CN112333804A - Performance optimization method and system for cognitive network - Google Patents
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Abstract
The embodiment of the invention provides a performance optimization method and system for a cognitive network, which achieve the purposes of effectively improving the interruption performance of the cognitive network, reducing the interruption probability of the cognitive network and improving the energy efficiency of the cognitive network by adopting a cooperative technology through a combined optimization scheme of spectrum sensing, power distribution and energy loss. The system for realizing the method comprises the following steps: the system comprises a spectrum sensing module, an interruption performance optimization module, a transmission performance optimization module and an energy consumption optimization module. In the energy consumption optimization module, the optimal hop count and the appropriate number of secondary users are selected through statistical analysis of energy in the cognitive network and distribution probability and energy estimation of the nodes, so that the aim of optimizing network energy consumption is fulfilled.
Description
Technical Field
The invention relates to a performance optimization method and system for a cognitive network, in particular to the technical field of H04W wireless communication networks.
Background
The cognitive network is defined as a network which has cognitive ability, can sense the current network environment, and makes analysis, decision and action according to the conditions. The method has the self-adaptive capacity to the network environment, and also has the capacity of judging the previous decision and learning the future decision by taking an end-to-end network as a target. The cognitive radio network comprises at least one cognitive node as a traditional wireless network, and can realize dynamic spectrum access, so that a cognitive user can use an idle authorized spectrum. Meanwhile, the network cognitive node can fully utilize spectrum resources and relieve the problem of shortage of wireless communication spectrum by dynamically sensing and reconstructing spectrum allocation.
In practical application, due to the existence of adverse factors such as noise, temperature, wireless fading and the like, mutual interference exists between a master user network and a cognitive network caused by missed detection and false alarm, so that the communication of the master user is harmfully interfered. When the service quality requirement of the master user is strict, the opportunity that the cognitive user uses the authorized spectrum is reduced, so that the transmission throughput of the cognitive network is limited, the energy consumption of nodes in the transmission process is reduced to the maximum extent, the energy balance among the nodes is ensured, the life cycle of the network is prolonged, and the problem which cannot be solved by the prior art is still solved.
Disclosure of Invention
The purpose of the invention is as follows: an object is to provide a performance optimization method and system for a cognitive network, so as to solve the above problems in the prior art. In the cognitive network communication process, the cognitive network performance is improved through the joint design and optimization aiming at the two aspects of spectrum sensing and data transmission.
The technical scheme is as follows: in a first aspect, a performance optimization method for a cognitive network is provided, where the method includes the following steps:
establishing a cognitive network scene model, wherein the cognitive network scene model comprises primary user nodes and secondary user nodes, the secondary user nodes in a certain range form a node cluster, and each node cluster comprises a node cluster head; the primary user node is used for sending frequency spectrum information to all nodes; the secondary user node is used for sensing the frequency spectrum and the network environment and sending the frequency spectrum and the network environment to the cluster head; the node cluster is used for communicating with other network nodes in the cognitive network scene model.
In the cognitive network scene model, a frequency spectrum sensing model of the node cluster is constructed according to local sensing of the node cluster, signal change in data transmission is analyzed, and interruption performance is optimized.
In a cognitive network scene model, the influence of power distribution and node cluster head positions on interrupt performance is integrated, and the joint optimization of the power distribution and the node cluster head positions is carried out; when the position of the optimal node cluster head has obstacles, the communication is assisted by the node cluster heads with good surrounding performance.
And constructing an energy consumption model of the cognitive network scene model, and further introducing gamma distribution for analysis.
And selecting a corresponding optimization scheme according to the comprehensive analysis result.
In some implementations of the first aspect, the node cluster can independently perform local sensing, and the local sensing of the node cluster is divided into two states, namely that a master user is inactive and the master user is using an authorized channel:
in the formula,
indicating that the current primary user node is inactive,
indicating that the ith cluster head received the nth sampled signal,
representing independent identically distributed gaussian noise.
In the formula,
indicating that the primary user node is using the grant channel,
represents the power of the primary user node,
indicating the nth sampled signal transmitted by the primary user node,
representing the complex channel gain of the perceived channel of the primary user node and the ith secondary user node,
representing independent identically distributed gaussian noise.
And according to the local sensing result, the secondary user node performs data communication through the sensed spectrum hole after detecting the idle authorized spectrum. The data communication mode is further divided into two time slots, and in the first time slot, the secondary user node transmits information to the node cluster head and the destination node; and in a second time slot, the secondary user node and the node cluster simultaneously transmit information to a destination node.
In the data communication process, when the channel capacity is lower than the data transmission rate, interruption occurs, and interruption probability is obtained according to interruption analysis under different conditions, so that a scheme for improving the interruption performance of the cognitive network is made.
In some implementation manners of the first aspect, the interruption analysis under different conditions is further divided into three conditions, where the first interruption condition occurs at a stage where the secondary user node to the node cluster head and then to the destination node, and the channels from the secondary user node to the destination node are both subjected to deep fading, at this time, the primary user node is inactive, the detection result of the secondary user node is correct, and the channel capacity is lower than the data transmission rate; the second interruption condition occurs in the stage of generating the false alarm condition, at this time, the primary user node is inactive, and the secondary user node judges that the result is wrong; the third interruption occurs in the stage of generating the missing detection phenomenon, at this time, the primary user node is active, the secondary user node judges that the primary user node does not exist, and the channel capacity is lower than the data transmission rate.
According to the energy detection judgment result of the secondary user node and the judgment results of the inactivity of the primary user node and the existence of the primary user node, the total interruption probability of the cognitive network is as follows:
in the formula,
indicating the probability of the first kind of outage situation,
indicating the probability of the second kind of outage situation,
the probability of a third kind of outage situation is indicated,
indicating when the secondary user node perceives the usage of the white space,
represents a fusion criteria threshold in cooperative spectrum sensing,
representing the decision threshold for energy detection.
In some realizations of the first aspect, the joint optimization further includes optimizing the interruption performance according to two parameters, namely, power allocation and node cluster head position; the secondary user node and the target node are positioned on two focuses of the constructed ellipse, and the node cluster head is positioned on a curve of the ellipse; when the information receiving end is known and the transmitting end is unknown, the time-varying channel fading coefficient is:
in the formula,
representing the channel fading coefficients of the secondary user node to the destination node,
represents the signal fading coefficient from the secondary user node to the ith node cluster head,
representing the channel fading coefficient from the ith node cluster head to the destination node;
representing the distance from the secondary user node to the destination node;
representing the distance from the secondary user node to the ith node cluster head;
representing the distance from the ith node cluster head to the destination node;
represents a path loss factor;
representing a zero mean unit variance rayleigh channel between the secondary user node and the destination node;
representing a zero-mean unit variance Rayleigh channel from a secondary user node to an ith node cluster head;
and representing a zero-mean unit variance Rayleigh channel between the ith node cluster head and the destination node.
Normalizing the channel capacity according to the constraint condition, wherein the optimization process is to meet the requirement that the channel capacity is maximum when the multi-node cluster head is used for cooperative communication, and further to meet the requirement that the signal-to-noise ratio of a target node end is maximum; wherein the constraint condition is:
in the formula,
which represents the total transmitted power, is,
represents the transmit power of the secondary user node,
denotes a transmission power of an ith node cluster head, N denotes the number of node cluster heads,
indicating the distance from the secondary user node to the ith node cluster head,
representing the distance from the ith node cluster head to the destination node;
represents the major semi-axis length of the ellipse;
the normalized channel capacity is:
in the formula,
represents the transmit power of the secondary user node,
denotes a transmission power of an ith node cluster head, N denotes the number of node cluster heads,
indicating the distance from the secondary user node to the ith node cluster head,
representing the distance from the ith node cluster head to the destination node;
represents a path loss factor;
representing a zero mean unit variance rayleigh channel between the secondary user node and the destination node;
representing a zero-mean unit variance Rayleigh channel from a secondary user node to an ith node cluster head;
representing a zero-mean unit variance Rayleigh channel between the ith node cluster head and the destination node;
the variance is indicated.
In some implementations of the first aspect, the sending is from the secondary user node based on a cognitive network context model
The energy consumption model from the bit data to the destination node is as follows:
in the formula,
representing the transmission energy consumption of the secondary user node,
is shown at a distance
Down transferThe radiation power necessary for one bit,
which is indicative of the path loss factor,
which represents the energy consumption of the destination node,
representing the distance of the secondary user node to the destination node.
In the multi-hop network, the total energy consumption of the node cluster head is as follows:
in the formula,
indicating the distance between the jth node cluster head and the (j + 1) th node cluster head,
represents the energy consumption of the jth node cluster head,
indicates the total number of node cluster heads,
representing the density of the network space.
In some implementations of the first aspect, the total energy consumption of the node cluster head is specifically:
in the formula,
indicating the distance between the jth node cluster head and the (j + 1) th node cluster head,
represents the energy consumption of the jth node cluster head,
indicates the total number of node cluster heads,
represents the total energy needed by the jth node cluster head to transmit data,
represents the total amount of energy consumed by sensing data at the jth node cluster head,
which is indicative of the path loss factor,
representing the density of the network space; wherein,
、
the following expression is further satisfied:
in the formula,
indicating energy consumption for processing a unit of dataThe value of the consumption is calculated,
indicates the total number of node cluster heads,
indicating the number of neighbor node cluster heads of the jth node cluster head,
indicating a data transmission rate between the jth node cluster head and the (j + 1) th node cluster head,
representing the communication time of the secondary user node for sending data to the node cluster head;
indicating the transmission rate between the node cluster heads,
indicating the communication time between the node cluster heads.
In some implementations of the first aspect, when the gamma distribution is introduced into the cognitive network scene model, the total energy consumption of the secondary user node and the node cluster head is:
in the formula,
represents the total energy consumption of the secondary user node,
indicates the total energy consumption of the node cluster head,
representing secondary user node awarenessThe spectrum information and the energy for sending the sensing information to the node cluster head meet the requirement
，
Indicates the total number of node cluster heads,
represents the number of cognitive network sensing nodes,
representing the number of samples per second in the sensing interval,
indicating the time each sensing of the secondary user node continues to operate,
indicating the communication time of the secondary user node with the node cluster head,
indicating the data transmission rate of the secondary user node and the node cluster head.
In a second aspect, a performance optimization system for a cognitive network is provided, the system including: the system comprises a spectrum sensing module, an interruption performance optimization module, a transmission performance optimization module and an energy consumption optimization module; the spectrum sensing module is used for constructing a cooperative spectrum sensing model; the interruption performance optimization module is used for sharing the antennas of different node cluster heads and the broadcast characteristics of wireless transmission, and improving the capacity of resisting channel fading; the transmission performance optimization module is used for analyzing and optimizing the performance of the cognitive network scene model in a combined manner according to power distribution analysis and the position distribution of the node cluster heads; the energy consumption optimization module is used for analyzing the communication node distribution probability in the cognitive network scene model by introducing gamma distribution according to the established energy consumption model, so that a scheme for improving the network energy efficiency is formulated.
In some implementations of the second aspect, the spectrum sensing model is:
wherein, the current primary user node is inactive,
indicating that the ith cluster head received the nth sampled signal,
representing independent identically distributed gaussian noise;
in the formula,
indicating that the primary user node is using the grant channel,
represents the power of the primary user node,
indicating the nth sampled signal transmitted by the primary user node,
representing the complex channel gain of the perceived channel of the primary user node and the ith secondary user node,
representing independent identically distributed gaussian noise.
In some implementation manners of the second aspect, the interruption performance optimization module further obtains an interruption probability according to interruption analysis under different conditions for interruption occurring when the channel capacity is lower than the data transmission rate in the data communication process, so as to make a scheme for improving the interruption performance of the cognitive network.
In some realizable manners of the second aspect, the transmission performance optimization module optimizes the interruption performance according to two parameters, namely power distribution and node cluster head position; the secondary user node and the target node are positioned on two focuses of the constructed ellipse, and the node cluster head is positioned on a curve of the ellipse; when the information receiving end is known and the transmitting end is unknown, the time-varying channel fading coefficient is:
in the formula,
representing the channel fading coefficients of the secondary user node to the destination node,
represents the signal fading coefficient from the secondary user node to the ith node cluster head,
representing the channel fading coefficient from the ith node cluster head to the destination node;
representing the distance from the secondary user node to the destination node;
representing the distance from the secondary user node to the ith node cluster head;
representing the distance from the ith node cluster head to the destination node;
represents a path loss factor;
representing a zero mean unit variance rayleigh channel between the secondary user node and the destination node;
representing a zero-mean unit variance Rayleigh channel from a secondary user node to an ith node cluster head;
and representing a zero-mean unit variance Rayleigh channel between the ith node cluster head and the destination node.
In some implementations of the second aspect, the energy consumption optimization module sends from the secondary user node based on the cognitive network scenario model
The energy consumption model from the bit data to the destination node is as follows:
in the formula,
representing the transmission energy consumption of the secondary user node,
is shown at a distance
The radiation power necessary for the next transmission of a bit,
which is indicative of the path loss factor,
which represents the energy consumption of the destination node,
representing the distance of the secondary user node to the destination node.
In some implementations of the second aspect, in the multi-hop network, the total energy consumption of the node cluster head is:
in the formula,
indicating the distance between the jth node cluster head and the (j + 1) th node cluster head,
represents the energy consumption of the jth node cluster head,
indicates the total number of node cluster heads,
representing the density of the network space.
In some implementations of the second aspect, analyzing different instances of the outage probability includes: the first interrupt condition occurs in a stage that a secondary user node to a node cluster head and then to a destination node and a channel from the secondary user node to the destination node is subjected to deep fading, at this time, the primary user node is inactive, a detection result of the secondary user node is correct, and the channel capacity is lower than a data transmission rate; the second interrupt condition occurs in the stage of generating the false alarm condition, at this time, the primary user node is inactive, and the secondary user node judges that the result is wrong; the third interruption occurs in the stage of generating the missing detection phenomenon, at this time, the primary user node is active, the secondary user node judges that the primary user node does not exist, and the channel capacity is lower than the data transmission rate.
Wherein the outage probability is further:
in the formula,
indicating the probability of the first kind of outage situation,
indicating the probability of the second kind of outage situation,
the probability of a third kind of outage situation is indicated,
indicating when the secondary user node perceives the usage of the white space,
represents a fusion criteria threshold in cooperative spectrum sensing,
representing the decision threshold for energy detection.
In some realizable manners of the second aspect, the transmission performance optimization module normalizes the channel capacity according to the constraint condition, and then the optimization process is that the channel capacity is the maximum when the multi-node cluster head cooperative communication is met, and further that the signal-to-noise ratio of the destination node end is the maximum; wherein the constraint conditions are as follows:
in the formula,
which represents the total transmitted power, is,
represents the transmit power of the secondary user node,
denotes a transmission power of an ith node cluster head, N denotes the number of node cluster heads,
indicating the distance from the secondary user node to the ith node cluster head,
representing the distance from the ith node cluster head to the destination node;
representing the major semi-axis length of the ellipse.
In some implementations of the second aspect, the normalized channel capacity is:
in the formula,
represents the transmit power of the secondary user node,
denotes a transmission power of an ith node cluster head, N denotes the number of node cluster heads,
indicating the distance from the secondary user node to the ith node cluster head,
representing the distance from the ith node cluster head to the destination node;
represents a path loss factor;
representing a zero mean unit variance rayleigh channel between the secondary user node and the destination node;
representing a zero-mean unit variance Rayleigh channel from a secondary user node to an ith node cluster head;
representing a zero-mean unit variance Rayleigh channel between the ith node cluster head and the destination node;
the variance is indicated.
In some implementations of the second aspect, the energy consumption optimization module is distributed in the multi-hop network according to gamma, and the total energy consumption of the secondary user node and the node cluster head is:
in the formula,
represents the total energy consumption of the secondary user node,
indicates the total energy consumption of the node cluster head,
representing the energy of the secondary user node sensing frequency spectrum information and sending the sensing information to the node cluster head
，
Indicates the total number of node cluster heads,
represents the number of cognitive network sensing nodes,
representing the number of samples per second in the sensing interval,
indicating the time each sensing of the secondary user node continues to operate,
indicating the communication time of the secondary user node with the node cluster head,
indicating the data transmission rate of the secondary user node and the node cluster head,
representing a scale parameter.
Has the advantages that: the invention provides a performance optimization method facing a cognitive network and a system for realizing the method. Aiming at the optimization scheme of the interrupt performance, the invention provides a joint optimization scheme of power distribution and node cluster head positions, so that the interrupt probability is minimum. In the aspect of energy analysis in the cognitive network, a gamma distribution model is introduced, so that the defects of the existing cognitive network research field in the aspect of energy analysis are overcome, and meanwhile, the accuracy of estimating the distribution probability and the energy consumption of the node cluster heads is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a cognitive network scene model provided in an embodiment of the present invention.
Fig. 2 is a schematic diagram of data communication according to an embodiment of the present invention.
FIG. 3 is an ellipse diagram of the joint optimization according to the embodiment of the present invention.
Fig. 4 is a block diagram of a cognitive network performance optimization system provided in an embodiment of the present invention.
Detailed Description
The invention realizes the purpose of optimizing the network performance facing to the cognitive network through the performance optimization method and the performance optimization system facing to the cognitive network. The present invention will be further described in detail with reference to the following examples and accompanying drawings.
Fig. 1 shows a network scenario architecture diagram according to an embodiment of the present invention, which includes primary user nodes and secondary user nodes, where the secondary user nodes in a certain range form a node cluster, and each node cluster includes a node cluster head. The primary user node is used for sending frequency spectrum information to all the nodes, the secondary user node is used for sensing frequency spectrums and network environments and sending the frequency spectrums and the network environments to the cluster head, and the node cluster is used for communicating with other network nodes in the cognitive network scene model.
The first embodiment is as follows:
the performance optimization method facing the cognitive network specifically comprises the following steps: firstly, establishing a cognitive network scene model; secondly, according to local sensing of the node cluster, a frequency spectrum sensing model of the node cluster is constructed, signal change in data transmission is analyzed, and interruption performance is optimized; thirdly, combining the influence of the power distribution and the node cluster head position on the interrupt performance, and performing joint optimization of the power distribution and the node cluster head position, wherein when the optimal node cluster head position has an obstacle, the node cluster head with good peripheral performance is used for assisting communication; secondly, performing model construction analysis on the energy loss of the cognitive network, and introducing gamma distribution to improve the accuracy; and finally, selecting a corresponding optimization scheme according to the comprehensive analysis result.
Example two:
on the basis of the first embodiment, when each node cluster head can perform local sensing, the local sensing of the ith node cluster head is as follows:
in the formula,
indicating that the current primary user node is inactive,
indicating that the ith cluster head received the nth sampled signal,
representing independent identically distributed gaussian noise;
in the formula,
indicating that the primary user node is using the grant channel,
represents the power of the primary user node,
indicating the nth sampled signal transmitted by the primary user node,
representing the complex channel gain of the perceived channel of the primary user node and the ith secondary user node,
representing independent identically distributed gaussian noise.
According to spectrum sensing, nodes in the cognitive network can detect spectrum holes which are not occupied by the main user node, and then data communication is carried out through the sensed spectrum holes. According to the numerical analysis and simulation test results, the more cognitive users participating in cooperative spectrum sensing, the better the sensing performance.
Fig. 2 is a schematic diagram of data communication, which includes a secondary user node, a node cluster head, and a destination node. The data transmission process is divided into two time slots, and in the first time slot, the secondary user node transmits information to the node cluster head and the destination node; and in the second time slot, the secondary user node and the node cluster simultaneously transmit information to the destination node.
And judging the spectrum sensing performance by adopting the detection probability and the false alarm probability according to the model value of local sensing, wherein from the perspective of the cognitive users in the network, the smaller the false alarm probability is, the more the idle authorized channel is used by the cognitive users. In the data communication process, when the channel capacity is lower than the data transmission rate, interruption occurs, and interruption probability is obtained according to interruption analysis under different conditions, so that a scheme for improving the interruption performance of the cognitive network is made. The cooperative spectrum sensing can effectively improve the interruption performance of the cognitive network and shorten the sensing time.
Example three:
on the basis of the second embodiment, the interrupt analysis under different conditions is further divided into three conditions, wherein the first interrupt condition occurs in a stage that the secondary user node to the node cluster head and then to the destination node, and the channels from the secondary user node to the destination node are subjected to deep fading, at this time, the primary user node is inactive, the detection result of the secondary user node is correct, and the channel capacity is lower than the data transmission rate; the second interruption condition occurs in the stage of generating the false alarm condition, at this time, the primary user node is inactive, and the secondary user node judges that the result is wrong; the third interruption occurs in the stage of generating the missing detection phenomenon, at this time, the primary user node is active, the secondary user node judges that the primary user node does not exist, and the channel capacity is lower than the data transmission rate.
Example four:
on the basis of the second embodiment, according to the energy detection judgment result of the secondary user node and the judgment result of the inactivity of the primary user node and the existence of the primary user node, the total outage probability of the cognitive network is as follows:
in the formula,
indicating the probability of the first kind of outage situation,
indicating the probability of the second kind of outage situation,
the probability of a third kind of outage situation is indicated,
indicating when the secondary user node perceives the usage of the white space,
represents a fusion criteria threshold in cooperative spectrum sensing,
representing the decision threshold for energy detection.
Example five:
on the basis of the first embodiment, in order to overcome the relay position influence under equal power distribution only considered in the aspects of relay selection and power distribution in the prior art, the first embodiment performs joint optimization under the condition of certain total transmitting power, and further performs interruption performance optimization according to two parameters of power distribution and node cluster head position; as shown in fig. 3, the secondary user node and the destination node are located at two focuses of the constructed ellipse, and the node cluster head is located on the curve of the ellipse; when the information receiving end is known and the transmitting end is unknown, the time-varying channel fading coefficient is:
in the formula,
representing the channel fading coefficients of the secondary user node to the destination node,
represents the signal fading coefficient from the secondary user node to the ith node cluster head,
representing the channel fading coefficient from the ith node cluster head to the destination node;
representing the distance from the secondary user node to the destination node;
representing the distance from the secondary user node to the ith node cluster head;
representing the distance from the ith node cluster head to the destination node;
represents a path loss factor;
representing a zero mean unit variance rayleigh channel between the secondary user node and the destination node;
representing a zero-mean unit variance Rayleigh channel from a secondary user node to an ith node cluster head;
and representing a zero-mean unit variance Rayleigh channel between the ith node cluster head and the destination node.
Normalizing the channel capacity according to the constraint condition, wherein the optimization process is to meet the requirement that the channel capacity is maximum when the multi-node cluster head is used for cooperative communication, and further to meet the requirement that the signal-to-noise ratio of a target node end is maximum; wherein the constraint conditions are as follows:
in the formula,
which represents the total transmitted power, is,
represents the transmit power of the secondary user node,
denotes a transmission power of an ith node cluster head, N denotes the number of node cluster heads,
indicating the distance from the secondary user node to the ith node cluster head,
representing the distance from the ith node cluster head to the destination node;
representing the major semi-axis length of the ellipse.
The normalized channel capacity is:
in the formula,
represents the transmit power of the secondary user node,
denotes a transmission power of an ith node cluster head, N denotes the number of node cluster heads,
indicating the distance from the secondary user node to the ith node cluster head,
representing the distance from the ith node cluster head to the destination node;
represents a path loss factor;
representing a zero mean unit variance rayleigh channel between the secondary user node and the destination node;
representing a zero-mean unit variance Rayleigh channel from a secondary user node to an ith node cluster head;
representing a zero-mean unit variance Rayleigh channel between the ith node cluster head and the destination node;
the variance is indicated.
Example six:
on the basis of the first embodiment, the method constructs the transmission from the secondary user node based on the cognitive network scene model
The energy consumption model of the bit data to the destination node is as follows:
in the formula,
representing the transmission energy consumption of the secondary user node,
is shown at a distance
The radiation power necessary for the next transmission of a bit,
which is indicative of the path loss factor,
which represents the energy consumption of the destination node,
representing the distance of the secondary user node to the destination node.
In a multi-hop network, the total energy consumption of a node cluster head is:
in the formula,
indicating the distance between the jth node cluster head and the (j + 1) th node cluster head,
represents the energy consumption of the jth node cluster head,
indicates the total number of node cluster heads,
represents the total energy needed by the jth node cluster head to transmit data,
represents the total amount of energy consumed by sensing data at the jth node cluster head,
which is indicative of the path loss factor,
representing the density of the network space; wherein,
、
the following expression is further satisfied:
in the formula,
representing the amount of energy expended to process a unit of data,
indicates the total number of node cluster heads,
indicating the number of neighbor node cluster heads of the jth node cluster head,
indicating a data transmission rate between the jth node cluster head and the (j + 1) th node cluster head,
representing the communication time of the secondary user node for sending data to the node cluster head;
indicating the transmission rate between the node cluster heads,
indicating the communication time between the node cluster heads.
In the multi-hop network, the total energy consumption of the secondary user node and the node cluster head is as follows:
in the formula,
represents the total energy consumption of the secondary user node,
indicates the total energy consumption of the node cluster head,
representing secondary user node perceptual spectrum information and clustering to nodesThe head transmits the energy of the perception information to satisfy
，
Indicates the total number of node cluster heads,
represents the number of cognitive network sensing nodes,
representing the number of samples per second in the sensing interval,
indicating the time each sensing of the secondary user node continues to operate,
indicating a communication time of the secondary user node with the node cluster head, indicating a data transmission rate of the secondary user node with the node cluster head,
representing a scale parameter.
And obtaining a relational expression of the number of secondary user nodes in the cluster, the hop count and the energy consumption total amount of spectrum sensing according to the analyzed data result, and better selecting the optimal hop count and introducing a proper amount of secondary user number through the expression to optimize network energy consumption.
Example seven:
on the basis of the first embodiment, a performance optimization system of a cognitive network is provided, as shown in fig. 4, the system includes a spectrum sensing module, an interruption performance optimization module, a transmission performance optimization module, and an energy consumption optimization module. The spectrum sensing module is used for constructing a cooperative spectrum sensing model; the interruption performance optimization module is used for sharing the antennas of different node cluster heads and the broadcast characteristics of wireless transmission, and improving the capacity of resisting channel fading; the transmission performance optimization module is used for analyzing and optimizing the performance of the cognitive network scene model in a combined manner according to power distribution analysis and the position distribution of the node cluster heads; the energy consumption optimization module is used for analyzing the communication node distribution probability in the cognitive network scene model by introducing gamma distribution according to the established energy consumption model, so that a scheme for improving the network energy efficiency is formulated.
As noted above, while the present invention has been shown and described with reference to certain preferred embodiments, it is not to be construed as limited thereto. Various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A performance optimization method facing a cognitive network is characterized by comprising the following steps:
establishing a cognitive network scene model; the cognitive network scene model comprises primary user nodes and secondary user nodes, wherein the secondary user nodes in a predefined range form a node cluster, and each node cluster comprises a node cluster head; the primary user node is used for sending frequency spectrum information to all nodes; the secondary user node is used for sensing frequency spectrum and network environment and sending the frequency spectrum and the network environment to the cluster head; the node cluster is used for communicating with other network nodes in the cognitive network scene model;
in the cognitive network scene model, according to the local perception of the node cluster, a spectrum perception model of the node cluster is built, signal change in data transmission is analyzed, and interruption performance is optimized;
in the cognitive network scene model, the influence of power distribution and node cluster head positions on the interrupt performance is integrated, and the joint optimization of the power distribution and the node cluster head positions is carried out; when the position of the optimal node cluster head has an obstacle, the node cluster heads with outstanding surrounding performance are utilized to assist communication;
constructing an energy consumption model of the cognitive network scene model; further introducing gamma distribution for analysis according to the energy consumption model;
and selecting a corresponding optimization scheme according to the comprehensive analysis result.
2. The performance optimization method for the cognitive network according to claim 1, wherein the node cluster is capable of independently performing local sensing, and the local sensing of the node cluster is divided into two states, namely a state where a primary user is inactive and a state where the primary user uses an authorized channel:
in the formula,
indicating that the current primary user node is inactive,
indicating that the ith cluster head received the nth sampled signal,
representing independent identically distributed gaussian noise;
in the formula,
indicating that the primary user node is using the grant channel,
represents the power of the primary user node,
indicating the nth sampled signal transmitted by the primary user node,
representing the complex channel gain of the perceived channel of the primary user node and the ith secondary user node,
representing independent identically distributed gaussian noise;
according to a local sensing result, after the secondary user node detects the idle authorized spectrum, data communication is carried out through a sensed spectrum hole; the data communication mode is further divided into two time slots, and in the first time slot, the secondary user node transmits information to the node cluster head and the destination node; in a second time slot, the secondary user node and the node cluster transmit information to a destination node simultaneously;
in the data communication process, when the channel capacity is lower than the data transmission rate, interruption occurs, and interruption probability is obtained according to interruption analysis under different conditions, so that a scheme for improving the interruption performance of the cognitive network is made.
3. The performance optimization method oriented to the cognitive network according to claim 2, wherein the interrupt analysis under different conditions further divides three conditions, the first interrupt condition occurs at a stage where the secondary user node to the node cluster head and then to the destination node, and the channels from the secondary user node to the destination node are all subjected to deep fading, at this time, the primary user node is inactive, the detection result of the secondary user node is correct, and the channel capacity is lower than the data transmission rate; the second interrupt condition occurs in the stage of generating the false alarm condition, at this time, the primary user node is inactive, and the secondary user node judges that the result is wrong; the third interruption condition occurs in the stage of generating the missing detection phenomenon, at this time, the primary user node is active, the secondary user node judges that the primary user node does not exist, and the channel capacity is lower than the data transmission rate;
according to the energy detection judgment result of the secondary user node and the judgment results of the inactivity of the primary user node and the existence of the primary user node, the total interruption probability of the cognitive network is as follows:
in the formula,
indicating the probability of the first kind of outage situation,
indicating the probability of the second kind of outage situation,
the probability of a third kind of outage situation is indicated,
indicating when the secondary user node perceives the usage of the white space,
represents a fusion criteria threshold in cooperative spectrum sensing,
representing the decision threshold for energy detection.
4. The performance optimization method for the cognitive network according to claim 1, wherein the joint optimization further comprises performing interrupt performance optimization according to two parameters of power allocation and node cluster head position; the secondary user node and the target node are positioned on two focuses of the constructed ellipse, and the node cluster head is positioned on a curve of the ellipse; when the information receiving end is known and the transmitting end is unknown, the time-varying channel fading coefficient is:
in the formula,
representing the channel fading coefficients of the secondary user node to the destination node,
represents the signal fading coefficient from the secondary user node to the ith node cluster head,
representing the channel fading coefficient from the ith node cluster head to the destination node;
representing the distance from the secondary user node to the destination node;
representing the distance from the secondary user node to the ith node cluster head;
representing the distance from the ith node cluster head to the destination node;
represents a path loss factor;
representing a zero mean unit variance rayleigh channel between the secondary user node and the destination node;
representing a zero-mean unit variance Rayleigh channel from a secondary user node to an ith node cluster head;
representing a zero-mean unit variance Rayleigh channel between the ith node cluster head and the destination node;
normalizing the channel capacity according to the constraint condition, wherein the optimization process is to meet the requirement that the channel capacity is maximum when the multi-node cluster head is used for cooperative communication, and further to meet the requirement that the signal-to-noise ratio of a target node end is maximum; wherein the constraint condition is:
in the formula,
which represents the total transmitted power, is,
represents the transmit power of the secondary user node,
denotes a transmission power of an ith node cluster head, N denotes the number of node cluster heads,
indicating the distance from the secondary user node to the ith node cluster head,
representing the distance from the ith node cluster head to the destination node;
represents the major semi-axis length of the ellipse;
the normalized channel capacity is:
in the formula,
represents the transmit power of the secondary user node,
denotes a transmission power of an ith node cluster head, N denotes the number of node cluster heads,
indicating the distance from the secondary user node to the ith node cluster head,
representing the distance from the ith node cluster head to the destination node;
represents a path loss factor;
representing a zero mean unit variance rayleigh channel between the secondary user node and the destination node;
representing a zero-mean unit variance Rayleigh channel from a secondary user node to an ith node cluster head;
representing a zero-mean unit variance Rayleigh channel between the ith node cluster head and the destination node;
the variance is indicated.
5. The cognitive network-oriented performance optimization method according to claim 1, wherein the secondary system is selected from the group consisting of the secondary system and the secondary system based on the cognitive network scenario modelUser node transmission
The energy consumption model from the bit data to the destination node is as follows:
in the formula,
representing the transmission energy consumption of the secondary user node,
is shown at a distance
The radiation power necessary for the next transmission of a bit,
which is indicative of the path loss factor,
which represents the energy consumption of the destination node,
representing the distance from the secondary user node to the destination node;
in the multi-hop network, the total energy consumption of the node cluster head is as follows:
in the formula,
represents the jth node cluster head and the (j + 1) th nodeThe distance between the cluster heads is such that,
represents the energy consumption of the jth node cluster head,
indicates the total number of node cluster heads,
representing the density of the network space.
6. The performance optimization method for the cognitive network according to claim 5, wherein in the multihop network, the total energy consumption of the node cluster head specifically comprises:
in the formula,
indicating the distance between the jth node cluster head and the (j + 1) th node cluster head,
represents the energy consumption of the jth node cluster head,
indicates the total number of node cluster heads,
represents the total energy needed by the jth node cluster head to transmit data,
represents the total amount of energy consumed by sensing data at the jth node cluster head,
which is indicative of the path loss factor,
representing the density of the network space; wherein,
、
the following expression is further satisfied:
in the formula,
representing the amount of energy expended to process a unit of data,
indicates the total number of node cluster heads,
indicating the number of neighbor node cluster heads of the jth node cluster head,
indicating a data transmission rate between the jth node cluster head and the (j + 1) th node cluster head,
indicating that the secondary user node sends data toCommunication time of a node cluster head;
indicating the transmission rate between the node cluster heads,
indicating the communication time between the node cluster heads.
7. The performance optimization method for the cognitive network according to claim 6, wherein according to the gamma distribution, in the multihop network, the total energy consumption of the secondary user node and the node cluster head is as follows:
in the formula,
represents the total energy consumption of the secondary user node,
indicates the total energy consumption of the node cluster head,
representing the energy of the secondary user node sensing frequency spectrum information and sending the sensing information to the node cluster head
，
Indicates the total number of node cluster heads,
represents the number of cognitive network sensing nodes,
representing the number of samples per second in the sensing interval,
indicating the time each sensing of the secondary user node continues to operate,
indicating the communication time of the secondary user node with the node cluster head,
indicating the data transmission rate of the secondary user node and the node cluster head,
representing a scale parameter.
8. A performance optimization system for a cognitive network, which is used for implementing the method of any one of claims 1 to 7, and is characterized by comprising: the system comprises a spectrum sensing module, an interruption performance optimization module, a transmission performance optimization module and an energy consumption optimization module; the spectrum sensing module is used for constructing a cooperative spectrum sensing model; the interruption performance optimization module is used for sharing the antennas of different node cluster heads and the broadcast characteristics of wireless transmission, and improving the capacity of resisting channel fading; the transmission performance optimization module is used for analyzing and optimizing the performance of the cognitive network scene model in a combined manner according to power distribution analysis and the position distribution of the node cluster heads; the energy consumption optimization module is used for analyzing the communication node distribution probability in the cognitive network scene model by introducing gamma distribution according to the established energy consumption model, so that a scheme for improving the network energy efficiency is formulated.
9. The cognitive network-oriented performance optimization system according to claim 8, wherein the spectrum sensing model is:
in the formula,
indicating that the current primary user node is inactive,
indicating that the ith cluster head received the nth sampled signal,
representing independent identically distributed gaussian noise;
in the formula,
indicating that the primary user node is using the grant channel,
represents the power of the primary user node,
indicating the nth sampled signal transmitted by the primary user node,
representing the complex channel gain of the perceived channel of the primary user node and the ith secondary user node,
representing independent identically distributed gaussian noise;
the interruption performance optimization module is further used for generating interruption when the channel capacity is lower than the data transmission rate in the data communication process, and acquiring interruption probability according to interruption analysis under different conditions so as to make a scheme for improving the interruption performance of the cognitive network;
the transmission performance optimization module optimizes the interruption performance according to two parameters of power distribution and node cluster head position; the secondary user node and the target node are positioned on two focuses of the constructed ellipse, and the node cluster head is positioned on a curve of the ellipse; when the information receiving end is known and the transmitting end is unknown, the time-varying channel fading coefficient is:
in the formula,
representing the channel fading coefficients of the secondary user node to the destination node,
represents the signal fading coefficient from the secondary user node to the ith node cluster head,
representing the channel fading coefficient from the ith node cluster head to the destination node;
representing the distance from the secondary user node to the destination node;
representing the distance from the secondary user node to the ith node cluster head;
representing the distance from the ith node cluster head to the destination node;
represents a path loss factor;
representing a zero mean unit variance rayleigh channel between the secondary user node and the destination node;
representing a zero-mean unit variance Rayleigh channel from a secondary user node to an ith node cluster head;
representing a zero-mean unit variance Rayleigh channel between the ith node cluster head and the destination node;
the energy consumption optimization module sends the energy consumption optimization data from the secondary user node based on the cognitive network scene model
The energy consumption model from the bit data to the destination node is as follows:
in the formula,
representing the transmission energy consumption of the secondary user node,
is shown at a distance
The radiation power necessary for the next transmission of a bit,
which is indicative of the path loss factor,
which represents the energy consumption of the destination node,
representing the distance from the secondary user node to the destination node;
in the multi-hop network, the total energy consumption of the node cluster head is as follows:
in the formula,
indicating the distance between the jth node cluster head and the (j + 1) th node cluster head,
represents the energy consumption of the jth node cluster head,
indicates the total number of node cluster heads,
representing the density of the network space.
10. The system of claim 9, wherein the different conditions comprise: the first interrupt condition occurs in a stage that a secondary user node to a node cluster head and then to a destination node and a channel from the secondary user node to the destination node is subjected to deep fading, at this time, the primary user node is inactive, a detection result of the secondary user node is correct, and the channel capacity is lower than a data transmission rate; the second interrupt condition occurs in the stage of generating the false alarm condition, at this time, the primary user node is inactive, and the secondary user node judges that the result is wrong; the third interruption condition occurs in the stage of generating the missing detection phenomenon, at this time, the primary user node is active, the secondary user node judges that the primary user node does not exist, and the channel capacity is lower than the data transmission rate;
the outage probability is further:
in the formula,
indicating the probability of the first kind of outage situation,
indicating the probability of the second kind of outage situation,
the probability of a third kind of outage situation is indicated,
indicating when the secondary user node perceives the usage of the white space,
represents a fusion criteria threshold in cooperative spectrum sensing,
a decision threshold representing energy detection;
the transmission performance optimizing module normalizes the channel capacity according to the constraint condition, and the optimizing process is that the channel capacity is the maximum when the multi-node cluster head cooperative communication is met, and further the signal-to-noise ratio of a target node end is the maximum; wherein the constraint condition is:
in the formula,
which represents the total transmitted power, is,
represents the transmit power of the secondary user node,
denotes a transmission power of an ith node cluster head, N denotes the number of node cluster heads,
indicating the distance from the secondary user node to the ith node cluster head,
representing the distance from the ith node cluster head to the destination node;
represents the major semi-axis length of the ellipse;
the normalized channel capacity is:
in the formula,
represents the transmit power of the secondary user node,
denotes a transmission power of an ith node cluster head, N denotes the number of node cluster heads,
indicating the distance from the secondary user node to the ith node cluster head,
representing the distance from the ith node cluster head to the destination node;
represents a path loss factor;
representing a zero mean unit variance rayleigh channel between the secondary user node and the destination node;
representing a zero-mean unit variance Rayleigh channel from a secondary user node to an ith node cluster head;
representing a zero-mean unit variance Rayleigh channel between the ith node cluster head and the destination node;
represents the variance;
the energy consumption optimization module is distributed in the multi-hop network according to gamma, and the total energy consumption of the secondary user nodes and the node cluster heads is as follows:
in the formula,
represents the total energy consumption of the secondary user node,
indicates the total energy consumption of the node cluster head,
representing the energy of the secondary user node sensing frequency spectrum information and sending the sensing information to the node cluster head
，
Indicates the total number of node cluster heads,
represents the number of cognitive network sensing nodes,
representing the number of samples per second in the sensing interval,
indicating the time each sensing of the secondary user node continues to operate,
indicating the communication time of the secondary user node with the node cluster head,
indicating the data transmission rate of the secondary user node and the node cluster head,
representing a scale parameter.
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