CN111615111A - Distributed robust security resource allocation method for small cell network - Google Patents

Distributed robust security resource allocation method for small cell network Download PDF

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CN111615111A
CN111615111A CN202010362941.5A CN202010362941A CN111615111A CN 111615111 A CN111615111 A CN 111615111A CN 202010362941 A CN202010362941 A CN 202010362941A CN 111615111 A CN111615111 A CN 111615111A
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small cell
users
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CN111615111B (en
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唐晓
张若南
王洋
蓝驯强
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Northwestern Polytechnical University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/02—Resource partitioning among network components, e.g. reuse partitioning
    • H04W16/10—Dynamic resource partitioning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/02—Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • 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
    • H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
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Abstract

The invention belongs to the technical field of wireless communication, and discloses a distributed steady safe resource allocation method facing a small cell network, which analyzes and obtains the steady private rate of small cell users; analyzing the interference under the worst condition based on the uncertainty of an interference channel between a small cell user and a macro user; a pricing mechanism is introduced, and an optimization model of a small cell user side is designed; solving an optimization model of the small cell users to obtain a safe transmission strategy of the small cell users; the equilibrium state of the whole network is achieved through the iterative updating of the security strategy among the users of the small cells; updating the price factor based on the obtained network balance state, and broadcasting in the whole network; and repeating the fourth step and the sixth step based on the updated price factor until the price factor is converged, and finally, balancing the network in the state, namely, the safe transmission scheme of all the small cell users. The invention realizes the reliable protection of macro users and the steady safety of small cell users under the condition of uncertain information.

Description

Distributed robust security resource allocation method for small cell network
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a distributed robust security resource allocation method for a small cell network.
Background
At present, small-cell networks (small-cell networks) deploy microcells, picocells and femtocells on the basis of traditional macrocells to form a multi-layer heterogeneous network structure, and are basic components of 5G and later 5G era infrastructures. The small cell base station has the characteristics of low cost, low power consumption and deployment on demand, and realizes a flexible network architecture. Meanwhile, the small cell network can realize seamless coverage service and high-efficiency spectrum efficiency, and further enhance user experience. In the existing wireless network system, security defense measures mainly depend on encryption technology, but with the rapid increase of the number of wireless devices and the continuous improvement of wireless network heterogeneity, an encryption method faces great challenges in terms of key management and distribution problems. The small cell base stations, however, have limited computational power and resources and may not be able to efficiently support the computational complexity required by the encryption techniques. In this respect, the physical layer security technology has a great potential, and it utilizes the inherent characteristics of the wireless medium, such as fading, interference and noise, to realize keyless secure transmission, which is convenient for implementation in a hierarchical heterogeneous small cell network.
For small cell networks, in addition to designing a link-level security transmission strategy, the interaction between different users and their impact on the overall security performance of the network need to be considered. Especially for small cells, which typically operate in an autonomous manner, users need to adjust their security transmission strategies taking into account their wireless environment and the behavior of other users. Moreover, the complex interference caused by simultaneous transmission between heterogeneous user layers can be fully utilized to reduce the eavesdropping performance and further improve the security. Furthermore, due to limited resources and lightweight signaling interactions in small cells, users need to independently determine their security policies in a distributed manner. However, in a small cell network with a hierarchical structure, macro cell users generally have higher priority, and thus reliable transmission performance guarantees are required. For this reason, the security problem of the small cell needs to be considered under the condition of the quality of service constraint of the macro cell user. Meanwhile, because the small cell base station has limited capability, all ideal channel state information may not be obtained, and accordingly, the design of the safe transmission scheme needs to take information uncertainty into consideration to realize robust safety guarantee. Therefore, physical layer security schemes for small cell networks need to jointly consider global interference constraints and uncertainty of channel information in distributed security policy design in order to provide a robust and reliable solution for practical systems.
Through the above analysis, the problems and disadvantages of the prior art include:
(1) the existing security mechanism relies on key distribution at the upper layer of a protocol, and as the number of users increases, key distribution and management become more difficult and complex; moreover, as the computing power of the device increases, the risk of the encryption being cracked increases.
(2) The existing physical layer security solution focuses on the security guarantee of a single link, but neglects the security of multi-user transmission under the network condition.
(3) Existing security schemes lack the consideration of potentially multiple uncertainties for the actual network.
The difficulty in solving the above problems and defects is: the existing security mechanism is based on a key system, and the physical layer security is a brand new solution. In the scenario considered by the present invention, the small cell network employs a distributed physical layer security mechanism, which requires small cell users to independently determine their own transmission strategies, but needs to satisfy common interference constraints, and therefore requires a distributed coordination mechanism. Furthermore, due to limited user capabilities of small cells, they cannot obtain global information of the whole network, and therefore need to make decisions with uncertainty involved.
The significance of solving the problems and the defects is as follows: therefore, the invention adopts a physical layer security scheme, does not need a secret key and has lower complexity; the invention considers the safe transmission of a plurality of coexisting small cell users in a double-layer network, and simultaneously guarantees the service quality of macro cell users through the interference condition constraint; meanwhile, double uncertainty factors in the network are considered, including uncertainty of a small cell user eavesdropping link and uncertainty of a macro cell interference link, and the scheme realizes robust safety of the small cell user and robust interference protection of the macro user.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a distributed robust security resource allocation method facing a small cell network.
The invention is realized in such a way that a distributed robust security resource allocation method facing a small cell network comprises the following steps:
the method comprises the steps that firstly, the robust privacy rate of small cell users is obtained based on uncertainty analysis of channel information of eavesdroppers in each small cell;
secondly, analyzing the interference under the worst condition based on the uncertainty of an interference channel between a small cell user and a macro user;
thirdly, introducing a pricing mechanism and designing an optimization model of a small cell user side;
fourthly, solving an optimization model of the small cell users to obtain a safe transmission strategy of the small cell users;
fifthly, the equilibrium state of the whole network is achieved through the iterative updating of the security strategy among the users of the small cells;
sixthly, updating the price factor based on the obtained network balance state, and broadcasting in the whole network;
and seventhly, repeating the fourth step and the sixth step based on the updated price factor until the price factor is converged, and finally, balancing the network in a final state, namely, the safe transmission scheme of all small cell users.
Further, the signal to interference plus noise ratio SINR of the small cell user SUE-j legally transmitted on the channel k in the distributed robust security resource allocation method for the small cell network is as follows:
Figure RE-GDA0002540799300000031
wherein the interference includes interference and noise in both macro cell and small cell,
Figure RE-GDA0002540799300000032
representing background noise, the signal-to-interference-and-noise ratio SINR of the eavesdropper on the channel k of the small cell user SUE-j is as follows:
Figure RE-GDA0002540799300000033
summing all channels to obtain the privacy rate of the small cell user SUE-j:
Figure RE-GDA0002540799300000041
and protecting the reception of the macro cell users by interference constraint, wherein the interference of all the small base stations to the macro cell users is lower than a threshold value:
Figure RE-GDA0002540799300000042
wherein
Figure RE-GDA0002540799300000043
Is the interference threshold of the macrocell base station MUE-n.
Further, the first step is based on uncertainty analysis of channel information of each small cell internal eavesdropper, and channel state information g of small cell users SUE-j on channel kj(k)＝[gij(k)]i∈J∪{0}The method comprises the following steps:
Figure RE-GDA0002540799300000044
Figure RE-GDA0002540799300000045
is an estimate based on prior information that,
Figure RE-GDA0002540799300000046
the uncertain part of the information about the eavesdropper channel state in the small base station is defined as:
Figure RE-GDA0002540799300000047
∈ thereinj(k) Andj(k) a constant specifying an uncertainty region;
the robust privacy rate for small cell users is expressed as:
Figure RE-GDA0002540799300000048
the solution to the optimization problem is expressed as:
Figure RE-GDA0002540799300000049
wherein:
Figure RE-GDA00025407993000000410
Figure RE-GDA0002540799300000051
further, the second step is for an interfering channel associated with a macrocell user MUE-n
Figure RE-GDA0002540799300000052
The model is represented as:
Figure RE-GDA0002540799300000053
Figure RE-GDA0002540799300000054
is a known estimate of the value of the signal,
Figure RE-GDA0002540799300000055
is the uncertain part of the channel state information, expressed as:
Figure RE-GDA0002540799300000056
wherein e isnIt is a constant that determines the size of the defined area, introducing the worst case interference, i.e. robust protection for the macro cell users:
Figure RE-GDA0002540799300000057
the solution to this problem is obtained as:
Figure RE-GDA0002540799300000058
further, the third step is based on a price factor k ═ kn]n∈NDefining:
Figure RE-GDA0002540799300000059
a robust privacy rate based on interference cost, wherein a price factor satisfies a condition:
0≤κ⊥ζ≤0。
further, the optimization problem of the fourth step small cell user is as follows:
Figure RE-GDA00025407993000000510
solving is carried out to obtain a resource allocation scheme of a single small cell user, and a Lagrangian dual method is utilized to obtain a Lagrangian function of the problem as follows:
Figure RE-GDA0002540799300000061
wherein xjIs a lagrange multiplier;
let its first derivative be zero, we can obtain:
Figure RE-GDA0002540799300000062
solving the optimal power distribution scheme of the single small cell user is carried out as follows, and the maximum value and the minimum value of the Lagrange multiplier are respectively
Figure RE-GDA0002540799300000063
And
Figure RE-GDA0002540799300000064
in addition
Figure RE-GDA0002540799300000065
Substituting into an equation, and obtaining the power p of the small cell user on each channel by using a dichotomyj(k)，
Figure RE-GDA0002540799300000066
If it is
Figure RE-GDA0002540799300000067
Then
Figure RE-GDA0002540799300000068
Otherwise
Figure RE-GDA0002540799300000069
Repeating the above steps using the updated upper and lower bounds of the Lagrangian multiplier until
Figure RE-GDA00025407993000000610
Wherein iota is a predefined threshold, and the power on each channel obtained by utilizing the dichotomy is the power distribution scheme of the current small cell user;
the fifth step obtains network balance through the optimal response iteration among small cell users, and the network balance is initialized to be assigned with a given price factor kappa and meets the power distribution
Figure RE-GDA00025407993000000611
Under the condition of (1), randomly assigning the power of all small cell users to p (t), wherein t is the iteration frequency, and sigma is the threshold value of a predefined termination algorithm;
the cyclic content is to solve the fourth step for each small cell user and update the self power
Figure RE-GDA00025407993000000612
Wherein
Figure RE-GDA00025407993000000613
For the power obtained in the fourth step, all users repeat the step until the convergence condition | | | p (t) -p (t-1) | |/| | p (t-1) | < σ is met, and each user obtains the optimal transmitting power in the current state to achieve balance;
said sixth step determining an optimal price factor, τ representing the number of iterations,
Figure RE-GDA00025407993000000614
initializing a step length rho for a predefined threshold value of a termination algorithm, randomly assigning a price factor, and obtaining balance by utilizing an iterative process in a fifth step based on a current price factor kappa (tau)
Figure RE-GDA00025407993000000615
On the basis, updating iteration time to be τ ← τ +1, and updating corresponding price factor to be τ ← τ +1
Figure RE-GDA0002540799300000071
And broadcasting in the whole network;
the seventh step, based on the updated price factor, repeating the price factor updating and the corresponding network balance updating process of the fourth, fifth and sixth steps until the convergence condition is satisfied
Figure RE-GDA0002540799300000072
It is another object of the present invention to provide a program storage medium for receiving user input, the stored computer program causing an electronic device to perform the steps comprising:
the method comprises the steps that firstly, the robust privacy rate of small cell users is obtained based on uncertainty analysis of channel information of eavesdroppers in each small cell;
secondly, analyzing the interference under the worst condition based on the uncertainty of an interference channel between a small cell user and a macro user;
thirdly, introducing a pricing mechanism and designing an optimization model of a small cell user side;
fourthly, solving an optimization model of the small cell users to obtain a safe transmission strategy of the small cell users;
fifthly, the equilibrium state of the whole network is achieved through the iterative updating of the security strategy among the users of the small cells;
sixthly, updating the price factor based on the obtained network balance state, and broadcasting in the whole network;
and seventhly, repeating the fourth step and the sixth step based on the updated price factor until the price factor is converged, and finally, balancing the network in a final state, namely, the safe transmission scheme of all small cell users.
It is another object of the present invention to provide a computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface to implement the method for distributed robust security resource allocation for small cell networks when executed on an electronic device.
Another object of the present invention is to provide a resource allocation system for implementing the distributed robust security resource allocation method for small cell networks, the resource allocation system comprising:
the robust privacy rate acquisition module is used for obtaining the robust privacy rate of the small cell users based on the uncertainty analysis of the information of the eavesdropper channel inside each small cell;
the interference analysis module is used for analyzing the interference under the worst condition based on the uncertainty of an interference channel between a small cell user and a macro user;
the optimization model design module is used for introducing a pricing mechanism and designing an optimization model of a small cell user side;
the safe transmission strategy module is used for solving the optimization model of the small cell users to obtain the safe transmission strategy of the small cell users;
the security strategy iterative updating module is used for achieving the equilibrium state of the whole network through the security strategy iterative updating among the small cell users;
the price factor updating module is used for updating the price factor based on the obtained network balance state and broadcasting the price factor in the whole network;
and the network balancing module is used for balancing the network in the final state based on the updated price factor until the price factor is converged, namely the safe transmission scheme of all small cell users.
Another object of the present invention is to provide a radio terminal equipped with the above resource allocation system.
By combining all the technical schemes, the invention has the advantages and positive effects that: the invention can provide reliable safety guarantee for the users in the small cell under the condition of strictly meeting the interference of the users in the macro cell under the condition of uncertain multiple information; first, the small cell user obtains its own robust privacy rate considering its uncertainty about the eavesdropper channel state information, while obtaining worst-case interference considering the uncertainty of the small cell user to the macro user channel. Secondly, on the basis of introducing the price factor, the small cell user maximizes the robust privacy rate of the small cell user at the cost of the worst interference. And thirdly, the balance among the users is realized by iteration of an optimal power strategy among the users in the small cell in the network, and the price factor is updated based on a projection method on the basis of the balance. Repeating the steps until convergence. By the scheme of the invention, the reliable protection of macro users and the steady safety of small cell users under the condition of uncertain information are realized. Meanwhile, the scheme of the invention is based on distributed iteration among users, and can be conveniently realized in a network.
In a dual-layer small cell network, it is considered that the transmission of small-cell users (SUEs) is threatened by an eavesdropper, and therefore, the private rate of the small-cell users is maximized. However, the transmission of small cell users is limited by the interference constraints of macro cell users (MUEs). Meanwhile, considering the implementation in a practical system, the transmission decision of the small cell user faces uncertainty of information. The uncertainty includes two aspects, on one hand, the channel state information of the eavesdropper cannot be confirmed; on the other hand, it cannot ascertain its interfering channel state information with the user. Therefore, the invention provides a distributed safety transmission scheme aiming at the problem, realizes the maximization of the steady safety rate of small cell users, and simultaneously provides the steady interference protection for macro cell users. The game model is utilized to model resource competition among users in the small cells, a pricing mechanism is introduced to solve the problem, and a safe transmission scheme is designed on the basis.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
Fig. 1 is a flowchart of a distributed robust security resource allocation method for a small cell network according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a resource allocation system according to an embodiment of the present invention;
in the figure: 1. a robust privacy rate acquisition module; 2. an interference analysis module; 3. an optimization model design module; 4. a secure transmission policy module; 5. a security policy iteration update module; 6. a price factor updating module; 7. And a network balancing module.
FIG. 3 is a diagram illustrating comparison of performance of different transmitters at a distance from an eavesdropper, according to an embodiment of the present invention;
in the figure: (a) interference power suffered by macro cell users; (b) small cell users and privacy rates.
Figure 4 is a comparison diagram of performance under different small cell user number conditions provided by the embodiment of the present invention;
in the figure: (a) interference power suffered by macro cell users; (b) small cell users and privacy rates.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems in the prior art, the present invention provides a distributed robust security resource allocation method for a small cell network, and the present invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the distributed robust security resource allocation method for small cell networks provided by the present invention includes the following steps:
s101: obtaining a steady privacy rate of small cell users based on uncertainty analysis of channel information of eavesdroppers in each small cell;
s102: analyzing the interference under the worst condition based on the uncertainty of an interference channel between a small cell user and a macro user;
s103: a pricing mechanism is introduced, and an optimization model of a small cell user side is designed;
s104: solving an optimization model of the small cell users to obtain a safe transmission strategy of the small cell users;
s105: the equilibrium state of the whole network is achieved through the iterative updating of the security strategy among the users of the small cells;
s106: updating the price factor based on the obtained network balance state, and broadcasting in the whole network;
s107: based on the updated price factor, repeating S104-S106 until the price factor converges, and finally, balancing the network in a final state, namely, the safe transmission scheme of all small cell users.
The distributed robust safety resource allocation method for the small cell network, provided by the invention, has the specific implementation steps as follows: consider a small cell network comprisingThe macro cell base station provides wireless service for N macro users; meanwhile, there are J small cell base stations, there is one small cell user in each small cell, and the small cell user set can be represented as
Figure RE-GDA0002540799300000101
There is also an eavesdropper in the small cell. There are K orthogonal channels in the network, denoted as
Figure RE-GDA0002540799300000102
Both macro cell users and small cell users may share usage. The transmission power of SUE-j on channel k is p for small cell usersj(k) P under a limited power budgetj＝[pj(k)]k∈KThe method comprises the following steps:
Figure RE-GDA0002540799300000103
wherein
Figure RE-GDA0002540799300000104
Is the power limit for each of the channels,
Figure RE-GDA0002540799300000105
is the maximum allowed transmit power. Similarly, with p0＝[p0(k)]k∈KRepresenting the transmit power of the macrocell base station. For transmission on channel k from cell user SUE-j to small cell user SUE-i, the link gain is denoted as hij(k) The link gain from the macrocell base station to the small cell users SUE-j is denoted by h0j(k) In that respect For eavesdropping on the channel, gij(k) Representing the gain of the eavesdropper link on channel k from the small cell user SUE-i to eavesdropping on the small cell user SUE-j, g0j(k) Representing the link gain of an eavesdropper on channel k from the macrocell base station to the small cell user SUE-j. On the other hand, the transmission between the users in the small cell also affects the reception of the users in the macro cell, and the user uses hjn(k) Representing the link gain on channel k from the cell user SUE-j to the macrocell user MUE-n.
Based on the above definitions, the present invention can obtain the SINR of the signal to interference plus noise ratio (SINR) of the small cell user SUE-j legally transmitted on the channel k as follows:
Figure RE-GDA0002540799300000111
wherein the interference includes interference and noise in both macro cell and small cell,
Figure RE-GDA0002540799300000112
representing background noise. Since the present invention focuses on interference limited communications, the present invention assumes that the noise power on all channels is the same for all small cell users. Similarly, the signal-to-interference-and-noise ratio SINR of the eavesdropper on the channel k of the small cell user SUE-j can also be obtained as follows:
Figure RE-GDA0002540799300000113
it can be seen from the present invention that eavesdropping in a small cell system is not only interfered by other small cell users, but also by macro cell users. According to the above two formulas, the privacy rate of small cell user SUE-j can be obtained by summing all channels:
Figure RE-GDA0002540799300000114
in consideration of the interference exerted by the small cell users to the macro cell users, the present invention proposes an interference constraint to protect the reception of the macro cell users, i.e. the interference of all the small base stations to the macro cell users is lower than a threshold,
Figure RE-GDA0002540799300000115
wherein
Figure RE-GDA0002540799300000121
Is the interference threshold of the macrocell base station MUE-n.
In the first step, considering the implicit assumption that perfect channel state information is adopted, perfect channel state is not easily obtained due to limited capability and resources of small cell users. From a practical implementation point of view, small cell users do not interact directly with eavesdroppers and macro cell users, and it may be difficult to obtain relevant information. Therefore, an uncertainty analysis based on channel information for each small cell internal eavesdropper is considered first.
In particular, the channel state information g for small cell user SUE-j on channel kj(k)＝[gij(k)]i∈J∪{0}The method comprises the following steps:
Figure RE-GDA0002540799300000122
Figure RE-GDA0002540799300000123
is an estimate based on prior information that,
Figure RE-GDA0002540799300000124
the uncertain part of the small base station internal information about the eavesdropper channel state is defined as:
Figure RE-GDA0002540799300000125
∈ thereinj(k) Andj(k) a constant specifying the uncertainty region.
Further, the robust privacy rate of small cell users may be expressed as:
Figure RE-GDA0002540799300000126
the solution to the optimization problem can be expressed as:
Figure RE-GDA0002540799300000127
wherein:
Figure RE-GDA0002540799300000128
Figure RE-GDA0002540799300000129
and secondly, analyzing the worst interference based on the uncertainty of the interference channel between the small cell user and the macro cell user. In particular, for the interference channel associated with the macrocell user MUE-n
Figure RE-GDA0002540799300000131
The model can be represented as:
Figure RE-GDA0002540799300000132
Figure RE-GDA0002540799300000133
is a known estimate of the value of the signal,
Figure RE-GDA0002540799300000134
is the uncertain part of the channel state information, expressed as:
Figure RE-GDA0002540799300000135
wherein e isnIs a constant that determines the size of the defined area. This may introduce worst-case interference, i.e. robust protection for macro cell users:
Figure RE-GDA0002540799300000136
a solution to this problem can be found as:
Figure RE-GDA0002540799300000137
and thirdly, after the uncertainty of the two aspects is analyzed, an optimization model of the user side of the small cell can be designed. Since the competition privacy rate between small cell users is maximized and simultaneously influenced by the interference constraint of macro cell users, the invention introduces a pricing mechanism to solve the problem based on the price factor k ═ kn]n∈NThe following definitions are made:
Figure RE-GDA0002540799300000138
i.e. a robust privacy rate based on interference cost, where the price factor satisfies the condition:
0≤κ⊥ζ≤0。
and fourthly, combining the steps, the optimization problem of the small cell users is as follows:
Figure RE-GDA0002540799300000139
the above problem is solved to obtain a resource allocation scheme for a single small cell user. Here, with the lagrange dual method, the lagrange function that can be used to obtain the above problem is:
Figure RE-GDA0002540799300000141
wherein xjIs a lagrange multiplier.
Let its first derivative be zero, we can obtain:
Figure RE-GDA0002540799300000142
on the basis, solving the optimal power allocation scheme of the single small cell user is carried out as follows. Setting the maximum value and the minimum value of the Lagrange multiplier to be respectively
Figure RE-GDA0002540799300000143
And
Figure RE-GDA0002540799300000144
in addition
Figure RE-GDA0002540799300000145
On the basis, the equation is substituted, and the power p of the small cell user on each channel is obtained by utilizing the dichotomyj(k)，
Figure RE-GDA0002540799300000146
On the basis of this, if the judgment is made
Figure RE-GDA0002540799300000147
Then
Figure RE-GDA0002540799300000148
Otherwise
Figure RE-GDA0002540799300000149
Repeating the above steps using the updated upper and lower bounds of the Lagrangian multiplier until
Figure RE-GDA00025407993000001410
Where iota is a predefined threshold. The power on each channel obtained by the dichotomy under the condition is the power distribution scheme of the current small cell users.
Fifthly, after the individual optimal problem of each small cell user is obtained, network balance can be obtained through the optimal response iteration among the small cell users under the inspiration of the balanced fixed-point property. Initialising to a given price factor k, at the point of satisfying the power allocation
Figure RE-GDA00025407993000001411
In the case of (2), the power of all small cell users is randomly assigned to p (t), t is the number of iterations, and σ is the threshold value of the predefined termination algorithm.
The cyclic content is to solve the fourth step for each small cell user and update the self power
Figure RE-GDA00025407993000001412
Wherein
Figure RE-GDA00025407993000001413
All users repeat the step for the power obtained in the fourth step until the convergence condition p (t) -p (t-1)/p (t-1) < sigma is satisfied, and each user obtains the optimal transmitting power in the current state to achieve balance.
Sixthly, determining an optimal price factor, wherein tau represents the iteration number,
Figure RE-GDA0002540799300000151
is a threshold value of a predefined termination algorithm. Initializing step length rho, and randomly assigning price factors. On the basis, firstly, based on the current price factor k (tau), the equilibrium is obtained by using the iteration process in the fifth step
Figure RE-GDA0002540799300000152
On the basis, updating iteration time to be τ ← τ +1, and updating corresponding price factor to be τ ← τ +1
Figure RE-GDA0002540799300000153
And broadcast over the entire network.
Seventhly, based on the updated price factors, repeating the price factor updating of the fourth, fifth and sixth steps and the corresponding network balance updating process until the convergence condition is met
Figure RE-GDA0002540799300000154
When the loop is terminated, the obtained price factor can ensure that the value of the interference function is small enough to satisfy the interference constraint and provide robust protection for the MUE. Meanwhile, price balance is realized, and the maximization of the steady privacy rate of each SUE is ensured.
As shown in fig. 2, the resource allocation system provided by the present invention includes:
the robust privacy rate acquisition module 1 is configured to obtain the robust privacy rate of the small cell user based on an uncertainty analysis of channel information of an eavesdropper inside each small cell.
And the interference analysis module 2 is configured to analyze the worst interference based on uncertainty of an interference channel between a small cell user and a macro user.
And the optimization model design module 3 is used for introducing a pricing mechanism and designing an optimization model of the small cell user side.
And the safe transmission strategy module 4 is used for solving the optimization model of the small cell users to obtain the safe transmission strategy of the small cell users.
And the security strategy iterative updating module 5 is used for achieving the equilibrium state of the whole network through the security strategy iterative updating among the small cell users.
And the price factor updating module 6 is used for updating the price factor based on the obtained network balance state and broadcasting the price factor in the whole network.
And the network balancing module 7 is configured to balance the network in the final state, that is, the safe transmission scheme of all small cell users, based on the updated price factor until the price factor converges.
The technical solution of the present invention is further described with reference to the following specific examples.
The distributed robust safe resource allocation method facing the small cell network provided by the embodiment of the invention specifically comprises the following steps:
the first step is as follows: and obtaining the robust privacy rate of the small cell users based on the uncertainty analysis of the channel information of each eavesdropper inside the small cell.
(1) The method comprises the steps that a macro cell base station is contained in a small cell network and provides wireless service for N macro users; meanwhile, there are J small cell base stations, there is one small cell user in each small cell, and the small cell user set can be represented as
Figure RE-GDA0002540799300000161
There is also an eavesdropper in the small cell. There are K orthogonal channels in the network, denoted as
Figure RE-GDA0002540799300000162
Both macro cell users and small cell users may share usage. For small cell usersSUE-j has a transmit power p on channel kj(k) The transmission power of the macrocell base station is p0＝[p0(k)]k∈K。
For transmission on channel k from cell user SUE-j to small cell user SUE-i, the link gain is denoted as hij(k) The link gain from the macrocell base station to the small cell users SUE-j is denoted by h0j(k) In that respect For eavesdropping on the channel, gij(k) Representing the eavesdropper link gain in channel k from the small cell user SUE-i to eavesdropping on the small cell user SUE-j, g0j(k) Representing the link gain of an eavesdropper on channel k from the macrocell base station to the small cell user SUE-j. On the other hand, the transmission between small cell SUEs also affects the reception of MUEs in macro cell, and uses hjn(k) Representing the link gain on channel k from the cell user SUE-j to the macrocell user MUE-n.
Based on the above definition, the signal to interference plus noise ratio SINR for legal transmission of small cell user SUE-j on channel k can be obtained as follows:
Figure RE-GDA0002540799300000163
wherein the interference includes interference and noise in both macro cell and small cell,
Figure RE-GDA0002540799300000164
representing background noise. Similarly, the signal-to-interference-and-noise ratio SINR of the eavesdropper on channel k for small cell user SUE-j is:
Figure RE-GDA0002540799300000165
according to the above two formulas, the privacy rate of small cell user SUE-j can be obtained by summing all channels:
Figure RE-GDA0002540799300000171
meanwhile, namely, the interference of all the small base stations to the macro cell users is lower than a threshold:
Figure RE-GDA0002540799300000172
wherein
Figure RE-GDA0002540799300000173
Is the interference threshold of the macrocell base station MUE-n.
(2) Channel state information g for small cell user SUE-j on channel kj(k)＝[gij(k)]i∈J∪{0}Is provided with
Figure RE-GDA0002540799300000174
Wherein
Figure RE-GDA0002540799300000175
Is an estimate based on prior information that,
Figure RE-GDA0002540799300000176
is the uncertain part of the small base station internal information about the eavesdropper channel state:
Figure RE-GDA0002540799300000177
∈ thereinj(k) Andj(k) a constant specifying the uncertainty region.
(3) The robust privacy rate of small cell users may represent:
Figure RE-GDA0002540799300000178
the solution to the optimization problem can be expressed as:
Figure RE-GDA0002540799300000179
wherein:
Figure RE-GDA00025407993000001710
Figure RE-GDA00025407993000001711
the second step is that: the worst-case interference is analyzed based on the uncertainty of the interference channel between small cell users and macro cell users.
(1) For interference channels related to MUE-n of macro cell users
Figure RE-GDA0002540799300000181
Can be expressed as:
Figure RE-GDA0002540799300000182
Figure RE-GDA0002540799300000183
is a known estimate of the value of the signal,
Figure RE-GDA0002540799300000184
is the uncertain part of the channel state information, expressed as:
Figure RE-GDA0002540799300000185
wherein e isnIs a constant that determines the size of the defined area.
(2) This introduces worst-case interference, i.e. robust protection for macro cell users:
Figure RE-GDA0002540799300000186
a solution to this problem can be found as:
Figure RE-GDA0002540799300000187
the third step: and (4) introducing a pricing mechanism and designing an optimization model of the small cell user side.
Based on the price factor k ═ k [ k ]n]n∈NDefining:
Figure RE-GDA0002540799300000188
i.e. a robust privacy rate based on interference cost, where the price factor satisfies the condition:
0≤κ⊥ζ≤0,
the fourth step: and solving the optimization model of the small cell users to obtain the safe transmission strategy of the small cell users.
The optimization problem for small cell users is as follows,
Figure RE-GDA0002540799300000189
(1) using the lagrange dual method, the lagrange function that can be used to obtain the above problem is:
Figure RE-GDA0002540799300000191
wherein xjIs a lagrange multiplier. Let its first derivative be zero, we can obtain:
Figure RE-GDA0002540799300000192
(2) setting the maximum value and the minimum value of the Lagrange multiplier to be respectively
Figure RE-GDA0002540799300000193
And
Figure RE-GDA0002540799300000194
in addition
Figure RE-GDA0002540799300000195
Binary method for obtaining power p of small cell user on each channel by taking into the above formulaj(k)，
Figure RE-GDA0002540799300000196
(3) On the basis of this, if the judgment is made
Figure RE-GDA0002540799300000197
Then
Figure RE-GDA0002540799300000198
Otherwise
Figure RE-GDA0002540799300000199
Repeating the above steps using the updated upper and lower bounds of the Lagrangian multiplier until
Figure RE-GDA00025407993000001910
Where iota is a predefined threshold. The power on each channel obtained by the dichotomy under the condition is the power distribution scheme of the current small cell users.
The fifth step: the equilibrium state of the whole network is achieved through the iterative updating of the security strategy among the users of the small cells;
(1) for a given price factor k, the power distribution is satisfied
Figure RE-GDA00025407993000001911
In the case of (2), the power of all small cell users is randomly assigned to p (t), where t is the number of iterations. The predefined sigma is a threshold value for the termination algorithm.
(2) Solving the fourth step for each small cell user and updating the self power
Figure RE-GDA00025407993000001912
Wherein
Figure RE-GDA00025407993000001913
The power determined in the fourth step. All users repeat the steps until the convergence condition | | p (t) -p (t-1) |/| p (t-1) | | < sigma is met, and each user obtains the optimal transmitting power in the current state to achieve balance.
And a sixth step: updating the price factor based on the obtained network balance state, and broadcasting in the whole network;
(1) tau represents the number of iterations and,
Figure RE-GDA00025407993000001914
is a threshold value of a predefined termination algorithm. Initializing a step length rho, and randomly assigning a price factor;
(2) based on the current price factor k (tau), a balance is obtained using the iterative procedure in the fifth step
Figure RE-GDA0002540799300000201
(3) Updating iteration time to be tau ← tau +1, and the corresponding price factor is updated to be
Figure RE-GDA0002540799300000202
And broadcast over the entire network.
The seventh step: repeating the fourth step and the sixth step based on the updated price factor until the price factor is converged and the convergence condition is met
Figure RE-GDA0002540799300000203
Network equalization in the final state is the safe transmission scheme of all small cell users.
The technical solution of the present invention is further described below with reference to experiments.
Fig. 3 shows the performance of the whole network as a function of the distance between the transmitter and the eavesdropper, and it can be seen from fig. 3(a) that the scheme of the present invention faces multiple information uncertainties as soon as possible under different network configuration conditions, and still strictly meets the interference condition of the macro user. In contrast, under the conventional scheme, the macro user faces back to severe interference. As can be seen from fig. 3(b), the network and privacy rates under the scheme of the present invention are reduced compared to the conventional method due to the strict satisfaction of the interference condition. Numerically, the interference caused by the conventional method is approximately 20dB higher than the result of the scheme of the present invention. The result is combined to see that the scheme of the invention strictly protects the transmission performance of the macro user at the cost of partially sacrificing the network security performance.
Figure 4 gives the variation of the performance of the whole network with the number of small cell users. The scheme can strictly ensure the interference condition of macro users when facing multiple uncertainties under different user numbers. The interference generated under the traditional method is about 20dB higher than that of the scheme of the invention, and in an actual network, the overall performance of macro users can be seriously influenced. Meanwhile, due to the strict interference protection of the scheme of the invention for the macro user, the private rate of the small cell is partially reduced.
It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. A distributed robust security resource allocation method facing a small cell network is characterized in that the distributed robust security resource allocation method facing the small cell network comprises the following steps:
the method comprises the steps that firstly, the robust privacy rate of small cell users is obtained based on uncertainty analysis of channel information of eavesdroppers in each small cell;
secondly, analyzing the interference under the worst condition based on the uncertainty of an interference channel between a small cell user and a macro user;
thirdly, introducing a pricing mechanism and designing an optimization model of a small cell user side;
fourthly, solving an optimization model of the small cell users to obtain a safe transmission strategy of the small cell users;
fifthly, the equilibrium state of the whole network is achieved through the iterative updating of the security strategy among the users of the small cells;
sixthly, updating the price factor based on the obtained network balance state, and broadcasting in the whole network;
and seventhly, repeating the fourth step and the sixth step based on the updated price factor until the price factor is converged, and finally, balancing the network in a final state, namely, the safe transmission scheme of all small cell users.
2. The small cell network-oriented distributed robust safety resource allocation method according to claim 1, wherein the signal to interference and noise ratio SINR of legitimate transmissions of small cell users SUE-j on channel k is:
Figure FDA0002475707500000011
wherein the interference includes interference and noise in both macro cell and small cell,
Figure FDA0002475707500000012
representing background noise, the signal-to-interference-and-noise ratio SINR of the eavesdropper on the channel k of the small cell user SUE-j is as follows:
Figure FDA0002475707500000013
summing all channels to obtain the privacy rate of the small cell user SUE-j:
Figure FDA0002475707500000021
and protecting the reception of the macro cell users by interference constraint, wherein the interference of all the small base stations to the macro cell users is lower than a threshold value:
Figure FDA0002475707500000022
wherein
Figure FDA0002475707500000023
Is the interference threshold of the macrocell base station MUE-n.
3. The small cell network-oriented distributed robust security resource allocation method according to claim 1, wherein the first step is based on uncertainty analysis of each small cell internal eavesdropper channel information, for channel state information g of small cell user SUE-j on channel kj(k)＝[gij(k)]i∈J∪{0}The method comprises the following steps:
Figure FDA0002475707500000024
Figure FDA0002475707500000025
is an estimate based on prior information that,
Figure FDA0002475707500000026
the uncertain part of the information about the eavesdropper channel state in the small base station is defined as:
Figure FDA0002475707500000027
∈ thereinj(k) Andj(k) a constant specifying an uncertainty region;
the robust privacy rate for small cell users is expressed as:
Figure FDA0002475707500000028
the solution to the optimization problem is expressed as:
Figure FDA0002475707500000029
wherein:
Figure FDA0002475707500000031
Figure FDA0002475707500000032
4. the small cell network-oriented distributed robust security resource allocation method of claim 1, wherein the second step is for an interference channel related to macro cell users MUE-n
Figure FDA0002475707500000033
The model is represented as:
Figure FDA0002475707500000034
Figure FDA0002475707500000035
is a known estimate of the value of the signal,
Figure FDA0002475707500000036
is the uncertain part of the channel state information, expressed as:
Figure FDA0002475707500000037
wherein e isnIt is a constant that determines the size of the defined area, introducing the worst case interference, i.e. robust protection for the macro cell users:
Figure FDA0002475707500000038
the solution to this problem is obtained as:
Figure FDA0002475707500000039
5. the small cell network-oriented distributed robust security resource allocation method according to claim 1, wherein the third step is based on a price factor k ═ k [ -k [ ]n]n∈NDefining:
Figure FDA00024757075000000310
a robust privacy rate based on interference cost, wherein a price factor satisfies a condition:
0≤κ⊥ζ≤0。
6. the small cell network-oriented distributed robust security resource allocation method according to claim 1, wherein the fourth step optimization problem for small cell users is as follows:
Figure FDA0002475707500000041
solving is carried out to obtain a resource allocation scheme of a single small cell user, and a Lagrangian dual method is utilized to obtain a Lagrangian function of the problem as follows:
Figure FDA0002475707500000042
wherein xjIs a lagrange multiplier;
let its first derivative be zero, we can obtain:
Figure FDA0002475707500000043
solving the optimal power distribution scheme of the single small cell user is carried out as follows, and the maximum value and the minimum value of the Lagrange multiplier are respectively
Figure FDA0002475707500000044
And
Figure FDA0002475707500000045
in addition
Figure FDA0002475707500000046
Substituting into an equation, and obtaining the power p of the small cell user on each channel by using a dichotomyj(k)，
Figure FDA0002475707500000047
If it is
Figure FDA0002475707500000048
Then
Figure FDA0002475707500000049
Otherwise
Figure FDA00024757075000000410
Repeating the above steps using the updated upper and lower bounds of the Lagrangian multiplier until
Figure FDA00024757075000000411
Wherein iotaThe power on each channel obtained by utilizing a dichotomy is a predefined threshold value, namely the power distribution scheme of the current small cell user;
the fifth step obtains network balance through the optimal response iteration among small cell users, and the network balance is initialized to be assigned with a given price factor kappa and meets the power distribution
Figure FDA00024757075000000412
Under the condition of (1), randomly assigning the power of all small cell users to p (t), wherein t is the iteration frequency, and sigma is the threshold value of a predefined termination algorithm;
the cyclic content is to solve the fourth step for each small cell user and update the self power
Figure FDA00024757075000000413
Wherein
Figure FDA00024757075000000414
For the power obtained in the fourth step, all users repeat the step until the convergence condition | | | p (t) -p (t-1) | |/| | p (t-1) | < σ is met, and each user obtains the optimal transmitting power in the current state to achieve balance;
said sixth step determining an optimal price factor, τ representing the number of iterations,
Figure FDA0002475707500000052
initializing a step length rho for a predefined threshold value of a termination algorithm, randomly assigning a price factor, and obtaining balance by utilizing an iterative process in a fifth step based on a current price factor kappa (tau)
Figure FDA0002475707500000051
On the basis, updating iteration time to be τ ← τ +1, and updating corresponding price factor to be τ ← τ +1
Figure FDA0002475707500000053
And broadcasting in the whole network;
in the seventh step, the first step is carried out,based on the updated price factors, the price factor updating of the fourth, fifth and sixth steps and the corresponding network balance updating process are repeated until the convergence condition is met
Figure FDA0002475707500000054
7. A program storage medium for receiving user input, the stored computer program causing an electronic device to perform the steps comprising:
the method comprises the steps that firstly, the robust privacy rate of small cell users is obtained based on uncertainty analysis of channel information of eavesdroppers in each small cell;
secondly, analyzing the interference under the worst condition based on the uncertainty of an interference channel between a small cell user and a macro user;
thirdly, introducing a pricing mechanism and designing an optimization model of a small cell user side;
fourthly, solving an optimization model of the small cell users to obtain a safe transmission strategy of the small cell users;
fifthly, the equilibrium state of the whole network is achieved through the iterative updating of the security strategy among the users of the small cells;
sixthly, updating the price factor based on the obtained network balance state, and broadcasting in the whole network;
and seventhly, repeating the fourth step and the sixth step based on the updated price factor until the price factor is converged, and finally, balancing the network in a final state, namely, the safe transmission scheme of all small cell users.
8. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface for implementing the small cell network oriented distributed robust security resource allocation method of any one of claims 1 to 6 when executed on an electronic device.
9. A resource allocation system for implementing the distributed robust security resource allocation method for small cell networks according to any one of claims 1 to 6, wherein the resource allocation system comprises:
the robust privacy rate acquisition module is used for obtaining the robust privacy rate of the small cell users based on the uncertainty analysis of the information of the eavesdropper channel inside each small cell;
the interference analysis module is used for analyzing the interference under the worst condition based on the uncertainty of an interference channel between a small cell user and a macro user;
the optimization model design module is used for introducing a pricing mechanism and designing an optimization model of a small cell user side;
the safe transmission strategy module is used for solving the optimization model of the small cell users to obtain the safe transmission strategy of the small cell users;
the security strategy iterative updating module is used for achieving the equilibrium state of the whole network through the security strategy iterative updating among the small cell users;
the price factor updating module is used for updating the price factor based on the obtained network balance state and broadcasting the price factor in the whole network;
and the network balancing module is used for balancing the network in the final state based on the updated price factor until the price factor is converged, namely the safe transmission scheme of all small cell users.
10. A wireless terminal equipped with the resource allocation system according to claim 9.
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