CN110602730B - Resource allocation method of NOMA (non-orthogonal multiple access) heterogeneous network based on wireless energy carrying - Google Patents

Resource allocation method of NOMA (non-orthogonal multiple access) heterogeneous network based on wireless energy carrying Download PDF

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CN110602730B
CN110602730B CN201910883988.3A CN201910883988A CN110602730B CN 110602730 B CN110602730 B CN 110602730B CN 201910883988 A CN201910883988 A CN 201910883988A CN 110602730 B CN110602730 B CN 110602730B
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femto
femtocell
channel
kth
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CN110602730A (en
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徐勇军
刘子腱
谷博文
陈前斌
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Shenzhen Hongyue Enterprise Management Consulting Co ltd
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Chongqing University of Post and Telecommunications
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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
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/0215—Traffic management, e.g. flow control or congestion control based on user or device properties, e.g. MTC-capable devices
    • H04W28/0221—Traffic management, e.g. flow control or congestion control based on user or device properties, e.g. MTC-capable devices power availability or consumption
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/0268—Traffic management, e.g. flow control or congestion control using specific QoS parameters for wireless networks, e.g. QoS class identifier [QCI] or guaranteed bit rate [GBR]
    • 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

Abstract

The invention belongs to the technical field of communication, and particularly relates to a resource allocation method of an NOMA (non-orthogonal multiple access) heterogeneous network based on wireless energy carrying, which comprises the steps of deploying a macro base station and a femto base station in an NOMA wireless energy carrying communication system, wherein each femto user receiving end has the functions of NOMA and wireless energy carrying communication, and the femto user is provided with an energy collection rectification circuit; constructing a resource allocation model based on channel uncertainty, and converting a resource optimization model into a convex optimization problem based on a Butkelbach method; solving the convex optimization problem by using a convex optimization solution or a Lagrange dual method to obtain the transmitting power distributed to each user by the femto and the transmission time for information decoding, namely obtaining a resource distribution scheme; the invention effectively restrains the interference and meets the service quality requirement of the user while ensuring the maximum total energy efficiency of the system.

Description

Resource allocation method of NOMA (non-orthogonal multiple access) heterogeneous network based on wireless energy carrying
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a resource allocation method of a NOMA (non-access-oriented ma) heterogeneous network based on wireless energy carrying.
Background
With the continuous growth of wireless data services, the existing spectrum resources have been unable to meet the rapidly growing demand for high-rate data transmission of current communication systems. This trend makes Spectral Efficiency (SE) a major performance indicator for design and optimization in wireless communication systems.
Non-orthogonal multiple access techniques (NOMA), which can improve the spectral efficiency of a communication system by allowing more users to share the same sub-channel, and heterogeneous networks (HetNets), which allow multiple low-power small cells to access a macro-cellular network in an opportunistic manner, are two major techniques to improve SE. Therefore, NOMA heterogeneous cellular networks are one of the hot candidates for 5G communication networks. Meanwhile, the energy consumption problem of the communication network draws attention, the energy consumption problem has serious influence on economic and ecological cost, and the wireless energy carrying technology can collect surrounding radio frequency energy in a wireless network environment to charge the mobile terminal, so that the energy consumption is reduced, and green communication is realized.
Many documents research on a NOMA heterogeneous communication network based on a wireless energy carrying technology, and consider the energy efficiency optimization problem under ideal channel conditions. In fact, it is difficult to obtain accurate channel gain due to SIC residual error and channel delay.
Disclosure of Invention
In order to ensure the communication quality of macro users and maximize the energy efficiency of a femtocell system under the condition of channel uncertainty, the invention provides a resource allocation method of a wireless energy-carrying NOMA (non-orthogonal multiple access) heterogeneous network, which comprises the following steps:
s1, deploying a macro base station and a femto base station in the NOMA wireless energy-carrying communication system, wherein each femtocell user receiving end has the functions of NOMA and wireless energy-carrying communication, and each femtocell user is provided with an energy collection rectification circuit;
s2, constructing a resource distribution model based on channel uncertainty, and converting a resource optimization model into a convex optimization problem based on a Buckbach method;
s3, solving the optimization problem by using a CVX or Lagrange dual method, obtaining the transmitting power of the femto to each user and the transmission time for information decoding, and transmitting information by the user according to the transmitting power distributed by the femto.
Further, the process of constructing the resource allocation model based on the channel uncertainty includes:
s21, establishing a resource allocation model meeting the minimum data rate of the femtocell user based on cross-layer interference constraint and the transmission power constraint of the femtocell;
s22, optimizing the resource allocation model into a robust resource allocation model with outage probability according to a random optimization theory;
and S23, establishing a resource allocation model based on channel uncertainty based on the minimum maximum probability machine method on the basis of the robust resource allocation model with the outage probability.
Further, the resource allocation model based on the channel uncertainty is represented as:
Figure BDA0002206729060000021
Figure BDA0002206729060000022
Figure BDA0002206729060000023
Figure BDA0002206729060000024
Figure BDA0002206729060000025
Figure BDA0002206729060000026
wherein K represents femtocell users, and K is the number of femtocell users; m is a macro user; etaEThe energy efficiency of the system; x is the number ofkRepresenting a transmission time for decoding information; p is a radical ofkTransmit power for the femto to the kk femtocell user; p is a radical ofmaxIs the maximum transmit power of the femto base station;
Figure BDA0002206729060000027
to account for the estimated channel gain between the kth femtocell user to the mth macrocell user at the channel uncertainty;
Figure BDA0002206729060000028
represents the maximum value of the cross-layer interference which the macro user can bear;
Figure BDA0002206729060000029
an uncertainty set that is an uncertainty set of channel gains to h; Δ hkRepresenting the channel gain error between the femto-cell to the kth femto-cell user;
Figure BDA0002206729060000031
to account for the data rate of the femtocell user under channel uncertainty;
Figure BDA0002206729060000032
the minimum rate required by the femtocell user.
Furthermore, by adopting a wireless energy carrying technology, the collected energy can counteract part of power consumption, so that the system energy efficiency etaEExpressed as:
Figure BDA0002206729060000033
wherein R istotalIs the femto total data rate; ptotalActual power consumption; rkData rate of femtocell user k;
Figure BDA0002206729060000034
total power consumption for the femtocell network; etotalTotal collected energy for the femtocell network; b is the bandwidth of the system; sigmakIs the noise power.
The method of the invention effectively restrains the interference and meets the requirement of the quality of service (QoS) of the user while ensuring the maximum total energy efficiency of the system.
Drawings
FIG. 1 is a system model of the present invention;
FIG. 2 is a flow chart of the solution of the method of the present invention;
FIG. 3 is a graph of total energy efficiency of femtocell users versus interruption probability threshold of macro users under different channel estimation errors according to the present invention;
fig. 4 is a graph of actual received interference power of macro users about uncertainty of an interference link under different channel estimation errors according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a resource allocation method based on a wireless energy-carrying NOMA heterogeneous network, as shown in FIG. 2, comprising the following steps:
s1, deploying a macro base station and a femto base station in the NOMA wireless energy-carrying communication system, wherein each femtocell user receiving end has the functions of NOMA and wireless energy-carrying communication, and each femtocell user is provided with an energy collection rectification circuit;
s2, constructing a resource distribution model based on channel uncertainty, and converting a resource optimization model into a convex optimization problem based on a Buckbach method;
s3, solving the convex optimization problem by using a CVX or Lagrange dual method, obtaining the transmitting power distributed to each user by the femto and the transmission time for information decoding, and sending information by the user according to the transmitting power distributed by the femto.
Further, the process of constructing the resource allocation model based on the channel uncertainty includes:
s21, establishing a resource allocation model meeting the minimum data rate of the femtocell user based on cross-layer interference constraint and the transmission power constraint of the femtocell;
s22, optimizing the resource allocation model into a robust resource allocation model with interruption probability according to a random optimization theory;
and S23, establishing a resource allocation model based on channel uncertainty based on the minimum maximum probability machine method on the basis of the robust resource allocation model with the outage probability.
In the embodiment, as shown in fig. 1, a macro base station and a femto base station are deployed in a NOMA wireless portable communication system, and a macro user set is defined
Figure BDA0002206729060000041
Femtocell user set
Figure BDA0002206729060000042
Channel gain h from femto to femto1≤h2≤···hk≤···≤hKBoth the user and the base station are configured with a single antenna and do not consider beam forming; each femtocell user receiving end has the functions of NOMA and wireless energy-carrying communication.
The NOMA technique allows multiple femtocells to multiplex the same sub-channel simultaneously, and in order to reduce mutual interference and improve system performance, Serial Interference Cancellation (SIC) is adopted on a terminal device, and the SIC is in the ascending order of channel gains. The k-th femtocell user detects the information of the i-th femtocell user, i < k, and treats the information of the i-th user as interference cancellation, so that the data rate R of the k-th femtocell userkComprises the following steps:
Figure BDA0002206729060000051
wherein x iskIndicating a transmission time for the kth femtocell user for information decoding; p is a radical ofkRepresenting a transmit power of a kth femtocell user; h iskChannel gain between the femto base station to the kth femto subscriber; sigmakRepresenting the noise power of the kth femtocell user.
Furthermore, since the femtocell subscribers are equipped with energy harvesting rectification circuits, it is possibleTo collect energy E of userkWriting:
Figure BDA0002206729060000052
where η represents the power conversion efficiency at the energy harvesting end.
Modeling total power consumption of femtocell network as linear model
Figure BDA0002206729060000053
Expressed as:
Figure BDA0002206729060000054
wherein, PcRepresents the power consumption of the hardware circuitry of the femto base station;
Figure BDA0002206729060000055
representing the inverse of the power amplifier drain efficiency.
Because the wireless energy carrying technology is adopted, the collected energy can counteract part of the power consumption, so the actual power consumption PtotalComprises the following steps:
Figure BDA0002206729060000056
wherein E istotalThe energy is collected by adopting a wireless energy carrying technology.
In summary, the energy efficiency of the system is expressed as:
Figure BDA0002206729060000057
wherein R istotalIs the femto total data rate; ptotalActual power consumption; and B is the bandwidth of the system.
Combining cross-layer interference constraint and transmitting power constraint of the femto base station, establishing a power distribution optimization problem meeting the minimum data rate of each femto user, wherein the optimization problem is represented as:
Figure BDA0002206729060000061
Figure BDA0002206729060000062
Figure BDA0002206729060000063
Figure BDA0002206729060000064
Figure BDA0002206729060000065
wherein the constraint conditions in the above formula include: a transmission time constraint and a transmit power non-negative constraint C1, a maximum power constraint C2 of the femto base station, a minimum data rate constraint C3 of each user, a cross-layer interference constraint C4; p is a radical ofmaxIs the maximum transmit power of the femto base station;
Figure BDA0002206729060000066
a minimum rate required for a femtocell user; gk,mChannel gain between the kth femtocell user and the mth macrocell user;
Figure BDA0002206729060000067
indicating the maximum value at which the mth macro user can tolerate cross-layer interference.
The optimization problem is non-robust optimization (also called nominal optimization), i.e. the channel condition is
Figure BDA0002206729060000068
And
Figure BDA0002206729060000069
wherein the channel estimation error ah k0 and Δ gk,mHowever, such an assumption is too ideal, and it is difficult to obtain an accurate channel gain due to SIC residual error and channel delay, so a robust energy efficiency resource allocation algorithm based on a NOMA heterogeneous network of a wireless energy carrying technology under channel uncertainty needs to be considered. In order to limit the user outage probability to a certain target value, based on the random optimization theory, the above optimization problem is re-described as a robust resource allocation problem with outage probability constraint, which is expressed as:
Figure BDA0002206729060000071
s.t.C1,C2
Figure BDA0002206729060000072
Figure BDA0002206729060000073
Figure BDA0002206729060000074
wherein, the constraint conditions in the above formula include: a transmission time constraint and a transmit power non-negative constraint C1, a maximum power constraint C2 of the femto base station, a minimum data rate constraint C3 of each user, a cross-layer interference constraint C4; channel gain constraint C5; alpha is alphakRepresenting a outage probability threshold for a kth femtocell user; beta is amAn interruption probability threshold representing the mth macro user;
Figure BDA0002206729060000075
representing the estimated channel gain between the femto base station to the k-th user; Δ hkRepresenting the channel gain error between the femto base station to the kth user;
Figure BDA0002206729060000076
estimating channel gain for the k-th femtocell user to the m-th macrocell user; Δ gk,mThe channel gain error between the kth femtocell user and the mth macrocell user.
The above problem is an infinite dimension optimization problem due to the channel uncertainty. Therefore, a new minimum maximum probability machine method is introduced, which does not depend on an accurate statistical model and only requires the mean and variance of random parameters. The following satisfying probability constraints need to be considered:
Figure BDA0002206729060000077
wherein, Pr (#) represents the probability; inf represents infimum; sup denotes supremum; y represents an uncertain parameter vector and,
Figure BDA0002206729060000078
representing the mean value of an uncertain parameter y, e representing the variance of an estimation error, epsilon (0,1) representing the interrupt probability level, and simultaneously, a optimizing variables, wherein the variables needing to be optimized are the transmitting power of a user; b is a constant, which can be the maximum value of the interference borne by the user in the invention; it is possible to obtain:
Figure BDA0002206729060000079
Figure BDA00022067290600000710
where d represents the minimum distance of the estimation error, d2The values of (A) are:
Figure BDA0002206729060000081
thus, it is possible to obtain:
Figure BDA0002206729060000082
Figure BDA0002206729060000083
if it is not
Figure BDA0002206729060000084
Then it is possible to obtain:
Figure BDA0002206729060000085
order to
Figure BDA0002206729060000086
For an uncertainty set of femto-to-user channel gain vectors, femto-to-user channel gain is denoted as h ═ h1,h2,…,hk,…,hK]T，hkA channel gain vector for the femto-to-kth user; order to
Figure BDA0002206729060000087
For the uncertainty set of channel gain vectors between femto-to-macro users, the channel gain between all femto-to-macro users m is denoted as gm＝[g1,m,g2,m,…,gk,m,…,gK,m]T，gk,mChannel gain between the kth femtocell user and the mth macrocell user; based on the ellipsoid uncertainty set, one can obtain:
Figure BDA0002206729060000088
Figure BDA0002206729060000089
wherein, vmThe sum of the uncertainty of all channel links from one femtocell user to the mth macrocell is greater than or equal to 0;
Figure BDA00022067290600000810
is an upper bound on the sum of channel link uncertainties from the femto to all femto users.
Defining a transmission power vector of a femto base station to a femto user as p ═ p1,p2,…,pk,…,pK]T，pkRepresenting the transmitting power of the femto-base station to the k-th femto-user; defining the channel gain error as Δ gm＝[Δg1,m,Δg2,m,…,Δgk,m,…,ΔgK,m]T，Δgk,mRepresenting a channel gain error between a k-th femtocell user and an m-th macro user; defining an estimated channel gain of
Figure BDA0002206729060000091
Figure BDA0002206729060000092
Representing the estimated channel gain between the femto to the kth user, C4 in the constraint problem may be rewritten as:
Figure BDA0002206729060000093
wherein the content of the first and second substances,
Figure BDA0002206729060000094
is expressed as approximate risk factor
Figure BDA0002206729060000095
βmAn interruption probability threshold representing the mth macro user; Δ g ═ diag (Δ g)m). To guarantee QoS for macro users, it is available：
Figure BDA0002206729060000096
Thus, cross-layer interference constraints can be derived that take into account channel uncertainty
Figure BDA0002206729060000097
Expressed as:
Figure BDA0002206729060000098
wherein the content of the first and second substances,
Figure BDA0002206729060000099
similarly, femtocell user outage probability constraints
Figure BDA00022067290600000910
Expressed as:
Figure BDA00022067290600000911
wherein the content of the first and second substances,
Figure BDA00022067290600000912
and
Figure BDA00022067290600000913
considering the channel uncertainty into the objective function, the following optimization problem can be derived:
Figure BDA00022067290600000914
s.t.C1,C2
Figure BDA00022067290600000915
Figure BDA00022067290600000916
Figure BDA00022067290600000917
wherein the channel estimation error is constrained to
Figure BDA00022067290600000918
The above problem is still in a fractional form, so that a minimum maximum probability machine method is introduced, uncertainty of a channel is considered in an optimization problem based on an ellipsoid uncertainty set, and then an objective function is written as the following local optimization problem through a Dinkebach method, which is expressed as:
Figure BDA0002206729060000101
Figure BDA0002206729060000102
wherein the content of the first and second substances,
Figure BDA0002206729060000103
according to the Cauchy-Schwarz inequality, one can obtain:
Figure BDA0002206729060000104
thus, the following optimization problem can be derived:
Figure BDA0002206729060000105
Figure BDA0002206729060000106
Figure BDA0002206729060000107
Figure BDA0002206729060000108
Figure BDA0002206729060000109
the above formula can be proved to be a convex function by solving a hessian matrix, so that the optimal allocation strategy can be solved by using a CVX (composite partitioning value) or Lagrangian dual method.
In this embodiment, the robust resource allocation method and the non-robust method of the NOMA heterogeneous cellular network based on the wireless energy carrying technology are compared, and it can be seen from fig. 3 that the relationship between the total energy efficiency of the femtocell user and the interruption probability threshold of the macro user, and as the interruption probability threshold of the macro user increases, the robust algorithm is obviously due to the non-robust algorithm, and the upper bound (v) of the variance of the channel uncertaintym) The larger the energy efficiency.
From fig. 4, it can be seen that the relationship between the interference power actually received by the macro user and the channel uncertainty is that the interference experienced by the macro user is larger when the channel uncertainty increases, and the non-robust method receives larger interference than the robust resource allocation method proposed by the present invention.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (6)

1. A resource allocation method based on a wireless energy-carrying NOMA heterogeneous network is characterized by comprising the following steps:
s1, deploying a macro base station and a femto base station in the NOMA wireless energy-carrying communication system, wherein each femto user receiving end has the functions of NOMA and wireless energy-carrying communication, and the femto user is provided with an energy collection rectification circuit and adopts a serial interference elimination technology;
s2, constructing a resource distribution model based on channel uncertainty, and converting a resource optimization model into a convex optimization problem based on a Buckbach method; the method specifically comprises the following steps:
s21, establishing a resource allocation model meeting the minimum data rate of the femto based on cross-layer interference constraint and femto transmission power constraint, wherein the model is expressed as:
Figure FDA0003487323070000011
Figure FDA0003487323070000012
Figure FDA0003487323070000013
Figure FDA0003487323070000014
Figure FDA0003487323070000015
wherein K represents the kth femtocell user, and K is the number of femtocell users; etaEThe energy efficiency of the system; x is the number ofkRepresenting a transmission time for decoding information; p is a radical ofkTransmit power for the femto to the kth femtocell user; p is a radical ofmaxIs the maximum transmit power of the femto base station; rkIs as followsA rate of k femtocell user demands;
Figure FDA0003487323070000016
a minimum rate required for the kth femtocell user; gk,mChannel gain between the kth femtocell user and the mth macrocell user;
Figure FDA0003487323070000017
represents the maximum value of the cross-layer interference which the mth macro user can bear;
s22, optimizing the resource allocation model into a robust resource allocation model with interruption probability according to a random optimization theory, wherein the model is expressed as:
Figure FDA0003487323070000021
Figure FDA0003487323070000022
Figure FDA0003487323070000023
Figure FDA0003487323070000024
Figure FDA0003487323070000025
Figure FDA0003487323070000026
wherein m represents the mth macro user; h iskIndicating femto-base station to kth femto-subscriberThe channel gain of (a); p is a radical ofmaxIs the maximum transmit power of the femto base station; alpha is alphakAn outage probability threshold representing the kth femto-user; beta is amAn interruption probability threshold representing the mth macro user;
Figure FDA0003487323070000027
representing the estimated channel gain between the femto base station to the k-th user; Δ hkRepresenting the channel gain error between the femto base station to the kth user;
Figure FDA0003487323070000028
estimating channel gain for the k-th femtocell user to the m-th macrocell user; Δ gk,mChannel gain error between the kth femtocell user and the mth macrocell user;
s23, on the basis of the robust resource allocation model with the interruption probability, establishing a resource allocation model based on the channel uncertainty based on the minimum maximum probability machine method;
s3, solving the convex optimization problem by using a convex optimization solution or Lagrange dual method, obtaining the transmitting power distributed to each user by the femto and the transmission time for information decoding, and transmitting information by the user according to the transmitting power distributed by the femto.
2. The method of claim 1, wherein the resource allocation model based on channel uncertainty comprises:
Figure FDA0003487323070000029
Figure FDA00034873230700000210
Figure FDA00034873230700000211
Figure FDA00034873230700000212
Figure FDA00034873230700000213
Figure FDA00034873230700000214
wherein the content of the first and second substances,
Figure FDA0003487323070000031
to account for the estimated channel gain between the kth femtocell user to the mth macrocell user at the channel uncertainty;
Figure FDA00034873230700000315
an uncertainty set for channel gain towards h; Δ hkRepresenting the channel gain error between the femto-cell to the kth femto-cell user;
Figure FDA0003487323070000032
to account for the data rate of the femtocell user under channel uncertainty.
3. The method of claim 2, wherein substituting channel uncertainty into constraints comprises: order to
Figure FDA0003487323070000033
Is the set of uncertainties for the channel gain vector h, denoted h ═ h1,h2,…,hk]TLet us order
Figure FDA0003487323070000034
Is a channel gain vector gmIs expressed as gm＝[g1,m,g2,m,…,gK,m]TBased on the set of ellipsoid uncertainties, will
Figure FDA0003487323070000035
Expressed as:
Figure FDA0003487323070000036
Figure FDA0003487323070000037
the improved cross-layer interference constraint is expressed as:
Figure FDA0003487323070000038
in order to guarantee the quality of service,
Figure FDA0003487323070000039
the requirements are satisfied:
Figure FDA00034873230700000310
according to constraints of quality of service, order
Figure FDA00034873230700000311
Cross-layer interference constraints under consideration of channel uncertainty
Figure FDA00034873230700000312
Expressed as:
Figure FDA00034873230700000313
wherein v ismThe sum of the uncertainty of all channel links from one femtocell user to the mth macrocell is greater than or equal to 0;
Figure FDA00034873230700000314
an upper bound for the sum of channel link uncertainties for all femtocell users; Δ g is an intermediate parameter, denoted Δ g ═ diag (Δ g)m) And diag (x) denotes the diagonal matrix, i.e. Δ g is Δ gmA diagonal matrix of (a); p is a transmission power vector of the femtocell to the femtocell user; Δ gmA channel gain error vector between the femtocell user and the mth macro user; Δ hmRepresenting the channel gain error between the femto base station and the mth macro user;
Figure FDA0003487323070000041
an estimated channel gain vector between the femto-cell and the femto-cell users;
Figure FDA0003487323070000042
estimating a channel gain vector for the femtocell user to the mth macrocell user;
Figure FDA0003487323070000043
is an approximate risk factor.
4. The method of claim 2, wherein the data rate of the femtocell user is determined by the data rate of the femtocell user
Figure FDA0003487323070000044
Expressed as:
Figure FDA0003487323070000045
wherein σkIs the noise power;
Figure FDA0003487323070000046
is a table representing the channel gain between the femto base station to the kth user under channel uncertainty; p is a radical ofiInterference power for the ith femtocell user to the kth femtocell user; and B is the system bandwidth.
5. The resource allocation method based on the wireless energy-carrying NOMA heterogeneous network as claimed in claim 1, wherein the resource optimization model is converted into a convex optimization problem based on the Butkelbach method:
Figure FDA0003487323070000047
Figure FDA0003487323070000048
Figure FDA0003487323070000049
Figure FDA00034873230700000410
Figure FDA00034873230700000411
wherein, B is the bandwidth of the system; sigmakIs the noise power;
Figure FDA00034873230700000412
is the inverse of the power amplifier drain efficiency; pcRepresenting hardware circuits of femto-base stationsPower consumption; η represents the power conversion efficiency of the energy harvesting end;
Figure FDA00034873230700000413
representing the channel gain between the femto-cell to the kth user under channel uncertainty;
Figure FDA00034873230700000414
an upper bound for the sum of all femtocell user link channel uncertainties; p is a radical ofiThe interference power of the ith femtocell user to the kth femtocell user is obtained.
6. The method of claim 1, wherein the collected energy can be used to offset a portion of power consumption by using wireless energy-carrying technology, so that system energy efficiency ηEExpressed as:
Figure FDA0003487323070000051
wherein R istotalIs the femto total data rate; ptotalActual power consumption;
Figure FDA0003487323070000052
total power consumption for the femtocell network; etotalTotal collected energy for the femtocell network; b is the bandwidth of the system; sigmakIs the noise power;
Figure FDA0003487323070000053
is the inverse of the power amplifier drain efficiency; p is a radical ofiInterference power for the ith femtocell user to the kth femtocell user; sigmakIs the noise power.
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