CN116056181A - Relay node selection method based on D2D communication - Google Patents
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Abstract
The invention discloses a relay node selection method based on D2D communication, which comprises the following steps: before formal communication, a base station firstly transmits a broadcast signal, a circular range with a cluster range being a center of a circle and a midpoint of two ends of a target node being a radius is determined based on constraint of a distance and a social relationship, after receiving broadcast test information of the base station, a relay node in the cluster range returns corresponding signal-to-noise ratio information, meanwhile, the relay node transmits the test signal to the target node, the target node returns corresponding signal-to-noise ratio information to the base station, and a relay node with the minimum signal-to-noise ratio is continuously removed according to a threshold value of the distance and the social relationship, and a plurality of better relay nodes are selected. The invention adopts the relay node selection method based on D2D communication, and combines MRC by using the maximum ratio between the link between the relay node and the destination node, thereby converting the problem into selecting the optimal signal-to-noise ratio between the source node and the relay node.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a relay node selection method based on D2D communication.
Background
D2D (device-to-device) communication is a popular technology in the current environment that does not require signaling through a base station while having lower energy consumption than conventional cellular networks. However, how to select an appropriate relay to assist the D2D device in communication is an urgent problem to be solved. Qian Hongzhi introduces a social threshold, and based on a Q learning algorithm, proposes an optimal relay selection algorithm for maximizing the total rate of the D2D link, so as to improve the system rate. Pan Xin and the like propose a mixed scheme based on distance and social relations, so that the communication rate of equipment is successfully improved. Ushik Shrestha Khwakhali et al propose a social relationship based relay selection scheme to improve the average throughput of the network by selecting a relay that is socially connected to the source and located near the midpoint of the source and destination. Zhang Mengyuan and the like propose an optimal social perception relay selection strategy based on an optimal stopping theory, so that the throughput of the system is improved. Zhang Zufang et al propose a self-adaptive relay selection method using a social network, and build a model based on a physical domain and a social domain, so as to improve the probability of successful relay selection, reduce the burden of a cellular network, and improve the system performance.
In the design of the prior relay node selection algorithm, the problems are mainly solved by methods such as a game algorithm, a traditional algorithm and the like, but the methods are poor in universality and inconsistent in performance under different scenes.
They often have the following problems:
1. failing to accommodate as many relay nodes as possible;
2. the link with the minimum signal-to-noise ratio is generally selected from the source node to the relay node and from the relay node to the destination node, so that resource waste is caused;
3. the algorithm time complexity is high.
Based on the above analysis, existing studies mostly solve the resource allocation and energy efficiency problems from the physical layer or the social layer. The invention provides a relay selection scheme combining a social layer and a physical layer, so that the energy efficiency and throughput of a remote D2D user are improved.
Disclosure of Invention
The invention aims to provide a relay node selection method based on D2D communication, which solves the problems in the background technology.
In order to achieve the above object, the present invention provides a relay node selection method based on D2D communication, the method comprising: before formal communication, a base station firstly transmits a broadcast signal, a circular range with a cluster range being a center of a circle and a midpoint of two ends of a target node being a radius is determined based on constraint of a distance and a social relationship, after receiving broadcast test information of the base station, a relay node in the cluster range returns corresponding signal-to-noise ratio information, meanwhile, the relay node transmits the test signal to the target node, the target node returns corresponding signal-to-noise ratio information to the base station, and a relay node with the minimum signal-to-noise ratio is continuously removed according to a threshold value of the distance and the social relationship, and a plurality of better relay nodes are selected.
Preferably, assuming that there is no interference between nodes and between a node and D2D users within the cluster, only signal interference from the base station is received, and a set of channel resources can only be multiplexed by a pair of D2D users;
a set of D2D pairs, d= { D, is denoted by D 1 ,D 2 ,D 3 ,...,D n Di represents the ith D2D pair, P s Representing the maximum transmit power of a D2D user, P r Representing the maximum transmit power of the relay node, N 0 Representing additive white gaussian noise, eta i,j Representing trust value between two devices g i,j Representing channel gains between the devices i and j, wherein gi and ri represent channel gains between the device i and an ri-th relay node in the cluster range, and signal to noise ratios from a source node to the relay node and from the relay node to a destination node are respectively represented as
Order the
If the number of relay nodes in the cluster is not less than 2, the signal to noise ratios from the source node to the cluster and from the cluster to the destination node are respectively expressed as
γ s,r ＝min(γ s,ri ),γ r,d ＝min(γ ri,d )
Preferably, the whole communication process is divided into two phases, the first phase is from a source node to a relay node in the cluster, the second phase is from the relay node to a destination node, the spectrum resource is divided into two equal parts, one phase uses one part, and the total signal to noise ratio in the cluster is according to the principle of decoding and forwarding
γ s,r,d ＝min{γ s,r ,γ r,d }
The instantaneous data rate of the D2D link is expressed as
W represents the spectrum bandwidth used by the D2D link, while 1/2 represents half of the spectrum resources used by the communication link;
EE is defined as the ratio of data rate to power, P is used t Representing the total power in the entire link, the following equation is derived
Assuming that the circuit power of different users is the same and expressed, the total power of the D2D link may be expressed as
P cir Representing circuit power, 1/2 represents that the power consumption of two phases in the D2D link communication process are independent and do not occur simultaneously, and n represents the number of relay nodes in the cluster.
Preferably, when the relay nodes in the cluster range do not meet the requirements, a direct communication technology is adopted to perform data transmission, and the signal-to-noise ratio, the instantaneous data rate and the power loss from the destination node to the source node are respectively
R s,d ＝Wlog 2 (1+γ s,d )
P t1 ＝P s +P cir
Deriving EE for direct communication from instantaneous data rate and power loss
Preferably, the EE-based optimal power optimization model is denoted by P1:
P1:
η i,j ∈{0,1},i∈D,j∈n
γ s,r,d ≥γ d ,
α∈{0,1}
for the P1 problem, two cases are classified, and when α=1, only relay communication is performed to obtain the optimal power
And
two assumptions are made, assuming the following:
suppose 1: when gamma is s,r,d ＝min{γ s,r ,γ r,d }＝γ s,r I.e. phi s,r ≤Φ r,d Then the objective function in P1 is rewritten as
As can be seen from the current assumption, when P s Φ s,r ≤P r Φ r,d ，P r Maximum value of (2)
Suppose 2: when gamma is s,r,d ＝min{γ s,r ,γ r,d }＝γ r,d I.e. phi r,d ≤Φ s,r Then the objective function in P1 is rewritten as
From the currentIt is assumed that when P r Φ r,d ≤P s Φ s,r ，P r Maximum value of (2)
From the above two assumptions, it can be seen if and only if gamma s,r ＝γ r,d At this time, EE takes its maximum value.
Preferably, use is made of
Replacing the objective function in the P1 problem to obtain the optimal power +.>
And->
Problem P1 is equivalently rewritten as problem P2:
η i,j ∈{0,1},i∈D,j∈n
γ s,r,d ≥γ d ,
P s Φ s,r ≤P r Φ r,d
preferably, problem P2 is converted to problem P3 according to a theoretical method,
where u=wleg 2 (1+P s Φ s,r )-q(P s +nP r +2(n+1)P cir )
γ s,r,d ≥γ d ,
EE takes the maximum value if and only if u=0, where q is a temporary optimal solution of EE maximum;
preferably, U is proved to be a concave function, and P is calculated for the function U respectively s And P r The first partial derivative of (2) gives the following formula
The first partial derivative is again derived to obtain the U's Heisen matrix as follows
The first-order cis-form main component of the Heisen matrix is complex, the second-order cis-form main component is zero, the Heisen matrix is a negative semi-definite matrix, and the function U is a concave function, so that the maximum value can be obtained;
after proving, use
P in substitution problem P3 r Then find the relation P for the function U s Is derived from the partial derivative of (2) to obtain the following formula
Wherein [ x ]] + Represents max {0, x }, in combination with constraint
Will be
Bringing in to an optimal transmit power of
Preferably, P s Iteration is continued until u=0, and P is solved according to the above derivation r Is the optimal solution of (a); when alpha=0, only direct communication is performed to make
Will phi s,d Substituted into
Is obtained by
Problem P1 is converted into problem P4 according to the theoretical method:
wherein z=wleg 2 (1+P s Φ s,d )-q(P s +P cir )
γ s,r,d ≥γ d ,
Regarding Z, regarding P s First and second partial derivatives of (2) to obtain
Since the second derivative of Z is less than zero, Z is proved to be a concave function when
When Z is at a maximum value,
combining constraints
Obtaining the product
And finally obtaining the optimal transmitting power required by the source node in the direct communication scene.
Therefore, the relay node selection method based on the D2D communication has the following beneficial effects:
1. the method provided by the invention is different from the traditional relay scheme, and a circular cluster area is constructed between the midpoints of the transmitting end and the receiving end, which is called a social-aware cellular cluster-assisted D2D communication network model, so that the problem of insufficient coverage of relay nodes in the traditional scheme is solved.
2. The method provided by the invention adopts a downlink to carry out D2D communication, a base station sends a broadcast signal, the signal-to-noise ratio information of relay nodes in a cluster is collected, and a plurality of better nodes far away from the base station are selected according to the threshold value of the distance and the social relationship; the MRC (maximum ratio combining) is used for the link between the relay node and the destination node, thereby converting the problem into selecting an optimal SNR (signal-to-noise ratio) between the source node and the relay node, and converting the conventional problem of selecting an optimal SNR between the source node and the relay node and between the relay node and the destination node into the problem of selecting an optimal SNR between the source node and the relay node.
3. An optimal power iterative algorithm based on social relation obtains optimal transmitting power through lower time complexity, and therefore optimal energy efficiency is calculated.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
Fig. 1 is a schematic diagram of a relay node selection method based on D2D communication according to the present invention;
FIG. 2 is a schematic diagram of an optimal power iterative algorithm of the present invention;
fig. 3 is a schematic diagram of a trunking relay selection algorithm according to the present invention.
Detailed Description
The technical scheme of the invention is further described below through the attached drawings and the embodiments.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
Examples
Fig. 1 is a schematic diagram of a relay node selection method based on D2D communication according to the present invention; FIG. 2 is a schematic diagram of an optimal power iterative algorithm of the present invention; fig. 3 is a schematic diagram of a trunking relay selection algorithm according to the present invention.
As shown in the figure, the method for selecting a relay node based on D2D communication according to the present invention includes: before formal communication, the base station firstly transmits a broadcast signal, and determines a cluster range as a circular range with a center point of two ends of a source node and a destination node and a half of a distance as a radius based on constraint of the distance and a social relationship. After receiving the broadcast test information of the base station, the relay nodes in the cluster range return corresponding signal-to-noise ratio (SNR) information, meanwhile, the relay nodes send test signals to the target nodes, the target nodes return the corresponding signal-to-noise ratio (SNR) information to the base station, the relay nodes with the minimum signal-to-noise ratio are continuously removed according to the threshold value of the distance and the social relationship, and a plurality of better relay nodes are selected.
For ease of analysis, it is assumed that within the cluster of source and destination nodes, there is no interference between the nodes and D2D users, only signal interference from the base station. One set of channel resources can only be multiplexed by a pair of D2D users, so in this case, there is no interference between different D2D groups, and interference exists only within each D2D group user.
A set of D2D pairs, d= { D, is denoted by D 1 ,D 2 ,D 3 ,...,D n Di represents the ith D2D pair, P s Representing the maximum transmit power of a D2D user, P r Representing the maximum transmit power of the relay node, N 0 Representing additive white gaussian noise, eta i,j Representing trust value between two devices g i,j Representing channel gains between the devices i and j, wherein gi and ri represent channel gains between the device i and an ri-th relay node in the cluster range, and signal to noise ratios from a source node to the relay node and from the relay node to a destination node are respectively represented as
For convenience of description, let
If the number of relay nodes in the cluster is not less than 2, the signal to noise ratios from the source node to the cluster and from the cluster to the destination node are respectively expressed as
γ s,r ＝min(γ s,ri ),γ r,d ＝min(γ ri,d )
The relay protocol can be roughly classified into Amplification Forwarding (AF) and Decoding Forwarding (DF) according to a signal processing procedure at the relay node. The whole communication process is divided into two phases, the first phase is from the source node to the relay node in the cluster, and the second phase is from the relay node to the destination node. At the same time, the spectrum resource is divided into two equal parts, and one part is used at one stage, so that the mutual interference in the cluster is eliminated. According to the principle of decoding and forwarding, the total signal to noise ratio in the cluster is
γ s,r,d ＝min{γ s,r ,γ r,d }
The instantaneous data rate of the D2D link is expressed as
W represents the spectrum bandwidth used by the D2D link, while 1/2 represents half of the spectrum resources used by the communication link;
EE is defined as the ratio of data rate to power, P is used t Representing the total power in the entire link, the following equation is derived
The total power is mainly composed of two parts, namely, the transmitting power and the circuit power. The transmit power is used to transmit data and the circuit power is used to process data, including mixing, analog-to-digital (a/D) conversion, and digital-to-analog (D/a) conversion. Assuming that the circuit power of different users is the same and expressed, the total power of the D2D link may be expressed as
P cir Representing circuit power, 1/2 represents that the power consumption of two phases in the D2D link communication process are independent and do not occur simultaneously, and n represents the number of relay nodes in the cluster.
Because of the selfish property of the relay nodes and the screening mechanism of the trust parameters, when the relay nodes in the cluster range do not meet the requirements, the direct communication technology is adopted for data transmission. The signal-to-noise ratio, the instantaneous data rate and the power loss from the destination node to the source node are respectively
R s,d ＝Wlog 2 (1+γ s,d )
P t1 ＝P s +P cir
Deriving EE for direct communication from instantaneous data rate and power loss
In the model, an optimal solution for energy efficiency needs to be obtained by obtaining an optimal transmit power and an optimal relay power in the link. The EE-based optimal power optimization model is denoted by P1:
η i,j ∈{0,1},i∈D,j∈n
γ s,r,d ≥γ d ,
α∈{0,1}
For the P1 problem, two cases are classified, when α=1, only relay communication is performed to solve the optimum power in the scenario
And->
Two assumptions are made, assuming the following:
suppose 1: when gamma is s,r,d ＝min{γ s,r ,γ r,d }＝γ s,r I.e. phi s,r ≤Φ r,d Then the objective function in P1 is rewritten as
At R s,d ＝Wlog 2 (1+γ s,d ) EE is related to P r Monotonically decreasing function if one is to obtainMaximum of EE, then P r Is the minimum value. As can be seen from the current assumption, when P s Φ s,r ≤P r Φ r,d ，P r Maximum value of (2)
Suppose 2: when gamma is s,r,d ＝min{γ s,r ,γ r,d }＝γ r,d I.e. phi r,d ≤Φ s,r Then the objective function in P1 is rewritten as
Obviously, in
Wherein EE is related to P s Monotonically decreasing function, if EE maximum is to be obtained, P is required r Is the minimum value. As can be seen from the current assumption, when P r Φ r,d ≤P s Φ s,r ，P r Maximum value of (2)
From the above two assumptions, it can be seen if and only if gamma s,r ＝γ r,d At this time, EE takes its maximum value.
In subsequent analysis, use is made of
Replacing the objective function in the P1 problem to obtain the optimal power +.>
And->
Problem P1Is equivalently rewritten as problem P2:
η i,j ∈{0,1},i∈D,j∈n
γ s,r,d ≥γ d ,
P s Φ s,r ≤P r Φ r,d
obviously, the objective function is a nonlinear partial programming problem that is difficult to solve for the problem P2, but can be transformed into a nonlinear parameter programming by using the transformation method in the existing document On nonlinear fractional programming. Therefore, problem P2 is converted to problem P3 according to the theoretical method,
where u=wleg 2 (1+P s Φ s,r )-q(P s +nP r +2(n+1)P cir )
γ s,r,d ≥γ d ,
It is known from document On nonlinear fractional programming that EE takes a maximum value if and only if u=0. Wherein q is a temporary optimal solution of EE maximum;
proving U as a concave function, firstly, respectively solving P for the function U s And P r The first partial derivative of (2) gives the following formula
The first partial derivative is again derived to obtain the U's Heisen matrix as follows
The first order cis-form of the jersey matrix is complex, the second order cis-form is zero, and the jessey matrix is a negative semi-definite matrix. Whereby the function U is a concave function, capable of taking a maximum value;
after proving, use
P in substitution problem P3 r Then find the relation P for the function U s Is derived from the partial derivative of (2) to obtain the following formula
Wherein [ x ]] + Represents max {0, x }, in combination with constraint
Will be
Bringing in to an optimal transmit power of
So far, only the first calculated optimal transmit power is found, while the last power optimal solution is related to the iteration factor. Thus, P s The iteration needs to be continued until u=0. In a similar manner, according to the above-mentioned derivation,solving for P r Is a solution to the optimization of (3).
When alpha=0, only direct communication is performed, and for simplifying the description, the method comprises
From the following components
EE is known s,d Concerning P r Is a monotonically decreasing function of P r EE when the minimum value is obtained s,d The maximum value is taken. Will phi s,d Substituted into->
Is obtained by
Using the method in document On nonlinear fractional programming, problem P1 can be converted into problem P4:
wherein z=wleg 2 (1+P s Φ s,d )-q(P s +P cir )
γ s,r,d ≥γ d ,
Regarding Z, regarding P s First and second partial derivatives of (2) to obtain
Since the second derivative of Z is less than zero, Z is proved to be a concave function when
At this time, Z takes the maximum value.
Combining constraints
Obtaining the product
In summary, the optimal transmit power required by the source node in the direct communication scenario is obtained.
The calculated optimal power is only a solution of the P3 problem and is not a solution of the P2 problem, and the solution of the P2 problem is related to the iteration factor and can be obtained through iteration of an optimal power iteration algorithm and a cluster relay selection algorithm. The optimal power iterative algorithm is shown in fig. 2, and the cluster relay selection algorithm is shown in fig. 3. In fig. 3, the total number of idle nodes is represented by F, the relay radius r of D2D is half of that of the source node to the destination node, meanwhile, the distance between the idle nodes and the source node is represented by D, and the trust threshold is η.
Therefore, the invention adopts the relay node selection method based on D2D communication, D2D communication is carried out through a downlink, the base station sends a broadcast signal, the signal-to-noise ratio information of the relay nodes in the cluster is collected, and a plurality of better nodes far away from the base station are selected according to the threshold value of the distance and the social relationship. The link between the relay node and the destination node uses a maximum ratio combining MRC to translate the problem into selecting an optimal signal-to-noise ratio SNR between the source node and the relay node.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention and not for limiting it, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that: the technical scheme of the invention can be modified or replaced by the same, and the modified technical scheme cannot deviate from the spirit and scope of the technical scheme of the invention.
Claims (9)
1. A relay node selection method based on D2D communication is characterized in that: the method comprises the following steps: before formal communication, a base station firstly transmits a broadcast signal, a circular range with a cluster range being a center of a circle and a midpoint of two ends of a target node being a radius is determined based on constraint of a distance and a social relationship, after receiving broadcast test information of the base station, a relay node in the cluster range returns corresponding signal-to-noise ratio information, meanwhile, the relay node transmits the test signal to the target node, the target node returns corresponding signal-to-noise ratio information to the base station, and a relay node with the minimum signal-to-noise ratio is continuously removed according to a threshold value of the distance and the social relationship, and a plurality of better relay nodes are selected.
2. The relay node selection method based on D2D communication according to claim 1, wherein: assuming that there is no interference between nodes and between a node and D2D users within the cluster, only signal interference from the base station is received, and a set of channel resources can only be multiplexed by a pair of D2D users;
a set of D2D pairs, d= { D, is denoted by D 1 ,D 2 ,D 3 ,...,D n Di represents the ith D2D pair, P s Representing the maximum transmit power of a D2D user, P r Representing the maximum transmit power of the relay node, N 0 Representing additive white gaussian noise, eta i,j Representing trust value between two devices g i,j Representing channel gains between devices i to j, where gi, ri represent channel gains between device i and the ri-th relay node in the cluster range, source node to relay node, relay node to destinationThe signal to noise ratio of the nodes of (a) are respectively expressed as
Order the
If the number of relay nodes in the cluster is not less than 2, the signal to noise ratios from the source node to the cluster and from the cluster to the destination node are respectively expressed as
γ s,r ＝min(γ s,ri ),γ r,d ＝min(γ ri,d )
3. The relay node selection method based on D2D communication according to claim 2, wherein: dividing the whole communication process into two phases, wherein the first phase is from a source node to a relay node in a cluster, the second phase is from the relay node to a destination node, spectrum resources are divided into two equal parts, one phase uses one part, and the total signal to noise ratio in the cluster is according to the principle of decoding and forwarding
γ s,r,d ＝min{γ s,r ,γ r,d }
The instantaneous data rate of the D2D link is expressed as
W represents the spectrum bandwidth used by the D2D link, while 1/2 represents half of the spectrum resources used by the communication link;
EE is defined as the ratio of data rate to power, P is used t Representing the total power in the entire link, the following equation is derived
Assuming that the circuit power of different users is the same and expressed, the total power of the D2D link may be expressed as
P cir Representing circuit power, 1/2 represents that the power consumption of two phases in the D2D link communication process are independent and do not occur simultaneously, and n represents the number of relay nodes in the cluster.
4. The relay node selection method based on D2D communication according to claim 3, wherein: when the relay nodes in the cluster range do not meet the requirements, adopting a direct communication technology to perform data transmission, wherein the signal-to-noise ratio, the instantaneous data rate and the power loss from the destination node to the source node are respectively as follows
R s,d ＝Wlog 2 (1+γ s,d )
P t1 ＝P s +P cir
Deriving EE for direct communication from instantaneous data rate and power loss
5. The relay node selection method based on D2D communication according to claim 4, wherein: the EE-based optimal power optimization model is denoted by P1:
s.t.0＜P≤P max
s s
0＜P≤P max
r r
η i,j ∈{0,1},i∈D,j∈n
γ s,r,d ≥γ d ,
α∈{0,1}
P s max and P r max Respectively representing the maximum transmitting power of the source node and the maximum transmitting power of the relay node, gamma d Representing the minimum signal-to-noise ratio requirement of the link for communication, wherein alpha represents the working coefficient of the system, and the direct communication and the relay communication are independent and do not simultaneously;
for the P1 problem, two cases are classified, and when α=1, only relay communication is performed to obtain the optimal power P s opt And P r opt Two assumptions are made, assuming the following:
suppose 1: when gamma is s,r,d ＝min{γ s,r ,γ r,d }＝γ s,r I.e. phi s,r ≤Φ r,d Then the objective function in P1 is rewritten as
As can be seen from the current assumption, when P s Φ s,r ≤P r Φ r,d ，P r Maximum value of (2)
Suppose 2: when gamma is s,r,d ＝min{γ s,r ,γ r,d }＝γ r,d I.e. phi r,d ≤Φ s,r Then the objective function in P1 is rewritten as
As can be seen from the current assumption, when P r Φ r,d ≤P s Φ s,r ，P r Maximum value of (2)
From the above two assumptions, it can be seen if and only if gamma s,r ＝γ r,d At this time, EE takes its maximum value.
6. The relay node selection method based on D2D communication according to claim 5, wherein: using
Replacing the objective function in the P1 problem to obtain the optimal power P s opt And P r opt Problem P1 is equivalently rewritten as problem P2:
s.t.0＜P≤P max
s s
0＜P≤P max
r r
η i,j ∈{0,1},i∈D,j∈n
γ s,r,d ≥γ d ,
P s Φ s,r ≤P r Φ r,d
7. the relay node selection method based on D2D communication according to claim 6, wherein: problem P2 is converted to problem P3 according to a theoretical method,
where u=wleg 2 (1+P s Φ s,r )-q(P s +nP r +2(n+1)P cir )
γ s,r,d ≥γ d ,
EE takes the maximum value if and only if u=0, where q is a temporary optimal solution of EE maximum.
8. The relay node selection method based on D2D communication according to claim 7, wherein: proving U as a concave function, firstly, respectively solving P for the function U s And P r The first partial derivative of (2) gives the following formula
The first partial derivative is again derived to obtain the U's Heisen matrix as follows
The first-order cis-form main component of the Heisen matrix is complex, the second-order cis-form main component is zero, the Heisen matrix is a negative semi-definite matrix, and the function U is a concave function, so that the maximum value can be obtained;
after proving, use
P in substitution problem P3 r Then find the relation P for the function U s Is derived from the partial derivative of (2) to obtain the following formula
Wherein [ x ]] + Represents max {0, x }, in combination with constraint
Will->
Bringing in to an optimal transmit power of
9. The D2D communication based relay node selection method according to claim 8, wherein: p (P) s Iteration is continued until u=0, and P is solved according to the above derivation r Is the optimal solution of (a); when alpha=0, only direct communication is performed to make
Will phi s,d Substituted into
Is obtained by
Problem P1 is converted into problem P4 according to the theoretical method:
wherein z=wleg 2 (1+P s Φ s,d )-q(P s +P cir )
s.t.0＜P≤P max
s s
0＜P≤P max
r r
γ s,r,d ≥γ d ,
Regarding Z, regarding P s First order partial derivative and second order partial derivative of (2)The order partial derivative is obtained
Since the second derivative of Z is less than zero, Z is proved to be a concave function when
When Z is at a maximum value,
combining constraint condition 0 < P s ≤P s max Obtaining the product
P s ＝min{P s * ,P s max }
And finally obtaining the optimal transmitting power required by the source node in the direct communication scene.
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