CN107708157A - Intensive small cell network resource allocation methods based on efficiency - Google Patents
Intensive small cell network resource allocation methods based on efficiency Download PDFInfo
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- 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
- H04W28/08—Load balancing or load distribution
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W48/00—Access restriction; Network selection; Access point selection
- H04W48/20—Selecting an access point
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/30—TPC using constraints in the total amount of available transmission power
- H04W52/34—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/02—Selection of wireless resources by user or terminal
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
- H04W84/04—Large scale networks; Deep hierarchical networks
- H04W84/042—Public Land Mobile systems, e.g. cellular systems
- H04W84/045—Public Land Mobile systems, e.g. cellular systems using private Base Stations, e.g. femto Base Stations, home Node B
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Abstract
The present invention relates to moving communicating field, specially a kind of intensive small cell network resource allocation methods based on efficiency, including：Cell optimal selection is determined based on load balancing；Channel assignment scheme is determined according to the cell optimal selection；According to the channel assignment scheme, optimization network energy efficiency determines optimal power allocation scheme, the cell optimal selection of the present invention is under the transmission requirement that ensure that user, user is controlled to be successfully accessed the probability of base station by modeling load balancing, so as to realize load balancing, while improve the energy efficiency of network；Based on fixed load balancing scheme, combined optimization network energy efficiency realizes rational channel distribution and power allocation scheme.
Description
Technical Field
The invention relates to the field of mobile communication, in particular to a method for allocating energy-efficiency-based dense small cellular network resources.
Background
With the wide application of the internet and the popularization of mobile intelligent terminals, the demand of people for mobile data services is increasing day by day, and people are now accustomed to and depend on ubiquitous and untimely wireless networks. The frequency bands used by the existing 4G network mobile communication system and the next generation mobile communication system are high frequency bands of about 2GHz, the high frequency wireless signals have very strong penetration capability, and the transmission energy loss is very large, so that the indoor signals are greatly attenuated, and even in some closed environments, the phenomena of weak coverage and even coverage of 'holes' occur. In such a case, the communication demand of the indoor user cannot be satisfied by only the macro base station. In addition, it is investigated that 70% of mobile data services occur indoors, and 20% to 40% of europe, 40% to 50% of the usa, and 60% of china mobile phone calls occur indoors.
In order to meet the increasing high-rate service requirement, it is considered as an effective solution to deploy a small cell network, the small cell has the characteristics of low power, small coverage area and flexible networking, and is particularly suitable for blind repair and hot spot area shunting in urban areas, and 80% of traffic can be shunted in busy areas. Therefore, small cells have become one of the key technologies of 5G in recent years.
With the continuous richness of service types and the rapid increase of application requirements, especially the emergence of services with high bandwidth requirements, people have an increasing demand for resources in wireless networks, and the contradiction between the increasing resource requirements and the limited network resource supply has become one of the main factors restricting the development of wireless communication. Therefore, achieving reasonable and efficient utilization of wireless network resources is one of the technical challenges that need to be solved urgently in dense small cellular networks. In a complex and variable heterogeneous convergence network environment, because base station equipment in the network consumes huge energy, in addition to efficient utilization of network resources and guarantee of Quality of Service (QoS), how to effectively reduce energy consumption in the heterogeneous network and improve network energy efficiency on the premise of satisfying user Service data transmission is also one of the key challenges that the current heterogeneous wireless network faces in serving users.
In summary, it is a main objective of radio resource management to reasonably allocate resources on a limited network resource supply to maximize network energy efficiency.
Disclosure of Invention
In view of this, the present invention is directed to how to implement an optimal selection scheme for users and cell base stations under load balancing, and reasonably allocate channels and power to each user while improving network energy efficiency.
The invention provides an energy efficiency-based dense small cellular network resource allocation method which can effectively improve network energy efficiency and guarantee user service requirements. This will have a very important significance for increasing the overall performance of the network.
The technical scheme provided by the invention for solving the problems is a method for allocating the resources of the dense small cellular network based on energy efficiency, which comprises the following steps: the method comprises the following steps:
s1, determining optimal selection of a cell based on load balancing;
s2, determining a channel allocation scheme according to the optimal selection of the cell;
and S3, optimizing the network energy efficiency according to the channel allocation scheme to determine an optimal power allocation scheme.
Further, for determining the optimal selection of the cells, each user can establish a list of candidate cells according to the minimum service rate requirement of the user, that is, a set of cells meeting the minimum rate requirement of the user, and define the priority of the user as the number of base stations in the list of candidate cells. And the user with high priority preferentially selects the base station with the maximum utility function value as the service base station of the user.
Further, for the channel allocation scheme, to reduce the same-layer and cross-layer interference, cell and user clustering is performed first, and then channel allocation is performed. Firstly, cells with neighbor relation are divided into clusters, and then user clustering is carried out in the cell clusters with the aim of minimizing the relevant interference of users. After clustering is completed, the users in the same user cluster select the sub-channel which enables the average energy efficiency of the cluster to be maximum, and the process is iterated until all the sub-channels are distributed.
Further, for the power allocation scheme, since one base station can connect to multiple users, the power allocation is limited by the maximum transmission power of the base station, that is, the maximum transmission power is limitedBased on the consideration, the algorithm jointly optimizes the network energy efficiency and determines the optimal power distribution scheme by combining the channel distribution scheme.
The invention has the beneficial effects that: the method of the invention not only can effectively ensure the load balance under different types of base stations in the dense small cellular network, but also realizes the optimal channel allocation scheme and power allocation scheme by combining the maximized network energy efficiency, thereby improving the transmission performance of the whole network.
Drawings
FIG. 1 is a diagram of a dense small cell network scene model of the present invention;
fig. 2 is a flowchart of a method for energy-efficiency-based dense small-cell network resource allocation according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly and completely apparent, the technical solutions in the embodiments of the present invention are described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention will be further described with reference to the following detailed description of embodiments and with reference to the accompanying drawings in which: the invention is realized based on the dense small cellular network scene as shown in fig. 1, wherein macro base stations are distributed in the center of the network, small base stations are randomly distributed in the macro cellular network, and in fig. 1, MBS represents a macro base station; PBS stands for microcell base station; FBS denotes home base stations, users being randomly distributed in the system.
The energy efficiency-based dense small cellular network resource allocation method of the invention, as shown in fig. 2, includes:
s1, determining optimal selection of a cell based on load balancing;
s2, determining a channel allocation scheme according to the optimal selection of the cell;
and S3, optimizing the network energy efficiency according to the channel allocation scheme to determine an optimal power allocation scheme.
Further, the determining the optimal selection of the cell based on the load balancing comprises: the user selects the service base stations in sequence according to the size of the utility function, and the method comprises the following steps: the user establishes an alternative cell list according to the minimum service rate requirement of the user; determining the priority of the user according to the number of the base stations in the candidate cell list of the user, namely, the user with the larger number of the base stations in the candidate cell list has the higher priority; and according to the priority sequence, enabling the user with higher priority to select the base station with larger value of the utility function as the service base station of the user with higher priority.
Since the transmission power, coverage area and maximum number of users that can be served by different types of base stations in a small cellular network are different, the present invention calls the number of users served by a base station m as the load L of the base station m And the maximum load of a base station is the number of users which can be served by the base station at most, wherein the maximum load is related to not only the maximum number of transmission resources of the base station, but also the scheduling method of the base station. By L m_b To indicate the maximum number of users that the base station m can simultaneously serve in one Transmission Time Interval (TTI), L m_max Is that said base station m is in a transmission time intervalThe number of users which can be served simultaneously in every TTI is twice that of the users; each TTI is equivalent to two slots in an LTE network, and the time length occupied by each TTI is 1ms.
When the load of the base station satisfies L m >L m_b In time, it cannot be determined that the base station is overloaded, because some appropriate scheduling method can be adopted to allow the user to obtain service in the next TTI;
preferably, the present invention uses Round Robin Scheduling (RRS) to allow the user to obtain service in the next TTI.
Furthermore, according to the 'zero' delay requirement put forward for future networks, the device-to-device delay must be limited within the range of 1ms, so under the polling scheduling method, the maximum number L of users that a base station can serve m_max Exactly twice as many users as can be served in one TTI thereof.
Further, the load balancing factor is:
the modeling load balancing factor is used for expressing the probability that a user K successfully accesses a base station m, and K belongs to 1, 2.., K; wherein L is m Representing the number of users currently served by a base station M, wherein M belongs to 1, 2. L is m_max Indicates the maximum number of service users, L, of the base station m m_b Indicating the maximum number of users that the base station m can simultaneously serve in one TTI.
By usingRepresenting the energy efficiency of the user k accessing the base station m, and further modeling a utility function Is the signal to interference plus noise ratio, P, of user k accessing base station m m Is the maximum transmission power of base station m.
Further, in order to ensure the minimum service requirement of the user, the cells meeting the minimum speed requirement of the user are listed in an alternative cell list, the number of base stations in the list is defined as the priority of the user k, and the user with high priority preferentially selects the base stations; and in accordance withDetermining a base station which can maximize the energy efficiency value as a service base station, and updating the current load and cell list of the base station after a user finishes selecting once, namely: the user establishes an alternative cell list according to the minimum service rate requirement of the user; determining the priority of the user according to the number of the base stations in the candidate cell list of the user, namely the priority of the user is higher for the user with the smaller number of the base stations in the candidate cell list; and according to the priority sequence, enabling the user with higher priority to select the base station with larger utility function value as the service base station of the user with higher priority.
The channel allocation through cell clustering and user clustering specifically comprises the following steps: dividing cells with neighbor relation into clusters; and clustering the users according to the related interference of the minimized users of the cell cluster.
Preferably, cell clustering and user clustering are performed to reduce peer-to-peer and cross-peer interference. The cell clustering is to cluster cells with neighbor relation, and the user clustering is to cluster users with small related interference by taking the cell cluster as a unit. Then, the user cluster selects the sub-channel which enables the average energy efficiency of the user cluster to be maximum, and the process is iterated until all the sub-channels are distributed.
Further, the cell clustering process includes: SINR that user k in base station i will receive from base station j j Reporting to a base station i, wherein the base station i receives the SINR sent by the user k i And SINR j After the information, judging if SINR i -SINR j <I th The base station j is judged as a neighbor base station of the base station i, and the base station i adds the sequence number j of the base station j into the adjacent cell list of the base station i to finish cell clustering; wherein, the SINR i Representing the SINR, from the serving base station i j Representing the signal-to-interference-and-noise ratio from other base stations, wherein i, j belongs to B, B is a set of base stations, and i is not equal to j; i is th Is a threshold value of the signal to interference plus noise ratio difference, I th Is an adjustable parameter.
Further, the clustering users according to the minimum user correlation interference of the cell cluster comprises: dividing users with small related interference into a cluster by minimizing the interference weight sum of the user cluster; the method specifically comprises the following steps: starting from randomly selecting a vertex in the network, traversing the subgraph by adding the vertices from other cells, and minimizing the weight of the current user cluster; when the sum of the interference weights of all vertexes of the current user cluster reaches an upper limit, the current user cluster is independently clustered, and a new user cluster is generated from the rest vertexes; wherein the interference weight of the user cluster comprises:
the interference weight of the user cluster is the reciprocal of the signal-to-interference-and-noise ratio of the users in the cell cluster, wherein:is the SINR, h, of user k accessing base station m k,n,m Is the channel gain from base station m to user k; p k,n,m Is the transmit power from base station m to user k; k' belongs to U, U is the user set, h k',n,j Channel gain, P, expressed as an interference signal k',n,j Transmission power, N, expressed as interference signal 0 Is additive white gaussian noise;
the sum of the correlated interference of two users is the weight of the edge between the two vertices, expressed as:
wherein the content of the first and second substances,respectively represent users k 1 ,k 2 Interference weight of, Δ th Is an upper bound on the interference of the user cluster.
Preferably, the determining the channel allocation specifically includes: iteratively assigning subchannels to a user cluster according to maximizing energy efficiency of the user cluster, comprising: randomly selecting a user cluster, wherein the user cluster selects a subchannel enabling the energy efficiency of the user cluster to be maximum, namely:selecting the channel which enables the energy efficiency of the user cluster to be maximum from the rest sub-channels by other user clusters, and iteratively executing the process until all the channels are distributed;
wherein argmax [ · is]Representing the value of the corresponding argument when the maximum value is obtained, N representing the subchannel, N being the total number of channels, EE n Representing the energy efficiency of the user cluster.
Furthermore, the optimization model of the energy efficiency introduces a constraint condition sigma j∈B,j≠m,k'∈U,k'≠k h k',n,j P k',n,j ≤I max Constructing a Lagrange function, and solving a power distribution strategy by using the updated Lagrange multiplier, wherein the method specifically comprises the following steps:
further, the energy efficiency model based on the modeling of the invention is as follows:
order toq * The maximum energy efficiency corresponding to q, namely:
if and only if:
can realize the maximum energy efficiency q * 。
Wherein, the first and the second end of the pipe are connected with each other,for the load balancing factor, W is expressed as the bandwidth, h k,n,m Is the channel gain from base station m to user k; p k,n,m Is the transmit power from base station m to user k; b represents a set of base stations, B = {1, 2.., M }, M represents a total number of base stations; u represents a user set, U = {1, 2.., K }, and K represents a total number of users; n is a subchannel, N is the total number of channels, N 0 Is additive white Gaussian noise, P cir Expressed as circuit power, h k',n,j Channel gain, P, expressed as an interference signal k',n,j A transmit power represented as an interference signal;is the optimum power.
Because the clustering method is adopted to reduce the interference, the constraint condition sigma is introduced j∈B,j≠m,k'∈U,k'≠k h k',n,j P k',n,j ≤I max ，I max Can be regarded as the maximum interference in each sub-channel, and the constraint can be reduced toDuring the calculation, default | B>1；
Further, a distributed manner is used to independently execute the power allocation scheme for each base station in the network. Thus, for each cell, its optimal power allocation scheme can be obtained by solving the following convex function optimization problem:
further, a Lagrangian function is constructed:
wherein γ is a constraintMu is a constraint conditionThe lagrange multiplier of (a) is,is a constraint conditionP is expressed as power, whereIs the rate obtained after user k connects to base station m,is the minimum rate requirement for user k; i is max Is the maximum interference in each subchannel;
since the Karash-Kuhn-Tucker (KKT) condition is an essential condition for solving the optimization problem, the Lagrangian function is used for solving the KKT conditionTo P k,n,m And solving the partial derivative and making the partial derivative equal to zero, wherein the obtained transmitting power from the base station m to the user k is as follows:
further, preferably, the lagrangian multiplier is updated by using a sub-gradient solution, and the power distribution strategy is solved:
wherein l is more than or equal to 0 and is iteration times, ξ i Is a minimum iteration step, i belongs to {1,2,3};representing the maximum transmission power of the base station m, and iteratively updating the solution power P k,n,m And lagrange multiplierUntil the algorithm converges.
According to the invention, the Lagrangian function is constructed, the updated Lagrangian multiplier is used for solving the power distribution strategy, the optimization problem is solved according to the KKT condition, and the network energy efficiency can be effectively improved.
The invention maximizes the network energy efficiency by combining cell selection and channel and power allocation, and adopts a clustering mode to solve the same-layer and cross-layer interference before channel allocation, thereby achieving the purposes that: the energy efficiency of the network can be maximized, the interference can be reduced, and the channels and the power can be reasonably distributed.
The above preferred embodiments are only intended to illustrate the technical solution of the present invention and not to limit, and although the present invention has been described in detail by the above preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention defined by the claims.
Claims (10)
1. A method for energy efficiency-based resource allocation of a dense small cellular network is characterized by comprising the following steps:
s1, determining optimal selection of a cell based on load balancing;
s2, determining a channel allocation scheme according to the optimal selection of the cell;
and S3, optimizing the network energy efficiency according to the channel allocation scheme to determine an optimal power allocation scheme.
2. The energy efficiency-based dense small cell network resource allocation method according to claim 1, wherein the determining of the optimal selection of the cell based on the load balancing comprises: the method for sequentially selecting the serving base stations by the user according to the utility function specifically comprises the following steps: the user establishes an alternative cell list according to the minimum service rate requirement of the user; determining the priority of the user according to the number of the base stations in the alternative cell list of the user, namely, the priority of the user is higher when the number of the base stations in the alternative cell list is smaller; and according to the priority sequence, enabling the user with higher priority to select the base station with larger utility function value as the service base station of the user with higher priority.
3. The method according to claim 2, wherein the utility function is as follows:wherein the content of the first and second substances,representing a load balancing factor;representing the energy efficiency of user k accessing base station m,P m is the maximum transmission of base station mThe power is transmitted to the power transmission device,the signal-to-interference-and-noise ratio of a user k accessing a base station m; according to a formulaSequentially selects base station m as the serving base station for the user, argmax [ ·]The value of the corresponding argument when the maximum value is obtained is expressed.
4. The method according to claim 3, wherein the load balancing factor comprises:
the load balancing factorThe method is used for representing the probability of successful access of a user K to a base station m, wherein K belongs to 1, 2. Wherein L is m Representing the number of users currently served by a base station M, wherein M belongs to 1,2, and M is the total number of the base stations; l is m_max Indicates the maximum number of service users, L, of the base station m m_max ＝2L m_b ，L m_b Representing the maximum number of users that the base station m can serve simultaneously in one transmission time interval TTI.
5. The method of claim 1, wherein the determining a channel allocation scheme according to the cell-optimal selection comprises: the channel allocation through cell clustering and user clustering specifically comprises the following steps: dividing cells with neighbor relation into clusters; and clustering the users according to the related interference of the minimized users of the cell cluster.
6. The energy efficiency basis according to claim 5The method for allocating dense small cell network resources, wherein the cell clustering process comprises: SINR that user k in base station i will receive from base station j j Reporting to a base station i, wherein the base station i receives the SINR sent by the user k i And SINR j After the information, if SINR i -SINR j <I th The base station j is judged as a neighbor base station of the base station i, and the base station i adds the sequence number j of the base station j into the adjacent cell list of the base station i to finish cell clustering; wherein, the SINR i Representing the SINR, from the serving base station i j Representing the signal-to-interference-and-noise ratio from other base stations, wherein i, j belongs to B, B is a set of base stations, and i is not equal to j; i is th Is the threshold for the signal to interference plus noise ratio difference.
7. The method of claim 5, wherein the clustering users according to their associated interference minimization across cell clusters comprises: dividing users with small related interference into a cluster by minimizing the interference weight sum of the user cluster; the method specifically comprises the following steps: starting from randomly selecting a vertex in the network, traversing the subgraph by adding vertices from other cells, and minimizing the interference weight of the current user cluster; when the sum of the interference weights of all vertexes of the current user cluster reaches an upper limit, the current user cluster is independently clustered, and a new user cluster is generated from the rest vertexes; wherein the interference weight of the user cluster comprises:
the interference weight of the user cluster is the reciprocal of the signal-to-interference-and-noise ratio of the users in the cell cluster, wherein:is the SINR, h, of user k accessing base station m k,n,m Is the channel gain from base station m to user k; p k,n,m Is the transmission power of base station m to user kRate; k' belongs to U, U is a user set, U = {1, 2., K }, and K represents the total number of users; h is k',n,j Channel gain, P, expressed as an interference signal k',n,j Transmission power, N, expressed as interference signal 0 Is additive white gaussian noise;
the sum of the correlated interference of two users is the weight of the edge between the two vertices, expressed as:
wherein the content of the first and second substances,respectively represent users k 1 ,k 2 Interference weight of, Δ th Is an upper bound of interference for the user cluster.
8. The energy efficiency-based dense small cell network resource allocation method according to claim 5, wherein the performing channel allocation specifically includes: iteratively assigning subchannels to user clusters based on maximizing energy efficiency of the user clusters, comprising: randomly selecting a user cluster, wherein the user cluster selects a subchannel enabling the energy efficiency of the user cluster to be maximum, namely:selecting the channel which enables the energy efficiency of the user cluster to be maximum from the rest sub-channels by other user clusters, and iteratively executing the process until all the channels are distributed;
wherein argmax [ · is]Representing the value of the corresponding argument when the maximum value is obtained, N representing the subchannel, N being the total number of channels, EE n Representing the energy efficiency of the user cluster.
9. The energy efficiency-based dense small cell network resource allocation method according to claim 1, wherein the step of determining the optimal power allocation scheme by optimizing network energy efficiency according to the channel allocation scheme comprises: the above-mentionedThe network energy efficiency is limited by the maximum transmission power of the base station, i.e.Under the condition of the air conditioner, the air conditioner is provided with a fan,represents the maximum transmission power of base station m; the optimization model of the energy efficiency is expressed as follows:
the maximum energy efficiency corresponding to the energy efficiency optimization model is as follows:
wherein, the first and the second end of the pipe are connected with each other,for the load balancing factor, P represents power, W represents bandwidth, h k,n,m Is the channel gain from base station m to user k; p is k,n,m Is the transmit power from base station m to user k; b denotes a set of base stations, B = {1, 2.., M }; m represents the total number of base stations; k' is equal to U, U represents a user set, U = {1, 2., K }, and K represents the total number of users; n is a subchannel, N is the total number of channels, N 0 Is additive white Gaussian noise, P cir Represents the circuit power, h k',n,j Representing the channel gain, P, of the interfering signal k',n,j Representing the transmit power of the interfering signal.
10. The method according to claim 9, wherein the energy efficiency optimization model introduces a constraint condition Σ j∈B,j≠m,k'∈U,k'≠k h k',n,j P k',n,j ≤I max Constructing a Lagrangian function, and solving a power distribution strategy by using the updated Lagrangian multiplier, specificallyThe method comprises the following steps:
the lagrange function is:
wherein γ is a constraintIs the lagrange multiplier, mu is the constraintThe lagrange multiplier of (a) is,is a constraint conditionP represents power, whereinIs the rate obtained after user k connects to base station m,is the minimum rate requirement for user k; i is max Is the maximum interference in each subchannel;
by solving the KKT condition, the transmit power of base station m to user k is expressed as:
updating lagrange multipliers by using a sub-gradient solution:
wherein l is more than or equal to 0 and is iteration number, ξ i Is a very small iteration step size, i ∈ {1,2,3}.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108449149A (en) * | 2018-03-26 | 2018-08-24 | 南京邮电大学 | A kind of small base station resource distribution method of energy acquisition based on matching game |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103957532A (en) * | 2014-04-15 | 2014-07-30 | 电子科技大学 | Method for improving energy efficiency of heterogeneous cellular network |
| CN104038945A (en) * | 2014-07-01 | 2014-09-10 | 重庆邮电大学 | Heterogeneous cellular network energy efficiency optimization method based on independent sets |
| CN105554771A (en) * | 2015-12-15 | 2016-05-04 | 重庆邮电大学 | Femto user resource distribution method based on dense distribution |
| CN106412988A (en) * | 2016-10-31 | 2017-02-15 | 东南大学 | Improved weighted graph-based super dense heterogeneous network interference coordination method |
| CN106454850A (en) * | 2016-10-14 | 2017-02-22 | 重庆邮电大学 | Resource distribution method for energy efficiency optimization of honeycomb heterogeneous network |
-
2017
- 2017-11-22 CN CN201711176251.5A patent/CN107708157A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103957532A (en) * | 2014-04-15 | 2014-07-30 | 电子科技大学 | Method for improving energy efficiency of heterogeneous cellular network |
| CN104038945A (en) * | 2014-07-01 | 2014-09-10 | 重庆邮电大学 | Heterogeneous cellular network energy efficiency optimization method based on independent sets |
| CN105554771A (en) * | 2015-12-15 | 2016-05-04 | 重庆邮电大学 | Femto user resource distribution method based on dense distribution |
| CN106454850A (en) * | 2016-10-14 | 2017-02-22 | 重庆邮电大学 | Resource distribution method for energy efficiency optimization of honeycomb heterogeneous network |
| CN106412988A (en) * | 2016-10-31 | 2017-02-15 | 东南大学 | Improved weighted graph-based super dense heterogeneous network interference coordination method |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108449149A (en) * | 2018-03-26 | 2018-08-24 | 南京邮电大学 | A kind of small base station resource distribution method of energy acquisition based on matching game |
| CN108811068A (en) * | 2018-07-24 | 2018-11-13 | 广东工业大学 | A kind of resource allocation methods, device and computer readable storage medium |
| CN108811068B (en) * | 2018-07-24 | 2021-07-02 | 广东工业大学 | Resource allocation method, device and computer readable storage medium |
| CN110012509A (en) * | 2019-04-11 | 2019-07-12 | 重庆邮电大学 | Resource allocation methods based on user mobility in a kind of 5G small cell network |
| CN110012509B (en) * | 2019-04-11 | 2022-04-19 | 重庆邮电大学 | Resource allocation method based on user mobility in 5G small cellular network |
| CN110049506A (en) * | 2019-04-23 | 2019-07-23 | 重庆邮电大学 | A kind of network energy efficiency method for improving based on cluster and geometry water filling resource allocation |
| CN111065121A (en) * | 2019-12-27 | 2020-04-24 | 烟台大学 | Intensive network energy consumption and energy efficiency combined optimization method considering cell difference |
| CN111065121B (en) * | 2019-12-27 | 2022-04-08 | 烟台大学 | Intensive network energy consumption and energy efficiency combined optimization method considering cell difference |
| CN111314938A (en) * | 2020-02-24 | 2020-06-19 | 厦门大学 | Optimization method for time-frequency domain resource allocation of cellular network of single cell |
| CN112367152A (en) * | 2020-10-29 | 2021-02-12 | 国网甘肃省电力公司信息通信公司 | Power wireless private network resource allocation method based on service priority |
| CN112583566A (en) * | 2020-12-03 | 2021-03-30 | 国网甘肃省电力公司信息通信公司 | Network resource allocation method based on air-space-ground integrated system |
| CN112583566B (en) * | 2020-12-03 | 2023-03-31 | 国网甘肃省电力公司信息通信公司 | Network resource allocation method based on air-space-ground integrated system |
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