CN105764063B - Small cell network power distribution method based on broad sense Nash Equilibrium - Google Patents
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- H—ELECTRICITY
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- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
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- H04W52/18—TPC being performed according to specific parameters
- H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
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Abstract
The invention discloses a kind of small cell network power distribution methods based on broad sense Nash Equilibrium, the broad sense Nash Equilibrium for the non-cooperative game problem that this method is made of solution macrocellular and cellulor, the transmission power of macro base station and small base station is calculated in a distributed manner, to obtain macro base station and small base station power distribution on each channel and traffic rate.Information interaction amount of the present invention is less, interference is smaller, sharing frequency spectrum resource is more efficient, the scope of application is wider.
Description
Technical Field
The invention relates to a communication technology, in particular to a small cellular network power distribution method based on generalized Nash equilibrium.
Background
With the development of smart phones and mobile applications, the explosive growth of wireless data has made current cellular networks already saturated. Current mobile communication technology has been unable to meet the demand, and therefore next-generation mobile communication systems have to seek new technology to meet these challenges, and Small Cells (Small Cells) are considered as a key technology of next-generation wireless communication technology. Small Cells are low power radio access nodes that can cover a range of 10 meters to 200 meters. By densely deploying Small cells with low power consumption in a macro Cell, the load of a macro base station can be reduced, the capacity of a system can be improved, the coverage area can be increased, and the spectrum utilization rate can be improved, so that the difficulties of lack of spectrum resources and insufficient capacity can be relieved.
The deployment of Small cells in macrocells naturally forms a two-layer heterogeneous network, which enables users to access the network through nearby Small cells to improve transmission rate, reduce macrocell load, and improve system capacity. Small cells are essentially a complement to Macro cells, so the Quality of service requirements of the two networks are different, and usually macrocells have higher priority and strict Quality of service (QoS) requirements to guarantee the communication of macrocellular users. Since the deployment density of Small Base Stations (SBS) is much higher than that of Macro Base Stations (MBS), if Small Cell and Macro Cell share spectrum resources, the deployment of Small Cell will have a great influence on QoS requirements of Macro Cell users, and therefore some restrictions must be made on the transmission power of Small Cell; in turn, the MBS transmit power also affects the Small Cell performance, and Small Cell users (SUEs) also interfere with each other, so that the Small Cell and Macro Cell must be jointly optimized. The following three difficulties must be faced in order to achieve joint optimization of Small cells and Macro cells: one, some smallcalls such as the home Cell (Femto Cell) are deployed by user installation rather than operator planning ahead; secondly, the deployment density of the SmallCells is usually far higher than that of the MBS, and the information interaction quantity among the SmallCells is very Small; thirdly, the backhaul link of some Small cells is ethernet or other wireless network such as Relay (Relay), so there is a large delay in the network. This presents a significant challenge to jointly optimize Small cells and Macro cells, greatly limiting system performance. In view of the high density random deployment of SBS, researchers have proposed ways to use wireless backhaul for Small cells. Therefore, interference management of Small Cell networks in such a transmission environment must limit information interaction between the base stations. In order to reduce the interaction amount of information, the power of the SBS and the MBS can be jointly optimized by designing a distributed algorithm.
With the development of 5G communications, more and more Small cells will be attached to cellular networks, which undoubtedly presents a huge challenge to current communication technologies.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the invention provides a small cellular network power distribution method based on generalized Nash equilibrium.
The technical scheme is as follows: the invention discloses a small cell network power distribution method based on generalized Nash equilibrium, which calculates the transmitting power of a macro base station and a small base station in a distributed manner by solving the generalized Nash equilibrium of a non-cooperative game problem formed by macro cells and small cells, thereby obtaining the power distribution and the communication rate of the macro base station and the small base station on each channel.
Further, the step of calculating the transmission power of the macro base station and the small base station in a distributed manner by solving the generalized nash equilibrium of the non-cooperative game problem formed by the macro cell and the small cell to obtain the distributed power and the communication rate of the macro base station and the small base station on each channel specifically includes:
step 1: macro base station sets initial dual variable mu0Not less than 0, precision is epsilon1And step length sequenceAnd will dually vary the mu0Broadcast to other small cells within the macro cell; the dual variable is a vector of QoS constraints corresponding to each channel user of the macro base station;
step 2: the macro base station sets an initial feasible transmit power matrix p0Initializing the internal iteration time t to be 0; the transmission power matrix is a matrix formed by the transmission powers of the macro base station and other small base stations on all channels;
and step 3: the macro base station and other small base stations maximize the effectiveness thereof and update respective transmitting power vectors according to the current dual variables by the problem of non-cooperative game due to mutual interferencei ═ 0.., M; wherein,representing vectors formed by the transmitting power of the base station i on all channels when the t +1 internal iterations are performed, wherein M represents the total number of small base stations;
and 4, step 4: each base station i judges separatelySending the judgment result to the macro base station;
and 5: the macro base station checks whether all base stations i are subjected to step 4 according to the received information: if both are true, go to step 6; otherwise, if one is not true, making t equal to t +1, and going to step 3;
step 6: macro base station according to step length sequenceAnd the dual variable mu at the last iterationnAnd calculating to obtain a dual variable mu at the current iterationn+1；
And 7: determine | mun+1-μn|≤∈1If not, determining whether the current situation is satisfied; if yes, the current transmission power matrix pt+1If not, making n equal to n +1, and going to step 2;
and 8: and the macro base station calculates according to the equilibrium solution to obtain the power distribution and the communication rate of the macro base station and the small base station on each channel.
Further, the transmission power vector in the step 3Comprises the following steps:
where K is the total number of channels, the base station with i equal to 0 is a macro base station, i equal to 1, …, the base station with M is a small base station,represents the transmit power on channel k at the t +1 internal iteration of the ith base station,the specific calculation is as follows:
wherein is represented by Represents all interference signals received by base station i on channel k at the t-th iteration, anσi(k) Representing the thermal noise, p, of base station i on channel kj t(k) Represents the transmit power, h, of base station j on channel k at the t-th internal iterationij(k) Represents the channel gain of base station i to base station j serving a user on channel k, andγ k represents the minimum transmission rate that macro cell users need to meet;a matrix of transmit powers of all small base stations except the macro base station at the t-th iteration,representing a matrix formed by the transmitting powers of all the small base stations except the base station i and the macro base station in the t iteration;λiis that makeSmallest positive integer of true, pi(k) Denotes the allocated transmission power of base station i on channel k, N isThe number of the small base stations is,represents the maximum sum power allowed for transmission by base station i;represents the maximum transmission power allowed by the base station i on the channel k;and the dual variables are the corresponding QoS constraints of the kth channel user of the macro base station in n +1 external iterations.
Further, the transmission power matrix p in the step 7t+1Comprises the following steps:
further, the dual variable μ in the step 6n+1Comprises the following steps:
wherein,[·]+＝max{0，·}；
in the formula,represents the QoS constraints, rho, of the kth channel user of the macro base stationnIs from a sequence of step sizesThe extracted nth value.
Further, the communication rate in step 8 is:
wherein R isik(pi,p-i) Representing the communication rate of base station i to channel user k.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages:
1. the distributed algorithm can be realized only by interacting dual variables and a small amount of control information, and the interference is less;
2. in the invention, the same cellular frequency band can be shared by all Small cells at the same time, but is not limited to be used by one Small Cell or Macro Cell, so that the spectrum resource sharing efficiency is higher;
3. the invention is suitable for various conditions that all base stations have power limit and independent power limit, cellular users have QoS limit and the like, and has wide application range;
4. the invention is suitable for the condition that the transmitting power of the MBS is adjustable, and can more flexibly and conveniently improve the system performance;
5. the method provided by the invention can be realized in a distributed manner, is suitable for scenes with more Small cells, and can improve the flexibility of Small Cell access.
Drawings
FIG. 1 is a schematic flow diagram of one embodiment of the present invention;
FIG. 2 is a diagram illustrating Small Cell network downlink interference;
FIG. 3 is a schematic diagram of randomly distributed locations of macro cell users, small cell sites, and small cell users;
fig. 4 is a schematic diagram of power convergence of a macro base station partial carrier;
FIG. 5 is a diagram illustrating power convergence of a first small cell on a portion of carriers;
FIG. 6 is a graph comparing the performance of an embodiment of the present invention and a conventional Nash equalization algorithm;
fig. 7 is a diagram of a comparison of macro cell user rates for a conventional nash equalization algorithm and a nash equalization algorithm that does not take into account macro cell rate requirements, in accordance with an embodiment of the present invention.
Detailed Description
The embodiment provides a small cell network power allocation method based on generalized Nash equilibrium, which calculates the transmitting power of a macro base station and a small base station in a distributed manner by solving the generalized Nash equilibrium of a non-cooperative game problem formed by macro cells and small cells, so as to obtain the power allocation and the communication rate of the macro base station and the small base station on each channel.
As shown in fig. 1, the method of this embodiment specifically includes:
step 1: macro base station sets initial dual variable mu0Accuracy e1And step length sequenceAnd will dually vary the mu0Broadcast to other small base stations within the macro cell.
And the dual variable is a vector of QoS constraint corresponding to each channel user of the macro base station.
Step 2: the macro base station sets an initial feasible transmit power matrix p0The number of internal iterations t is initialized to 0.
The transmission power matrix is a matrix formed by the transmission powers of the macro base station and other small base stations.
And step 3: the macro base station and other small base stations, through the problem of non-cooperative gaming due to mutual interference,according to the current dual variable, maximizing the utility of the transmission power vector and updating the respective transmission power vectori＝0,…,M。
Wherein,representing vectors formed by the transmitting power of the base station i on all channels when the t +1 internal iterations are performed, wherein M represents the total number of small base stations; vector of transmitted powerComprises the following steps:
where K is the total number of channels, the base station with i equal to 0 is a macro base station, i equal to 1, …, the base station with M is a small base station,represents the transmit power on channel k at the t +1 internal iteration of the ith base station,the specific calculation is as follows:
wherein is represented byRepresents all interference signals received by base station i on channel k at the t-th iteration, anσi(k) Representing the thermal noise, p, of base station i on channel kj t(k) Represents the transmit power, h, of base station j on channel k at the t-th internal iterationij(k) Represents the channel gain of base station i to base station j serving a user on channel k, andγkrepresents the minimum transmission rate that the macro cell user needs to meet;a matrix of transmit powers of all small base stations except the macro base station at the t-th iteration,representing a matrix formed by the transmitting powers of all the small base stations except the base station i and the macro base station in the t iteration;λiis that makeSmallest positive integer of true, pi(k) Indicating the allocated transmit power of base station i on channel k, N being the number of small base stations,represents the maximum sum power allowed for transmission by base station i;represents the maximum transmission power allowed by the base station i on the channel k;and the dual variables are the corresponding QoS constraints of the kth channel user of the macro base station in n +1 external iterations.
The small base stations have an autonomous function and can calculate respective transmitting power, so that each base station can maximize the self utility under the condition of ensuring the communication rate of cellular users.
And 4, step 4: each base station i judges separatelyAnd sending the judgment result to the macro base station.
Wherein,represents the transmit power on channel k at the t internal iteration of the ith base station, i.e. the transmit power on channel k at the previous internal iteration of the ith base station,the transmit power on channel k for the ith base station at the current inner iteration.
And 5: and the macro base station checks whether all the base stations i are satisfied according to the received information, and the step 4 is satisfied.
If both are true, go to step 6; otherwise, if there is a failure, let t be t +1, and go to step 3.
Step 6: macro base station according to step length sequenceAnd the dual variable mu at the last iterationnAnd calculating to obtain a dual variable mu at the current iterationn+1。
Wherein[·]+＝max{0，·}；
In the formula,represents the QoS constraints, rho, of the kth channel user of the macro base stationnIs from the sequence of step sizes { p }l}l＝1The nth value.
And 7: determine | mun+1-μn|≤∈1Whether or not this is true.
If true, the current transmit power matrix pt+1I.e. the equalization solution of the generalized nash equalization, otherwise let n be n +1, and go to step 2.
Wherein p ist+1The transmit power matrix when t +1 internal iterations are represented specifically as:
and 8: and the macro base station calculates according to the equilibrium solution to obtain the power distribution and the communication rate of the macro base station and the small base station on each channel.
The power distribution of the macro base station and the small base station on each channel can be directly obtained from the final transmitting power matrix, and the calculation formula of the communication rate is as follows:
wherein R isik(pi,p-i) Representing the communication rate, R, of a base station i to a channel user k0k(p0,p-0) Representing the communication rate of the macro base station to the channel user k.
The sum communication rate of base station i on all cellular channels is then:
in this method, the optimization objective is to maximize the communication rate of each base station, i.e.:since there are multiple users in a cellular network, the lowest communication rate constraint, R, is considered for each cellular channel user0k(p0,p-0)≥Rmin(k) (ii) a For the downlink, the independent transmit power of the macro base station on band k is limited toIndependent transmit power limitation of small cell i on cellular channel kThe sum power of the base station is limited toSum power limitation of small base station i transmitting end
The present embodiment is subjected to simulation verification as follows.
As shown in fig. 2 and fig. 3, wherein the coverage radius of the macro cell is 500m, 6 small cells are deployed in the coverage area of the macro cell, the frequency band of the macro cell is 10MHz, the macro cell is divided into 10 subcarriers, all the frequency bands are shared by the macro cell and the small cells, and each frequency band is divided into each macro cell user and small cell user, assuming that the coverage radius of the small cell is 100m, wherein the small cell users and the macro cell users are both randomly distributed in the coverage area, and considering large-scale fading, path fading is logarithmically 128.1+37.6log10d (km) dB attenuation, where d represents the distance between the base station and the user. Small-scale fading, due to buildings or other factors, is considered herein to be a cyclic complex gaussian variable. To show performance, the simulation considers only the total power constraint, i.e. fetchWhere i is 1, …, M.
As shown in fig. 4 and 5, the power convergence situation of the macro base station and the small base station is shown. It can be seen that the method provided by the invention can be adopted to converge in about 40 steps, and has a faster rate.
As shown in fig. 6, the system performance is shown as a function of the maximum allowed transmit power of the macro base station, and it can be seen that as the maximum allowed transmit power increases, the total sum rate becomes larger continuously. Meanwhile, in order to embody the fairness of the algorithm performance, the Nash equilibrium algorithm considers the conversion of the global QoS constraint into the single power constraint, namely h exists between the base station i and the channel ki0(k)pi(k)≤ξi,kWhereini＝1,…,M，it can be seen that, under the condition that the QoS constraints are all satisfied, the method of the embodiment can dynamically adjust the power allocation instead of solving like the conventional nash balance problem, so that the method is more flexible and the obtained result is more excellent.
As shown in fig. 7, three different algorithms are shown to satisfy QoS constraints, and the generalized nash equalization method proposed in this embodiment can satisfy the communication rate of the macro cell user, but the nash equalization algorithm without considering the QoS constraints cannot satisfy the communication of the macro cell user. As can be seen from the results, firstly, if the QoS constraint is not considered, many macro-cellular users do not transmit signals, which results in that some macro-cellular users cannot communicate, that is, if the QoS constraint is not considered, the case of no communication does exist, and the power distribution is very uneven; secondly, it can be seen that the generalized nash equalization algorithm and the nash equalization algorithm both satisfy the macro cell user communication, but it can be found that the value of the nash equalization algorithm on each carrier is greater than the value of the generalized nash equalization algorithm, which indicates that a part of resources are wasted due to the conversion of the global QoS constraint into the single power constraint, and the resources are not fully and reasonably utilized, but the algorithm of the present invention can just make up for the defect, and the resource allocation is considered from the global perspective, so that the performance is more reasonably optimized under the condition of satisfying the transmission rate of the macro cell user.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages:
1. the distributed algorithm can be realized only by interacting dual variables and a small amount of control information, and the interference is less;
2. in the invention, the same cellular frequency band can be shared by all Small cells at the same time, but is not limited to be used by one Small Cell or Macro Cell, so that the spectrum resource sharing efficiency is higher;
3. the invention is suitable for various conditions that all base stations have power limit and independent power limit, cellular users have QoS limit and the like, and has wide application range;
4. the invention is suitable for the condition that the transmitting power of the MBS is adjustable, and can more flexibly and conveniently improve the system performance;
5. the method provided by the invention can be realized in a distributed manner, is suitable for scenes with more Small cells, and can improve the flexibility of Small Cell access.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (5)
1. A small cellular network power distribution method based on generalized Nash equilibrium is characterized in that: calculating the transmitting power of the macro base station and the small base station in a distributed manner by solving the generalized Nash equilibrium of the non-cooperative game problem formed by the macro cell and the small cell, thereby obtaining the power distribution and the communication rate of the macro base station and the small base station on each channel; the method specifically comprises the following steps:
(1) macro base station sets initial dual variable mu0Not less than 0, precision is epsilon1And step length sequenceAnd will dually vary the mu0Broadcast to other small cells within the macro cell; the dual variable is a vector of QoS constraints corresponding to each channel user of the macro base station;
(2) the macro base station sets an initial feasible transmit power matrix p0Initializing the internal iteration time t to be 0; the transmission power matrix is a matrix formed by the transmission powers of the macro base station and other small base stations on all channels;
(3) the macro base station and other small base stations maximize the effectiveness thereof and update respective transmitting power vectors according to the current dual variables by the problem of non-cooperative game due to mutual interferenceWherein,representing vectors formed by the transmitting power of the base station i on all channels when the t +1 internal iterations are performed, wherein M represents the total number of small base stations;
(4) each base station i judges separatelySending the judgment result to the macro base station;
(5) the macro base station checks whether all base stations i are subjected to the step (4) according to the received information: if both are true, turning to the step (6); otherwise, if there is a failure, making t ═ t +1, and going to step (3);
(6) macro base station according to step length sequenceAnd the dual variable mu at the last iterationnAnd calculating to obtain a dual variable mu at the current iterationn+1；
(7) Determine | mun+1-μn|≤∈1Whether the result is true or not; if yes, the current transmission power matrix pt+1And (3) an equalization solution of generalized Nash equalization, otherwise, making n equal to n +1, and turning to the step (2);
(8) and the macro base station calculates according to the equilibrium solution to obtain the power distribution and the communication rate of the macro base station and the small base station on each channel.
2. The method of claim 1 for power allocation in small cell networks based on generalized nash equalization, characterized in that: the transmit power vector in (3)Comprises the following steps:
where K is the total number of channels, the base station with i equal to 0 is a macro base station, i equal to 1, the base station with M is a small base station,represents the transmit power on channel k at the t +1 internal iteration of the ith base station,the specific calculation is as follows:
wherein is represented by Represents all interference signals received by base station i on channel k at the t-th iteration, anσi(k) Representing the thermal noise, p, of base station i on channel kj t(k) Represents the transmit power, h, of base station j on channel k at the t-th internal iterationij(k) Represents the channel gain of base station i to base station j serving a user on channel k, andγkrepresents the minimum transmission rate that the macro cell user needs to meet;a matrix of transmit powers of all small base stations except the macro base station at the t-th iteration,representing a matrix formed by the transmitting powers of all the small base stations except the base station i and the macro base station in the t iteration;λiis that makeSmallest positive integer of true, pi(k) Indicating the allocated transmit power of base station i on channel k, N being the number of small base stations,represents the maximum sum power allowed for transmission by base station i;represents the maximum transmission power allowed by the base station i on the channel k;and the dual variables are the corresponding QoS constraints of the kth channel user of the macro base station in n +1 external iterations.
3. The method of claim 2 for power allocation in a small cell network based on generalized nash equalization, wherein: the transmit power matrix p in the step (7)t+1Comprises the following steps:
4. the method of claim 2 for power allocation in a small cell network based on generalized nash equalization, wherein: the dual variable μ in the step (6)n+1Comprises the following steps:
wherein,[·]+＝max{0,·}；
in the formula,represents the QoS constraints, rho, of the kth channel user of the macro base stationnIs from a sequence of step sizesThe extracted nth value.
5. The method of claim 2 for power allocation in a small cell network based on generalized nash equalization, wherein: the communication rate in the step (8) is as follows:
wherein,Rik(pi,p-i) Representing the communication rate of base station i to channel user k.
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