CN111615111B - Small cell network-oriented distributed robust security resource allocation method - Google Patents
Small cell network-oriented distributed robust security resource allocation method Download PDFInfo
- Publication number
- CN111615111B CN111615111B CN202010362941.5A CN202010362941A CN111615111B CN 111615111 B CN111615111 B CN 111615111B CN 202010362941 A CN202010362941 A CN 202010362941A CN 111615111 B CN111615111 B CN 111615111B
- Authority
- CN
- China
- Prior art keywords
- small cell
- user
- users
- network
- interference
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000013468 resource allocation Methods 0.000 title claims abstract description 28
- 230000005540 biological transmission Effects 0.000 claims abstract description 48
- 238000005457 optimization Methods 0.000 claims abstract description 29
- 230000007246 mechanism Effects 0.000 claims abstract description 16
- 238000013076 uncertainty analysis Methods 0.000 claims description 12
- 238000013461 design Methods 0.000 claims description 7
- 238000009826 distribution Methods 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 5
- 230000009977 dual effect Effects 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 2
- 238000004891 communication Methods 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 6
- 230000008901 benefit Effects 0.000 description 3
- 229920002430 Fibre-reinforced plastic Polymers 0.000 description 2
- 238000003491 array Methods 0.000 description 2
- 239000002131 composite material Substances 0.000 description 2
- 238000007796 conventional method Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000011151 fibre-reinforced plastic Substances 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012804 iterative process Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000006399 behavior Effects 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000005562 fading Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 230000035755 proliferation Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000011664 signaling Effects 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
Classifications
-
- 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
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Computer Security & Cryptography (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention belongs to the technical field of wireless communication, and discloses a distributed robust safe resource allocation method for a small cell network, which is used for analyzing and obtaining the robust privacy rate of a small cell user; analyzing the interference in the worst case based on the uncertainty of the interference channel between the small cell user and the macro user; a pricing mechanism is introduced, and an optimization model of a small cell user terminal is designed; solving an optimization model of the small cell user to obtain a safe transmission strategy of the small cell user; the equilibrium state of the whole network is achieved through the iterative updating of the security policies among the small cell users; updating price factors based on the obtained network equilibrium state, and broadcasting in the whole network; and repeating the fourth step to the sixth step based on the updated price factor until the price factor converges, wherein the network equalization in the final state is the safe transmission scheme of all 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
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a distributed robust safety resource allocation method for a small cell network.
Background
Currently, small cell networks (sms) deploy microcells, picocells, and femtocells on the basis of conventional macrocell cells to form a multi-layer heterogeneous network structure, which is an essential component of 5G and later 5G times infrastructure. The small cell base station has the characteristics of low cost, low power consumption and deployment according to requirements, and realizes a flexible network architecture. Meanwhile, the small cell network can realize seamless coverage service and high-efficiency spectrum efficiency, so that user experience is further enhanced. In existing wireless network systems, security defenses mainly depend on encryption technology, but with the proliferation of the number of wireless devices and the continuous improvement of wireless network heterogeneity, encryption methods face great challenges in key management and distribution. While the small cell base station has limited computational power and resources, it may not be possible to efficiently support the computational complexity required for encryption techniques. In this respect, the physical layer security technology has great potential, and it takes advantage of the inherent characteristics of the wireless medium, such as fading, interference, and noise, so as to implement keyless secure transmission, which is convenient to implement in the hierarchical heterogeneous small cell network.
For small cell networks, in addition to designing link-level security transmission policies, interactions 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 fully consider their wireless environment and the behavior of other users to adjust their secure transmission policies. Moreover, the complex interference caused by simultaneous transmission among heterogeneous user layers can be fully utilized to reduce the eavesdropping performance and further improve the safety. 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 small cell networks with hierarchical structures, macrocell users often have higher priorities and thus require reliable transmission performance guarantees. 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 macrocell user. Meanwhile, due to the limited capability of the small cell base station, all ideal channel state information may not be obtained, and accordingly, the design of the secure transmission scheme needs to take information uncertainty into consideration to realize robust security. Therefore, the physical layer security scheme of the small cell network needs to jointly consider the global interference constraint and the uncertainty of the channel information in the distributed security policy design in order to provide a robust and reliable solution for the actual system.
Through the above analysis, the problems and drawbacks of the prior art include:
(1) The existing security mechanism relies on key distribution of an upper layer of a protocol, and as the number of users increases, the distribution and management of keys become more difficult and complex; moreover, as the computing power of devices increases, the risk of encryption being broken increases.
(2) The existing physical layer security solution focuses on the security guarantee of a single link, and ignores the security of multi-user transmission under the network condition.
(3) Existing security schemes lack consideration of the multiple uncertainties underlying an actual network.
The difficulty of solving the problems and the defects is as follows: the existing security mechanism is based on a key system, and physical layer security is a brand new solution. In the scenario considered by the present invention, the small cell network adopts a distributed physical layer security mechanism, which requires that the small cell users independently determine their own transmission policy, but need to satisfy a common interference constraint, thus requiring a distributed coordination mechanism. Furthermore, because of the limited small cell user capability, it is not possible to obtain global information for the entire network, so decisions need to be made involving uncertainty factors.
The meaning of solving the problems and the defects is as follows: the invention thus employs a physical layer security scheme that does not require keys and has low complexity; the invention considers the safety transmission of a plurality of coexisting small cell users in the double-layer network, and simultaneously ensures the service quality of macro cell users through the constraint of interference conditions; meanwhile, the dual 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 provided scheme realizes robust security of the small cell user and robust interference protection of the macro user.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a distributed robust and safe 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 to 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 the small cell user and the macro user;
thirdly, a pricing mechanism is introduced, and an optimization model of a small cell user terminal is designed;
step four, solving an optimization model of the small cell user to obtain a safe transmission strategy of the small cell user;
fifthly, iteratively updating the security policy among the users of the small cell to achieve the equilibrium state of the whole network;
sixth, updating price factors based on the obtained network equilibrium 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 converges, wherein the network equalization in the final state is the safe transmission scheme of all small cell users.
Further, the distributed robust security resource allocation method facing the small cell network is characterized in that the signal-to-interference-plus-noise ratio SINR of legal transmission of the small cell user SUE-j on the channel k is as follows:
where interference includes both macro and small cell interference and noise,the signal-to-interference-plus-noise ratio SINR representing background noise for small cell user SUE-j to eavesdropper on channel k is:
summing all channels to obtain the privacy rate of the small cell user SUE-j:
the interference constraint protects the reception of the macrocell user, and the interference of all the small base stations to the macrocell user is lower than a threshold value:
wherein the method comprises the steps ofIs the interference threshold of the macrocell base station MUE-n.
Further, the first step is based on an uncertainty analysis of the channel information of the eavesdropper inside each small cell, channel state information g for small cell users SUE-j on channel k j (k)＝[g ij (k)] i∈J∪{0} The method comprises the following steps:
is based on an estimate of the prior information, +.>Is an uncertain part of the information about the channel state of an eavesdropper inside a small base station, defined as:
wherein E is j (k) And epsilon j (k) Specifying a constant of the uncertainty region;
the robust privacy rate for small cell users is expressed as:
the solution to this optimization problem is expressed as:
wherein:
further, the second step is for an interference channel related to the macrocell user MUE-nThe model is expressed as:
is a known estimate, +.>Is an uncertain part of the channel state information, expressed as:
wherein e n Is a constant that determines the size of the defined area, introducing worst-case interference, i.e. robust protection for macrocell users:
the solution to this problem is:
further, the third step is based on a price factor κ= [ κ ] n ] n∈N Definition:
robust privacy rate based on interference cost, wherein the price factor satisfies the condition:
0≤κ⊥ζ≤0。
further, the optimization problem of the fourth step small cell user is as follows:
solving to obtain a resource allocation scheme of a single small cell user, and obtaining a Lagrange function of the problem by using a Lagrange dual method, wherein the Lagrange function is as follows:
wherein χ is j Is a Lagrangian multiplier;
let its first derivative be zero, it is possible to obtain:
the optimal power allocation scheme for solving the users of the single small cell is carried out in the following way that the maximum value and the minimum value of the Lagrangian multiplier are respectivelyAnd->In addition->Taking into equation, using dichotomy to find power p of small cell users on each channel j (k)，If->Then->Otherwise->Repeating the above steps with the upper and lower bounds of the updated Lagrangian multiplier until +.>Wherein iota is a predefined threshold, the power on each channel obtained by the dichotomy is the power allocation scheme of the current small cell user;
the fifth step obtains network balance through the optimal response iteration among the small cell users, initializes given price factor kappa assignment, and satisfies the power distributionIn the case of (a), all small cells are to be countedThe power of the user is randomly assigned with p (t), t is the iteration number, and sigma is a threshold value of a predefined termination algorithm;
the circulation content is that solving the fourth step for each small cell user, updating the self powerWherein->For the power obtained in the fourth step, all users repeat the step until convergence conditions of ||p (t) -p (t-1) |/|p (t-1) | < sigma are met, and each user obtains the optimal transmitting power in the current state to achieve balance;
the sixth step of determining an optimal price factor, τ representing the number of iterations,for a predefined threshold value of the termination algorithm, initializing a step size ρ, randomly assigning a price factor, first obtaining an equalization ++using an iterative procedure in a fifth step based on the current price factor κ (τ)>On the basis, the updating iteration time is tau≡tau+1, and the corresponding price factor is updated asAnd broadcast over the whole network;
the seventh step, based on the updated price factor, repeating the price factor updating and corresponding network equalization updating processes of the fourth, fifth and sixth steps until meeting the convergence condition
It is a further object of the present invention to provide a storage medium for receiving user input, the stored computer program causing an electronic device to perform the steps of any one of the claims 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 the small cell user and the macro user;
thirdly, a pricing mechanism is introduced, and an optimization model of a small cell user terminal is designed;
step four, solving an optimization model of the small cell user to obtain a safe transmission strategy of the small cell user;
fifthly, iteratively updating the security policy among the users of the small cell to achieve the equilibrium state of the whole network;
sixth, updating price factors based on the obtained network equilibrium 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 converges, wherein the network equalization in the final state is the safe transmission scheme of all small cell users.
It is a further 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 for implementing said method of distributed robust secure 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 implementing the distributed robust and secure resource allocation method for a small cell network, 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 channel information of the eavesdropper in each small cell;
the interference analysis module is used for analyzing the interference under the worst condition based on the uncertainty of the interference channel between the small cell user and the macro user;
the optimizing model design module is used for introducing a pricing mechanism and designing an optimizing model of the small cell user side;
the safety transmission strategy module is used for solving an optimization model of the small cell user to obtain a safety transmission strategy of the small cell user;
the security policy iteration updating module is used for achieving the equilibrium state of the whole network through security policy iteration updating among small cell users;
the price factor updating module is used for updating the price factor based on the obtained network equilibrium state and broadcasting the price factor in the whole network;
and the network equalization module is used for equalizing the network in a final state based on the updated price factor until the price factor converges, namely, the safe transmission scheme of all small cell users.
Another object of the present invention is to provide a wireless terminal that mounts the 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 small cell users under the condition of strictly meeting the interference condition of macro cell users under the condition of uncertain multiple information; first, the small cell user obtains its own robust privacy rate taking into account its uncertainty about the eavesdropper channel state information, while the worst case interference is obtained taking into account the small cell user's uncertainty to the macro user channel. Second, on the basis of introducing a price factor, the small cell users maximize their own robust privacy rate at the cost of worst interference. And thirdly, realizing balance among users through optimal power strategy iteration among users in small cells in the network, and updating the price factor based on a projection method on the basis of balance. Repeating the above steps until convergence. By the scheme of the invention, reliable protection of macro users and robust 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 two-layer small cell network, it is considered that the transmission of small cell users (SUEs) is threatened by an eavesdropper, for which purpose the privacy rate itself is maximized. However, the transmission of small cell users is limited by the interference constraints of Macrocell Users (MUEs). Meanwhile, in consideration of implementation in an actual system, transmission decisions of small cell users face uncertainty of information. The uncertainty of the method comprises two aspects, on one hand, the method cannot know the channel state information of an eavesdropper; on the other hand, it cannot ascertain its interfering channel state information with the user. Therefore, the invention provides a distributed safe transmission scheme aiming at the problem, realizes the maximization of the steady safe rate of the small cell user, and simultaneously provides steady interference protection for the macro cell user. The method is characterized in that the resource competition among small cell users is modeled by using a game model, a pricing mechanism is introduced to solve the problem, and a safe transmission scheme is designed on the basis.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly explain the drawings needed in the embodiments of the present application, and it is obvious that the drawings described below are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
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 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 updating module; 6. a price factor updating module; 7. and a network equalization module.
FIG. 3 is a graph showing a comparison of performance at different distances from an eavesdropper for different transmitters according to an embodiment of the present invention;
in the figure: (a) interference power experienced by macrocell users; (b) small cell user and privacy rate.
Fig. 4 is a schematic diagram showing performance comparison under different small cell user numbers according to an embodiment of the present invention;
in the figure: (a) interference power experienced by macrocell users; (b) small cell user and privacy rate.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Aiming at the problems existing in the prior art, the invention provides a distributed robust and safe resource allocation method for a small cell network, and the 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 a small cell network provided by the invention comprises the following steps:
s101: obtaining a robust privacy rate of the small cell users based on uncertainty analysis of the channel information of the eavesdroppers in each small cell;
s102: analyzing the interference in the worst case based on the uncertainty of the interference channel between the small cell user and the macro user;
s103: a pricing mechanism is introduced, and an optimization model of a small cell user terminal is designed;
s104: solving an optimization model of the small cell user to obtain a safe transmission strategy of the small cell user;
s105: the equilibrium state of the whole network is achieved through the iterative updating of the security policies among the small cell users;
s106: updating price factors based on the obtained network equilibrium state, and broadcasting in the whole network;
s107: and based on the updated price factor, repeating S104-S106 until the price factor converges, and balancing the network in a final state, namely, the safe transmission scheme of all small cell users.
The specific implementation steps of the distributed robust safety resource allocation method facing the small cell network provided by the invention are explained as follows: consider a small cell network comprising a macrocell base station providing wireless service to N macrocell users; at the same time, there are JA small cell base station, one small cell user exists in each small cell, and a small cell user set can be expressed asAn eavesdropper is also present in the small cell. K orthogonal channels in the network are shown as +.>Both macrocell users and small cell users can share use. The transmission power on channel k for small cell user SUE-j is p j (k) P under a limited power budget j ＝[p j (k)] k∈K The method is to satisfy the following steps:
wherein the method comprises the steps ofIs the power limit per channel, +.>Is the maximum allowed transmit power. Similarly, with p 0 ＝[p 0 (k)] k∈K Representing the transmit power of the macrocell base station. For transmissions from small cell user SUE-j to small cell user SUE-i on channel k, the link gain is denoted as h ij (k) The link gain from the macrocell base station to the small cell user SUE-j is denoted as h 0j (k) A. The invention relates to a method for producing a fibre-reinforced plastic composite For eavesdropping on the channel g ij (k) Representing the eavesdropper link gain, g, from small cell user SUE-i to eavesdropping on small cell user SUE-j on channel k 0j (k) Representing the link gain from the macrocell base station to an eavesdropper of the small cell user SUE-j on channel k. On the other hand, the transmission among the users in the small cell can also influence the reception of the users in the macro cell, and h is used jn (k) Representing the link gain from the small cell user SUE-j to the macrocell user MUE-n on channel k.
Based on the definition, the invention can obtain the SINR (signal to interference plus noise ratio) of the legal transmission of the small cell user SUE-j on the channel k as follows:
where interference includes both macro and small cell interference and noise,representing background noise. Since the present invention focuses on interference limited communications, the present invention assumes that the noise power is the same for all small cell users on all channels. The signal-to-interference-plus-noise ratio SINR of the small cell user SUE-j eavesdropper on channel k is similarly obtained as:
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 interfered by macro cell users. According to the two formulas, the private rate of the small cell user SUE-j can be obtained by summing all channels:
considering the interference that small cell users impose on macro cell users, the present invention proposes an interference constraint to protect the reception of macro cell users, i.e. all small base stations have an interference to macro cell users below a threshold,
wherein the method comprises the steps ofIs the interference threshold of the macrocell base station MUE-n.
First, considering the implicit assumption that perfect channel state information is adopted, perfect channel state is not easily available due to limited capability and resources of small cell users. From a practical implementation point of view, the small cell user does not interact directly with the eavesdropper and the macrocell user, but it may be difficult to obtain relevant information. Thus, consider first an uncertainty analysis based on the information of the eavesdropper channel inside each small cell.
Specifically, for small cell user SUE-j on channel k, channel state information g j (k)＝[g ij (k)] i∈J∪{0} The method comprises the following steps:
is based on an estimate of the prior information, +.>Is an uncertain part of the information about the channel state of an eavesdropper inside a small base station, which is defined by the present invention as:
wherein E is j (k) And epsilon j (k) A constant of the uncertainty region is specified.
Further, the robust privacy rate of small cell users can be expressed as:
the solution to this optimization problem can be expressed as:
wherein:
and secondly, analyzing the interference in the worst case based on the uncertainty of the interference channel between the small cell user and the macro cell user. In particular, for interference channels related to macrocell users MUE-nThe model can be expressed as:
is a known estimate, +.>Is an uncertain part of the channel state information, expressed as:
wherein e n Is a constant that determines the size of the defined area. The worst case interference, i.e. robust protection for macrocell users, can thus be introduced:
the solution to this problem can be found as:
and thirdly, after the uncertainty of the two aspects is analyzed, an optimization model of the small cell user terminal can be designed. Since the competition privacy rate between small cell users is maximized and the interference constraint of macro cell users is influenced, the invention introduces a pricing mechanism to solve the problem, and is based on price factor kappa= [ kappa ] n ] n∈N The following definitions are made:
i.e. a robust privacy rate based on interference costs, where the price factor satisfies the condition:
0≤κ⊥ζ≤0。
fourth, combining the above steps, the optimization problem of the small cell user is as follows:
and solving the problem to obtain a resource allocation scheme of the single small cell user. Here, the lagrangian function for the above problem can be obtained by using the lagrangian dual method as follows:
wherein χ is j Is a lagrange multiplier.
Let its first derivative be zero, it is possible to obtain:
on the basis, solve for single smallThe optimal power allocation scheme for the zone users proceeds as follows. Setting the maximum value and the minimum value of the Lagrangian multiplier as respectivelyAnd->In addition->On the basis of the above equation, the power p of the small cell user on each channel is obtained by using the dichotomy j (k)，On the basis of this, a judgment is made if +.>Then->Otherwise->Repeating the above steps with the upper and lower bounds of the updated Lagrangian multiplier until +.>Where iota is a predefined threshold. The power on each channel, i.e. the power allocation scheme for the current small cell user, is determined by the dichotomy under this condition.
And fifthly, after obtaining the individual optimal problem of each small cell user, under the heuristic of the fixed point property of the equalization, network equalization can be obtained through optimal response iteration among the small cell users. Initializing given price factor kappa assignment, when power allocation is satisfiedIn the case of (a), will allThe power of the small cell user is randomly assigned p (t), t is the iteration number, and sigma is the threshold value of the predefined termination algorithm.
The circulation content is that solving the fourth step for each small cell user, updating the self powerWherein->For the power obtained in the fourth step, all users repeat this step in this way until the convergence condition p (t) -p (t-1)/p (t-1) < σ is satisfied, and each user obtains the optimal transmit power in the current state, so as to achieve equalization.
Sixth, determining optimal price factor, τ represents iteration number,is a threshold value for a predefined termination algorithm. Initializing step length rho, and randomly assigning price factors. On the basis of this, firstly, based on the current price factor k (τ), an equalization is obtained by means of an iterative process in the fifth step>On the basis, the updating iteration time is tau≡tau+1, and the corresponding price factor is updated to +.>And broadcast over the whole network. />
Seventh, based on the updated price factor, repeating the price factor updating and corresponding network equalization updating processes of the fourth, fifth and sixth steps until meeting the convergence conditionWhen the cycle is terminated, the price factor obtained can ensure that the value of the interference function is small enough to meet the interference constraint and provide robust protection for the MUE. At the same time, price balance is realized, and the steady privacy rate maximization of each SUE is ensured。
As shown in fig. 2, the resource allocation system provided by the present invention includes:
and the robust privacy rate acquisition module 1 is used for obtaining the robust privacy rate of the small cell users based on the uncertainty analysis of the channel information of the eavesdropper in each small cell.
And the interference analysis module 2 is used for analyzing the interference under the worst condition based on the uncertainty of the interference channel between the small cell user and the 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 user to obtain the safe transmission strategy of the small cell user.
And the security policy iteration updating module 5 is used for achieving the equilibrium state of the whole network through security policy iteration updating among small cell users.
And the price factor updating module 6 is used for updating the price factor based on the obtained network equilibrium state and broadcasting the price factor in the whole network.
And the network equalization module 7 is used for equalizing the network in a final state, namely the safe transmission scheme of all small cell users, based on the updated price factor until the price factor converges.
The technical scheme of the invention is further described below with reference to specific embodiments.
The distributed robust security resource allocation method for the small cell network provided by the embodiment of the invention specifically comprises the following steps:
the first step: the robust privacy rate of the small cell users is derived based on an uncertainty analysis of the eavesdropper channel information inside each small cell.
(1) A macro cell base station is included in a small cell network to provide 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 expressed asAn eavesdropper is also present in the small cell. K orthogonal channels in the network are shown as +.>Both macrocell users and small cell users can share use. The transmission power on channel k for small cell user SUE-j is p j (k) The transmit power of the macrocell base station is p 0 ＝[p 0 (k)] k∈K 。
For transmissions from small cell user SUE-j to small cell user SUE-i on channel k, the link gain is denoted as h ij (k) The link gain from the macrocell base station to the small cell user SUE-j is denoted as h 0j (k) A. The invention relates to a method for producing a fibre-reinforced plastic composite For eavesdropping on the channel g ij (k) Indicating the eavesdropper link gain, g, in channel k from small cell user SUE-i to eavesdropping on small cell user SUE-j 0j (k) Representing the link gain from the macrocell base station to an eavesdropper of the small cell user SUE-j on channel k. On the other hand, the transmission between the SUEs of the small cell also affects the reception of MUEs in the macro cell, using h jn (k) Representing the link gain from the small cell user SUE-j to the macrocell user MUE-n on channel k.
Based on the definition, the SINR (signal to interference plus noise ratio) of the legal transmission of the small cell user SUE-j on the channel k can be obtained as follows:
where interference includes both macro and small cell interference and noise,representing background noise. Similarly, the signal-to-interference-plus-noise ratio SINR of the small cell user SUE-j for an eavesdropper on channel k is:
according to the two formulas, the private rate of the small cell user SUE-j can be obtained by summing all channels:
meanwhile, the interference of all small base stations to the macrocell user is lower than a threshold value:
wherein the method comprises the steps ofIs the interference threshold of the macrocell base station MUE-n.
(2) Channel state information g for small cell user SUE-j on channel k j (k)＝[g ij (k)] i∈J∪{0} Has the following componentsWherein->Is based on an estimate of the prior information, +.>Is an uncertain part of the information about the channel state of an eavesdropper inside the small base station:
wherein E is j (k) And epsilon j (k) A constant of the uncertainty region is specified.
(3) The robust privacy rate of the small cell user may represent:
the solution to this optimization problem can be expressed as:
wherein:
and a second step of: the worst case interference is analyzed based on the uncertainty of the interference channel between the small cell user and the macrocell user.
(1) For interference channels related to macrocell users MUE-nCan be expressed as:
is a known estimate, +.>Is an uncertain part of the channel state information, expressed as:
wherein e n Is a constant that determines the size of the defined area.
(2) Thereby introducing worst case interference, i.e. robust protection for macrocell users:
the solution to this problem can be found as:
and a third step of: and (3) introducing a pricing mechanism, and designing an optimization model of the small cell user terminal.
Based on price factor kappa= [ kappa ] n ] n∈N Definition:
i.e. a robust privacy rate based on interference costs, where the price factor satisfies the condition:
0≤κ⊥ζ≤0,
fourth step: and solving an optimization model of the small cell user to obtain a safe transmission strategy of the small cell user.
The optimization problem for small cell users is as follows,
(1) The lagrangian function for the above problem can be obtained using the lagrangian dual method as follows:
wherein χ is j Is a lagrange multiplier. Let its first derivative be zero, it is possible to obtain:
(2) Setting the maximum value and the minimum value of the Lagrangian multiplier as respectivelyAnd->In addition, anotherCarrying-in method for obtaining power p of small cell user on each channel by using dichotomy j (k)，
(3) On the basis of which judgment is made, ifThen->Otherwise->Repeating the above steps with the upper and lower bounds of the updated Lagrangian multiplier until +.>Where iota is a predefined threshold. The power on each channel, i.e. the power allocation scheme for the current small cell user, is determined by the dichotomy under this condition.
Fifth step: the equilibrium state of the whole network is achieved through the iterative updating of the security policies among the small cell users;
(1) Assigning a value for a given price factor k, in satisfying power allocationIn the case of (2), the power of all small cell users is randomly assigned p (t), and t is the iteration number. Predefining sigma as finalAnd stopping the threshold value of the algorithm.
(2) Solving the fourth step for each small cell user, and updating the self powerWherein->The power determined in the fourth step. All users repeat the step until convergence conditions of p (t) -p (t-1) |/|p (t-1) | < sigma are met, and each user obtains the optimal transmitting power in the current state to achieve balance.
Sixth step: updating price factors based on the obtained network equilibrium state, and broadcasting in the whole network;
(1) τ represents the number of iterations and,is a threshold value for a predefined termination algorithm. Initializing a step length rho, and randomly assigning a price factor;
(2) Based on the current price factor k (tau), an equalization is obtained using an iterative process in a fifth step
(3) The updating iteration time is tau≡tau+1, and the corresponding price factor is updated asAnd broadcast over the whole network.
Seventh step: repeating the fourth step-the sixth step based on the updated price factor until the price factor converges, satisfying the convergence conditionNetwork equalization in the final state is the secure transmission scheme for all small cell users.
The technical scheme of the invention is further described in the following in connection with experiments.
Fig. 3 shows the variation of the performance of the whole network along with the distance between the transmitter and the eavesdropper, and as can be seen from fig. 3 (a), the scheme of the 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, macro users face serious 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 conditions. The interference caused by the conventional method is approximately 20dB higher than the result of the scheme of the invention. From the above results, it can be seen that the scheme of the present invention protects the transmission performance of macro users strictly at the cost of partially sacrificing the network security performance.
Fig. 4 shows the performance of the entire network as a function of the number of small cell users. The proposal faces multiple uncertainties under different numbers of users, and can strictly ensure the interference condition of macro users. The interference generated by the traditional method is approximately 20dB higher than that of the scheme of the invention, and the overall performance of macro users can be seriously affected in an actual network. Meanwhile, the scheme of the invention can cause partial reduction of the private rate of the small cell due to strict interference protection to the macro user.
It should be noted that the embodiments of the present invention can be realized in 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 special purpose design hardware. Those of ordinary skill 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 as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (5)
1. The distributed robust security resource allocation method for the small cell network is characterized by comprising the following steps of:
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 the small cell user and the macro user;
thirdly, a pricing mechanism is introduced, and an optimization model of a small cell user terminal is designed;
step four, solving an optimization model of the small cell user to obtain a safe transmission strategy of the small cell user;
fifthly, iteratively updating the security policy among the users of the small cell to achieve the equilibrium state of the whole network;
sixth, updating price factors based on the obtained network equilibrium state, and broadcasting in the whole network;
seventhly, repeating the fourth step and the sixth step based on the updated price factor until the price factor converges, wherein the network in the final state is balanced, namely the safe transmission scheme of all small cell users;
the first step is based on an uncertainty analysis of the channel information of the eavesdropper inside each small cell, the channel state information g for the small cell user SUE-j on channel k j (k)＝[g ij (k)] i∈J∪{0} The method comprises the following steps:
is based on an estimate of the prior information, +.>Is an uncertain part of the information about the channel state of an eavesdropper inside a small base station, defined as:
wherein E is j (k) And epsilon j (k) Specifying a constant of the uncertainty region;
the robust privacy rate for small cell users is expressed as:
the solution to this optimization problem is expressed as:
wherein:
wherein,,representing background noise;
the second step is for interference channels related to macrocell user MUE-nThe model is expressed as:
is a known estimate, +.>Is an uncertain part of the channel state information, expressed as:
wherein e n Is a constant that determines the size of the defined area, introducing worst-case interference, i.e. robust protection for macrocell users:
wherein,,is the interference threshold of the macrocell base station MUE-n;
the solution to get robust protection for macrocell users is:
the third step is based on a price factor kappa= [ kappa ] n ] n∈N Definition:
robust privacy rate based on interference cost, wherein the price factor satisfies the condition:
0≤κ⊥ζ≤0；
the optimization problem of the fourth step small cell user is as follows:
solving to obtain a resource allocation scheme of a single small cell user, and obtaining a Lagrange function of the problem by using a Lagrange dual method, wherein the Lagrange function is as follows:
wherein χ is j Is a Lagrangian multiplier;
let its first derivative be zero, it is possible to obtain:
the optimal power allocation scheme for solving the users of the single small cell is carried out in the following way that the maximum value and the minimum value of the Lagrangian multiplier are respectivelyAnd->In addition->Taking into equation, using dichotomy to find power p of small cell users on each channel j (k)，If->Then->Otherwise->Repeatedly solving the optimal power distribution scheme of the single small cell user by using the upper and lower bounds of the updated Lagrangian multiplier untilWherein iota is a predefined threshold, the power on each channel obtained by the dichotomy is the power allocation scheme of the current small cell user;
the fifth step obtains network balance through the optimal response iteration among the small cell users, initializes given price factor kappa assignment, and satisfies the power distributionIn the case of (a), willThe power of all small cell users is randomly assigned with p (t), t is the iteration number, and sigma is a threshold value of a predefined termination algorithm;
the circulation content is that solving the fourth step for each small cell user, updating the self powerWherein->For the power obtained in the fourth step, all users repeat the step until convergence conditions of ||p (t) -p (t-1) |/|p (t-1) | < sigma are met, and each user obtains the optimal transmitting power in the current state to achieve balance;
the sixth step of determining an optimal price factor, τ representing the number of iterations,for a predefined threshold value of the termination algorithm, initializing a step size ρ, randomly assigning a price factor, first obtaining an equalization ++using an iterative procedure in a fifth step based on the current price factor κ (τ)>On the basis, the updating iteration time is tau≡tau+1, and the corresponding price factor is updated asAnd broadcast over the whole network;
the seventh step, based on the updated price factor, repeating the price factor updating and corresponding network equalization updating processes of the fourth, fifth and sixth steps until meeting the convergence condition
2. The method for distributing distributed robust security resources towards a small cell network according to claim 1, wherein the method for distributing distributed robust security resources towards a small cell network comprises the following steps:
where interference includes both macro and small cell interference and noise,the signal-to-interference-plus-noise ratio SINR representing background noise for small cell user SUE-j to eavesdropper on channel k is:
summing all channels to obtain the privacy rate of the small cell user SUE-j:
the interference constraint protects the reception of the macrocell user, and the interference of all the small base stations to the macrocell user is lower than a threshold value:
wherein the method comprises the steps ofIs the interference threshold of the macrocell base station MUE-n.
3. A storage medium for receiving user input, the stored computer program causing an electronic device to perform the method for distributed robust security resource allocation for a small cell network according to any of claims 1-2, comprising the steps of:
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 the small cell user and the macro user;
thirdly, a pricing mechanism is introduced, and an optimization model of a small cell user terminal is designed;
step four, solving an optimization model of the small cell user to obtain a safe transmission strategy of the small cell user;
fifthly, iteratively updating the security policy among the users of the small cell to achieve the equilibrium state of the whole network;
sixth, updating price factors based on the obtained network equilibrium 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 converges, wherein the network equalization in the final state is the safe transmission scheme of all small cell users.
4. A resource allocation system implementing a method of distributed robust security resource allocation for a small cell network according to any of claims 1-2, characterized in that 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 channel information of the eavesdropper in each small cell;
the interference analysis module is used for analyzing the interference under the worst condition based on the uncertainty of the interference channel between the small cell user and the macro user;
the optimizing model design module is used for introducing a pricing mechanism and designing an optimizing model of the small cell user side;
the safety transmission strategy module is used for solving an optimization model of the small cell user to obtain a safety transmission strategy of the small cell user;
the security policy iteration updating module is used for achieving the equilibrium state of the whole network through security policy iteration updating among small cell users;
the price factor updating module is used for updating the price factor based on the obtained network equilibrium state and broadcasting the price factor in the whole network;
and the network equalization module is used for equalizing the network in a final state based on the updated price factor until the price factor converges, namely, the safe transmission scheme of all small cell users.
5. A wireless terminal, wherein the wireless terminal is equipped with the resource allocation system according to claim 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010362941.5A CN111615111B (en) | 2020-04-30 | 2020-04-30 | Small cell network-oriented distributed robust security resource allocation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010362941.5A CN111615111B (en) | 2020-04-30 | 2020-04-30 | Small cell network-oriented distributed robust security resource allocation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111615111A CN111615111A (en) | 2020-09-01 |
| CN111615111B true CN111615111B (en) | 2023-07-25 |
Family
ID=72201185
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010362941.5A Active CN111615111B (en) | 2020-04-30 | 2020-04-30 | Small cell network-oriented distributed robust security resource allocation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111615111B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113422628B (en) * | 2020-12-24 | 2023-02-03 | 西北工业大学 | Safe transmission method and terminal system based on reconfigurable intelligent surface and active interference |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106954227A (en) * | 2017-02-24 | 2017-07-14 | 南京邮电大学 | Efficiency resource allocation methods of the ultra dense set network based on interference coordination |
| CN108430109A (en) * | 2018-03-02 | 2018-08-21 | 北京科技大学 | A kind of the cooperative game resource allocation method and its system of the small subzone network of cognition |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180054806A1 (en) * | 2016-08-22 | 2018-02-22 | Alcatel-Lucent Usa, Inc. | Systems and methods for decoupling control and data channels in wireless networks |
| US10080200B2 (en) * | 2016-10-27 | 2018-09-18 | Yuan Ze University | Intelligent deployment cascade control device based on an FDD-OFDMA indoor small cell in multi-user and interference environments |
-
2020
- 2020-04-30 CN CN202010362941.5A patent/CN111615111B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106954227A (en) * | 2017-02-24 | 2017-07-14 | 南京邮电大学 | Efficiency resource allocation methods of the ultra dense set network based on interference coordination |
| CN108430109A (en) * | 2018-03-02 | 2018-08-21 | 北京科技大学 | A kind of the cooperative game resource allocation method and its system of the small subzone network of cognition |
Non-Patent Citations (4)
| Title |
|---|
| Haining Wang.Joint power and rate optimization for co-channel small cells using Frank-Wolfe algorithm.《IEEE》.2016,全文. * |
| 张海波 ; 彭星萤 ; 陈善学 ; .宏-飞蜂窝双层网络中基于毫微微基站分组的资源分配.计算机应用.2017,(05),全文. * |
| 徐勇军 ; 李国权 ; 陈前斌 ; 林金朝 ; .异构无线网络干扰效率最大顽健资源分配算法.电子学报.2020,(03),全文. * |
| 李文超.超密集网络中基于分簇的无线网络资源分配技术研究.《中国硕士论文全文数据库》.2019,全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111615111A (en) | 2020-09-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Achievable rate maximization for cognitive hybrid satellite-terrestrial networks with AF-relays | |
| Guo et al. | Robust resource allocation with imperfect channel estimation in NOMA-based heterogeneous vehicular networks | |
| Tabassum et al. | Modeling and analysis of uplink non-orthogonal multiple access in large-scale cellular networks using poisson cluster processes | |
| Khan et al. | Efficient power allocation in downlink multi‐cell multi‐user NOMA networks | |
| Kalamkar et al. | Resource allocation and fairness in wireless powered cooperative cognitive radio networks | |
| Sun et al. | D2D enhanced heterogeneous cellular networks with dynamic TDD | |
| Dao et al. | Resource-aware relay selection for inter-cell interference avoidance in 5G heterogeneous network for Internet of Things systems | |
| Zhang et al. | Spectrum allocation and power control in full-duplex ultra-dense heterogeneous networks | |
| Ahmed et al. | Backscatter sensors communication for 6G low-powered NOMA-enabled IoT networks under imperfect SIC | |
| Jangsher et al. | Resource allocation in moving small cell network | |
| LeAnh et al. | Distributed power and channel allocation for cognitive femtocell network using a coalitional game in partition-form approach | |
| CN114006645B (en) | Relay-assisted safe transmission method and system for cognitive unmanned aerial vehicle | |
| Lu et al. | Advanced NOMA assisted semi-grant-free transmission schemes for randomly distributed users | |
| Nouri et al. | Multi-UAV placement and user association in uplink MIMO ultra-dense wireless networks | |
| Singh et al. | Exploiting cache-free/cache-aided TWR-NOMA in cognitive hybrid satellite-terrestrial networks | |
| Mirzaei | A novel approach to QoS‐aware resource allocation in NOMA cellular HetNets using multi‐layer optimization | |
| CN107567055A (en) | Robust resource allocation methods based on user's outage probability in two layers of heterogeneous wireless network | |
| Yao et al. | Distributed ABS-slot access in dense heterogeneous networks: A potential game approach with generalized interference model | |
| Chen et al. | Downlink interference management in cell-free VLC network | |
| Elhattab et al. | Opportunistic device association for heterogeneous cellular networks with H2H/IoT co-existence under QoS guarantee | |
| Guo et al. | Outage performance of ris-assisted cognitive non-terrestrial network with noma | |
| Esmat et al. | Joint channel selection and optimal power allocation for multi‐cell D2D communications underlaying cellular networks | |
| Koseoglu et al. | Cross-layer energy minimization for underwater ALOHA networks | |
| CN111615111B (en) | Small cell network-oriented distributed robust security resource allocation method | |
| Wang et al. | Performance analysis of cooperative aerial base station-assisted networks with non-orthogonal multiple access |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |