CN113747458A - Resource allocation method, device, base station and readable storage medium for D2D communication system - Google Patents
Resource allocation method, device, base station and readable storage medium for D2D communication system Download PDFInfo
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Abstract
The invention discloses a resource allocation method, a device, a base station and a readable storage medium of a D2D communication system, wherein the method comprises the following steps: clustering according to the mutual interference degree of all D2D users, and reusing the same CUE communication link channel resource by the D2D users in each cluster set; defining each cluster set as a whole, defining each CUE user as another whole, allocating CUE communication link channel resources to the cluster sets by using a Hungarian matching algorithm, and determining a final channel multiplexing result; energy efficiency is defined as the ratio η of spectral efficiency to total power consumedEEAnd obtaining a system optimization objective function, equivalently transforming the system optimization objective function into a difference with a convex function, namely a D.C. structural form, and obtaining an optimal power distribution scheme by utilizing an interior point method. When there are multiple pairs of D2D users to reuse the honeycombIn the case of a user, the embodiment of the present invention adopts a solution of allocating power after allocating channels, so as to maximize the energy efficiency of the system while ensuring the communication quality between the cellular user and the D2D user.
Description
Technical Field
The invention relates to the technical field of vehicle navigation and positioning, in particular to a resource allocation method, a resource allocation device, a base station and a readable storage medium for a D2D communication system.
Background
With the explosive increase of the number of mobile terminals and communication devices of the internet of things, the energy consumption ratio of the existing communication system is gradually increased, which requires that the energy consumption of unit communication traffic should be reduced as much as possible while optimizing the configuration of wireless resources, so that the problem of system energy efficiency needs to be considered while optimizing resources. A Distributed Antenna System (DAS) is a highly efficient and flexible communication System, which is composed of a plurality of antennas Distributed at different spatial locations, and can be regarded as an extension of a mimo System. DAS has many advantages compared to centralized antenna systems: the frequency spectrum efficiency and the energy efficiency are improved, the communication coverage is enlarged, the transmitting power is reduced, and users in a cell are served more efficiently. Direct communication within a short distance can effectively improve spectrum efficiency, wherein device-to-device (D2D) communication is a novel technology that allows terminals to directly communicate by multiplexing cell resources under the control of a system, and solves the problem of lack of spectrum resources of a wireless communication system to a certain extent. The D2D technique may be applied to mobile cellular networks to improve resource utilization and network capacity. Each D2D communication link occupies the same resources as one cellular communication link. Currently, D2D users mainly access the wireless communication network through three communication modes, namely, an orthogonal mode, a relay mode and a multiplexing mode. More communication freedom also means higher network performance, with higher energy efficiency and spectrum utilization in the reuse mode.
However, the D2D user in reuse mode also causes severe co-channel interference to the cellular user, so effective interference management is necessary. In the distributed antenna D2D communication system with a plurality of pairs of D2D communication users, the interference situation becomes more complicated than that of the centralized system, and in the distributed antenna system with a plurality of pairs of D2D users, only the communication reliability of the D2D users or the link capacity of the cellular users are considered to be insufficient, after the D2D communication is introduced into the distributed antenna system, the spectrum utilization rate of the distributed antenna D2D communication system is the highest in the multiplexing mode, but the problem of co-channel interference is caused at the same time.
Disclosure of Invention
In view of the above, the present invention provides a method, an apparatus, a base station and a readable storage medium for allocating resources of a D2D communication system, so as to solve the technical problem of co-channel interference caused by spectrum multiplexing in a distributed antenna D2D communication system in a multiplexing mode. The technical scheme adopted by the invention for solving the technical problems is as follows:
according to a first aspect of the present invention, there is provided a resource allocation method for a D2D communication system, the method comprising:
defining that in a cell with the radius of R, a plurality of pairs of equipment-to-equipment D2D users multiplex downlink CUE communication link channel resources in a distributed antenna system; wherein, the cell has N remote access units RAU, K randomly distributed cellular user equipment CUE and M D2D pairs, and M is more than K;
clustering according to the mutual interference degree of all the D2D users, and reusing the same CUE communication link channel resource by the D2D users in each cluster set;
defining each cluster set as a whole, defining each CUE user as another whole, allocating channel resources of the CUE communication link to the cluster sets by using a Hungarian matching algorithm, and determining a final channel multiplexing result;
energy efficiency is defined as the ratio η of spectral efficiency to total power consumedEEAnd obtaining a system optimization objective function, equivalently transforming the system optimization objective function into a D.C. structural form, and obtaining an optimal power distribution scheme by utilizing an interior point method.
According to a second aspect of the present invention, there is provided a resource allocation apparatus for a D2D communication system, which is applied to a distributed antenna system, the apparatus including:
a channel allocation module, configured to cluster according to mutual interference degrees of all D2D users, where the D2D users in each cluster set reuse the same CUE communication link channel resource; defining each cluster set as a whole, defining each CUE user as another whole, allocating channel resources of the CUE communication link to the cluster sets by using a Hungarian matching algorithm, and determining a final channel multiplexing result;
a power distribution module for defining energy efficiency as a ratio η of spectral efficiency to total power consumedEEAnd obtaining a system optimization objective function, equivalently transforming the system optimization objective function into a D.C. structural form, and obtaining an optimal power distribution scheme by utilizing an interior point method.
According to a third aspect of the present invention, there is provided a base station comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the computer program when executed by the processor implements the steps of the above-mentioned D2D communication system resource allocation method.
According to the fourth aspect of the present invention, there is also provided a computer readable storage medium, which stores a resource allocation program, and when the resource allocation program is executed by a processor, the resource allocation program realizes the steps of the resource allocation method of the D2D communication system.
According to the resource allocation method, the resource allocation device, the base station and the readable storage medium of the D2D communication system, when a plurality of pairs of D2D users multiplex cellular users in the distributed antenna D2D communication system, the energy efficiency of the system is maximized while the communication quality of the cellular users and the D2D users is ensured; because the problem is a non-convex nonlinear combination optimization problem and a closed solution cannot be directly obtained, the problem is solved by using the idea of firstly allocating channels and then allocating power; firstly, mapping the interference situation of D2D users to an idea graph, allocating D2D users to a proper set by using graph theory knowledge under the criterion of minimizing the total interference in a cluster, and then allocating channel resources to the users in the cluster by adopting a Hungarian matching algorithm; at this point, the problem has been transformed into a non-convex and non-linear optimization problem. According to the fractional programming theory, a non-Convex optimization target is firstly transformed into an equivalent subtraction form, and then is split into an optimization problem with a Difference of Convex Function (d.c.) structure, so that an optimal power distribution scheme can be obtained by using an interior point method, and a final system energy efficiency is obtained by using a Concave-Convex program (CCCP) algorithm.
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Fig. 1 is a flowchart of a resource allocation method of a D2D communication system according to embodiment 1 of the present invention.
Fig. 2 is a block diagram of a resource allocation apparatus of a D2D communication system according to embodiment 2 of the present invention.
Fig. 3 is a block diagram of a base station according to embodiment 3 of the present invention.
Fig. 4 is a schematic diagram of a D2D user multiplexing distributed antenna system model according to an embodiment of the present invention.
Fig. 5 is a D2D user interference perception diagram according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of a channel allocation result based on the hungarian matching algorithm according to an embodiment of the present invention.
Fig. 7 is a graph showing a variation of the energy efficiency EE with the maximum transmission power according to the embodiment of the present invention.
Fig. 8 is a graph of energy efficiency EE as a function of D2D logarithmic quantity according to an embodiment of the present invention.
FIG. 9 is a graph of performance between different methods involved in an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
As shown in fig. 3, a method for allocating resources of a D2D communication system according to an embodiment of the present invention includes:
defining that in a cell with the radius of R, a plurality of pairs of D2D users multiplex the channel resources of a downlink CUE communication link in a distributed antenna system; the cell is provided with N Remote Access Units (RAUs), K randomly distributed Cellular User Equipment (CUEs) and M pairs of D2D, wherein M is larger than K.
Specifically, as shown in fig. 4, each device is assumed to be equipped with only a single antenna, and the remote access Unit located in the center of the cell is a Central Processing Unit (CPU), which is a special RAU and is denoted as RAU 1. The other remote access units are low-power and low-cost antenna units and are connected with a CPU (central processing unit) in the center of the cell through optical fibers. The cell adopts the working mode of the cooperative DAS, that is, a plurality of RAUs can simultaneously serve one CUE user. At present, K CUE users occupy K channel resources of corresponding numbers in a cell system, and channels among the K CUE users are mutually orthogonal and have equal bandwidth, so that mutual interference does not exist. To highlight the role of D2D communication, assuming the system is fully loaded, no excess spectrum resources in the cell can be allocated to D2D users, so that M D2D pairs can only communicate by multiplexing cellular user channel resources.
When a radio signal is transmitted in an environment, the transmission quality is disturbed by the surrounding environment. In the embodiment of the present invention, the influence of large-scale fading and small-scale fading on the signal quality is comprehensively considered, and taking the channel spectrum response between the RAU and the CUE as an example, the channel gain between the nth RAU and the kth CUE is modeled as follows:
Hn,k＝Ln,kGn,k (1)
wherein G isn,kRepresenting small-scale fading, G, of a wireless channeln,kIs an independent identically distributed complex gaussian random variable with zero mean and unit variance. L isn,kRepresenting a large scale fading, G, between the nth RAU and the kth CUEn,kAnd Ln,kAre independent of each other. L isn,kIt can be specifically expressed as follows:
wherein s isn,kRepresents the log-normal shadowing fading variable between the nth RAU and the kth CUE, and c is a path loss constant. dn,kThe distance between the nth RAU and the kth CUE is expressed, and alpha is a path loss exponent and is usually between 3.0 and 5.0.
Similarly, it can be obtained that when the ith D2D pair multiplexes channels of the kth CUE user, the channel power gain on the i-th through link of D2D user is equal to
The interference channel power gain between the D2D transmitter i and the D2D receiver j that multiplexes the kth CUE user is
The interference channel power gain between D2D transmitter i and the kth CUE user is
And the interference channel gain between the kth CUE user and the ith D2D receiver is
S101, clustering according to the mutual interference degree of all the D2D users, and reusing the same CUE communication link channel resource by the D2D users in each cluster set;
wherein, each D2D pair is defined to multiplex the channel resource of only one most suitable CUE user, and the channel resource of one CUE can be multiplexed by a plurality of D2D pairs, and the D2D user set multiplexing the same CUE channel resource is called a cluster set.
Specifically, the clustering rule is to maximize the interference of D2D users between different sets after clustering, and minimize the sum of the mutual interference of D2D users in all sets.
S102, defining each cluster set as a whole, defining each CUE user as another whole, allocating channel resources of the CUE communication link to the cluster sets by using a Hungarian matching algorithm, and determining a final channel multiplexing result;
the result of multiplexing the CUE channel is decided using the hungarian matching algorithm to reduce interference of the D2D user to the CUE. Through the above two steps, it can be ensured that the interference in the D2D cluster and the interference of the D2D user to the CUE are both minimal, thereby obtaining higher system performance.
Specifically, the mutual interference situations of M D2D pairs are mapped into an idea graph G, as shown in fig. 5. The vertices of the awareness graph G represent D2D users, the channel gain of each D2D link is mapped to one edge of the graph, and the two-way edges are used to represent the mutual interference between two pairs of D2D, and the weights of the edges represent the interference levels. Considering that it is difficult to obtain accurate small-scale fading and it needs to spend large system overhead, the interference level between the i D2D and the i' D2D pairs is captured directly from the large-scale fading channel gain in the early stage, and the weight of the edge is represented as wi,i'＝Li,i'. The edge set of the idea graph G is represented by E (G), the edge vertex set is represented by V (G),
represents the sum of the weights of the edges (a, b) of two disjoint clusters,
and the edge weights represent the mutual interference between any two pairs of D2D in the same cluster set.
Bisecting M D2D into K different cluster sets { R }1,…,RK}，RkK, i.e., decomposing the awareness graph G into K disjoint sets, with users in the sets satisfying R1∪…∪RK＝V(G)，
Make the weight of the edge
And max. Since the total number of edge sets of the awareness graph G is fixed, i.e. the sum of the weights of all the edges in the awareness graph G is fixed:
after decomposition, maximizing inter-cluster interference means maximizing the sum of edge weights between different clusters
While minimizing the sum of all intra-cluster interferences, i.e. minimizing the sum of edge weights within all clusters
Thus, a pair of D2D users is given the least incremental contribution to interference within the system cluster by finding a suitable cluster join, i.e., by finding in which cluster set the pair of users contribute least to interference within the system cluster.
Therefore, the step of clustering according to the mutual interference degree of all the D2D users includes:
s201, randomly distributing K different D2D links to the K sets to form an initial cluster;
s202, calculating the mutual interference levels of the (M-K) unassigned D2D links and all clusters one by one; wherein the intra-cluster interference sum after adding a new D2D link i' in the kth cluster
S203, finding out the cluster k which is most suitable for the D2D link i' to join*I.e. by
And add i' to the k*In a set;
s204, looping step S202 and step S203, and returning all the D2D user clustering results until (M-K) D2D links are all allocated into K sets.
4. The D2D communication system resource allocation method according to claim 3, wherein the method includes:
after D2D is clustered by a Graph-based Channel Assignment Algorithm (GCAA) Assignment Algorithm, the Channel Assignment result needs to be further determined. And regarding each cluster set as a whole, regarding each CUE user as another whole, and adopting an algorithm based on Hungarian matching to determine a channel multiplexing result so as to minimize the interference of the D2D user to the CUE. The channel allocation result can be shown in fig. 6. Specifically, the GCAA channel allocation algorithm is executed as follows:
TABLE 1 GCAA channel Allocation Algorithm
S103, defining the energy efficiency as the ratio eta of the spectrum efficiency to the total power consumptionEEAnd obtaining a system optimization objective function, equivalently transforming the system optimization objective function into a difference D.C. structural form with a convex function, and obtaining an optimal power distribution scheme by utilizing an interior point method.
For the kth CUE, a kth channel resource is used to communicate with the base station. The D2D user has now formed a cluster and determined the CUE channel resources to be multiplexed. Suppose that there is currently SkD2D multiplexes channel resources of the k CUE to a cluster set, which is marked as Gk. Normalization of bandwidth to 1, then GkThe spectral efficiency of the jth D2D pair can be expressed as:
where j ∈ Gk，
Representing the spectral efficiency of the jth D2D pair multiplexing the kth cellular user channel resource.
Represents the transmit power of the jth D2D pair transmitter,
represents GkTransmission power, P, of the i-th D2D usern,kRepresenting the transmit power of the nth RAU to the kth cellular user. Hj,jDenotes the through link channel gain, H, of the jth D2D pairi,jRepresenting the interference channel gain, Hn,jIndicating the interfering channel gain for the nth RAU to the jth D2D pair.
Representing additive white gaussian noise.
Likewise, the spectral efficiency of the kth cellular user can be expressed as:
wherein SkRepresenting the number of pairs of D2D multiplexing the kth cellular user channel resource,
representing additive white gaussian noise.
The spectral efficiency of the system can be expressed as:
energy Efficiency (EE) is defined as the ratio of spectral Efficiency to total power consumed. The energy efficiency of the distributed antenna D2D communication system can therefore be expressed as:
where M represents the number of pairs of D2D in the system, PotherRepresents other power consumption of the system, including static power consumption and dynamic power consumption, and represents a power amplification factor.
After considering the minimum capacity requirements of the CUE and D2D users, the system optimization objective function can be expressed as:
wherein R ismin1Representing the minimum capacity requirement, R, of the CUEmin2Representing the minimum capacity requirement, R, of D2D usersiRepresenting the capacity, P, obtained by the ith D2D usermaxRepresenting the maximum transmit power of the D2D user. To simplify the calculation, it is assumed that the transmit power of the cellular users is unchanged.
At present, the optimization objective function is a non-convex and non-linear function, and an optimal solution is difficult to obtain. By introducing a scalar φ, the original optimization problem can be redefined as an equivalent optimization problem with fractional form with a sign reduction:
theorem 1: order to
And
if and only if F (phi)*) 0 and f (phi)*)＝P*When satisfied, the optimal power allocation scheme of (8) is the optimal power allocation of (7).
From theorem 1, it can be seen that any function with similar fractional programming can be equivalently approximated as a non-fractional programming problem with a reduced form. Meanwhile, the solution of the original problem and the solution of the transformed problem have equivalence. Through analysis, the set formed by the constraints of the optimization problem (8) is a convex set, and meanwhile, the objective function can be reconstructed into a form of a sum of two concave and convex parts, namely, converted into a D.C structure, so that an equivalent problem with the D.C structure is obtained:
wherein the content of the first and second substances,
representing the concave and convex parts of the objective function, respectively. Using a first order taylor expansion on the convex part of the objective function, the following power iteration equation is obtained:
where t represents the number of iterations, P ═ P1,...,PM]，PTThe transpose representing P is a convex set C composed of the constraints of problem (8)1And (4) forming.
Representing the derivative of the convex part of the objective function, for a fixed P(t)P can be obtained by interior point method(t+1)The value of (c). To this end, the problem may employ the CCCP algorithm to obtain a solution to the optimization problem.
Table 2 power allocation algorithm for maximizing energy efficiency of distributed antenna D2D communication system
The algorithm provided by the embodiment of the invention solves the problem of resource allocation of a distributed antenna D2D communication system, realizes the maximization of the system energy efficiency and ensures the communication quality of cellular users and D2D users.
Example two
As shown in fig. 2, a resource allocation apparatus 20 of a D2D communication system according to an embodiment of the present invention is applied to a distributed antenna system, and the apparatus includes:
a channel allocation module 21, configured to cluster according to the mutual interference degrees of all D2D users, where the D2D users in each cluster set reuse the same CUE communication link channel resource; defining each cluster set as a whole, defining each CUE user as another whole, allocating channel resources of the CUE communication link to the cluster sets by using a Hungarian matching algorithm, and determining a final channel multiplexing result;
a power distribution module 22 for defining energy efficiency as a ratio η of spectral efficiency to total power consumedEEAnd obtaining a system optimization objective function, equivalently transforming the system optimization objective function into a difference with a convex function, namely a D.C. structural form, and obtaining an optimal power distribution scheme by utilizing an interior point method.
The Monte-Carlo method is used for verifying that the energy efficiency of the distributed antenna D2D communication system can be effectively improved by using the channel allocation algorithm based on the graph theory. Assume that the cell center remote access unit RAU1 is a central processing unit with polar coordinates (0,0), and the polar coordinates of the other remote access unit locations are expressed as:
k cellular users are randomly generated in the cell, and M D2D pairs. The systematic simulation parameters are shown in table 3.
TABLE 3 Main simulation parameters of the System
As shown in fig. 7, the difference of energy efficiency obtained by the system using the graph theory-based channel allocation algorithm and the system employing the random channel allocation algorithm in the distributed antenna system is compared. In the simulation, the number M of D2D users is set to 9. It can be seen from the figure that as the maximum transmission power of the D2D user increases, the energy efficiency of the system increases gradually and decreases slightly. Meanwhile, it can be concluded from the figure that the energy efficiency of the system is far less than that of the system using the clustering based on the graph theory and the channel allocation algorithm based on the Hungarian matching after the random channel allocation method is simply adopted. For example, when the transmitting power of the D2D user is 9dBm, the energy efficiency obtained by the method based on the graph theory is improved by 1.5bit/Joule/Hz compared with the energy efficiency obtained by the method of random allocation. This shows that the channel resource of the system can be effectively distributed by adopting the channel distribution algorithm based on the graph theory, the interference suffered by the users in the system is increased when the transmitting power is increased, and the interference suffered by each user can be effectively balanced by the algorithm, thereby obtaining the system energy efficiency higher than that of the random method.
As shown in fig. 8, the EE variation curve was simulated as D2D increases in number. The maximum transmit power for the D2D user was set to 8dBm for the simulation. As can be seen, as the number of D2D user pairs increases, the system energy efficiency also continues to increase. Although the cluster of D2D becomes larger when the number of D2D users increases, which results in stronger and stronger co-channel interference, it can be seen from the figure that the energy efficiency of the system obtained by using the channel allocation method based on the graph theory is always higher than that of the method using random allocation, which illustrates that when the number of D2D users increases, the channel allocation method based on the graph theory can better allocate channel resources, suppress interference, and obtain higher system performance.
As shown in fig. 9, the performance difference between the optimization method proposed herein and the different methods when calculating EE using the equal power algorithm was simulated as the number of D2D increased. The curve of the red line represents the result obtained by using the equal power scheme after the channel resource allocation method of the graph theory, and is referred to as the ECA method. The blue curve represents the result of using the optimization algorithm proposed in this chapter after using the channel resource allocation scheme based on graph theory, and is recorded as the PCA method. As a whole, system energy efficiency is increasing with increasing number of D2D. It is evident from the figure that the performance obtained with the PCA method is much higher than that obtained with the ECA method. And it can be seen by observation that as the number of D2D pairs increases, the performance gap between the two methods also gradually increases, and when the number of D2D pairs reaches 12, the performance gap reaches 4 bit/Joule/Hz. Therefore, the optimization method provided by the invention can effectively improve the system performance.
EXAMPLE III
As shown in fig. 3, in the base station 30 according to the embodiment of the present invention, specifically, the base station 30 at least includes a processor 31, a memory 32, and a data bus 33. The data bus 33 is used for implementing connection communication between the processor 31 and the memory 32, and the memory 32 is a computer-readable storage medium that can store at least one computer program, which can be read, compiled and executed by the processor 31, so as to implement the corresponding processing flow. In the present embodiment, the memory 32 is used as a computer readable storage medium, in which a resource allocation program is stored, and the program is executable by the processor 31, and when the computer program is executed by the processor, the steps of the resource allocation method of the D2D communication system are implemented.
Example four
An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a resource allocation program, and when the resource allocation program is executed by a processor, the resource allocation program is configured to implement the following steps of the resource allocation method for the D2D communication system:
s101, clustering according to the mutual interference degree of all the D2D users, and reusing the same CUE communication link channel resource by the D2D users in each cluster set;
s102, defining each cluster set as a whole, defining each CUE user as another whole, allocating channel resources of the CUE communication link to the cluster sets by using a Hungarian matching algorithm, and determining a final channel multiplexing result;
s103, defining the energy efficiency as the ratio eta of the spectrum efficiency to the total power consumptionEEAnd obtaining a system optimization objective function, equivalently transforming the system optimization objective function into a difference D.C. structural form with a convex function, and obtaining an optimal power distribution scheme by utilizing an interior point method.
According to the resource allocation method, the resource allocation device, the base station and the readable storage medium of the D2D communication system, when a plurality of pairs of D2D users multiplex cellular users in the distributed antenna D2D communication system, the energy efficiency of the system is maximized while the communication quality of the cellular users and the D2D users is ensured; because the problem is a non-convex nonlinear combination optimization problem and a closed solution cannot be directly obtained, the problem is solved by using the idea of firstly allocating channels and then allocating power; firstly, mapping the interference situation of D2D users to an idea graph, allocating D2D users to a proper set by using graph theory knowledge under the criterion of minimizing the total interference in a cluster, and then allocating channel resources to the users in the cluster by adopting a Hungarian matching algorithm; at this point, the problem has been transformed into a non-convex and non-linear optimization problem. According to the fractional programming theory, a non-Convex optimization target is firstly transformed into an equivalent subtraction form, and then is split into an optimization problem with a Difference of Convex Function (d.c.) structure, so that an optimal power distribution scheme can be obtained by using an interior point method, and a final system energy efficiency is obtained by using a Concave-Convex program (CCCP) algorithm.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a computer-readable storage medium (such as ROM/RAM, magnetic disk, optical disk), and includes instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device) to execute the method for allocating resources in a D2D communication system according to the embodiments of the present invention.
The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, and are not to be construed as limiting the scope of the invention. Any modifications, equivalents and improvements which may occur to those skilled in the art without departing from the scope and spirit of the present invention are intended to be within the scope of the claims.
Claims (10)
1. A method for allocating resources in a D2D communication system, the method comprising:
defining that in a cell with the radius of R, a plurality of pairs of equipment-to-equipment D2D users multiplex downlink CUE communication link channel resources in a distributed antenna system; wherein, the cell has N remote access units RAU, K randomly distributed cellular user equipment CUE and M D2D pairs, and M > K;
s101, clustering according to the mutual interference degree of all the D2D users, and reusing the same CUE communication link channel resource by the D2D users in each cluster set;
s102, defining each cluster set as a whole, defining each CUE user as another whole, allocating channel resources of the CUE communication link to the cluster sets by using a Hungarian matching algorithm, and determining a final channel multiplexing result;
s103, defining the energy efficiency as the ratio eta of the spectrum efficiency to the total power consumptionEEAnd obtaining a system optimization objective function, equivalently transforming the system optimization objective function into a D.C. structural form, and obtaining an optimal power distribution scheme by utilizing an interior point method.
2. The method for allocating resources in a D2D communication system as claimed in claim 1, wherein said step of clustering according to the mutual interference degree of all D2D users comprises:
s201, randomly distributing K different D2D links to the K sets to form an initial cluster;
s202, calculating the mutual interference levels of the (M-K) unassigned D2D links and all clusters one by one; wherein the intra-cluster interference sum after adding a new D2D link i' in the kth cluster
S203, finding out the cluster k which is most suitable for the D2D link i' to join*I.e. by
And add i' to the k*In a set;
s204, looping step S202 and step S203, and returning all the D2D user clustering results until (M-K) D2D links are all allocated into K sets.
3. The D2D communication system resource allocation method according to claim 1, wherein, prior to the step of allocating the CUE communication link channel resource to a set of clusters using the hungarian matching algorithm, the method comprises:
301. mapping the mutual interference situations of the M D2D pairs into an idea graph G; wherein the vertex of the awareness graph G represents a D2D user, the channel gain of each D2D link is mapped to an edge of the awareness graph G, the bidirectional edge represents the mutual interference between two pairs of D2D, and the weight of the edge represents the interference level;
302. capturing the mutual interference level according to the large scale fading channel gain between the ith D2D pair and the ith' D2D pair, and expressing the weight of the edge as wi,i'＝Li,i'(ii) a The edge set of the idea graph G is represented by E (G), the edge vertex set is represented by V (G),
represents the sum of the weights of the edges (a, b) of two disjoint clusters,
edge weights representing mutual interference between any two pairs of D2D pairs in the same cluster set;
303. bisecting M D2D into K different cluster sets { R }1,…,RK}，RkE.g. R, K1, 21∪…∪RK＝V(G)，
Make the weight of the edge
And max.
4. The D2D communication system resource allocation method according to claim 3, wherein the method includes:
s401, initializing K CUEs, enabling M D2D pairs to be randomly distributed in the cell, initializing K cluster sets,
s402, randomly distributing K different D2D pairs to K cluster sets;
s403, repeatedly executing for (M-K) unallocated D2D users: use of
Calculating an interference increase value when the user i' of D2D joins the kth cluster set, wherein K is 1: K;
s404, according to
Obtaining the cluster set to which D2D should be added to i', and updating the cluster setAggregating the D2D user information until all the D2D users are allocated into corresponding cluster sets;
s405, the CUE communication link channel resources are sent to a cluster set by using a Hungarian matching algorithm, a final channel multiplexing result is determined, and the D2D user clustering result and the channel allocation result are returned.
5. The D2D communication system resource allocation method of claim 1, wherein the energy efficiency is defined as a ratio η of a spectrum efficiency to a total power consumptionEEThe step of obtaining the system optimization objective function specifically comprises:
s501, is defined as SkMultiplexing the channel resource of the kth CUE by the D2D pair group set, wherein the group set is marked as Gk(ii) a The bandwidth is normalized to 1, then G iskThe spectral efficiency of the jth D2D pair in (j) is expressed as:
wherein j ∈ Gk，
Represents the spectral efficiency of the jth D2D pair multiplexing the kth cellular user channel resource;
denotes the transmission power, P, of the j-th D2D to the transmitteri kRepresents GkTransmission power, P, of the i-th D2D usern,kRepresents the transmit power of the nth RAU to the kth cellular user; hj,jDenotes the through link channel gain, H, of the jth D2D pairi,jRepresenting the interference channel gain, Hn,jRepresents the interference channel gain for the nth RAU to the jth D2D pair;
representing additive gaussiansWhite noise;
s502, the spectral efficiency of the kth cellular user is expressed as:
wherein S iskRepresenting the number of pairs of D2D multiplexing the kth cellular user channel resource,
representing additive white gaussian noise;
s503, the spectral efficiency of the system is expressed as:
s504, energy efficiency is defined as the ratio eta of the spectral efficiency to the total power consumptionEESystem energy efficiency ηEEComprises the following steps:
where M represents the number of pairs of D2D in the system, PotherRepresenting other power consumption of the system, including static power consumption and dynamic power consumption, wherein tau represents a power amplification factor;
s505, according to the CUE and the minimum capacity requirement of the D2D user, the system optimization objective function is as follows:
wherein R ismin1Representing the minimum capacity requirement, R, of the CUEmin2Representing the minimum capacity requirement, R, of D2D usersiRepresenting the capacity, P, obtained by the ith D2D usermaxMaximum for D2D userThe transmit power.
6. The method of claim 5, wherein the system optimization objective function is equivalently transformed into D.C. structure form, and the step of obtaining the optimal power allocation scheme by using interior point method comprises:
s601, introducing a scalar phi, and redefining the system optimization objective function (5) into an equivalent optimization objective function with a decreasing sign and a fractional form:
s602, converting the equivalent optimization objective function (6) into a D.C structure to obtain an equivalent objective function (7) with the D.C structure:
wherein the content of the first and second substances,
a concave part (8) and a convex part (9) representing the equivalent objective function (7), respectively;
s603, using first-order Taylor expansion on the convex part (9) of the equivalent objective function (7) to obtain the following power iterative equation:
wherein t represents an iterationThe number of generations, P ═ P1,...,PM]，PTThe transpose representing P is a convex set C composed of constraints of an equivalent optimization objective function (6)1Forming;
a derivative representing the convex part of the objective function;
s604, for a fixed P(t)P can be obtained by interior point method(t+1)To obtain the best power allocation scheme.
7. The method of allocating resources in a D2D communication system according to claim 5, wherein the system optimization objective function is equivalently transformed into d.c. structure form, and after the step of obtaining the optimal power allocation scheme by using the interior point method, the method further comprises:
obtaining the optimal energy efficiency eta by using a concave-convex program algorithm CCCPEE。
8. A resource allocation apparatus for D2D communication system, applied to a distributed antenna system, the apparatus comprising:
a channel allocation module, configured to cluster according to mutual interference degrees of all D2D users, where the D2D users in each cluster set reuse the same CUE communication link channel resource; defining each cluster set as a whole, defining each CUE user as another whole, allocating channel resources of the CUE communication link to the cluster sets by using a Hungarian matching algorithm, and determining a final channel multiplexing result;
a power distribution module for defining energy efficiency as a ratio η of spectral efficiency to total power consumedEEAnd obtaining a system optimization objective function, equivalently transforming the system optimization objective function into a D.C. structural form, and obtaining an optimal power distribution scheme by utilizing an interior point method.
9. A base station, characterized in that the base station comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the D2D communication system resource allocation method according to any one of claims 1-7.
10. A computer-readable storage medium, having stored thereon a resource allocation program which, when executed by a processor, carries out the steps of the D2D communication system resource allocation method according to any one of claims 1-7.
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