862B CN 201910723703 A CN201910723703 A CN 201910723703A CN 110493862 B CN110493862 B CN 110493862B
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马丕明
林朋
马艳波
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Shandong University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/34—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/34—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
    • H04W52/346—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading distributing total power among users or channels
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/38—TPC being performed in particular situations
    • H04W52/50—TPC being performed in particular situations at the moment of starting communication in a multiple access environment

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Abstract

A resource allocation and energy management method for a hybrid energy supply heterogeneous cloud wireless access network belongs to the technical field of wireless communication. The method establishes a heterogeneous cloud wireless access network in a hybrid power supply mode, wherein a Remote Radio Head (RRH) node acquires low-price renewable energy through an energy collection technology on one hand, and guarantees the operation stability and the reliability of a communication network by adopting a traditional power grid power supply mode on the other hand. Meanwhile, the surplus RRH nodes can sell the surplus energy to other RRH nodes with energy shortage, so that the utilization rate of green energy is improved, the total amount of electric energy of the whole system for purchasing a traditional power grid is reduced, and the cost efficiency of the system is improved.

Description

Resource allocation and energy management method for hybrid energy supply heterogeneous cloud wireless access network
Technical Field
The invention relates to a resource allocation and energy management method of a hybrid energy supply heterogeneous cloud wireless access network, and belongs to the technical field of wireless communication.
Background
With the continuous emergence of new application scenarios, the number of user equipments and the demand for data rate increase exponentially, so the academic world proposes a heterogeneous cloud radio access network (H-CRAN) as an access network solution for the next generation mobile communication system. At the same time, the explosion in data throughput has led to a dramatic increase in the energy consumption of the H-CRAN system. In order to implement the concept of green communication and improve the energy utilization rate, researchers begin to research how to apply the energy collection technology to the traditional communication network.
However, in practical applications, the energy collection technology is easily affected by environmental factors, and in addition, the limited collection rate of the energy collection technology cannot meet the energy requirement of a high-power base station. In order to improve the operational reliability of communication networks, hybrid energy supply schemes combining energy harvesting technologies with traditional power grids are becoming a new trend.
In addition, since the unit cost of acquiring energy from different energy sources by the wireless communication network with hybrid energy supply is different, the energy efficiency is replaced by the cost efficiency as a technical index for measuring the resource saving degree of the hybrid energy supply system.
To date, there has been no research precedent for joint resource allocation and energy management methods for cost-effective hybrid powered H-CRAN systems.
Disclosure of Invention
In order to make up for the defects of the prior art, the invention provides a resource allocation and energy management method of a hybrid energy supply heterogeneous cloud wireless access network, which considers the minimum data rate requirement of user equipment and the maximum cost limit of a system, and takes the cost efficiency of the maximized system as an optimization target.
The technical scheme of the invention is as follows:
a resource allocation and energy management method of a hybrid energy supply heterogeneous cloud wireless access network is realized by the following systems: the system comprises a centralized baseband unit Pool (BBU Pool), a heterogeneous Macro Base Station (MBS), N Remote Radio Head (RRH) nodes and M User Equipments (UE) dynamically accessed to the MBS or RRH node; wherein
Figure BDA0002158157870000011
Represents the nth RRH node and the nth RRH node,
Figure BDA0002158157870000012
Figure BDA0002158157870000013
it means that the m-th UE,
Figure BDA0002158157870000014
the BBU Pool is responsible for baseband signal processing and resource dynamic allocation of the system, the RRH node and the macro base station MBS are responsible for frequency band processing and user access, and the RRH node and the macro base station MBS are respectively accessed to the BBU Pool through a fronthaul link and a backhaul link;
in the H-CRAN system, an RRH node is used for meeting the requirement of a user on higher communication rate in a hotspot region; the MBS can supplement the coverage loophole of the RRH as necessary, so as to ensure the seamless coverage of the user access; in order to reduce energy consumption, each UE should be assigned to a base station with the best channel condition; order to
Figure BDA0002158157870000021
And
Figure BDA0002158157870000022
respectively representing the access state indication functions of RRH and MBS to UE, when the nth RRH node in the system isWhen the mth UE provides the user access service,
Figure BDA0002158157870000023
when the access service is not provided for,
Figure BDA0002158157870000024
when the macro base station MBS provides user access service for the mth UE in the system,
Figure BDA0002158157870000025
when the access service is not provided for,
Figure BDA0002158157870000026
the system can dynamically adjust the access states of the MBS or RRH node and each user, but one UE can only access the MBS or one RRH node at the same time;
researching the downlink of the H-CRAN system, and respectively representing the user gains of the nth RRH node and the MBS to the mth UE as
Figure BDA0002158157870000027
And
Figure BDA0002158157870000028
the channel gain independently follows Rayleigh fading distribution and keeps stable in the same time slot; setting all RRH nodes and MBS links to respective UE to share the same frequency band;
in the whole network, a macro base station MBS adopts a traditional power grid for power supply, energy sources of RRH nodes comprise collected renewable energy and energy shared by the power grid and other RRH nodes, namely when the energy collected by an energy collection module of a certain RRH node in the H-CRAN network is excessive, the residual energy can be sold to other RRH nodes with energy shortage, so that the quantity of electric energy purchased by the whole network is reduced, and the cost efficiency of the system is improved; the RRH node adopts a pricing strategy to determine the priority and specific quantity of energy obtained from various energy sources; the method comprises the following specific steps:
1) calculating the data transmission rate of each user equipment and the sum of the data transmission rates of the total system
According to the Shannon formula, when the nth RRH node in the system provides user access service for the mth UE, the data transmission rate of the UE is
Figure BDA0002158157870000029
Similarly, when the macro base station MBS provides the user access service for the mth UE, the data transmission rate of the UE is
Figure BDA00021581578700000210
Wherein the content of the first and second substances,
Figure BDA00021581578700000211
and
Figure BDA00021581578700000212
the signal transmission power distributed to the mth UE by the nth RRH node and MBS respectively, B0Is the channel bandwidth;
Figure BDA00021581578700000213
and
Figure BDA00021581578700000214
carrier to interference and noise ratio (CINR) of the nth RRH node to MBS to mth UE, respectively; in the expression of CINR, N0A power spectral density representative of additive white gaussian noise;
the total data transmission rate of the system is expressed as
Figure BDA0002158157870000031
Where the symbol Σ indicates that the summation is performed within the range limited by its subscript;
2) calculating energy consumption of each base station
Power consumption P of RRH noden RAnd power consumption P of MBSMAre divided into two parts of static basic power consumption and transmission power consumption which are respectively expressed as
Figure BDA0002158157870000032
Figure BDA0002158157870000033
Wherein P isn RBAnd PMBStatic base power consumption, ψ, of the nth RRH node and MBS, respectivelyRAnd psiMThe power amplifier dissipation coefficients of RRH and MBS;
3) calculating cost of each base station and total system cost
Let enRepresenting the renewable energy collected by the energy collection device of the nth RRH node at the current time slot,
Figure BDA0002158157870000034
and
Figure BDA0002158157870000035
respectively representing the consumption of the energy of the battery of the node and the electric quantity purchased from a traditional power grid, and the price is aBAnd aG；
Figure BDA0002158157870000036
And
Figure BDA0002158157870000037
respectively representing the increment of the energy of the battery of the node and the electric quantity sold to the traditional power grid, and the price is bBAnd bG(ii) a Similarly, we represent the sum of the renewable energy purchased by the nth RRH node from other RRH nodes as
Figure BDA0002158157870000038
Figure BDA0002158157870000039
It represents the sum of the renewable energy that the node sells to the remaining RRH nodes,
Figure BDA00021581578700000310
and
Figure BDA00021581578700000311
respectively at a price of
Figure BDA00021581578700000312
And
Figure BDA00021581578700000313
to ensure that RRH nodes preferentially use battery energy and stimulate energy sharing among RRH nodes, assume
Figure BDA00021581578700000314
Cost of nth RRH node
Figure BDA00021581578700000315
The difference between the total price for purchasing energy and the total price for selling energy is defined as:
Figure BDA00021581578700000316
wherein
Figure BDA00021581578700000317
The symbol T is a transposed symbol;
in addition, cost C of MBSMIs defined as
CM＝aGPM (7)
Therefore, the total system cost is composed of the cost of RRH and the cost of MBS, i.e.
Figure BDA0002158157870000041
4) Determining an optimization problem
Cost efficiency is defined as the ratio of the total data transmission rate of the system to the total cost of the system; taking the maximum system total cost efficiency as an objective function, and taking the data transmission rate requirement of the UE, the system total cost upper limit and the battery reserve limit of the RRH node as constraint conditions, constructing the following optimization problem:
Figure BDA0002158157870000042
where Θ: ═ { X, P, Q } is the set of optimized variables, where the symbol: ═ represents the definition,
Figure BDA0002158157870000043
which indicates the access of the user to the mobile station,
Figure BDA0002158157870000044
it is meant that the power allocation is,
Figure BDA0002158157870000045
representing RRH node energy management;
Figure BDA0002158157870000046
minimum transmission rate requirement for mth UE, CmaxUpper limit of the total cost of the system, vnAnd VnThe initial battery storage and the upper battery storage limit of the nth RRH node are respectively set; the maximum value symbol is represented by a symbol maximize, the symbol subject to is a constraint symbol, C1-C6 are constraint conditions, and represent that under the constraint conditions of access limit of each UE, minimum transmission rate requirement of each UE, total cost upper limit of the system, battery storage limit of each RRH node and shared energy conservation among the RRH nodes, the maximum value of an objective function, namely an equation after maximize, is solved, and the maximization problem is also referred to as an original problem in the following description;
5) equivalence conversion of original question (outer circulation)
Because the objective function is in a fractional form, the original problem cannot be solved directly by a convex optimization method, and the equivalent transformation of the original problem needs to be solved into a convex problem; by non-negative variationThe quantity y represents the cost efficiency of the system, if an optimum cost efficiency y can be found*Then the objective function in the form of a score in the original problem (9) is equivalent to the objective function in the form of a subtraction in the original problem (10);
Figure BDA0002158157870000051
the maximization problem represented by the formula (10) is also referred to as an equivalence problem in the following description; based on the Dinkelbach method, the optimal cost efficiency gamma is obtained through finite iteration updating*(ii) a The specific process of iteratively updating the cost efficiency γ is as follows:
A) setting the initial iteration number i to be 1, and setting the cost efficiency initial value gamma (1) to be 0; setting a convergence precision tau;
B) when the iteration number is i, representing the current updated cost efficiency by using gamma (i), substituting the cost efficiency gamma (i) into the equivalence problem to solve, and calculating to obtain the total data transmission rate R of the systemtotalAnd total cost of the system Ctotal；
C) Let gamma be*If convergence condition | R ═ γ (i)total-γ(i)Ctotal|<If τ is satisfied, the optimum cost efficiency γ is output*Otherwise, let γ (i +1) ═ Rtotal/CtotalI +1, repeating step B) until a convergence condition is satisfied;
6) solving the equivalence problem (inner loop)
The equivalence problem is a mixed binary integer programming problem because the integral variables and the continuous variables are simultaneously contained; will be in constraint C6
Figure BDA0002158157870000052
Is relaxed to
Figure BDA0002158157870000053
The equivalence problem can be relaxed to a convex problem, where the notation a, b denotes a set of only two elements a, b, the notation a, b]Represents a closed interval from a to b; the relaxed convex problem has a unique optimal solution, and the Lagrangian dual principle is utilizedIn theory, a relationship between a maximization problem, i.e. an equivalence problem, and a minimization problem, i.e. a dual problem, is established, the equivalence problem has strong dual, an optimal value of the equivalence problem can be obtained by solving the dual problem, and a lagrangian function of the equivalence problem is expressed as:
Figure BDA0002158157870000054
wherein
Figure BDA0002158157870000061
Is a set of pairs of even factors,
Figure BDA0002158157870000062
respectively representing dual factors corresponding to the limiting conditions C2-C5 in the formula (10);
the dual function of the equivalence function is:
Figure BDA0002158157870000063
the dual problem for the dual function is as follows:
Figure BDA0002158157870000064
under the constraint condition that the set lambda is larger than or equal to 0, the minimum value of a dual function D (lambda) is solved by optimizing the lambda, the equivalent problem is known to have strong duality, and the optimal value obtained by the dual problem (13) is the optimal value of the equivalent problem; optimal value Λ for even factor set*The solution can be realized by a sub-gradient iterative algorithm, and the specific solution process is as follows:
A) setting the initial iteration time t to be 0, setting the initial value Λ (0) of the dual factor to be a non-negative real number, and setting the iteration precision;
B) when the iteration number is t, representing the currently updated dual factor by Λ (t), substituting the dual factor set Λ (t) into the formula (12) according to Karush-Kuhn-Tucker (KKT)Obtaining the optimal transmitting power of each RRH node and MBS on each UE
Figure BDA0002158157870000065
And optimal user access variables
Figure BDA0002158157870000066
And solving the optimal energy management of the RRH nodes according to the priority of the RRH nodes for obtaining and selling energy in different channels determined by the pricing strategy, and respectively deducing
Figure BDA0002158157870000067
And
Figure BDA0002158157870000068
C) respectively updating 4 dual factors according to the following formula 4
Figure BDA0002158157870000069
Wherein stp _ λ (t),
Figure BDA00021581578700000610
stp _ ω (t) and stp _ μ (t) respectively represent iteration steps corresponding to respective Lagrangian duality factors, symbols 2]+Expression [ 2 ]]The fraction in (1) takes a non-negative value;
D) let Λ*Λ (t +1), if Λ*If the preset precision requirement is met, the optimal pair even factor set Lambda is output*Otherwise, making t equal to t +1, and repeating the step B) and the step C) until the preset precision requirement is met;
E) according to the obtained optimal pair even factor set Lambda*And calculating to obtain the optimal power distribution X of the H-CRAN system under the condition of meeting the minimum data transmission rate of each UE, the total system cost upper limit and the battery reserve limit of each RRH node*User access P*And energy management Q*Obtaining the optimal optimization variable set theta*。
The invention provides a combined resource allocation and energy management method of a hybrid energy supply heterogeneous cloud wireless access network based on cost efficiency. In the heterogeneous cloud wireless access network, on one hand, the RRH node reduces the cost of the system through an energy collection technology, and on the other hand, the RRH node ensures the operation stability and the reliability of a communication network by using a traditional power grid power supply mode. Meanwhile, the collected RRH nodes with excess energy can sell the residual energy to other RRH nodes with energy shortage, so that the utilization rate of green energy is improved, and the cost efficiency of the whole network system is maximized.
Drawings
Fig. 1 is a schematic diagram of a communication system according to the present invention.
Detailed Description
The invention is further described below, but not limited to, with reference to the following figures and examples.
Example (b):
the embodiment of the invention is shown in fig. 1, and a resource allocation and energy management method for a hybrid energy supply heterogeneous cloud wireless access network is implemented by the following systems: the system comprises a centralized baseband unit Pool (BBU Pool), a heterogeneous Macro Base Station (MBS), N Remote Radio Head (RRH) nodes and M User Equipments (UE) dynamically accessed to the MBS or RRH node; wherein
Figure BDA0002158157870000071
Represents the nth RRH node and the nth RRH node,
Figure BDA0002158157870000072
Figure BDA0002158157870000073
it means that the m-th UE,
Figure BDA0002158157870000074
BBU Pool is responsible for the baseband signal processing and resource dynamic allocation of the system, RRH node and macro base station MBS are responsible for the frequency band processing and user access, and the two are respectively connected through the forward link and the backward linkAdding BBU Pool;
in the H-CRAN system, an RRH node is used for meeting the requirement of a user on higher communication rate in a hotspot region; the MBS can supplement the coverage loophole of the RRH as necessary, so as to ensure the seamless coverage of the user access; in order to reduce energy consumption, each UE should be assigned to a base station with the best channel condition; order to
Figure BDA0002158157870000075
And
Figure BDA0002158157870000076
respectively representing the access state indication functions of RRH and MBS to UE, when the nth RRH node in the system provides user access service for the mth UE,
Figure BDA0002158157870000077
when the access service is not provided for,
Figure BDA0002158157870000078
when the macro base station MBS provides user access service for the mth UE in the system,
Figure BDA0002158157870000079
when the access service is not provided for,
Figure BDA0002158157870000081
the system can dynamically adjust the access states of the MBS or RRH node and each user, but one UE can only access the MBS or one RRH node at the same time;
researching the downlink of the H-CRAN system, and respectively representing the user gains of the nth RRH node and the MBS to the mth UE as
Figure BDA0002158157870000082
And
Figure BDA0002158157870000083
the channel gain independently follows Rayleigh fading distribution and keeps stable in the same time slot; setting all RRH nodes and MBS links to respective UE to share the same frequency band;
in the whole network, a macro base station MBS adopts a traditional power grid for power supply, energy sources of RRH nodes comprise collected renewable energy and energy shared by the power grid and other RRH nodes, namely when the energy collected by an energy collection module of a certain RRH node in the H-CRAN network is excessive, the residual energy can be sold to other RRH nodes with energy shortage, so that the quantity of electric energy purchased by the whole network is reduced, and the cost efficiency of the system is improved; the RRH node adopts a pricing strategy to determine the priority and specific quantity of energy obtained from various energy sources; the method comprises the following specific steps:
1) calculating the data transmission rate of each user equipment and the sum of the data transmission rates of the total system
According to the Shannon formula, when the nth RRH node in the system provides user access service for the mth UE, the data transmission rate of the UE is
Figure BDA0002158157870000084
Similarly, when the macro base station MBS provides the user access service for the mth UE, the data transmission rate of the UE is
Figure BDA0002158157870000085
Wherein the content of the first and second substances,
Figure BDA0002158157870000086
and
Figure BDA0002158157870000087
the signal transmission power distributed to the mth UE by the nth RRH node and MBS respectively, B0Is the channel bandwidth;
Figure BDA0002158157870000088
and
Figure BDA0002158157870000089
the nth RRH node and the MBS to the mth RRH nodeA carrier to interference and noise ratio (CINR) of the UE; in the expression of CINR, N0A power spectral density representative of additive white gaussian noise;
the total data transmission rate of the system is expressed as
Figure BDA00021581578700000810
Where the symbol Σ indicates that the summation is performed within the range limited by its subscript;
2) calculating energy consumption of each base station
Power consumption P of RRH noden RAnd power consumption P of MBSMAre divided into two parts of static basic power consumption and transmission power consumption which are respectively expressed as
Figure BDA0002158157870000091
Figure BDA0002158157870000092
Wherein P isn RBAnd PMBStatic base power consumption, ψ, of the nth RRH node and MBS, respectivelyRAnd psiMThe power amplifier dissipation coefficients of RRH and MBS;
3) calculating cost of each base station and total system cost
Let enRepresenting the renewable energy collected by the energy collection device of the nth RRH node at the current time slot,
Figure BDA0002158157870000093
and
Figure BDA0002158157870000094
respectively representing the consumption of the energy of the battery of the node and the electric quantity purchased from a traditional power grid, and the price is aBAnd aG；
Figure BDA0002158157870000095
And
Figure BDA0002158157870000096
respectively representing the increment of the energy of the battery of the node and the electric quantity sold to the traditional power grid, and the price is bBAnd bG(ii) a Similarly, we represent the sum of the renewable energy purchased by the nth RRH node from other RRH nodes as
Figure BDA0002158157870000097
Figure BDA0002158157870000098
It represents the sum of the renewable energy that the node sells to the remaining RRH nodes,
Figure BDA0002158157870000099
and
Figure BDA00021581578700000910
respectively at a price of
Figure BDA00021581578700000911
And
Figure BDA00021581578700000912
to ensure that RRH nodes preferentially use battery energy and stimulate energy sharing among RRH nodes, assume
Figure BDA00021581578700000913
Cost of nth RRH node
Figure BDA00021581578700000914
The difference between the total price for purchasing energy and the total price for selling energy is defined as:
Figure BDA00021581578700000915
wherein
Figure BDA00021581578700000916
SymbolTIs a transposed symbol;
in addition, cost C of MBSMIs defined as
CM＝aGPM (7)
Therefore, the total system cost is composed of the cost of RRH and the cost of MBS, i.e.
Figure BDA00021581578700000917
4) Determining an optimization problem
Cost efficiency is defined as the ratio of the total data transmission rate of the system to the total cost of the system; taking the maximum system total cost efficiency as an objective function, and taking the data transmission rate requirement of the UE, the system total cost upper limit and the battery reserve limit of the RRH node as constraint conditions, constructing the following optimization problem:
Figure BDA0002158157870000101
where Θ: ═ { X, P, Q } is the set of optimized variables, where the symbol: ═ represents the definition,
Figure BDA0002158157870000102
which indicates the access of the user to the mobile station,
Figure BDA0002158157870000103
it is meant that the power allocation is,
Figure BDA0002158157870000104
representing RRH node energy management;
Figure BDA0002158157870000105
minimum transmission rate requirement for mth UE, CmaxUpper limit of the total cost of the system, vnAnd VnThe initial battery reserve and the upper battery reserve limit of the nth RRH node(ii) a The maximum value symbol is represented by a symbol maximize, the symbol subject to is a constraint symbol, C1-C6 are constraint conditions, and represent that under the constraint conditions of access limit of each UE, minimum transmission rate requirement of each UE, total cost upper limit of the system, battery storage limit of each RRH node and shared energy conservation among the RRH nodes, the maximum value of an objective function, namely an equation after maximize, is solved, and the maximization problem is also referred to as an original problem in the following description;
5) equivalence conversion of original question (outer circulation)
Because the objective function is in a fractional form, the original problem cannot be solved directly by a convex optimization method, and the equivalent transformation of the original problem needs to be solved into a convex problem; expressing the cost efficiency of the system by a non-negative variable gamma if an optimal cost efficiency gamma can be obtained*Then the objective function in the form of a score in the original problem (9) is equivalent to the objective function in the form of a subtraction in the original problem (10);
Figure BDA0002158157870000106
the maximization problem represented by the formula (10) is also referred to as an equivalence problem in the following description; based on the Dinkelbach method, the optimal cost efficiency gamma is obtained through finite iteration updating*(ii) a The specific process of iteratively updating the cost efficiency γ is as follows:
A) setting the initial iteration number i to be 1, and setting the cost efficiency initial value gamma (1) to be 0; setting a convergence precision tau;
B) when the iteration number is i, representing the current updated cost efficiency by using gamma (i), substituting the cost efficiency gamma (i) into the equivalence problem to solve, and calculating to obtain the total data transmission rate R of the systemtotalAnd total cost of the system Ctotal；
C) Let gamma be*If convergence condition | R ═ γ (i)total-γ(i)Ctotal|<If τ is satisfied, the optimum cost efficiency γ is output*Otherwise, let γ (i +1) ═ Rtotal/CtotalI +1, repeating step B) until a convergence condition is satisfied;
6) solving the equivalence problem (inner loop)
The equivalence problem is a mixed binary integer programming problem because the integral variables and the continuous variables are simultaneously contained; will be in constraint C6
Figure BDA0002158157870000111
Is relaxed to
Figure BDA0002158157870000112
The equivalence problem can be relaxed to a convex problem, where the notation a, b denotes a set of only two elements a, b, the notation a, b]Represents a closed interval from a to b; the relaxed convex problem has a unique optimal solution, the Lagrangian dual theory is utilized to establish the relationship between the maximization problem, namely the equivalence problem, and the minimization problem, namely the dual problem, the equivalence problem has strong duality, the optimal value of the equivalence problem can be obtained by solving the dual problem, and the Lagrangian function of the equivalence problem is expressed as:
Figure BDA0002158157870000113
wherein
Figure BDA0002158157870000114
Is a set of pairs of even factors,
Figure BDA0002158157870000115
respectively representing dual factors corresponding to the limiting conditions C2-C5 in the formula (10);
the dual function of the equivalence function is:
Figure BDA0002158157870000116
the dual problem for the dual function is as follows:
Figure BDA0002158157870000121
under the constraint condition that the set lambda is larger than or equal to 0, the minimum value of a dual function D (lambda) is solved by optimizing the lambda, the equivalent problem is known to have strong duality, and the optimal value obtained by the dual problem (13) is the optimal value of the equivalent problem; optimal value Λ for even factor set*The solution can be realized by a sub-gradient iterative algorithm, and the specific solution process is as follows:
A) setting the initial iteration time t to be 0, setting the initial value Λ (0) of the dual factor to be a non-negative real number, and setting the iteration precision;
B) when the iteration number is t, representing the currently updated dual factor by Λ (t), substituting the dual factor set Λ (t) into the formula (12), and obtaining the optimal transmitting power of each RRH node and MBS on each UE according to the conditions of Karush-Kuhn-Tucker (KKT)
Figure BDA0002158157870000122
And optimal user access variables
Figure BDA0002158157870000123
And solving the optimal energy management of the RRH nodes according to the priority of the RRH nodes for obtaining and selling energy in different channels determined by the pricing strategy, and respectively deducing
Figure BDA0002158157870000124
And
Figure BDA0002158157870000125
C) respectively updating 4 dual factors according to the following formula 4
Figure BDA0002158157870000126
Wherein stp _ λ (t),
Figure BDA0002158157870000127
stp _ ω (t) and stp _ μ (t) respectively represent iteration steps corresponding to respective Lagrangian duality factors, symbols 2]+Expression [ 2 ]]Is partially takenA non-negative value;
D) let Λ*Λ (t +1), if Λ*If the preset precision requirement is met, the optimal pair even factor set Lambda is output*Otherwise, making t equal to t +1, and repeating the step B) and the step C) until the preset precision requirement is met;
E) according to the obtained optimal pair even factor set Lambda*And calculating to obtain the optimal power distribution X of the H-CRAN system under the condition of meeting the minimum data transmission rate of each UE, the total system cost upper limit and the battery reserve limit of each RRH node*User access P*And energy management Q*Obtaining the optimal optimization variable set theta*。

Claims (1)

1. A resource allocation and energy management method of a hybrid energy supply heterogeneous cloud wireless access network is realized by the following systems: the system comprises a centralized baseband processing unit Pool BBU Pool, a heterogeneous macro base station MBS, N remote radio head RRH nodes and M user equipment UE dynamically accessed to the MBS or RRH node; wherein
Figure FDA0002929169310000011
Represents the nth RRH node and the nth RRH node,
Figure FDA0002929169310000012
Figure FDA0002929169310000013
it means that the m-th UE,
Figure FDA0002929169310000014
the BBU Pool is responsible for the baseband signal processing and resource dynamic allocation of the system, the RRH node and the MBS are responsible for the frequency band processing and the user access, and the RRH node and the MBS are respectively accessed to the BBU Pool through a fronthaul link and a backhaul link;
in the H-CRAN system, an RRH node is used for meeting the requirement of a user on higher communication rate in a hotspot region; the MBS can supplement the coverage loophole of the RRH as necessary, so as to ensure the seamless coverage of the user access; to reduceEnergy consumption, each UE should be assigned to a base station with the best channel condition; order to
Figure FDA0002929169310000015
And
Figure FDA0002929169310000016
respectively representing the access state indication functions of the RRH node and the MBS to the UE, when the nth RRH node in the system provides user access service for the mth UE,
Figure FDA0002929169310000017
when the access service is not provided for,
Figure FDA0002929169310000018
when the macro base station MBS provides user access service for the mth UE in the system,
Figure FDA0002929169310000019
when the access service is not provided for,
Figure FDA00029291693100000110
the system can dynamically adjust the access states of the MBS or RRH node and each user, but one UE can only access the MBS or one RRH node at the same time;
researching the downlink of the H-CRAN system, and respectively representing the user gains of the nth RRH node and the MBS to the mth UE as
Figure FDA00029291693100000111
And
Figure FDA00029291693100000112
the channel gain independently follows Rayleigh fading distribution and keeps stable in the same time slot; setting all RRH nodes and MBS links to respective UE to share the same frequency band;
in the whole network, a macro base station MBS adopts a traditional power grid for power supply, energy sources of RRH nodes comprise collected renewable energy and energy shared by the power grid and other RRH nodes, namely when the energy collected by an energy collection module of a certain RRH node in the H-CRAN network is excessive, the residual energy can be sold to other RRH nodes with energy shortage, so that the quantity of electric energy purchased by the whole network is reduced, and the cost efficiency of the system is improved; the RRH node adopts a pricing strategy to determine the priority and specific quantity of energy obtained from various energy sources; the method comprises the following specific steps:
1) calculating the data transmission rate of each user equipment and the sum of the data transmission rates of the total system
According to the Shannon formula, when the nth RRH node in the system provides user access service for the mth UE, the data transmission rate of the UE is
Figure FDA00029291693100000113
Similarly, when the macro base station MBS provides the user access service for the mth UE, the data transmission rate of the UE is
Figure FDA0002929169310000021
Wherein the content of the first and second substances,
Figure FDA0002929169310000022
and
Figure FDA0002929169310000023
the signal transmission power distributed to the mth UE by the nth RRH node and MBS respectively, B0Is the channel bandwidth;
Figure FDA0002929169310000024
and
Figure FDA0002929169310000025
carrier to interference and noise ratio, N, of the nth RRH node to MBS to mth UE, respectively0A power spectral density representative of additive white gaussian noise;
the total data transmission rate of the system is expressed as
Figure FDA0002929169310000026
Where the symbol Σ indicates that the summation is performed within the range limited by its subscript;
2) calculating energy consumption of each base station
Power consumption P of RRH noden RAnd power consumption P of MBSMAre divided into two parts of static basic power consumption and transmission power consumption which are respectively expressed as
Figure FDA0002929169310000027
Figure FDA0002929169310000028
Wherein P isn RBAnd PMBStatic base power consumption, ψ, of the nth RRH node and MBS, respectivelyRAnd psiMThe power amplifier dissipation coefficients of RRH and MBS;
3) calculating cost of each base station and total system cost
Let enRepresenting the renewable energy collected by the energy collection device of the nth RRH node at the current time slot,
Figure FDA0002929169310000029
and
Figure FDA00029291693100000210
respectively representing the consumption of the energy of the battery of the node and the electric quantity purchased from a traditional power grid, and the price is aBAnd aG；
Figure FDA00029291693100000211
And
Figure FDA00029291693100000212
respectively representing the increment of the energy of the battery of the node and the electric quantity sold to the traditional power grid, and the price is bBAnd bG(ii) a Similarly, the sum of the renewable energy purchased by the nth RRH node from other RRH nodes is represented as
Figure FDA00029291693100000213
It represents the sum of the renewable energy that the node sells to the remaining RRH nodes,
Figure FDA00029291693100000214
and
Figure FDA00029291693100000215
respectively at a price of
Figure FDA00029291693100000216
And
Figure FDA00029291693100000217
to ensure that RRH nodes preferentially use battery energy and stimulate energy sharing among RRH nodes, assume
Figure FDA00029291693100000218
Cost of nth RRH node
Figure FDA00029291693100000219
The difference between the total price for purchasing energy and the total price for selling energy is defined as:
Figure FDA0002929169310000031
wherein
Figure FDA0002929169310000032
SymbolTIs a transposed symbol;
in addition, cost C of MBSMIs defined as
CM＝aGPM (7)
Therefore, the total system cost is composed of the cost of RRH and the cost of MBS, i.e.
Figure FDA0002929169310000033
4) Determining an optimization problem
Cost efficiency is defined as the ratio of the total data transmission rate of the system to the total cost of the system; taking the maximum system total cost efficiency as an objective function, and taking the data transmission rate requirement of the UE, the system total cost upper limit and the battery reserve limit of the RRH node as constraint conditions, constructing the following optimization problem:
Figure FDA0002929169310000034
where Θ: ═ { X, P, Q } is the set of optimized variables, where the symbol: ═ represents the definition,
Figure FDA0002929169310000035
which indicates the access of the user to the mobile station,
Figure FDA0002929169310000036
it is meant that the power allocation is,
Figure FDA0002929169310000037
representing RRH node energy management;
Figure FDA0002929169310000038
minimum transmission rate requirement for mth UE, CmaxUpper limit of the total cost of the system, vnAnd VnAre respectively asThe battery initial reserve and the battery reserve upper limit of the nth RRH node; the maximum value symbol is represented by a symbol maximize, the symbol subject to is a constraint symbol, C1-C6 are constraint conditions, and represent that under the constraint conditions of access limit of each UE, minimum transmission rate requirement of each UE, total cost upper limit of the system, battery storage limit of each RRH node and shared energy conservation among the RRH nodes, the maximum value of an objective function, namely an equation after maximize, is solved, and the maximization problem is also referred to as an original problem in the following description;
5) original problem equivalence transformation
Because the objective function is in a fractional form, the original problem cannot be solved directly by a convex optimization method, and the equivalent transformation of the original problem needs to be solved into a convex problem; expressing the cost efficiency of the system by a non-negative variable gamma if an optimal cost efficiency gamma can be obtained*Then the objective function in the form of a score in the original problem (9) is equivalent to the objective function in the form of a subtraction in the original problem (10);
Figure FDA0002929169310000041
the maximization problem represented by the formula (10) is also referred to as an equivalence problem in the following description; based on the Dinkelbach method, the optimal cost efficiency gamma is obtained through finite iteration updating*(ii) a The specific process of iteratively updating the cost efficiency γ is as follows:
A) setting the initial iteration number i to be 1, and setting the cost efficiency initial value gamma (1) to be 0; setting a convergence precision tau;
B) when the iteration number is i, representing the current updated cost efficiency by using gamma (i), substituting the cost efficiency gamma (i) into the equivalence problem to solve, and calculating to obtain the total data transmission rate R of the systemtotalAnd total cost of the system Ctotal；
C) Let gamma be*If convergence condition | R ═ γ (i)total-γ(i)Ctotal|<If τ is satisfied, the optimum cost efficiency γ is output*Otherwise, let γ (i +1) ═ Rtotal/CtotalI +1, repeating step B) until a convergence condition is satisfied；
6) Solving equivalence problems
The equivalence problem is a mixed binary integer programming problem because the integral variables and the continuous variables are simultaneously contained; will be in constraint C6
Figure FDA0002929169310000042
Is relaxed to
Figure FDA0002929169310000043
The equivalence problem can be relaxed to a convex problem, where the notation a, b denotes a set of only two elements a, b, the notation a, b]Represents a closed interval from a to b; the relaxed convex problem has a unique optimal solution, the Lagrangian dual theory is utilized to establish the relationship between the maximization problem, namely the equivalence problem, and the minimization problem, namely the dual problem, the equivalence problem has strong duality, the optimal value of the equivalence problem can be obtained by solving the dual problem, and the Lagrangian function of the equivalence problem is expressed as:
Figure FDA0002929169310000051
wherein
Figure FDA0002929169310000052
Is a set of parities, λm,
Figure FDA0002929169310000053
ωnμ represents the dual factor corresponding to the constraint conditions C2-C5 in formula (10), respectively, and γ represents cost efficiency;
the dual function of the equivalence function is:
Figure FDA0002929169310000054
the dual problem for the dual function is as follows:
Figure FDA0002929169310000055
under the constraint condition that the set lambda is larger than or equal to 0, the minimum value of a dual function D (lambda) is solved by optimizing the lambda, the equivalent problem is known to have strong duality, and the optimal value obtained by the dual problem (13) is the optimal value of the equivalent problem; optimal value Λ for even factor set*The solution can be realized by a sub-gradient iterative algorithm, and the specific solution process is as follows:
A) setting the initial iteration time t to be 0, setting the initial value Λ (0) of the dual factor to be a non-negative real number, and setting the iteration precision;
B) when the iteration number is t, representing the currently updated dual factor by Λ (t), substituting the dual factor set Λ (t) into the formula (12), and obtaining the optimal transmitting power of each corresponding RRH node and MBS on each UE according to the Karush-Kuhn-Tucker condition
Figure FDA0002929169310000056
And optimal user access variables
Figure FDA0002929169310000057
And solving the optimal energy management of the RRH nodes according to the priority of the RRH nodes for obtaining and selling energy in different channels determined by the pricing strategy, and respectively deducing
Figure FDA0002929169310000058
And
Figure FDA0002929169310000059
C) respectively updating 4 dual factors according to the following formula 4
Figure FDA0002929169310000061
Wherein stp _ λ (t),
Figure FDA0002929169310000062
stp _ ω (t) and stp _ μ (t) respectively represent iteration steps corresponding to respective Lagrangian duality factors, symbols 2]+Expression [ 2 ]]The fraction in (1) takes a non-negative value;
D) let Λ*Λ (t +1), if Λ*If the preset precision requirement is met, the optimal pair even factor set Lambda is output*Otherwise, making t equal to t +1, and repeating the step B) and the step C) until the preset precision requirement is met;
E) according to the obtained optimal pair even factor set Lambda*And calculating to obtain the optimal power distribution X of the H-CRAN system under the condition of meeting the minimum data transmission rate of each UE, the total system cost upper limit and the battery reserve limit of each RRH node*User access P*And energy management Q*Obtaining the optimal optimization variable set theta*。
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